{"id": "ba9f4bd2fe3f7f3521e3f5347c068ef8d41531fef70030d3cd04c9e9791e56ce", "sources": ["arxiv", "semantic_scholar"], "title": "Categorical Prior Lock-in: Why In-Context Learning Fails for Structured Data", "abstract": "Large language models (LLMs) are increasingly used as conditional generators for structured data, relying on in-context learning (ICL) to adapt to new distributions without parameter updates. We investigate the limits of ICL for structured generation under distribution mismatch, using high-cardinality tabular data as a controlled test case, and identify a structural failure mode we term \\textit{categorical prior lock-in}: the inability of ICL to update the model's prior over token distributions inherited from pre-training. Across two 7B-parameter open-weight models, ICL improves numerical fidelity with additional examples but exhibits a sharp ceiling on categorical distributions, failing to reproduce rare classes entirely. Parameter-efficient fine-tuning (LoRA) overcomes these limitations but introduces measurable memorization risk and, in some cases, destabilizes structured output generation, highlighting a fundamental trade-off between adaptability and privacy.", "authors": ["Antonio Pelusi", "Stefano Braghin", "Alberto Trombetta"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-06-10", "url": "https://arxiv.org/abs/2606.11961", "pdf_url": "https://arxiv.org/pdf/2606.11961v1", "arxiv_id": "2606.11961", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "4468b28148dbdb0015d75ed2fda3269df71d399ff9dbae28a81f01037160a553", "sources": ["arxiv", "semantic_scholar"], "title": "Fixed-Parameter Tractability of Private Synthetic Data Generation", "abstract": "We study the problem of generating synthetic data under differential privacy. We establish fixed-parameter tractability (FPT) for this problem where the parameter is the treewidth of the query family's incidence graph. Our algorithms attain optimal error rates across all regimes and are realized by two different approaches: the first is based on linear programming (LP) and the FPT of the separation problem for the LP dual; the second is based on a subsampled private multiplicative weights method, where we obtain FPT for sampling from Gibbs distributions. Both approaches are unified by a dynamic programming framework over a tree decomposition.", "authors": ["Badih Ghazi", "Cristóbal Guzmán", "Pritish Kamath", "Alexander Knop", "Ravi Kumar", "Pasin Manurangsi"], "categories": ["cs.DS", "cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2026-06-09", "url": "https://arxiv.org/abs/2606.11283", "pdf_url": "https://arxiv.org/pdf/2606.11283v1", "arxiv_id": "2606.11283", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "6fcb8750efc3ce0a2815966bdc391e18544d0f9565b2e02cf1d21fea42cae6f5", "sources": ["arxiv", "semantic_scholar"], "title": "Cross-Modal Knowledge Distillation without Paired Data: Theoretical Foundation and Algorithm", "abstract": "Cross-modal knowledge distillation (CMKD) studies how a (large) teacher model trained on one type of data (e.g., images) can guide a (smaller) student model building on another type of data (e.g., text/audio). Existing CMKD methods often require paired multi-modal data with aligned semantics, but obtaining such paired data are often costly and impractical. To mitigate this limitation, we develop a new CMKD framework for the more challenging setting where paired data are unavailable. In particular, we establish a cross-modal distributional relationship between teacher and student models, which reveals two fundamental quantities governing effective distillation: feature alignment and label alignment. These quantities characterize semantic discrepancy between modalities at the levels of representation and prediction distributions, respectively. Motivated by this insight, we propose a principled framework, with theoretical guarantees, that enables effective cross-modal knowledge distillation by aligning distributions rather than individual samples. Extensive experiments across a wide range of multimodal benchmarks show that our framework is highly effective in both unpaired and paired data settings, improving significantly over prior work.", "authors": ["Trong Khiem Tran", "Anh Duc Chu", "Quang Hung Pham", "Phi Le Nguyen", "Trong Nghia Hoang"], "categories": ["cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-06-09", "url": "https://arxiv.org/abs/2606.10504", "pdf_url": "https://arxiv.org/pdf/2606.10504v1", "arxiv_id": "2606.10504", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "da98b269aa44709d5e736c5a89ae287bdd942ec2a60938258b89f90c82b1ceda", "sources": ["arxiv", "semantic_scholar"], "title": "Non-covalent Interactions at cm$^{-1}$ Accuracy: Data Efficient Physics-Informed Distillation for Machine Learning Interatomic Potentials", "abstract": "Foundation models in atomistic machine learning encode interaction physics across diverse atomic environments, but whether that structure can be transferred when building specialist potentials at quantum-chemical accuracy remains open. Here we show that knowledge distillation from a pretrained universal machine-learning interatomic potential (MLIP), followed by coupled-cluster fine-tuning with single and double excitations and perturbative triples [CCSD(T)], transfers not only low-cost labels but a physically meaningful prior on interaction length scales, anisotropy, and the repulsive-dispersive balance, which CCSD(T) data then sharpens to quantum-chemical accuracy. For He--benzene, fine-tuning with 30% of the CCSD(T) data outperforms direct training using the full 80%; a 60% reduction in the high-fidelity compute budget. A symmetry-adapted perturbation theory (SAPT)-informed adaptive short-range/long-range architecture further lowers the validation MAE from 0.75 1/cm to 0.49 1/cm. Across a circumarene series of polycyclic aromatic hydrocarbons (PAHs), swapping the MLIP teacher under an otherwise identical pipeline changes the coronene error by an order of magnitude while leaving the larger PAHs stable, direct evidence that distillation transfers physical structure, not labels alone. Together, these results identify the choice of pretrained teacher as a primary design axis for data-efficient quantum-chemical-accuracy potentials, alongside architecture and training protocol.", "authors": ["Yulin Shen", "Shahzad Akram", "Louis Primeau", "Gen Zu", "Konstantinos D. Vogiatzis", "Yang Zhang", "Adrian Del Maestro"], "categories": ["physics.chem-ph"], "fields_of_study": ["Physics"], "published_date": "2026-06-03", "url": "https://arxiv.org/abs/2606.05127", "pdf_url": "https://arxiv.org/pdf/2606.05127v1", "arxiv_id": "2606.05127", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/DelMaestroGroup/papers-code-mlip-distillation-sapt", "venue": null, "quality_score": 0.65}
{"id": "c82a462f67c0410d7f4ecde4b69f966745a35460e6ba791b31b5e2f6b717c62a", "sources": ["arxiv", "semantic_scholar"], "title": "State Machine Guided Multi-Relational Synthetic Data from Logs for Anomaly Detection", "abstract": "Software systems generate massive unstructured logs that record execution behavior, failures, and interactions across components, yet existing log anomaly detection methods treat these logs primarily as flat sequences of templates, overlooking the relational execution structure that governs how events co-occur and evolve over time. We propose a framework that discovers this hidden structure by recovering an execution state machine directly from logs and inducing a corresponding multi-table relational schema connecting traces, events, states, transitions, and parameters. This discovered state machine serves as a generative prior to produce realistic multi-relational synthetic data that preserves structural, temporal, and process constraints while amplifying rare but valid execution behaviors. We assess the fidelity of the generated data through constraint validation, distributional similarity, and process-level metrics, and demonstrate its usefulness by showing that augmenting real logs with the synthetic relational data significantly improves anomaly and bug detection on held-out real datasets compared to sequence-based baselines and naive oversampling. Our results show that execution logs implicitly encode a relational database governed by a latent state machine, and that recovering this structure enables principled synthetic data generation for robust and interpretable anomaly detection.", "authors": ["Aja Khanal", "Apurva Narayan"], "categories": ["cs.MA"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-30", "url": "https://arxiv.org/abs/2606.00531", "pdf_url": "https://arxiv.org/pdf/2606.00531v1", "arxiv_id": "2606.00531", "doi": "10.1145/3770855.3818134", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (KDD 2026)", "quality_score": 0.55}
{"id": "931e08dd31d97511721aa0df16883280d2cf5c0dc6c8b1a7378c44ad86ec49ce", "sources": ["arxiv", "semantic_scholar"], "title": "SilentRetrieval: Hijacking Retrieval-Augmented Generation via Semantically-Preserving Adversarial Data Poisoning", "abstract": "Retrieval-Augmented Generation (RAG) mitigates LLM hallucinations but introduces a critical vulnerability: corpus integrity. We present SilentRetrieval, a two-stage data poisoning attack that hijacks RAG systems through adversarially crafted yet fluent documents. Stage 1 uses Coordinated Beam Search, a multi-token joint optimization method with a fluency-similarity objective, to keep a poisoned host document retrievable while constraining perplexity. Stage 2 uses Context-Adaptive Trigger Generation, a lightweight trigger-fusion step driven by a frozen LLM, to integrate manipulation triggers into document content. Under a one-poisoned-document-per-query evaluation with synthetic target answers, SilentRetrieval achieves 84.6%/81.3% HR@10 and 57.5%/54.8% ASR-LLM on Natural Questions and MS MARCO, while maintaining near-benign perplexity. Cross-model evaluation across four target LLMs shows nontrivial effectiveness under a fixed trigger generator, and transfer tests against unseen retrievers, including ColBERT and commercial embedding models, yield 64.7% average HR@10 under the same injected-corpus protocol. In a sampled Wikipedia-scale evaluation, SilentRetrieval retains 74.2% HR@10 at a 0.016% poisoning ratio. Combined retrieval-side and generation-side defenses reduce attack success substantially but incur a latency trade-off. Human evaluation shows substantially lower flag rates than disfluent baselines, while remaining numerically more suspicious than benign content at the current sample size.", "authors": ["Jiachen Qian"], "categories": ["cs.CR", "cs.CL", "cs.IR"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-27", "url": "https://arxiv.org/abs/2605.28074", "pdf_url": "https://arxiv.org/pdf/2605.28074v1", "arxiv_id": "2605.28074", "doi": "10.1145/3770855.3818186", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (KDD '26), August 09--13, 2026, Jeju Island, Republic of Korea", "quality_score": 0.55}
{"id": "24a1583360e38bf4298c904d4413dc4c4335a8475f7e579570e5e73499f360bb", "sources": ["arxiv", "semantic_scholar"], "title": "Knowledge Graphs as the Missing Data Layer for LLM-Based Industrial Asset Operations", "abstract": "LLM-based agents for industrial asset operations show limited accuracy when reasoning over flat document stores. AssetOpsBench (KDD 2026) establishes that GPT-4 agents achieve 65% on 139 industrial maintenance scenarios, and compares LLM orchestration paradigms (Agent-As-Tool vs. Plan-Execute) on a fixed data layer. We ask the orthogonal question: how much does the data model behind the tools matter? We treat a typed knowledge graph as a grounding substrate and route each question by how it is best answered: (i) LLM-generated Cypher for structured retrieval, which lifts the same GPT-4 model from 65% to 82-83%; (ii) native graph and optimization primitives, with no LLM, reaching 99% on graph-answerable scenarios; and (iii) generation-augmented knowledge (GAK) for answers absent from the data -- the engine's agent materializes the missing facts as provenance-tagged graph nodes, then answers. A recurring theme is inverted LLM usage: we constrain the LLM to query generation or one-shot enrichment from a typed schema and let the graph execute deterministically. On the 88 real AssetOpsBench failure-mode scenarios the benchmark itself flags non-deterministic -- ten equipment types absent from the graph -- GAK lifts answerability from zero to 100% of equipment types and answers 81.8% of scenarios, every materialized fact tagged source:LLM-derived for auditability. We also contribute 40 graph-native scenarios. For structured operational domains the data layer -- not the LLM orchestration -- is the primary lever, and a typed knowledge graph serves as a grounding substrate between raw industrial data and LLM reasoning.", "authors": ["Madhulatha Mandarapu", "Sandeep Kunkunuru"], "categories": ["cs.DB", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-26", "url": "https://arxiv.org/abs/2605.26874", "pdf_url": "https://arxiv.org/pdf/2605.26874v2", "arxiv_id": "2605.26874", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": null, "quality_score": 0.65}
{"id": "b1d42184dcb41fae9648a55dd958fc9a48abb82e54f100acf3bf788edc729388", "sources": ["arxiv", "semantic_scholar"], "title": "Generating Logically Consistent Synthetic Supply Chain Data with LLM-Driven Knowledge Graph Reasoning", "abstract": "Synthetic data offers a promising solution to two persistent barriers in supply chain analytics: data scarcity and data privacy. However, for synthetic data to support operational simulation and decision-making, it must do more than reproduce the statistical distributions of real records, and also preserve the \\emph{operational logic} that governs supply chain processes, including the temporal orderings, mathematical dependencies, hierarchical taxonomies, and conditional rules that make a record operationally plausible. We consider this logic as the ``physics'' of supply chain data. Existing tabular generative models are primarily optimized for distributional fidelity and downstream predictive utility, and therefore often generate records that appear statistically realistic but violate fundamental operational constraints. This paper introduces \\textbf{\\textit{TabKG}}, a knowledge-graph-guided framework for logically consistent synthetic supply chain tabular data generation. TabKG constructs a \\textbf{\\textit{Column Relationship Knowledge Graph (CR-KG)}} to represent data operational dependencies. It uses a multi-LLM ensemble with majority voting to propose candidate relationships from column metadata, validates these relationships against real data to remove hallucinated or unsupported edges, and then uses the validated CR-KG to guide generation. Specifically, TabKG compresses the original table into independent columns, generates these columns using a latent diffusion model, and deterministically reconstructs dependent columns according to the validated relationships, enforcing logical consistency by construction with respect to the discovered operational rules.", "authors": ["Yunbo Long", "Ge Zheng", "Liming Xu", "Alexandra Brintrup"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-26", "url": "https://arxiv.org/abs/2605.26823", "pdf_url": "https://arxiv.org/pdf/2605.26823v1", "arxiv_id": "2605.26823", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "45247d95e2be1a8c2ea3875bd787dda61845ed8b880c7ec8b3a8410efe105917", "sources": ["arxiv", "semantic_scholar"], "title": "Re-defining Humor Data Objects for AI Humor Research", "abstract": "In most existing AI humor research, humor was treated as either \"present\" or \"not present.\" We explore the concept of humor as a social interaction with context and explanations. During this project, we defined a humor reasoning data object and developed a way to prompt LLMs to generate an explanation of humor effective for general population. We iterated from an earlier prompt to an improved prompt, found that the later version reduced important errors, and then scaled generation to a large number of data objects which have the potential to enable data synthesis and data augmentation for AI humor research. Our main takeaway is that better prompting of an LLM improves humor explanation quality, especially by handling missing context, multi-modality, and transcript issues more carefully. These results establish a strong foundation for future work on AI understanding of humor as social behavior. All code and data are available at: https://github.com/anna-arnett/ai-humor/ .", "authors": ["Anna Arnett", "Bang Nguyen", "Meng Jiang"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-24", "url": "https://arxiv.org/abs/2605.25171", "pdf_url": "https://arxiv.org/pdf/2605.25171v2", "arxiv_id": "2605.25171", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/anna-arnett/ai-humor/", "venue": null, "quality_score": 0.65}
{"id": "7670b3ceb9be16b6bc4a3afb2bccb6aeb65925999ad9cf1356a3245f1b69c691", "sources": ["arxiv", "semantic_scholar"], "title": "Muon Nuclear Data Development Project", "abstract": "Negative muon-induced nuclear reactions play a critical role in a wide range of scientific and technological applications; however, comprehensive nuclear data for these processes remain unavailable. To address this gap, we have launched the Muon Nuclear Data (muND) Development Project in Japan, aiming to construct a dedicated data library for muon capture reactions. The library consists of four sub-libraries: muonic X-ray energies and intensities (XR), lifetimes of muonic atoms and nuclear capture rates (LT), energy spectra of emitted particles (ES), and production branching ratios of residual nuclei (BR). This project integrates experimental measurements, theoretical modeling, and machine learning techniques to compile and evaluate the data. We report the current status and recent progress of each sub-library.", "authors": ["Yukinobu Watanabe", "Megumi Niikura", "Shinichiro Abe", "Sayani Biswas", "Hiroki Iwamoto", "Adrian Hillier", "Naritoshi Kawamura", "Shoichiro Kawase", "Teiichiro Matsuzaki", "Futoshi Minato", "Rurie Mizuno", "Dai Tomono", "Yuji Yamaguchi"], "categories": ["nucl-ex", "nucl-th"], "fields_of_study": ["Physics"], "published_date": "2026-05-15", "url": "https://arxiv.org/abs/2605.15539", "pdf_url": "https://arxiv.org/pdf/2605.15539v1", "arxiv_id": "2605.15539", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "259cdbba7e1ef534abbe5f8f7c58b3160748be9378fa1b1acbbd5ff37d09e222", "sources": ["arxiv", "semantic_scholar"], "title": "The Nova Synthetic Data Base: A Principal Component/AI Analysis of Novae Synoptic Spectra", "abstract": "The Nova Synthetic Data Base (NSDB) is presented as the first publicly available database of synthetic spectra for classical nova shells, spanning an unprecedented range of physical parameters (e.g., ejecta mass, chemical composition, temperature, and luminosity of the white dwarf) at several post-eruption ages. Generated using detailed 3D photoionization models, this homogeneous database enables a systematic exploration of spectral features in novae. In this work, we introduce a principal component analysis/AI-based framework to derive time-dependent proxies for retrieving the physical properties of novae from limited spectral data. By analyzing the correlations between the eigenspectra and the grid's variables, a reduced set of diagnostic spectral lines is derived, paving the way for robust multiregressor machine-learning algorithms with a minimal effort observational set. The prediction capability of the method is high and robust to data noise. The results establish a proof of concept for the use of model grids combined with physically controlled AI as a tool to interpret novae observations in the context of the large number of events expected from future wide-area surveys.", "authors": ["Bruno C. Santos", "Marcos P. Diaz", "Larissa Takeda"], "categories": ["astro-ph.SR", "astro-ph.IM"], "fields_of_study": ["Physics"], "published_date": "2026-05-14", "url": "https://arxiv.org/abs/2605.15432", "pdf_url": "https://arxiv.org/pdf/2605.15432v1", "arxiv_id": "2605.15432", "doi": "10.3847/1538-4365/ae5641", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Astrophys. J. Suppl. Ser. 284, 24 (2026)", "quality_score": 0.55}
{"id": "f6333e4806e8d00584543ee7a209a25700f49fa15333991ffd185a9b6ce59266", "sources": ["arxiv", "semantic_scholar"], "title": "A Toolbox to Understand the Physics of Quantum Data Management", "abstract": "The application of quantum computing to data management has attracted growing interest, yet remains constrained by a limited understanding of how the physical behaviour of quantum devices relates to the structure and difficulty of database problems. In particular, evaluating quantum annealing approaches for combinatorial optimisation, which is central to many data management tasks, poses significant challenges beyond the scope of conventional empirical and complexity-theoretic methods. We present a computational toolbox for the systematic numerical analysis of quantum annealing processes derived from data management problem formulations. Adopting a physics-informed perspective, the toolbox enables the study of spectral and dynamical properties -- such as energy gaps and eigenstate structure -- that are inaccessible through direct hardware measurements, yet essential for understanding computational hardness and scaling behaviour. Our approach further provides derived quantities and visualisation techniques that support the interpretation of optimisation dynamics, the identification of structural similarities to canonical physical models, and the construction of reduced effective descriptions. By bridging methodological gaps between quantum computing and database systems research, this work establishes a principled foundation for evaluating quantum approaches and guiding future co-design efforts.", "authors": ["Wolfgang Mauerer", "Manuel Schönberger"], "categories": ["quant-ph", "cs.DB"], "fields_of_study": ["Physics", "Computer Science"], "published_date": "2026-05-14", "url": "https://arxiv.org/abs/2605.14719", "pdf_url": "https://arxiv.org/pdf/2605.14719v1", "arxiv_id": "2605.14719", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "0d9c902e7642ec06ef0ae3b512f58b6d8afe48168f22643dab6367b0d3f55cbb", "sources": ["arxiv", "semantic_scholar"], "title": "Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection", "abstract": "Weakly supervised graph anomaly detection aims to unveil unusual graph instances, e.g., nodes, whose behaviors significantly differ from normal ones, given only a limited number of annotated anomalies and abundant unlabeled samples. A major challenge is to learn a meaningful latent feature representation that reduces intra-class variance among normal data while remaining highly sensitive to anomalies. Although recent works have applied self-supervised feature learning for graph anomaly detection, their strategies are not specifically tailored to its unique requirements, motivating our exploration of a more domain-specific approach. In this paper, we introduce a weakly supervised graph anomaly detection method that leverages a feature learning strategy tailored for graph anomalies. Our approach is built upon a multi-task learning scheme that extracts robust feature representations through synthesized anomalies. We generate synthetic anomalies by perturbing the normal graph in various ways and assign a dedicated detection head to each anomaly type, ensuring that learned features are sensitive to potential deviations from normal patterns. Although synthetic anomalies may not perfectly replicate real-world patterns, they provide valuable auxiliary data for effective feature learnin, much like features learned from ImageNet classification transfer to downstream vision tasks. Additionally, we adopt a two-phase learning strategy: an initial warm-up phase using only synthetic samples, followed by a full-training phase integrating both tasks, to balance the influence of synthetic and real data. Extensive experiments on public datasets demonstrate the superior performance of our method over its competitors. Code is available at https://github.com/yj-zhou/SAWGAD.", "authors": ["Yingjie Zhou", "Yuqin Xie", "Fanxing Liu", "Dongjin Song", "Ce Zhu", "Lingqiao Liu"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-12", "url": "https://arxiv.org/abs/2605.11749", "pdf_url": "https://arxiv.org/pdf/2605.11749v1", "arxiv_id": "2605.11749", "doi": "10.1109/TKDE.2026.3656821", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/yj-zhou/SAWGAD", "venue": "IEEE Transactions on Knowledge and Data Engineering", "quality_score": 0.85}
{"id": "01d9f31e1dbf08cc0141b84af2aab52d981400138216b7f767da4ec50f5e135d", "sources": ["arxiv", "semantic_scholar"], "title": "Automated Big Data Quality Assessment using Knowledge Graph Embeddings", "abstract": "Automated data quality assessment is crucial for managing big data, but existing solutions face challenges in achieving accurate context-aware assessment. This paper presents a novel knowledge-based approach to enhance automated data quality assessment. Our approach utilizes knowledge graph embeddings to predict missing edges between the input dataset's context representation and the relevant quality rules and dimensions within a knowledge graph representing contextual data characteristics and the required quality assessment operations. We surpass conventional practices by integrating diverse representations within the knowledge graph, drawing insights from contextual information from a thorough literature investigation. This integration allows us to develop a comprehensive and context-specific data quality assessment plan tailored to each context. Leveraging the knowledge graph improves our understanding of the input dataset's context, overcoming the limitations of traditional methods that rely solely on strict matching and overlook contextual characteristics. By injecting numerical edge attributes, we assign corresponding weights to each predicted quality measurement, providing a comprehensive data quality assessment plan for the input dataset. To evaluate our approach, we leverage AmpliGraph, a framework developed and benchmarked by AccentureLabs. The evaluation involves employing a real-world radiation sensors dataset provided by the Lebanese Atomic Energy Commission (LAEC-CNRS). The results obtained from this evaluation demonstrate the capability of our solution to generate a comprehensive data quality assessment plan for the given input dataset.", "authors": ["Hadi Fadlallah", "Rima Kilany", "Mitri Haber", "Ali Jaber"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-12", "url": "https://arxiv.org/abs/2605.18833", "pdf_url": "https://arxiv.org/pdf/2605.18833v1", "arxiv_id": "2605.18833", "doi": "10.1504/IJDMMM.2025.150987", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Journal of Data Mining Modelling and Management", "quality_score": 0.55}
{"id": "1cdd15cbecb299f7f994abea1bc9c190ce9962fdedeb2c3735c7a46f69207b9c", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation", "abstract": "The Room Acoustics and Speaker Distance Estimation (SDE) Challenge at ICASSP 2025 explores the effectiveness of augmented room impulse response (RIR) data for improving SDE model performance. This challenge at GenDARA involves generating RIRs to supplement sparse datasets and fine-tuning SDE models with the augmented data. We employ the open-source fast diffuse room impulse response generator (FastRIR) conditioned only on speaker and listener locations. We design a quality filter to ensure generated RIR alignment with challenge RIRs, and hyperparameter optimization is employed for model fine-tuning. Our approach reduces the mean absolute error (MAE) of the five positions from 1.66m to 0.6m for GWA rooms and from 2.18m to 0.69m for Treble rooms, with results demonstrating that the augmentation approach significantly improves estimation accuracy, particularly at medium to long distances.", "authors": ["Anton Ratnarajah", "Mehmet Ergezer", "Arun Nair", "Mrudula Athi"], "categories": ["cs.SD", "cs.AI", "eess.AS", "eess.SP"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2026-05-01", "url": "https://arxiv.org/abs/2605.00721", "pdf_url": "https://arxiv.org/pdf/2605.00721v1", "arxiv_id": "2605.00721", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "Generative Data Augmentation for Real-World Signal Processing Applications (GenDA 2025). An ICASSP 2025 Satellite Workshop and IEEE Data Science and Learning Workshop", "quality_score": 0.85}
{"id": "31c533c54c4f33e49192d3c8c20859a3df47b03cf5ea17fbeef00d1e5f2fb917", "sources": ["arxiv", "semantic_scholar"], "title": "Multimodal Data Curation Through Ranked Retrieval", "abstract": "Shared embedding spaces are widely used for multimodal search and data curation. In practice, two problems often limit how well this works. First, embeddings can reflect modality more than meaning, so examples cluster by input type even when the underlying content matches. Second, the paired supervision used to train these spaces is often noisy. When we blend many heterogeneous, human-labeled datasets, these issues reinforce each other and degrade cross-modal retrieval. We present a framework that improves alignment by acting on both the training pairs and the embedding model. Symmetric Nucleus Subsampling (SNS) refines training pairs by trimming raw inputs and annotations to the portions that best support each other. Expert Embedding Engine (EEE) combines complementary embedding experts using a learned projection network, together with a bias-aware objective that reduces modality-driven separation in the embedding space. We demonstrate that this approach collapses the modality gap by over 90% on average vs base embedding experts and is a strong data curator, with datablends from our method outperforming stratified sampling and traditional curation baselines in downstream model performance.", "authors": ["Pratyush Muthukumar", "Harshil Kotamreddy", "Sarah Amiraslani", "Tomo Kanazawa", "Ramani Akkati", "Shaan Jain", "Andrew Mathau"], "categories": ["cs.IR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-01", "url": "https://arxiv.org/abs/2605.01163", "pdf_url": "https://arxiv.org/pdf/2605.01163v1", "arxiv_id": "2605.01163", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "e2b7c068efaba712c4070107b3de420ea1861bb585a7376e9bc6e26db010d777", "sources": ["arxiv", "semantic_scholar"], "title": "The Solar System Notification Alert Processing System (SNAPS): Public access to SNAPS data and products", "abstract": "The Solar System Notification Alert Processing System, SNAPS, is a downstream broker that ingests moving object data from ZTF and LSST and serves these data and derived properties to the public. This document describes how users can access our SNAPS data and products. This is intended to be a living document that will be updated on the arXiv when significant improvements are made to our data access schemes, and will therefore always contain the most up to date information about interacting with our databases and infrastructure. This is version 1.0.", "authors": ["David E. Trilling", "Michael Gowanlock", "Revanth Munugala", "Daniel R. Kramer", "Maria Chernyavskaya", "Erin Clark", "Graceson Mule", "Savannah Chappus"], "categories": ["astro-ph.EP", "astro-ph.IM"], "fields_of_study": ["Physics"], "published_date": "2026-04-30", "url": "https://arxiv.org/abs/2604.27420", "pdf_url": "https://arxiv.org/pdf/2604.27420v1", "arxiv_id": "2604.27420", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "f3816b51589742c7f15bc625ba37732627c2ebaa94badc721d8a20d123864c87", "sources": ["arxiv", "semantic_scholar"], "title": "Diverse Image Priors for Black-box Data-free Knowledge Distillation", "abstract": "Knowledge distillation (KD) represents a vital mechanism to transfer expertise from complex teacher networks to efficient student models. However, in decentralized or secure AI ecosystems, privacy regulations and proprietary interests often restrict access to the teacher's interface and original datasets. These constraints define a challenging black-box data-free KD scenario where only top-1 predictions and no training data are available. While recent approaches utilize synthetic data, they still face limitations in data diversity and distillation signals. We propose Diverse Image Priors Knowledge Distillation (DIP-KD), a framework that addresses these challenges through a three-phase collaborative pipeline: (1) Synthesis of image priors to capture diverse visual patterns and semantics; (2) Contrast to enhance the collective distinction between synthetic samples via contrastive learning; and (3) Distillation via a novel primer student that enables soft-probability KD. Our evaluation across 12 benchmarks shows that DIP-KD achieves state-of-the-art performance, with ablations confirming data diversity as critical for knowledge acquisition in restricted AI environments.", "authors": ["Tri-Nhan Vo", "Dang Nguyen", "Trung Le", "Kien Do", "Sunil Gupta"], "categories": ["cs.LG", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2026-04-28", "url": "https://arxiv.org/abs/2604.25794", "pdf_url": "https://arxiv.org/pdf/2604.25794v1", "arxiv_id": "2604.25794", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "772160fcefbfed49c0c7c0aad1871e234ec5a8bd058ca9008ef8e141cc16d4c2", "sources": ["arxiv", "semantic_scholar"], "title": "Cross-Domain Data Selection and Augmentation for Automatic Compliance Detection", "abstract": "Automating the detection of regulatory compliance remains a challenging task due to the complexity and variability of legal texts. Models trained on one regulation often fail to generalise to others. This limitation underscores the need for principled methods to improve cross-domain transfer. We study data selection as a strategy to mitigate negative transfer in compliance detection framed as a natural language inference (NLI) task. Specifically, we evaluate four approaches for selecting augmentation data from a larger source domain: random sampling, Moore-Lewis's cross-entropy difference, importance weighting, and embedding-based retrieval. We systematically vary the proportion of selected data to analyse its effect on cross-domain adaptation. Our findings demonstrate that targeted data selection substantially reduces negative transfer, offering a practical path toward scalable and reliable compliance automation across heterogeneous regulations.", "authors": ["Fariz Ikhwantri", "Dusica Marijan"], "categories": ["cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-04-23", "url": "https://arxiv.org/abs/2604.21469", "pdf_url": "https://arxiv.org/pdf/2604.21469v1", "arxiv_id": "2604.21469", "doi": "10.1109/BigData66926.2025.11401435", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "BigData Congress [Services Society]", "quality_score": 0.55}
{"id": "03e2fb5bf339d69f9b6038ac057380d0044af7c7ce07ad7f83278a6ba1a0ec85", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetic Flight Data Generation Using Generative Models", "abstract": "The increasing adoption of synthetic data in aviation research offers a promising solution to data scarcity and confidentiality challenges. This study investigates the potential of generative models to produce realistic synthetic flight data and evaluates their quality through a comprehensive four-stage assessment framework. The need for synthetic flight data arises from their potential to serve as an alternative to confidential real-world records and to augment rare events in historical datasets. These enhanced datasets can then be used to train machine learning models that predict critical events, such as flight delays, cancellations, diversions, and turnaround times. Two generative models, Tabular Variational Autoencoder (TVAE) and Gaussian Copula (GC), are adapted to generate synthetic flight information and compared based on their ability to preserve statistical similarity, fidelity, diversity, and predictive utility. Results indicate that while GC achieves higher statistical similarity and fidelity, its computational cost hinders its applicability to large datasets. In contrast, TVAE efficiently handles large datasets and enables scalable synthetic data generation. The findings demonstrate that synthetic data can support flight delay prediction models with accuracy comparable to those trained on real data. These results pave the way for leveraging synthetic flight data to enhance predictive modeling in air transportation.", "authors": ["Karim Aly", "Alexei Sharpanskykh"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-04-22", "url": "https://arxiv.org/abs/2604.20293", "pdf_url": "https://arxiv.org/pdf/2604.20293v1", "arxiv_id": "2604.20293", "doi": "10.1109/ICNS65417.2025.10976960", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Networking and Services", "quality_score": 0.55}
{"id": "1929b528bc78fa06d97d0561f18ebc9181070c1e529042b57280c37eec779b88", "sources": ["arxiv", "semantic_scholar"], "title": "Toward Cross-Lingual Quality Classifiers for Multilingual Pretraining Data Selection", "abstract": "As Large Language Models (LLMs) scale, data curation has shifted from maximizing volume to optimizing the signal-to-noise ratio by performing quality filtering. However, for many languages, native high quality data is insufficient to train robust quality classifiers. This work investigates the idea that quality markers in embedding space may show cross-lingual consistency, which would allow high-resource languages to subsidize the filtering of low-resource ones. We evaluate various filtering strategies, including cross-lingual transfer, third quartile sampling (Q3), and retention rate tuning. Our results demonstrate that massive multilingual pooling frequently outperforms monolingual baselines in both rank stability and aggregate accuracy for a 1B model trained on 103B tokens, delivering gains for high resource languages (1.2% increase in aggregate normalized accuracy for French) and matching or exceeding monolingual baselines for low-resource languages. However, we find that scale alone does not guarantee stability. Furthermore, for high-resource languages like French, we show that refining the decision boundary through third quartile sampling (Q3) or tuning the retention rate is necessary to fully leverage the multilingual signal.", "authors": ["Yassine Turki", "Vinko Sabolčec", "Bettina Messmer", "Martin Jaggi"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-04-22", "url": "https://arxiv.org/abs/2604.20549", "pdf_url": "https://arxiv.org/pdf/2604.20549v1", "arxiv_id": "2604.20549", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "cf2ca4ce0aa4f0df2db5f2f82271c09e1cd6f2aa8d43ec45a7375bf0eb0eea2b", "sources": ["arxiv", "semantic_scholar"], "title": "Adversarial Arena: Crowdsourcing Data Generation through Interactive Competition", "abstract": "Post-training Large Language Models requires diverse, high-quality data which is rare and costly to obtain, especially in low resource domains and for multi-turn conversations. Common solutions are crowdsourcing or synthetic generation, but both often yield low-quality or low-diversity data. We introduce Adversarial Arena for building high quality conversational datasets by framing data generation as an adversarial task: attackers create prompts, and defenders generate responses. This interactive competition between multiple teams naturally produces diverse and complex data. We validated this approach by conducting a competition with 10 academic teams from top US and European universities, each building attacker or defender bots. The competition, focused on safety alignment of LLMs in cybersecurity, generated 19,683 multi-turn conversations. Fine-tuning an open-source model on this dataset produced an 18.47% improvement in secure code generation on CyberSecEval-Instruct and 29.42% improvement on CyberSecEval-MITRE.", "authors": ["Prasoon Goyal", "Sattvik Sahai", "Michael Johnston", "Hangjie Shi", "Yao Lu", "Shaohua Liu", "Anna Rumshisky", "Rahul Gupta", "Anna Gottardi", "Desheng Zhang", "Lavina Vaz", "Leslie Ball", "Lucy Hu", "Luke Dai", "Samyuth Sagi", "Maureen Murray", "Sankaranarayanan Ananthakrishnan"], "categories": ["cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-04-20", "url": "https://arxiv.org/abs/2604.17803", "pdf_url": "https://arxiv.org/pdf/2604.17803v1", "arxiv_id": "2604.17803", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": null, "quality_score": 0.65}
{"id": "a676b4b6b4ea554ec42f2950b89dc5724fec7a9ef66a5b0dab98ba916da9c066", "sources": ["arxiv", "semantic_scholar"], "title": "Reverse Constitutional AI: A Framework for Controllable Toxic Data Generation via Probability-Clamped RLAIF", "abstract": "Ensuring the safety of large language models (LLMs) requires robust red teaming, yet the systematic synthesis of high-quality toxic data remains under-explored. We propose Reverse Constitutional AI (R-CAI), a framework for automated and controllable adversarial data generation that moves beyond isolated jailbreak prompts. By inverting a harmless constitution into a constitution of toxicity and iteratively refining model outputs through a critique--revision pipeline, R-CAI enables scalable synthesis of multi-dimensional adversarial data without human annotation. Optimizing solely for toxicity-related rewards, however, can lead to reward hacking and degraded semantic coherence. To address this challenge, we introduce probability clamping within reinforcement learning from AI feedback, which stabilizes adversarial optimization while preserving adversarial intent. Experiments demonstrate that R-CAI generates diverse, high-quality toxic data and that probability clamping substantially improves semantic coherence (15%) without sacrificing adversarial strength. Overall, R-CAI provides a fully automated framework for red teaming data generation and systematic safety evaluation of aligned language models.", "authors": ["Yuan Fang", "Yiming Luo", "Aimin Zhou", "Fei Tan"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-04-20", "url": "https://arxiv.org/abs/2604.17769", "pdf_url": "https://arxiv.org/pdf/2604.17769v1", "arxiv_id": "2604.17769", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/ZeroLoss-Lab/R-CAI", "venue": null, "quality_score": 0.65}
{"id": "29791699448af97d6314ddde2cc6a1e332f8b9dad65cba22b8a5d805e0f2e8f8", "sources": ["arxiv", "semantic_scholar"], "title": "A Complexity Agnostic Clustering Engine for Time Projection Chambers and its Implementation in FPGA", "abstract": "A clustering functional block implemented in field-programable-gate-array (FPGA) for time projection chambers (TPC) operating with predictable time regardless the complexity of the event is described in this paper. The clustering functional block reorganizes input data and the hits data belonging to the same clusters are output together for further process in the later stages. The clustering operation consists of two phases, data filling phase and data outputting phase, and the later uses the same number of clock cycles as the data filling phase. The clustering block can accommodate events with arbitrary number of clusters and number of hits per cluster as long as the total number of hits is within a predesigned limit. The operation time is exactly twice of the data filling time with no residual O(n2) term. The clustering block has been implemented with operating frequency of 200 MHz in a low-cost FPGA evaluation module and test results confirm the expected performance.", "authors": ["Jinyuan Wu", "Michael Wang", "Datao Gong"], "categories": ["physics.ins-det"], "fields_of_study": ["Physics"], "published_date": "2026-04-17", "url": "https://arxiv.org/abs/2604.16253", "pdf_url": "https://arxiv.org/pdf/2604.16253v1", "arxiv_id": "2604.16253", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "608423cb3d036d22d0f2445d13c6d8793526e766c8848e2355fb6a3395877219", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetic Tabular Generators Fail to Preserve Behavioral Fraud Patterns: A Benchmark on Temporal, Velocity, and Multi-Account Signals", "abstract": "We introduce behavioral fidelity -- a third evaluation dimension for synthetic tabular data that measures whether generated data preserves the temporal, sequential, and structural behavioral patterns that distinguish real-world entity activity. Existing frameworks evaluate statistical fidelity (marginal distributions and correlations) and downstream utility (classifier AUROC on synthetic-trained models), but neither tests for the behavioral signals that operational detection and analysis systems actually rely on. We formalize a taxonomy of four behavioral fraud patterns (P1-P4) covering inter-event timing, burst structure, multi-account graph motifs, and velocity-rule trigger rates; define a degradation ratio metric calibrated to a real-data noise floor (1.0 = matches real variability, k = k-times worse); and prove that row-independent generators -- the dominant paradigm -- are structurally incapable of reproducing P3 graph motifs (Proposition 1) and produce non-positive within-entity IET autocorrelation (Proposition 2), making the positive burst fingerprint of fraud sequences unachievable regardless of architecture or training data size. We benchmark CTGAN, TVAE, GaussianCopula, and TabularARGN on IEEE-CIS Fraud Detection and the Amazon Fraud Dataset. All four fail severely: on IEEE-CIS composite degradation ratios range from 24.4x (TVAE) to 39.0x (GaussianCopula); on Amazon FDB, row-independent generators score 81.6-99.7x, while TabularARGN achieves 17.2x. We document generator-specific failure modes and their resolutions. The P1-P4 framework extends to any domain with entity-level sequential tabular data, including healthcare and network security. We release our evaluation framework as open source.", "authors": ["Bhavana Sajja"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-04-13", "url": "https://arxiv.org/abs/2604.13125", "pdf_url": "https://arxiv.org/pdf/2604.13125v1", "arxiv_id": "2604.13125", "doi": "10.5281/zenodo.19545114", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/bhavana3/synthetic-data-experiments", "venue": null, "quality_score": 0.6446}
{"id": "23ba24289a7f42386fab4894d40e37a41551da1c40e934681ec1287f6ce0901e", "sources": ["arxiv", "semantic_scholar"], "title": "Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning", "abstract": "The rapid growth of generative artificial intelligence (AI) has introduced unprecedented computational demands, driving significant increases in the energy footprint of data centers. However, existing power consumption data is largely proprietary and reported at varying resolutions, creating challenges for estimating whole-facility energy use and planning infrastructure. In this work, we present a methodology that bridges this gap by linking high-resolution workload power measurements to whole-facility energy demand. Using NLR's high-performance computing data center equipped with NVIDIA H100 GPUs, we measure power consumption of AI workloads at 0.1-second resolution for AI training, fine-tuning and inference jobs. Workloads are characterized using MLCommons benchmarks for model training and fine-tuning, and vLLM benchmarks for inference, enabling reproducible and standardized workload profiling. The dataset of power consumption profiles is made publicly available. These power profiles are then scaled to the whole-facility-level using a bottom-up, event-driven, data center energy model. The resulting whole-facility energy profiles capture realistic temporal fluctuations driven by AI workloads and user-behavior, and can be used to inform infrastructure planning for grid connection, on-site energy generation, and distributed microgrids.", "authors": ["Roberto Vercellino", "Jared Willard", "Gustavo Campos", "Weslley da Silva Pereira", "Olivia Hull", "Matthew Selensky", "Juliane Mueller"], "categories": ["eess.SY", "cs.DC", "cs.LG"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2026-04-08", "url": "https://arxiv.org/abs/2604.07345", "pdf_url": "https://arxiv.org/pdf/2604.07345v1", "arxiv_id": "2604.07345", "doi": null, "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3434}
{"id": "71460b735a6560680e233d9e30db2bd5dd90f88099920406d1fb1a7f0649c1ab", "sources": ["arxiv", "semantic_scholar"], "title": "Stop Fixating on Prompts: Reasoning Hijacking and Constraint Tightening for Red-Teaming LLM Agents", "abstract": "With the widespread application of LLM-based agents across various domains, their complexity has introduced new security threats. Existing red-team methods mostly rely on modifying user prompts, which lack adaptability to new data and may impact the agent's performance. To address the challenge, this paper proposes the JailAgent framework, which completely avoids modifying the user prompt. Specifically, it implicitly manipulates the agent's reasoning trajectory and memory retrieval with three key stages: Trigger Extraction, Reasoning Hijacking, and Constraint Tightening. Through precise trigger identification, real-time adaptive mechanisms, and an optimized objective function, JailAgent demonstrates outstanding performance in cross-model and cross-scenario environments.", "authors": ["Yanxu Mao", "Peipei Liu", "Tiehan Cui", "Congying Liu", "Mingzhe Xing", "Datao You"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2026-04-07", "url": "https://arxiv.org/abs/2604.05549", "pdf_url": "https://arxiv.org/pdf/2604.05549v2", "arxiv_id": "2604.05549", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3427}
{"id": "7226f11ff1ae9ab933b7bb22fecc1fd85550470db3356f93d1bb4d0322f51bab", "sources": ["arxiv", "semantic_scholar"], "title": "A Synthetic Eye Movement Dataset for Script Reading Detection: Real Trajectory Replay on a 3D Simulator", "abstract": "Large vision-language models have achieved remarkable capabilities by training on massive internet-scale data, yet a fundamental asymmetry persists: while LLMs can leverage self-supervised pretraining on abundant text and image data, the same is not true for many behavioral modalities. Video-based behavioral data -- gestures, eye movements, social signals -- remains scarce, expensive to annotate, and privacy-sensitive. A promising alternative is simulation: replace real data collection with controlled synthetic generation to produce automatically labeled data at scale. We introduce infrastructure for this paradigm applied to eye movement, a behavioral signal with applications across vision-language modeling, virtual reality, robotics, accessibility systems, and cognitive science. We present a pipeline for generating synthetic labeled eye movement video by extracting real human iris trajectories from reference videos and replaying them on a 3D eye movement simulator via headless browser automation. Applying this to the task of script-reading detection during video interviews, we release final_dataset_v1: 144 sessions (72 reading, 72 conversation) totaling 12 hours of synthetic eye movement video at 25fps. Evaluation shows that generated trajectories preserve the temporal dynamics of the source data (KS D < 0.14 across all metrics). A matched frame-by-frame comparison reveals that the 3D simulator exhibits bounded sensitivity at reading-scale movements, attributable to the absence of coupled head movement -- a finding that informs future simulator design. The pipeline, dataset, and evaluation tools are released to support downstream behavioral classifier development at the intersection of behavioral modeling and vision-language systems.", "authors": ["Kidus Zewde", "Yuchen Zhou", "Dennis Ng", "Neo Tiangratanakul", "Tommy Duong", "Ankit Raj", "Yuxin Zhang", "Xingyu Shen", "Simiao Ren"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2026-04-07", "url": "https://arxiv.org/abs/2604.05475", "pdf_url": "https://arxiv.org/pdf/2604.05475v1", "arxiv_id": "2604.05475", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3427}
{"id": "bb0c0b1b8916fdee6107a2d676210ecb1f73aab25cb1cd9fd36980488bf9723f", "sources": ["arxiv", "semantic_scholar"], "title": "Stable and Privacy-Preserving Synthetic Educational Data with Empirical Marginals: A Copula-Based Approach", "abstract": "To advance Educational Data Mining (EDM) within strict privacy-protecting regulatory frameworks, researchers must develop methods that enable data-driven analysis while protecting sensitive student information. Synthetic data generation is one such approach, enabling the release of statistically generated samples instead of real student records; however, existing deep learning and parametric generators often distort marginal distributions and degrade under iterative regeneration, leading to distribution drift and progressive loss of distributional support that compromise reliability. In response, we introduce the Non-Parametric Gaussian Copula (NPGC), a plug-and-play synthesis method that replaces deep learning and parametric optimization with empirical statistical anchoring to preserve the observed marginal distributions while modeling dependencies through a copula framework. NPGC integrates Differential Privacy (DP) at both the marginal and correlation levels, supports heterogeneous variable types, and treats missing data as an explicit state to retain informative absence patterns. We evaluate NPGC against deep learning and parametric baselines on five benchmark datasets and demonstrate that it remains stable across multiple regeneration cycles and achieves competitive downstream performance at substantially lower computational cost. We further validate NPGC through deployment in a real-world online learning platform, demonstrating its practicality for privacy-preserving research.", "authors": ["Gabriel Diaz Ramos", "Lorenzo Luzi", "Debshila Basu Mallick", "Richard Baraniuk"], "categories": ["cs.LG", "cs.CY"], "fields_of_study": ["Computer Science"], "published_date": "2026-04-05", "url": "https://arxiv.org/abs/2604.04195", "pdf_url": "https://arxiv.org/pdf/2604.04195v1", "arxiv_id": "2604.04195", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3412}
{"id": "9e6d1c1647ec7547fc139aa03b739e1b9dfdc33a17417af179a2f7cb0327e262", "sources": ["arxiv", "semantic_scholar"], "title": "The IAEA Fusion Data Lake Project -- Accelerating AI and Big Data Applications through Open Science and FAIR Data", "abstract": "AI applications in fusion is a maturing field, playing a key role as surrogate models and digital twins to overcome computational expense limitations and insufficiently characterised phenomena, and expanding the horizon for real-time applications. The IAEA is supporting this activity through the AI for Fusion Coordinated Research Project (CRP), a five-year initiative launched in 2022, which involves 24 institutions across 11 countries. A key goal is to support the development of modern data infrastructure required to enable the development of agnostic AI models that can be safely extrapolate into the parameter space of future fusion power plants. The IAEA is playing an active role in contributing to the data infrastructure with the Fusion Data Lake project. A modern data platform to enable the development of AI workflows in line with FAIR data principles. The platform comprises three major components: 1. An international data catalogue; 2. A centralised medium-term storage; and 3. A data federation of the various fusion data platforms around the world. The current proof of concept (PoC) demonstrates the data cataloguing and federation capacity by integrating with the UKAEA's MAST Data Catalog. Currently, the second phase of the PoC will demonstrate scalability by integrating two additional experimental fusion device catalogues. This report presents a high-level project overview, including: - Technical architecture and design, collaborations and contributions, and the PoC solution; - Data and metadata model development and ontological concepts; and - The approach to data governance and terms of service. This illustrates the approach, results, and direction of the work, highlighting the high potential value to the fusion community of increasing the visibility and accessibility of the numerous international experimental data sets.", "authors": ["Daljeet Singh Gahle", "Matteo Barbarino"], "categories": ["physics.plasm-ph", "physics.app-ph"], "fields_of_study": ["Physics"], "published_date": "2026-04-02", "url": "https://arxiv.org/abs/2604.01797", "pdf_url": "https://arxiv.org/pdf/2604.01797v1", "arxiv_id": "2604.01797", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3391}
{"id": "932602d25b3ecf66a3676c4ca72aa2c5dbc0018ad8f5d208b0f98a8020aa5164", "sources": ["arxiv", "semantic_scholar"], "title": "KMTNet Synoptic Survey of Southern Sky III: The First Data Release", "abstract": "We present the first public data release (DR1) of the KMTNet Synoptic Survey of Southern Sky (KS4). This deep, wide-field imaging survey covers a southern footprint of -85$^{\\circ}$ < Decl. < -28.8$^{\\circ}$ in the $B$, $V$, $R$, and $I$ bands using a network of three 1.6-m telescopes. Although primarily designed to secure reference imaging for gravitational wave counterpart identification, DR1 delivers science-ready data for $\\sim$4,000 deg$^{2}$ to enable a broad range of astrophysical research. The release includes deep co-added images reaching median 5$σ$ depths of 22.0-23.5 AB mag. It is accompanied by two source catalogs containing over 200 million sources with SNR $>5$: an $I$-band-selected forced-photometry catalog optimized for consistent colors, and a band-merged catalog offering enhanced completeness. Validation demonstrates robust data quality, characterized by mean astrometric offsets of $+0.054 \\pm 0.129$ arcsec in RA and $-0.015 \\pm 0.120$ arcsec in Dec relative to Gaia DR3. {\\refbf Photometric uniformity for point sources is maintained within $\\pm 0.03$ mag relative to Gaia XP for 97.5--99.8\\% of the footprint across all four bands.} A key advantage of KS4 is its uniform and contiguous spatial coverage. It extends to fainter magnitudes than other uniform surveys while filling irregular gaps in existing deep datasets. All data products are publicly available via the CDS and NOIRLab's Astro Data Lab.", "authors": ["Seo-Won Chang", "Myungshin Im", "Mankeun Jeong", "Joonho Kim", "Bomi Park", "Jaewon Lee", "David A. H. Buckley", "Jeff Cooke", "Sungho Jung", "Dong-Jin Kim", "Ji Hoon Kim", "Yongjung Kim", "Chung-Uk Lee", "Seong-Kook Lee", "Gregory S. H. Paek", "Jiseop Shin"], "categories": ["astro-ph.IM", "astro-ph.GA"], "fields_of_study": ["Physics"], "published_date": "2026-03-30", "url": "https://arxiv.org/abs/2603.28089", "pdf_url": "https://arxiv.org/pdf/2603.28089v1", "arxiv_id": "2603.28089", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3369}
{"id": "2724bbc5ce66cc9d196fc1beac09c89a773859389ad2e6ea9138f85519ab6f8c", "sources": ["arxiv", "semantic_scholar"], "title": "Mapping data literacy trajectories in K-12 education", "abstract": "Data literacy skills are fundamental in computer science education. However, understanding how data-driven systems work represents a paradigm shift from traditional rule-based programming. We conducted a systematic literature review of 84 studies to understand K-12 learners' engagement with data across disciplines and contexts. We propose the data paradigms framework that categorises learning activities along two dimensions: (i) logic (knowledge-based or data-driven systems), and (ii) explainability (transparent or opaque models). We further apply the notion of learning trajectories to visualize the pathways learners follow across these distinct paradigms. We detail four distinct trajectories as a provocation for researchers and educators to reflect on how the notion of data literacy varies depending on the learning context. We suggest these trajectories could be useful to those concerned with the design of data literacy learning environments within and beyond CS education.", "authors": ["Robert Whyte", "Manni Cheung", "Katharine Childs", "Jane Waite", "Sue Sentance"], "categories": ["cs.CY", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-30", "url": "https://arxiv.org/abs/2603.28317", "pdf_url": "https://arxiv.org/pdf/2603.28317v1", "arxiv_id": "2603.28317", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3369}
{"id": "a6bb067eaec23056cd6d6be54052544ed5d6779aff21ac76d3150b444199c7ee", "sources": ["arxiv", "semantic_scholar"], "title": "Text Data Integration", "abstract": "Data comes in many forms. From a shallow perspective, they can be viewed as being either in structured (e.g., as a relation, as key-value pairs) or unstructured (e.g., text, image) formats. So far, machines have been fairly good at processing and reasoning over structured data that follows a precise schema. However, the heterogeneity of data poses a significant challenge on how well diverse categories of data can be meaningfully stored and processed. Data Integration, a crucial part of the data engineering pipeline, addresses this by combining disparate data sources and providing unified data access to end-users. Until now, most data integration systems have leaned on only combining structured data sources. Nevertheless, unstructured data (a.k.a. free text) also contains a plethora of knowledge waiting to be utilized. Thus, in this chapter, we firstly make the case for the integration of textual data, to later present its challenges, state of the art and open problems.", "authors": ["Md Ataur Rahman", "Dimitris Sacharidis", "Oscar Romero", "Sergi Nadal"], "categories": ["cs.CL", "cs.IR"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-28", "url": "https://arxiv.org/abs/2603.27055", "pdf_url": "https://arxiv.org/pdf/2603.27055v1", "arxiv_id": "2603.27055", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3354}
{"id": "9d2b8caa681bcc51265d2af7e81c21061edc253ec3c7a036a777ae73017b29c5", "sources": ["arxiv", "semantic_scholar"], "title": "KI-Adventskalender: An Informal Learning Intervention for Data & AI Literacy", "abstract": "Secondary school students increasingly encounter AI systems whose outputs depend on data quality, evaluation choices and modeling assumptions. To provide accessible entry points to these interconnected concepts, we developed KI-Adventskalender, a free web-based extracurricular initiative with 24 didactically curated, short, guided micro-challenges released daily in December, targeting data-centric competencies and socio-technical themes that shape how data are interpreted in practice. Drawing on two annual iterations, we report aggregate platform traces characterizing participation and task-level engagement. Participation increased substantially in 2025, but early attrition persists. Progression stabilized after midpoint: among users reaching Day 12 in 2025, more than 75% completed the calendar. Competence cluster performance shifted across years; higher revision rates co-occurred with strong pass rates, suggesting sustained engagement. We use these observations to motivate a next-step measurement agenda: tighter task instrumentation, embedded micro-assessments and mixed-method evaluation designs that can distinguish persistence from conceptual uptake, knowledge progression and durable learning outcomes.", "authors": ["Rahul Sharma", "Lars Henrich", "Larisa Ivanova", "Arsalan Karimzadmotallebiazar", "Annette Bieniusa", "Leo Van Waveren", "Sebastian Vollmer"], "categories": ["cs.HC"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-27", "url": "https://arxiv.org/abs/2603.26906", "pdf_url": "https://arxiv.org/pdf/2603.26906v2", "arxiv_id": "2603.26906", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3347}
{"id": "9a0d7b6cc2c0a6a564241d1a0ebf75136fbae7b7d6c027e80530c6533a1af822", "sources": ["arxiv", "semantic_scholar"], "title": "Data Gravity and the Energy Limits of Computation", "abstract": "Unlike the von Neumann architecture, which separates computation from memory, the brain tightly integrates them, an organization that large language models increasingly resemble. The crucial difference lies in the ratio of energy spent on computation versus data access: in the brain, most energy fuels compute, while in von Neumann architectures, data movement dominates. To capture this imbalance, we introduce the \\emph{operation-operand disjunction constant} $G_d$, a dimensionless measure of the energy required for data transport relative to computation. As part of this framework, we propose the metaphor of \\emph{data gravity}: just as mass exerts gravitational pull, large and frequently accessed data sets attract computation. We develop expressions for optimal computation placement and show that bringing the computation closer to the data can reduce energy consumption by a factor of $G_d^{(β- 1)/2}$, where $β\\in (1, 3)$ captures the empirically observed distance-dependent energy scaling. We demonstrate that these findings are consistent with measurements across processors from 45\\,nm to 7\\,nm, as well as with results from processing-in-memory (PIM) architectures. High $G_d$ values are limiting; as $G_d$ increases, the energy required for data movement threatens to stall progress, slowing the scaling of large language models and pushing modern computing toward a plateau. Unless computation is realigned with data gravity, the growth of AI may be capped not by algorithms but by physics.", "authors": ["Wonsuk Lee", "Jehoshua Bruck"], "categories": ["cs.AR"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-27", "url": "https://arxiv.org/abs/2603.26053", "pdf_url": "https://arxiv.org/pdf/2603.26053v1", "arxiv_id": "2603.26053", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3347}
{"id": "cb21c0e491970e7b3dbdaab622bd18621aa83d2dcfe0eff6c51aca334d1b099d", "sources": ["arxiv", "semantic_scholar"], "title": "DFLOP: A Data-driven Framework for Multimodal LLM Training Pipeline Optimization", "abstract": "Multimodal Large Language Models (MLLMs) have achieved remarkable advances by integrating text, image, and audio understanding within a unified architecture. However, existing distributed training frameworks remain fundamentally data-blind: they parallelize computation without accounting for variations in input data characteristics. This data unawareness leads to severe computation skew across stages and microbatches, where heterogeneous multimodal inputs incur different processing costs. Consequently, GPU resources are unevenly utilized, synchronization delays accumulate, and overall training efficiency degrades. To address this limitation, we present DFLOP, a data-driven framework for multimodal LLM training pipeline optimization. DFLOP continuously profiles runtime behavior to capture data-induced computation variance and employs predictive scheduling to balance workloads across stages and microbatches. By coupling data characteristics with execution planning, DFLOP substantially improves GPU utilization and throughput. Extensive experiments on large-scale multimodal benchmarks show that DFLOP achieves up to 3.6x faster training compared to state-of-the-art distributed training frameworks.", "authors": ["Hyeonjun An", "Sihyun Kim", "Chaerim Lim", "Hyunjoon Kim", "Rathijit Sen", "Sangmin Jung", "Hyeonsoo Lee", "Dongwook Kim", "Takki Yu", "Jinkyu Jeong", "Youngsok Kim", "Kwanghyun Park"], "categories": ["cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-26", "url": "https://arxiv.org/abs/2603.25120", "pdf_url": "https://arxiv.org/pdf/2603.25120v1", "arxiv_id": "2603.25120", "doi": "10.1145/3802037", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Proc. ACM Manag. Data 4, 3, Article 160 (June 2026), 29 pages", "quality_score": 0.5248}
{"id": "6df5cb857d195ab32a84179c0511644b03b9048bd27dcb8082a4ec6bdf3a137a", "sources": ["arxiv", "semantic_scholar"], "title": "Comparing Natural and Synthetic Structured Data: A Study of the Passive Verb Alternation in French and Italian", "abstract": "This study compares the impact of natural and synthetic data on training and evaluating large language models (LLMs), using the case of passive verb alternation in French and Italian. We use Blackbird Language Matrices (BLMs), structured datasets designed to probe linguistic knowledge of underlying patterns across sentence sets. We compare structured templates instantiated with natural sentences extracted from Universal Dependencies to structured templates of synthetic sentences. Experiments show that while models achieve ceiling performance when trained and tested on synthetic datasets, they do not reliably generalize to natural sentences. In contrast, models trained on natural data exhibit robust performance across both natural and synthetic test suites, demonstrating their superior ability to capture abstract linguistic patterns. These results corroborate the value of natural data and of structured set ups in linguistic evaluation for probing LLMs' syntactic and semantic knowledge.", "authors": ["Giuseppe Samo", "Paola Merlo"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-26", "url": "https://arxiv.org/abs/2603.25227", "pdf_url": "https://arxiv.org/pdf/2603.25227v1", "arxiv_id": "2603.25227", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.334}
{"id": "e637766eeee16df1e15d7b36e1ed2f34abcb17c56462e9510c52748d9ba9cc71", "sources": ["arxiv", "semantic_scholar"], "title": "Knowledge-Guided Retrieval-Augmented Generation for Zero-Shot Psychiatric Data: Privacy Preserving Synthetic Data Generation", "abstract": "AI systems in healthcare research have shown potential to increase patient throughput and assist clinicians, yet progress is constrained by limited access to real patient data. To address this issue, we present a zero-shot, knowledge-guided framework for psychiatric tabular data in which large language models (LLMs) are steered via Retrieval-Augmented Generation using the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and the International Classification of Diseases (ICD-10). We conducted experiments using different combinations of knowledge bases to generate privacy-preserving synthetic data. The resulting models were benchmarked against two state-of-the-art deep learning models for synthetic tabular data generation, namely CTGAN and TVAE, both of which rely on real data and therefore entail potential privacy risks. Evaluation was performed on six anxiety-related disorders: specific phobia, social anxiety disorder, agoraphobia, generalized anxiety disorder, separation anxiety disorder, and panic disorder. CTGAN typically achieves the best marginals and multivariate structure, while the knowledge-augmented LLM is competitive on pairwise structure and attains the lowest pairwise error in separation anxiety and social anxiety. An ablation study shows that clinical retrieval reliably improves univariate and pairwise fidelity over a no-retrieval LLM. Privacy analyses indicate that the real data-free LLM yields modest overlaps and a low average linkage risk comparable to CTGAN, whereas TVAE exhibits extensive duplication despite a low k-map score. Overall, grounding an LLM in clinical knowledge enables high-quality, privacy-preserving synthetic psychiatric data when real datasets are unavailable or cannot be shared.", "authors": ["Adam Jakobsen", "Sushant Gautam", "Hugo Lewi Hammer", "Susanne Olofsdotter", "Miriam S Johanson", "Pål Halvorsen", "Vajira Thambawita"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-26", "url": "https://arxiv.org/abs/2603.25186", "pdf_url": "https://arxiv.org/pdf/2603.25186v1", "arxiv_id": "2603.25186", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.334}
{"id": "75179b0045f788fe725dc6ecb9272aff660e8df526b2224a9d1e8960cb0273a1", "sources": ["arxiv", "semantic_scholar"], "title": "Attack Assessment and Augmented Identity Recognition for Human Skeleton Data", "abstract": "Machine learning models trained on small data sets for security applications are especially vulnerable to adversarial attacks. Person identification from LiDAR based skeleton data requires time consuming and expensive data acquisition for each subject identity. Recently, Assessment and Augmented Identity Recognition for Skeletons (AAIRS) has been used to train Hierarchical Co-occurrence Networks for Person Identification (HCN-ID) with small LiDAR based skeleton data sets. However, AAIRS does not evaluate robustness of HCN-ID to adversarial attacks or inoculate the model to defend against such attacks. Popular perturbation-based approaches to generating adversarial attacks are constrained to targeted perturbations added to real training samples, which is not ideal for inoculating models with small training sets. Thus, we propose Attack-AAIRS, a novel addition to the AAIRS framework. Attack-AAIRS leverages a small real data set and a GAN generated synthetic data set to assess and improve model robustness against unseen adversarial attacks. Rather than being constrained to perturbations of limited real training samples, the GAN learns the distribution of adversarial attack samples that exploit weaknesses in HCN-ID. Attack samples drawn from this distribution augment training for inoculation of the HCN-ID to improve robustness. Ten-fold cross validation of Attack-AAIRS yields increased robustness to unseen attacks- including FGSM, PGD, Additive Gaussian Noise, MI-FGSM, and BIM. The HCN-ID Synthetic Data Quality Score for Attack-AAIRS indicates that generated attack samples are of similar quality to the original benign synthetic samples generated by AAIRS. Furthermore, inoculated models show consistent final test accuracy with the original model trained on real data, demonstrating that our method improves robustness to adversarial attacks without reducing test performance on real data.", "authors": ["Joseph G. Zalameda", "Megan A. Witherow", "Alexander M. Glandon", "Jose Aguilera", "Khan M. Iftekharuddin"], "categories": ["cs.LG", "cs.CR", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-25", "url": "https://arxiv.org/abs/2603.24232", "pdf_url": "https://arxiv.org/pdf/2603.24232v1", "arxiv_id": "2603.24232", "doi": "10.1109/IJCNN54540.2023.10191835", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE International Joint Conference on Neural Network", "quality_score": 0.5236}
{"id": "0853a12b5820406fae704bce050fdd2424476f020b5b6850fe9e3b4fc8643b9c", "sources": ["arxiv", "semantic_scholar"], "title": "Data Mixing for Large Language Models Pretraining: A Survey and Outlook", "abstract": "Large language models (LLMs) rely on pretraining on massive and heterogeneous corpora, where training data composition has a decisive impact on training efficiency and downstream generalization under realistic compute and data budget constraints. Unlike sample-level data selection, data mixing optimizes domain-level sampling weights to allocate limited budgets more effectively. In recent years, a growing body of work has proposed principled data mixing methods for LLM pretraining; however, the literature remains fragmented and lacks a dedicated, systematic survey. This paper provides a comprehensive review of data mixing for LLM pretraining. We first formalize data mixture optimization as a bilevel problem on the probability simplex and clarify the role of data mixing in the pretraining pipeline, and briefly explain how existing methods make this formulation tractable in practice. We then introduce a fine-grained taxonomy that organizes existing methods along two dimensions: static versus dynamic mixing. Static mixing is further categorized into rule-based and learning-based methods, while dynamic mixing is grouped into adaptive and externally guided families. For each class, we summarize representative approaches and analyze their strengths and limitations from a performance-cost trade-off perspective. Building on this analysis, we highlight challenges that cut across methods, including limited transferability across data domains, optimization objectives, models, and validation sets, as well as unstandardized evaluation protocols and benchmarks, and the inherent tension between performance gains and cost control in learning-based methods. Finally, we outline several exploratory directions, including finer-grained domain partitioning and inverse data mixing, as well as pipeline-aware designs, aiming to provide conceptual and methodological insights for future research.", "authors": ["Zhuo Chen", "Yuxuan Miao", " Supryadi", "Deyi Xiong"], "categories": ["cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-25", "url": "https://arxiv.org/abs/2604.16380", "pdf_url": "https://arxiv.org/pdf/2604.16380v1", "arxiv_id": "2604.16380", "doi": "10.3724/2096-7004.di.2026.0055", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Data Intelligence", "quality_score": 0.5236}
{"id": "ca5baaf7a190773b27113ff79e48c3681371723308769a7e007675b92a7a0a13", "sources": ["arxiv", "semantic_scholar"], "title": "Why Does Self-Distillation (Sometimes) Degrade the Reasoning Capability of LLMs?", "abstract": "Self-distillation has emerged as an effective post-training paradigm for LLMs, often improving performance while shortening reasoning traces. However, in mathematical reasoning, we find that it can reduce response length while degrading performance. We trace this degradation to the suppression of epistemic verbalization - the model's expression of uncertainty during reasoning. Through controlled experiments varying conditioning context richness and task coverage, we show that conditioning the teacher on rich information suppresses uncertainty expression, enabling rapid in-domain optimization with limited task coverage but harming OOD performance, where unseen problems benefit from expressing uncertainty and adjusting accordingly. Across Qwen3-1.7B/8B, DeepSeek-Distill-Qwen-7B, and Olmo3-7B-Instruct, we observe performance drops of up to 40%. Our findings highlight that exposing appropriate levels of uncertainty is crucial for robust reasoning and underscore the importance of optimizing reasoning behavior beyond merely reinforcing correct answer traces.", "authors": ["Jeonghye Kim", "Xufang Luo", "Minbeom Kim", "Sangmook Lee", "Dohyung Kim", "Jiwon Jeon", "Dongsheng Li", "Yuqing Yang"], "categories": ["cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-25", "url": "https://arxiv.org/abs/2603.24472", "pdf_url": "https://arxiv.org/pdf/2603.24472v3", "arxiv_id": "2603.24472", "doi": null, "citation_count": 42, "influential_citation_count": 7, "has_code": true, "code_url": "https://github.com/beanie00/self-distillation-analysis", "venue": null, "quality_score": 0.6189}
{"id": "2b3306ad51b8ec0ffae1b8f3a005c13e7327834e4e9ba5cdbc396df0a0c1fee5", "sources": ["arxiv", "semantic_scholar"], "title": "Spatial Analysis on Value-Based Quadtrees of Rasterized Vector Data", "abstract": "Mobility data science offers insights into the complex interconnections of spatial data of moving objects and their surroundings, often based on a combination of vector and raster data. For example, mobility traces are usually in vector format, weather data are often in raster format. Yet, available spatial analysis tools for exploratory data science push data scientists towards one or the other, providing only limited support for the respective other. In this paper, we contribute to this problem space with a value-based quadtree index, which serves as a bridge builder to support joint spatial analysis on vector and raster data leveraging their unique autocorrelation property. We achieve a 90% reduction in median Point-in-Polygon query latency, while keeping the accuracy of query responses at equal level.", "authors": ["Diana Baumann", "Nils Japke", "Tim C. Rese", "David Bermbach"], "categories": ["cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-24", "url": "https://arxiv.org/abs/2603.23105", "pdf_url": "https://arxiv.org/pdf/2603.23105v4", "arxiv_id": "2603.23105", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3325}
{"id": "d7802482b9934d7e2a406a7f991574f9733bc95bc43fa46d664a4bc76a054093", "sources": ["arxiv", "semantic_scholar"], "title": "Assessing Data Literacy in K-12 Education: Challenges and Opportunities", "abstract": "Data literacy has become a key learning objective in K-12 education, but it remains an ambiguous concept as teachers interpret it differently. When creating assessments, teachers turn broad ideas about \"working with data\" into concrete decisions about what materials to include. Since working with data visualizations is a core component of data literacy, teachers' decisions about how to include them on assessments offer insight into how they interpret data literacy more broadly. Drawing on interviews with 13 teachers, we identify four challenges in enacting data literacy in assessments: (1) conceptual ambiguity between data visualization and data literacy, (2) tradeoffs between using real-world or synthetic data, (3) difficulty finding and adapting domain-appropriate visual representations and data visualizations, and (4) balancing assessing data literacy and domain-specific learning goals. Drawing on lessons from data visualization, human-computer interaction, and the learning sciences, we discuss opportunities to better support teachers in assessing data literacy.", "authors": ["Annabel Goldman", "Yuan Cui", "Matthew Kay"], "categories": ["cs.HC"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-22", "url": "https://arxiv.org/abs/2603.21382", "pdf_url": "https://arxiv.org/pdf/2603.21382v2", "arxiv_id": "2603.21382", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.331}
{"id": "79162a472af0a620006cec89b7bb9a71f631487192a6e39b62ecd723e5b17a1d", "sources": ["arxiv", "semantic_scholar"], "title": "R&D: Balancing Reliability and Diversity in Synthetic Data Augmentation for Semantic Segmentation", "abstract": "Collecting and annotating datasets for pixel-level semantic segmentation tasks are highly labor-intensive. Data augmentation provides a viable solution by enhancing model generalization without additional real-world data collection. Traditional augmentation techniques, such as translation, scaling, and color transformations, create geometric variations but fail to generate new structures. While generative models have been employed to extend semantic information of datasets, they often struggle to maintain consistency between the original and generated images, particularly for pixel-level tasks. In this work, we propose a novel synthetic data augmentation pipeline that integrates controllable diffusion models. Our approach balances diversity and reliability data, effectively bridging the gap between synthetic and real data. We utilize class-aware prompting and visual prior blending to improve image quality further, ensuring precise alignment with segmentation labels. By evaluating benchmark datasets such as PASCAL VOC and BDD100K, we demonstrate that our method significantly enhances semantic segmentation performance, especially in data-scarce scenarios, while improving model robustness in real-world applications. Our code is available at \\href{https://github.com/chequanghuy/Enhanced-Generative-Data-Augmentation-for-Semantic-Segmentation-via-Stronger-Guidance}{https://github.com/chequanghuy/Enhanced-Generative-Data-Augmentation-for-Semantic-Segmentation-via-Stronger-Guidance}.", "authors": ["Huy Che", "Dinh-Duy Phan", "Duc-Khai Lam"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-19", "url": "https://arxiv.org/abs/2603.18427", "pdf_url": "https://arxiv.org/pdf/2603.18427v1", "arxiv_id": "2603.18427", "doi": "10.1007/978-3-032-09321-9_30", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/chequanghuy/Enhanced-Generative-Data-Augmentation-for-Semantic-Segmentation-via-Stronger-Guidance}{https://github.com/chequanghuy/Enhanced-Generative-Data-Augmentation-for-Semantic-Segmentation-via-Stronger-Guidance}", "venue": "International Conference on Computational Collective Intelligence", "quality_score": 0.7986}
{"id": "0d2c47403c4d43f5d5555a0c1ed3335f3abce0913ba5a4e92af8c5fc99b01f5d", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetic Data, Information, and Prior Knowledge: Why Synthetic Data Augmentation to Boost Sample Doesn't Work for Statistical Inference", "abstract": "The use of synthetic data to deidentify data and to improve predictive models is well-attested to. The augmentation of datasets using synthetically generated data is an alluring proposition: in the best case, it generates realistic data \\textit{in silico} at a fraction of the cost of authentic data which may be found \\textit{in vivo} or \\textit{in vitro}. This poses novel epistemic challenges. We contend that synthetic data augmentation is best understood as a novel way of accounting for prior knowledge. In this manuscript, we propose a definition of synthetic distributions and analyze how synthetic data augmentation interplays with standard accounts of maximum likelihood and Bayesian estimation. We observe that the marginal Fisher information contributed by synthetic data processes is subject to fundamental bounds, and enumerate obstacles to the use of synthetic data augmentation to aid in inferential tasks. We then articulate a Bayesian formulation of the way that synthetic data augmentation can be coherently understood, but argue that naive approaches to the specification of the prior are epistemically unjustifiable. This suggests that enhanced scrutiny must be placed on identifying justifiable priors to warrant the use and inclusion of data drawn from specific synthetic distributions. While our analysis shows the challenges and limitations of using synthetic data augmentation to improve upon traditional statistical model reasoning, it does suggest that augmentation is the principal approach analysts using outcome reasoning (i.e. using train/test splits to justify the analysis) can constrain an otherwise high-dimensional model space, providing an alternative to trying to encode the constraints into the potentially complex architecture of the algorithm.", "authors": ["Reid Dale", "Jordan Rodu", "Mike Baiocchi"], "categories": ["stat.ME"], "fields_of_study": ["Mathematics"], "published_date": "2026-03-18", "url": "https://arxiv.org/abs/2603.18345", "pdf_url": "https://arxiv.org/pdf/2603.18345v1", "arxiv_id": "2603.18345", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3281}
{"id": "c2a924e1cb0a1f8d78f1c9005e5d314102cc55772bec73746b5b8c02f666614d", "sources": ["arxiv", "semantic_scholar"], "title": "Knowledge Distillation for Large Language Models", "abstract": "We propose a resource-efficient framework for compressing large language models through knowledge distillation, combined with guided chain-of-thought reinforcement learning. Using Qwen 3B as the teacher and Qwen 0.5B as the student, we apply knowledge distillation across English Dolly-15k, Spanish Dolly-15k, and code BugNet and PyTorrent datasets, with hyperparameters tuned in the English setting to optimize student performance. Across tasks, the distilled student retains a substantial portion of the teacher's capability while remaining significantly smaller: 70% to 91% in English, up to 95% in Spanish, and up to 93.5% Rouge-L in code. For coding tasks, integrating chain-of-thought prompting with Group Relative Policy Optimization using CoT-annotated Codeforces data improves reasoning coherence and solution correctness compared to knowledge distillation alone. Post-training 4-bit weight quantization further reduces memory footprint and inference latency. These results show that knowledge distillation combined with chain-of-thought guided reinforcement learning can produce compact, efficient models suitable for deployment in resource-constrained settings.", "authors": ["Alejandro Paredes La Torre", "Barbara Flores", "Diego Rodriguez"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-14", "url": "https://arxiv.org/abs/2603.13765", "pdf_url": "https://arxiv.org/pdf/2603.13765v1", "arxiv_id": "2603.13765", "doi": null, "citation_count": 8, "influential_citation_count": 2, "has_code": true, "code_url": "https://github.com/AlejandroParedesLT/knowledge_distillLLM", "venue": null, "quality_score": 0.604}
{"id": "25966d3d72fc71b385dc432c50057dd063605d9371b5bfe5ea4a640d2d4ff7db", "sources": ["arxiv", "semantic_scholar"], "title": "Greedy Information Projection for LLM Data Selection", "abstract": "We present \\emph{Greedy Information Projection} (\\textsc{GIP}), a principled framework for choosing training examples for large language model fine-tuning. \\textsc{GIP} casts selection as maximizing mutual information between a subset of examples and task-specific query signals, which may originate from LLM quality judgments, metadata, or other sources. The framework involves optimizing a closed-form mutual information objective defined using both data and query embeddings, naturally balancing {\\it quality} and {\\it diversity}. Optimizing this score is equivalent to maximizing the projection of the query embedding matrix onto the span of the selected data, which provides a geometric explanation for the co-emergence of quality and diversity. Building on this view, we employ a fast greedy matching-pursuit procedure with efficient projection-based updates. On instruction-following and mathematical reasoning datasets, \\textsc{GIP} selects small subsets that match full-data fine-tuning while using only a fraction of examples and compute, unifying quality-aware and diversity-aware selection for efficient fine-tuning.", "authors": ["Victor Ye Dong", "Kuan-Yun Lee", "Jiamei Shuai", "Shengfei Liu", "Yi Liu", "Jian Jiao"], "categories": ["cs.LG", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-14", "url": "https://arxiv.org/abs/2603.13790", "pdf_url": "https://arxiv.org/pdf/2603.13790v1", "arxiv_id": "2603.13790", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3252}
{"id": "7ac96d80eabc6195405b55e9c394859da6db6906979806be0c3872e7be032baf", "sources": ["arxiv", "semantic_scholar"], "title": "Grounding Synthetic Data Generation With Vision and Language Models", "abstract": "Deep learning models benefit from increasing data diversity and volume, motivating synthetic data augmentation to improve existing datasets. However, existing evaluation metrics for synthetic data typically calculate latent feature similarity, which is difficult to interpret and does not always correlate with the contribution to downstream tasks. We propose a vision-language grounded framework for interpretable synthetic data augmentation and evaluation in remote sensing. Our approach combines generative models, semantic segmentation and image captioning with vision and language models. Based on this framework, we introduce ARAS400k: A large-scale Remote sensing dataset Augmented with Synthetic data for segmentation and captioning, containing 100k real images and 300k synthetic images, each paired with segmentation maps and descriptions. ARAS400k enables the automated evaluation of synthetic data by analyzing semantic composition, minimizing caption redundancy, and verifying cross-modal consistency between visual structures and language descriptions. Experimental results indicate that while models trained exclusively on synthetic data reach competitive performance levels, those trained with augmented data (a combination of real and synthetic images) consistently outperform real-data baselines. Consequently, this work establishes a scalable benchmark for remote sensing tasks, specifically in semantic segmentation and image captioning. The dataset is available at zenodo.org/records/18890661 and the code base at github.com/caglarmert/ARAS400k.", "authors": ["Ümit Mert Çağlar", "Alptekin Temizel"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-10", "url": "https://arxiv.org/abs/2603.09625", "pdf_url": "https://arxiv.org/pdf/2603.09625v2", "arxiv_id": "2603.09625", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": null, "quality_score": 0.5985}
{"id": "2ee3fd21d15b910cedaf6ee3f131ec68cf3a65167a6237a01259aefba6acbe75", "sources": ["arxiv", "semantic_scholar"], "title": "Improving TabPFN's Synthetic Data Generation by Integrating Causal Structure", "abstract": "Synthetic tabular data generation addresses data scarcity and privacy constraints in a variety of domains. Tabular Prior-Data Fitted Network (TabPFN), a recent foundation model for tabular data, has been shown capable of generating high-quality synthetic tabular data. However, TabPFN is autoregressive: features are generated sequentially by conditioning on the previous ones, depending on the order in which they appear in the input data. We demonstrate that when the feature order conflicts with causal structure, the model produces spurious correlations that impair its ability to generate synthetic data and preserve causal effects. We address this limitation by integrating causal structure into TabPFN's generation process through two complementary approaches: Directed Acyclic Graph (DAG)-aware conditioning, which samples each variable given its causal parents, and a Completed Partially Directed Acyclic Graph (CPDAG)-based strategy for scenarios with partial causal knowledge. We evaluate these approaches on controlled benchmarks and six CSuite datasets, assessing structural fidelity, distributional alignment, privacy preservation, and Average Treatment Effect (ATE) preservation. Across most settings, DAG-aware conditioning improves the quality and stability of synthetic data relative to vanilla TabPFN. The CPDAG-based strategy shows moderate improvements, with effectiveness depending on the number of oriented edges. These results indicate that injecting causal structure into autoregressive generation enhances the reliability of synthetic tabular data.", "authors": ["Davide Tugnoli", "Andrea De Lorenzo", "Marco Virgolin", "Giovanni Cinà"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-10", "url": "https://arxiv.org/abs/2603.10254", "pdf_url": "https://arxiv.org/pdf/2603.10254v1", "arxiv_id": "2603.10254", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/DavideTugnoli/tabpfn-causal-synthetic", "venue": null, "quality_score": 0.5985}
{"id": "0dbfca4e111be9e745db13f8b56201e4e33d0f3b6898b4b27f3cd057ed411daf", "sources": ["arxiv", "semantic_scholar"], "title": "MAcPNN: Mutual Assisted Learning on Data Streams with Temporal Dependence", "abstract": "Internet of Things (IoT) Analytics often involves applying machine learning (ML) models on data streams. In such scenarios, traditional ML paradigms face obstacles related to continuous learning while dealing with concept drifts, temporal dependence, and avoiding forgetting. Moreover, in IoT, different edge devices build up a network. When learning models on those devices, connecting them could be useful in improving performance and reusing others' knowledge. This work proposes Mutual Assisted Learning, a learning paradigm grounded on Vygotsky's popular Sociocultural Theory of Cognitive Development. Each device is autonomous and does not need a central orchestrator. Whenever it degrades its performance due to a concept drift, it asks for assistance from others and decides whether their knowledge is useful for solving the new problem. This way, the number of connections is drastically reduced compared to the classical Federated Learning approaches, where the devices communicate at each training round. Every device is equipped with a Continuous Progressive Neural Network (cPNN) to handle the dynamic nature of data streams. We call this implementation Mutual Assisted cPNN (MAcPNN). To implement it, we allow cPNNs for single data point predictions and apply quantization to reduce the memory footprint. Experimental results prove the effectiveness of MAcPNN in boosting performance on synthetic and real data streams.", "authors": ["Federico Giannini", "Emanuele Della Valle"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-09", "url": "https://arxiv.org/abs/2603.08972", "pdf_url": "https://arxiv.org/pdf/2603.08972v1", "arxiv_id": "2603.08972", "doi": "10.1109/BigData62323.2024.10825150", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "BigData Congress [Services Society]", "quality_score": 0.5053}
{"id": "8db0d2aeadabb2f4263aff65e7c6f92bc3513abdd335a1e128541c888407f7a2", "sources": ["arxiv", "semantic_scholar"], "title": "Query-Guided Analysis and Mitigation of Data Verification Errors (Extended Version)", "abstract": "Data verification, the process of labeling data items as correct or incorrect, is a preprocessing step that may critically affect the quality of results in data-driven pipelines. Despite recent advances, verification can still produce erroneous labels that propagate to downstream query results in complex ways. We present a framework that complements existing verification tools by assessing the impact of potential labeling errors on query outputs and guiding additional verification steps to improve result reliability. To this end, we introduce Maximal Error Score (MES), a worst-case uncertainty metric that quantifies the reliability of query output tuples independently of the underlying data distribution. As an auxiliary indicator, we identify risky tuples - input tuples for which reducing label uncertainty may counterintuitively increase the output uncertainty. We then develop efficient algorithms for computing MES and detecting risky tuples, as well as a generic algorithm, named MESReduce, that builds on both indicators and interacts with external verifiers to select effective additional verification steps. We implement our techniques in a prototype system and evaluate them on real and synthetic datasets, demonstrating that MESReduce can substantially and effectively reduce the MES and improve the accuracy of verification results.", "authors": ["Ran Schreiber", "Yael Amsterdamer"], "categories": ["cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-09", "url": "https://arxiv.org/abs/2603.08612", "pdf_url": "https://arxiv.org/pdf/2603.08612v1", "arxiv_id": "2603.08612", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3216}
{"id": "141e02aa64830e2ccdf25286769ddaeb065edd93387335ff40b0a3622603c7b9", "sources": ["arxiv", "semantic_scholar"], "title": "Visualization Retrieval for Data Literacy: Position Paper", "abstract": "Current resources for data literacy education, such as visualization galleries and datasets, provide useful examples but lack mechanisms for learners to query, compare, and navigate the visualization design space efficiently. This position paper advocates for visualization retrieval as essential infrastructure for data literacy, transforming static collections into dynamic, inquiry-based learning environments. We analyze the role of retrieval across the data lifecycle, demonstrating how it facilitates design space exploration and vocabulary expansion, supports data consumption through visualization comparison and critique, and aids data management via resource curation. We outline key opportunities for future research and system design, including integrated retrieval-authoring environments, pedagogical relevance modeling, and collaborative educational corpora. Ultimately, we argue that visualization retrieval systems empower learners to articulate intent, bridge technical barriers, and proactively reason with data.", "authors": ["Huyen N. Nguyen", "Nils Gehlenborg"], "categories": ["cs.HC"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-06", "url": "https://arxiv.org/abs/2604.09598", "pdf_url": "https://arxiv.org/pdf/2604.09598v1", "arxiv_id": "2604.09598", "doi": "10.5281/zenodo.19240985", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3194}
{"id": "21319c3f20387e68a1225a2fbb05f1b53dd556137934cc72c26f00fe18f3c702", "sources": ["arxiv", "semantic_scholar"], "title": "FairFinGAN: Fairness-aware Synthetic Financial Data Generation", "abstract": "Financial datasets often suffer from bias that can lead to unfair decision-making in automated systems. In this work, we propose FairFinGAN, a WGAN-based framework designed to generate synthetic financial data while mitigating bias with respect to the protected attribute. Our approach incorporates fairness constraints directly into the training process through a classifier, ensuring that the synthetic data is both fair and preserves utility for downstream predictive tasks. We evaluate our proposed model on five real-world financial datasets and compare it with existing GAN-based data generation methods. Experimental results show that our approach achieves superior fairness metrics without significant loss in data utility, demonstrating its potential as a tool for bias-aware data generation in financial applications.", "authors": ["Tai Le Quy", "Dung Nguyen Tuan", "Trung Nguyen Thanh", "Duy Tran Cong", "Huyen Giang Thi Thu", "Frank Hopfgartner"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-05", "url": "https://arxiv.org/abs/2603.05327", "pdf_url": "https://arxiv.org/pdf/2603.05327v1", "arxiv_id": "2603.05327", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3186}
{"id": "cd4cf9f5e4f178b89aa0a95dc6272374a695575a3fc87b56d0ac6c9c5e28d599", "sources": ["arxiv", "semantic_scholar"], "title": "A Late-Fusion Multimodal AI Framework for Privacy-Preserving Deduplication in National Healthcare Data Environments", "abstract": "Duplicate records pose significant challenges in customer relationship management (CRM)and healthcare, often leading to inaccuracies in analytics, impaired user experiences, and compliance risks. Traditional deduplication methods rely heavily on direct identifiers such as names, emails, or Social Security Numbers (SSNs), making them ineffective under strict privacy regulations like GDPR and HIPAA, where such personally identifiable information (PII) is restricted or masked. In this research, I propose a novel, scalable, multimodal AI framework for detecting duplicates without depending on sensitive information. This system leverages three distinct modalities: semantic embeddings derived from textual fields (names, cities) using pre-trained DistilBERT models, behavioral patterns extracted from user login timestamps, and device metadata encoded through categorical embeddings. These heterogeneous modalities are combined using a late fusion approach and clustered via DBSCAN, an unsupervised density-based algorithm. This proposed model is evaluated against a traditional string-matching baseline on a synthetic CRM dataset specifically designed to reflect privacy-preserving constraints. The multimodal framework demonstrated good performance, achieving a good F1-score by effectively identifying duplicates despite variations and noise inherent in the data. This approach offers a privacy-compliant solution to entity resolution and supports secure digital infrastructure, enhances the reliability of public health analytics, and promotes ethical AI adoption across government and enterprise settings. It is well-suited for integration into national health data modernization efforts, aligning with broader goals of privacy-first innovation.", "authors": ["Mohammed Omer Shakeel Ahmed"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-04", "url": "https://arxiv.org/abs/2603.04595", "pdf_url": "https://arxiv.org/pdf/2603.04595v1", "arxiv_id": "2603.04595", "doi": "10.1109/FMLDS67896.2025.00021", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "2025 IEEE International Conference on Future Machine Learning and Data Science (FMLDS)", "quality_score": 0.4996}
{"id": "59fdc3d3e3aec027e41bca47d2a5b0a2893c01a1a13cbc59581a306082dd01ff", "sources": ["arxiv", "semantic_scholar"], "title": "SEAnet: A Deep Learning Architecture for Data Series Similarity Search", "abstract": "A key operation for massive data series collection analysis is similarity search. According to recent studies, SAX-based indexes offer state-of-the-art performance for similarity search tasks. However, their performance lags under high-frequency, weakly correlated, excessively noisy, or other dataset-specific properties. In this work, we propose Deep Embedding Approximation (DEA), a novel family of data series summarization techniques based on deep neural networks. Moreover, we describe SEAnet, a novel architecture especially designed for learning DEA, that introduces the Sum of Squares preservation property into the deep network design. We further enhance SEAnet with SEAtrans encoder. Finally, we propose novel sampling strategies, SEAsam and SEAsamE, that allow SEAnet to effectively train on massive datasets. Comprehensive experiments on 7 diverse synthetic and real datasets verify the advantages of DEA learned using SEAnet in providing high-quality data series summarizations and similarity search results.", "authors": ["Qitong Wang", "Themis Palpanas"], "categories": ["cs.DB", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-02", "url": "https://arxiv.org/abs/2603.01448", "pdf_url": "https://arxiv.org/pdf/2603.01448v2", "arxiv_id": "2603.01448", "doi": "10.1109/TKDE.2023.3270264", "citation_count": 12, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Knowledge and Data Engineering", "quality_score": 0.4973}
{"id": "20b6a4ae5ef453c0cb3d573ae624418cf74014e964190d29adb77ab16d4da022", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetic Data in MR Spectroscopy: Current Practices, Applications, and Considerations", "abstract": "The use of synthetic data has emerged as an essential tool in Magnetic Resonance Spectroscopy (MRS) research and applications, providing advantages for optimization of acquisition, software validation, deep learning applications, and enhanced reproducibility. Importantly, synthetic data addresses challenges of limited training data availability, particularly for clinical populations, and offers controlled solutions for investigating uncertainties and unexplained variance with in vivo data. This work provides a review and evaluation of current practices in the use and generation of synthetic data within the MRS field. Conducted by the MRS Synthetic Data Working Group under the Code & Data Sharing Committee of the MRS Study Group of the International Society for Magnetic Resonance in Medicine (ISMRM), this manuscript encompasses existing literature, supplemented by collective experience and in-house methodologies.", "authors": ["John T. LaMaster", "Aaron T. Gudmundson", "Alireza Abaei", "Seyma Alcicek", "Arturo Alvarado", "Ovidiu Andronesi", "Tiffany K. Bell", "Wolfgang Bogner", "Hanna Bugler", "Alexander R Craven", "Cristina Cudalbu", "Alma Davidson", "Christopher W. Davies-Jenkins", "Dinesh Deelchand", "Richard A. E. Edden", "Morteza Esmaeili", "Candace C Fleischer", "Abdelrahman Gad", "Guglielmo Genovese", "Saumya Gurbani", "Ashley D. Harris", "Pierre-Gilles Henry", "Kay Chioma Igwe", "Ajin Joy", "Margarida Julià-Sapé", "Hyeonjin Kim", "Roland Kreis", "Fan Lam", "Karl Landheer", "Bernard Lanz", "Chu-Yu Lee", "Clémence Ligneul", "Julian P. Merkofer", "Jack J. Miller", "Jessie Mosso", "Stanislav Motyka", "Eloïse Mougel", "Paul G. Mullins", "Saipavitra Murali-Manohar", "Chloé Najac", "Shinichiro Nakajima", "Georg Oeltzschner", "Esin Ozturk-Isik", "Marco Palombo", "Ulrich Pilatus", "Justyna Platek", "Emma Van Praagh", "Xiaobo Qu", "Rudy Rizzo", "Christopher T. Rodgers", "Esau Poblador Rodriguez", "Yeison Rodriguez", "Manoj K Sammi", "Dennis M. J. van de Sande", "Manoj Kumar Sarma", "Francesca Saviola", "Anouk Schrantee", "Amirmohammad Shamaei", "Dunja Simicic", "Brian J Soher", "Nico Sollmann", "Yulu Song", "Jeffrey A Stanley", "Bernhard Strasser", "Antonia Susnjar", "Kelley M. Swanberg", "M. Albert Thomas", "Ivan Tkáč", "Zhangren Tu", "Paul J. Weiser", "Mark Widmaier", "Martin Wilson", "Christopher J. Wu", "Lijing Xin", "Helge J. Zöllner", "İpek Özdemir", "MRS Synthetic Data Working Group", "Antonia Kaiser"], "categories": ["physics.med-ph"], "fields_of_study": ["Physics"], "published_date": "2026-02-26", "url": "https://arxiv.org/abs/2602.23463", "pdf_url": "https://arxiv.org/pdf/2602.23463v2", "arxiv_id": "2602.23463", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3135}
{"id": "f2e901a57c6ceea018e18c7819f1ca18d344d4f062d697206e92269913c3e8af", "sources": ["arxiv", "semantic_scholar"], "title": "Forecasting Antimicrobial Resistance Trends Using Machine Learning on WHO GLASS Surveillance Data: A Retrieval-Augmented Generation Approach for Policy Decision Support", "abstract": "Antimicrobial resistance (AMR) is a growing global crisis projected to cause 10 million deaths per year by 2050. While the WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS) provides standardized surveillance data across 44 countries, few studies have applied machine learning to forecast population-level resistance trends from this data. This paper presents a two-component framework for AMR trend forecasting and evidence-grounded policy decision support. We benchmark six models -- Naive, Linear Regression, Ridge Regression, XGBoost, LightGBM, and LSTM -- on 5,909 WHO GLASS observations across six WHO regions (2021-2023). XGBoost achieved the best performance with a test MAE of 7.07% and R-squared of 0.854, outperforming the naive baseline by 83.1%. Feature importance analysis identified the prior-year resistance rate as the dominant predictor (50.5% importance), while regional MAE ranged from 4.16% (European Region) to 10.14% (South-East Asia Region). We additionally implemented a Retrieval-Augmented Generation (RAG) pipeline combining a ChromaDB vector store of WHO policy documents with a locally deployed Phi-3 Mini language model, producing source-attributed, hallucination-constrained policy answers. Code and data are available at https://github.com/TanvirTurja", "authors": ["Md Tanvir Hasan Turja"], "categories": ["cs.LG", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2026-02-26", "url": "https://arxiv.org/abs/2602.22673", "pdf_url": "https://arxiv.org/pdf/2602.22673v1", "arxiv_id": "2602.22673", "doi": "10.48550/arXiv.2602.22673", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/TanvirTurja", "venue": "arXiv.org", "quality_score": 0.7615}
{"id": "4f6d795c8995a6ad9aa2313992b0d42eefbfe701252942e1430d4aa8c0f845b3", "sources": ["arxiv", "semantic_scholar"], "title": "Workload-Aware Incremental Reclustering in Cloud Data Warehouses", "abstract": "Modern cloud data warehouses store data in micro-partitions and rely on metadata (e.g., zonemaps) for efficient data pruning during query processing. Maintaining data clustering in a large-scale table is crucial for effective data pruning. Existing automatic clustering approaches lack the flexibility required in dynamic cloud environments with continuous data ingestion and evolving workloads. This paper advocates a clean separation between reclustering policy and clustering-key selection. We introduce the concept of boundary micro-partitions that sit on the boundary of query ranges. We then present WAIR, a workload-aware algorithm to identify and recluster only boundary micro-partitions most critical for pruning efficiency. WAIR achieves near-optimal (with respect to fully sorted table layouts) query performance but incurs significantly lower reclustering cost with a theoretical upper bound. We further implement the algorithm into a prototype reclustering service and evaluate on standard benchmarks (TPC-H, DSB) and a real-world workload. Results show that WAIR improves query performance and reduces the overall cost compared to existing solutions.", "authors": ["Yipeng Liu", "Renfei Zhou", "Jiaqi Yan", "Huanchen Zhang"], "categories": ["cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2026-02-26", "url": "https://arxiv.org/abs/2602.23289", "pdf_url": "https://arxiv.org/pdf/2602.23289v2", "arxiv_id": "2602.23289", "doi": "10.1145/3802127", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3135}
{"id": "8704a8eb0c1960934b5dbaa47900db39bf4df329ec8dc6818e0afdfb6fff407e", "sources": ["arxiv", "semantic_scholar"], "title": "Bayesian Generative Adversarial Networks via Gaussian Approximation for Tabular Data Synthesis", "abstract": "Generative Adversarial Networks (GAN) have been used in many studies to synthesise mixed tabular data. Conditional tabular GAN (CTGAN) have been the most popular variant but struggle to effectively navigate the risk-utility trade-off. Bayesian GAN have received less attention for tabular data, but have been explored with unstructured data such as images and text. The most used technique employed in Bayesian GAN is Markov Chain Monte Carlo (MCMC), but it is computationally intensive, particularly in terms of weight storage. In this paper, we introduce Gaussian Approximation of CTGAN (GACTGAN), an integration of the Bayesian posterior approximation technique using Stochastic Weight Averaging-Gaussian (SWAG) within the CTGAN generator to synthesise tabular data, reducing computational overhead after the training phase. We demonstrate that GACTGAN yields better synthetic data compared to CTGAN, achieving better preservation of tabular structure and inferential statistics with less privacy risk. These results highlight GACTGAN as a simpler, effective implementation of Bayesian tabular synthesis.", "authors": ["Bahrul Ilmi Nasution", "Mark Elliot", "Richard Allmendinger"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2026-02-25", "url": "https://arxiv.org/abs/2602.21948", "pdf_url": "https://arxiv.org/pdf/2602.21948v1", "arxiv_id": "2602.21948", "doi": "10.48550/arXiv.2602.21948", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4916}
{"id": "58bd721d0fd273d93eb3a6b78aed5c2713689238f56e5973df7234530d3a5777", "sources": ["arxiv", "semantic_scholar"], "title": "Seasoning Data Modeling Education with GARLIC: A Participatory Co-Design Framework", "abstract": "Entity-Relationship (ER) modeling is commonly taught as a primarily technical activity, despite its central role in shaping how data systems represent people, processes, and institutions. Prior research in participatory design demonstrates that involving diverse stakeholders in modeling can surface tacit knowledge, challenge implicit assumptions, and produce more inclusive data representations. However, database education currently lacks structured pedagogical approaches for teaching participatory ER modeling in practice. We introduce the GARLIC methodology for teaching and learning participatory ER modeling. GARLIC adapts and extends the ONION participatory ER modeling framework of Makovska et al.(HILDA 2025) into a workshop-based learning format that combines role-playing, collaborative synthesis, guided critique, and iterative refinement. GARLIC is designed to develop both technical modeling skills and critical awareness of the social and ethical dimensions of data representation. GARLIC lowers the barrier to participatory ER modeling and equips students with practical skills for collaborative, inclusive data model design.", "authors": ["Viktoriia Makovska", "Ihor Michurin", "Mariia Tokhtamysh", "George Fletcher", "Julia Stoyanovich"], "categories": ["cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2026-02-20", "url": "https://arxiv.org/abs/2602.18274", "pdf_url": "https://arxiv.org/pdf/2602.18274v1", "arxiv_id": "2602.18274", "doi": "10.48550/arXiv.2602.18274", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4858}
{"id": "ffd4c7f8a6af305c960bcd8fb055bb357281c2546865e7a3e06c5fe258f73f4e", "sources": ["arxiv", "semantic_scholar"], "title": "DeepFusion: Accelerating MoE Training via Federated Knowledge Distillation from Heterogeneous Edge Devices", "abstract": "Recent Mixture-of-Experts (MoE)-based large language models (LLMs) such as Qwen-MoE and DeepSeek-MoE are transforming generative AI in natural language processing. However, these models require vast and diverse training data. Federated learning (FL) addresses this challenge by leveraging private data from heterogeneous edge devices for privacy-preserving MoE training. Nonetheless, traditional FL approaches require devices to host local MoE models, which is impractical for resource-constrained devices due to large model sizes. To address this, we propose DeepFusion, the first scalable federated MoE training framework that enables the fusion of heterogeneous on-device LLM knowledge via federated knowledge distillation, yielding a knowledge-abundant global MoE model. Specifically, DeepFusion features each device to independently configure and train an on-device LLM tailored to its own needs and hardware limitations. Furthermore, we propose a novel View-Aligned Attention (VAA) module that integrates multi-stage feature representations from the global MoE model to construct a predictive perspective aligned with on-device LLMs, thereby enabling effective cross-architecture knowledge distillation. By explicitly aligning predictive perspectives, VAA resolves the view-mismatch problem in traditional federated knowledge distillation, which arises from heterogeneity in model architectures and prediction behaviors between on-device LLMs and the global MoE model. Experiments with industry-level MoE models (Qwen-MoE and DeepSeek-MoE) and real-world datasets (medical and finance) demonstrate that DeepFusion achieves performance close to centralized MoE training. Compared with key federated MoE baselines, DeepFusion reduces communication costs by up to 71% and improves token perplexity by up to 5.28%.", "authors": ["Songyuan Li", "Jia Hu", "Ahmed M. Abdelmoniem", "Geyong Min", "Haojun Huang", "Jiwei Huang"], "categories": ["cs.LG", "cs.AI", "cs.MA"], "fields_of_study": ["Computer Science"], "published_date": "2026-02-15", "url": "https://arxiv.org/abs/2602.14301", "pdf_url": "https://arxiv.org/pdf/2602.14301v1", "arxiv_id": "2602.14301", "doi": "10.48550/arXiv.2602.14301", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4801}
{"id": "0a4763bd80f526d169372342ef7383ee64033651fafb06a68020fbde8fd77537", "sources": ["arxiv", "semantic_scholar"], "title": "DistillLens: Symmetric Knowledge Distillation Through Logit Lens", "abstract": "Standard Knowledge Distillation (KD) compresses Large Language Models (LLMs) by optimizing final outputs, yet it typically treats the teacher's intermediate layer's thought process as a black box. While feature-based distillation attempts to bridge this gap, existing methods (e.g., MSE and asymmetric KL divergence) ignore the rich uncertainty profiles required for the final output. In this paper, we introduce DistillLens, a framework that symmetrically aligns the evolving thought processes of student and teacher models. By projecting intermediate hidden states into the vocabulary space via the Logit Lens, we enforce structural alignment using a symmetric divergence objective. Our analysis proves that this constraint imposes a dual-sided penalty, preventing both overconfidence and underconfidence while preserving the high-entropy information conduits essential for final deduction. Extensive experiments on GPT-2 and Llama architectures demonstrate that DistillLens consistently outperforms standard KD and feature-transfer baselines on diverse instruction-following benchmarks. The code is available at https://github.com/manishdhakal/DistillLens.", "authors": ["Manish Dhakal", "Uthman Jinadu", "Anjila Budathoki", "Rajshekhar Sunderraman", "Yi Ding"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2026-02-14", "url": "https://arxiv.org/abs/2602.13567", "pdf_url": "https://arxiv.org/pdf/2602.13567v1", "arxiv_id": "2602.13567", "doi": "10.48550/arXiv.2602.13567", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/manishdhakal/DistillLens", "venue": "arXiv.org", "quality_score": 0.7402}
{"id": "15e488d3e25f7f7133294d4f1ae81fd945454c3c976abd29fca90dc80019bc37", "sources": ["arxiv", "semantic_scholar"], "title": "Pedagogically-Inspired Data Synthesis for Language Model Knowledge Distillation", "abstract": "Knowledge distillation from Large Language Models (LLMs) to smaller models has emerged as a critical technique for deploying efficient AI systems. However, current methods for distillation via synthetic data lack pedagogical awareness, treating knowledge transfer as a one-off data synthesis and training task rather than a systematic learning process. In this paper, we propose a novel pedagogically-inspired framework for LLM knowledge distillation that draws from fundamental educational principles. Our approach introduces a three-stage pipeline -- Knowledge Identifier, Organizer, and Adapter (IOA) -- that systematically identifies knowledge deficiencies in student models, organizes knowledge delivery through progressive curricula, and adapts representations to match the cognitive capacity of student models. We integrate Bloom's Mastery Learning Principles and Vygotsky's Zone of Proximal Development to create a dynamic distillation process where student models approach teacher model's performance on prerequisite knowledge before advancing, and new knowledge is introduced with controlled, gradual difficulty increments. Extensive experiments using LLaMA-3.1/3.2 and Qwen2.5 as student models demonstrate that IOA achieves significant improvements over baseline distillation methods, with student models retaining 94.7% of teacher performance on DollyEval while using less than 1/10th of the parameters. Our framework particularly excels in complex reasoning tasks, showing 19.2% improvement on MATH and 22.3% on HumanEval compared with state-of-the-art baselines.", "authors": ["Bowei He", "Yankai Chen", "Xiaokun Zhang", "Linghe Kong", "Philip S. Yu", "Xue Liu", "Chen Ma"], "categories": ["cs.AI", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2026-02-12", "url": "https://arxiv.org/abs/2602.12172", "pdf_url": "https://arxiv.org/pdf/2602.12172v1", "arxiv_id": "2602.12172", "doi": "10.48550/arXiv.2602.12172", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4767}
{"id": "475cae0c0fab2f03e362e52f58ae624ac4c070da0274b3156e7e22fb324c298f", "sources": ["arxiv", "semantic_scholar"], "title": "Benchmarking Knowledge-Extraction Attack and Defense on Retrieval-Augmented Generation", "abstract": "Retrieval-Augmented Generation (RAG) has become a cornerstone of knowledge-intensive applications, including enterprise chatbots, healthcare assistants, and agentic memory management. However, recent studies show that knowledge-extraction attacks can recover sensitive knowledge-base content through maliciously crafted queries, raising serious intellectual property and privacy concerns. While prior work has explored individual attack and defense techniques, the research landscape remains fragmented, spanning heterogeneous retrieval embeddings, diverse generation models, and evaluations based on non-standardized metrics and inconsistent datasets. To address this gap, we introduce the first systematic benchmark for knowledge-extraction attacks on RAG systems. Our benchmark covers broad attack/defense strategies, representative retrieval embedding models, open/closed-source generators, (non) graph-based indexing, all evaluated under a unified experimental framework with standardized protocols across multiple datasets spanning diverse languages. By consolidating the experimental landscape and enabling reproducible, comparable evaluation, this benchmark provides actionable insights and a practical foundation for developing privacy-preserving RAG systems in the face of emerging knowledge extraction threats.", "authors": ["Zhisheng Qi", "Utkarsh Sahu", "Li Ma", "Haoyu Han", "Ryan Rossi", "Franck Dernoncourt", "Mahantesh Halappanavar", "Nesreen Ahmed", "Yushun Dong", "Yue Zhao", "Yu Zhang", "Yu Wang"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2026-02-10", "url": "https://arxiv.org/abs/2602.09319", "pdf_url": "https://arxiv.org/pdf/2602.09319v3", "arxiv_id": "2602.09319", "doi": "10.1145/3770855.3817524", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4744}
{"id": "57afa3aa573e0a7c289d6ce59fa19c924cad0bbf81a33c629f36b47812d64254", "sources": ["arxiv", "semantic_scholar"], "title": "COMBOOD: A Semiparametric Approach for Detecting Out-of-distribution Data for Image Classification", "abstract": "Identifying out-of-distribution (OOD) data at inference time is crucial for many machine learning applications, especially for automation. We present a novel unsupervised semi-parametric framework COMBOOD for OOD detection with respect to image recognition. Our framework combines signals from two distance metrics, nearest-neighbor and Mahalanobis, to derive a confidence score for an inference point to be out-of-distribution. The former provides a non-parametric approach to OOD detection. The latter provides a parametric, simple, yet effective method for detecting OOD data points, especially, in the far OOD scenario, where the inference point is far apart from the training data set in the embedding space. However, its performance is not satisfactory in the near OOD scenarios that arise in practical situations. Our COMBOOD framework combines the two signals in a semi-parametric setting to provide a confidence score that is accurate both for the near-OOD and far-OOD scenarios. We show experimental results with the COMBOOD framework for different types of feature extraction strategies. We demonstrate experimentally that COMBOOD outperforms state-of-the-art OOD detection methods on the OpenOOD (both version 1 and most recent version 1.5) benchmark datasets (for both far-OOD and near-OOD) as well as on the documents dataset in terms of accuracy. On a majority of the benchmark datasets, the improvements in accuracy resulting from the COMBOOD framework are statistically significant. COMBOOD scales linearly with the size of the embedding space, making it ideal for many real-life applications.", "authors": ["Magesh Rajasekaran", "Md Saiful Islam Sajol", "Frej Berglind", "Supratik Mukhopadhyay", "Kamalika Das"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2026-02-04", "url": "https://arxiv.org/abs/2602.07042", "pdf_url": "https://arxiv.org/pdf/2602.07042v1", "arxiv_id": "2602.07042", "doi": "10.1137/1.9781611978032.74", "citation_count": 10, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "SDM", "quality_score": 0.4675}
{"id": "7c4f1f4d7af8edcb3c1f7c3d5bd459ca1b831b41cb006b745fd579985d3ef397", "sources": ["arxiv", "semantic_scholar"], "title": "Should I use Synthetic Data for That? An Analysis of the Suitability of Synthetic Data for Data Sharing and Augmentation", "abstract": "Recent advances in generative modelling have led many to see synthetic data as the go-to solution for a range of problems around data access, scarcity, and under-representation. In this paper, we study three prominent use cases: (1) Sharing synthetic data as a proxy for proprietary datasets to enable statistical analyses while protecting privacy, (2) Augmenting machine learning training sets with synthetic data to improve model performance, and (3) Augmenting datasets with synthetic data to reduce variance in statistical estimation. For each use case, we formalise the problem setting and study, through formal analysis and case studies, under which conditions synthetic data can achieve its intended objectives. We identify fundamental and practical limits that constrain when synthetic data can serve as an effective solution for a particular problem. Our analysis reveals that due to these limits many existing or envisioned use cases of synthetic data are a poor problem fit. Our formalisations and classification of synthetic data use cases enable decision makers to assess whether synthetic data is a suitable approach for their specific data availability problem.", "authors": ["Bogdan Kulynych", "Theresa Stadler", "Jean Louis Raisaro", "Carmela Troncoso"], "categories": ["cs.LG", "cs.CY"], "fields_of_study": ["Computer Science"], "published_date": "2026-02-03", "url": "https://arxiv.org/abs/2602.03791", "pdf_url": "https://arxiv.org/pdf/2602.03791v1", "arxiv_id": "2602.03791", "doi": "10.48550/arXiv.2602.03791", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4664}
{"id": "accddd0e575b15fd4cdb0817a95c3d54046f8fe1d42639fc58fc12d373a49fe8", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetic Data Augmentation for Medical Audio Classification: A Preliminary Evaluation", "abstract": "Medical audio classification remains challenging due to low signal-to-noise ratios, subtle discriminative features, and substantial intra-class variability, often compounded by class imbalance and limited training data. Synthetic data augmentation has been proposed as a potential strategy to mitigate these constraints; however, prior studies report inconsistent methodological approaches and mixed empirical results. In this preliminary study, we explore the impact of synthetic augmentation on respiratory sound classification using a baseline deep convolutional neural network trained on a moderately imbalanced dataset (73%:27%). Three generative augmentation strategies (variational autoencoders, generative adversarial networks, and diffusion models) were assessed under controlled experimental conditions. The baseline model without augmentation achieved an F1-score of 0.645. Across individual augmentation strategies, performance gains were not observed, with several configurations demonstrating neutral or degraded classification performance. Only an ensemble of augmented models yielded a modest improvement in F1-score (0.664). These findings suggest that, for medical audio classification, synthetic augmentation may not consistently enhance performance when applied to a standard CNN classifier. Future work should focus on delineating task-specific data characteristics, model-augmentation compatibility, and evaluation frameworks necessary for synthetic augmentation to be effective in medical audio applications.", "authors": ["David McShannon", "Anthony Mella", "Nicholas Dietrich"], "categories": ["cs.SD", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-02-03", "url": "https://arxiv.org/abs/2602.02955", "pdf_url": "https://arxiv.org/pdf/2602.02955v1", "arxiv_id": "2602.02955", "doi": "10.48550/arXiv.2602.02955", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4664}
{"id": "8091a0db71b1f0448ec6fffe77277bee1bb544f6dcae129f3b724afcfc32f039", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy Amplification Persists under Unlimited Synthetic Data Release", "abstract": "We study privacy amplification by synthetic data release, a phenomenon in which differential privacy guarantees are improved by releasing only synthetic data rather than the private generative model itself. Recent work by Pierquin et al. (2025) established the first formal amplification guarantees for a linear generator, but they apply only in asymptotic regimes where the model dimension far exceeds the number of released synthetic records, limiting their practical relevance. In this work, we show a surprising result: under a bounded-parameter assumption, privacy amplification persists even when releasing an unbounded number of synthetic records, thereby improving upon the bounds of Pierquin et al. (2025). Our analysis provides structural insights that may guide the development of tighter privacy guarantees for more complex release mechanisms.", "authors": ["Clément Pierquin", "Aurélien Bellet", "Marc Tommasi", "Matthieu Boussard"], "categories": ["cs.CR", "cs.DS", "cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2026-02-03", "url": "https://arxiv.org/abs/2602.04895", "pdf_url": "https://arxiv.org/pdf/2602.04895v1", "arxiv_id": "2602.04895", "doi": "10.48550/arXiv.2602.04895", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4664}
{"id": "a949099a0abe09527d25c5f84b4b0bf3e6b7a0ae7e7928eafbaa0be10dd2c13f", "sources": ["arxiv", "semantic_scholar"], "title": "A hybrid approach for building fuzzy numbers based on data and expert knowledge", "abstract": "This paper presents a hybrid socio-technical methodology for constructing fuzzy numbers from numerical data while incorporating expert knowledge through an interactive Deck of Cards (DoC) process. The approach extends the existing DoC membership function construction framework by introducing a data-driven pipeline based on a convex version of fuzzy $k$-Means in which each computational step produces intermediate outputs that are translated into card-based structures for expert validation and tuning. The proposed method ensures interpretability, adaptability, and consistency between empirical evidence and expert semantics.", "authors": ["Diego García-Zamora", "José Rui Figueira", "Miguel Couceiro"], "categories": ["math.GM"], "fields_of_study": ["Mathematics"], "published_date": "2026-02-01", "url": "https://arxiv.org/abs/2602.01192", "pdf_url": "https://arxiv.org/pdf/2602.01192v1", "arxiv_id": "2602.01192", "doi": "10.1016/j.fss.2026.110000", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Diego García-Zamora, José Rui Figueira, Miguel Couceiro, A hybrid approach for building fuzzy numbers based on data and expert knowledge, Fuzzy Sets and Systems, Volume 542, 2026, 110000, ISSN 0165-0114", "quality_score": 0.4641}
{"id": "5d6220129167ade8ea1b85dd25bde67236011fbd813c32bdf457e7ece45408d4", "sources": ["arxiv", "semantic_scholar"], "title": "Learning from Synthetic Data: Limitations of ERM", "abstract": "The prevalence and low cost of LLMs have led to a rise of synthetic content. From review sites to court documents, \"natural\" content has been contaminated by data points that appear similar to natural data, but are in fact LLM-generated. In this work we revisit fundamental learning theory questions in this, now ubiquitous, setting. We model this scenario as a sequence of learning tasks where the input is a mix of natural and synthetic data, and the learning algorithms are oblivious to the origin of any individual example. We study the possibilities and limitations of ERM in this setting. For the problem of estimating the mean of an arbitrary $d$-dimensional distribution, we find that while ERM converges to the true mean, it is outperformed by an algorithm that assigns non-uniform weights to examples from different generations of data. For the PAC learning setting, the disparity is even more stark. We find that ERM does not always converge to the true concept, echoing the model collapse literature. However, we show there are algorithms capable of learning the correct hypothesis for arbitrary VC classes and arbitrary amounts of contamination.", "authors": ["Kareem Amin", "Alex Bie", "Weiwei Kong", "Umar Syed", "Sergei Vassilvitskii"], "categories": ["cs.LG", "cs.DS", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2026-01-21", "url": "https://arxiv.org/abs/2601.15468", "pdf_url": "https://arxiv.org/pdf/2601.15468v2", "arxiv_id": "2601.15468", "doi": "10.48550/arXiv.2601.15468", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4515}
{"id": "1f05a0ea84eb9971fc2149c17378489b7aa48bd267e4437f077c3acfd8013452", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetic Data Augmentation for Multi-Task Chinese Porcelain Classification: A Stable Diffusion Approach", "abstract": "The scarcity of training data presents a fundamental challenge in applying deep learning to archaeological artifact classification, particularly for the rare types of Chinese porcelain. This study investigates whether synthetic images generated through Stable Diffusion with Low-Rank Adaptation (LoRA) can effectively augment limited real datasets for multi-task CNN-based porcelain classification. Using MobileNetV3 with transfer learning, we conducted controlled experiments comparing models trained on pure real data against those trained on mixed real-synthetic datasets (95:5 and 90:10 ratios) across four classification tasks: dynasty, glaze, kiln and type identification. Results demonstrate task-specific benefits: type classification showed the most substantial improvement (5.5\\% F1-macro increase with 90:10 ratio), while dynasty and kiln tasks exhibited modest gains (3-4\\%), suggesting that synthetic augmentation effectiveness depends on the alignment between generated features and task-relevant visual signatures. Our work contributes practical guidelines for deploying generative AI in archaeological research, demonstrating both the potential and limitations of synthetic data when archaeological authenticity must be balanced with data diversity.", "authors": ["Ziyao Ling", "Silvia Mirri", "Paola Salomoni", "Giovanni Delnevo"], "categories": ["cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-01-21", "url": "https://arxiv.org/abs/2601.14791", "pdf_url": "https://arxiv.org/pdf/2601.14791v1", "arxiv_id": "2601.14791", "doi": "10.48550/arXiv.2601.14791", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4515}
{"id": "4797b14cc83b097f1285a45b822103b576f20716c6cce425a35372f9157609ec", "sources": ["arxiv", "semantic_scholar"], "title": "Derivative free data-driven stabilization of continuous-time linear systems from input-output data", "abstract": "This letter presents a data-driven framework for the design of stabilizing controllers from input-output data in the continuous-time, linear, and time-invariant domain. Rather than relying on measurements or reliable estimates of input and output time derivatives, the proposed approach uses filters to derive a parameterization of the system dynamics. This parameterization is amenable to the application of linear matrix inequalities enabling the design of stabilizing output feedback controllers from input-output data and the knowledge of the order of the system.", "authors": ["Corrado Possieri"], "categories": ["math.OC", "math.DS"], "fields_of_study": ["Mathematics", "Computer Science"], "published_date": "2026-01-20", "url": "https://arxiv.org/abs/2601.13848", "pdf_url": "https://arxiv.org/pdf/2601.13848v2", "arxiv_id": "2601.13848", "doi": "10.1109/LCSYS.2026.3658297", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Control Systems Letters", "quality_score": 0.4503}
{"id": "a65b00beb2e0a5aad333f5d6f12827d9856b9e30c15bf99373010126c672c7ad", "sources": ["arxiv", "semantic_scholar"], "title": "Approximating splits for decision trees quickly in sparse data streams", "abstract": "Decision trees are one of the most popular classifiers in the machine learning literature. While the most common decision tree learning algorithms treat data as a batch, numerous algorithms have been proposed to construct decision trees from a data stream. A standard training strategy involves augmenting the current tree by changing a leaf node into a split. Here we typically maintain counters in each leaf which allow us to determine the optimal split, and whether the split should be done. In this paper we focus on how to speed up the search for the optimal split when dealing with sparse binary features and a binary class. We focus on finding splits that have the approximately optimal information gain or Gini index. In both cases finding the optimal split can be done in $O(d)$ time, where $d$ is the number of features. We propose an algorithm that yields $(1 + α)$ approximation when using conditional entropy in amortized $O(α^{-1}(1 + m\\log d) \\log \\log n)$ time, where $m$ is the number of 1s in a data point, and $n$ is the number of data points. Similarly, for Gini index, we achieve $(1 + α)$ approximation in amortized $O(α^{-1} + m \\log d)$ time. Our approach is beneficial for sparse data where $m \\ll d$. In our experiments we find almost-optimal splits efficiently, faster than the baseline, overperforming the theoretical approximation guarantees.", "authors": ["Nikolaj Tatti"], "categories": ["cs.LG", "cs.DS"], "fields_of_study": ["Computer Science"], "published_date": "2026-01-18", "url": "https://arxiv.org/abs/2601.12525", "pdf_url": "https://arxiv.org/pdf/2601.12525v1", "arxiv_id": "2601.12525", "doi": "10.1137/1.9781611978520.69", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "SDM", "quality_score": 0.448}
{"id": "a75863447534c699c5b55e4408a4103b7e5d05b51d4fcfd0900bb0b26e0ed80a", "sources": ["arxiv", "semantic_scholar"], "title": "Big Data Workload Profiling for Energy-Aware Cloud Resource Management", "abstract": "Cloud data centers face increasing pressure to reduce operational energy consumption as big data workloads continue to grow in scale and complexity. This paper presents a workload aware and energy efficient scheduling framework that profiles CPU utilization, memory demand, and storage IO behavior to guide virtual machine placement decisions. By combining historical execution logs with real time telemetry, the proposed system predicts the energy and performance impact of candidate placements and enables adaptive consolidation while preserving service level agreement compliance. The framework is evaluated using representative Hadoop MapReduce, Spark MLlib, and ETL workloads deployed on a multi node cloud testbed. Experimental results demonstrate consistent energy savings of 15 to 20 percent compared to a baseline scheduler, with negligible performance degradation. These findings highlight workload profiling as a practical and scalable strategy for improving the sustainability of cloud based big data processing environments.", "authors": ["Milan Parikh", "Aniket Abhishek Soni", "Sneja Mitinbhai Shah", "Ayush Raj Jha"], "categories": ["cs.DC", "cs.AI", "cs.SE"], "fields_of_study": ["Computer Science"], "published_date": "2026-01-17", "url": "https://arxiv.org/abs/2601.11935", "pdf_url": "https://arxiv.org/pdf/2601.11935v1", "arxiv_id": "2601.11935", "doi": "10.48550/arXiv.2601.11935", "citation_count": 3, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4469}
{"id": "87ad79c902e8816a181dd8fbdb4a6a906429cf2e5193e2c2719478bf3cf10cc0", "sources": ["arxiv", "semantic_scholar"], "title": "Translating database mathematical schemes into relational database software applications with MatBase", "abstract": "We present a pseudocode algorithm for translating our (Elementary) Mathematical Data Model schemes into relational ones and associated sets of non-relational constraints, used by MatBase, our intelligent data and knowledge base management system prototype. We prove that this algorithm is very fast, solid, complete, and optimal. We apply it to a Mathema tical scheme modeling the genealogical trees subuniverse. We also provide examples of SQL and VBA code for enforcing some of its non-relational constraints, as well as guidelines to develop code for enforcing such constraints.", "authors": ["Christian Mancas", "Diana Christina Mancas"], "categories": ["cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2026-01-15", "url": "https://arxiv.org/abs/2601.10604", "pdf_url": "https://arxiv.org/pdf/2601.10604v4", "arxiv_id": "2601.10604", "doi": "10.54364/cybersecurityjournal.2026.3124", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Advances in Knowledge-Based Systems, Data Science, and Cybersecurity 2026, 3(1): 497-517", "quality_score": 0.4446}
{"id": "3551a47333765357895f55e0d6a3c21eb277fe9ff191e6ae71f98fe615a08e7c", "sources": ["arxiv", "semantic_scholar"], "title": "FilDeep: Learning Large Deformations of Elastic-Plastic Solids with Multi-Fidelity Data", "abstract": "The scientific computation of large deformations in elastic-plastic solids is crucial in various manufacturing applications. Traditional numerical methods exhibit several inherent limitations, prompting Deep Learning (DL) as a promising alternative. The effectiveness of current DL techniques typically depends on the availability of high-quantity and high-accuracy datasets, which are yet difficult to obtain in large deformation problems. During the dataset construction process, a dilemma stands between data quantity and data accuracy, leading to suboptimal performance in the DL models. To address this challenge, we focus on a representative application of large deformations, the stretch bending problem, and propose FilDeep, a Fidelity-based Deep Learning framework for large Deformation of elastic-plastic solids. Our FilDeep aims to resolve the quantity-accuracy dilemma by simultaneously training with both low-fidelity and high-fidelity data, where the former provides greater quantity but lower accuracy, while the latter offers higher accuracy but in less quantity. In FilDeep, we provide meticulous designs for the practical large deformation problem. Particularly, we propose attention-enabled cross-fidelity modules to effectively capture long-range physical interactions across MF data. To the best of our knowledge, our FilDeep presents the first DL framework for large deformation problems using MF data. Extensive experiments demonstrate that our FilDeep consistently achieves state-of-the-art performance and can be efficiently deployed in manufacturing.", "authors": ["Jianheng Tang", "Shilong Tao", "Zhe Feng", "Haonan Sun", "Menglu Wang", "Zhanxing Zhu", "Yunhuai Liu"], "categories": ["cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-01-15", "url": "https://arxiv.org/abs/2601.10031", "pdf_url": "https://arxiv.org/pdf/2601.10031v1", "arxiv_id": "2601.10031", "doi": "10.1145/3770854.3783959", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4446}
{"id": "d620f22c230e98dbd8d8a68c9694f5c3ee6e07bb0b5de9adf42c68c4b72ad077", "sources": ["arxiv", "semantic_scholar"], "title": "Radiation Resistance of Ge-doped Multi-Mode Fiber for Optical Links in Collider Experiments", "abstract": "The applications of optical links in collider experiments provide the advantage of high-speed data transmission with low mass fibers over distances of a few hundred meters. Ge-doped multi-mode fibers are evaluated for radiation tolerance in ionizing doses of Co-60 gamma rays. The Radiation-Induced Attenuation (RIA) varies significantly depending on doping substances and fabrication technologies. A type of telecom-grade fiber has demonstrated an RIA of 0.05 dB/m under a total ionizing dose of 300 kGy(SiO2). The dependence on dose rate is compared in the range between 5 Gy/hr and 1.4 kGy/hr, and the annealing recovery is observed after the Co-60 source is shielded. The temperature dependence is investigated across a range of -15 oC to room temperature. At cold temperatures, stagnant annealing leads to a substantially higher RIA during irradiation. The recovery of radiation-induced defects is typically within a few hours, resulting in similar RIA levels regardless of the dose rate and temperature during exposure. Ge-doped fibers of chosen fabrication methods are capable of enduring high ionizing doses for use in high-energy physics experiments.", "authors": ["Datao Gong", "Suen Hou", "Bo-Jing Juang", "Chonghan Liu", "Tiankuan Liu", "Ming Qi", "Jingbo Ye", "Lei Zhang", "Li Zhang"], "categories": ["hep-ex"], "fields_of_study": ["Physics"], "published_date": "2026-01-11", "url": "https://arxiv.org/abs/2601.06822", "pdf_url": "https://arxiv.org/pdf/2601.06822v2", "arxiv_id": "2601.06822", "doi": "10.1016/j.nima.2026.171699", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Nuclear Instruments and Methods in Physics Research Section A : Accelerators, Spectrometers, Detectors and Associated Equipment", "quality_score": 0.44}
{"id": "a5887c0d073eb4b88b0840bb6d1fabaa309cc5f7085e9ff6ac9d4305a11d7c5d", "sources": ["arxiv", "semantic_scholar"], "title": "Causal Data Augmentation for Robust Fine-Tuning of Tabular Foundation Models", "abstract": "Fine-tuning tabular foundation models (TFMs) under data scarcity is challenging, as early stopping on even scarcer validation data often fails to capture true generalization performance. We propose CausalMixFT, a method that enhances fine-tuning robustness and downstream performance by generating structurally consistent synthetic samples using Structural Causal Models (SCMs) fitted on the target dataset. This approach augments limited real data with causally informed synthetic examples, preserving feature dependencies while expanding training diversity. Evaluated across 33 classification datasets from TabArena and over 2300 fine-tuning runs, our CausalMixFT method consistently improves median normalized ROC-AUC from 0.10 (standard fine-tuning) to 0.12, outperforming purely statistical generators such as CTGAN (-0.01), TabEBM (-0.04), and TableAugment (-0.09). Moreover, it narrows the median validation-test performance correlation gap from 0.67 to 0.30, enabling more reliable validation-based early stopping, a key step toward improving fine-tuning stability under data scarcity. These results demonstrate that incorporating causal structure into data augmentation provides an effective and principled route to fine-tuning tabular foundation models in low-data regimes.", "authors": ["Magnus Bühler", "Lennart Purucker", "Frank Hutter"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-01-07", "url": "https://arxiv.org/abs/2601.04110", "pdf_url": "https://arxiv.org/pdf/2601.04110v2", "arxiv_id": "2601.04110", "doi": "10.48550/arXiv.2601.04110", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4354}
{"id": "1d489b88b9487b84692966ccad37f63bfe42f64744bea28ac3d91de5452e31a2", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Compositional Generalization of LLMs via Skill Taxonomy Guided Data Synthesis", "abstract": "Large Language Models (LLMs) and agent-based systems often struggle with compositional generalization due to a data bottleneck in which complex skill combinations follow a long-tailed, power-law distribution, limiting both instruction-following performance and generalization in agent-centric tasks. To address this challenge, we propose STEPS, a Skill Taxonomy guided Entropy-based Post-training data Synthesis framework for generating compositionally challenging data. STEPS explicitly targets compositional generalization by uncovering latent relationships among skills and organizing them into an interpretable, hierarchical skill taxonomy using structural information theory. Building on this taxonomy, we formulate data synthesis as a constrained information maximization problem, selecting skill combinations that maximize marginal structural information within the hierarchy while preserving semantic coherence. Experiments on challenging instruction-following benchmarks show that STEPS outperforms existing data synthesis baselines, while also yielding improved compositional generalization in downstream agent-based evaluations.", "authors": ["Yifan Wei", "Li Du", "Xiaoyan Yu", "Yang Feng", "Angsheng Li"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-01-07", "url": "https://arxiv.org/abs/2601.03676", "pdf_url": "https://arxiv.org/pdf/2601.03676v1", "arxiv_id": "2601.03676", "doi": "10.48550/arXiv.2601.03676", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/weiyifan1023/STEPS", "venue": "arXiv.org", "quality_score": 0.6729}
{"id": "663a830d4e987480f0b6e13a00d9108fa65762116ff30fda55d25986ea7f1b0c", "sources": ["arxiv", "semantic_scholar"], "title": "AIS-CycleGen: A CycleGAN-Based Framework for High-Fidelity Synthetic AIS Data Generation and Augmentation", "abstract": "Automatic Identification System (AIS) data are vital for maritime domain awareness, yet they often suffer from domain shifts, data sparsity, and class imbalance, which hinder the performance of predictive models. In this paper, we propose a robust data augmentation method, AISCycleGen, based on Cycle-Consistent Generative Adversarial Networks (CycleGAN), which is tailored for AIS datasets. Unlike traditional methods, AISCycleGen leverages unpaired domain translation to generate high-fidelity synthetic AIS data sequences without requiring paired source-target data. The framework employs a 1D convolutional generator with adaptive noise injection to preserve the spatiotemporal structure of AIS trajectories, enhancing the diversity and realism of the generated data. To demonstrate its efficacy, we apply AISCycleGen to several baseline regression models, showing improvements in performance across various maritime domains. The results indicate that AISCycleGen outperforms contemporary GAN-based augmentation techniques, achieving a PSNR value of 30.5 and an FID score of 38.9. These findings underscore AISCycleGen's potential as an effective and generalizable solution for augmenting AIS datasets, improving downstream model performance in real-world maritime intelligence applications.", "authors": ["SM Ashfaq uz Zaman", "Faizan Qamar", "Masnizah Mohd", "Nur Hanis Sabrina Suhaimi", "Amith Khandakar"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-01-04", "url": "https://arxiv.org/abs/2601.06127", "pdf_url": "https://arxiv.org/pdf/2601.06127v1", "arxiv_id": "2601.06127", "doi": "10.48550/arXiv.2601.06127", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.432}
{"id": "be9284933f3b32bae7eb1768b56422ff717008f5d271beb71847080b5b4d8d97", "sources": ["arxiv", "semantic_scholar"], "title": "Exploring the Heterogeneity of Tabular Data: A Diversity-aware Data Generator via LLMs", "abstract": "Tabular data generation has become increasingly essential for enabling robust machine learning applications, which require large-scale, high-quality data. Existing solutions leverage generative models to learn original data distributions. However, real-world data are naturally heterogeneous with diverse distributions, making it challenging to obtain a universally good model for diverse data generation. To address this limitation, we introduce Diversity-Aware Tabular data gEnerator (DATE), a framework that (i) prepares high-quality and distributionally distinct examples for in-context learning by effectively partitioning the original heterogeneous data into multiple diverse subsets; (ii) harnesses Large Language Models (LLMs) to explore the diversity of the partitioned distribution with decision tree reasoning as feedback, generating high-quality labeled data for each subset. However, the massive generated data inherently involves a trade-off between diversity and quality. To integrate this issue, existing solutions greedily select the validation-best data. However, we prove that the selection in heterogeneous settings does not possess the greedy-choice property, and design a Multi-Arm Bandit-based sampling algorithm that balances the diversity and quality of generated data. Extensive experiments on tabular classification and regression benchmarks demonstrate that DATE consistently outperforms state-of-the-art GAN-based and LLM-based methods. On average, DATE achieves a 23.75% reduction in error rate with just 100 generated data. Empirically, we demonstrate that data generated by DATE can improve the accuracy of Direct Preference Optimization (DPO) and enhance the reasoning capability of LLMs on the target data. Code is available at https://github.com/windblow32/DATE.", "authors": ["Yafeng Tang", "Xiaoou Ding", "Jianzhuo Du", "Zishuo Yan", "Zhuang Ma", "Zheng Liang", "Zekai Qian", "Hongzhi Wang"], "categories": ["cs.LG", "cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-26", "url": "https://arxiv.org/abs/2512.21915", "pdf_url": "https://arxiv.org/pdf/2512.21915v1", "arxiv_id": "2512.21915", "doi": "10.48550/arXiv.2512.21915", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/windblow32/DATE", "venue": "arXiv.org", "quality_score": 0.6517}
{"id": "fd32c417ab649a9da7980ced39cead11c6a7dad28fe9bd1345a0b204f7227532", "sources": ["arxiv", "semantic_scholar"], "title": "Data relativistic uncertainty framework for low-illumination anime scenery image enhancement", "abstract": "By contrast with the prevailing works of low-light enhancement in natural images and videos, this study copes with the low-illumination quality degradation in anime scenery images to bridge the domain gap. For such an underexplored enhancement task, we first curate images from various sources and construct an unpaired anime scenery dataset with diverse environments and illumination conditions to address the data scarcity. To exploit the power of uncertainty information inherent with the diverse illumination conditions, we propose a Data Relativistic Uncertainty (DRU) framework, motivated by the idea from Relativistic GAN. By analogy with the wave-particle duality of light, our framework interpretably defines and quantifies the illumination uncertainty of dark/bright samples, which is leveraged to dynamically adjust the objective functions to recalibrate the model learning under data uncertainty. Extensive experiments demonstrate the effectiveness of DRU framework by training several versions of EnlightenGANs, yielding superior perceptual and aesthetic qualities beyond the state-of-the-art methods that are incapable of learning from data uncertainty perspective. We hope our framework can expose a novel paradigm of data-centric learning for potential visual and language domains. Code is available.", "authors": ["Yiquan Gao", "John See"], "categories": ["cs.CV", "cs.LG", "cs.MM"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-26", "url": "https://arxiv.org/abs/2512.21944", "pdf_url": "https://arxiv.org/pdf/2512.21944v3", "arxiv_id": "2512.21944", "doi": "10.48550/arXiv.2512.21944", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.6517}
{"id": "d909f2ba13cc906b98e738f398b1a8a6bd3f503c97143f5d0188ee7682ea48db", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetic Financial Data Generation for Enhanced Financial Modelling", "abstract": "Data scarcity and confidentiality in finance often impede model development and robust testing. This paper presents a unified multi-criteria evaluation framework for synthetic financial data and applies it to three representative generative paradigms: the statistical ARIMA-GARCH baseline, Variational Autoencoders (VAEs), and Time-series Generative Adversarial Networks (TimeGAN). Using historical S and P 500 daily data, we evaluate fidelity (Maximum Mean Discrepancy, MMD), temporal structure (autocorrelation and volatility clustering), and practical utility in downstream tasks, specifically mean-variance portfolio optimization and volatility forecasting. Empirical results indicate that ARIMA-GARCH captures linear trends and conditional volatility but fails to reproduce nonlinear dynamics; VAEs produce smooth trajectories that underestimate extreme events; and TimeGAN achieves the best trade-off between realism and temporal coherence (e.g., TimeGAN attained the lowest MMD: 1.84e-3, average over 5 seeds). Finally, we articulate practical guidelines for selecting generative models according to application needs and computational constraints. Our unified evaluation protocol and reproducible codebase aim to standardize benchmarking in synthetic financial data research.", "authors": ["Christophe D. Hounwanou", "Yae Ulrich Gaba", "Pierre Ntakirutimana"], "categories": ["cs.LG", "q-fin.CP"], "fields_of_study": ["Computer Science", "Economics"], "published_date": "2025-12-25", "url": "https://arxiv.org/abs/2512.21791", "pdf_url": "https://arxiv.org/pdf/2512.21791v1", "arxiv_id": "2512.21791", "doi": "10.48550/arXiv.2512.21791", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4205}
{"id": "6ec908c4f5973cf68452cff992d73cec5364457c75b06e46e42cba5f88e54d26", "sources": ["arxiv", "semantic_scholar"], "title": "Deep Generative Models for Synthetic Financial Data: Applications to Portfolio and Risk Modeling", "abstract": "Synthetic financial data provides a practical solution to the privacy, accessibility, and reproducibility challenges that often constrain empirical research in quantitative finance. This paper investigates the use of deep generative models, specifically Time-series Generative Adversarial Networks (TimeGAN) and Variational Autoencoders (VAEs) to generate realistic synthetic financial return series for portfolio construction and risk modeling applications. Using historical daily returns from the S and P 500 as a benchmark, we generate synthetic datasets under comparable market conditions and evaluate them using statistical similarity metrics, temporal structure tests, and downstream financial tasks. The study shows that TimeGAN produces synthetic data with distributional shapes, volatility patterns, and autocorrelation behaviour that are close to those observed in real returns. When applied to mean--variance portfolio optimization, the resulting synthetic datasets lead to portfolio weights, Sharpe ratios, and risk levels that remain close to those obtained from real data. The VAE provides more stable training but tends to smooth extreme market movements, which affects risk estimation. Finally, the analysis supports the use of synthetic datasets as substitutes for real financial data in portfolio analysis and risk simulation, particularly when models are able to capture temporal dynamics. Synthetic data therefore provides a privacy-preserving, cost-effective, and reproducible tool for financial experimentation and model development.", "authors": ["Christophe D. Hounwanou", "Yae Ulrich Gaba"], "categories": ["q-fin.ST", "cs.AI"], "fields_of_study": ["Computer Science", "Economics"], "published_date": "2025-12-25", "url": "https://arxiv.org/abs/2512.21798", "pdf_url": "https://arxiv.org/pdf/2512.21798v2", "arxiv_id": "2512.21798", "doi": "10.48550/arXiv.2512.21798", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4205}
{"id": "0d35c83b6a241c398a574308cc8e50f3eece198e82581cef63354bdde00b4d74", "sources": ["arxiv", "semantic_scholar"], "title": "Generative Spatiotemporal Data Augmentation", "abstract": "We explore spatiotemporal data augmentation using video foundation models to diversify both camera viewpoints and scene dynamics. Unlike existing approaches based on simple geometric transforms or appearance perturbations, our method leverages off-the-shelf video diffusion models to generate realistic 3D spatial and temporal variations from a given image dataset. Incorporating these synthesized video clips as supplemental training data yields consistent performance gains in low-data settings, such as UAV-captured imagery where annotations are scarce. Beyond empirical improvements, we provide practical guidelines for (i) choosing an appropriate spatiotemporal generative setup, (ii) transferring annotations to synthetic frames, and (iii) addressing disocclusion - regions newly revealed and unlabeled in generated views. Experiments on COCO subsets and UAV-captured datasets show that, when applied judiciously, spatiotemporal augmentation broadens the data distribution along axes underrepresented by traditional and prior generative methods, offering an effective lever for improving model performance in data-scarce regimes.", "authors": ["Jinfan Zhou", "Lixin Luo", "Sungmin Eum", "Heesung Kwon", "Jeong Joon Park"], "categories": ["cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-14", "url": "https://arxiv.org/abs/2512.12508", "pdf_url": "https://arxiv.org/pdf/2512.12508v1", "arxiv_id": "2512.12508", "doi": "10.48550/arXiv.2512.12508", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4079}
{"id": "b1b97843d559f8da40570f457fe225c23225e32e198a248e47818df093221729", "sources": ["arxiv", "semantic_scholar"], "title": "Improving Translation Quality by Selecting Better Data for LLM Fine-Tuning: A Comparative Analysis", "abstract": "We investigated the impact of data selection on machine translation fine-tuning for open LLMs. Using Japanese-English corpora, we compare five selectors: TF-IDF, COMET Kiwi, QuRate, FD-Score, and random selection, under controlled training conditions. We observed that semantic selectors consistently outperform lexical and geometry-based heuristics, and that even when the selected data differ by less than 3%, the impact on model performance is substantial, underscoring the sensitivity of fine-tuning to data quality.", "authors": ["Felipe Ribeiro Fujita de Mello", "Hideyuki Takada"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-12", "url": "https://arxiv.org/abs/2512.11388", "pdf_url": "https://arxiv.org/pdf/2512.11388v1", "arxiv_id": "2512.11388", "doi": "10.1109/BigData66926.2025.11402145", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "BigData Congress [Services Society]", "quality_score": 0.4056}
{"id": "d2be2167e4f89152383af2c4a58a9563713f8485fe7bc5d21fe73862d9c18f6f", "sources": ["arxiv", "semantic_scholar"], "title": "A Conditional Generative Framework for Synthetic Data Augmentation in Segmenting Thin and Elongated Structures in Biological Images", "abstract": "Thin and elongated filamentous structures, such as microtubules and actin filaments, often play important roles in biological systems. Segmenting these filaments in biological images is a fundamental step for quantitative analysis. Recent advances in deep learning have significantly improved the performance of filament segmentation. However, there is a big challenge in acquiring high quality pixel-level annotated dataset for filamentous structures, as the dense distribution and geometric properties of filaments making manual annotation extremely laborious and time-consuming. To address the data shortage problem, we propose a conditional generative framework based on the Pix2Pix architecture to generate realistic filaments in microscopy images from binary masks. We also propose a filament-aware structural loss to improve the structure similarity when generating synthetic images. Our experiments have demonstrated the effectiveness of our approach and outperformed existing model trained without synthetic data.", "authors": ["Yi Liu", "Yichi Zhang"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-11", "url": "https://arxiv.org/abs/2512.10334", "pdf_url": "https://arxiv.org/pdf/2512.10334v3", "arxiv_id": "2512.10334", "doi": "10.1109/ACDSA67686.2026.11467901", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2574}
{"id": "edced3f56bdd8c0ee1b787a0f65a7142123c1c12557adc2e583aa85eb7438a27", "sources": ["arxiv", "semantic_scholar"], "title": "Geometric Data Science", "abstract": "This book introduces the new research area of Geometric Data Science, where data can represent any real objects through geometric measurements. The first part of the book focuses on finite point sets. The most important result is a complete and continuous classification of all finite clouds of unordered points under rigid motion in any Euclidean space. The key challenge was to avoid the exponential complexity arising from permutations of the given unordered points. For a fixed dimension of the ambient Euclidean space, the times of all algorithms for the resulting invariants and distance metrics depend polynomially on the number of points. The second part of the book advances a similar classification in the much more difficult case of periodic point sets, which model all periodic crystals at the atomic scale. The most significant result is the hierarchy of invariants from the ultra-fast to complete ones. The key challenge was to resolve the discontinuity of crystal representations that break down under almost any noise. Experimental validation on all major materials databases confirmed the Crystal Isometry Principle: any real periodic crystal has a unique location in a common moduli space of all periodic structures under rigid motion. The resulting moduli space contains all known and not yet discovered periodic crystals and hence continuously extends Mendeleev's table to the full crystal universe.", "authors": ["Olga D Anosova", "Vitaliy A Kurlin"], "categories": ["math.MG", "cond-mat.mtrl-sci", "cs.CG"], "fields_of_study": ["Mathematics", "Physics", "Computer Science"], "published_date": "2025-12-04", "url": "https://arxiv.org/abs/2512.05040", "pdf_url": "https://arxiv.org/pdf/2512.05040v1", "arxiv_id": "2512.05040", "doi": "10.48550/arXiv.2512.05040", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3965}
{"id": "5e1745291926c4aa4265b37c593a8649e6d340e5fe278290067df8fa49c22fc7", "sources": ["arxiv", "semantic_scholar"], "title": "MechDetect: Detecting Data-Dependent Errors", "abstract": "Data quality monitoring is a core challenge in modern information processing systems. While many approaches to detect data errors or shifts have been proposed, few studies investigate the mechanisms governing error generation. We argue that knowing how errors were generated can be key to tracing and fixing them. In this study, we build on existing work in the statistics literature on missing values and propose MechDetect, a simple algorithm to investigate error generation mechanisms. Given a tabular data set and a corresponding error mask, the algorithm estimates whether or not the errors depend on the data using machine learning models. Our work extends established approaches to detect mechanisms underlying missing values and can be readily applied to other error types, provided that an error mask is available. We demonstrate the effectiveness of MechDetect in experiments on established benchmark datasets.", "authors": ["Philipp Jung", "Nicholas Chandler", "Sebastian Jäger", "Felix Biessmann"], "categories": ["cs.LG", "cs.DB", "cs.IR"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-03", "url": "https://arxiv.org/abs/2512.04138", "pdf_url": "https://arxiv.org/pdf/2512.04138v1", "arxiv_id": "2512.04138", "doi": "10.1109/DSIS67228.2025.11390600", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2516}
{"id": "076eb10c9b44e8d3f571c13281929d29efecb57f25b088538eda6c796a8beae9", "sources": ["arxiv", "semantic_scholar"], "title": "Robust Tabular Foundation Models", "abstract": "The development of tabular foundation models (TFMs) has accelerated in recent years, showing strong potential to outperform traditional ML methods for structured data. A key finding is that TFMs can be pretrained entirely on synthetic datasets, opening opportunities to design data generators that encourage desirable model properties. Prior work has mainly focused on crafting high-quality priors over generators to improve overall pretraining performance. Our insight is that parameterizing the generator distribution enables an adversarial robustness perspective: during training, we can adapt the generator to emphasize datasets that are particularly challenging for the model. We formalize this by introducing an optimality gap measure, given by the difference between TFM performance and the best achievable performance as estimated by strong baselines such as XGBoost, CatBoost, and Random Forests. Building on this idea, we propose Robust Tabular Foundation Models (RTFM), a model-agnostic adversarial training framework. Applied to the TabPFN V2 classifier, RTFM improves benchmark performance, with up to a 6% increase in mean normalized AUC over the original TabPFN and other baseline algorithms, while requiring less than 100k additional synthetic datasets. These results highlight a promising new direction for targeted adversarial training and fine-tuning of TFMs using synthetic data alone.", "authors": ["Matthew Peroni", "Franck Le", "Vadim Sheinin"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-02", "url": "https://arxiv.org/abs/2512.03307", "pdf_url": "https://arxiv.org/pdf/2512.03307v1", "arxiv_id": "2512.03307", "doi": "10.48550/arXiv.2512.03307", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3942}
{"id": "dd1acc7a5efb4a19021e0fc44397ed8275b7d5aebae0d64784c8cf56b3821ae7", "sources": ["arxiv", "semantic_scholar"], "title": "Fast Gaussian Process Approximations for Autocorrelated Data", "abstract": "This paper is concerned with the problem of how to speed up computation for Gaussian process models trained on autocorrelated data. The Gaussian process model is a powerful tool commonly used in nonlinear regression applications. Standard regression modeling assumes random samples and an independently, identically distributed noise. Various fast approximations that speed up Gaussian process regression work under this standard setting. But for autocorrelated data, failing to account for autocorrelation leads to a phenomenon known as temporal overfitting that deteriorates model performance on new test instances. To handle autocorrelated data, existing fast Gaussian process approximations have to be modified; one such approach is to segment the originally correlated data points into blocks in which the blocked data are de-correlated. This work explains how to make some of the existing Gaussian process approximations work with blocked data. Numerical experiments across diverse application datasets demonstrate that the proposed approaches can remarkably accelerate computation for Gaussian process regression on autocorrelated data without compromising model prediction performance.", "authors": ["Ahmadreza Chokhachian", "Matthias Katzfuss", "Yu Ding"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2025-12-02", "url": "https://arxiv.org/abs/2512.02925", "pdf_url": "https://arxiv.org/pdf/2512.02925v1", "arxiv_id": "2512.02925", "doi": "10.1287/ijds.2025.0087", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "INFORMS Journal on Data Science", "quality_score": 0.3942}
{"id": "259aefb8dbfcdd572c54998f8d322270d63ee4630c2347272f5f5478933844a5", "sources": ["arxiv", "semantic_scholar"], "title": "Challenges of Heterogeneity in Big Data: A Comparative Study of Classification in Large-Scale Structured and Unstructured Domains", "abstract": "This study analyzes the impact of heterogeneity (\"Variety\") in Big Data by comparing classification strategies across structured (Epsilon) and unstructured (Rest-Mex, IMDB) domains. A dual methodology was implemented: evolutionary and Bayesian hyperparameter optimization (Genetic Algorithms, Optuna) in Python for numerical data, and distributed processing in Apache Spark for massive textual corpora. The results reveal a \"complexity paradox\": in high-dimensional spaces, optimized linear models (SVM, Logistic Regression) outperformed deep architectures and Gradient Boosting. Conversely, in text-based domains, the constraints of distributed fine-tuning led to overfitting in complex models, whereas robust feature engineering -- specifically Transformer-based embeddings (ROBERTa) and Bayesian Target Encoding -- enabled simpler models to generalize effectively. This work provides a unified framework for algorithm selection based on data nature and infrastructure constraints.", "authors": ["González Trigueros Jesús Eduardo", "Alonso Sánchez Alejandro", "Muñoz Rivera Emilio", "Peñarán Prieto Mariana Jaqueline", "Mendoza González Camila Natalia"], "categories": ["cs.LG", "cs.CL", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-29", "url": "https://arxiv.org/abs/2512.00298", "pdf_url": "https://arxiv.org/pdf/2512.00298v1", "arxiv_id": "2512.00298", "doi": "10.48550/arXiv.2512.00298", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3907}
{"id": "4b9e89c25ae90f37cf0a6c013a4e7b0fa267ea762e71e5b3f4e257f4bddbdab2", "sources": ["arxiv", "semantic_scholar"], "title": "Robust Spectral Watermark for Synthetic Tabular Data", "abstract": "The rise of generative AI has enabled the production of high-fidelity synthetic tabular data across fields such as healthcare, finance, and public policy, raising growing concerns about data provenance and misuse. Watermarking offers a promising solution to address these concerns by ensuring the traceability of synthetic data, but existing methods face many limitations: they are computationally expensive due to reliance on the inverse process of large diffusion models, struggle with mixed discrete-continuous data, or lack robustness to common post-processing attacks. To address these limitations, we propose TAB-DRW, an efficient and robust post-editing watermarking scheme for synthetic tabular data. TAB-DRW embeds watermark signals in the frequency domain: it normalizes heterogeneous features via the Yeo-Johnson transformation and standardization, applies the discrete Fourier transform (DFT), and adjusts the imaginary parts of adaptively selected entries according to precomputed pseudorandom bits. To further enhance robustness and efficiency, we introduce a novel rank-based pseudorandom bit generation method that enables row-wise retrieval without incurring storage overhead. Experiments on five benchmark tabular datasets show that TAB-DRW achieves strong detectability and robustness against post-processing and adaptive attacks, while preserving high data fidelity and fully supporting mixed-type features.", "authors": ["Yizhou Zhao", "Xiang Li", "Peter Song", "Qi Long", "Weijie Su"], "categories": ["cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-26", "url": "https://arxiv.org/abs/2511.21600", "pdf_url": "https://arxiv.org/pdf/2511.21600v2", "arxiv_id": "2511.21600", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2465}
{"id": "a220fbc00a09cdb67dba3511070e87191da6a21781f9b9e2bc7247f84d2065a1", "sources": ["arxiv", "semantic_scholar"], "title": "Data-Driven Assessment of Concrete Slab Integrity via Impact-Echo Signals and Neural Networks", "abstract": "Subsurface defects such as delamination, voids, and honeycombing critically affect the durability of concrete bridge decks but are difficult to detect reliably using visual inspection or manual sounding. This paper presents a machine learning based Impact Echo (IE) framework that automates both defect localization and multi-class classification of common concrete defects. Raw IE signals from Federal Highway Administration (FHWA) laboratory slabs and in-service bridge decks are transformed via Fast Fourier Transform (FFT) into dominant peak-frequency features and interpolated into spatial maps for defect zone visualization. Unsupervised k-means clustering highlights low-frequency, defect-prone regions, while Ground Truth Masks (GTMs) derived from seeded lab defects are used to validate spatial accuracy and generate high-confidence training labels. From these validated regions, spatially ordered peak-frequency sequences are constructed and fed into a stacked Long Short-Term Memory (LSTM) network that classifies four defect types shallow delamination, deep delamination, voids, and honeycombing with 73% overall accuracy. Field validation on the bridge deck demonstrates that models trained on laboratory data generalize under realistic coupling, noise, and environmental variability. The proposed framework enhances the objectivity, scalability, and repeatability of Non-Destructive Evaluation (NDE), supporting intelligent, data-driven bridge health monitoring at a network scale.", "authors": ["Yeswanth Ravichandran", "Duoduo Liao", "Charan Teja Kurakula"], "categories": ["eess.SP", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2025-11-26", "url": "https://arxiv.org/abs/2511.21080", "pdf_url": "https://arxiv.org/pdf/2511.21080v1", "arxiv_id": "2511.21080", "doi": "10.1109/BigData66926.2025.11402462", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "BigData Congress [Services Society]", "quality_score": 0.3873}
{"id": "53c9efdf73f8fb6917076b9089aa1583aaa33520ca3b050cb967cd8e997ae3f3", "sources": ["arxiv"], "title": "A review on data fusion in multimodal learning analytics and educational data mining", "abstract": "The new educational models such as smart learning environments use of digital and context-aware devices to facilitate the learning process. In this new educational scenario, a huge quantity of multimodal students' data from a variety of different sources can be captured, fused, and analyze. It offers to researchers and educators a unique opportunity of being able to discover new knowledge to better understand the learning process and to intervene if necessary. However, it is necessary to apply correctly data fusion approaches and techniques in order to combine various sources of multimodal learning analytics (MLA). These sources or modalities in MLA include audio, video, electrodermal activity data, eye-tracking, user logs, and click-stream data, but also learning artifacts and more natural human signals such as gestures, gaze, speech, or writing. This survey introduces data fusion in learning analytics (LA) and educational data mining (EDM) and how these data fusion techniques have been applied in smart learning. It shows the current state of the art by reviewing the main publications, the main type of fused educational data, and the data fusion approaches and techniques used in EDM/LA, as well as the main open problems, trends, and challenges in this specific research area.", "authors": ["Wilson Chango", "Juan A. Lara", "Rebeca Cerezo", "Cristóbal Romero"], "categories": ["cs.CY", "cs.LG"], "fields_of_study": [], "published_date": "2025-11-25", "url": "https://arxiv.org/abs/2511.20871", "pdf_url": "https://arxiv.org/pdf/2511.20871v1", "arxiv_id": "2511.20871", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "WIREs Data Mining and Knowledge Discovery, 12(4), e1458 (2022)", "quality_score": 0.3861}
{"id": "42e8e740a42ca8fb0d0fb0703ef6c2ab07d6fcb8f28bc4efd854ecd0dddb484b", "sources": ["arxiv", "semantic_scholar"], "title": "Post-Pruning Accuracy Recovery via Data-Free Knowledge Distillation", "abstract": "Model pruning is a widely adopted technique to reduce the computational complexity and memory footprint of Deep Neural Networks (DNNs). However, global unstructured pruning often leads to significant degradation in accuracy, typically necessitating fine-tuning on the original training dataset to recover performance. In privacy-sensitive domains such as healthcare or finance, access to the original training data is often restricted post-deployment due to regulations (e.g., GDPR, HIPAA). This paper proposes a Data-Free Knowledge Distillation framework to bridge the gap between model compression and data privacy. We utilize DeepInversion to synthesize privacy-preserving ``dream'' images from the pre-trained teacher model by inverting Batch Normalization (BN) statistics. These synthetic images serve as a transfer set to distill knowledge from the original teacher to the pruned student network. Experimental results on CIFAR-10 across various architectures (ResNet, MobileNet, VGG) demonstrate that our method significantly recovers accuracy lost during pruning without accessing a single real data point.", "authors": ["Chinmay Tripurwar", "Utkarsh Maurya", " Dishant"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-24", "url": "https://arxiv.org/abs/2511.20702", "pdf_url": "https://arxiv.org/pdf/2511.20702v1", "arxiv_id": "2511.20702", "doi": "10.48550/arXiv.2511.20702", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.385}
{"id": "58359336632406d7ed0d19935b76739e35f182764b04a32d8146d2ecdf2b466b", "sources": ["arxiv", "semantic_scholar"], "title": "An Interpretability-Guided Framework for Responsible Synthetic Data Generation in Emotional Text", "abstract": "Emotion recognition from social media is critical for understanding public sentiment, but accessing training data has become prohibitively expensive due to escalating API costs and platform restrictions. We introduce an interpretability-guided framework where Shapley Additive Explanations (SHAP) provide principled guidance for LLM-based synthetic data generation. With sufficient seed data, SHAP-guided approach matches real data performance, significantly outperforms naïve generation, and substantially improves classification for underrepresented emotion classes. However, our linguistic analysis reveals that synthetic text exhibits reduced vocabulary richness and fewer personal or temporally complex expressions than authentic posts. This work provides both a practical framework for responsible synthetic data generation and a critical perspective on its limitations, underscoring that the future of trustworthy AI depends on navigating the trade-offs between synthetic utility and real-world authenticity.", "authors": ["Paula Joy B. Martinez", "Jose Marie Antonio Miñoza", "Sebastian C. Ibañez"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-20", "url": "https://arxiv.org/abs/2511.16132", "pdf_url": "https://arxiv.org/pdf/2511.16132v1", "arxiv_id": "2511.16132", "doi": "10.48550/arXiv.2511.16132", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3804}
{"id": "8ab06786b6866d7ac54912fc3d7ba3f8064447e79a6f49a5f036f5813d3b2b4d", "sources": ["arxiv", "semantic_scholar"], "title": "Oversampling techniques for predicting COVID-19 patient length of stay", "abstract": "COVID-19 is a respiratory disease that caused a global pandemic in 2019. It is highly infectious and has the following symptoms: fever or chills, cough, shortness of breath, fatigue, muscle or body aches, headache, the new loss of taste or smell, sore throat, congestion or runny nose, nausea or vomiting, and diarrhea. These symptoms vary in severity; some people with many risk factors have been known to have lengthy hospital stays or die from the disease. In this paper, we analyze patients' electronic health records (EHR) to predict the severity of their COVID-19 infection using the length of stay (LOS) as our measurement of severity. This is an imbalanced classification problem, as many people have a shorter LOS rather than a longer one. To combat this problem, we synthetically create alternate oversampled training data sets. Once we have this oversampled data, we run it through an Artificial Neural Network (ANN), which during training has its hyperparameters tuned using Bayesian optimization. We select the model with the best F1 score and then evaluate it and discuss it.", "authors": ["Zachariah Farahany", "Jiawei Wu", "K M Sajjadul Islam", "Praveen Madiraju"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-19", "url": "https://arxiv.org/abs/2511.15048", "pdf_url": "https://arxiv.org/pdf/2511.15048v1", "arxiv_id": "2511.15048", "doi": "10.1109/BigData55660.2022.10020253", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan, 17-20 December 2022", "quality_score": 0.3793}
{"id": "caeeba3569b4d6a02e83546ba8bf44f3dd8c32ba65cf1639e5309dce56f53ba4", "sources": ["arxiv", "semantic_scholar"], "title": "Bi-View Embedding Fusion: A Hybrid Learning Approach for Knowledge Graph's Nodes Classification Addressing Problems with Limited Data", "abstract": "Traditional Machine Learning (ML) methods require large amounts of data to perform well, limiting their applicability in sparse or incomplete scenarios and forcing the usage of additional synthetic data to improve the model training. To overcome this challenge, the research community is looking more and more at Graph Machine Learning (GML) as it offers a powerful alternative by using relationships within data. However, this method also faces limitations, particularly when dealing with Knowledge Graphs (KGs), which can hide huge information due to their semantic nature. This study introduces Bi-View, a novel hybrid approach that increases the informative content of node features in KGs to generate enhanced Graph Embeddings (GEs) that are used to improve GML models without relying on additional synthetic data. The proposed work combines two complementary GE techniques: Node2Vec, which captures structural patterns through unsupervised random walks, and GraphSAGE, which aggregates neighbourhood information in a supervised way. Node2Vec embeddings are first computed to represent the graph topology, and node features are then enriched with centrality-based metrics, which are used as input for the GraphSAGE model. Moreover, a fusion layer combines the original Node2Vec embeddings with the GraphSAGE-influenced representations, resulting in a dual-perspective embedding space. Such a fusion captures both topological and semantic properties of the graph, enabling the model to exploit informative features that may exist in the dataset but that are not explicitly represented. Our approach improves downstream task performance, especially in scenarios with poor initial features, giving the basis for more accurate and precise KG-enanched GML models.", "authors": ["Rosario Napoli", "Giovanni Lonia", "Antonio Celesti", "Massimo Villari", "Maria Fazio"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-17", "url": "https://arxiv.org/abs/2511.13044", "pdf_url": "https://arxiv.org/pdf/2511.13044v1", "arxiv_id": "2511.13044", "doi": "10.48550/arXiv.2511.13044", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Knowledge Graphs, Springer Nature Singapore, 2026, pp. 19-34", "quality_score": 0.377}
{"id": "2521105ef9df190fa0ec0335650bc7bada62146e104634e85e459517f099d374", "sources": ["arxiv", "semantic_scholar"], "title": "Advanced Data Analysis of Spontaneous Biophoton Emission: A Multi-Method Approach", "abstract": "Ultra-weak photon emission (UPE) from living systems is widely hypothesized to reflect un-derlying self-organization and long-range coordination in biological dynamics. However, distin-guishing biologically driven correlations from trivial stochastic or instrumental effects requires a robust, multi-method framework. In this work, we establish and benchmark a comprehensive anal-ysis pipeline for photon-count time series, combining Distribution Entropy Analysis, Rényi entro-py, Detrended Fluctuation Analysis, its generalization Multifractal Detrended Fluctuation Analysis, and tail-statistics characterization. Surrogate signals constructed from Poisson processes, Fractional Gaussian Noise, and Renewal Processes with power-law waiting times are used to validate sensitivity to memory, intermittency, and multifractality. Across all methods, a coherent hierarchy of dynamical regimes is recovered, demonstrating internal methodological consistency. Application to experimental dark-count data and attenuated coherent-laser emission confirm Poisson-like behavior, establishing an essential statistical baseline for UPE studies. The combined results show that this multi-resolution approach reliably separates trivial photon-counting statistics from struc-tured long-range organization, providing a validated methodological foundation for future biological UPE measurements and their interpretation in the context of non-equilibrium statistical physics, information dynamics, and prospective markers of biological coherence.", "authors": ["M. Benfatto", "L. De Paolis", "L. Tonello", "P. Grigolini"], "categories": ["physics.bio-ph", "nlin.AO", "physics.data-an", "q-bio.QM"], "fields_of_study": ["Physics", "Biology"], "published_date": "2025-11-14", "url": "https://arxiv.org/abs/2511.11080", "pdf_url": "https://arxiv.org/pdf/2511.11080v1", "arxiv_id": "2511.11080", "doi": null, "citation_count": 2, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2377}
{"id": "3b9891c05534de9cba4d5791ecaa151261a3c37e17785e92d22fd8735bbd77d5", "sources": ["arxiv", "semantic_scholar"], "title": "STELLAR: Scene Text Editor for Low-Resource Languages and Real-World Data", "abstract": "Scene Text Editing (STE) is the task of modifying text content in an image while preserving its visual style, such as font, color, and background. While recent diffusion-based approaches have shown improvements in visual quality, key limitations remain: lack of support for low-resource languages, domain gap between synthetic and real data, and the absence of appropriate metrics for evaluating text style preservation. To address these challenges, we propose STELLAR (Scene Text Editor for Low-resource LAnguages and Real-world data). STELLAR enables reliable multilingual editing through a language-adaptive glyph encoder and a multi-stage training strategy that first pre-trains on synthetic data and then fine-tunes on real images. We also construct a new dataset, STIPLAR(Scene Text Image Pairs of Low-resource lAnguages and Real-world data), for training and evaluation. Furthermore, we propose Text Appearance Similarity (TAS), a novel metric that assesses style preservation by independently measuring font, color, and background similarity, enabling robust evaluation even without ground truth. Experimental results demonstrate that STELLAR outperforms state-of-the-art models in visual consistency and recognition accuracy, achieving an average TAS improvement of 2.2% across languages over the baselines.", "authors": ["Yongdeuk Seo", "Hyun-seok Min", "Sungchul Choi"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-13", "url": "https://arxiv.org/abs/2511.09977", "pdf_url": "https://arxiv.org/pdf/2511.09977v2", "arxiv_id": "2511.09977", "doi": "10.48550/arXiv.2511.09977", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3724}
{"id": "37401637c8a4aa563ce24d7d69ceb258b644b3e5a341761753cbfb631baf614a", "sources": ["arxiv", "semantic_scholar"], "title": "Equilibrium Dynamics and Mitigation of Gender Bias in Synthetically Generated Data", "abstract": "Recursive prompting with large language models enables scalable synthetic dataset generation but introduces the risk of bias amplification. We investigate gender bias dynamics across three generations of recursive text generation using three complementary evaluation frameworks: rule-based pattern matching, embedding-based semantic similarity, and downstream task performance. Experiments with three initial bias levels (0.1, 0.3, 0.6) and four mitigation strategies reveal equilibrium dynamics rather than monotonic amplification. The low initial bias amplifies toward the model's inherent bias level (+36%), whereas the high initial bias decays toward it (-26%). Among mitigation methods, contrastive augmentation, which introduces gender-swapped variants, achieves significant downstream bias reduction (98.8% for low initial bias and 91% on average) despite producing higher embedding-based bias scores. This paradox demonstrates that semantic similarity metrics may diverge from behavioral fairness outcomes, highlighting the need for multidimensional evaluation in responsible synthetic data generation.", "authors": ["Ashish Kattamuri", "Arpita Vats", "Harshwardhan Fartale", "Rahul Raja", "Akshata Kishore Moharir", "Ishita Prasad"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-12", "url": "https://arxiv.org/abs/2511.10689", "pdf_url": "https://arxiv.org/pdf/2511.10689v1", "arxiv_id": "2511.10689", "doi": "10.48550/arXiv.2511.10689", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3713}
{"id": "ef57f69ce4e0ee31d6504e7ed77f14f629f2447326e620f8931bacf2fd2af084", "sources": ["arxiv", "semantic_scholar"], "title": "Methodological Precedence in Health Tech: Why ML/Big Data Analysis Must Follow Basic Epidemiological Consistency. A Case Study", "abstract": "The integration of advanced analytical tools, including Machine Learning (ML) and massive data processing, has revolutionized health research, promising unprecedented accuracy in diagnosis and risk prediction. However, the rigor of these complex methods is fundamentally dependent on the quality and integrity of the underlying datasets and the validity of their statistical design. We propose an emblematic case where advanced analysis (ML/Big Data) must necessarily be subsequent to the verification of basic methodological coherence and adherence to established medical protocols, such as the STROBE Statement. This study highlights a crucial cautionary principle: sophisticated analyses amplify, rather than correct, severe methodological flaws rooted in basic design choices, leading to misleading or contradictory findings. By applying simple, standard descriptive statistical methods and established national epidemiological benchmarks to a recently published cohort study on COVID-19 vaccine outcomes and severe adverse events, like cancer, we expose multiple, statistically irreconcilable paradoxes. These paradoxes, specifically the contradictory finding of an increased cancer incidence within an exposure subgroup, concurrent with a suppressed overall Crude Incidence Rate compared to national standards, definitively invalidate the reported risk of increased cancer in the total population. We demonstrate that the observed effects are mathematical artifacts stemming from an uncorrected selection bias in the cohort construction. This analysis serves as a robust reminder that even the most complex health studies must first pass the test of basic epidemiological consistency before any conclusion drawn from subsequent advanced statistical modeling can be considered valid or publishable.", "authors": ["Marco Roccetti"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-10", "url": "https://arxiv.org/abs/2511.07500", "pdf_url": "https://arxiv.org/pdf/2511.07500v2", "arxiv_id": "2511.07500", "doi": "10.48550/arXiv.2511.07500", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.369}
{"id": "ce4101ce0d994f11b11dcbb4ece7e0cec65277a0cca607c52ffa4015bb332729", "sources": ["arxiv"], "title": "A Multi-Agent System for Semantic Mapping of Relational Data to Knowledge Graphs", "abstract": "Enterprises often maintain multiple databases for storing critical business data in siloed systems, resulting in inefficiencies and challenges with data interoperability. A key to overcoming these challenges lies in integrating disparate data sources, enabling businesses to unlock the full potential of their data. Our work presents a novel approach for integrating multiple databases using knowledge graphs, focusing on the application of large language models as semantic agents for mapping and connecting structured data across systems by leveraging existing vocabularies. The proposed methodology introduces a semantic layer above tables in relational databases, utilizing a system comprising multiple LLM agents that map tables and columns to Schema.org terms. Our approach achieves a mapping accuracy of over 90% in multiple domains.", "authors": ["Milena Trajanoska", "Riste Stojanov", "Dimitar Trajanov"], "categories": ["cs.DB", "cs.AI"], "fields_of_study": [], "published_date": "2025-11-09", "url": "https://arxiv.org/abs/2511.06455", "pdf_url": "https://arxiv.org/pdf/2511.06455v1", "arxiv_id": "2511.06455", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "The 1st GOBLIN Workshop on Knowledge Graph Technologies, June 12, 2025 in Leipzig, Germany", "quality_score": 0.3678}
{"id": "492b5be5b3c0ef62c1aa2797ce3f9347f03d4dbadda3ef04dd6bef7a327562cb", "sources": ["arxiv", "semantic_scholar"], "title": "Are We Asking the Right Questions? On Ambiguity in Natural Language Queries for Tabular Data Analysis", "abstract": "Natural language interfaces to tabular data must handle ambiguities inherent to queries. Instead of treating ambiguity as a deficiency, we reframe it as a feature of cooperative interaction where users are intentional about the degree to which they specify queries. We develop a principled framework based on a shared responsibility of query specification between user and system, distinguishing unambiguous and ambiguous cooperative queries, which systems can resolve through reasonable inference, from uncooperative queries that cannot be resolved. Applying the framework to evaluations for tabular question answering and analysis, we analyze queries in 15 datasets, and observe an uncontrolled mixing of query types neither adequate for evaluating a system's accuracy nor for evaluating interpretation capabilities. This conceptualization around cooperation in resolving queries informs how to design and evaluate natural language interfaces for tabular data analysis, for which we distill concrete directions for future research and broader implications.", "authors": ["Daniel Gomm", "Cornelius Wolff", "Madelon Hulsebos"], "categories": ["cs.AI", "cs.CL", "cs.DB", "cs.HC"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-06", "url": "https://arxiv.org/abs/2511.04584", "pdf_url": "https://arxiv.org/pdf/2511.04584v4", "arxiv_id": "2511.04584", "doi": "10.48550/arXiv.2511.04584", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3644}
{"id": "b314331d94ce9fba3cc989711199cc485c281e7b4696c98209927f79eb3bc852", "sources": ["arxiv", "semantic_scholar"], "title": "ENDF/B-VIII.1: Updated Nuclear Reaction Data Library for Science and Applications", "abstract": "The ENDF/B-VIII.1 library is the newest recommended evaluated nuclear data file by the Cross Section Evaluation Working Group (CSEWG) for use in nuclear science and technology applications, and incorporates advances made in the six years since the release of ENDF/B-VIII.0. Among key advances made are that the $^{239}$Pu file was reevaluated by a joint international effort and that updated $^{16,18}$O, $^{19}$F, $^{28-30}$Si, $^{50-54}$Cr, $^{55}$Mn, $^{54,56,57}$Fe, $^{63,65}$Cu, $^{139}$La, $^{233,235,238}$U, and $^{240,241}$Pu neutron nuclear data from the IAEA coordinated INDEN collaboration were adopted. Over 60 neutron dosimetry cross sections were adopted from the IAEA's IRDFF-II library. In addition, the new library includes significant changes for $^3$He, $^6$Li,$^9$Be, $^{51}$V, $^{88}$Sr, $^{103}$Rh, $^{140,142}$Ce, Dy, $^{181}$Ta, Pt, $^{206-208}$Pb, and $^{234,236}$U neutron data, and new nuclear data for the photonuclear, charged-particle and atomic sublibraries. Numerous thermal neutron scattering kernels were reevaluated or provided for the very first time. On the covariance side, work was undertaken to introduce better uncertainty quantification standards and testing for nuclear data covariances. The significant effort to reevaluate important nuclides has reduced bias in the simulations of many integral experiments with particular progress noted for fluorine, copper, and stainless steel containing benchmarks. Data issues hindered the successful deployment of the previous ENDF/B-VIII.0 for commercial nuclear power applications in high burnup situations. These issues were addressed by improving the $^{238}$U and $^{239,240,241}$Pu evaluated data in the resonance region. The new library performance as a function of burnup is similar to the reference ENDF/B-VII.1 library. The ENDF/B-VIII.1 data are available in ENDF-6 and GNDS format at https://doi.org/10.11578/endf/2571019.", "authors": ["G. P. A. Nobre", "R. Capote", "M. T. Pigni", "A. Trkov", "C. M. Mattoon", "D. Neudecker", "D. A. Brown", "M. B. Chadwick", "A. C. Kahler", "N. A. Kleedtke", "M. Zerkle", "A. I. Hawari", "C. W. Chapman", "N. C. Fleming", "J. L. Wormald", "K. Ramić", "Y. Danon", "N. A. Gibson", "P. Brain", "M. W. Paris", "G. M. Hale", "I. J. Thompson", "D. P. Barry", "I. Stetcu", "W. Haeck", "A. E. Lovell", "M. R. Mumpower", "G. Potel", "K. Kravvaris", "G. Noguere", "J. D. McDonnell", "A. D. Carlson", "M. Dunn", "T. Kawano", "D. Wiarda", "I. Al-Qasir", "G. Arbanas", "R. Arcilla", "B. Beck", "D. Bernard", "R. Beyer", "J. M. Brown", "O. Cabellos", "R. J. Casperson", "Y. Cheng", "E. V. Chimanski", "R. Coles", "M. Cornock", "J. Cotchen", "J. P. W. Crozier", "D. E. Cullen", "A. Daskalakis", "M. -A. Descalle", "D. D. DiJulio", "P. Dimitriou", "A. C. Dreyfuss", "I. Durán", "R. Ferrer", "T. Gaines", "V. Gillette", "G. Gert", "K. H. Guber", "J. D. Haverkamp", "M. W. Herman", "J. Holmes", "M. Hursin", "N. Jisrawi", "A. R. Junghans", "K. J. Kelly", "H. I. Kim", "K. S. Kim", "A. J. Koning", "M. Koštál", "B. K. Laramee", "A. Lauer-Coles", "L. Leal", "H. Y. Lee", "A. M. Lewis", "J. Malec", "J. I. Márquez Damián", "W. J. Marshall", "A. Mattera", "G. Muhrer", "A. Ney", "W. E. Ormand", "D. K. Parsons", "C. M. Percher", "V. G. Pronyaev", "A. Qteish", "S. Quaglioni", "M. Rapp", "J. J. Ressler", "M. Rising", "D. Rochman", "P. K. Romano", "D. Roubtsov", "G. Schnabel", "M. Schulc", "G. J. Siemers", "A. A. Sonzogni", "P. Talou", "J. Thompson", "T. H. Trumbull", "S. C. van der Marck", "M. Vorabbi", "C. Wemple", "K. A. Wendt", "M. White", "R. Q. Wright"], "categories": ["physics.app-ph", "nucl-ex", "nucl-th"], "fields_of_study": ["Physics"], "published_date": "2025-11-05", "url": "https://arxiv.org/abs/2511.03564", "pdf_url": "https://arxiv.org/pdf/2511.03564v2", "arxiv_id": "2511.03564", "doi": "10.1016/j.nds.2026.04.001", "citation_count": 12, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "Nuclear Data Sheets", "quality_score": 0.3632}
{"id": "e68ad5aa3b0001e66c166ae2d24f2869361218f5190c6d2a62d12e6999e5e1bb", "sources": ["arxiv", "semantic_scholar"], "title": "The Real-Time Data Processor Framework for Data Handling and Analysis of High-Energy Instruments", "abstract": "We implemented a real-time data processor (rta-dp) framework that can be used to develop real-time analysis pipelines and data handling systems to manage high-throughput data streams with distributed applications in the context of ground and space astrophysical projects and high-energy instruments. The rta-dp is based on the ZeroMQ in-memory communication framework to receive input data, share data between distributed processes, and send or receive commands and pipeline configuration. The rta-dp framework has a flexible architecture that allows the implementation of distributed analysis systems customized to the requirements of several scenarios. The rta-dp framework also provides monitoring capabilities for the running processes and sends housekeeping, logging, alarms, and informative messages that a monitoring process can acquire. We are using the rta-dp in several contexts, such as acquiring and processing data from X-ray detectors to the data quality system of the ASTRI Project, as well as reprocessing and archiving data.", "authors": ["A. Bulgarelli", "N. Parmiggiani", "L. Castaldini", "R. Falco", "A. Di Piano", "V. Fioretti", "G. Panebianco", "A. Rizzo"], "categories": ["astro-ph.IM"], "fields_of_study": ["Physics"], "published_date": "2025-11-05", "url": "https://arxiv.org/abs/2511.03760", "pdf_url": "https://arxiv.org/pdf/2511.03760v1", "arxiv_id": "2511.03760", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2311}
{"id": "782fafbb22bb021d83041595ff109127b27eb4973b9c4d3aeaa317302771fc10", "sources": ["arxiv", "semantic_scholar"], "title": "What's the next frontier for Data-centric AI? Data Savvy Agents", "abstract": "The recent surge in AI agents that autonomously communicate, collaborate with humans and use diverse tools has unlocked promising opportunities in various real-world settings. However, a vital aspect remains underexplored: how agents handle data. Scalable autonomy demands agents that continuously acquire, process, and evolve their data. In this paper, we argue that data-savvy capabilities should be a top priority in the design of agentic systems to ensure reliable real-world deployment. Specifically, we propose four key capabilities to realize this vision: (1) Proactive data acquisition: enabling agents to autonomously gather task-critical knowledge or solicit human input to address data gaps; (2) Sophisticated data processing: requiring context-aware and flexible handling of diverse data challenges and inputs; (3) Interactive test data synthesis: shifting from static benchmarks to dynamically generated interactive test data for agent evaluation; and (4) Continual adaptation: empowering agents to iteratively refine their data and background knowledge to adapt to shifting environments. While current agent research predominantly emphasizes reasoning, we hope to inspire a reflection on the role of data-savvy agents as the next frontier in data-centric AI.", "authors": ["Nabeel Seedat", "Jiashuo Liu", "Mihaela van der Schaar"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-02", "url": "https://arxiv.org/abs/2511.01015", "pdf_url": "https://arxiv.org/pdf/2511.01015v1", "arxiv_id": "2511.01015", "doi": "10.48550/arXiv.2511.01015", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3598}
{"id": "4bab6ecad8294868242b2e47511f186ee0069d1ce15e957f0237928ffb01f7f8", "sources": ["arxiv", "semantic_scholar"], "title": "MedEqualizer: A Framework Investigating Bias in Synthetic Medical Data and Mitigation via Augmentation", "abstract": "Synthetic healthcare data generation presents a viable approach to enhance data accessibility and support research by overcoming limitations associated with real-world medical datasets. However, ensuring fairness across protected attributes in synthetic data is critical to avoid biased or misleading results in clinical research and decision-making. In this study, we assess the fairness of synthetic data generated by multiple generative adversarial network (GAN)-based models using the MIMIC-III dataset, with a focus on representativeness across protected demographic attributes. We measure subgroup representation using the logarithmic disparity metric and observe significant imbalances, with many subgroups either underrepresented or overrepresented in the synthetic data, compared to the real data. To mitigate these disparities, we introduce MedEqualizer, a model-agnostic augmentation framework that enriches the underrepresented subgroups prior to synthetic data generation. Our results show that MedEqualizer significantly improves demographic balance in the resulting synthetic datasets, offering a viable path towards more equitable and representative healthcare data synthesis.", "authors": ["Sama Salarian", "Yue Zhang", "Swati Padhee", "Srinivasan Parthasarathy"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-02", "url": "https://arxiv.org/abs/2511.01054", "pdf_url": "https://arxiv.org/pdf/2511.01054v1", "arxiv_id": "2511.01054", "doi": "10.48550/arXiv.2511.01054", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3598}
{"id": "dcc139e82a48867d74daac7b5f0f3afaca1eee96cc074df47f67b08d60d54636", "sources": ["arxiv", "semantic_scholar"], "title": "Scheduling Data-Intensive Workloads in Large-Scale Distributed Systems: Trends and Challenges", "abstract": "With the explosive growth of big data, workloads tend to get more complex and computationally demanding. Such applications are processed on distributed interconnected resources that are becoming larger in scale and computational capacity. Data-intensive applications may have different degrees of parallelism and must effectively exploit data locality. Furthermore, they may impose several Quality of Service requirements, such as time constraints and resilience against failures, as well as other objectives, like energy efficiency. These features of the workloads, as well as the inherent characteristics of the computing resources required to process them, present major challenges that require the employment of effective scheduling techniques. In this chapter, a classification of data-intensive workloads is proposed and an overview of the most commonly used approaches for their scheduling in large-scale distributed systems is given. We present novel strategies that have been proposed in the literature and shed light on open challenges and future directions.", "authors": ["Georgios L. Stavrinides", "Helen D. Karatza"], "categories": ["cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2025-10-29", "url": "https://arxiv.org/abs/2510.25362", "pdf_url": "https://arxiv.org/pdf/2510.25362v1", "arxiv_id": "2510.25362", "doi": "10.1007/978-3-319-73767-6_2", "citation_count": 21, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Modeling and Simulation in HPC and Cloud Systems, ser. Studies in Big Data, Feb. 2018, vol. 36, pp. 19-43", "quality_score": 0.3552}
{"id": "0581c6a0bd0da4f435eff4932f07e9285898eb1b500849214cedfc411563c9ec", "sources": ["arxiv", "semantic_scholar"], "title": "UtilGen: Utility-Centric Generative Data Augmentation with Dual-Level Task Adaptation", "abstract": "Data augmentation using generative models has emerged as a powerful paradigm for enhancing performance in computer vision tasks. However, most existing augmentation approaches primarily focus on optimizing intrinsic data attributes -- such as fidelity and diversity -- to generate visually high-quality synthetic data, while often neglecting task-specific requirements. Yet, it is essential for data generators to account for the needs of downstream tasks, as training data requirements can vary significantly across different tasks and network architectures. To address these limitations, we propose UtilGen, a novel utility-centric data augmentation framework that adaptively optimizes the data generation process to produce task-specific, high-utility training data via downstream task feedback. Specifically, we first introduce a weight allocation network to evaluate the task-specific utility of each synthetic sample. Guided by these evaluations, UtilGen iteratively refines the data generation process using a dual-level optimization strategy to maximize the synthetic data utility: (1) model-level optimization tailors the generative model to the downstream task, and (2) instance-level optimization adjusts generation policies -- such as prompt embeddings and initial noise -- at each generation round. Extensive experiments on eight benchmark datasets of varying complexity and granularity demonstrate that UtilGen consistently achieves superior performance, with an average accuracy improvement of 3.87% over previous SOTA. Further analysis of data influence and distribution reveals that UtilGen produces more impactful and task-relevant synthetic data, validating the effectiveness of the paradigm shift from visual characteristics-centric to task utility-centric data augmentation.", "authors": ["Jiyu Guo", "Shuo Yang", "Yiming Huang", "Yancheng Long", "Xiaobo Xia", "Xiu Su", "Bo Zhao", "Zeke Xie", "Liqiang Nie"], "categories": ["cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-10-28", "url": "https://arxiv.org/abs/2510.24262", "pdf_url": "https://arxiv.org/pdf/2510.24262v1", "arxiv_id": "2510.24262", "doi": "10.48550/arXiv.2510.24262", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3541}
{"id": "2f67b54f364bf7091889d8bf19d2fa58049e26cb916320bb4c7e6a90309a5767", "sources": ["arxiv", "semantic_scholar"], "title": "A Survey of Data Agents: Emerging Paradigm or Overstated Hype?", "abstract": "The rapid advancement of large language models (LLMs) has spurred the emergence of data agents, autonomous systems designed to orchestrate Data + AI ecosystems for tackling complex data-related tasks. However, the term \"data agent\" currently suffers from terminological ambiguity and inconsistent adoption, conflating simple query responders with sophisticated autonomous architectures. This terminological ambiguity fosters mismatched user expectations, accountability challenges, and barriers to industry growth. Inspired by the SAE J3016 standard for driving automation, this survey introduces the first systematic hierarchical taxonomy for data agents, comprising six levels that delineate and trace progressive shifts in autonomy, from manual operations (L0) to a vision of generative, fully autonomous data agents (L5), thereby clarifying capability boundaries and responsibility allocation. Through this lens, we offer a structured review of existing research arranged by increasing autonomy, encompassing specialized data agents for data management, preparation, and analysis, alongside emerging efforts toward versatile, comprehensive systems with enhanced autonomy. We further analyze critical evolutionary leaps and technical gaps for advancing data agents, especially the ongoing L2-to-L3 transition, where data agents evolve from procedural execution to autonomous orchestration. Finally, we conclude with a forward-looking roadmap, envisioning proactive, generative data agents.", "authors": ["Yizhang Zhu", "Liangwei Wang", "Chenyu Yang", "Xiaotian Lin", "Boyan Li", "Wei Zhou", "Xinyu Liu", "Zhangyang Peng", "Tianqi Luo", "Yu Li", "Chengliang Chai", "Chong Chen", "Shimin Di", "Ju Fan", "Ji Sun", "Nan Tang", "Fugee Tsung", "Jiannan Wang", "Chenglin Wu", "Yanwei Xu", "Shaolei Zhang", "Yong Zhang", "Xuanhe Zhou", "Guoliang Li", "Yuyu Luo"], "categories": ["cs.DB", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-10-27", "url": "https://arxiv.org/abs/2510.23587", "pdf_url": "https://arxiv.org/pdf/2510.23587v2", "arxiv_id": "2510.23587", "doi": "10.48550/arXiv.2510.23587", "citation_count": 27, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/HKUSTDial/awesome-data-agents", "venue": "arXiv.org", "quality_score": 0.5454}
{"id": "9ff37d7c29016160aaa8997a98cdd8e6160fb9210015910cdcd06841402872c9", "sources": ["arxiv", "semantic_scholar"], "title": "TerraGen: A Unified Multi-Task Layout Generation Framework for Remote Sensing Data Augmentation", "abstract": "Remote sensing vision tasks require extensive labeled data across multiple, interconnected domains. However, current generative data augmentation frameworks are task-isolated, i.e., each vision task requires training an independent generative model, and ignores the modeling of geographical information and spatial constraints. To address these issues, we propose \\textbf{TerraGen}, a unified layout-to-image generation framework that enables flexible, spatially controllable synthesis of remote sensing imagery for various high-level vision tasks, e.g., detection, segmentation, and extraction. Specifically, TerraGen introduces a geographic-spatial layout encoder that unifies bounding box and segmentation mask inputs, combined with a multi-scale injection scheme and mask-weighted loss to explicitly encode spatial constraints, from global structures to fine details. Also, we construct the first large-scale multi-task remote sensing layout generation dataset containing 45k images and establish a standardized evaluation protocol for this task. Experimental results show that our TerraGen can achieve the best generation image quality across diverse tasks. Additionally, TerraGen can be used as a universal data-augmentation generator, enhancing downstream task performance significantly and demonstrating robust cross-task generalisation in both full-data and few-shot scenarios.", "authors": ["Datao Tang", "Hao Wang", "Yudeng Xin", "Hui Qiao", "Dongsheng Jiang", "Yin Li", "Zhiheng Yu", "Xiangyong Cao"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-10-24", "url": "https://arxiv.org/abs/2510.21391", "pdf_url": "https://arxiv.org/pdf/2510.21391v1", "arxiv_id": "2510.21391", "doi": "10.48550/arXiv.2510.21391", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Geoscience and Remote Sensing", "quality_score": 0.3495}
{"id": "19e1054767dcabf349163dea9e040697a84809f0726a204d9e479e9df7b50f38", "sources": ["arxiv", "semantic_scholar"], "title": "Guiding diffusion models to reconstruct flow fields from sparse data", "abstract": "The reconstruction of unsteady flow fields from limited measurements is a challenging and crucial task for many engineering applications. Machine learning models are gaining popularity for solving this problem due to their ability to learn complex patterns from data and to generalize across diverse conditions. Among these, diffusion models have emerged as being particularly powerful for generative tasks, producing high-quality samples by iteratively refining noisy inputs. In contrast to other methods, these generative models are capable of reconstructing the smallest scales of the fluid spectrum. In this work, we introduce a novel sampling method for diffusion models that enables the reconstruction of high-fidelity samples by guiding the reverse process using the available sparse data. Moreover, we enhance the reconstructions with available physics knowledge using a conflict-free update method during training. To evaluate the effectiveness of our method, we conduct experiments on 2 and 3-dimensional turbulent flow data. Our method consistently outperforms other diffusion-based methods in predicting the fluid's structure and in pixel-wise accuracy. This study underscores the remarkable potential of diffusion models in reconstructing flow field data, paving the way for leveraging them in fluid dynamics research and applications ranging from super-resolution to reconstructions of experiments.", "authors": ["Marc Amorós-Trepat", "Luis Medrano-Navarro", "Qiang Liu", "Luca Guastoni", "Nils Thuerey"], "categories": ["physics.flu-dyn", "cs.LG"], "fields_of_study": ["Computer Science", "Physics"], "published_date": "2025-10-22", "url": "https://arxiv.org/abs/2510.19971", "pdf_url": "https://arxiv.org/pdf/2510.19971v2", "arxiv_id": "2510.19971", "doi": "10.1063/5.0304492", "citation_count": 13, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/tum-pbs/sparse-reconstruction", "venue": "The Physics of Fluids", "quality_score": 0.5366}
{"id": "aa1881802d5b804805e68c48aad19fde088adc2f2ff0e4c8abada55837ad16b2", "sources": ["arxiv", "semantic_scholar"], "title": "Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods", "abstract": "Machine learning (ML) holds great potential to advance anomaly detection (AD) in chemical processes. However, the development of ML-based methods is hindered by the lack of openly available experimental data. To address this gap, we have set up a laboratory-scale batch distillation plant and operated it to generate an extensive experimental database, covering fault-free experiments and experiments in which anomalies were intentionally induced, for training advanced ML-based AD methods. In total, 119 experiments were conducted across a wide range of operating conditions and mixtures. Most experiments containing anomalies were paired with a corresponding fault-free one. The database that we provide here includes time-series data from numerous sensors and actuators, along with estimates of measurement uncertainty. In addition, unconventional data sources -- such as concentration profiles obtained via online benchtop NMR spectroscopy and video and audio recordings -- are provided. Extensive metadata and expert annotations of all experiments are included. The anomaly annotations are based on an ontology developed in this work. The data are organized in a structured database and made freely available via doi.org/10.5281/zenodo.17395543. This new database paves the way for the development of advanced ML-based AD methods. As it includes information on the causes of anomalies, it further enables the development of interpretable and explainable ML approaches, as well as methods for anomaly mitigation.", "authors": ["Justus Arweiler", "Indra Jungjohann", "Aparna Muraleedharan", "Heike Leitte", "Jakob Burger", "Kerstin Münnemann", "Fabian Jirasek", "Hans Hasse"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science", "Medicine"], "published_date": "2025-10-20", "url": "https://arxiv.org/abs/2510.18075", "pdf_url": "https://arxiv.org/pdf/2510.18075v2", "arxiv_id": "2510.18075", "doi": "10.1038/s41597-026-07124-3", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Scientific Data", "quality_score": 0.3449}
{"id": "1e05141812d523c3907fd5b85e0b3c61009bab94361c3ffe3c01303222bab10f", "sources": ["arxiv", "semantic_scholar"], "title": "A fully automated and scalable Parallel Data Augmentation for Low Resource Languages using Image and Text Analytics", "abstract": "Linguistic diversity across the world creates a disparity with the availability of good quality digital language resources thereby restricting the technological benefits to majority of human population. The lack or absence of data resources makes it difficult to perform NLP tasks for low-resource languages. This paper presents a novel scalable and fully automated methodology to extract bilingual parallel corpora from newspaper articles using image and text analytics. We validate our approach by building parallel data corpus for two different language combinations and demonstrate the value of this dataset through a downstream task of machine translation and improve over the current baseline by close to 3 BLEU points.", "authors": ["Prawaal Sharma", "Navneet Goyal", "Poonam Goyal", "Vishnupriyan R"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-10-15", "url": "https://arxiv.org/abs/2510.13211", "pdf_url": "https://arxiv.org/pdf/2510.13211v1", "arxiv_id": "2510.13211", "doi": "10.1145/3555776.3577788", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "ACM Symposium on Applied Computing", "quality_score": 0.3392}
{"id": "625ee27c2deffa254ba3c98902529eb4d8ab53eafd8b2e4e094b5cf707990511", "sources": ["arxiv", "semantic_scholar"], "title": "The Impact of Synthetic Data on Object Detection Model Performance: A Comparative Analysis with Real-World Data", "abstract": "Recent advances in generative AI, particularly in computer vision (CV), offer new opportunities to optimize workflows across industries, including logistics and manufacturing. However, many AI applications are limited by a lack of expertise and resources, which forces a reliance on general-purpose models. Success with these models often requires domain-specific data for fine-tuning, which can be costly and inefficient. Thus, using synthetic data for fine-tuning is a popular, cost-effective alternative to gathering real-world data. This work investigates the impact of synthetic data on the performance of object detection models, compared to models trained on real-world data only, specifically within the domain of warehouse logistics. To this end, we examined the impact of synthetic data generated using the NVIDIA Omniverse Replicator tool on the effectiveness of object detection models in real-world scenarios. It comprises experiments focused on pallet detection in a warehouse setting, utilizing both real and various synthetic dataset generation strategies. Our findings provide valuable insights into the practical applications of synthetic image data in computer vision, suggesting that a balanced integration of synthetic and real data can lead to robust and efficient object detection models.", "authors": ["Muammer Bay", "Timo von Marcard", "Dren Fazlija"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-10-14", "url": "https://arxiv.org/abs/2510.12208", "pdf_url": "https://arxiv.org/pdf/2510.12208v1", "arxiv_id": "2510.12208", "doi": "10.48550/arXiv.2510.12208", "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/MuammerBay/omniverse-replicator-sim2real-analysis", "venue": "arXiv.org", "quality_score": 0.5224}
{"id": "8a37e451b8314ee1aca085c2c6e5219c00fecdc88ab0d7148d160bdc067c9d4f", "sources": ["arxiv", "semantic_scholar"], "title": "Knowledge-Decoupled Functionally Invariant Path with Synthetic Personal Data for Personalized ASR", "abstract": "Fine-tuning generic ASR models with large-scale synthetic personal data can enhance the personalization of ASR models, but it introduces challenges in adapting to synthetic personal data without forgetting real knowledge, and in adapting to personal data without forgetting generic knowledge. Considering that the functionally invariant path (FIP) framework enables model adaptation while preserving prior knowledge, in this letter, we introduce FIP into synthetic-data-augmented personalized ASR models. However, the model still struggles to balance the learning of synthetic, personalized, and generic knowledge when applying FIP to train the model on all three types of data simultaneously. To decouple this learning process and further address the above two challenges, we integrate a gated parameter-isolation strategy into FIP and propose a knowledge-decoupled functionally invariant path (KDFIP) framework, which stores generic and personalized knowledge in separate modules and applies FIP to them sequentially. Specifically, KDFIP adapts the personalized module to synthetic and real personal data and the generic module to generic data. Both modules are updated along personalization-invariant paths, and their outputs are dynamically fused through a gating mechanism. With augmented synthetic data, KDFIP achieves a 29.38% relative character error rate reduction on target speakers and maintains comparable generalization performance to the unadapted ASR baseline.", "authors": ["Yue Gu", "Zhihao Du", "Ying Shi", "Jiqing Han", "Yongjun He"], "categories": ["cs.SD"], "fields_of_study": ["Computer Science"], "published_date": "2025-10-12", "url": "https://arxiv.org/abs/2510.10401", "pdf_url": "https://arxiv.org/pdf/2510.10401v1", "arxiv_id": "2510.10401", "doi": "10.1109/LSP.2025.3621332", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Signal Processing Letters", "quality_score": 0.3357}
{"id": "8d17110b8d0d61d1d190a352742ca2bd6c9610ff87e214ca6e642f7294752b12", "sources": ["arxiv", "semantic_scholar"], "title": "Semi-disentangled spatiotemporal implicit neural representations of longitudinal neuroimaging data for trajectory classification", "abstract": "The human brain undergoes dynamic, potentially pathology-driven, structural changes throughout a lifespan. Longitudinal Magnetic Resonance Imaging (MRI) and other neuroimaging data are valuable for characterizing trajectories of change associated with typical and atypical aging. However, the analysis of such data is highly challenging given their discrete nature with different spatial and temporal image sampling patterns within individuals and across populations. This leads to computational problems for most traditional deep learning methods that cannot represent the underlying continuous biological process. To address these limitations, we present a new, fully data-driven method for representing aging trajectories across the entire brain by modelling subject-specific longitudinal T1-weighted MRI data as continuous functions using Implicit Neural Representations (INRs). Therefore, we introduce a novel INR architecture capable of partially disentangling spatial and temporal trajectory parameters and design an efficient framework that directly operates on the INRs' parameter space to classify brain aging trajectories. To evaluate our method in a controlled data environment, we develop a biologically grounded trajectory simulation and generate T1-weighted 3D MRI data for 450 healthy and dementia-like subjects at regularly and irregularly sampled timepoints. In the more realistic irregular sampling experiment, our INR-based method achieves 81.3% accuracy for the brain aging trajectory classification task, outperforming a standard deep learning baseline model (73.7%).", "authors": ["Agampreet Aulakh", "Nils D. Forkert", "Matthias Wilms"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-10-11", "url": "https://arxiv.org/abs/2510.09936", "pdf_url": "https://arxiv.org/pdf/2510.09936v1", "arxiv_id": "2510.09936", "doi": "10.48550/arXiv.2510.09936", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3346}
{"id": "4ff9c55f61843a23110d4d8b3b6d492dcb9a866e332658def60b91ea98d1f269", "sources": ["arxiv", "semantic_scholar"], "title": "Prompting Test-Time Scaling Is A Strong LLM Reasoning Data Augmentation", "abstract": "Large language models (LLMs) have demonstrated impressive reasoning capabilities when provided with chain-of-thought exemplars, but curating large reasoning datasets remains laborious and resource-intensive. In this work, we introduce Prompting Test-Time Scaling (P-TTS), a simple yet effective inference-time data augmentation strategy for enhancing LLM reasoning through finetuning. Rather than collecting thousands or even millions of examples, P-TTS leverages a small pool of only 90 manually selected reasoning instances and systematically varies exemplar augmentation through principled instruction prompting intensities at test time to synthesize diverse reasoning trajectory contexts. Then we finetune the various sizes of Qwen-2.5 models on P-TTS data. Across a suite of mathematical reasoning AIME2024 & 25, MATH500, and GPQA-Diamond, our P-TTS-7B and 32B models outperform the prior competitive baselines like S1 and S1.1 (1K-shot), achieving absolute accuracy gains of +26.66% and +30.00% on AIME'24 (7B), and +13.34% and +6.67% on AIME'25 (7B); P-TTS-32B yields gains of +23.33% and +16.63% on AIME'24, and +26.63% and +3.33% on AIME'25 (vs. S1 and S1.1, respectively), with comparable or better performance on MATH500 and GPQA-Diamond. We further show that P-TTS enhances zero-shot generalization accuracy on out-of-domain reasoning benchmarks of Gaokao, Kaoyan, OlympiadBench, AMC23, GradeSchoolMath, and Minerva. Our analysis suggests that test-time scaling effectively explores the latent space of reasoning patterns, amplifying LLM problem-solving with minimal annotation overhead, and further unlocking the reasoning potential and capabilities of LLMs. Prompting Test-Time Scaling offers a practical, low-cost way to elicit LLM reasoning in resource-constrained or rapidly evolving domains.", "authors": ["Sondos Mahmoud Bsharat", "Zhiqiang Shen"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-10-10", "url": "https://arxiv.org/abs/2510.09599", "pdf_url": "https://arxiv.org/pdf/2510.09599v1", "arxiv_id": "2510.09599", "doi": "10.48550/arXiv.2510.09599", "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/VILA-Lab/PTTS", "venue": "arXiv.org", "quality_score": 0.5153}
{"id": "7ff49377079a3c412240be06a106fef780efc7a565eb888a193646f2a43a0900", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetic Object Compositions for Scalable and Accurate Learning in Detection, Segmentation, and Grounding", "abstract": "Visual grouping -- operationalized through tasks such as instance segmentation, visual grounding, and object detection -- enables applications ranging from robotic perception to photo editing. These fundamental problems in computer vision are powered by large-scale, painstakingly annotated datasets. Despite their impact, these datasets are costly to build, biased in coverage, and difficult to scale. Synthetic datasets offer a promising alternative but struggle with flexibility, accuracy, and compositional diversity. We introduce Synthetic Object Compositions (SOC), an accurate and scalable data synthesis pipeline via a novel object-centric composition strategy. It composes high-quality synthetic object segments into new images using 3D geometric layout augmentation and camera configuration augmentation with generative harmonization and mask-area-weighted blending, yielding accurate and diverse masks, boxes, and referring expressions. Models trained on just 100K of our synthetic images outperform those trained on larger real datasets (GRIT 20M, V3Det 200K) and synthetic pipelines (Copy-Paste, X-Paste, SynGround, SegGen) by +24-36% -- achieving +10.9 AP on LVIS and +8.4 NAcc on gRefCOCO. Beyond the general open-vocabulary setup, SOC also enables controllable dataset construction for different use cases and boosts performance in both low-data and closed-vocabulary scenarios. Augmenting LVIS and COCO with synthetic object segments delivers strong performance across different real-data scales and yields even greater improvements under extremely limited real-data conditions, including +6.59 AP on a 1% COCO data setup. Furthermore, this controllability enables targeted data generation for intra-class referring, a diagnostic grounding task we propose that requires fine-grained attribute discrimination.", "authors": ["Weikai Huang", "Jieyu Zhang", "Taoyang Jia", "Chenhao Zheng", "Ziqi Gao", "Jae Sung Park", "Winson Han", "Ranjay Krishna"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-10-10", "url": "https://arxiv.org/abs/2510.09110", "pdf_url": "https://arxiv.org/pdf/2510.09110v4", "arxiv_id": "2510.09110", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/weikaih04/Synthetic-Detection-Segmentation-Grounding-Data", "venue": null, "quality_score": 0.3941}
{"id": "e69deebc663d586efaf59c18c09bd15db968d87ab4c9ad2adcd461a9b34cf1a1", "sources": ["arxiv", "semantic_scholar"], "title": "Explaining raw data complexity to improve satellite onboard processing", "abstract": "With increasing processing power, deploying AI models for remote sensing directly onboard satellites is becoming feasible. However, new constraints arise, mainly when using raw, unprocessed sensor data instead of preprocessed ground-based products. While current solutions primarily rely on preprocessed sensor images, few approaches directly leverage raw data. This study investigates the effects of utilising raw data on deep learning models for object detection and classification tasks. We introduce a simulation workflow to generate raw-like products from high-resolution L1 imagery, enabling systemic evaluation. Two object detection models (YOLOv11n and YOLOX-S) are trained on both raw and L1 datasets, and their performance is compared using standard detection metrics and explainability tools. Results indicate that while both models perform similarly at low to medium confidence thresholds, the model trained on raw data struggles with object boundary identification at high confidence levels. It suggests that adapting AI architectures with improved contouring methods can enhance object detection on raw images, improving onboard AI for remote sensing.", "authors": ["Adrien Dorise", "Marjorie Bellizzi", "Adrien Girard", "Benjamin Francesconi", "Stéphane May"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-10-08", "url": "https://arxiv.org/abs/2510.06858", "pdf_url": "https://arxiv.org/pdf/2510.06858v2", "arxiv_id": "2510.06858", "doi": "10.48550/arXiv.2510.06858", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2107}
{"id": "78ef8950086abe8512e67d1222bdb36c51d7429914366342a47774426ceb47b9", "sources": ["arxiv", "semantic_scholar"], "title": "LLM-Based Data Science Agents: A Survey of Capabilities, Challenges, and Future Directions", "abstract": "Recent advances in large language models (LLMs) have enabled a new class of AI agents that automate multiple stages of the data science workflow by integrating planning, tool use, and multimodal reasoning across text, code, tables, and visuals. This survey presents the first comprehensive, lifecycle-aligned taxonomy of data science agents, systematically analyzing and mapping forty-five systems onto the six stages of the end-to-end data science process: business understanding and data acquisition, exploratory analysis and visualization, feature engineering, model building and selection, interpretation and explanation, and deployment and monitoring. In addition to lifecycle coverage, we annotate each agent along five cross-cutting design dimensions: reasoning and planning style, modality integration, tool orchestration depth, learning and alignment methods, and trust, safety, and governance mechanisms. Beyond classification, we provide a critical synthesis of agent capabilities, highlight strengths and limitations at each stage, and review emerging benchmarks and evaluation practices. Our analysis identifies three key trends: most systems emphasize exploratory analysis, visualization, and modeling while neglecting business understanding, deployment, and monitoring; multimodal reasoning and tool orchestration remain unresolved challenges; and over 90% lack explicit trust and safety mechanisms. We conclude by outlining open challenges in alignment stability, explainability, governance, and robust evaluation frameworks, and propose future research directions to guide the development of robust, trustworthy, low-latency, transparent, and broadly accessible data science agents.", "authors": ["Mizanur Rahman", "Amran Bhuiyan", "Mohammed Saidul Islam", "Md Tahmid Rahman Laskar", "Ridwan Mahbub", "Ahmed Masry", "Shafiq Joty", "Enamul Hoque"], "categories": ["cs.AI", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-10-05", "url": "https://arxiv.org/abs/2510.04023", "pdf_url": "https://arxiv.org/pdf/2510.04023v1", "arxiv_id": "2510.04023", "doi": "10.48550/arXiv.2510.04023", "citation_count": 9, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3277}
{"id": "658c7c77da914200d04aacc788d35df837c250d0904ef587b04c56e0370c758d", "sources": ["arxiv", "semantic_scholar"], "title": "The Impact of Scaling Training Data on Adversarial Robustness", "abstract": "Deep neural networks remain vulnerable to adversarial examples despite advances in architectures and training paradigms. We investigate how training data characteristics affect adversarial robustness across 36 state-of-the-art vision models spanning supervised, self-supervised, and contrastive learning approaches, trained on datasets from 1.2M to 22B images. Models were evaluated under six black-box attack categories: random perturbations, two types of geometric masks, COCO object manipulations, ImageNet-C corruptions, and ImageNet-R style shifts. Robustness follows a logarithmic scaling law with both data volume and model size: a tenfold increase in data reduces attack success rate (ASR) on average by ~3.2%, whereas a tenfold increase in model size reduces ASR on average by ~13.4%. Notably, some self-supervised models trained on curated datasets, such as DINOv2, outperform others trained on much larger but less curated datasets, challenging the assumption that scale alone drives robustness. Adversarial fine-tuning of ResNet50s improves generalization across structural variations but not across color distributions. Human evaluation reveals persistent gaps between human and machine vision. These results show that while scaling improves robustness, data quality, architecture, and training objectives play a more decisive role than raw scale in achieving broad-spectrum adversarial resilience.", "authors": ["Marco Zimmerli", "Andreas Plesner", "Till Aczel", "Roger Wattenhofer"], "categories": ["cs.CV", "cs.AI", "cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-30", "url": "https://arxiv.org/abs/2509.25927", "pdf_url": "https://arxiv.org/pdf/2509.25927v1", "arxiv_id": "2509.25927", "doi": "10.48550/arXiv.2509.25927", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.322}
{"id": "24b89d8e7fb53f2637adfdd36d0962bf07e5b5011d1893bff77161dcc0954a86", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Learning-Augmented Data Structures", "abstract": "Learning-augmented data structures use predicted frequency estimates to retrieve frequently occurring database elements faster than standard data structures. Recent work has developed data structures that optimally exploit these frequency estimates while maintaining robustness to adversarial prediction errors. However, the privacy and security implications of this setting remain largely unexplored. In the event of a security breach, data structures should reveal minimal information beyond their current contents. This is even more crucial for learning-augmented data structures, whose layout adapts to the data. A data structure is history independent if its memory representation reveals no information about past operations except what is inferred from its current contents. In this work, we take the first step towards privacy and security guarantees in this setting by proposing the first learning-augmented data structure that is strongly history independent, robust, and supports dynamic updates. To achieve this, we introduce two techniques: thresholding, which automatically makes any learning-augmented data structure robust, and pairing, a simple technique that provides strong history independence in the dynamic setting. Our experimental results demonstrate a tradeoff between security and efficiency but are still competitive with the state of the art.", "authors": ["Prabhav Goyal", "Vinesh Sridhar", "Wilson Zheng"], "categories": ["cs.IR", "cs.AI", "cs.DS"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-30", "url": "https://arxiv.org/abs/2510.00165", "pdf_url": "https://arxiv.org/pdf/2510.00165v1", "arxiv_id": "2510.00165", "doi": "10.48550/arXiv.2510.00165", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.322}
{"id": "d123f09308e26734162305b33287131b0ef12cd030ce879bc307dd6b8792e422", "sources": ["arxiv", "semantic_scholar"], "title": "A Law of Data Reconstruction for Random Features (and Beyond)", "abstract": "Large-scale deep learning models are known to memorize parts of the training set. In machine learning theory, memorization is often framed as interpolation or label fitting, and classical results show that this can be achieved when the number of parameters $p$ in the model is larger than the number of training samples $n$. In this work, we consider memorization from the perspective of data reconstruction, demonstrating that this can be achieved when $p$ is larger than $dn$, where $d$ is the dimensionality of the data. More specifically, we show that, in the random features model, when $p \\gg dn$, the subspace spanned by the training samples in feature space gives sufficient information to identify the individual samples in input space. Our analysis suggests an optimization method to reconstruct the dataset from the model parameters, and we demonstrate that this method performs well on various architectures (random features, two-layer fully-connected and deep residual networks). Our results reveal a law of data reconstruction, according to which the entire training dataset can be recovered as $p$ exceeds the threshold $dn$.", "authors": ["Leonardo Iurada", "Simone Bombari", "Tatiana Tommasi", "Marco Mondelli"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-26", "url": "https://arxiv.org/abs/2509.22214", "pdf_url": "https://arxiv.org/pdf/2509.22214v2", "arxiv_id": "2509.22214", "doi": "10.48550/arXiv.2509.22214", "citation_count": 2, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/iurada/data-reconstruction-law", "venue": "arXiv.org", "quality_score": 0.4905}
{"id": "9fa0a7d32acad3f44ec7fcc12e91d5c76d9b4c306bc0944b2d157a6d2debfd35", "sources": ["arxiv", "semantic_scholar"], "title": "Extracting Actionable Insights from Building Energy Data using Vision LLMs on Wavelet and 3D Recurrence Representations", "abstract": "The analysis of complex building time-series for actionable insights and recommendations remains challenging due to the nonlinear and multi-scale characteristics of energy data. To address this, we propose a framework that fine-tunes visual language large models (VLLMs) on 3D graphical representations of the data. The approach converts 1D time-series into 3D representations using continuous wavelet transforms (CWTs) and recurrence plots (RPs), which capture temporal dynamics and localize frequency anomalies. These 3D encodings enable VLLMs to visually interpret energy-consumption patterns, detect anomalies, and provide recommendations for energy efficiency. We demonstrate the framework on real-world building-energy datasets, where fine-tuned VLLMs successfully monitor building states, identify recurring anomalies, and generate optimization recommendations. Quantitatively, the Idefics-7B VLLM achieves validation losses of 0.0952 with CWTs and 0.1064 with RPs on the University of Sharjah energy dataset, outperforming direct fine-tuning on raw time-series data (0.1176) for anomaly detection. This work bridges time-series analysis and visualization, providing a scalable and interpretable framework for energy analytics.", "authors": ["Amine Bechar", "Adel Oulefki", "Abbes Amira", "Fatih Kurogollu", "Yassine Himeur"], "categories": ["cs.LG", "cs.CY"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-26", "url": "https://arxiv.org/abs/2509.21934", "pdf_url": "https://arxiv.org/pdf/2509.21934v1", "arxiv_id": "2509.21934", "doi": "10.1109/ICDM65498.2025.00112", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Industrial Conference on Data Mining", "quality_score": 0.3174}
{"id": "e130e7ccab5b7f7a8c0d849d68053c68d2e6c7257219ef788ca3ce112b864cbb", "sources": ["arxiv", "semantic_scholar"], "title": "TABFAIRGDT: A Fast Fair Tabular Data Generator using Autoregressive Decision Trees", "abstract": "Ensuring fairness in machine learning remains a significant challenge, as models often inherit biases from their training data. Generative models have recently emerged as a promising approach to mitigate bias at the data level while preserving utility. However, many rely on deep architectures, despite evidence that simpler models can be highly effective for tabular data. In this work, we introduce TABFAIRGDT, a novel method for generating fair synthetic tabular data using autoregressive decision trees. To enforce fairness, we propose a soft leaf resampling technique that adjusts decision tree outputs to reduce bias while preserving predictive performance. Our approach is non-parametric, effectively capturing complex relationships between mixed feature types, without relying on assumptions about the underlying data distributions. We evaluate TABFAIRGDT on benchmark fairness datasets and demonstrate that it outperforms state-of-the-art (SOTA) deep generative models, achieving better fairness-utility trade-off for downstream tasks, as well as higher synthetic data quality. Moreover, our method is lightweight, highly efficient, and CPU-compatible, requiring no data pre-processing. Remarkably, TABFAIRGDT achieves a 72% average speedup over the fastest SOTA baseline across various dataset sizes, and can generate fair synthetic data for medium-sized datasets (10 features, 10K samples) in just one second on a standard CPU, making it an ideal solution for real-world fairness-sensitive applications.", "authors": ["Emmanouil Panagiotou", "Benoît Ronval", "Arjun Roy", "Ludwig Bothmann", "Bernd Bischl", "Siegfried Nijssen", "Eirini Ntoutsi"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-24", "url": "https://arxiv.org/abs/2509.19927", "pdf_url": "https://arxiv.org/pdf/2509.19927v1", "arxiv_id": "2509.19927", "doi": "10.1109/ICDM65498.2025.00156", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Industrial Conference on Data Mining", "quality_score": 0.3151}
{"id": "2b787b9cd7812240c01e6ee8c8d0f73aecc4c03ee1d3275a175489a62ed74c43", "sources": ["arxiv", "semantic_scholar"], "title": "ROPA: Synthetic Robot Pose Generation for RGB-D Bimanual Data Augmentation", "abstract": "Training robust bimanual manipulation policies via imitation learning requires demonstration data with broad coverage over robot poses, contacts, and scene contexts. However, collecting diverse and precise real-world demonstrations is costly and time-consuming, which hinders scalability. Prior works have addressed this with data augmentation, typically for either eye-in-hand (wrist camera) setups with RGB inputs or for generating novel images without paired actions, leaving augmentation for eye-to-hand (third-person) RGB-D training with new action labels less explored. In this paper, we propose Synthetic Robot Pose Generation for RGB-D Bimanual Data Augmentation (ROPA), an offline imitation learning data augmentation method that fine-tunes Stable Diffusion to synthesize third-person RGB and RGB-D observations of novel robot poses. Our approach simultaneously generates corresponding joint-space action labels while employing constrained optimization to enforce physical consistency through appropriate gripper-to-object contact constraints in bimanual scenarios. We evaluate our method on 5 simulated and 3 real-world tasks. Our results across 2625 simulation trials and 300 real-world trials demonstrate that ROPA outperforms baselines and ablations, showing its potential for scalable RGB and RGB-D data augmentation in eye-to-hand bimanual manipulation. Our project website is available at: https://ropaaug.github.io/.", "authors": ["Jason Chen", "I-Chun Arthur Liu", "Gaurav Sukhatme", "Daniel Seita"], "categories": ["cs.RO", "cs.AI", "cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-23", "url": "https://arxiv.org/abs/2509.19454", "pdf_url": "https://arxiv.org/pdf/2509.19454v2", "arxiv_id": "2509.19454", "doi": "10.48550/arXiv.2509.19454", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.314}
{"id": "13a277badf61b1c6d4ca9c3a43e69d8c836a03eaa71d0c04b94fdd8892bf3a18", "sources": ["arxiv", "semantic_scholar"], "title": "OpenGVL -- Benchmarking Visual Temporal Progress for Data Curation", "abstract": "Data scarcity remains one of the most limiting factors in driving progress in robotics. However, the amount of available robotics data in the wild is growing exponentially, creating new opportunities for large-scale data utilization. Reliable temporal task completion prediction could help automatically annotate and curate this data at scale. The Generative Value Learning (GVL) approach was recently proposed, leveraging the knowledge embedded in vision-language models (VLMs) to predict task progress from visual observations. Building upon GVL, we propose OpenGVL, a comprehensive benchmark for estimating task progress across diverse challenging manipulation tasks involving both robotic and human embodiments. We evaluate the capabilities of publicly available open-source foundation models, showing that open-source model families significantly underperform closed-source counterparts, achieving only approximately $70\\%$ of their performance on temporal progress prediction tasks. Furthermore, we demonstrate how OpenGVL can serve as a practical tool for automated data curation and filtering, enabling efficient quality assessment of large-scale robotics datasets. We release the benchmark along with the complete codebase at \\href{github.com/budzianowski/opengvl}{OpenGVL}.", "authors": ["Paweł Budzianowski", "Emilia Wiśnios", "Michał Tyrolski", "Gracjan Góral", "Igor Kulakov", "Viktor Petrenko", "Krzysztof Walas"], "categories": ["cs.RO", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-22", "url": "https://arxiv.org/abs/2509.17321", "pdf_url": "https://arxiv.org/pdf/2509.17321v4", "arxiv_id": "2509.17321", "doi": "10.48550/arXiv.2509.17321", "citation_count": 2, "influential_citation_count": 1, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4834}
{"id": "0f1ec5a365cb664e20183c979a525a65fda0b6274b787489f16e79873738afb6", "sources": ["arxiv", "semantic_scholar"], "title": "Knowledge Distillation for Variational Quantum Convolutional Neural Networks on Heterogeneous Data", "abstract": "Distributed quantum machine learning faces significant challenges due to heterogeneous client data and variations in local model structures, which hinder global model aggregation. To address these challenges, we propose a knowledge distillation framework for variational quantum convolutional neural networks on heterogeneous data. The framework features a quantum gate number estimation mechanism based on client data, which guides the construction of resource-adaptive VQCNN circuits. Particle swarm optimization is employed to efficiently generate personalized quantum models tailored to local data characteristics. During aggregation, a knowledge distillation strategy integrating both soft-label and hard-label supervision consolidates knowledge from heterogeneous clients using a public dataset, forming a global model while avoiding parameter exposure and privacy leakage. Theoretical analysis shows that proposed framework benefits from quantum high-dimensional representation, offering advantages over classical approaches, and minimizes communication by exchanging only model indices and test outputs. Extensive simulations on the PennyLane platform validate the effectiveness of the gate number estimation and distillation-based aggregation. Experimental results demonstrate that the aggregated global model achieves accuracy close to fully supervised centralized training. These results shown that proposed methods can effectively handle heterogeneity, reduce resource consumption, and maintain performance, highlighting its potential for scalable and privacy-preserving distributed quantum learning.", "authors": ["Kai Yu", "Binbin Cai", "Song Lin"], "categories": ["quant-ph", "cs.LG"], "fields_of_study": ["Physics", "Computer Science"], "published_date": "2025-09-20", "url": "https://arxiv.org/abs/2509.16699", "pdf_url": "https://arxiv.org/pdf/2509.16699v1", "arxiv_id": "2509.16699", "doi": "10.48550/arXiv.2509.16699", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3105}
{"id": "58c408523f4c48e903a30c0ba4d3e9929920ceef252345f68d2b52b7db0fcce1", "sources": ["arxiv", "semantic_scholar"], "title": "Efficient Long-Tail Learning in Latent Space by sampling Synthetic Data", "abstract": "Imbalanced classification datasets pose significant challenges in machine learning, often leading to biased models that perform poorly on underrepresented classes. With the rise of foundation models, recent research has focused on the full, partial, and parameter-efficient fine-tuning of these models to deal with long-tail classification. Despite the impressive performance of these works on the benchmark datasets, they still fail to close the gap with the networks trained using the balanced datasets and still require substantial computational resources, even for relatively smaller datasets. Underscoring the importance of computational efficiency and simplicity, in this work we propose a novel framework that leverages the rich semantic latent space of Vision Foundation Models to generate synthetic data and train a simple linear classifier using a mixture of real and synthetic data for long-tail classification. The computational efficiency gain arises from the number of trainable parameters that are reduced to just the number of parameters in the linear model. Our method sets a new state-of-the-art for the CIFAR-100-LT benchmark and demonstrates strong performance on the Places-LT benchmark, highlighting the effectiveness and adaptability of our simple and effective approach.", "authors": ["Nakul Sharma"], "categories": ["cs.LG", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-19", "url": "https://arxiv.org/abs/2509.15859", "pdf_url": "https://arxiv.org/pdf/2509.15859v1", "arxiv_id": "2509.15859", "doi": "10.48550/arXiv.2509.15859", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3094}
{"id": "731db86b652046d68f295341601e7012b2094c2bd4e4a51bd374f4ebefaf2bd5", "sources": ["arxiv", "semantic_scholar"], "title": "Autoguided Online Data Curation for Diffusion Model Training", "abstract": "The costs of generative model compute rekindled promises and hopes for efficient data curation. In this work, we investigate whether recently developed autoguidance and online data selection methods can improve the time and sample efficiency of training generative diffusion models. We integrate joint example selection (JEST) and autoguidance into a unified code base for fast ablation and benchmarking. We evaluate combinations of data curation on a controlled 2-D synthetic data generation task as well as (3x64x64)-D image generation. Our comparisons are made at equal wall-clock time and equal number of samples, explicitly accounting for the overhead of selection. Across experiments, autoguidance consistently improves sample quality and diversity. Early AJEST (applying selection only at the beginning of training) can match or modestly exceed autoguidance alone in data efficiency on both tasks. However, its time overhead and added complexity make autoguidance or uniform random data selection preferable in most situations. These findings suggest that while targeted online selection can yield efficiency gains in early training, robust sample quality improvements are primarily driven by autoguidance. We discuss limitations and scope, and outline when data selection may be beneficial.", "authors": ["Valeria Pais", "Luis Oala", "Daniele Faccio", "Marco Aversa"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-18", "url": "https://arxiv.org/abs/2509.15267", "pdf_url": "https://arxiv.org/pdf/2509.15267v2", "arxiv_id": "2509.15267", "doi": "10.48550/arXiv.2509.15267", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3082}
{"id": "9fabcf3f3985b8a07fba74502b2bf8cbd81585a72e9407d4b997aa09d458f708", "sources": ["arxiv", "semantic_scholar"], "title": "Task Decoding based on Eye Movements using Synthetic Data Augmentation", "abstract": "Machine learning has been extensively used in various applications related to eye-tracking research. Understanding eye movement is one of the most significant subsets of eye-tracking research that reveals the scanning pattern of an individual. Researchers have thoroughly analyzed eye movement data to understand various eye-tracking applications, such as attention mechanisms, navigational behavior, task understanding, etc. The outcome of traditional machine learning algorithms used for decoding tasks based on eye movement data has received a mixed reaction to Yarbus' claim that it is possible to decode the observer's task from their eye movements. In this paper, to support the hypothesis by Yarbus, we are decoding tasks categories while generating synthetic data samples using well-known Synthetic Data Generators CTGAN and its variations such as CopulaGAN and Gretel AI Synthetic Data generators on available data from an in-person user study. Our results show that augmenting more eye movement data combined with additional synthetically generated improves classification accuracy even with traditional machine learning algorithms. We see a significant improvement in task decoding accuracy from 28.1% using Random Forest to 82% using Inception Time when five times more data is added in addition to the 320 real eye movement dataset sample. Our proposed framework outperforms all the available studies on this dataset because of the use of additional synthetic datasets. We validated our claim with various algorithms and combinations of real and synthetic data to show how decoding accuracy increases with the increase in the augmentation of generated data to real data.", "authors": ["Shanmuka Sadhu", "Arca Baran", "Preeti Pandey", "Ayush Kumar"], "categories": ["cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-15", "url": "https://arxiv.org/abs/2509.11547", "pdf_url": "https://arxiv.org/pdf/2509.11547v1", "arxiv_id": "2509.11547", "doi": "10.48550/arXiv.2509.11547", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3048}
{"id": "142358cbabbf5ced1b93093fcbfdcc79dddd7e13ff416ce000615e0330bd05ec", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetic Homes: A Multimodal Generative AI Pipeline for Residential Building Data Generation under Data Scarcity", "abstract": "Computational models have emerged as powerful tools for multi-scale energy modeling research at the building and urban scale, supporting data-driven analysis across building and urban energy systems. However, these models require large amounts of building parameter data that is often inaccessible, expensive to collect, or subject to privacy constraints. We introduce a modular, multimodal generative Artificial Intelligence (AI) framework that integrates image, tabular, and simulation-based components and produces synthetic residential building datasets from publicly available county records and images, and present an end-to-end pipeline instantiating this framework. To reduce typical Large Language Model (LLM) challenges, we evaluate our model's components using occlusion-based visual focus analysis. Our analysis demonstrates that our selected vision-language model achieves greater visual focus than a GPT-based alternative for building image processing. We also assess realism of our results against a national reference dataset, finding that our synthetic data overlaps more than 95% for three of the four selected variables. This work reduces dependence on costly or restricted data sources, lowering barriers to building-scale energy research and Machine Learning (ML)-driven urban energy modeling, and therefore enabling scalable downstream tasks such as energy modeling, retrofit analysis, and urban-scale simulation under data scarcity.", "authors": ["Jackson Eshbaugh", "Chetan Tiwari", "Jorge Silveyra"], "categories": ["cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-11", "url": "https://arxiv.org/abs/2509.09794", "pdf_url": "https://arxiv.org/pdf/2509.09794v5", "arxiv_id": "2509.09794", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Lafayette-EshbaughSilveyra-Group/synthetic-homes", "venue": null, "quality_score": 0.3548}
{"id": "80b7f1fd2e31176a1806e5f262fde60048f50304db5b961054f1391a3db71b94", "sources": ["arxiv", "semantic_scholar"], "title": "\"A 6 or a 9?\": Ensemble Learning Through the Multiplicity of Performant Models and Explanations", "abstract": "Creating models from past observations and ensuring their effectiveness on new data is the essence of machine learning. However, selecting models that generalize well remains a challenging task. Related to this topic, the Rashomon Effect refers to cases where multiple models perform similarly well for a given learning problem. This often occurs in real-world scenarios, like the manufacturing process or medical diagnosis, where diverse patterns in data lead to multiple high-performing solutions. We propose the Rashomon Ensemble, a method that strategically selects models from these diverse high-performing solutions to improve generalization. By grouping models based on both their performance and explanations, we construct ensembles that maximize diversity while maintaining predictive accuracy. This selection ensures that each model covers a distinct region of the solution space, making the ensemble more robust to distribution shifts and variations in unseen data. We validate our approach on both open and proprietary collaborative real-world datasets, demonstrating up to 0.20+ AUROC improvements in scenarios where the Rashomon ratio is large. Additionally, we demonstrate tangible benefits for businesses in various real-world applications, highlighting the robustness, practicality, and effectiveness of our approach.", "authors": ["Gianlucca Zuin", "Adriano Veloso"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-11", "url": "https://arxiv.org/abs/2509.09073", "pdf_url": "https://arxiv.org/pdf/2509.09073v2", "arxiv_id": "2509.09073", "doi": "10.1145/3767735", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "ACM Transactions on Knowledge Discovery from Data", "quality_score": 0.3002}
{"id": "c83975e39b524e835581a134321072984a1d8f95afc4498afd1dfdc4f07159b8", "sources": ["arxiv", "semantic_scholar"], "title": "From Scarcity to Efficiency: Investigating the Effects of Data Augmentation on African Machine Translation", "abstract": "The linguistic diversity across the African continent presents different challenges and opportunities for machine translation. This study explores the effects of data augmentation techniques in improving translation systems in low-resource African languages. We focus on two data augmentation techniques: sentence concatenation with back translation and switch-out, applying them across six African languages. Our experiments show significant improvements in machine translation performance, with a minimum increase of 25\\% in BLEU score across all six languages. We provide a comprehensive analysis and highlight the potential of these techniques to improve machine translation systems for low-resource languages, contributing to the development of more robust translation systems for under-resourced languages.", "authors": ["Mardiyyah Oduwole", "Oluwatosin Olajide", "Jamiu Suleiman", "Faith Hunja", "Busayo Awobade", "Fatimo Adebanjo", "Comfort Akanni", "Chinonyelum Igwe", "Peace Ododo", "Promise Omoigui", "Abraham Owodunni", "Steven Kolawole"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-09", "url": "https://arxiv.org/abs/2509.07471", "pdf_url": "https://arxiv.org/pdf/2509.07471v2", "arxiv_id": "2509.07471", "doi": "10.48550/arXiv.2509.07471", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2979}
{"id": "63101aba14fd4cbccaea8c446fbe4b08dd4f4947d018b77b542c821b6bd0adb5", "sources": ["arxiv", "semantic_scholar"], "title": "Multi-Label Transfer Learning in Non-Stationary Data Streams", "abstract": "Label concepts in multi-label data streams often experience drift in non-stationary environments, either independently or in relation to other labels. Transferring knowledge between related labels can accelerate adaptation, yet research on multi-label transfer learning for data streams remains limited. To address this, we propose two novel transfer learning methods: BR-MARLENE leverages knowledge from different labels in both source and target streams for multi-label classification; BRPW-MARLENE builds on this by explicitly modelling and transferring pairwise label dependencies to enhance learning performance. Comprehensive experiments show that both methods outperform state-of-the-art multi-label stream approaches in non-stationary environments, demonstrating the effectiveness of inter-label knowledge transfer for improved predictive performance.", "authors": ["Honghui Du", "Leandro Minku", "Aonghus Lawlor", "Huiyu Zhou"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-09", "url": "https://arxiv.org/abs/2509.08181", "pdf_url": "https://arxiv.org/pdf/2509.08181v1", "arxiv_id": "2509.08181", "doi": "10.1109/ICDM65498.2025.00029", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Industrial Conference on Data Mining", "quality_score": 0.2979}
{"id": "90c54cc2f5caa6d7f6ce369452c242d8457cabe06e57a877fb98f61a97727ac0", "sources": ["arxiv", "semantic_scholar"], "title": "Data-Augmented Quantization-Aware Knowledge Distillation", "abstract": "Quantization-aware training (QAT) and Knowledge Distillation (KD) are combined to achieve competitive performance in creating low-bit deep learning models. Existing KD and QAT works focus on improving the accuracy of quantized models from the network output perspective by designing better KD loss functions or optimizing QAT's forward and backward propagation. However, limited attention has been given to understanding the impact of input transformations, such as data augmentation (DA). The relationship between quantization-aware KD and DA remains unexplored. In this paper, we address the question: how to select a good DA in quantization-aware KD, especially for the models with low precisions? We propose a novel metric which evaluates DAs according to their capacity to maximize the Contextual Mutual Information--the information not directly related to an image's label--while also ensuring the predictions for each class are close to the ground truth labels on average. The proposed method automatically ranks and selects DAs, requiring minimal training overhead, and it is compatible with any KD or QAT algorithm. Extensive evaluations demonstrate that selecting DA strategies using our metric significantly improves state-of-the-art QAT and KD works across various model architectures and datasets.", "authors": ["Justin Kur", "Kaiqi Zhao"], "categories": ["cs.LG", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-04", "url": "https://arxiv.org/abs/2509.03850", "pdf_url": "https://arxiv.org/pdf/2509.03850v1", "arxiv_id": "2509.03850", "doi": "10.48550/arXiv.2509.03850", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2922}
{"id": "ab5ba4b55511e43a96cb7d942e0a6a6fcb31ea8d6b4325740e375d2e651cc301", "sources": ["arxiv", "semantic_scholar"], "title": "Data Cartography for Detecting Memorization Hotspots and Guiding Data Interventions in Generative Models", "abstract": "Modern generative models risk overfitting and unintentionally memorizing rare training examples, which can be extracted by adversaries or inflate benchmark performance. We propose Generative Data Cartography (GenDataCarto), a data-centric framework that assigns each pretraining sample a difficulty score (early-epoch loss) and a memorization score (frequency of ``forget events''), then partitions examples into four quadrants to guide targeted pruning and up-/down-weighting. We prove that our memorization score lower-bounds classical influence under smoothness assumptions and that down-weighting high-memorization hotspots provably decreases the generalization gap via uniform stability bounds. Empirically, GenDataCarto reduces synthetic canary extraction success by over 40\\% at just 10\\% data pruning, while increasing validation perplexity by less than 0.5\\%. These results demonstrate that principled data interventions can dramatically mitigate leakage with minimal cost to generative performance.", "authors": ["Laksh Patel", "Neel Shanbhag"], "categories": ["cs.LG", "cs.AI", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-27", "url": "https://arxiv.org/abs/2509.00083", "pdf_url": "https://arxiv.org/pdf/2509.00083v1", "arxiv_id": "2509.00083", "doi": "10.48550/arXiv.2509.00083", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.283}
{"id": "1c2c5cd73db824a47eb69bddb5a248be04cf1fe19b89134ee634a91593433d5c", "sources": ["arxiv", "semantic_scholar"], "title": "Generative Models for Synthetic Data: Transforming Data Mining in the GenAI Era", "abstract": "Generative models such as Large Language Models, Diffusion Models, and generative adversarial networks have recently revolutionized the creation of synthetic data, offering scalable solutions to data scarcity, privacy, and annotation challenges in data mining. This tutorial introduces the foundations and latest advances in synthetic data generation, covers key methodologies and practical frameworks, and discusses evaluation strategies and applications. Attendees will gain actionable insights into leveraging generative synthetic data to enhance data mining research and practice. More information can be found on our website: https://syndata4dm.github.io/.", "authors": ["Dawei Li", "Yue Huang", "Ming Li", "Tianyi Zhou", "Xiangliang Zhang", "Huan Liu"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-27", "url": "https://arxiv.org/abs/2508.19570", "pdf_url": "https://arxiv.org/pdf/2508.19570v1", "arxiv_id": "2508.19570", "doi": "10.1145/3746252.3761455", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Information and Knowledge Management", "quality_score": 0.283}
{"id": "6263dc1c1247a9ea4c140cb0a01bde670e47f95706f1c38955b152a290423255", "sources": ["arxiv", "semantic_scholar"], "title": "Database Entity Recognition with Data Augmentation and Deep Learning", "abstract": "This paper addresses the challenge of Database Entity Recognition (DB-ER) in Natural Language Queries (NLQ). We present several key contributions to advance this field: (1) a human-annotated benchmark for DB-ER task, derived from popular text-to-sql benchmarks, (2) a novel data augmentation procedure that leverages automatic annotation of NLQs based on the corresponding SQL queries which are available in popular text-to-SQL benchmarks, (3) a specialized language model based entity recognition model using T5 as a backbone and two down-stream DB-ER tasks: sequence tagging and token classification for fine-tuning of backend and performing DB-ER respectively. We compared our DB-ER tagger with two state-of-the-art NER taggers, and observed better performance in both precision and recall for our model. The ablation evaluation shows that data augmentation boosts precision and recall by over 10%, while fine-tuning of the T5 backbone boosts these metrics by 5-10%.", "authors": ["Zikun Fu", "Chen Yang", "Kourosh Davoudi", "Ken Q. Pu"], "categories": ["cs.CL", "cs.AI", "cs.DB", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-26", "url": "https://arxiv.org/abs/2508.19372", "pdf_url": "https://arxiv.org/pdf/2508.19372v1", "arxiv_id": "2508.19372", "doi": "10.1109/IRI66576.2025.00071", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE International Conference on Information Reuse and Integration", "quality_score": 0.2819}
{"id": "c0501e5a8c4c4cce739e296c8fd88a451ed2512a366b4235a8e334caa0c1c144", "sources": ["arxiv", "semantic_scholar"], "title": "Metrics, KPIs, and Taxonomy for Data Valuation and Monetisation -- A Systematic Literature Review", "abstract": "Data valuation and data monetisation are complex subjects but essential to most organisations today. Unfortunately, they still lack standard procedures and frameworks for organisations to follow. In this survey, we introduce the reader to the concepts by providing the definitions and the background required to better understand data, monetisation strategies, and finally metrics and KPIs used in these strategies. We have conducted a systematic literature review on metrics and KPIs used in data valuation and monetisation, in every aspect of an organisation's business, and by a variety of stakeholders. We provide an expansive list of such metrics and KPIs with 162 references. We then categorise all the metrics and KPIs found into a large taxonomy, following the Balanced Scorecard (BSC) approach with further subclustering to cover every aspect of an organisation's business. This taxonomy will help every level of data management understand the complex landscape of the domain. We also discuss the difficulty in creating a standard framework for data valuation and data monetisation and the major challenges the domain is currently facing.", "authors": ["Eduardo Vyhmeister", "Bastien Pietropaoli", "Alejando Martinez Molina", "Montserrat Gonzalez-Ferreiro", "Gabriel Gonzalez-Castane", "Jordi Arjona Aroca", "Andrea Visentin"], "categories": ["cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-25", "url": "https://arxiv.org/abs/2508.18331", "pdf_url": "https://arxiv.org/pdf/2508.18331v1", "arxiv_id": "2508.18331", "doi": "10.48550/arXiv.2508.18331", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2807}
{"id": "2eb0f5517e1b5166b53e2aeea1256193ad7489a9cea1074f04411d0419159428", "sources": ["arxiv", "semantic_scholar"], "title": "CultranAI at PalmX 2025: Data Augmentation for Cultural Knowledge Representation", "abstract": "In this paper, we report our participation to the PalmX cultural evaluation shared task. Our system, CultranAI, focused on data augmentation and LoRA fine-tuning of large language models (LLMs) for Arabic cultural knowledge representation. We benchmarked several LLMs to identify the best-performing model for the task. In addition to utilizing the PalmX dataset, we augmented it by incorporating the Palm dataset and curated a new dataset of over 22K culturally grounded multiple-choice questions (MCQs). Our experiments showed that the Fanar-1-9B-Instruct model achieved the highest performance. We fine-tuned this model on the combined augmented dataset of 22K+ MCQs. On the blind test set, our submitted system ranked 5th with an accuracy of 70.50%, while on the PalmX development set, it achieved an accuracy of 84.1%.", "authors": ["Hunzalah Hassan Bhatti", "Youssef Ahmed", "Md Arid Hasan", "Firoj Alam"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-24", "url": "https://arxiv.org/abs/2508.17324", "pdf_url": "https://arxiv.org/pdf/2508.17324v2", "arxiv_id": "2508.17324", "doi": "10.48550/arXiv.2508.17324", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1779}
{"id": "e8f376d61aecfc62dc5babc3ddb3007cd6082782f938391c1acd85745b385040", "sources": ["arxiv", "semantic_scholar"], "title": "HandCraft: Dynamic Sign Generation for Synthetic Data Augmentation", "abstract": "Sign Language Recognition (SLR) models face significant performance limitations due to insufficient training data availability. In this article, we address the challenge of limited data in SLR by introducing a novel and lightweight sign generation model based on CMLPe. This model, coupled with a synthetic data pretraining approach, consistently improves recognition accuracy, establishing new state-of-the-art results for the LSFB and DiSPLaY datasets using our Mamba-SL and Transformer-SL classifiers. Our findings reveal that synthetic data pretraining outperforms traditional augmentation methods in some cases and yields complementary benefits when implemented alongside them. Our approach democratizes sign generation and synthetic data pretraining for SLR by providing computationally efficient methods that achieve significant performance improvements across diverse datasets.", "authors": ["Gaston Gustavo Rios", "Pedro Dal Bianco", "Franco Ronchetti", "Facundo Quiroga", "Oscar Stanchi", "Santiago Ponte Ahón", "Waldo Hasperué"], "categories": ["cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-20", "url": "https://arxiv.org/abs/2508.14345", "pdf_url": "https://arxiv.org/pdf/2508.14345v2", "arxiv_id": "2508.14345", "doi": "10.48550/arXiv.2508.14345", "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/okason97/HandCraft", "venue": "arXiv.org", "quality_score": 0.425}
{"id": "ab3b17ad1bfbbbb385be06448ba7f1524d88f549f65ceec830a377be89203881", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetic Adaptive Guided Embeddings (SAGE): A Novel Knowledge Distillation Method", "abstract": "Model distillation enables the transfer of knowledge from large-scale models to compact student models, facilitating deployment in resource-constrained environments. However, conventional distillation approaches often suffer from computational overhead and limited generalization. We propose a novel adaptive distillation framework that dynamically augments training data in regions of high student model loss. Using UMAP-based dimensionality reduction and nearest neighbor sampling, our method identifies underperforming regions in the embedding space and generates targeted synthetic examples to guide student learning. To further improve efficiency, we introduce a lightweight teacher-student interface that bypasses the teacher's input layer, enabling direct distillation on vectorized representations. Experiments across standard NLP benchmarks demonstrate that our 66M-parameter student model consistently matches or surpasses established baselines, achieving 91.2% on QNLI and 92.3% on SST-2, while training with fewer epochs. These results highlight the promise of loss-aware data augmentation and vectorized distillation for efficient and effective model compression.", "authors": ["Suleyman Olcay Polat", "Poli A. Nemkova", "Mark V. Albert"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-20", "url": "https://arxiv.org/abs/2508.14783", "pdf_url": "https://arxiv.org/pdf/2508.14783v1", "arxiv_id": "2508.14783", "doi": "10.48550/arXiv.2508.14783", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.275}
{"id": "22dc0e512b0f70b0dad9143b463d73045866bc4b94216a14b549c6f99ea87f33", "sources": ["arxiv", "semantic_scholar"], "title": "TOM: An Open-Source Tongue Segmentation Method with Multi-Teacher Distillation and Task-Specific Data Augmentation", "abstract": "Tongue imaging serves as a valuable diagnostic tool, particularly in Traditional Chinese Medicine (TCM). The quality of tongue surface segmentation significantly affects the accuracy of tongue image classification and subsequent diagnosis in intelligent tongue diagnosis systems. However, existing research on tongue image segmentation faces notable limitations, and there is a lack of robust and user-friendly segmentation tools. This paper proposes a tongue image segmentation model (TOM) based on multi-teacher knowledge distillation. By incorporating a novel diffusion-based data augmentation method, we enhanced the generalization ability of the segmentation model while reducing its parameter size. Notably, after reducing the parameter count by 96.6% compared to the teacher models, the student model still achieves an impressive segmentation performance of 95.22% mIoU. Furthermore, we packaged and deployed the trained model as both an online and offline segmentation tool (available at https://itongue.cn/), allowing TCM practitioners and researchers to use it without any programming experience. We also present a case study on TCM constitution classification using segmented tongue patches. Experimental results demonstrate that training with tongue patches yields higher classification performance and better interpretability than original tongue images. To our knowledge, this is the first open-source and freely available tongue image segmentation tool.", "authors": ["Jiacheng Xie", "Ziyang Zhang", "Biplab Poudel", "Congyu Guo", "Yang Yu", "Guanghui An", "Xiaoting Tang", "Lening Zhao", "Chunhui Xu", "Dong Xu"], "categories": ["eess.IV", "cs.AI", "q-bio.QM"], "fields_of_study": ["Computer Science", "Engineering", "Biology"], "published_date": "2025-08-19", "url": "https://arxiv.org/abs/2508.14932", "pdf_url": "https://arxiv.org/pdf/2508.14932v1", "arxiv_id": "2508.14932", "doi": "10.48550/arXiv.2508.14932", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "Expert systems with applications", "quality_score": 0.4232}
{"id": "05f14f3655a561f3327947df19928392d06ef8201bc7af476f8010c718f6a7cd", "sources": ["arxiv", "semantic_scholar"], "title": "Effect of Data Augmentation on Conformal Prediction for Diabetic Retinopathy", "abstract": "The clinical deployment of deep learning models for high-stakes tasks such as diabetic retinopathy (DR) grading requires demonstrable reliability. While models achieve high accuracy, their clinical utility is limited by a lack of robust uncertainty quantification. Conformal prediction (CP) offers a distribution-free framework to generate prediction sets with statistical guarantees of coverage. However, the interaction between standard training practices like data augmentation and the validity of these guarantees is not well understood. In this study, we systematically investigate how different data augmentation strategies affect the performance of conformal predictors for DR grading. Using the DDR dataset, we evaluate two backbone architectures -- ResNet-50 and a Co-Scale Conv-Attentional Transformer (CoaT) -- trained under five augmentation regimes: no augmentation, standard geometric transforms, CLAHE, Mixup, and CutMix. We analyze the downstream effects on conformal metrics, including empirical coverage, average prediction set size, and correct efficiency. Our results demonstrate that sample-mixing strategies like Mixup and CutMix not only improve predictive accuracy but also yield more reliable and efficient uncertainty estimates. Conversely, methods like CLAHE can negatively impact model certainty. These findings highlight the need to co-design augmentation strategies with downstream uncertainty quantification in mind to build genuinely trustworthy AI systems for medical imaging.", "authors": ["Rizwan Ahamed", "Annahita Amireskandari", "Joel Palko", "Carol Laxson", "Binod Bhattarai", "Prashnna Gyawali"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-19", "url": "https://arxiv.org/abs/2508.14266", "pdf_url": "https://arxiv.org/pdf/2508.14266v1", "arxiv_id": "2508.14266", "doi": "10.48550/arXiv.2508.14266", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1743}
{"id": "21e3aa29f6bb44887f4598bb379e57e6b7d4bbafc5315f1d9c7f5e9c32401696", "sources": ["arxiv", "semantic_scholar"], "title": "Leveraging the Christoffel Function for Outlier Detection in Data Streams", "abstract": "Outlier detection holds significant importance in the realm of data mining, particularly with the growing pervasiveness of data acquisition methods. The ability to identify outliers in data streams is essential for maintaining data quality and detecting faults. However, dealing with data streams presents challenges due to the non-stationary nature of distributions and the ever-increasing data volume. While numerous methods have been proposed to tackle this challenge, a common drawback is the lack of straightforward parameterization in many of them. This article introduces two novel methods: DyCF and DyCG. DyCF leverages the Christoffel function from the theory of approximation and orthogonal polynomials. Conversely, DyCG capitalizes on the growth properties of the Christoffel function, eliminating the need for tuning parameters. Both approaches are firmly rooted in a well-defined algebraic framework, meeting crucial demands for data stream processing, with a specific focus on addressing low-dimensional aspects and maintaining data history without memory cost. A comprehensive comparison between DyCF, DyCG, and state-of-the-art methods is presented, using both synthetic and real industrial data streams. The results show that DyCF outperforms fine-tuning methods, offering superior performance in terms of execution time and memory usage. DyCG performs less well, but has the considerable advantage of requiring no tuning at all.", "authors": ["Kévin Ducharlet", "Louise Travé-Massuyès", "Jean-Bernard Lasserre", "Marie-Véronique Le Lann", "Youssef Miloudi"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-13", "url": "https://arxiv.org/abs/2508.16617", "pdf_url": "https://arxiv.org/pdf/2508.16617v1", "arxiv_id": "2508.16617", "doi": "10.1007/s41060-024-00581-2", "citation_count": 6, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "International Journal of Data Science and Analysis", "quality_score": 0.267}
{"id": "79c008908f6d3ea0df9c8d715d23a747f4a412f187eccbe80eee972cdeb29bf4", "sources": ["arxiv", "semantic_scholar"], "title": "Difficulty-Based Preference Data Selection by DPO Implicit Reward Gap", "abstract": "Aligning large language models (LLMs) with human preferences is a critical challenge in AI research. While methods like Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO) are widely used, they often rely on large, costly preference datasets. The current work lacks methods for high-quality data selection specifically for preference data. In this work, we introduce a novel difficulty-based data selection strategy for preference datasets, grounded in the DPO implicit reward mechanism. By selecting preference data examples with smaller DPO implicit reward gaps, which are indicative of more challenging cases, we improve data efficiency and model alignment. Our approach consistently outperforms five strong baselines across multiple datasets and alignment tasks, achieving superior performance with only 10\\% of the original data. This principled, efficient selection method offers a promising solution for scaling LLM alignment with limited resources.", "authors": ["Xuan Qi", "Rongwu Xu", "Zhijing Jin"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-06", "url": "https://arxiv.org/abs/2508.04149", "pdf_url": "https://arxiv.org/pdf/2508.04149v2", "arxiv_id": "2508.04149", "doi": "10.48550/arXiv.2508.04149", "citation_count": 7, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Difficulty-Based-Preference-Data-Select/Difficulty-Based-Preference-Data-Select", "venue": "arXiv.org", "quality_score": 0.4002}
{"id": "b3d6186c6838e1b6db1b948cf7a4a8663669106a887497ac569d32cff998d18d", "sources": ["arxiv", "semantic_scholar"], "title": "Managing Data for Scalable and Interactive Event Sequence Visualization", "abstract": "Parallel event sequences, such as those collected in program execution traces and automated manufacturing pipelines, are typically visualized as interactive parallel timelines. As the dataset size grows, these charts frequently experience lag during common interactions such as zooming, panning, and filtering. Summarization approaches can improve interaction performance, but at the cost of accuracy in representation. To address this challenge, we introduce ESeMan (Event Sequence Manager), an event sequence management system designed to support interactive rendering of timeline visualizations with tunable accuracy. ESeMan employs hierarchical data structures and intelligent caching to provide visualizations with only the data necessary to generate accurate summarizations with significantly reduced data fetch time. We evaluate ESeMan's query times against summed area tables, M4 aggregation, and statistical sub-sampling on a variety of program execution traces. Our results demonstrate ESeMan provides better performance, achieving sub-100ms fetch times while maintaining visualization accuracy at the pixel level. We further present our benchmarking harness, enabling future performance evaluations for event sequence visualization.", "authors": ["Sayef Azad Sakin", "Katherine E. Isaacs"], "categories": ["cs.HC"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-05", "url": "https://arxiv.org/abs/2508.03974", "pdf_url": "https://arxiv.org/pdf/2508.03974v2", "arxiv_id": "2508.03974", "doi": "10.1109/LDAV68558.2025.00008", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Symposium on Large Data Analysis and Visualization", "quality_score": 0.2578}
{"id": "060a7d709dd23f0f1d969e50ec341b141fc2cf2e64ca24d2d9ab08b869c1144d", "sources": ["arxiv", "semantic_scholar"], "title": "Decoupling Data and Tooling in Interactive Visualization", "abstract": "Interactive data visualization is a major part of modern exploratory data analysis, with web-based technologies enabling a rich ecosystem of both specialized and general tools. However, current visualization tools often lack support for transformation or wrangling of data and are forced to re-implement their own solutions to load and ingest data. This redundancy creates substantial development overhead for tool creators, steeper learning curves for users who must master different data handling interfaces across tools and a degraded user experience as data handling is usually seen as an after-thought. We propose a modular approach that separates data wrangling and loading capabilities from visualization components. This architecture allows visualization tools to concentrate on their core strengths while providing the opportunity to develop a unified, powerful interface for data handling. An additional benefit of this approach is that it allows for multiple tools to exist and be used side by side. We demonstrate the feasibility of this approach by building an early prototype using web technologies to encapsulate visualization tools and manage data flow between them. We discuss future research directions, including downstream integrations with other tooling, such as IDEs, literate programming notebooks and applications, as well as incorporation of new technologies for efficient data transformations. We seek input from the community to better understand the requirements towards this approach.", "authors": ["Jan Simson"], "categories": ["cs.HC"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-31", "url": "https://arxiv.org/abs/2508.00107", "pdf_url": "https://arxiv.org/pdf/2508.00107v2", "arxiv_id": "2508.00107", "doi": "10.48550/arXiv.2508.00107", "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/jansim/data-studio/blob/main/extra/vis-data-studio-poster.pdf", "venue": "arXiv.org", "quality_score": 0.3896}
{"id": "6faeae814806a6e8274f61e1632325b2191b18f99783730d6614bb756a5c43b9", "sources": ["arxiv", "semantic_scholar"], "title": "CoT-Self-Instruct: Building high-quality synthetic prompts for reasoning and non-reasoning tasks", "abstract": "We propose CoT-Self-Instruct, a synthetic data generation method that instructs LLMs to first reason and plan via Chain-of-Thought (CoT) based on given seed tasks, and then generate a new synthetic example of similar quality and complexity. This is followed by a filtering step to select high-quality data using automatic metrics, which are then used for LLM training. In verifiable reasoning, our synthetic data significantly outperforms existing training datasets, such as s1k and OpenMathReasoning, when evaluated on MATH500, AMC23, AIME24, and GPQA-Diamond. For non-verifiable instruction-following tasks, our method surpasses the performance of both human and standard Self-Instruct training data on the AlpacaEval 2.0 and Arena-Hard benchmarks.", "authors": ["Ping Yu", "Jack Lanchantin", "Tianlu Wang", "Weizhe Yuan", "Olga Golovneva", "Ilia Kulikov", "Sainbayar Sukhbaatar", "Jason Weston", "Jing Xu"], "categories": ["cs.AI", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-31", "url": "https://arxiv.org/abs/2507.23751", "pdf_url": "https://arxiv.org/pdf/2507.23751v2", "arxiv_id": "2507.23751", "doi": "10.48550/arXiv.2507.23751", "citation_count": 23, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3451}
{"id": "47f47399c6854d8df4c3526def9ded4b39147f36ebdf15ed73f72a83fa38d488", "sources": ["arxiv", "semantic_scholar"], "title": "How Sovereign Is Sovereign Compute? A Review of 775 Non-U.S. Data Centers", "abstract": "Previous literature has proposed that the companies operating data centers enforce government regulations on AI companies. Using a new dataset of 775 non-U.S. data center projects, this paper estimates how often data centers could be subject to foreign legal authorities due to the nationality of the data center operators. We find that U.S. companies operate 48% of all non-U.S. data center projects in our dataset when weighted by investment value - a proxy for compute capacity. This is an approximation based on public data and should be interpreted as an initial estimate. For the United States, our findings suggest that data center operators offer a lever for internationally governing AI that complements traditional export controls, since operators can be used to regulate computing resources already deployed in non-U.S. data centers. For other countries, our results show that building data centers locally does not guarantee digital sovereignty if those facilities are run by foreign entities. To support future research, we release our dataset, which documents over 20 variables relating to each data center, including the year it was announced, the investment value, and its operator's national affiliation. The dataset also includes over 1,000 quotes describing these data centers' strategic motivations, operational challenges, and engagement with U.S. and Chinese entities.", "authors": ["Aris Richardson", "Haley Yi", "Michelle Nie", "Simon Wisdom", "Casey Price", "Ruben Weijers", "Steven Veld", "Mauricio Baker"], "categories": ["cs.CY"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-30", "url": "https://arxiv.org/abs/2508.00932", "pdf_url": "https://arxiv.org/pdf/2508.00932v2", "arxiv_id": "2508.00932", "doi": "10.48550/arXiv.2508.00932", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/alarichardson/non-us-data-center-registry", "venue": "arXiv.org", "quality_score": 0.3878}
{"id": "9b6fde86d4cad1c630aa77a6ec0974cce3fac227e6312bc2949ae12475bcb2c0", "sources": ["arxiv", "semantic_scholar"], "title": "Knowledge Augmentation via Synthetic Data: A Framework for Real-World ECG Image Classification", "abstract": "In real-world clinical practice, electrocardiograms (ECGs) are often captured and shared as photographs. However, publicly available ECG data, and thus most related research, relies on digital signals. This has led to a disconnect in which computer assisted interpretation of ECG cannot easily be applied to ECG images. The emergence of high-fidelity synthetic data generators has introduced practical alternatives by producing realistic, photo-like, ECG images derived from the digital signal that could help narrow this divide. To address this, we propose a novel knowledge augmentation framework that uses synthetic data generated from multiple sources to provide generalisable and accurate interpretation of ECG photographs. Our framework features two key contributions. First, we introduce a robust pre-processing pipeline designed to remove background artifacts and reduces visual differences between images. Second, we implement a two-stage training strategy: a Morphology Learning Stage, where the model captures broad morphological features from visually different, scan-like synthetic data, followed by a Task-Specific Adaptation Stage, where the model is fine-tuned on the photo-like target data. We tested the model on the British Heart Foundation Challenge dataset, to classify five common ECG findings: myocardial infarction (MI), atrial fibrillation, hypertrophy, conduction disturbance, and ST/T changes. Our approach, built upon the ConvNeXt backbone, outperforms a single-source training baseline and achieved \\textbf{1st} place in the challenge with an macro-AUROC of \\textbf{0.9677}. These results suggest that incorporating morphology learning from heterogeneous sources offers a more robust and generalizable paradigm than conventional single-source training.", "authors": ["Xiaoyu Wang", "Ramesh Nadarajah", "Zhiqiang Zhang", "David Wong"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-29", "url": "https://arxiv.org/abs/2507.21968", "pdf_url": "https://arxiv.org/pdf/2507.21968v2", "arxiv_id": "2507.21968", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.159}
{"id": "b70905c383be14ff2f9e2d9a9c165ce48694946db51817a413e70f7e93f10b77", "sources": ["arxiv", "semantic_scholar"], "title": "Permanent Data Encoding (PDE): A Visual Language for Semantic Compression and Knowledge Preservation in 3-Character Units", "abstract": "Permanent Data Encoding (PDE) is a visual language framework designed for long-term, human-readable, and electrically independent knowledge preservation. By encoding semantic content into compact 2-3 character alphanumeric codes, paired with public dictionaries and rule-based expansion structures, PDE enables information to be visually interpreted and logically reconstructed without reliance on digital systems. Unlike QR codes or binary data, PDE offers a transparent and self-contained method of encoding meaning. This paper outlines the PDE syntax, dictionary protocol, use cases in disaster resilience and AI integration, and its implications as a cross-generational semantic infrastructure.", "authors": ["Yoshiharu Tsuyuki", "Xianqi Li", "Yuji Kurihara", "Kenji Mitsudo"], "categories": ["cs.DL"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-27", "url": "https://arxiv.org/abs/2507.20131", "pdf_url": "https://arxiv.org/pdf/2507.20131v1", "arxiv_id": "2507.20131", "doi": "10.48550/arXiv.2507.20131", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2475}
{"id": "61c2cf6c1f48ee141d791d6e933ef90a59743446dffbb3c107d9575b7c87968e", "sources": ["arxiv", "semantic_scholar"], "title": "Handling Out-of-Distribution Data: A Survey", "abstract": "In the field of Machine Learning (ML) and data-driven applications, one of the significant challenge is the change in data distribution between the training and deployment stages, commonly known as distribution shift. This paper outlines different mechanisms for handling two main types of distribution shifts: (i) Covariate shift: where the value of features or covariates change between train and test data, and (ii) Concept/Semantic-shift: where model experiences shift in the concept learned during training due to emergence of novel classes in the test phase. We sum up our contributions in three folds. First, we formalize distribution shifts, recite on how the conventional method fails to handle them adequately and urge for a model that can simultaneously perform better in all types of distribution shifts. Second, we discuss why handling distribution shifts is important and provide an extensive review of the methods and techniques that have been developed to detect, measure, and mitigate the effects of these shifts. Third, we discuss the current state of distribution shift handling mechanisms and propose future research directions in this area. Overall, we provide a retrospective synopsis of the literature in the distribution shift, focusing on OOD data that had been overlooked in the existing surveys.", "authors": ["Lakpa Tamang", "Mohamed Reda Bouadjenek", "Richard Dazeley", "Sunil Aryal"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-25", "url": "https://arxiv.org/abs/2507.21160", "pdf_url": "https://arxiv.org/pdf/2507.21160v1", "arxiv_id": "2507.21160", "doi": "10.1109/TKDE.2025.3592614", "citation_count": 16, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Knowledge and Data Engineering", "quality_score": 0.3076}
{"id": "d1f65effa989c12d73ebc59847e0a8dfaff086acc511d87ecb8261ad8bc8ce5d", "sources": ["arxiv", "semantic_scholar"], "title": "C2G-KD: PCA-Constrained Generator for Data-Free Knowledge Distillation", "abstract": "We introduce C2G-KD, a data-free knowledge distillation framework where a class-conditional generator is trained to produce synthetic samples guided by a frozen teacher model and geometric constraints derived from PCA. The generator never observes real training data but instead learns to activate the teacher's output through a combination of semantic and structural losses. By constraining generated samples to lie within class-specific PCA subspaces estimated from as few as two real examples per class, we preserve topological consistency and diversity. Experiments on MNIST show that even minimal class structure is sufficient to bootstrap useful synthetic training pipelines.", "authors": ["Magnus Bengtsson", "Kenneth Östberg"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-24", "url": "https://arxiv.org/abs/2507.18533", "pdf_url": "https://arxiv.org/pdf/2507.18533v1", "arxiv_id": "2507.18533", "doi": "10.48550/arXiv.2507.18533", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2441}
{"id": "811f03d3b2f6c148542501cbb9444c21e4375b8f0142c31ff2398a8957f52447", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetic Data Augmentation for Enhanced Chicken Carcass Instance Segmentation", "abstract": "The poultry industry has been driven by broiler chicken production and has grown into the world's largest animal protein sector. Automated detection of chicken carcasses on processing lines is vital for quality control, food safety, and operational efficiency in slaughterhouses and poultry processing plants. However, developing robust deep learning models for tasks like instance segmentation in these fast-paced industrial environments is often hampered by the need for laborious acquisition and annotation of large-scale real-world image datasets. We present the first pipeline generating photo-realistic, automatically labeled synthetic images of chicken carcasses. We also introduce a new benchmark dataset containing 300 annotated real-world images, curated specifically for poultry segmentation research. Using these datasets, this study investigates the efficacy of synthetic data and automatic data annotation to enhance the instance segmentation of chicken carcasses, particularly when real annotated data from the processing line is scarce. A small real dataset with varying proportions of synthetic images was evaluated in prominent instance segmentation models. Results show that synthetic data significantly boosts segmentation performance for chicken carcasses across all models. This research underscores the value of synthetic data augmentation as a viable and effective strategy to mitigate data scarcity, reduce manual annotation efforts, and advance the development of robust AI-driven automated detection systems for chicken carcasses in the poultry processing industry.", "authors": ["Yihong Feng", "Chaitanya Pallerla", "Xiaomin Lin", "Pouya Sohrabipour", "Philip Crandall", "Wan Shou", "Yu She", "Dongyi Wang"], "categories": ["cs.CV", "eess.IV"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2025-07-24", "url": "https://arxiv.org/abs/2507.18558", "pdf_url": "https://arxiv.org/pdf/2507.18558v1", "arxiv_id": "2507.18558", "doi": "10.1109/TAFE.2025.3644764", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Transactions on AgriFood Electronics", "quality_score": 0.2441}
{"id": "96245aed7c868d3640c1c76f2de51e3284b784e2636eb3dc4956d52bf8467c9f", "sources": ["arxiv", "semantic_scholar"], "title": "Small Data Explainer -- The impact of small data methods in everyday life", "abstract": "The emergence of breakthrough artificial intelligence (AI) techniques has led to a renewed focus on how small data settings, i.e., settings with limited information, can benefit from such developments. This includes societal issues such as how best to include under-represented groups in data-driven policy and decision making, or the health benefits of assistive technologies such as wearables. We provide a conceptual overview, in particular contrasting small data with big data, and identify common themes from exemplary case studies and application areas. Potential solutions are described in a more detailed technical overview of current data analysis and modelling techniques, highlighting contributions from different disciplines, such as knowledge-driven modelling from statistics and data-driven modelling from computer science. By linking application settings, conceptual contributions and specific techniques, we highlight what is already feasible and suggest what an agenda for fully leveraging small data might look like.", "authors": ["Maren Hackenberg", "Sophia G. Connor", "Fabian Kabus", "June Brawner", "Ella Markham", "Mahi Hardalupas", "Areeq Chowdhury", "Rolf Backofen", "Anna Köttgen", "Angelika Rohde", "Nadine Binder", "Harald Binder", "the Collaborative Research Center 1597 Small Data"], "categories": ["cs.CY", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-15", "url": "https://arxiv.org/abs/2507.11773", "pdf_url": "https://arxiv.org/pdf/2507.11773v1", "arxiv_id": "2507.11773", "doi": "10.48550/arXiv.2507.11773", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2338}
{"id": "973cc83c52b963a03136e623490b80012694753ba6f2f0d92fb908878a2e40aa", "sources": ["arxiv", "semantic_scholar"], "title": "Class-Proportional Coreset Selection for Difficulty-Separable Data", "abstract": "High-quality training data is essential for building reliable and efficient machine learning systems. One-shot coreset selection addresses this by pruning the dataset while maintaining or even improving model performance, often relying on training-dynamics-based data difficulty scores. However, most existing methods implicitly assume class-wise homogeneity in data difficulty, overlooking variation in data difficulty across different classes. In this work, we challenge this assumption by showing that, in domains such as network intrusion detection and medical imaging, data difficulty often clusters by class. We formalize this as class-difficulty separability and introduce the Class Difficulty Separability Coefficient (CDSC) as a quantitative measure. We demonstrate that high CDSC values correlate with performance degradation in class-agnostic coreset methods, which tend to overrepresent easy majority classes while neglecting rare but informative ones. To address this, we introduce class-proportional variants of multiple sampling strategies. Evaluated on five diverse datasets spanning security and medical domains, our methods consistently achieve state-of-the-art performance. For instance, on CTU-13, at an extreme 99% pruning rate, a class-proportional variant of Coverage-centric Coreset Selection (CCS-CP) shows remarkable stability, with accuracy dropping only 2.58%, precision 0.49%, and recall 0.19%. In contrast, the class-agnostic CCS baseline, the next best method, suffers sharper declines of 7.59% in accuracy, 4.57% in precision, and 4.11% in recall. We further show that aggressive pruning enhances generalization in noisy, imbalanced, and large-scale datasets. Our results underscore that explicitly modeling class-difficulty separability leads to more effective, robust, and generalizable data pruning, particularly in high-stakes scenarios.", "authors": ["Elisa Tsai", "Haizhong Zheng", "Atul Prakash"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-15", "url": "https://arxiv.org/abs/2507.10904", "pdf_url": "https://arxiv.org/pdf/2507.10904v2", "arxiv_id": "2507.10904", "doi": "10.1109/ICCVW69036.2025.00716", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1487}
{"id": "a780c3df4d6cff021b027301c20832504775791d99db5144070b64df19cd61ef", "sources": ["arxiv", "semantic_scholar"], "title": "Investigating the Reliability of the AfriTEC Model During the Descending Phase of Solar Cycle 24 Across East Africa", "abstract": "This study investigates the reliability of the African Regional Ionospheric Total Electron Content (AfriTEC) model during the descending phase of Solar Cycle 24 (2016-2017) across East Africa. Using GNSS-derived TEC data from five equatorial and low-latitude stations MOIU, MAL2, ZAMB, ADIS, and MBAR the model's performance is assessed through statistical metrics, including Mean Absolute Error (MAE) and correlation coefficient r. Results indicate that the AfriTEC model effectively captures the diurnal and seasonal behavior of TEC, particularly during equinoxes, with MAE values generally below 1.5 TECU and correlation coefficients exceeding 0.80. However, discrepancies emerge during solstice periods and post-sunset hours, reflecting the model's limitations in representing complex ionospheric processes such as the Equatorial Ionization Anomaly (EIA). To benchmark its performance, AfriTEC is also compared against the widely used NeQuick model. AfriTEC demonstrates superior regional adaptability and reduced error under most conditions, though it remains sensitive to localized ionospheric disturbances. These findings suggest that while AfriTEC is a valuable tool for ionospheric modeling in whole Africa especially at East African sector, enhancements incorporating real-time solar and geomagnetic indices could further improve its predictive capabilities.", "authors": ["Efrem Amanuel Data", "Daniel Izuikedinachi Okoh", "Emmanuel Daudi Sulungu", "Dejene Ambisa Terefe"], "categories": ["physics.space-ph", "astro-ph.SR"], "fields_of_study": ["Physics"], "published_date": "2025-07-14", "url": "https://arxiv.org/abs/2507.10275", "pdf_url": "https://arxiv.org/pdf/2507.10275v1", "arxiv_id": "2507.10275", "doi": "10.1007/s10509-025-04462-3", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Astrophysics and Space Science", "quality_score": 0.2326}
{"id": "ecbbdd0411952106928bb2daeff48a082afbba90a7d1f81ad0c14c677d1ecbf7", "sources": ["arxiv", "semantic_scholar"], "title": "Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout", "abstract": "Federated Learning (FL) is a promising distributed machine learning approach that enables collaborative training of a global model using multiple edge devices. The data distributed among the edge devices is highly heterogeneous. Thus, FL faces the challenge of data distribution and heterogeneity, where non-Independent and Identically Distributed (non-IID) data across edge devices may yield in significant accuracy drop. Furthermore, the limited computation and communication capabilities of edge devices increase the likelihood of stragglers, thus leading to slow model convergence. In this paper, we propose the FedDHAD FL framework, which comes with two novel methods: Dynamic Heterogeneous model aggregation (FedDH) and Adaptive Dropout (FedAD). FedDH dynamically adjusts the weights of each local model within the model aggregation process based on the non-IID degree of heterogeneous data to deal with the statistical data heterogeneity. FedAD performs neuron-adaptive operations in response to heterogeneous devices to improve accuracy while achieving superb efficiency. The combination of these two methods makes FedDHAD significantly outperform state-of-the-art solutions in terms of accuracy (up to 6.7% higher), efficiency (up to 2.02 times faster), and computation cost (up to 15.0% smaller).", "authors": ["Ji Liu", "Beichen Ma", "Qiaolin Yu", "Ruoming Jin", "Jingbo Zhou", "Yang Zhou", "Huaiyu Dai", "Haixun Wang", "Dejing Dou", "Patrick Valduriez"], "categories": ["cs.DC", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-14", "url": "https://arxiv.org/abs/2507.10430", "pdf_url": "https://arxiv.org/pdf/2507.10430v2", "arxiv_id": "2507.10430", "doi": "10.1145/3749376", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "ACM Transactions on Knowledge Discovery from Data", "quality_score": 0.2326}
{"id": "cd00dbc4748f7eb5a7f75ad2d704ddae87ac481dacdb673bcf47b6408cd46754", "sources": ["arxiv", "semantic_scholar"], "title": "SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework", "abstract": "The growing reliance on data-driven applications in sectors such as healthcare, finance, and law enforcement underscores the need for secure, privacy-preserving, and scalable mechanisms for data generation and sharing. Synthetic data generation (SDG) has emerged as a promising approach but often relies on centralized or external processing, raising concerns about data sovereignty, domain ownership, and compliance with evolving regulatory standards. To overcome these issues, we introduce SynthGuard, a framework designed to ensure computational governance by enabling data owners to maintain control over SDG workflows. SynthGuard supports modular and privacy-preserving workflows, ensuring secure, auditable, and reproducible execution across diverse environments. In this paper, we demonstrate how SynthGuard addresses the complexities at the intersection of domain-specific needs and scalable SDG by aligning with requirements for data sovereignty and regulatory compliance. Developed iteratively with domain expert input, SynthGuard has been validated through real-world use cases, demonstrating its ability to balance security, privacy, and scalability while ensuring compliance. The evaluation confirms its effectiveness in implementing and executing SDG workflows and integrating privacy and utility assessments across various computational environments.", "authors": ["Eduardo Brito", "Mahmoud Shoush", "Kristian Tamm", "Paula Etti", "Liina Kamm"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-14", "url": "https://arxiv.org/abs/2507.10489", "pdf_url": "https://arxiv.org/pdf/2507.10489v1", "arxiv_id": "2507.10489", "doi": "10.1007/978-3-032-00633-2_12", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "ARES", "quality_score": 0.2326}
{"id": "cc1867ef68f697a10e52ffaf185b27468a5dca21d78ab738b8262f20d56320db", "sources": ["arxiv"], "title": "Towards Benchmarking Foundation Models for Tabular Data With Text", "abstract": "Foundation models for tabular data are rapidly evolving, with increasing interest in extending them to support additional modalities such as free-text features. However, existing benchmarks for tabular data rarely include textual columns, and identifying real-world tabular datasets with semantically rich text features is non-trivial. We propose a series of simple yet effective ablation-style strategies for incorporating text into conventional tabular pipelines. Moreover, we benchmark how state-of-the-art tabular foundation models can handle textual data by manually curating a collection of real-world tabular datasets with meaningful textual features. Our study is an important step towards improving benchmarking of foundation models for tabular data with text.", "authors": ["Martin Mráz", "Breenda Das", "Anshul Gupta", "Lennart Purucker", "Frank Hutter"], "categories": ["cs.LG"], "fields_of_study": [], "published_date": "2025-07-10", "url": "https://arxiv.org/abs/2507.07829", "pdf_url": "https://arxiv.org/pdf/2507.07829v1", "arxiv_id": "2507.07829", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1451}
{"id": "d68d8c3b08ab7c6261ce0567356f670bfe4eca288a966b9f3e14b320a2db21a5", "sources": ["arxiv", "semantic_scholar"], "title": "Universal Embeddings of Tabular Data", "abstract": "Tabular data in relational databases represents a significant portion of industrial data. Hence, analyzing and interpreting tabular data is of utmost importance. Application tasks on tabular data are manifold and are often not specified when setting up an industrial database. To address this, we present a novel framework for generating universal, i.e., task-independent embeddings of tabular data for performing downstream tasks without predefined targets. Our method transforms tabular data into a graph structure, leverages Graph Auto-Encoders to create entity embeddings, which are subsequently aggregated to obtain embeddings for each table row, i.e., each data sample. This two-step approach has the advantage that unseen samples, consisting of similar entities, can be embedded without additional training. Downstream tasks such as regression, classification or outlier detection, can then be performed by applying a distance-based similarity measure in the embedding space. Experiments on real-world datasets demonstrate that our method achieves superior performance compared to existing universal tabular data embedding techniques.", "authors": ["Astrid Franz", "Frederik Hoppe", "Marianne Michaelis", "Udo Göbel"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-08", "url": "https://arxiv.org/abs/2507.05904", "pdf_url": "https://arxiv.org/pdf/2507.05904v1", "arxiv_id": "2507.05904", "doi": "10.48550/arXiv.2507.05904", "citation_count": 1, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2257}
{"id": "bebd123aa96bcb6b9e3c6d1e9619679922e7bb581f81694c1003e92ceecbff85", "sources": ["arxiv", "semantic_scholar"], "title": "Voltage Regulation in Distribution Systems with Data Center Loads", "abstract": "Recent boom in foundation models and AI computing have raised growing concerns on the power and energy trajectories of large-scale data centers. This paper focuses on the voltage issues caused by volatile and intensity of data center power demand, which also aligns with recent observations of more frequent voltage disturbances in power grids. To address these data center integration challenges, we propose a dynamic voltage control scheme by harnessing data center's load regulation capabilities. By taking local voltage measurements and adjusting power injections at each data center buses through the dynamic voltage and frequency scaling (DVFS) scheme, we are able to maintain safe voltage magnitude in a distributed fashion with higher data center computing load. Simulations using real large language model (LLM) inference load validate the effectiveness of our proposed mechanism. Both the LLM power data and proposed control scheme are open sourced.", "authors": ["Yize Chen", "Baosen Zhang"], "categories": ["eess.SY"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2025-07-08", "url": "https://arxiv.org/abs/2507.06416", "pdf_url": "https://arxiv.org/pdf/2507.06416v1", "arxiv_id": "2507.06416", "doi": "10.48550/arXiv.2507.06416", "citation_count": 4, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/chennnnnyize/voltage-regulation-with-data-centers", "venue": "arXiv.org", "quality_score": 0.3489}
{"id": "fb8aeeb610c002ae4254562ecfb4e8c8a4a49b0135b7940c13423c0aeaa75f9f", "sources": ["arxiv", "semantic_scholar"], "title": "Backdoors in Conditional Diffusion: Threats to Responsible Synthetic Data Pipelines", "abstract": "Text-to-image diffusion models achieve high-fidelity image generation from natural language prompts. ControlNets extend these models by enabling conditioning on structural inputs (e.g., edge maps, depth, pose), providing fine-grained control over outputs. Yet their reliance on large, publicly scraped datasets and community fine-tuning makes them vulnerable to data poisoning. We introduce a model-poisoning attack that embeds a covert backdoor into a ControlNet, causing it to produce attacker-specified content when exposed to visual triggers, without textual prompts. Experiments show that poisoning only 1% of the fine-tuning corpus yields a 90-98% attack success rate, while 5% further strengthens the backdoor, all while preserving normal generation quality. To mitigate this risk, we propose clean fine-tuning (CFT): freezing the diffusion backbone and fine-tuning only the ControlNet on a sanitized dataset with a reduced learning rate. CFT lowers attack success rates on held-out data. These results expose a critical security weakness in open-source, ControlNet-guided diffusion pipelines and demonstrate that CFT offers a practical defense for responsible synthetic-data pipelines.", "authors": ["Raz Lapid", "Almog Dubin"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-07", "url": "https://arxiv.org/abs/2507.04726", "pdf_url": "https://arxiv.org/pdf/2507.04726v2", "arxiv_id": "2507.04726", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "AAAI 2026 Workshop on Shaping Responsible Synthetic Data in the Era of Foundation Models", "quality_score": 0.3471}
{"id": "ed65225c520615771181d0d97a2127d10419731223e1bdb664a319d4817f7319", "sources": ["arxiv", "semantic_scholar"], "title": "ChangeBridge: Spatiotemporal Image Generation with Multimodal Controls for Remote Sensing", "abstract": "Spatiotemporal image generation is a highly meaningful task, which can generate future scenes conditioned on given observations. However, existing change generation methods can only handle event-driven changes (e.g., new buildings) and fail to model cross-temporal variations (e.g., seasonal shifts). In this work, we propose ChangeBridge, a conditional spatiotemporal image generation model for remote sensing. Given pre-event images and multimodal event controls, ChangeBridge generates post-event scenes that are both spatially and temporally coherent. The core idea is a drift-asynchronous diffusion bridge. Specifically, it consists of three main modules: a) Composed Bridge Initialization, which replaces noise initialization. It starts the diffusion from a composed pre-event state, modeling a diffusion bridge process. b) Asynchronous Drift Diffusion, which uses a pixel-wise drift map, assigning different drift magnitudes to event and temporal evolution. This enables differentiated generation during the pre-to-post transition. c) Drift-Aware Denoising, which embeds the drift map into the denoising network, guiding drift-aware reconstruction. Experiments show that ChangeBridge can generate better cross-spatiotemporal aligned scenarios compared to state-of-the-art methods. Additionally, ChangeBridge shows great potential for land-use planning and as a data generation engine for a series of change detection tasks. Code is available at https://github.com/zhenghuizhao/ChangeBridge", "authors": ["Zhenghui Zhao", "Chen Wu", "Xiangyong Cao", "Di Wang", "Hongruixuan Chen", "Datao Tang", "Liangpei Zhang", "Zhuo Zheng"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-07", "url": "https://arxiv.org/abs/2507.04678", "pdf_url": "https://arxiv.org/pdf/2507.04678v3", "arxiv_id": "2507.04678", "doi": "10.48550/arXiv.2507.04678", "citation_count": 3, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/zhenghuizhao/ChangeBridge", "venue": "arXiv.org", "quality_score": 0.3471}
{"id": "12f1e7e8aac3cc37d1114ce9fd20420c83ab25c0d0d8c02683b335f941d09d6b", "sources": ["arxiv", "semantic_scholar"], "title": "When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need", "abstract": "Data-free knowledge distillation (DFKD) transfers knowledge from a teacher to a student without access the real in-distribution (ID) data. Its common solution is to use a generator to synthesize fake data and use them as a substitute for real ID data. However, existing works typically assume teachers are trustworthy, leaving the robustness and security of DFKD from untrusted teachers largely unexplored. In this work, we conduct the first investigation into distilling non-transferable learning (NTL) teachers using DFKD, where the transferability from an ID domain to an out-of-distribution (OOD) domain is prohibited. We find that NTL teachers fool DFKD through divert the generator's attention from the useful ID knowledge to the misleading OOD knowledge. This hinders ID knowledge transfer but prioritizes OOD knowledge transfer. To mitigate this issue, we propose Adversarial Trap Escaping (ATEsc) to benefit DFKD by identifying and filtering out OOD-like synthetic samples. Specifically, inspired by the evidence that NTL teachers show stronger adversarial robustness on OOD samples than ID samples, we split synthetic samples into two groups according to their robustness. The fragile group is treated as ID-like data and used for normal knowledge distillation, while the robust group is seen as OOD-like data and utilized for forgetting OOD knowledge. Extensive experiments demonstrate the effectiveness of ATEsc for improving DFKD against NTL teachers. Code is released at https://github.com/tmllab/2025_ICML_ATEsc.", "authors": ["Ziming Hong", "Runnan Chen", "Zengmao Wang", "Bo Han", "Bo Du", "Tongliang Liu"], "categories": ["cs.LG", "cs.AI", "cs.CR", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-05", "url": "https://arxiv.org/abs/2507.04119", "pdf_url": "https://arxiv.org/pdf/2507.04119v1", "arxiv_id": "2507.04119", "doi": "10.48550/arXiv.2507.04119", "citation_count": 5, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/tmllab/2025_ICML_ATEsc", "venue": "International Conference on Machine Learning", "quality_score": 0.3435}
{"id": "85f6d7c9f6e065892676e9027615b20b43f9b5d19c7adeba0e6db26befe2ca73", "sources": ["arxiv", "semantic_scholar"], "title": "Exploring LLM Capabilities in Extracting DCAT-Compatible Metadata for Data Cataloging", "abstract": "Efficient data exploration is crucial as data becomes increasingly important for accelerating processes, improving forecasts and developing new business models. Data consumers often spend 25-98 % of their time searching for suitable data due to the exponential growth, heterogeneity and distribution of data. Data catalogs can support and accelerate data exploration by using metadata to answer user queries. However, as metadata creation and maintenance is often a manual process, it is time-consuming and requires expertise. This study investigates whether LLMs can automate metadata maintenance of text-based data and generate high-quality DCAT-compatible metadata. We tested zero-shot and few-shot prompting strategies with LLMs from different vendors for generating metadata such as titles and keywords, along with a fine-tuned model for classification. Our results show that LLMs can generate metadata comparable to human-created content, particularly on tasks that require advanced semantic understanding. Larger models outperformed smaller ones, and fine-tuning significantly improves classification accuracy, while few-shot prompting yields better results in most cases. Although LLMs offer a faster and reliable way to create metadata, a successful application requires careful consideration of task-specific criteria and domain context.", "authors": ["Lennart Busch", "Daniel Tebernum", "Gissel Velarde"], "categories": ["cs.IR", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-04", "url": "https://arxiv.org/abs/2507.05282", "pdf_url": "https://arxiv.org/pdf/2507.05282v1", "arxiv_id": "2507.05282", "doi": "10.5220/0013458500003967", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Data Technologies and Applications", "quality_score": 0.2211}
{"id": "ccf57ec86b5c61a658848d344fbcac8ae2c12d690eff218851adecea6f7e79ba", "sources": ["arxiv", "semantic_scholar"], "title": "Instant Particle Size Distribution Measurement Using CNNs Trained on Synthetic Data", "abstract": "Accurate particle size distribution (PSD) measurement is important in industries such as mining, pharmaceuticals, and fertilizer manufacturing, significantly influencing product quality and operational efficiency. Traditional PSD methods like sieve analysis and laser diffraction are manual, time-consuming, and limited by particle overlap. Recent developments in convolutional neural networks (CNNs) enable automated, real-time PSD estimation directly from particle images. In this work, we present a CNN-based methodology trained on realistic synthetic particle imagery generated using Blender's advanced rendering capabilities. Synthetic data sets using this method can replicate various industrial scenarios by systematically varying particle shapes, textures, lighting, and spatial arrangements that closely resemble the actual configurations. We evaluated three CNN-based architectures, ResNet-50, InceptionV3, and EfficientNet-B0, for predicting critical PSD parameters (d10, d50, d90). Results demonstrated comparable accuracy across models, with EfficientNet-B0 achieving the best computational efficiency suitable for real-time industrial deployment. This approach shows the effectiveness of realistic synthetic data for robust CNN training, which offers significant potential for automated industrial PSD monitoring. The code is released at : https://github.com/YasserElj/Synthetic-Granular-Gen", "authors": ["Yasser El Jarida", "Youssef Iraqi", "Loubna Mekouar"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-01", "url": "https://arxiv.org/abs/2507.00822", "pdf_url": "https://arxiv.org/pdf/2507.00822v1", "arxiv_id": "2507.00822", "doi": "10.48550/arXiv.2507.00822", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/YasserElj/Synthetic-Granular-Gen", "venue": "arXiv.org", "quality_score": 0.3365}
{"id": "f25910b836bef80a8d0d67459ddeb02edfc9251c6a0f476244e8621646ed9c0d", "sources": ["arxiv", "semantic_scholar"], "title": "Puzzles: Unbounded Video-Depth Augmentation for Scalable End-to-End 3D Reconstruction", "abstract": "Multi-view 3D reconstruction remains a core challenge in computer vision. Recent methods, such as DUST3R and its successors, directly regress pointmaps from image pairs without relying on known scene geometry or camera parameters. However, the performance of these models is constrained by the diversity and scale of available training data. In this work, we introduce Puzzles, a data augmentation strategy that synthesizes an unbounded volume of high-quality posed video-depth data from a single image or video clip. By simulating diverse camera trajectories and realistic scene geometry through targeted image transformations, Puzzles significantly enhances data variety. Extensive experiments show that integrating Puzzles into existing video-based 3D reconstruction pipelines consistently boosts performance without modifying the underlying network architecture. Notably, models trained on only ten percent of the original data augmented with Puzzles still achieve accuracy comparable to those trained on the full dataset. Code is available at https://jiahao-ma.github.io/puzzles/.", "authors": ["Jiahao Ma", "Lei Wang", "Miaomiao liu", "David Ahmedt-Aristizabal", "Chuong Nguyen"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-30", "url": "https://arxiv.org/abs/2506.23863", "pdf_url": "https://arxiv.org/pdf/2506.23863v1", "arxiv_id": "2506.23863", "doi": "10.48550/arXiv.2506.23863", "citation_count": 2, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3347}
{"id": "6fdffb01f81ee89a9cb958b618edf0d8290c8d17f521971d7cf632f59b0288cd", "sources": ["arxiv", "semantic_scholar"], "title": "Accessible Data Access and Analysis by People who are Blind or Have Low Vision", "abstract": "Our work aims to develop new assistive technologies that enable blind or low vision (BLV) people to explore and analyze data readily. At present, barriers exist for BLV people to explore and analyze data, restricting access to government, health and personal data, and limiting employment opportunities. This work explores the co-design and development of an innovative system to support data access, with a focus on the use of refreshable tactile displays (RTDs) and conversational agents. The envisaged system will use a combination of tactile graphics and speech to communicate with BLV users, and proactively assist with data analysis tasks. As well as addressing significant equity gaps, our work expects to produce innovations in assistive technology, multimodal interfaces, dialogue systems, and natural language understanding and generation.", "authors": ["Samuel Reinders", "Munazza Zaib", "Matthew Butler", "Bongshin Lee", "Ingrid Zukerman", "Lizhen Qu", "Kim Marriott"], "categories": ["cs.HC"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-30", "url": "https://arxiv.org/abs/2506.23443", "pdf_url": "https://arxiv.org/pdf/2506.23443v1", "arxiv_id": "2506.23443", "doi": "10.48550/arXiv.2506.23443", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2166}
{"id": "b2d762b82f5485ae5a4832d3589e566549c4e23b641be82109f265d0629e2eb8", "sources": ["arxiv", "semantic_scholar"], "title": "FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation", "abstract": "Synthetic data generation creates data based on real-world data using generative models. In health applications, generating high-quality data while maintaining fairness for sensitive attributes is essential for equitable outcomes. Existing GAN-based and LLM-based methods focus on counterfactual fairness and are primarily applied in finance and legal domains. Causal fairness provides a more comprehensive evaluation framework by preserving causal structure, but current synthetic data generation methods do not address it in health settings. To fill this gap, we develop the first LLM-augmented synthetic data generation method to enhance causal fairness using real-world tabular health data. Our generated data deviates by less than 10% from real data on causal fairness metrics. When trained on causally fair predictors, synthetic data reduces bias on the sensitive attribute by 70% compared to real data. This work improves access to fair synthetic data, supporting equitable health research and healthcare delivery.", "authors": ["Nitish Nagesh", "Ziyu Wang", "Amir M. Rahmani"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science", "Medicine"], "published_date": "2025-06-23", "url": "https://arxiv.org/abs/2506.19082", "pdf_url": "https://arxiv.org/pdf/2506.19082v1", "arxiv_id": "2506.19082", "doi": "10.1109/EMBC58623.2025.11252705", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Annual International Conference of the IEEE Engineering in Medicine and Biology Society", "quality_score": 0.2085}
{"id": "1b28f2f4e96fffccf354a47df164eeef906d31438aac0a73f2e2cb0776abc520", "sources": ["arxiv", "semantic_scholar"], "title": "Modeling and Visualization Reasoning for Stakeholders in Education and Industry Integration Systems: Research on Structured Synthetic Dialogue Data Generation Based on NIST Standards", "abstract": "This study addresses the structural complexity and semantic ambiguity in stakeholder interactions within the Education-Industry Integration (EII) system. The scarcity of real interview data, absence of structured variable modeling, and lack of interpretability in inference mechanisms have limited the analytical accuracy and policy responsiveness of EII research. To resolve these challenges, we propose a structural modeling paradigm based on the National Institute of Standards and Technology (NIST) synthetic data quality framework, focusing on consistency, authenticity, and traceability. We design a five-layer architecture that includes prompt-driven synthetic dialogue generation, a structured variable system covering skills, institutional, and emotional dimensions, dependency and causal path modeling, graph-based structure design, and an interactive inference engine. Empirical results demonstrate the effectiveness of the approach using a 15-segment synthetic corpus, with 41,597 tokens, 127 annotated variables, and 820 semantic relationship triples. The model exhibits strong structural consistency (Krippendorff alpha = 0.83), construct validity (RMSEA = 0.048, CFI = 0.93), and semantic alignment (mean cosine similarity > 0.78 via BERT). A key causal loop is identified: system mismatch leads to emotional frustration, reduced participation, skill gaps, and recurrence of mismatch, revealing a structural degradation cycle. This research introduces the first NIST-compliant AI modeling framework for stakeholder systems and provides a foundation for policy simulation, curriculum design, and collaborative strategy modeling.", "authors": ["Wei Meng"], "categories": ["cs.CY"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-20", "url": "https://arxiv.org/abs/2506.16952", "pdf_url": "https://arxiv.org/pdf/2506.16952v1", "arxiv_id": "2506.16952", "doi": "10.48550/arXiv.2506.16952", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2051}
{"id": "ea8046badd4af40bec3596c011fc3ef909eb73eb1643addf7a4b35b82e4364e4", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetic ALS-EEG Data Augmentation for ALS Diagnosis Using Conditional WGAN with Weight Clipping", "abstract": "Amyotrophic Lateral Sclerosis (ALS) is a rare neurodegenerative disease, and high-quality EEG data from ALS patients are scarce. This data scarcity, coupled with severe class imbalance between ALS and healthy control recordings, poses a challenge for training reliable machine learning classifiers. In this work, we address these issues by generating synthetic EEG signals for ALS patients using a Conditional Wasserstein Generative Adversarial Network (CWGAN). We train CWGAN on a private EEG dataset (ALS vs. non-ALS) to learn the distribution of ALS EEG signals and produce realistic synthetic samples. We preprocess and normalize EEG recordings, and train a CWGAN model to generate synthetic ALS signals. The CWGAN architecture and training routine are detailed, with key hyperparameters chosen for stable training. Qualitative evaluation of generated signals shows that they closely mimic real ALS EEG patterns. The CWGAN training converged with generator and discriminator loss curves stabilizing, indicating successful learning. The synthetic EEG signals appear realistic and have potential use as augmented data for training classifiers, helping to mitigate class imbalance and improve ALS detection accuracy. We discuss how this approach can facilitate data sharing and enhance diagnostic models.", "authors": ["Abdulvahap Mutlu", "Şengül Doğan", "Türker Tuncer"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-19", "url": "https://arxiv.org/abs/2506.16243", "pdf_url": "https://arxiv.org/pdf/2506.16243v1", "arxiv_id": "2506.16243", "doi": "10.48550/arXiv.2506.16243", "citation_count": 2, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/abdulvahapmutlu/als-synthetic-data-augmentation-wgan", "venue": "arXiv.org", "quality_score": 0.3152}
{"id": "cd5c2f1e49335df45ff035f731ce04a9ec1d7d41854a9b9054fa66ba34222078", "sources": ["arxiv", "semantic_scholar"], "title": "Job Market Cheat Codes: Prototyping Salary Prediction and Job Grouping with Synthetic Job Listings", "abstract": "This paper presents a machine learning methodology prototype using a large synthetic dataset of job listings to identify trends, predict salaries, and group similar job roles. Employing techniques such as regression, classification, clustering, and natural language processing (NLP) for text-based feature extraction and representation, this study aims to uncover the key features influencing job market dynamics and provide valuable insights for job seekers, employers, and researchers. Exploratory data analysis was conducted to understand the dataset's characteristics. Subsequently, regression models were developed to predict salaries, classification models to predict job titles, and clustering techniques were applied to group similar jobs. The analyses revealed significant factors influencing salary and job roles, and identified distinct job clusters based on the provided data. While the results are based on synthetic data and not intended for real-world deployment, the methodology demonstrates a transferable framework for job market analysis.", "authors": ["Abdel Rahman Alsheyab", "Mohammad Alkhasawneh", "Nidal Shahin"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-18", "url": "https://arxiv.org/abs/2506.15879", "pdf_url": "https://arxiv.org/pdf/2506.15879v1", "arxiv_id": "2506.15879", "doi": "10.48550/arXiv.2506.15879", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2028}
{"id": "f4b83f63f257c1002a487c24e947b944c3aa13b2799d988e166a3e757989e9b9", "sources": ["arxiv", "semantic_scholar"], "title": "Data-driven approaches to inverse problems", "abstract": "Inverse problems are concerned with the reconstruction of unknown physical quantities using indirect measurements and are fundamental across diverse fields such as medical imaging, remote sensing, and material sciences. These problems serve as critical tools for visualizing internal structures beyond what is visible to the naked eye, enabling quantification, diagnosis, prediction, and discovery. However, most inverse problems are ill-posed, necessitating robust mathematical treatment to yield meaningful solutions. While classical approaches provide mathematically rigorous and computationally stable solutions, they are constrained by the ability to accurately model solution properties and implement them efficiently. A more recent paradigm considers deriving solutions to inverse problems in a data-driven manner. Instead of relying on classical mathematical modeling, this approach utilizes highly over-parameterized models, typically deep neural networks, which are adapted to specific inverse problems using carefully selected training data. Current approaches that follow this new paradigm distinguish themselves through solution accuracy paired with computational efficiency that was previously inconceivable. These notes offer an introduction to this data-driven paradigm for inverse problems. The first part of these notes will provide an introduction to inverse problems, discuss classical solution strategies, and present some applications. The second part will delve into modern data-driven approaches, with a particular focus on adversarial regularization and provably convergent linear plug-and-play denoisers. Throughout the presentation of these methodologies, their theoretical properties will be discussed, and numerical examples will be provided. The lecture series will conclude with a discussion of open problems and future perspectives in the field.", "authors": ["Carola-Bibiane Schönlieb", "Zakhar Shumaylov"], "categories": ["math.NA", "cs.LG", "math.OC"], "fields_of_study": ["Mathematics", "Computer Science"], "published_date": "2025-06-13", "url": "https://arxiv.org/abs/2506.11732", "pdf_url": "https://arxiv.org/pdf/2506.11732v1", "arxiv_id": "2506.11732", "doi": "10.48550/arXiv.2506.11732", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1971}
{"id": "6b072b792e1837d0d0a081639b717bf48cf9afd6c0d05811fa4d085282566b40", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetic Human Action Video Data Generation with Pose Transfer", "abstract": "In video understanding tasks, particularly those involving human motion, synthetic data generation often suffers from uncanny features, diminishing its effectiveness for training. Tasks such as sign language translation, gesture recognition, and human motion understanding in autonomous driving have thus been unable to exploit the full potential of synthetic data. This paper proposes a method for generating synthetic human action video data using pose transfer (specifically, controllable 3D Gaussian avatar models). We evaluate this method on the Toyota Smarthome and NTU RGB+D datasets and show that it improves performance in action recognition tasks. Moreover, we demonstrate that the method can effectively scale few-shot datasets, making up for groups underrepresented in the real training data and adding diverse backgrounds. We open-source the method along with RANDOM People, a dataset with videos and avatars of novel human identities for pose transfer crowd-sourced from the internet.", "authors": ["Vaclav Knapp", "Matyas Bohacek"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-11", "url": "https://arxiv.org/abs/2506.09411", "pdf_url": "https://arxiv.org/pdf/2506.09411v1", "arxiv_id": "2506.09411", "doi": "10.48550/arXiv.2506.09411", "citation_count": 2, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.301}
{"id": "c3578d4a9a6d8b532c2fcbe6f45311c2ee0e21f6df793bbd059e7fcbe30204b2", "sources": ["arxiv", "semantic_scholar"], "title": "PGDA-KGQA: A Prompt-Guided Generative Framework with Multiple Data Augmentation Strategies for Knowledge Graph Question Answering", "abstract": "Knowledge Graph Question Answering (KGQA) is a crucial task in natural language processing that requires reasoning over knowledge graphs (KGs) to answer natural language questions. Recent methods utilizing large language models (LLMs) have shown remarkable semantic parsing capabilities but are limited by the scarcity of diverse annotated data and multi-hop reasoning samples. Traditional data augmentation approaches are focus mainly on single-hop questions and prone to semantic distortion, while LLM-based methods primarily address semantic distortion but usually neglect multi-hop reasoning, thus limiting data diversity. The scarcity of multi-hop samples further weakens models' generalization. To address these issues, we propose PGDA-KGQA, a prompt-guided generative framework with multiple data augmentation strategies for KGQA. At its core, PGDA-KGQA employs a unified prompt-design paradigm: by crafting meticulously engineered prompts that integrate the provided textual content, it leverages LLMs to generate large-scale (question, logical form) pairs for model training. Specifically, PGDA-KGQA enriches its training set by: (1) generating single-hop pseudo questions to improve the alignment of question semantics with KG relations; (2) applying semantic-preserving question rewriting to improve robustness against linguistic variations; (3) employing answer-guided reverse path exploration to create realistic multi-hop questions. By adopting an augment-generate-retrieve semantic parsing pipeline, PGDA-KGQA utilizes the augmented data to enhance the accuracy of logical form generation and thus improve answer retrieval performance. Experiments demonstrate that outperforms state-of-the-art methods on standard KGQA datasets, achieving improvements on WebQSP by 2.8%, 1.2%, and 3.1% and on ComplexWebQuestions by 1.8%, 1.1%, and 2.4% in F1, Hits@1, and Accuracy, respectively.", "authors": ["Xiujun Zhou", "Pingjian Zhang", "Deyou Tang"], "categories": ["cs.CL", "cs.IR"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-11", "url": "https://arxiv.org/abs/2506.09414", "pdf_url": "https://arxiv.org/pdf/2506.09414v1", "arxiv_id": "2506.09414", "doi": "10.48550/arXiv.2506.09414", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1948}
{"id": "1380a2ca7e83cc1cb06ba24550da037b7da627bc80f36775d14b96cd60c6e513", "sources": ["arxiv", "semantic_scholar"], "title": "Patient Similarity Computation for Clinical Decision Support: An Efficient Use of Data Transformation, Combining Static and Time Series Data", "abstract": "Patient similarity computation (PSC) is a fundamental problem in healthcare informatics. The aim of the patient similarity computation is to measure the similarity among patients according to their historical clinical records, which helps to improve clinical decision support. This paper presents a novel distributed patient similarity computation (DPSC) technique based on data transformation (DT) methods, utilizing an effective combination of time series and static data. Time series data are sensor-collected patients' information, including metrics like heart rate, blood pressure, Oxygen saturation, respiration, etc. The static data are mainly patient background and demographic data, including age, weight, height, gender, etc. Static data has been used for clustering the patients. Before feeding the static data to the machine learning model adaptive Weight-of-Evidence (aWOE) and Z-score data transformation (DT) methods have been performed, which improve the prediction performances. In aWOE-based patient similarity models, sensitive patient information has been processed using aWOE which preserves the data privacy of the trained models. We used the Dynamic Time Warping (DTW) approach, which is robust and very popular, for time series similarity. However, DTW is not suitable for big data due to the significant computational run-time. To overcome this problem, distributed DTW computation is used in this study. For Coronary Artery Disease, our DT based approach boosts prediction performance by as much as 11.4%, 10.20%, and 12.6% in terms of AUC, accuracy, and F-measure, respectively. In the case of Congestive Heart Failure (CHF), our proposed method achieves performance enhancement up to 15.9%, 10.5%, and 21.9% for the same measures, respectively. The proposed method reduces the computation time by as high as 40%.", "authors": ["Joydeb Kumar Sana", "Mohammad M. Masud", "M Sohel Rahman", "M Saifur Rahman"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-08", "url": "https://arxiv.org/abs/2506.07092", "pdf_url": "https://arxiv.org/pdf/2506.07092v1", "arxiv_id": "2506.07092", "doi": "10.48550/arXiv.2506.07092", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1914}
{"id": "982e6851651b78098012073b355d1f5d5915be373006234f61b8631851b5b065", "sources": ["arxiv", "semantic_scholar"], "title": "Learning based on neurovectors for tabular data: a new neural network approach", "abstract": "In this paper, we present a novel learning approach based on Neurovectors, an innovative paradigm that structures information through interconnected nodes and vector relationships for tabular data processing. Unlike traditional artificial neural networks that rely on weight adjustment through backpropagation, Neurovectors encode information by structuring data in vector spaces where energy propagation, rather than traditional weight updates, drives the learning process, enabling a more adaptable and explainable learning process. Our method generates dynamic representations of knowledge through neurovectors, thereby improving both the interpretability and efficiency of the predictive model. Experimental results using datasets from well-established repositories such as the UCI machine learning repository and Kaggle are reported both for classification and regression. To evaluate its performance, we compare our approach with standard machine learning and deep learning models, showing that Neurovectors achieve competitive accuracy.", "authors": ["J. C. Husillos", "A. Gallego", "A. Roma", "A. Troncoso"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-08", "url": "https://arxiv.org/abs/2506.07185", "pdf_url": "https://arxiv.org/pdf/2506.07185v1", "arxiv_id": "2506.07185", "doi": "10.48550/arXiv.2506.07185", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1914}
{"id": "768b1b1b2b783bc7ce9058231261bae12724329a4bf7016b89b3f424ffb3c62a", "sources": ["arxiv", "semantic_scholar"], "title": "An Active Learning-Based Streaming Pipeline for Reduced Data Training of Structure Finding Models in Neutron Diffractometry", "abstract": "Structure determination workloads in neutron diffractometry are computationally expensive and routinely require several hours to many days to determine the structure of a material from its neutron diffraction patterns. The potential for machine learning models trained on simulated neutron scattering patterns to significantly speed up these tasks have been reported recently. However, the amount of simulated data needed to train these models grows exponentially with the number of structural parameters to be predicted and poses a significant computational challenge. To overcome this challenge, we introduce a novel batch-mode active learning (AL) policy that uses uncertainty sampling to simulate training data drawn from a probability distribution that prefers labelled examples about which the model is least certain. We confirm its efficacy in training the same models with about 75% less training data while improving the accuracy. We then discuss the design of an efficient stream-based training workflow that uses this AL policy and present a performance study on two heterogeneous platforms to demonstrate that, compared with a conventional training workflow, the streaming workflow delivers about 20% shorter training time without any loss of accuracy.", "authors": ["Tianle Wang", "Jorge Ramirez", "Cristina Garcia-Cardona", "Thomas Proffen", "Shantenu Jha", "Sudip K. Seal"], "categories": ["cs.LG", "cs.AI", "cs.DC", "physics.atm-clus", "physics.data-an"], "fields_of_study": ["Computer Science", "Physics"], "published_date": "2025-06-06", "url": "https://arxiv.org/abs/2506.11100", "pdf_url": "https://arxiv.org/pdf/2506.11100v1", "arxiv_id": "2506.11100", "doi": "10.1109/BigData62323.2024.10825990", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "BigData Congress [Services Society]", "quality_score": 0.1891}
{"id": "69d54cdfe16078285d448b5b0fd0cb6bf68bfb0e398216c99efff2a17655d33c", "sources": ["arxiv", "semantic_scholar"], "title": "LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback", "abstract": "Large Action Models (LAMs) for AI Agents offer incredible potential but face challenges due to the need for high-quality training data, especially for multi-steps tasks that involve planning, executing tool calls, and responding to feedback. To address these issues, we present LAM SIMULATOR, a comprehensive framework designed for online exploration of agentic tasks with high-quality feedback. Our framework features a dynamic task query generator, an extensive collection of tools, and an interactive environment where Large Language Model (LLM) Agents can call tools and receive real-time feedback. This setup enables LLM Agents to explore and solve tasks autonomously, facilitating the discovery of multiple approaches to tackle any given task. The resulting action trajectory data are then used to create high-quality training datasets for LAMs. Our experiments on popular agentic benchmarks, ToolBench and CRMArena, highlight the effectiveness of LAM SIMULATOR: models trained with self-generated datasets using our framework achieve significant performance gains, up to a 49.3\\% improvement over their original baselines. LAM SIMULATOR requires minimal human input during dataset creation, highlighting LAM SIMULATOR's efficiency and effectiveness in speeding up development of AI agents.", "authors": ["Thai Hoang", "Kung-Hsiang Huang", "Shirley Kokane", "Jianguo Zhang", "Zuxin Liu", "Ming Zhu", "Jake Grigsby", "Tian Lan", "Michael S Ryoo", "Chien-Sheng Wu", "Shelby Heinecke", "Huan Wang", "Silvio Savarese", "Caiming Xiong", "Juan Carlos Niebles"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-02", "url": "https://arxiv.org/abs/2506.02298", "pdf_url": "https://arxiv.org/pdf/2506.02298v1", "arxiv_id": "2506.02298", "doi": "10.48550/arXiv.2506.02298", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Annual Meeting of the Association for Computational Linguistics", "quality_score": 0.2258}
{"id": "baaa20bba4ab7ab564bb0ca83ff97d0cb1cbcd1bac74c4e27b9b7351fa69a3a3", "sources": ["arxiv", "semantic_scholar"], "title": "Improving Knowledge Distillation Under Unknown Covariate Shift Through Confidence-Guided Data Augmentation", "abstract": "Large foundation models trained on extensive datasets demonstrate strong zero-shot capabilities in various domains. To replicate their success when data and model size are constrained, knowledge distillation has become an established tool for transferring knowledge from foundation models to small student networks. However, the effectiveness of distillation is critically limited by the available training data. This work addresses the common practical issue of covariate shift in knowledge distillation, where spurious features appear during training but not at test time. We ask the question: when these spurious features are unknown, yet a robust teacher is available, is it possible for a student to also become robust to them? We address this problem by introducing a novel diffusion-based data augmentation strategy that generates images by maximizing the disagreement between the teacher and the student, effectively creating challenging samples that the student struggles with. Experiments demonstrate that our approach significantly improves worst group and mean group accuracy on CelebA and SpuCo Birds as well as the spurious mAUC on spurious ImageNet under covariate shift, outperforming state-of-the-art diffusion-based data augmentation baselines", "authors": ["Niclas Popp", "Kevin Alexander Laube", "Matthias Hein", "Lukas Schott"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-02", "url": "https://arxiv.org/abs/2506.02294", "pdf_url": "https://arxiv.org/pdf/2506.02294v2", "arxiv_id": "2506.02294", "doi": "10.48550/arXiv.2506.02294", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1845}
{"id": "993c701224408a85402a10dcdd52bdeac3603c721622011d4148b040eca88951", "sources": ["arxiv", "semantic_scholar"], "title": "Data Heterogeneity Modeling for Trustworthy Machine Learning", "abstract": "Data heterogeneity plays a pivotal role in determining the performance of machine learning (ML) systems. Traditional algorithms, which are typically designed to optimize average performance, often overlook the intrinsic diversity within datasets. This oversight can lead to a myriad of issues, including unreliable decision-making, inadequate generalization across different domains, unfair outcomes, and false scientific inferences. Hence, a nuanced approach to modeling data heterogeneity is essential for the development of dependable, data-driven systems. In this survey paper, we present a thorough exploration of heterogeneity-aware machine learning, a paradigm that systematically integrates considerations of data heterogeneity throughout the entire ML pipeline -- from data collection and model training to model evaluation and deployment. By applying this approach to a variety of critical fields, including healthcare, agriculture, finance, and recommendation systems, we demonstrate the substantial benefits and potential of heterogeneity-aware ML. These applications underscore how a deeper understanding of data diversity can enhance model robustness, fairness, and reliability and help model diagnosis and improvements. Moreover, we delve into future directions and provide research opportunities for the whole data mining community, aiming to promote the development of heterogeneity-aware ML.", "authors": ["Jiashuo Liu", "Peng Cui"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-01", "url": "https://arxiv.org/abs/2506.00969", "pdf_url": "https://arxiv.org/pdf/2506.00969v1", "arxiv_id": "2506.00969", "doi": "10.1145/3711896.3736560", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Knowledge Discovery and Data Mining", "quality_score": 0.2386}
{"id": "9ff1bbdf6328d79ffb4d01711e8a119b56b054dfd892cf68b1dd83377b4310f3", "sources": ["arxiv", "semantic_scholar"], "title": "Data Swarms: Optimizable Generation of Synthetic Evaluation Data", "abstract": "We propose Data Swarms, an algorithm to optimize the generation of synthetic evaluation data and advance quantitative desiderata of LLM evaluation. We first train a swarm of initial data generators using existing data, and define various evaluation objectives to reflect the desired properties of evaluation (e.g., generate more difficult problems for the evaluated models) and quantitatively evaluate data generators. We then employ particle swarm optimization to optimize the swarm of data generators, where they collaboratively search through the model parameter space to find new generators that advance these objectives. We further extend it to Adversarial Swarms, where the data generator swarm generates harder data while the test taker model swarm learns from such data, co-evolving dynamically for better data and models simultaneously. Extensive experiments demonstrate that Data Swarms outperforms eight data generation baselines across five evaluation objectives, while Adversarial Swarms produce more robust learning of synthetic data and stronger generalization. Further analysis reveals that Data Swarms successfully optimizes compositions of multiple evaluation objectives and generalizes to new off-the-shelf LLMs, unseen at optimization time.", "authors": ["Shangbin Feng", "Yike Wang", "Weijia Shi", "Yulia Tsvetkov"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-31", "url": "https://arxiv.org/abs/2506.00741", "pdf_url": "https://arxiv.org/pdf/2506.00741v2", "arxiv_id": "2506.00741", "doi": "10.48550/arXiv.2506.00741", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1822}
{"id": "47f0c54333b24c514808729951a0ef20996a08af8f5acf965abde3f565675810", "sources": ["arxiv", "semantic_scholar"], "title": "Harnessing Negative Signals: Reinforcement Distillation from Teacher Data for LLM Reasoning", "abstract": "Recent advances in model distillation show that data from advanced reasoning models can effectively train smaller student models. However, standard practices discard incorrect reasoning traces -- valuable, yet underutilized data. This paper addresses the critical question: How can both positive and negative distilled reasoning traces be effectively leveraged to maximize LLM reasoning performance in an offline setting? We employ a two-stage training recipe: first, Supervised Fine-Tuning (SFT) on positive traces, followed by a refinement stage using both positive and negative traces. We find that a simple REINFORCE-style objective, which we term the Reinforcement Distillation (REDI) objective, outperforms established preference optimization methods like DPO and SimPO in this distillation context. Our empirical evaluations demonstrate the effectiveness of this approach. Notably, our Qwen-REDI-1.5B model, trained on just 131k traces from the open Open-R1 dataset, achieves an 83.1% score on MATH-500. Its performance matches that of DeepSeek-R1-Distill-Qwen-1.5B, a model trained on 800k proprietary data. This result showcases the remarkable data efficiency of utilizing previously discarded negative traces.", "authors": ["Shuyao Xu", "Cheng Peng", "Jiangxuan Long", "Weidi Xu", "Wei Chu", "Yuan Qi"], "categories": ["cs.LG", "cs.AI", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-30", "url": "https://arxiv.org/abs/2505.24850", "pdf_url": "https://arxiv.org/pdf/2505.24850v2", "arxiv_id": "2505.24850", "doi": "10.48550/arXiv.2505.24850", "citation_count": 2, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Tim-Siu/reinforcement-distillation", "venue": "arXiv.org", "quality_score": 0.2798}
{"id": "3558fc50875c612295c152043879dc94ffee54a05bd022d3ae88897f9a08a9da", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetic Generation and Latent Projection Denoising of Rim Lesions in Multiple Sclerosis", "abstract": "Quantitative susceptibility maps from magnetic resonance images can provide both prognostic and diagnostic information in multiple sclerosis, a neurodegenerative disease characterized by the formation of lesions in white matter brain tissue. In particular, susceptibility maps provide adequate contrast to distinguish between \"rim\" lesions, surrounded by deposited paramagnetic iron, and \"non-rim\" lesion types. These paramagnetic rim lesions (PRLs) are an emerging biomarker in multiple sclerosis. Much effort has been devoted to both detection and segmentation of such lesions to monitor longitudinal change. As paramagnetic rim lesions are rare, addressing this problem requires confronting the class imbalance between rim and non-rim lesions. We produce synthetic quantitative susceptibility maps of paramagnetic rim lesions and show that inclusion of such synthetic data improves classifier performance and provide a multi-channel extension to generate accompanying contrasts and probabilistic segmentation maps. We exploit the projection capability of our trained generative network to demonstrate a novel denoising approach that allows us to train on ambiguous rim cases and substantially increase the minority class. We show that both synthetic lesion synthesis and our proposed rim lesion label denoising method best approximate the unseen rim lesion distribution and improve detection in a clinically interpretable manner. We release our code and generated data at https://github.com/agr78/PRLx-GAN upon publication.", "authors": ["Alexandra G. Roberts", "Ha M. Luu", "Mert Şişman", "Alexey V. Dimov", "Ceren Tozlu", "Ilhami Kovanlikaya", "Susan A. Gauthier", "Thanh D. Nguyen", "Yi Wang"], "categories": ["eess.IV", "cs.AI", "cs.CV"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2025-05-29", "url": "https://arxiv.org/abs/2505.23353", "pdf_url": "https://arxiv.org/pdf/2505.23353v1", "arxiv_id": "2505.23353", "doi": "10.48550/arXiv.2505.23353", "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/agr78/PRLx-GAN", "venue": "arXiv.org", "quality_score": 0.278}
{"id": "69b55fb94d0512d98f7e25049575c0de602d175f71b511e9fff147475b0a26d9", "sources": ["arxiv", "semantic_scholar"], "title": "Leveraging Diffusion Models for Synthetic Data Augmentation in Protein Subcellular Localization Classification", "abstract": "We investigate whether synthetic images generated by diffusion models can enhance multi-label classification of protein subcellular localization. Specifically, we implement a simplified class-conditional denoising diffusion probabilistic model (DDPM) to produce label-consistent samples and explore their integration with real data via two hybrid training strategies: Mix Loss and Mix Representation. While these approaches yield promising validation performance, our proposed MixModel exhibits poor generalization to unseen test data, underscoring the challenges of leveraging synthetic data effectively. In contrast, baseline classifiers built on ResNet backbones with conventional loss functions demonstrate greater stability and test-time performance. Our findings highlight the importance of realistic data generation and robust supervision when incorporating generative augmentation into biomedical image classification.", "authors": ["Sylvey Lin", "Zhi-Yi Cao"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-28", "url": "https://arxiv.org/abs/2505.22926", "pdf_url": "https://arxiv.org/pdf/2505.22926v1", "arxiv_id": "2505.22926", "doi": "10.48550/arXiv.2505.22926", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1788}
{"id": "c1694ba0013358cf5915e38f67449303681da325d8a388987bc7e9cfc74c9575", "sources": ["arxiv", "semantic_scholar"], "title": "A Cross Modal Knowledge Distillation & Data Augmentation Recipe for Improving Transcriptomics Representations through Morphological Features", "abstract": "Understanding cellular responses to stimuli is crucial for biological discovery and drug development. Transcriptomics provides interpretable, gene-level insights, while microscopy imaging offers rich predictive features but is harder to interpret. Weakly paired datasets, where samples share biological states, enable multimodal learning but are scarce, limiting their utility for training and multimodal inference. We propose a framework to enhance transcriptomics by distilling knowledge from microscopy images. Using weakly paired data, our method aligns and binds modalities, enriching gene expression representations with morphological information. To address data scarcity, we introduce (1) Semi-Clipped, an adaptation of CLIP for cross-modal distillation using pretrained foundation models, achieving state-of-the-art results, and (2) PEA (Perturbation Embedding Augmentation), a novel augmentation technique that enhances transcriptomics data while preserving inherent biological information. These strategies improve the predictive power and retain the interpretability of transcriptomics, enabling rich unimodal representations for complex biological tasks.", "authors": ["Ihab Bendidi", "Yassir El Mesbahi", "Alisandra K. Denton", "Karush Suri", "Kian Kenyon-Dean", "Auguste Genovesio", "Emmanuel Noutahi"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-27", "url": "https://arxiv.org/abs/2505.21317", "pdf_url": "https://arxiv.org/pdf/2505.21317v1", "arxiv_id": "2505.21317", "doi": "10.48550/arXiv.2505.21317", "citation_count": 3, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "International Conference on Machine Learning", "quality_score": 0.1776}
{"id": "dc745690a65b358d53c7e15b82f2c5d93197b68eccf174a22f423bf760bb9b66", "sources": ["arxiv", "semantic_scholar"], "title": "Conversational Lexicography: Querying Lexicographic Data on Knowledge Graphs with SPARQL through Natural Language", "abstract": "Knowledge graphs offer an excellent solution for representing the lexical-semantic structures of lexicographic data. However, working with the SPARQL query language represents a considerable hurdle for many non-expert users who could benefit from the advantages of this technology. This paper addresses the challenge of creating natural language interfaces for lexicographic data retrieval on knowledge graphs such as Wikidata. We develop a multidimensional taxonomy capturing the complexity of Wikidata's lexicographic data ontology module through four dimensions and create a template-based dataset with over 1.2 million mappings from natural language utterances to SPARQL queries. Our experiments with GPT-2 (124M), Phi-1.5 (1.3B), and GPT-3.5-Turbo reveal significant differences in model capabilities. While all models perform well on familiar patterns, only GPT-3.5-Turbo demonstrates meaningful generalization capabilities, suggesting that model size and diverse pre-training are crucial for adaptability in this domain. However, significant challenges remain in achieving robust generalization, handling diverse linguistic data, and developing scalable solutions that can accommodate the full complexity of lexicographic knowledge representation.", "authors": ["Kilian Sennrich", "Sina Ahmadi"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-26", "url": "https://arxiv.org/abs/2505.19971", "pdf_url": "https://arxiv.org/pdf/2505.19971v1", "arxiv_id": "2505.19971", "doi": null, "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1765}
{"id": "975205efe50f083f67afa1928dc1b3f29f29d9b67e39a2be4b732350db84849b", "sources": ["arxiv", "semantic_scholar"], "title": "Adaptive Indexing for Approximate Query Processing in Exploratory Data Analysis", "abstract": "Minimizing data-to-analysis time while enabling real-time interaction and efficient analytical computations on large datasets are fundamental objectives of contemporary exploratory systems. Although some of the recent adaptive indexing and on-the-fly processing approaches address most of these needs, there are cases, where they do not always guarantee reliable performance. Some examples of such cases include: exploring areas with a high density of objects; executing the first exploratory queries or exploring previously unseen areas (where the index has not yet adapted sufficiently); and working with very large data files on commodity hardware, such as low-specification laptops. In such demanding cases, approximate and incremental techniques can be exploited to ensure efficiency and scalability by allowing users to prioritize response time over result accuracy, acknowledging that exact results are not always necessary. Therefore, approximation mechanisms that enable smooth user interaction by defining the trade-off between accuracy and performance based on vital factors (e.g., task, preferences, available resources) are of great importance. Considering the aforementioned, in this work, we present an adaptive approximate query processing framework for interactive on-the-fly analysis (with out a preprocessing phase) over large raw data. The core component of the framework is a main-memory adaptive indexing scheme (VALINOR-A) that interoperates with user-driven sampling and incremental aggregation computations. Additionally, an effective error-bounded approximation strategy is designed and integrated in the query processing process. We conduct extensive experiments using both real and synthetic datasets, demonstrating the efficiency and effectiveness of the proposed framework.", "authors": ["Stavros Maroulis", "Nikos Bikakis", "Vassilis Stamatopoulos", "George Papastefanatos"], "categories": ["cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-26", "url": "https://arxiv.org/abs/2505.19872", "pdf_url": "https://arxiv.org/pdf/2505.19872v1", "arxiv_id": "2505.19872", "doi": "10.48550/arXiv.2505.19872", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1765}
{"id": "3ac3ca979d994e85781b8772694de43a8b23c15a7e84faabaf804271afe90905", "sources": ["arxiv", "semantic_scholar"], "title": "What Does Information Science Offer for Data Science Research?: A Review of Data and Information Ethics Literature", "abstract": "This paper reviews literature pertaining to the development of data science as a discipline, current issues with data bias and ethics, and the role that the discipline of information science may play in addressing these concerns. Information science research and researchers have much to offer for data science, owing to their background as transdisciplinary scholars who apply human-centered and social-behavioral perspectives to issues within natural science disciplines. Information science researchers have already contributed to a humanistic approach to data ethics within the literature and an emphasis on data science within information schools all but ensures that this literature will continue to grow in coming decades. This review article serves as a reference for the history, current progress, and potential future directions of data ethics research within the corpus of information science literature.", "authors": ["Brady D. Lund", "Ting Wang"], "categories": ["cs.DL", "cs.CY"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-26", "url": "https://arxiv.org/abs/2506.03165", "pdf_url": "https://arxiv.org/pdf/2506.03165v1", "arxiv_id": "2506.03165", "doi": "10.2478/jdis-2022-0018", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Journal of Data and Information Science", "quality_score": 0.1765}
{"id": "1aae47d1228ff686074400183983fabed656419e7d00b6121392de876127ed81", "sources": ["arxiv", "semantic_scholar"], "title": "GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation", "abstract": "Fine-tuning for large language models (LLMs) typically requires substantial amounts of high-quality supervised data, which is both costly and labor-intensive to acquire. While synthetic data generation has emerged as a promising solution, existing approaches frequently suffer from factual inaccuracies, insufficient long-tail coverage, simplistic knowledge structures, and homogenized outputs. To address these challenges, we introduce GraphGen, a knowledge graph-guided framework designed for three key question-answering (QA) scenarios: atomic QA, aggregated QA, and multi-hop QA. It begins by constructing a fine-grained knowledge graph from the source text. It then identifies knowledge gaps in LLMs using the expected calibration error metric, prioritizing the generation of QA pairs that target high-value, long-tail knowledge. Furthermore, GraphGen incorporates multi-hop neighborhood sampling to capture complex relational information and employs style-controlled generation to diversify the resulting QA data. Experimental results on knowledge-intensive tasks under closed-book settings demonstrate that GraphGen outperforms conventional synthetic data methods, offering a more reliable and comprehensive solution to the data scarcity challenge in supervised fine-tuning. The code and data are publicly available at https://github.com/open-sciencelab/GraphGen.", "authors": ["Zihong Chen", "Wanli Jiang", "Jinzhe Li", "Zhonghang Yuan", "Huanjun Kong", "Wanli Ouyang", "Nanqing Dong"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-26", "url": "https://arxiv.org/abs/2505.20416", "pdf_url": "https://arxiv.org/pdf/2505.20416v1", "arxiv_id": "2505.20416", "doi": "10.48550/arXiv.2505.20416", "citation_count": 5, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/open-sciencelab/GraphGen", "venue": "arXiv.org", "quality_score": 0.2727}
{"id": "e536bda7129e4b1ab97c2e3475d420995ea7a47a9481acb9fc8d0e1ce421039c", "sources": ["arxiv", "semantic_scholar"], "title": "Mosaic: Data-Free Knowledge Distillation via Mixture-of-Experts for Heterogeneous Distributed Environments", "abstract": "Federated Learning (FL) is a decentralized machine learning paradigm that enables clients to collaboratively train models while preserving data privacy. However, the coexistence of model and data heterogeneity gives rise to inconsistent representations and divergent optimization dynamics across clients, ultimately hindering robust global performance. To transcend these challenges, we propose Mosaic, a novel data-free knowledge distillation framework tailored for heterogeneous distributed environments. Mosaic first trains local generative models to approximate each client's personalized distribution, enabling synthetic data generation that safeguards privacy through strict separation from real data. Subsequently, Mosaic forms a Mixture-of-Experts (MoE) from client models based on their specialized knowledge, and distills it into a global model using the generated data. To further enhance the MoE architecture, Mosaic integrates expert predictions via a lightweight meta model trained on a few representative prototypes. Extensive experiments on standard image classification benchmarks demonstrate that Mosaic consistently outperforms state-of-the-art approaches under both model and data heterogeneity. The source code has been published at https://github.com/Wings-Of-Disaster/Mosaic.", "authors": ["Junming Liu", "Yanting Gao", "Siyuan Meng", "Yifei Sun", "Aoqi Wu", "Yufei Jin", "Yirong Chen", "Ding Wang", "Guosun Zeng"], "categories": ["cs.LG", "cs.AI", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-26", "url": "https://arxiv.org/abs/2505.19699", "pdf_url": "https://arxiv.org/pdf/2505.19699v1", "arxiv_id": "2505.19699", "doi": "10.48550/arXiv.2505.19699", "citation_count": 3, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Wings-Of-Disaster/Mosaic", "venue": "arXiv.org", "quality_score": 0.2727}
{"id": "532da35de7266af6556f351f862ee9a5d5df0c95838ad2cbf018afdec8aa2ed1", "sources": ["arxiv", "semantic_scholar"], "title": "Improving Heart Rejection Detection in XPCI Images Using Synthetic Data Augmentation", "abstract": "Accurate identification of acute cellular rejection (ACR) in endomyocardial biopsies is essential for effective management of heart transplant patients. However, the rarity of high-grade rejection cases (3R) presents a significant challenge for training robust deep learning models. This work addresses the class imbalance problem by leveraging synthetic data generation using StyleGAN to augment the limited number of real 3R images. Prior to GAN training, histogram equalization was applied to standardize image appearance and improve the consistency of tissue representation. StyleGAN was trained on available 3R biopsy patches and subsequently used to generate 10,000 realistic synthetic images. These were combined with real 0R samples, that is samples without rejection, in various configurations to train ResNet-18 classifiers for binary rejection classification. Three classifier variants were evaluated: one trained on real 0R and synthetic 3R images, another using both synthetic and additional real samples, and a third trained solely on real data. All models were tested on an independent set of real biopsy images. Results demonstrate that synthetic data improves classification performance, particularly when used in combination with real samples. The highest-performing model, which used both real and synthetic images, achieved strong precision and recall for both classes. These findings underscore the value of hybrid training strategies and highlight the potential of GAN-based data augmentation in biomedical image analysis, especially in domains constrained by limited annotated datasets.", "authors": ["Jakov Samardžija", "Donik Vršnak", "Sven Lončarić"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-26", "url": "https://arxiv.org/abs/2505.19746", "pdf_url": "https://arxiv.org/pdf/2505.19746v2", "arxiv_id": "2505.19746", "doi": "10.48550/arXiv.2505.19746", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1765}
{"id": "56268a8b2bb2defcd8a6f5a472dc49a1b25baf01282358ad82f808106bcf39fa", "sources": ["arxiv", "semantic_scholar"], "title": "A Survey of LLM $\\times$ DATA", "abstract": "The integration of large language model (LLM) and data management (DATA) is rapidly redefining both domains. In this survey, we comprehensively review the bidirectional relationships. On the one hand, DATA4LLM, spanning large-scale data processing, storage, and serving, feeds LLMs with high quality, diversity, and timeliness of data required for stages like pre-training, post-training, retrieval-augmented generation, and agentic workflows: (i) Data processing for LLMs includes scalable acquisition, deduplication, filtering, selection, domain mixing, and synthetic augmentation; (ii) Data Storage for LLMs focuses on efficient data and model formats, distributed and heterogeneous storage hierarchies, KV-cache management, and fault-tolerant checkpointing; (iii) Data serving for LLMs tackles challenges in RAG (e.g., knowledge post-processing), LLM inference (e.g., prompt compression, data provenance), and training strategies (e.g., data packing and shuffling). On the other hand, in LLM4DATA, LLMs are emerging as general-purpose engines for data management. We review recent advances in (i) data manipulation, including automatic data cleaning, integration, discovery; (ii) data analysis, covering reasoning over structured, semi-structured, and unstructured data, and (iii) system optimization (e.g., configuration tuning, query rewriting, anomaly diagnosis), powered by LLM techniques like retrieval-augmented prompting, task-specialized fine-tuning, and multi-agent collaboration.", "authors": ["Xuanhe Zhou", "Junxuan He", "Wei Zhou", "Haodong Chen", "Zirui Tang", "Haoyu Zhao", "Xin Tong", "Guoliang Li", "Youmin Chen", "Jun Zhou", "Zhaojun Sun", "Binyuan Hui", "Shuo Wang", "Conghui He", "Zhiyuan Liu", "Jingren Zhou", "Fan Wu"], "categories": ["cs.DB", "cs.AI", "cs.CL", "cs.IR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-24", "url": "https://arxiv.org/abs/2505.18458", "pdf_url": "https://arxiv.org/pdf/2505.18458v3", "arxiv_id": "2505.18458", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/weAIDB/awesome-data-llm", "venue": null, "quality_score": 0.2058}
{"id": "2168330f8f4f2073641577ca14d3cfaaae127d9d68cb665d136fa11bc3529847", "sources": ["arxiv", "semantic_scholar"], "title": "4,500 Seconds: Small Data Training Approaches for Deep UAV Audio Classification", "abstract": "Unmanned aerial vehicle (UAV) usage is expected to surge in the coming decade, raising the need for heightened security measures to prevent airspace violations and security threats. This study investigates deep learning approaches to UAV classification focusing on the key issue of data scarcity. To investigate this we opted to train the models using a total of 4,500 seconds of audio samples, evenly distributed across a 9-class dataset. We leveraged parameter efficient fine-tuning (PEFT) and data augmentations to mitigate the data scarcity. This paper implements and compares the use of convolutional neural networks (CNNs) and attention-based transformers. Our results show that, CNNs outperform transformers by 1-2\\% accuracy, while still being more computationally efficient. These early findings, however, point to potential in using transformers models; suggesting that with more data and further optimizations they could outperform CNNs. Future works aims to upscale the dataset to better understand the trade-offs between these approaches.", "authors": ["Andrew P. Berg", "Qian Zhang", "Mia Y. Wang"], "categories": ["cs.SD", "cs.AI", "eess.AS"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2025-05-21", "url": "https://arxiv.org/abs/2505.23782", "pdf_url": "https://arxiv.org/pdf/2505.23782v1", "arxiv_id": "2505.23782", "doi": "10.5220/0000195100003967", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Data Technologies and Applications", "quality_score": 0.1747}
{"id": "f3ea80aa95434e36ecd8a53eb838070293ca3c829b96a825a86fd8fce3245629", "sources": ["arxiv", "semantic_scholar"], "title": "Score Distillation Beyond Acceleration: Generative Modeling from Corrupted Data", "abstract": "Learning generative models directly from corrupted observations is a long standing challenge across natural and scientific domains. We introduce Restoration Score Distillation (RSD), a unified framework for learning high fidelity, one step generative models using only degraded data and the mapping $A$ may be the identity or a non invertible corruption operator (e.g., blur, masking, subsampling, Fourier acquisition). RSD first pretrains a corruption aware diffusion teacher on the observed measurements, then distills it into an efficient one step generator whose samples are statistically closer to the clean distribution p_X. The framework subsumes identity corruption (denoising task) as a special case of our general formulation. Empirically, RSD consistently reduces Frechet Inception Distance (FID) relative to corruption aware diffusion teachers across noisy generation (CIFAR 10, FFHQ, CelebA HQ, AFHQ v2), image restoration (Gaussian deblurring, random inpainting, super resolution, and mixtures with additive noise), and multi coil MRI without access to any clean images. The distilled generator inherits one step sampling efficiency, yielding up to 30x speedups over multi step diffusion while surpassing the teachers after substantially fewer training iterations. These results establish score distillation as a practical tool for generative modeling from corrupted data, not merely for acceleration. We provide theoretical support for the use of distillation in enhancing generation quality in the Appendix.", "authors": ["Yasi Zhang", "Tianyu Chen", "Zhendong Wang", "Ying Nian Wu", "Mingyuan Zhou", "Oscar Leong"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-19", "url": "https://arxiv.org/abs/2505.13377", "pdf_url": "https://arxiv.org/pdf/2505.13377v2", "arxiv_id": "2505.13377", "doi": null, "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1193}
{"id": "0e543076998eddd78b5ad05c2803cd2f6bc937d3346a66288114b820660c24a4", "sources": ["arxiv", "semantic_scholar"], "title": "CleanPatrick: A Benchmark for Image Data Cleaning", "abstract": "Robust machine learning depends on clean data, yet current image data cleaning benchmarks rely on synthetic noise or narrow human studies, limiting comparison and real-world relevance. We introduce CleanPatrick, the first large-scale benchmark for data cleaning in the image domain, built upon the publicly available Fitzpatrick17k dermatology dataset. We collect 496,377 binary annotations from 933 medical crowd workers, identify off-topic samples (4%), near-duplicates (21%), and label errors (32%), and employ an aggregation model inspired by item-response theory followed by expert review to derive high-quality ground truth. CleanPatrick formalizes issue detection as a ranking task and employs standard ranking metrics that mirror real audit workflows. We benchmark classical anomaly detectors, perceptual hashing, SSIM, Confident Learning, NoiseRank, FINE, BHN, and SelfClean. On CleanPatrick, self-supervised representations excel at near-duplicate detection, classical methods achieve competitive off-topic detection under constrained review budgets, and detecting implausible labels under conservative human judgment remains challenging for fine-grained medical classification. By releasing both the dataset and the evaluation framework, CleanPatrick enables a systematic comparison of image-cleaning strategies.", "authors": ["Fabian Gröger", "Simone Lionetti", "Philippe Gottfrois", "Alvaro Gonzalez-Jimenez", "Ludovic Amruthalingam", "Elisabeth Victoria Goessinger", "Hanna Lindemann", "Marie Bargiela", "Marie Hofbauer", "Omar Badri", "Philipp Tschandl", "Arash Koochek", "Matthew Groh", "Alexander A. Navarini", "Marc Pouly"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-16", "url": "https://arxiv.org/abs/2505.11034", "pdf_url": "https://arxiv.org/pdf/2505.11034v2", "arxiv_id": "2505.11034", "doi": "10.48550/arXiv.2505.11034", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.165}
{"id": "23e737f6ebb336a07746343273a2e924ce1e5f6e94a715ba7771f0d918e76b39", "sources": ["arxiv", "semantic_scholar"], "title": "RouteNator: A Router-Based Multi-Modal Architecture for Generating Synthetic Training Data for Function Calling LLMs", "abstract": "This paper addresses fine-tuning Large Language Models (LLMs) for function calling tasks when real user interaction data is unavailable. In digital content creation tools, where users express their needs through natural language queries that must be mapped to API calls, the lack of real-world task-specific data and privacy constraints for training on it necessitate synthetic data generation. Existing approaches to synthetic data generation fall short in diversity and complexity, failing to replicate real-world data distributions and leading to suboptimal performance after LLM fine-tuning. We present a novel router-based architecture that leverages domain resources like content metadata and structured knowledge graphs, along with text-to-text and vision-to-text language models to generate high-quality synthetic training data. Our architecture's flexible routing mechanism enables synthetic data generation that matches observed real-world distributions, addressing a fundamental limitation of traditional approaches. Evaluation on a comprehensive set of real user queries demonstrates significant improvements in both function classification accuracy and API parameter selection. Models fine-tuned with our synthetic data consistently outperform traditional approaches, establishing new benchmarks for function calling tasks.", "authors": ["Vibha Belavadi", "Tushar Vatsa", "Dewang Sultania", "Suhas Suresha", "Ishita Verma", "Cheng Chen", "Tracy Holloway King", "Michael Friedrich"], "categories": ["cs.LG", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-15", "url": "https://arxiv.org/abs/2505.10495", "pdf_url": "https://arxiv.org/pdf/2505.10495v1", "arxiv_id": "2505.10495", "doi": "10.18653/v1/2025.knowledgenlp-1.10", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "https://aclanthology.org/2025.knowledgenlp-1.10/ KnowledgeNLP 2025", "quality_score": 0.1639}
{"id": "e6b819266afd6af72f9ff578b4768716d1af097049f4b1ab461f7a0a9228a948", "sources": ["arxiv", "semantic_scholar"], "title": "Emotion Knowledge Enhancement for Vision Large Language Models: A Self-Verification Approach for High-Quality Emotion Instruction Data Generation", "abstract": "Facial emotion perception in the vision large language model (VLLM) is crucial for achieving natural human-machine interaction. However, creating high-quality annotations for both coarse- and fine-grained facial emotion analysis demands costly expertise. The lack of such high-quality instruction data limits the performance of VLLMs in facial emotion perception. To address this, we propose a self-verification approach with emotion knowledge enhancement (SEKE), which generates high-quality instruction data for multi-grained emotion analysis cost-effectively using closed-source VLLM. This approach integrates prior human knowledge to VLLM inference, guided by the inherent correlations between three grained levels of emotion descriptions, i.e., discrete expression, valence-arousal, and action unit, to reliably generate comprehensive annotations. A self-verification strategy with Uncertainty-Aware Monte Carlo sampling (SV-UAMC) is further embedded to efficiently extract more accurate VLLM predictions, further improving annotation reliability. Consequently, we construct a facial emotion instruction dataset (FEID) containing three comprehensive descriptions, which provides coarse- and fine-grained emotional information for effective model training. Additionally, we introduce a facial emotion analysis benchmark (FEAB) to measure the VLLM's corresponding ability. Our method significantly outperforms state-of-the-art methods on three downstream facial emotion analysis tasks.", "authors": ["Feifan Wang", "Tengfei Song", "Minggui He", "Chang Su", "Zhanglin Wu", "Hao Yang", "Wenming Zheng", "Osamu Yoshie"], "categories": ["cs.LG", "cs.GR"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-14", "url": "https://arxiv.org/abs/2505.18168", "pdf_url": "https://arxiv.org/pdf/2505.18168v1", "arxiv_id": "2505.18168", "doi": "10.48550/arXiv.2505.18168", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1627}
{"id": "d92a0177300037a4ab056f8da1a4cd16f2db9af6def2ff3ee9a7588d2015ddbc", "sources": ["arxiv", "semantic_scholar"], "title": "Guiding Data Collection via Factored Scaling Curves", "abstract": "Generalist imitation learning policies trained on large datasets show great promise for solving diverse manipulation tasks. However, to ensure generalization to different conditions, policies need to be trained with data collected across a large set of environmental factor variations (e.g., camera pose, table height, distractors) $-$ a prohibitively expensive undertaking, if done exhaustively. We introduce a principled method for deciding what data to collect and how much to collect for each factor by constructing factored scaling curves (FSC), which quantify how policy performance varies as data scales along individual or paired factors. These curves enable targeted data acquisition for the most influential factor combinations within a given budget. We evaluate the proposed method through extensive simulated and real-world experiments, across both training-from-scratch and fine-tuning settings, and show that it boosts success rates in real-world tasks in new environments by up to 26% over existing data-collection strategies. We further demonstrate how factored scaling curves can effectively guide data collection using an offline metric, without requiring real-world evaluation at scale.", "authors": ["Lihan Zha", "Apurva Badithela", "Michael Zhang", "Justin Lidard", "Jeremy Bao", "Emily Zhou", "David Snyder", "Allen Z. Ren", "Dhruv Shah", "Anirudha Majumdar"], "categories": ["cs.RO", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-12", "url": "https://arxiv.org/abs/2505.07728", "pdf_url": "https://arxiv.org/pdf/2505.07728v1", "arxiv_id": "2505.07728", "doi": "10.48550/arXiv.2505.07728", "citation_count": 10, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2603}
{"id": "666b2c5f57a8c9ecbb18268f706a919ebc5f6e81a213b549b8ea8eb6e6e753ae", "sources": ["arxiv", "semantic_scholar"], "title": "DMRL: Data- and Model-aware Reward Learning for Data Extraction", "abstract": "Large language models (LLMs) are inherently vulnerable to unintended privacy breaches. Consequently, systematic red-teaming research is essential for developing robust defense mechanisms. However, current data extraction methods suffer from several limitations: (1) rely on dataset duplicates (addressable via deduplication), (2) depend on prompt engineering (now countered by detection and defense), and (3) rely on random-search adversarial generation. To address these challenges, we propose DMRL, a Data- and Model-aware Reward Learning approach for data extraction. This technique leverages inverse reinforcement learning to extract sensitive data from LLMs. Our method consists of two main components: (1) constructing an introspective reasoning dataset that captures leakage mindsets to guide model behavior, and (2) training reward models with Group Relative Policy Optimization (GRPO), dynamically tuning optimization based on task difficulty at both the data and model levels. Comprehensive experiments across various LLMs demonstrate that DMRL outperforms all baseline methods in data extraction performance.", "authors": ["Zhiqiang Wang", "Ruoxi Cheng"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-07", "url": "https://arxiv.org/abs/2505.06284", "pdf_url": "https://arxiv.org/pdf/2505.06284v1", "arxiv_id": "2505.06284", "doi": "10.48550/arXiv.2505.06284", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1547}
{"id": "147cd8ba0cac1f0883cfa88c1e845b729b4d19c37de5ceed98c608330a3ea8bd", "sources": ["arxiv", "semantic_scholar"], "title": "Are Synthetic Corruptions A Reliable Proxy For Real-World Corruptions?", "abstract": "Deep learning (DL) models are widely used in real-world applications but remain vulnerable to distribution shifts, especially due to weather and lighting changes. Collecting diverse real-world data for testing the robustness of DL models is resource-intensive, making synthetic corruptions an attractive alternative for robustness testing. However, are synthetic corruptions a reliable proxy for real-world corruptions? To answer this, we conduct the largest benchmarking study on semantic segmentation models, comparing performance on real-world corruptions and synthetic corruptions datasets. Our results reveal a strong correlation in mean performance, supporting the use of synthetic corruptions for robustness evaluation. We further analyze corruption-specific correlations, providing key insights to understand when synthetic corruptions succeed in representing real-world corruptions. Open-source Code: https://github.com/shashankskagnihotri/benchmarking_robustness/tree/segmentation_david/semantic_segmentation", "authors": ["Shashank Agnihotri", "David Schader", "Nico Sharei", "Mehmet Ege Kaçar", "Margret Keuper"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-07", "url": "https://arxiv.org/abs/2505.04835", "pdf_url": "https://arxiv.org/pdf/2505.04835v1", "arxiv_id": "2505.04835", "doi": "10.48550/arXiv.2505.04835", "citation_count": 5, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/shashankskagnihotri/benchmarking_robustness/tree/segmentation_david/semantic_segmentation", "venue": "arXiv.org", "quality_score": 0.2391}
{"id": "21a7d98c5b6aec6cd771f34c775c9433507a798c86f5ceefcbc13fa976c6a1c2", "sources": ["arxiv", "semantic_scholar"], "title": "Generating Synthetic Data via Augmentations for Improved Facial Resemblance in DreamBooth and InstantID", "abstract": "Personalizing Stable Diffusion for professional portrait generation from amateur photos faces challenges in maintaining facial resemblance. This paper evaluates the impact of augmentation strategies on two personalization methods: DreamBooth and InstantID. We compare classical augmentations (flipping, cropping, color adjustments) with generative augmentation using InstantID's synthetic images to enrich training data. Using SDXL and a new FaceDistance metric based on FaceNet, we quantitatively assess facial similarity. Results show classical augmentations can cause artifacts harming identity retention, while InstantID improves fidelity when balanced with real images to avoid overfitting. A user study with 97 participants confirms high photorealism and preferences for InstantID's polished look versus DreamBooth's identity accuracy. Our findings inform effective augmentation strategies for personalized text-to-image generation.", "authors": ["Koray Ulusan", "Benjamin Kiefer"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-06", "url": "https://arxiv.org/abs/2505.03557", "pdf_url": "https://arxiv.org/pdf/2505.03557v2", "arxiv_id": "2505.03557", "doi": "10.48550/arXiv.2505.03557", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1535}
{"id": "1f55af22ceb8f9c7a42e33e2c31cf28309e66d5a22975b8cb29afecbc1e55400", "sources": ["arxiv", "semantic_scholar"], "title": "Modeling supply chain compliance response strategies based on AI synthetic data with structural path regression: A Simulation Study of EU 2027 Mandatory Labor Regulations", "abstract": "In the context of the new mandatory labor compliance in the European Union (EU), which will be implemented in 2027, supply chain enterprises face stringent working hour management requirements and compliance risks. In order to scientifically predict the enterprises' coping behaviors and performance outcomes under the policy impact, this paper constructs a methodological framework that integrates the AI synthetic data generation mechanism and structural path regression modeling to simulate the enterprises' strategic transition paths under the new regulations. In terms of research methodology, this paper adopts high-quality simulation data generated based on Monte Carlo mechanism and NIST synthetic data standards to construct a structural path analysis model that includes multiple linear regression, logistic regression, mediation effect and moderating effect. The variable system covers 14 indicators such as enterprise working hours, compliance investment, response speed, automation level, policy dependence, etc. The variable set with explanatory power is screened out through exploratory data analysis (EDA) and VIF multicollinearity elimination. The findings show that compliance investment has a significant positive impact on firm survival and its effect is transmitted through the mediating path of the level of intelligence; meanwhile, firms' dependence on the EU market significantly moderates the strength of this mediating effect. It is concluded that AI synthetic data combined with structural path modeling provides an effective tool for high-intensity regulatory simulation, which can provide a quantitative basis for corporate strategic response, policy design and AI-assisted decision-making in the pre-prediction stage lacking real scenario data. Keywords: AI synthetic data, structural path regression modeling, compliance response strategy, EU 2027 mandatory labor regulation", "authors": ["Wei Meng"], "categories": ["cs.CY", "cs.AI", "stat.AP"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2025-05-04", "url": "https://arxiv.org/abs/2505.06261", "pdf_url": "https://arxiv.org/pdf/2505.06261v1", "arxiv_id": "2505.06261", "doi": "10.48550/arXiv.2505.06261", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Veredas do Direito", "quality_score": 0.1513}
{"id": "542aca1ca4913b9fc0c5f39efac9391575f68514240dbee863de501bdcbb814f", "sources": ["arxiv", "semantic_scholar"], "title": "Synthesize-on-Graph: Knowledgeable Synthetic Data Generation for Continue Pre-training of Large Language Models", "abstract": "Large Language Models (LLMs) have achieved remarkable success but remain data-inefficient, especially when learning from small, specialized corpora with limited and proprietary data. Existing synthetic data generation methods for continue pre-training focus on intra-document content and overlook cross-document knowledge associations, limiting content diversity and depth. We propose Synthetic-on-Graph (SoG), a synthetic data generation framework that incorporates cross-document knowledge associations for efficient corpus expansion. SoG constructs a context graph by extracting entities and concepts from the original corpus, representing cross-document associations, and employing a graph walk strategy for knowledge-associated sampling. This enhances synthetic data diversity and coherence, enabling models to learn complex knowledge structures and handle rare knowledge. To further improve the quality of synthetic data, we integrate two complementary strategies, Chain-of-Thought (CoT) and Contrastive Clarifying (CC), to enhance both reasoning capability and discriminative power. Extensive experiments demonstrate that SoG surpasses state-of-the-art (SOTA) methods on multi-hop and domain-specific question answering, while achieving competitive performance on long-context reading comprehension. These results highlight the superior generalization ability of SoG. Our work advances the paradigm of synthetic data generation and offers practical solutions for efficient knowledge acquisition in LLMs, particularly for downstream tasks and domains with limited training data.", "authors": ["Shengjie Ma", "Xuhui Jiang", "Chengjin Xu", "Cehao Yang", "Liyu Zhang", "Jian Guo"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-02", "url": "https://arxiv.org/abs/2505.00979", "pdf_url": "https://arxiv.org/pdf/2505.00979v3", "arxiv_id": "2505.00979", "doi": "10.48550/arXiv.2505.00979", "citation_count": 5, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1945}
{"id": "a2860135dd858a0eb78c3efe429838836eda8d522aa6039de2de90ebf56f8b7b", "sources": ["arxiv", "semantic_scholar"], "title": "CAE-DFKD: Bridging the Transferability Gap in Data-Free Knowledge Distillation", "abstract": "Data-Free Knowledge Distillation (DFKD) enables the knowledge transfer from the given pre-trained teacher network to the target student model without access to the real training data. Existing DFKD methods focus primarily on improving image recognition performance on associated datasets, often neglecting the crucial aspect of the transferability of learned representations. In this paper, we propose Category-Aware Embedding Data-Free Knowledge Distillation (CAE-DFKD), which addresses at the embedding level the limitations of previous rely on image-level methods to improve model generalization but fail when directly applied to DFKD. The superiority and flexibility of CAE-DFKD are extensively evaluated, including: \\textit{\\textbf{i.)}} Significant efficiency advantages resulting from altering the generator training paradigm; \\textit{\\textbf{ii.)}} Competitive performance with existing DFKD state-of-the-art methods on image recognition tasks; \\textit{\\textbf{iii.)}} Remarkable transferability of data-free learned representations demonstrated in downstream tasks.", "authors": ["Zherui Zhang", "Changwei Wang", "Rongtao Xu", "Wenhao Xu", "Shibiao Xu", "Yu Zhang", "Li Guo"], "categories": ["cs.CV", "cs.NE"], "fields_of_study": ["Computer Science"], "published_date": "2025-04-30", "url": "https://arxiv.org/abs/2504.21478", "pdf_url": "https://arxiv.org/pdf/2504.21478v1", "arxiv_id": "2504.21478", "doi": "10.1109/DAC63849.2025.11132975", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Design Automation Conference", "quality_score": 0.1467}
{"id": "6e31464bb9b7122e2677adb15cdbd7a6d3a32a7b4500d37a2fc04ede1e21ca26", "sources": ["arxiv", "semantic_scholar"], "title": "Tabular Data Adapters: Improving Outlier Detection for Unlabeled Private Data", "abstract": "The remarkable success of Deep Learning approaches is often based and demonstrated on large public datasets. However, when applying such approaches to internal, private datasets, one frequently faces challenges arising from structural differences in the datasets, domain shift, and the lack of labels. In this work, we introduce Tabular Data Adapters (TDA), a novel method for generating soft labels for unlabeled tabular data in outlier detection tasks. By identifying statistically similar public datasets and transforming private data (based on a shared autoencoder) into a format compatible with state-of-the-art public models, our approach enables the generation of weak labels. It thereby can help to mitigate the cold start problem of labeling by basing on existing outlier detection models for public datasets. In experiments on 50 tabular datasets across different domains, we demonstrate that our method is able to provide more accurate annotations than baseline approaches while reducing computational time. Our approach offers a scalable, efficient, and cost-effective solution, to bridge the gap between public research models and real-world industrial applications.", "authors": ["Dayananda Herurkar", "Jörn Hees", "Vesselin Tzvetkov", "Andreas Dengel"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-04-29", "url": "https://arxiv.org/abs/2504.20862", "pdf_url": "https://arxiv.org/pdf/2504.20862v1", "arxiv_id": "2504.20862", "doi": "10.48550/arXiv.2504.20862", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1455}
{"id": "931d2d7d3556e8cbf15b2d77079fc8ba36451c7cd55614a191e120162fc932c5", "sources": ["arxiv", "semantic_scholar"], "title": "Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking", "abstract": "Generative models have revolutionized multiple domains, yet their application to tabular data remains underexplored. Evaluating generative models for tabular data presents unique challenges due to structural complexity, large-scale variability, and mixed data types, making it difficult to intuitively capture intricate patterns. Existing evaluation metrics offer only partial insights, lacking a comprehensive measure of generative performance. To address this limitation, we propose three novel evaluation metrics: FAED, FPCAD, and RFIS. Our extensive experimental analysis, conducted on three standard network intrusion detection datasets, compares these metrics with established evaluation methods such as Fidelity, Utility, TSTR, and TRTS. Our results demonstrate that FAED effectively captures generative modeling issues overlooked by existing metrics. While FPCAD exhibits promising performance, further refinements are necessary to enhance its reliability. Our proposed framework provides a robust and practical approach for assessing generative models in tabular data applications.", "authors": ["Dayananda Herurkar", "Ahmad Ali", "Andreas Dengel"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-04-29", "url": "https://arxiv.org/abs/2504.20900", "pdf_url": "https://arxiv.org/pdf/2504.20900v1", "arxiv_id": "2504.20900", "doi": "10.48550/arXiv.2504.20900", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1455}
{"id": "f6f5b7fe20c146ecb82fe1b22c5bb2b67ca9d2e197faf66d6501bb074f3e78fd", "sources": ["arxiv", "semantic_scholar"], "title": "Knowledge-Driven Agentic Scientific Corpus Distillation Framework for Biomedical Large Language Models Training", "abstract": "Corpus distillation for biomedical large language models (LLMs) seeks to address the pressing challenge of insufficient quantity and quality in open-source annotated scientific corpora, which remains a bottleneck for effective LLM training in biomedical research. This paper proposes a knowledge-driven, agentic framework for scientific corpus distillation, tailored explicitly for LLM training in the biomedical domain, addressing the challenge posed by the complex hierarchy of biomedical knowledge. Central to our approach is a collaborative multi-agent architecture, where specialized agents, each guided by the Medical Subject Headings (MeSH) hierarchy, work in concert to autonomously extract, synthesize, and self-evaluate high-quality textual data from vast scientific literature. This agentic framework collectively generates and refines domain-specific question-answer pairs, ensuring comprehensive coverage and consistency with biomedical ontologies while minimizing manual involvement. Extensive experimental results show that language models trained on our multi-agent distilled datasets achieve notable improvements in biomedical question-answering tasks, outperforming both strong life sciences LLM baselines and advanced proprietary models. Notably, our AI-Ready dataset enables Llama3-70B to surpass GPT-4 with MedPrompt and Med-PaLM-2, despite their larger scale. Detailed ablation studies and case analyses further validate the effectiveness and synergy of each agent within the framework, highlighting the potential of multi-agent collaboration in biomedical LLM training.", "authors": ["Meng Xiao", "Xunxin Cai", "Qingqing Long", "Chengrui Wang", "Yuanchun Zhou", "Hengshu Zhu"], "categories": ["cs.CL", "cs.AI", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2025-04-28", "url": "https://arxiv.org/abs/2504.19565", "pdf_url": "https://arxiv.org/pdf/2504.19565v3", "arxiv_id": "2504.19565", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": null, "quality_score": 0.1706}
{"id": "a38de6294a2ff1a9cb8ef65f8aec7cb63ccbbf8b80027111fee71ac3edc61f1c", "sources": ["arxiv", "semantic_scholar"], "title": "A Comprehensive Survey of Synthetic Tabular Data Generation", "abstract": "Tabular data is one of the most prevalent and important data formats in real-world applications such as healthcare, finance, and education. However, its effective use in machine learning is often constrained by data scarcity, privacy concerns, and class imbalance. Synthetic tabular data generation has emerged as a powerful solution, leveraging generative models to learn underlying data distributions and produce realistic, privacy-preserving samples. Although this area has seen growing attention, most existing surveys focus narrowly on specific methods (e.g., GANs or privacy-enhancing techniques), lacking a unified and comprehensive view that integrates recent advances such as diffusion models and large language models (LLMs). In this survey, we present a structured and in-depth review of synthetic tabular data generation methods. Specifically, the survey is organized into three core components: (1) Background, which covers the overall generation pipeline, including problem definitions, synthetic tabular data generation methods, post processing, and evaluation; (2) Generation Methods, where we categorize existing approaches into traditional generation methods, diffusion model methods, and LLM-based methods, and compare them in terms of architecture, generation quality, and applicability; and (3) Applications and Challenges, which summarizes practical use cases, highlights common datasets, and discusses open challenges such as heterogeneity, data fidelity, and privacy protection. This survey aims to provide researchers and practitioners with a holistic understanding of the field and to highlight key directions for future work in synthetic tabular data generation.", "authors": ["Ruxue Shi", "Yili Wang", "Mengnan Du", "Xu Shen", "Yi Chang", "Xin Wang"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-04-23", "url": "https://arxiv.org/abs/2504.16506", "pdf_url": "https://arxiv.org/pdf/2504.16506v3", "arxiv_id": "2504.16506", "doi": "10.48550/arXiv.2504.16506", "citation_count": 34, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.386}
{"id": "79be7605e2a234c548cc9bf3ad96d3e44c1af1478dbaaeee87d816d8c8c48eb9", "sources": ["arxiv", "semantic_scholar"], "title": "A Statistical Approach for Synthetic EEG Data Generation", "abstract": "Electroencephalogram (EEG) data is crucial for diagnosing mental health conditions but is costly and time-consuming to collect at scale. Synthetic data generation offers a promising solution to augment datasets for machine learning applications. However, generating high-quality synthetic EEG that preserves emotional and mental health signals remains challenging. This study proposes a method combining correlation analysis and random sampling to generate realistic synthetic EEG data. We first analyze interdependencies between EEG frequency bands using correlation analysis. Guided by this structure, we generate synthetic samples via random sampling. Samples with high correlation to real data are retained and evaluated through distribution analysis and classification tasks. A Random Forest model trained to distinguish synthetic from real EEG performs at chance level, indicating high fidelity. The generated synthetic data closely match the statistical and structural properties of the original EEG, with similar correlation coefficients and no significant differences in PERMANOVA tests. This method provides a scalable, privacy-preserving approach for augmenting EEG datasets, enabling more efficient model training in mental health research.", "authors": ["Gideon Vos", "Maryam Ebrahimpour", "Liza van Eijk", "Zoltan Sarnyai", "Mostafa Rahimi Azghadi"], "categories": ["eess.SP", "cs.LG"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2025-04-22", "url": "https://arxiv.org/abs/2504.16143", "pdf_url": "https://arxiv.org/pdf/2504.16143v2", "arxiv_id": "2504.16143", "doi": "10.48550/arXiv.2504.16143", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1375}
{"id": "1c159ba848a2aa48b71a511a711e1d388c9920adaaed3f2e66231c621ff196c0", "sources": ["arxiv", "semantic_scholar"], "title": "Amplify Initiative: Building A Localized Data Platform for Globalized AI", "abstract": "Current AI models often fail to account for local context and language, given the predominance of English and Western internet content in their training data. This hinders the global relevance, usefulness, and safety of these models as they gain more users around the globe. Amplify Initiative, a data platform and methodology, leverages expert communities to collect diverse, high-quality data to address the limitations of these models. The platform is designed to enable co-creation of datasets, provide access to high-quality multilingual datasets, and offer recognition to data authors. This paper presents the approach to co-creating datasets with domain experts (e.g., health workers, teachers) through a pilot conducted in Sub-Saharan Africa (Ghana, Kenya, Malawi, Nigeria, and Uganda). In partnership with local researchers situated in these countries, the pilot demonstrated an end-to-end approach to co-creating data with 155 experts in sensitive domains (e.g., physicians, bankers, anthropologists, human and civil rights advocates). This approach, implemented with an Android app, resulted in an annotated dataset of 8,091 adversarial queries in seven languages (e.g., Luganda, Swahili, Chichewa), capturing nuanced and contextual information related to key themes such as misinformation and public interest topics. This dataset in turn can be used to evaluate models for their safety and cultural relevance within the context of these languages.", "authors": ["Qazi Mamunur Rashid", "Erin van Liemt", "Tiffany Shih", "Amber Ebinama", "Karla Barrios Ramos", "Madhurima Maji", "Aishwarya Verma", "Charu Kalia", "Jamila Smith-Loud", "Joyce Nakatumba-Nabende", "Rehema Baguma", "Andrew Katumba", "Chodrine Mutebi", "Jagen Marvin", "Eric Peter Wairagala", "Mugizi Bruce", "Peter Oketta", "Lawrence Nderu", "Obichi Obiajunwa", "Abigail Oppong", "Michael Zimba", "Data Authors"], "categories": ["cs.HC", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-04-18", "url": "https://arxiv.org/abs/2504.14105", "pdf_url": "https://arxiv.org/pdf/2504.14105v1", "arxiv_id": "2504.14105", "doi": "10.48550/arXiv.2504.14105", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "0def269ed0a59ec02d62aab061b96522c071959fa9ce5f85a1793bd350114628", "sources": ["arxiv", "semantic_scholar"], "title": "Effective Dual-Region Augmentation for Reduced Reliance on Large Amounts of Labeled Data", "abstract": "This paper introduces a novel dual-region augmentation approach designed to reduce reliance on large-scale labeled datasets while improving model robustness and adaptability across diverse computer vision tasks, including source-free domain adaptation (SFDA) and person re-identification (ReID). Our method performs targeted data transformations by applying random noise perturbations to foreground objects and spatially shuffling background patches. This effectively increases the diversity of the training data, improving model robustness and generalization. Evaluations on the PACS dataset for SFDA demonstrate that our augmentation strategy consistently outperforms existing methods, achieving significant accuracy improvements in both single-target and multi-target adaptation settings. By augmenting training data through structured transformations, our method enables model generalization across domains, providing a scalable solution for reducing reliance on manually annotated datasets. Furthermore, experiments on Market-1501 and DukeMTMC-reID datasets validate the effectiveness of our approach for person ReID, surpassing traditional augmentation techniques. The code is available at https://github.com/PrasannaPulakurthi/Foreground-Background-Augmentation", "authors": ["Prasanna Reddy Pulakurthi", "Majid Rabbani", "Celso M. de Melo", "Sohail A. Dianat", "Raghuveer M. Rao"], "categories": ["cs.CV"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2025-04-17", "url": "https://arxiv.org/abs/2504.13077", "pdf_url": "https://arxiv.org/pdf/2504.13077v2", "arxiv_id": "2504.13077", "doi": "10.1117/12.3058627", "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/PrasannaPulakurthi/Foreground-Background-Augmentation", "venue": "Proc. SPIE 13459, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications III, 134590I (2025)", "quality_score": 0.2036}
{"id": "0d1ec65e529248aad58493e226967e54d8df45aa4951d29f7af6d639d9cfa201", "sources": ["arxiv", "semantic_scholar"], "title": "Leveraging Vertical Public-Private Split for Improved Synthetic Data Generation", "abstract": "Differentially Private Synthetic Data Generation (DP-SDG) is a key enabler of private and secure tabular-data sharing, producing artificial data that carries through the underlying statistical properties of the input data. This typically involves adding carefully calibrated statistical noise to guarantee individual privacy, at the cost of synthetic data quality. Recent literature has explored scenarios where a small amount of public data is used to help enhance the quality of synthetic data. These methods study a horizontal public-private partitioning which assumes access to a small number of public rows that can be used for model initialization, providing a small utility gain. However, realistic datasets often naturally consist of public and private attributes, making a vertical public-private partitioning relevant for practical synthetic data deployments. We propose a novel framework that adapts horizontal public-assisted methods into the vertical setting. We compare this framework against our alternative approach that uses conditional generation, highlighting initial limitations of public-data assisted methods and proposing future research directions to address these challenges.", "authors": ["Samuel Maddock", "Shripad Gade", "Graham Cormode", "Will Bullock"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2025-04-15", "url": "https://arxiv.org/abs/2504.10987", "pdf_url": "https://arxiv.org/pdf/2504.10987v1", "arxiv_id": "2504.10987", "doi": "10.48550/arXiv.2504.10987", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1295}
{"id": "79a1d69dee8e722eb31571bcab2227268ba40e501f863d3ec628ae59fb58f836", "sources": ["arxiv", "semantic_scholar"], "title": "Transferable text data distillation by trajectory matching", "abstract": "In the realm of large language model (LLM), as the size of large models increases, it also brings higher training costs. There is a urgent need to minimize the data size in LLM training. Compared with data selection method, the data distillation method aims to synthesize a small number of data samples to achieve the training effect of the full data set and has better flexibility. Despite its successes in computer vision, the discreteness of text data has hitherto stymied its exploration in natural language processing (NLP). In this work, we proposed a method that involves learning pseudo prompt data based on trajectory matching and finding its nearest neighbor ID to achieve cross-architecture transfer. During the distillation process, we introduce a regularization loss to improve the robustness of our distilled data. To our best knowledge, this is the first data distillation work suitable for text generation tasks such as instruction tuning. Evaluations on two benchmarks, including ARC-Easy and MMLU instruction tuning datasets, established the superiority of our distillation approach over the SOTA data selection method LESS. Furthermore, our method demonstrates a good transferability over LLM structures (i.e., OPT to Llama).", "authors": ["Rong Yao", "Hailin Hu", "Yifei Fu", "Hanting Chen", "Wenyi Fang", "Fanyi Du", "Kai Han", "Yunhe Wang"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-04-14", "url": "https://arxiv.org/abs/2504.09818", "pdf_url": "https://arxiv.org/pdf/2504.09818v2", "arxiv_id": "2504.09818", "doi": "10.48550/arXiv.2504.09818", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1283}
{"id": "b5b488e57d0f04833f2739ff5c95e39cb3379c03fd99dc131dccefd46b1197b1", "sources": ["arxiv", "semantic_scholar"], "title": "Text To 3D Object Generation For Scalable Room Assembly", "abstract": "Modern machine learning models for scene understanding, such as depth estimation and object tracking, rely on large, high-quality datasets that mimic real-world deployment scenarios. To address data scarcity, we propose an end-to-end system for synthetic data generation for scalable, high-quality, and customizable 3D indoor scenes. By integrating and adapting text-to-image and multi-view diffusion models with Neural Radiance Field-based meshing, this system generates highfidelity 3D object assets from text prompts and incorporates them into pre-defined floor plans using a rendering tool. By introducing novel loss functions and training strategies into existing methods, the system supports on-demand scene generation, aiming to alleviate the scarcity of current available data, generally manually crafted by artists. This system advances the role of synthetic data in addressing machine learning training limitations, enabling more robust and generalizable models for real-world applications.", "authors": ["Sonia Laguna", "Alberto Garcia-Garcia", "Marie-Julie Rakotosaona", "Stylianos Moschoglou", "Leonhard Helminger", "Sergio Orts-Escolano"], "categories": ["cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-04-12", "url": "https://arxiv.org/abs/2504.09328", "pdf_url": "https://arxiv.org/pdf/2504.09328v1", "arxiv_id": "2504.09328", "doi": "10.48550/arXiv.2504.09328", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.126}
{"id": "825e7e0dca2b8f478d1a466a77ae3d1e2c716d4e2fd043baa903a5204980604d", "sources": ["arxiv", "semantic_scholar"], "title": "Understanding the Impact of Data Domain Extraction on Synthetic Data Privacy", "abstract": "Privacy attacks, particularly membership inference attacks (MIAs), are widely used to assess the privacy of generative models for tabular synthetic data, including those with Differential Privacy (DP) guarantees. These attacks often exploit outliers, which are especially vulnerable due to their position at the boundaries of the data domain (e.g., at the minimum and maximum values). However, the role of data domain extraction in generative models and its impact on privacy attacks have been overlooked. In this paper, we examine three strategies for defining the data domain: assuming it is externally provided (ideally from public data), extracting it directly from the input data, and extracting it with DP mechanisms. While common in popular implementations and libraries, we show that the second approach breaks end-to-end DP guarantees and leaves models vulnerable. While using a provided domain (if representative) is preferable, extracting it with DP can also defend against popular MIAs, even at high privacy budgets.", "authors": ["Georgi Ganev", "Meenatchi Sundaram Muthu Selva Annamalai", "Sofiane Mahiou", "Emiliano De Cristofaro"], "categories": ["cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-04-11", "url": "https://arxiv.org/abs/2504.08254", "pdf_url": "https://arxiv.org/pdf/2504.08254v2", "arxiv_id": "2504.08254", "doi": "10.48550/arXiv.2504.08254", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1249}
{"id": "9294a1dd3a69b312edec4ddb5ae66e7c1dbb0edcf60a6e94897aae7ddc3fb786", "sources": ["arxiv", "semantic_scholar"], "title": "Robustness of Online Identification-based Policy Iteration to Noisy Data", "abstract": "This article investigates the core mechanisms of indirect data-driven control for unknown systems, focusing on the application of policy iteration (PI) within the context of the linear quadratic regulator (LQR) optimal control problem. Specifically, we consider a setting where data is collected sequentially from a linear system subject to exogenous process noise, and is then used to refine estimates of the optimal control policy. We integrate recursive least squares (RLS) for online model estimation within a certainty-equivalent framework, and employ PI to iteratively update the control policy. In this work, we investigate first the convergence behavior of RLS under two different models of adversarial noise, namely point-wise and energy bounded noise, and then we provide a closed-loop analysis of the combined model identification and control design process. This iterative scheme is formulated as an algorithmic dynamical system consisting of the feedback interconnection between two algorithms expressed as discrete-time systems. This system theoretic viewpoint on indirect data-driven control allows us to establish convergence guarantees to the optimal controller in the face of uncertainty caused by noisy data. Simulations illustrate the theoretical results.", "authors": ["Bowen Song", "Andrea Iannelli"], "categories": ["eess.SY"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2025-04-10", "url": "https://arxiv.org/abs/2504.07627", "pdf_url": "https://arxiv.org/pdf/2504.07627v2", "arxiv_id": "2504.07627", "doi": "10.48550/arXiv.2504.07627", "citation_count": 4, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1747}
{"id": "9658a66b1cbe5eabbdd41c7231189bd20c6b27d2a6be2ba77896755cc1ab2637", "sources": ["arxiv", "semantic_scholar"], "title": "WorldMove, a global open data for human mobility", "abstract": "High-quality human mobility data is crucial for applications such as urban planning, transportation management, and public health, yet its collection is often hindered by privacy concerns and data scarcity-particularly in less-developed regions. To address this challenge, we introduce WorldMove, a large-scale synthetic mobility dataset covering over 1,600 cities across 179 countries and 6 continents. Our method leverages publicly available multi-source data, including gridded population distribution, point-of-interest (POI) maps, and commuting origin-destination (OD) flows-to generate realistic city-scale mobility trajectories using a diffusion-based generative model. The generation process involves defining city boundaries, collecting multi-source input features, and simulating individual-level movements that reflect plausible daily mobility behavior. Comprehensive validation demonstrates that the generated data closely aligns with real-world observations, both in terms of fine-grained individual mobility behavior and city-scale population flows. Alongside the pre-generated datasets, we release the trained model and a complete open-source pipeline, enabling researchers and practitioners to generate custom synthetic mobility data for any city worldwide. This work not only fills critical data gaps, but also lays a global foundation for scalable, privacy-preserving, and inclusive mobility research-empowering data-scarce regions and enabling universal access to human mobility insights.", "authors": ["Yuan Yuan", "Yuheng Zhang", "Jingtao Ding", "Yong Li"], "categories": ["cs.SI"], "fields_of_study": ["Medicine", "Computer Science"], "published_date": "2025-04-09", "url": "https://arxiv.org/abs/2504.10506", "pdf_url": "https://arxiv.org/pdf/2504.10506v2", "arxiv_id": "2504.10506", "doi": "10.1038/s41597-026-06555-2", "citation_count": 6, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "Scientific Data", "quality_score": 0.2113}
{"id": "8c6e7821e6e3e29d1c65da7985dc44e2bf7477f0115efa1b9e4c518e9f82bba6", "sources": ["arxiv", "semantic_scholar"], "title": "Polyspectral Mean based Time Series Clustering of Indian Stock Market", "abstract": "In this study, we employ k-means clustering algorithm of polyspectral means to analyze 49 stocks in the Indian stock market. We have used spectral and bispectral information obtained from the data, by using spectral and bispectral means with different weight functions that will give us varying insights into the temporal patterns of the stocks. In particular, the higher order polyspectral means can provide significantly more information than what we can gather from power spectra, and can also unveil nonlinear trends in a time series. Through rigorous analysis, we identify five distinctive clusters, uncovering nuanced market structures. Notably, one cluster emerges as that of a conglomerate powerhouse, featuring ADANI, BIRLA, TATA, and unexpectedly, government-owned bank SBI. Another cluster spotlights the IT sector with WIPRO and TCS, while a third combines private banks, government entities, and RELIANCE. The final cluster comprises publicly traded companies with dispersed ownership. Such clustering of stocks sheds light on intricate financial relationships within the stock market, providing valuable insights for investors and analysts navigating the dynamic landscape of the Indian stock market.", "authors": ["Dhrubajyoti Ghosh"], "categories": ["q-fin.ST", "stat.AP"], "fields_of_study": ["Economics", "Mathematics"], "published_date": "2025-04-09", "url": "https://arxiv.org/abs/2504.07021", "pdf_url": "https://arxiv.org/pdf/2504.07021v1", "arxiv_id": "2504.07021", "doi": "10.1007/s44248-025-00030-w", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Discover Data", "quality_score": 0.1226}
{"id": "76bfb63fafc9f8a6c4c59a8f0057fad589d64624c344d60077338926d9e0ed7e", "sources": ["arxiv", "semantic_scholar"], "title": "Characteristics of Ge-doped Multi-Mode Fibers in Total Ionizing Dose", "abstract": "Purpose: The fiber optical links in 850 nm band with Ge-doped multi-mode (MM) fibers are well developed for data transmission at 10 Gbps and higher. The applications in nuclear environments require radiation resistance. The characteristics of Ge-doped MM fibers are investigated for Radiation Induced Attenuation (RIA) in Total Ionizing Dose (TID). Methods: Commercial samples of Ge-doped MM fibers were irradiated in Go-60 gamma rays at dose rates of 5 to 1.4k Gy(SiO2)/hr. The fiber samples were packaged in water tanks maintained at constant temperatures in the range of -15 to 45 degC. The optical power transmitted through the fibers were recorded in irradiation, and in annealing when the source was shielded. The measurements of RIA in time are analyzed for dose rate and temperature dependences. Results: Ge-doped fiber samples of OM2 to OM4 grades were investigated for attenuation of optical power in radiation ionizing dose. Depending on the fabrication technology, two of the fiber types show radiation resistance with the RIAs of 0.2 dB/m and 0.05 dB/m, respectively, for the TID of 300 kGy(SiO2). At low dose rate of 5 Gy/hr, the RIA increases steadily and the annealing of low density ionizing defects does not cause notable deviation. At 1.4 kGy/hr the accumulated defects result to twice higher RIA during irradiation, and is worsen to a factor three in cold temperature. However, once the source is shielded the recovery is effective in a few hours. Conclusion: The telecom products of 850 nm Ge-doped MM fibers provide high speed communication in distances of a few hundred meters. The industrial fabrication methods provide fibers that can endure radiation ionizing dose for applications in nuclear instrumentation.", "authors": ["Datao Gong", "Suen Hou", "Bo-Jing Juang", "Bin Lin", "Chonghan Liu", "Tiankuan Liu", "Ming Qi", "Yi Yang", "Jingbo Ye", "Lei Zhang", "Li Zhang", "HuiPing Zhu"], "categories": ["physics.med-ph", "hep-ex"], "fields_of_study": ["Physics"], "published_date": "2025-04-07", "url": "https://arxiv.org/abs/2504.04871", "pdf_url": "https://arxiv.org/pdf/2504.04871v1", "arxiv_id": "2504.04871", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0766}
{"id": "50e4321f0ccc235f2dde44ce6473e09c3fdd2f6ccb5ccb09d32146fe03047d19", "sources": ["arxiv", "semantic_scholar"], "title": "Augmenting Anonymized Data with AI: Exploring the Feasibility and Limitations of Large Language Models in Data Enrichment", "abstract": "Large Language Models (LLMs) have demonstrated advanced capabilities in both text generation and comprehension, and their application to data archives might facilitate the privatization of sensitive information about the data subjects. In fact, the information contained in data often includes sensitive and personally identifiable details. This data, if not safeguarded, may bring privacy risks in terms of both disclosure and identification. Furthermore, the application of anonymisation techniques, such as k-anonymity, can lead to a significant reduction in the amount of data within data sources, which may reduce the efficacy of predictive processes. In our study, we investigate the capabilities offered by LLMs to enrich anonymized data sources without affecting their anonymity. To this end, we designed new ad-hoc prompt template engineering strategies to perform anonymized Data Augmentation and assess the effectiveness of LLM-based approaches in providing anonymized data. To validate the anonymization guarantees provided by LLMs, we exploited the pyCanon library, designed to assess the values of the parameters associated with the most common privacy-preserving techniques via anonymization. Our experiments conducted on real-world datasets demonstrate that LLMs yield promising results for this goal.", "authors": ["Stefano Cirillo", "Domenico Desiato", "Giuseppe Polese", "Monica Maria Lucia Sebillo", "Giandomenico Solimando"], "categories": ["cs.CR", "cs.ET"], "fields_of_study": ["Computer Science"], "published_date": "2025-04-03", "url": "https://arxiv.org/abs/2504.03778", "pdf_url": "https://arxiv.org/pdf/2504.03778v1", "arxiv_id": "2504.03778", "doi": "10.48550/arXiv.2504.03778", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1747}
{"id": "c053bca4c58244364a7c2d6b548fe06db9b2754ad80a7d6fbe9301e229543866", "sources": ["arxiv", "semantic_scholar"], "title": "Advancing Semantic Caching for LLMs with Domain-Specific Embeddings and Synthetic Data", "abstract": "This report investigates enhancing semantic caching effectiveness by employing specialized, fine-tuned embedding models. Semantic caching relies on embedding similarity rather than exact key matching, presenting unique challenges in balancing precision, query latency, and computational efficiency. We propose leveraging smaller, domain-specific embedding models, fine-tuned with targeted real-world and synthetically generated datasets. Our empirical evaluations demonstrate that compact embedding models fine-tuned for just one epoch on specialized datasets significantly surpass both state-of-the-art open-source and proprietary alternatives in precision and recall. Moreover, we introduce a novel synthetic data generation pipeline for the semantic cache that mitigates the challenge of limited domain-specific annotated data, further boosting embedding performance. Our approach effectively balances computational overhead and accuracy, establishing a viable and efficient strategy for practical semantic caching implementations.", "authors": ["Waris Gill", "Justin Cechmanek", "Tyler Hutcherson", "Srijith Rajamohan", "Jen Agarwal", "Muhammad Ali Gulzar", "Manvinder Singh", "Benoit Dion"], "categories": ["cs.LG", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-04-03", "url": "https://arxiv.org/abs/2504.02268", "pdf_url": "https://arxiv.org/pdf/2504.02268v1", "arxiv_id": "2504.02268", "doi": "10.48550/arXiv.2504.02268", "citation_count": 8, "influential_citation_count": 1, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2386}
{"id": "ffd1004d4c229e034e7686cd48a13132921ff70bc0a4dfef62d4013edd1092c3", "sources": ["arxiv", "semantic_scholar"], "title": "EEG-EyeTrack: A Benchmark for Time Series and Functional Data Analysis with Open Challenges and Baselines", "abstract": "A new benchmark dataset for functional data analysis (FDA) is presented, focusing on the reconstruction of eye movements from EEG data. The contribution is twofold: first, open challenges and evaluation metrics tailored to FDA applications are proposed. Second, functional neural networks are used to establish baseline results for the primary regression task of reconstructing eye movements from EEG signals. Baseline results are reported for the new dataset, based on consumer-grade hardware, and the EEGEyeNet dataset, based on research-grade hardware.", "authors": ["Tiago Vasconcelos Afonso", "Florian Heinrichs"], "categories": ["eess.SP", "cs.LG", "stat.ML"], "fields_of_study": ["Engineering", "Computer Science", "Mathematics"], "published_date": "2025-04-02", "url": "https://arxiv.org/abs/2504.03760", "pdf_url": "https://arxiv.org/pdf/2504.03760v1", "arxiv_id": "2504.03760", "doi": "10.48550/arXiv.2504.03760", "citation_count": 1, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "20f3d304cab944132334e819042ff3ab310d3b481dbc16400b84171961f8327c", "sources": ["arxiv", "semantic_scholar"], "title": "Data-free Knowledge Distillation with Diffusion Models", "abstract": "Recently Data-Free Knowledge Distillation (DFKD) has garnered attention and can transfer knowledge from a teacher neural network to a student neural network without requiring any access to training data. Although diffusion models are adept at synthesizing high-fidelity photorealistic images across various domains, existing methods cannot be easiliy implemented to DFKD. To bridge that gap, this paper proposes a novel approach based on diffusion models, DiffDFKD. Specifically, DiffDFKD involves targeted optimizations in two key areas. Firstly, DiffDFKD utilizes valuable information from teacher models to guide the pre-trained diffusion models' data synthesis, generating datasets that mirror the training data distribution and effectively bridge domain gaps. Secondly, to reduce computational burdens, DiffDFKD introduces Latent CutMix Augmentation, an efficient technique, to enhance the diversity of diffusion model-generated images for DFKD while preserving key attributes for effective knowledge transfer. Extensive experiments validate the efficacy of DiffDFKD, yielding state-of-the-art results exceeding existing DFKD approaches. We release our code at https://github.com/xhqi0109/DiffDFKD.", "authors": ["Xiaohua Qi", "Renda Li", "Long Peng", "Qiang Ling", "Jun Yu", "Ziyi Chen", "Peng Chang", "Mei Han", "Jing Xiao"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-04-01", "url": "https://arxiv.org/abs/2504.00870", "pdf_url": "https://arxiv.org/pdf/2504.00870v1", "arxiv_id": "2504.00870", "doi": "10.1109/ICME59968.2025.11209042", "citation_count": 15, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/xhqi0109/DiffDFKD", "venue": "IEEE International Conference on Multimedia and Expo", "quality_score": 0.301}
{"id": "bb94c435194b5a6f77ecc26dbee7a65bcdbb178d641d44f1dc79926166983563", "sources": ["arxiv", "semantic_scholar"], "title": "Enhancing Trust in Inter-Organisational Data Sharing: Levels of Assurance for Data Trustworthiness", "abstract": "As data is increasingly acknowledged as a highly valuable asset, much effort has been put into investigating inter-organisational data sharing, aiming at utilising the value of formerly unused data. Moreover, most researchers agree, that trust between actors is key for successful data sharing activities. However, existing research oftentimes focus on trust from a data provider perspective. Therefore, our work highlights the unbalanced view of trust, addressing it from a data consumer perspective. More specifically, our aim is to investigate trust enhancing measures on a data level, that is data trustworthiness. We found, that existing data trustworthiness enhancing solutions do not meet the requirements of the domain of inter-organisational data sharing. Therefore, our study addresses this gap. Conducting a rigorous design science research approach, this work proposes a new Levels of Assurance for Data Trustworthiness artifact. Built on existing artifacts, we demonstrate, how it addresses the identified challenges within the domain appropriately. We found that our novel approach requires more work to be suitable for adoption. Still, we are confident that our solution can increase consumer trust. We conclude by contributing to the body of design knowledge and emphasise the need for more attention to be put into consumer trust.", "authors": ["Florian Zimmer", "Janosch Haber", "Mayuko Kaneko"], "categories": ["cs.SI", "cs.CY", "econ.GN"], "fields_of_study": ["Computer Science", "Economics"], "published_date": "2025-03-31", "url": "https://arxiv.org/abs/2503.24149", "pdf_url": "https://arxiv.org/pdf/2503.24149v1", "arxiv_id": "2503.24149", "doi": "10.48550/arXiv.2503.24149", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Data Technologies and Applications", "quality_score": 0.1123}
{"id": "cd7ab79a27fcee9dbe0d9f01f7c5d62ccce3c551eae7069230fb359b815db17c", "sources": ["arxiv", "semantic_scholar"], "title": "WHERE and WHICH: Iterative Debate for Biomedical Synthetic Data Augmentation", "abstract": "In Biomedical Natural Language Processing (BioNLP) tasks, such as Relation Extraction, Named Entity Recognition, and Text Classification, the scarcity of high-quality data remains a significant challenge. This limitation poisons large language models to correctly understand relationships between biological entities, such as molecules and diseases, or drug interactions, and further results in potential misinterpretation of biomedical documents. To address this issue, current approaches generally adopt the Synthetic Data Augmentation method which involves similarity computation followed by word replacement, but counterfactual data are usually generated. As a result, these methods disrupt meaningful word sets or produce sentences with meanings that deviate substantially from the original context, rendering them ineffective in improving model performance. To this end, this paper proposes a biomedical-dedicated rationale-based synthetic data augmentation method. Beyond the naive lexicon similarity, specific bio-relation similarity is measured to hold the augmented instance having a strong correlation with bio-relation instead of simply increasing the diversity of augmented data. Moreover, a multi-agents-involved reflection mechanism helps the model iteratively distinguish different usage of similar entities to escape falling into the mis-replace trap. We evaluate our method on the BLURB and BigBIO benchmark, which includes 9 common datasets spanning four major BioNLP tasks. Our experimental results demonstrate consistent performance improvements across all tasks, highlighting the effectiveness of our approach in addressing the challenges associated with data scarcity and enhancing the overall performance of biomedical NLP models.", "authors": ["Zhengyi Zhao", "Shubo Zhang", "Bin Liang", "Binyang Li", "Kam-Fai Wong"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-31", "url": "https://arxiv.org/abs/2503.23673", "pdf_url": "https://arxiv.org/pdf/2503.23673v1", "arxiv_id": "2503.23673", "doi": "10.48550/arXiv.2503.23673", "citation_count": 1, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "e328a151437f05422a7e227a1d04869206843be074bdb58bef90d893724c2022", "sources": ["arxiv", "semantic_scholar"], "title": "A Survey on Unlearnable Data", "abstract": "Unlearnable data (ULD) has emerged as an innovative defense technique to prevent machine learning models from learning meaningful patterns from specific data, thus protecting data privacy and security. By introducing perturbations to the training data, ULD degrades model performance, making it difficult for unauthorized models to extract useful representations. Despite the growing significance of ULD, existing surveys predominantly focus on related fields, such as adversarial attacks and machine unlearning, with little attention given to ULD as an independent area of study. This survey fills that gap by offering a comprehensive review of ULD, examining unlearnable data generation methods, public benchmarks, evaluation metrics, theoretical foundations and practical applications. We compare and contrast different ULD approaches, analyzing their strengths, limitations, and trade-offs related to unlearnability, imperceptibility, efficiency and robustness. Moreover, we discuss key challenges, such as balancing perturbation imperceptibility with model degradation and the computational complexity of ULD generation. Finally, we highlight promising future research directions to advance the effectiveness and applicability of ULD, underscoring its potential to become a crucial tool in the evolving landscape of data protection in machine learning.", "authors": ["Jiahao Li", "Yiqiang Chen", "Yunbing Xing", "Yang Gu", "Xiangyuan Lan"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-30", "url": "https://arxiv.org/abs/2503.23536", "pdf_url": "https://arxiv.org/pdf/2503.23536v2", "arxiv_id": "2503.23536", "doi": "10.48550/arXiv.2503.23536", "citation_count": 3, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/LiJiahao-Alex/Awesome-UnLearnable-Data", "venue": "arXiv.org", "quality_score": 0.1718}
{"id": "c43acf4783e7b43e7d4abb2f9766296ac697f17487bb161dc66bf43a8caa62fe", "sources": ["arxiv", "semantic_scholar"], "title": "The Oxford Insights Government AI Readiness Index (GARI): An Analysis of its Data and Overcoming Obstacles, with a Case Study of Iraq", "abstract": "This research examines the \"Government AI Readines Index\" (GARI) issued by Oxford, analyzing data on governmental preparedness for adopting artificial intelligence acros different countrie. It highlights the evaluation criteria used to assess readiness, including technological infrastructure, human resources, supportive policies, and the level of innovation. The study specifically focuses on Iraq, exploring the challenge the Iraqi government face in adopting and implementing AI technology. It discussed economic, social, and political barriers that hinder this transition and provides concrete recommendations to overcome these obstacle. By analyzing Iraq case, the research aims to offer insight into improving collaboration between the public and private sectors to enhance the effective use of AI in governance and public administration. Additionally, the study emphasizes the importance of investing in education, training, and capacity building to develop a skilled workforce, enabling countries to harness AI potential and improve government service efficiency.", "authors": ["Ahmed Shaker Alalaq"], "categories": ["cs.CY"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-26", "url": "https://arxiv.org/abs/2503.20833", "pdf_url": "https://arxiv.org/pdf/2503.20833v1", "arxiv_id": "2503.20833", "doi": "10.48550/arXiv.2503.20833", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "138ee58c411e657c87d7eb776522b248cdd3fce955424ba9786262a992db1390", "sources": ["arxiv", "semantic_scholar"], "title": "Global Convergence of Continual Learning on Non-IID Data", "abstract": "Continual learning, which aims to learn multiple tasks sequentially, has gained extensive attention. However, most existing work focuses on empirical studies, and the theoretical aspect remains under-explored. Recently, a few investigations have considered the theory of continual learning only for linear regressions, establishes the results based on the strict independent and identically distributed (i.i.d.) assumption and the persistent excitation on the feature data that may be difficult to verify or guarantee in practice. To overcome this fundamental limitation, in this paper, we provide a general and comprehensive theoretical analysis for continual learning of regression models. By utilizing the stochastic Lyapunov function and martingale estimation techniques, we establish the almost sure convergence results of continual learning under a general data condition for the first time. Additionally, without any excitation condition imposed on the data, the convergence rates for the forgetting and regret metrics are provided.", "authors": ["Fei Zhu", "Yujing Liu", "Wenzhuo Liu", "Zhaoxiang Zhang"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-24", "url": "https://arxiv.org/abs/2503.18511", "pdf_url": "https://arxiv.org/pdf/2503.18511v1", "arxiv_id": "2503.18511", "doi": "10.48550/arXiv.2503.18511", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "59a84d361fbd44b900a0272c71f0ef327c636b535deca276734827b5ad9c9ed5", "sources": ["arxiv", "semantic_scholar"], "title": "GReaTER: Generate Realistic Tabular data after data Enhancement and Reduction", "abstract": "Tabular data synthesis involves not only multi-table synthesis but also generating multi-modal data (e.g., strings and categories), which enables diverse knowledge synthesis. However, separating numerical and categorical data has limited the effectiveness of tabular data generation. The GReaT (Generate Realistic Tabular Data) framework uses Large Language Models (LLMs) to encode entire rows, eliminating the need to partition data types. Despite this, the framework's performance is constrained by two issues: (1) tabular data entries lack sufficient semantic meaning, limiting LLM's ability to leverage pre-trained knowledge for in-context learning, and (2) complex multi-table datasets struggle to establish effective relationships for collaboration. To address these, we propose GReaTER (Generate Realistic Tabular Data after data Enhancement and Reduction), which includes: (1) a data semantic enhancement system that improves LLM's understanding of tabular data through mapping, enabling better in-context learning, and (2) a cross-table connecting method to establish efficient relationships across complex tables. Experimental results show that GReaTER outperforms the GReaT framework.", "authors": ["Tung Sum Thomas Kwok", "Chi-Hua Wang", "Guang Cheng"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-19", "url": "https://arxiv.org/abs/2503.15564", "pdf_url": "https://arxiv.org/pdf/2503.15564v1", "arxiv_id": "2503.15564", "doi": "10.1109/ICDEW67478.2025.00032", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1747}
{"id": "88e7ab4bc81590b67d01613961d24a2933f1449c6547fc9188bb5c83e72a4cfc", "sources": ["arxiv", "semantic_scholar"], "title": "Euclid Quick Data Release (Q1) -- Data release overview", "abstract": "The first Euclid Quick Data Release, Q1, comprises 63.1 sq deg of the Euclid Deep Fields (EDFs) to nominal wide-survey depth. It encompasses visible and near-infrared space-based imaging and spectroscopic data, ground-based photometry in the u, g, r, i and z bands, as well as corresponding masks. Overall, Q1 contains about 30 million objects in three areas near the ecliptic poles around the EDF-North and EDF-South, as well as the EDF-Fornax field in the constellation of the same name. The purpose of this data release -- and its associated technical papers -- is twofold. First, it is meant to inform the community of the enormous potential of the Euclid survey data, to describe what is contained in these data, and to help prepare expectations for the forthcoming first major data release DR1. Second, it enables a wide range of initial scientific projects with wide-survey Euclid data, ranging from the early Universe to the Solar System. The Q1 data were processed with early versions of the processing pipelines, which already demonstrate good performance, with numerous improvements in implementation compared to pre-launch development. In this paper, we describe the sky areas released in Q1, the observations, a top-level view of the data processing of Euclid and associated external data, the Q1 photometric masks, and how to access the data. We also give an overview of initial scientific results obtained using the Q1 data set by Euclid Consortium scientists, and conclude with important caveats when using the data. As a complementary product, Q1 also contains observations of a star-forming area in Lynd's Dark Nebula 1641 in the Orion~A Cloud, observed for technical purposes during Euclid's performance-verification phase. This is a unique target, of a type not commonly found in Euclid's nominal sky survey.", "authors": [" Euclid Collaboration", "H. Aussel", "I. Tereno", "M. Schirmer", "G. Alguero", "B. Altieri", "E. Balbinot", "T. de Boer", "P. Casenove", "P. Corcho-Caballero", "H. Furusawa", "J. Furusawa", "M. J. Hudson", "K. Jahnke", "G. Libet", "J. Macias-Perez", "N. Masoumzadeh", "J. J. Mohr", "J. Odier", "D. Scott", "T. Vassallo", "G. Verdoes Kleijn", "A. Zacchei", "N. Aghanim", "A. Amara", "S. Andreon", "N. Auricchio", "S. Awan", "R. Azzollini", "C. Baccigalupi", "M. Baldi", "A. Balestra", "S. Bardelli", "A. Basset", "P. Battaglia", "A. N. Belikov", "R. Bender", "A. Biviano", "A. Bonchi", "D. Bonino", "E. Branchini", "M. Brescia", "J. Brinchmann", "S. Camera", "G. Cañas-Herrera", "V. Capobianco", "C. Carbone", "V. F. Cardone", "J. Carretero", "S. Casas", "F. J. Castander", "M. Castellano", "G. Castignani", "S. Cavuoti", "K. C. Chambers", "A. Cimatti", "C. Colodro-Conde", "G. Congedo", "C. J. Conselice", "L. Conversi", "Y. Copin", "F. Courbin", "H. M. Courtois", "M. Cropper", "J. -G. Cuby", "A. Da Silva", "R. da Silva", "H. Degaudenzi", "J. T. A. de Jong", "G. De Lucia", "A. M. Di Giorgio", "J. Dinis", "C. Dolding", "H. Dole", "M. Douspis", "F. Dubath", "C. A. J. Duncan", "X. Dupac", "S. Dusini", "A. Ealet", "S. Escoffier", "M. Fabricius", "M. Farina", "R. Farinelli", "F. Faustini", "S. Ferriol", "S. Fotopoulou", "N. Fourmanoit", "M. Frailis", "E. Franceschi", "P. Franzetti", "S. Galeotta", "K. George", "W. Gillard", "B. Gillis", "C. Giocoli", "P. Gómez-Alvarez", "J. Gracia-Carpio", "B. R. Granett", "A. Grazian", "F. Grupp", "L. Guzzo", "S. Gwyn", "S. V. H. Haugan", "O. Herent", "J. Hoar", "H. Hoekstra", "M. S. Holliman", "W. Holmes", "I. M. Hook", "F. Hormuth", "A. Hornstrup", "P. Hudelot", "S. Ilić", "M. Jhabvala", "B. Joachimi", "E. Keihänen", "S. Kermiche", "A. Kiessling", "B. Kubik", "K. Kuijken", "M. Kümmel", "M. Kunz", "H. Kurki-Suonio", "O. Lahav", "Q. Le Boulc'h", "A. M. C. Le Brun", "D. Le Mignant", "P. Liebing", "S. Ligori", "P. B. Lilje", "V. Lindholm", "I. Lloro", "G. Mainetti", "D. Maino", "E. Maiorano", "O. Mansutti", "S. Marcin", "O. Marggraf", "K. Markovic", "M. Martinelli", "N. Martinet", "F. Marulli", "R. Massey", "S. Maurogordato", "H. J. McCracken", "E. Medinaceli", "S. Mei", "M. Melchior", "Y. Mellier", "M. Meneghetti", "E. Merlin", "G. Meylan", "A. Mora", "M. Moresco", "P. W. Morris", "L. Moscardini", "S. Mourre", "R. Nakajima", "C. Neissner", "R. C. Nichol", "S. -M. Niemi", "J. W. Nightingale", "T. Nutma", "C. Padilla", "S. Paltani", "F. Pasian", "J. A. Peacock", "K. Pedersen", "W. J. Percival", "V. Pettorino", "S. Pires", "G. Polenta", "J. E. Pollack", "M. Poncet", "L. A. Popa", "L. Pozzetti", "G. D. Racca", "F. Raison", "R. Rebolo", "A. Renzi", "J. Rhodes", "G. Riccio", "H. -W. Rix", "E. Romelli", "M. Roncarelli", "E. Rossetti", "B. Rusholme", "R. Saglia", "Z. Sakr", "A. G. Sánchez", "D. Sapone", "B. Sartoris", "M. Sauvage", "J. A. Schewtschenko", "P. Schneider", "M. Scodeggio", "A. Secroun", "E. Sefusatti", "G. Seidel", "M. Seiffert", "S. Serrano", "P. Simon", "C. Sirignano", "G. Sirri", "J. Skottfelt", "A. Spurio Mancini", "L. Stanco", "J. Steinwagner", "C. Surace", "P. Tallada-Crespí", "D. Tavagnacco", "A. N. Taylor", "H. I. Teplitz", "N. Tessore", "S. Toft", "R. Toledo-Moreo", "F. Torradeflot", "A. Tsyganov", "I. Tutusaus", "E. A. Valentijn", "L. Valenziano", "J. Valiviita", "A. Veropalumbo", "Y. Wang", "J. Weller", "O. R. Williams", "G. Zamorani", "F. M. Zerbi", "E. Zucca", "V. Allevato", "M. Ballardini", "R. P. Blake", "M. Bolzonella", "E. Bozzo", "C. Burigana", "R. Cabanac", "M. Calabrese", "A. Cappi", "D. Di Ferdinando", "J. A. Escartin Vigo", "L. Gabarra", "W. G. Hartley", "M. Huertas-Company", "J. Martín-Fleitas", "S. Matthew", "M. Maturi", "N. Mauri", "R. B. Metcalf", "A. Pezzotta", "M. Pöntinen", "C. Porciani", "I. Risso", "V. Scottez", "M. Sereno", "M. Tenti", "M. Viel", "M. Wiesmann", "Y. Akrami", "S. Alvi", "I. T. Andika", "S. Anselmi", "M. Archidiacono", "F. Atrio-Barandela", "S. Avila", "P. Bergamini", "D. Bertacca", "M. Bethermin", "L. Bisigello", "A. Blanchard", "L. Blot", "H. Böhringer", "S. Borgani", "A. S. Borlaff", "M. L. Brown", "S. Bruton", "F. Buitrago", "A. Calabro", "G. Calderone", "B. Camacho Quevedo", "F. Caro", "C. S. Carvalho", "T. Castro", "Y. Charles", "F. Cogato", "S. Conseil", "A. R. Cooray", "M. Costanzi", "O. Cucciati", "S. Davini", "F. De Paolis", "G. Desprez", "A. Díaz-Sánchez", "J. J. Diaz", "S. Di Domizio", "J. M. Diego", "P. Dimauro", "P. -A. Duc", "A. Enia", "Y. Fang", "A. M. N. Ferguson", "A. G. Ferrari", "A. Finoguenov", "A. Fontana", "F. Fontanot", "A. Franco", "J. García-Bellido", "T. Gasparetto", "R. Gavazzi", "E. Gaztanaga", "F. Giacomini", "F. Gianotti", "A. H. Gonzalez", "G. Gozaliasl", "A. Gruppuso", "M. Guidi", "C. M. Gutierrez", "A. Hall", "C. Hernández-Monteagudo", "H. Hildebrandt", "J. Hjorth", "J. Jacobson", "S. Joudaki", "J. J. E. Kajava", "Y. Kang", "V. Kansal", "D. Karagiannis", "K. Kiiveri", "C. C. Kirkpatrick", "S. Kruk", "F. Lacasa", "C. Laigle", "M. Lattanzi", "V. Le Brun", "J. Le Graet", "L. Legrand", "M. Lembo", "F. Lepori", "G. Leroy", "G. F. Lesci", "J. Lesgourgues", "L. Leuzzi", "T. I. Liaudat", "A. Loureiro", "M. Magliocchetti", "E. A. Magnier", "C. Mancini", "F. Mannucci", "R. Maoli", "C. J. A. P. Martins", "L. Maurin", "C. J. R. McPartland", "J. -B. Melin", "M. Migliaccio", "M. Miluzio", "P. Monaco", "A. Montoro", "C. Moretti", "G. Morgante", "C. Murray", "S. Nadathur", "K. Naidoo", "A. Navarro-Alsina", "S. Nesseris", "L. Nicastro", "M. Oguri", "F. Passalacqua", "K. Paterson", "L. Patrizii", "A. Pisani", "D. Potter", "S. Quai", "M. Radovich", "P. Reimberg", "P. -F. Rocci", "G. Rodighiero", "R. P. Rollins", "S. Sacquegna", "M. Sahlén", "D. B. Sanders", "E. Sarpa", "C. Scarlata", "J. Schaye", "A. Schneider", "M. Schultheis", "D. Sciotti", "D. Scognamiglio", "E. Sellentin", "F. Shankar", "L. C. Smith", "E. Soubrie", "S. A. Stanford", "K. Tanidis", "C. Tao", "G. Testera", "M. Tewes", "R. Teyssier", "S. Tosi", "A. Troja", "M. Tucci", "C. Valieri", "A. Venhola", "D. Vergani", "F. Vernizzi", "G. Verza", "P. Vielzeuf", "N. A. Walton", "J. R. Weaver", "J. Wilde", "L. Zalesky"], "categories": ["astro-ph.GA"], "fields_of_study": ["Physics"], "published_date": "2025-03-19", "url": "https://arxiv.org/abs/2503.15302", "pdf_url": "https://arxiv.org/pdf/2503.15302v1", "arxiv_id": "2503.15302", "doi": null, "citation_count": 6, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2113}
{"id": "fc5b634986a24fbe392ecade83299136e7e6d02c4d1bf454b81e8e5090c6299a", "sources": ["arxiv", "semantic_scholar"], "title": "Synthesizing Privacy-Preserving Text Data via Finetuning without Finetuning Billion-Scale LLMs", "abstract": "Synthetic data offers a promising path to train models while preserving data privacy. Differentially private (DP) finetuning of large language models (LLMs) as data generator is effective, but is impractical when computation resources are limited. Meanwhile, prompt-based methods such as private evolution depend heavily on the manual prompts, and ineffectively use private information in their iterative data selection process. To overcome these limitations, we propose CTCL (Data Synthesis with ConTrollability and CLustering), a novel framework for generating privacy-preserving synthetic data without extensive prompt engineering or billion-scale LLM finetuning. CTCL pretrains a lightweight 140M conditional generator and a clustering-based topic model on large-scale public data. To further adapt to the private domain, the generator is DP finetuned on private data for fine-grained textual information, while the topic model extracts a DP histogram representing distributional information. The DP generator then samples according to the DP histogram to synthesize a desired number of data examples. Evaluation across five diverse domains demonstrates the effectiveness of our framework, particularly in the strong privacy regime. Systematic ablation validates the design of each framework component and highlights the scalability of our approach.", "authors": ["Bowen Tan", "Zheng Xu", "Eric Xing", "Zhiting Hu", "Shanshan Wu"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-16", "url": "https://arxiv.org/abs/2503.12347", "pdf_url": "https://arxiv.org/pdf/2503.12347v2", "arxiv_id": "2503.12347", "doi": "10.48550/arXiv.2503.12347", "citation_count": 14, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/tanyuqian/synthetic-private-data", "venue": "International Conference on Machine Learning", "quality_score": 0.294}
{"id": "c918c766592f5cb343d5db6e5cfe31f24eadc47aee128a802f135d227d9367d6", "sources": ["arxiv", "semantic_scholar"], "title": "Hyperboloidal initial data without logarithmic singularities", "abstract": "Andersson and Chruściel showed that generic asymptotically hyperboloidal initial data sets admit polyhomogeneous expansions, and that only a non-generic subclass of solutions of the conformal constraint equations is free of logarithmic singularities. The purpose of this work is twofold. First, within the evolutionary framework of the constraint equations, we show that the existence of a well-defined Bondi mass brings the asymptotically hyperboloidal initial data sets into a subclass whose Cauchy development guaranteed to admit a smooth boundary, by virtue of the results of Andersson and Chruściel. Second, by generalizing a recent result of Beyer and Ritchie, we show that the existence of well-defined Bondi mass and angular momentum, together with some mild restrictions on the free data, implies that the generic solutions of the parabolic-hyperbolic form of the constraint equations are completely free of logarithmic singularities. We also provide numerical evidence to show that in the vicinity of Kerr, asymptotically hyperboloidal initial data without logarithmic singularities can indeed be constructed.", "authors": ["Károly Csukás", "István Rácz"], "categories": ["gr-qc", "math-ph", "math.DG"], "fields_of_study": ["Physics", "Mathematics"], "published_date": "2025-03-14", "url": "https://arxiv.org/abs/2503.11804", "pdf_url": "https://arxiv.org/pdf/2503.11804v2", "arxiv_id": "2503.11804", "doi": "10.1007/s10714-025-03424-y", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "General Relativity and Gravitation", "quality_score": 0.1505}
{"id": "fbf725f95803c3565c02683617be321749c7da2e91a07e8d248de3e1f5a2f6d1", "sources": ["arxiv", "semantic_scholar"], "title": "Data augmentation using diffusion models to enhance inverse Ising inference", "abstract": "Identifying model parameters from observed configurations poses a fundamental challenge in data science, especially with limited data. Recently, diffusion models have emerged as a novel paradigm in generative machine learning, capable of producing new samples that closely mimic observed data. These models learn the gradient of model probabilities, bypassing the need for cumbersome calculations of partition functions across all possible configurations. We explore whether diffusion models can enhance parameter inference by augmenting small datasets. Our findings demonstrate this potential through a synthetic task involving inverse Ising inference and a real-world application of reconstructing missing values in neural activity data. This study serves as a proof-of-concept for using diffusion models for data augmentation in physics-related problems, thereby opening new avenues in data science.", "authors": ["Yechan Lim", "Sangwon Lee", "Junghyo Jo"], "categories": ["physics.data-an", "cs.LG"], "fields_of_study": ["Physics", "Computer Science", "Medicine"], "published_date": "2025-03-13", "url": "https://arxiv.org/abs/2503.10154", "pdf_url": "https://arxiv.org/pdf/2503.10154v1", "arxiv_id": "2503.10154", "doi": "10.1103/physreve.111.045302", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Physical Review E", "quality_score": 0.0917}
{"id": "72d0fbc380ee3a545a7421a2fad6350d065df83435f94d32a935a0bcbc21754c", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetic Data Augmentation for Enhancing Harmful Algal Bloom Detection with Machine Learning", "abstract": "Harmful Algal Blooms (HABs) pose severe threats to aquatic ecosystems and public health, resulting in substantial economic losses globally. Early detection is crucial but often hindered by the scarcity of high-quality datasets necessary for training reliable machine learning (ML) models. This study investigates the use of synthetic data augmentation using Gaussian Copulas to enhance ML-based HAB detection systems. Synthetic datasets of varying sizes (100-1,000 samples) were generated using relevant environmental features$\\unicode{x2015}$water temperature, salinity, and UVB radiation$\\unicode{x2015}$with corrected Chlorophyll-a concentration as the target variable. Experimental results demonstrate that moderate synthetic augmentation significantly improves model performance (RMSE reduced from 0.4706 to 0.1850; $p < 0.001$). However, excessive synthetic data introduces noise and reduces predictive accuracy, emphasizing the need for a balanced approach to data augmentation. These findings highlight the potential of synthetic data to enhance HAB monitoring systems, offering a scalable and cost-effective method for early detection and mitigation of ecological and public health risks.", "authors": ["Tianyi Huang"], "categories": ["cs.LG", "cs.AI", "cs.CY"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-05", "url": "https://arxiv.org/abs/2503.03794", "pdf_url": "https://arxiv.org/pdf/2503.03794v1", "arxiv_id": "2503.03794", "doi": "10.1109/SusTech63138.2025.11025596", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Conference on Technologies for Sustainability", "quality_score": 0.0825}
{"id": "40c34e903408397fbd5ae9e1bb6bb88329f969bc2fd0d0c8cbb6fd574a15673e", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetic Tabular Data Detection In the Wild", "abstract": "Detecting synthetic tabular data is essential to prevent the distribution of false or manipulated datasets that could compromise data-driven decision-making. This study explores whether synthetic tabular data can be reliably identified across different tables. This challenge is unique to tabular data, where structures (such as number of columns, data types, and formats) can vary widely from one table to another. We propose four table-agnostic detectors combined with simple preprocessing schemes that we evaluate on six evaluation protocols, with different levels of ''wildness''. Our results show that cross-table learning on a restricted set of tables is possible even with naive preprocessing schemes. They confirm however that cross-table transfer (i.e. deployment on a table that has not been seen before) is challenging. This suggests that sophisticated encoding schemes are required to handle this problem.", "authors": ["G. Charbel N. Kindji", "Elisa Fromont", "Lina Maria Rojas-Barahona", "Tanguy Urvoy"], "categories": ["cs.LG", "cs.AI", "cs.DB", "cs.NE", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2025-03-03", "url": "https://arxiv.org/abs/2503.01937", "pdf_url": "https://arxiv.org/pdf/2503.01937v1", "arxiv_id": "2503.01937", "doi": "10.48550/arXiv.2503.01937", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Symposium on Intelligent Data Analysis", "quality_score": 0.1193}
{"id": "984dd21887fb058a1724e80e41b88ca7618fe1fca0f6be4bf6d7591981e6e559", "sources": ["arxiv", "semantic_scholar"], "title": "Synthesizing Tabular Data Using Selectivity Enhanced Generative Adversarial Networks", "abstract": "As E-commerce platforms face surging transactions during major shopping events like Black Friday, stress testing with synthesized data is crucial for resource planning. Most recent studies use Generative Adversarial Networks (GANs) to generate tabular data while ensuring privacy and machine learning utility. However, these methods overlook the computational demands of processing GAN-generated data, making them unsuitable for E-commerce stress testing. This thesis introduces a novel GAN-based approach incorporating query selectivity constraints, a key factor in database transaction processing. We integrate a pre-trained deep neural network to maintain selectivity consistency between real and synthetic data. Our method, tested on five real-world datasets, outperforms three state-of-the-art GANs and a VAE model, improving selectivity estimation accuracy by up to 20pct and machine learning utility by up to 6 pct.", "authors": ["Youran Zhou", "Jianzhong Qi"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-02-28", "url": "https://arxiv.org/abs/2502.21034", "pdf_url": "https://arxiv.org/pdf/2502.21034v1", "arxiv_id": "2502.21034", "doi": "10.48550/arXiv.2502.21034", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0768}
{"id": "301a5c507f2db7afdba6e03016123d0d724b93265561d35be317a004f8056bbc", "sources": ["arxiv", "semantic_scholar"], "title": "Data Jamboree: A Party of Open-Source Software Solving Real-World Data Science Problems", "abstract": "The evolving focus in statistics and data science education highlights the growing importance of computing. This paper presents the Data Jamboree, a live event that combines computational methods with traditional statistical techniques to address real-world data science problems. Participants, ranging from novices to experienced users, followed workshop leaders in using open-source tools like Julia, Python, and R to perform tasks such as data cleaning, manipulation, and predictive modeling. The Jamboree showcased the educational benefits of working with open data, providing participants with practical, hands-on experience. We compared the tools in terms of efficiency, flexibility, and statistical power, with Julia excelling in performance, Python in versatility, and R in statistical analysis and visualization. The paper concludes with recommendations for designing similar events to encourage collaborative learning and critical thinking in data science.", "authors": ["Lucy D'Agostino McGowan", "Shannon Tass", "Sam Tyner", "HaiYing Wang", "Jun Yan"], "categories": ["stat.OT"], "fields_of_study": ["Mathematics"], "published_date": "2025-02-27", "url": "https://arxiv.org/abs/2502.20281", "pdf_url": "https://arxiv.org/pdf/2502.20281v1", "arxiv_id": "2502.20281", "doi": "10.51387/25-NEJSDS79", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "The New England Journal of Statistics in Data Science", "quality_score": 0.1169}
{"id": "57da3818fa2ab85e088d9418f88c7cd7bc0fd3445482436b5416f1deac58d75d", "sources": ["arxiv", "semantic_scholar"], "title": "Mapping Research Data at the University of Bologna", "abstract": "Research data management (RDM) strategies and practices play a pivotal role in adhering to the paradigms of reproducibility and transparency by enabling research sharing in accordance with the principles of Open Science. Discipline-specificity is an essential factor when understanding RDM declinations, to tailor a comprehensive support service and to enhance interdisciplinarity. In this paper we present the results of a mapping carried out to gather information on research data generated and managed within the University of Bologna (UniBO). The aim is to identify differences and commonalities between disciplines and potential challenges for institutional support. We analyzed the data management plans (DMPs) of European competitive projects drafted by researchers affiliated with UniBO. We applied descriptive statistics to the collected variables to answer three main questions: How diverse is the range of data managed within the University of Bologna? Which trends of problems and patterns in terms of data management can influence/improve data stewardship service? Is there an interdisciplinary approach to data production within the University? The research work evidenced many points of contact between different disciplines in terms of data produced, formats used and modest predilection for data reuse. Hot topics such as data confidentiality, needed either on privacy or intellectual property rights (IPR) premises, and long-term preservation pose challenges to all researchers. These results show an increasing attention to RDM while highlighting the relevance of training and support to face the relatively new challenges posed by this approach.", "authors": ["C. Basalti", "G. Caldoni", "S. Coppini", "B. Gualandi", "M. Marino", "F. Masini", "S. Peroni"], "categories": ["cs.DL"], "fields_of_study": ["Computer Science"], "published_date": "2025-02-26", "url": "https://arxiv.org/abs/2503.13464", "pdf_url": "https://arxiv.org/pdf/2503.13464v2", "arxiv_id": "2503.13464", "doi": "10.5334/dsj-2025-038", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Data Science Journal", "quality_score": 0.0745}
{"id": "aef7fe8026759718bd9eac81c2c12a11388173ecb2f1414b1d708afea1456537", "sources": ["arxiv", "semantic_scholar"], "title": "Mixture models for data with unknown distributions", "abstract": "We describe and analyze a broad class of mixture models for real-valued multivariate data in which the probability density of observations within each component of the model is represented as an arbitrary combination of basis functions. Fits to these models give us a way to cluster data with distributions of unknown form, including strongly non-Gaussian or multimodal distributions, and return both a division of the data and an estimate of the distributions, effectively performing clustering and density estimation within each cluster at the same time. We describe two fitting methods, one using an expectation-maximization (EM) algorithm and the other a Bayesian non-parametric method using a collapsed Gibbs sampler. The former is numerically efficient, but gives only point estimates of the probability densities. The latter is more computationally demanding but returns a full Bayesian posterior and also an estimate of the number of components. We demonstrate our methods with a selection of illustrative applications and give code implementing both algorithms.", "authors": ["M. E. J. Newman"], "categories": ["stat.ME", "stat.ML"], "fields_of_study": ["Mathematics"], "published_date": "2025-02-26", "url": "https://arxiv.org/abs/2502.19605", "pdf_url": "https://arxiv.org/pdf/2502.19605v1", "arxiv_id": "2502.19605", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0474}
{"id": "0fd639dad4e5bd43f067089e18c882bc552d8b0e60b525fd6a2063370f22131f", "sources": ["arxiv", "semantic_scholar"], "title": "Diffusion and Flow Matching Models for Tabular Data: A Survey", "abstract": "Deep generative models have made rapid progress in image, text, audio, and video generation, and are increasingly being applied to structured records. For tabular data, however, generative modeling remains difficult: a dataset may contain numerical and categorical attributes, missing values, sensitive fields, imbalanced categories, complex feature dependencies, and domain constraints. Earlier tabular data modeling methods based on GANs or VAEs have achieved useful results, but they can suffer from unstable training, mode collapse, weak modeling of multimodal distributions, and fragile handling of mixed-type features. Diffusion models have therefore attracted growing interest because their noising-and-denoising formulation provides a flexible and stable way to model complex data distributions, and has been adapted to tabular synthesis, missing-value imputation, trustworthy data generation, and anomaly detection. Flow matching offers a closely related route by learning transport vector fields along probability paths, often with more direct control over path design and sampling efficiency. Despite this progress, the literature on diffusion and flow matching models for tabular data remains difficult to compare because methods target different tasks and rely on different representations, objectives, evaluation protocols, and domain assumptions. To the best of our knowledge, this is the first survey dedicated specifically to diffusion and flow matching models for tabular data. We review work from June 2015 to May 2026, organize it around data-engineering challenges, tasks, design choices, and evaluation dimensions, and discuss open problems in scalability, feature dependency modeling, privacy, fairness, benchmarking, and constraint-aware generation. We maintain updates in a GitHub repository.", "authors": ["Zhong Li", "Qi Huang", "Lincen Yang", "Jiayang Shi", "Zhao Yang", "Niki van Stein", "Thomas Bäck", "Matthijs van Leeuwen"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-02-24", "url": "https://arxiv.org/abs/2502.17119", "pdf_url": "https://arxiv.org/pdf/2502.17119v2", "arxiv_id": "2502.17119", "doi": null, "citation_count": 13, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2865}
{"id": "24327122d38aac2a4bf324505f7011c4e5cb536a6300cf30ad9cd95bb37da278", "sources": ["arxiv", "semantic_scholar"], "title": "In-context Learning of Evolving Data Streams with Tabular Foundational Models", "abstract": "State-of-the-art data stream mining has long drawn from ensembles of the Very Fast Decision Tree, a seminal algorithm honored with the 2015 KDD Test-of-Time Award. However, the emergence of large tabular models, i.e., transformers designed for structured numerical data, marks a significant paradigm shift. These models move beyond traditional weight updates, instead employing in-context learning through prompt tuning. By using on-the-fly sketches to summarize unbounded streaming data, one can feed this information into a pre-trained model for efficient processing. This work bridges advancements from both areas, highlighting how transformers' implicit meta-learning abilities, pre-training on drifting natural data, and reliance on context optimization directly address the core challenges of adaptive learning in dynamic environments. Exploring real-time model adaptation, this research demonstrates that TabPFN, coupled with a simple sliding memory strategy, consistently outperforms ensembles of Hoeffding trees, such as Adaptive Random Forest, and Streaming Random Patches, across all non-stationary benchmarks.", "authors": ["Afonso Lourenço", "João Gama", "Eric P. Xing", "Goreti Marreiros"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-02-24", "url": "https://arxiv.org/abs/2502.16840", "pdf_url": "https://arxiv.org/pdf/2502.16840v2", "arxiv_id": "2502.16840", "doi": "10.1145/3770854.3780305", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "4f463ad3d1ebdbfbcb9b7114166067868f4a6f5d814bca7b9e5c32fc7e3eea8b", "sources": ["arxiv", "semantic_scholar"], "title": "Secure Federated Data Distillation", "abstract": "Dataset Distillation (DD) is a powerful technique for reducing large datasets into compact, representative synthetic datasets, accelerating Machine Learning training. However, traditional DD methods operate in a centralized manner, which poses significant privacy threats and reduces its applicability. To mitigate these risks, we propose a Secure Federated Data Distillation (SFDD) framework to decentralize the distillation process while preserving privacy. Unlike existing Federated Distillation techniques that focus on training global models with distilled knowledge, our approach aims to produce a distilled dataset without exposing local contributions. We leverage the gradient-matching-based distillation method, adapting it for a distributed setting where clients contribute to the distillation process without sharing raw data. The central aggregator iteratively refines a synthetic dataset by integrating client-side updates while ensuring data confidentiality. To make our approach resilient to inference attacks perpetrated by the server that could exploit gradient updates to reconstruct private data, we create an optimized Local Differential Privacy approach, called LDPO-RLD. Furthermore, we assess the framework's resilience against malicious clients executing backdoor attacks (such as Doorping) and demonstrate robustness under the assumption of a sufficient number of participating clients. Our experimental results demonstrate the effectiveness of SFDD and that the proposed defense concretely mitigates the identified vulnerabilities, with minimal impact on the performance of the distilled dataset. By addressing the interplay between privacy and federation in dataset distillation, this work advances the field of privacy-preserving Machine Learning making our SFDD framework a viable solution for sensitive data-sharing applications.", "authors": ["Marco Arazzi", "Mert Cihangiroglu", "Serena Nicolazzo", "Antonino Nocera"], "categories": ["cs.CR", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-02-19", "url": "https://arxiv.org/abs/2502.13728", "pdf_url": "https://arxiv.org/pdf/2502.13728v2", "arxiv_id": "2502.13728", "doi": "10.48550/arXiv.2502.13728", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "dde1787b29ded07aa2b85cc184d13b571dd17794c8201b810c489074a5fa92f4", "sources": ["arxiv", "semantic_scholar"], "title": "LLM-Powered Proactive Data Systems", "abstract": "With the power of LLMs, we now have the ability to query data that was previously impossible to query, including text, images, and video. However, despite this enormous potential, most present-day data systems that leverage LLMs are reactive, reflecting our community's desire to map LLMs to known abstractions. Most data systems treat LLMs as an opaque black box that operates on user inputs and data as is, optimizing them much like any other approximate, expensive UDFs, in conjunction with other relational operators. Such data systems do as they are told, but fail to understand and leverage what the LLM is being asked to do (i.e. the underlying operations, which may be error-prone), the data the LLM is operating on (e.g., long, complex documents), or what the user really needs. They don't take advantage of the characteristics of the operations and/or the data at hand, or ensure correctness of results when there are imprecisions and ambiguities. We argue that data systems instead need to be proactive: they need to be given more agency -- armed with the power of LLMs -- to understand and rework the user inputs and the data and to make decisions on how the operations and the data should be represented and processed. By allowing the data system to parse, rewrite, and decompose user inputs and data, or to interact with the user in ways that go beyond the standard single-shot query-result paradigm, the data system is able to address user needs more efficiently and effectively. These new capabilities lead to a rich design space where the data system takes more initiative: they are empowered to perform optimization based on the transformation operations, data characteristics, and user intent. We discuss various successful examples of how this framework has been and can be applied in real-world tasks, and present future directions for this ambitious research agenda.", "authors": ["Sepanta Zeighami", "Yiming Lin", "Shreya Shankar", "Aditya Parameswaran"], "categories": ["cs.DB", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-02-18", "url": "https://arxiv.org/abs/2502.13016", "pdf_url": "https://arxiv.org/pdf/2502.13016v1", "arxiv_id": "2502.13016", "doi": "10.48550/arXiv.2502.13016", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Data Engineering Bulletin", "quality_score": 0.1505}
{"id": "0e44c94a64e23ce42cd874f059b3537cb63f0b49609d8e74a60ddf43f8c2c12d", "sources": ["arxiv", "semantic_scholar"], "title": "Does Training with Synthetic Data Truly Protect Privacy?", "abstract": "As synthetic data becomes increasingly popular in machine learning tasks, numerous methods--without formal differential privacy guarantees--use synthetic data for training. These methods often claim, either explicitly or implicitly, to protect the privacy of the original training data. In this work, we explore four different training paradigms: coreset selection, dataset distillation, data-free knowledge distillation, and synthetic data generated from diffusion models. While all these methods utilize synthetic data for training, they lead to vastly different conclusions regarding privacy preservation. We caution that empirical approaches to preserving data privacy require careful and rigorous evaluation; otherwise, they risk providing a false sense of privacy.", "authors": ["Yunpeng Zhao", "Jie Zhang"], "categories": ["cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-02-18", "url": "https://arxiv.org/abs/2502.12976", "pdf_url": "https://arxiv.org/pdf/2502.12976v1", "arxiv_id": "2502.12976", "doi": "10.48550/arXiv.2502.12976", "citation_count": 13, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Learning Representations", "quality_score": 0.2865}
{"id": "311fc6d10dcd69ced8797f035138f9b6be33b9667c4d61b96f837c73673a705f", "sources": ["arxiv", "semantic_scholar"], "title": "The Vendiscope: An Algorithmic Microscope For Data Collections", "abstract": "The evolution of microscopy, beginning with its invention in the late 16th century, has continuously enhanced our ability to explore and understand the microscopic world, enabling increasingly detailed observations of structures and phenomena. In parallel, the rise of data-driven science has underscored the need for sophisticated methods to explore and understand the composition of complex data collections. This paper introduces the Vendiscope, the first algorithmic microscope designed to extend traditional microscopy to computational analysis. The Vendiscope leverages the Vendi scores -- a family of differentiable diversity metrics rooted in ecology and quantum mechanics -- and assigns weights to data points based on their contribution to the overall diversity of the collection. These weights enable high-resolution data analysis at scale. We demonstrate this across biology, materials science, and machine learning (ML). We analyzed the $250$ million protein sequences in the protein universe, discovering that over $200$ million are near-duplicates and that AlphaFold fails on proteins with Gene Ontology (GO) functions that contribute most to diversity. Applying the Vendiscope to the Materials Project database led to similar findings: more than $85\\%$ of the crystals with formation energy data are near-duplicates and ML models perform poorly on materials that enhance diversity. Additionally, the Vendiscope can be used to study phenomena such as memorization in generative models. We used the Vendiscope to identify memorized training samples from $13$ different generative models and found that the best-performing ones often memorize the training samples that contribute least to diversity. Our findings demonstrate that the Vendiscope can serve as a powerful tool for data-driven science.", "authors": ["Amey P. Pasarkar", "Adji Bousso Dieng"], "categories": ["cs.LG", "cond-mat.mtrl-sci", "cs.AI", "q-bio.QM"], "fields_of_study": ["Computer Science", "Physics", "Biology"], "published_date": "2025-02-15", "url": "https://arxiv.org/abs/2502.10828", "pdf_url": "https://arxiv.org/pdf/2502.10828v1", "arxiv_id": "2502.10828", "doi": "10.48550/arXiv.2502.10828", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2113}
{"id": "7b01cfab230a09c9412ee7f85dbfd89f3966a6fd5fe22a008e438f0f5c3e2b1d", "sources": ["arxiv", "semantic_scholar"], "title": "Modeling Time-evolving Causality over Data Streams", "abstract": "Given an extensive, semi-infinite collection of multivariate coevolving data sequences (e.g., sensor/web activity streams) whose observations influence each other, how can we discover the time-changing cause-and-effect relationships in co-evolving data streams? How efficiently can we reveal dynamical patterns that allow us to forecast future values? In this paper, we present a novel streaming method, ModePlait, which is designed for modeling such causal relationships (i.e., time-evolving causality) in multivariate co-evolving data streams and forecasting their future values. The solution relies on characteristics of the causal relationships that evolve over time in accordance with the dynamic changes of exogenous variables. ModePlait has the following properties: (a) Effective: it discovers the time-evolving causality in multivariate co-evolving data streams by detecting the transitions of distinct dynamical patterns adaptively. (b) Accurate: it enables both the discovery of time-evolving causality and the forecasting of future values in a streaming fashion. (c) Scalable: our algorithm does not depend on data stream length and thus is applicable to very large sequences. Extensive experiments on both synthetic and real-world datasets demonstrate that our proposed model outperforms state-of-the-art methods in terms of discovering the time-evolving causality as well as forecasting.", "authors": ["Naoki Chihara", "Yasuko Matsubara", "Ren Fujiwara", "Yasushi Sakurai"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-02-13", "url": "https://arxiv.org/abs/2502.08963", "pdf_url": "https://arxiv.org/pdf/2502.08963v1", "arxiv_id": "2502.08963", "doi": "10.1145/3690624.3709283", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Knowledge Discovery and Data Mining", "quality_score": 0.2113}
{"id": "6c8fac916ff4aedc4f79e22f3445c6869431bd3c208a7e9a1c83b972af12b211", "sources": ["arxiv", "semantic_scholar"], "title": "Data Sharing in the PRIMED Consortium: Design, implementation, and recommendations for future policymaking", "abstract": "Sharing diverse genomic and other biomedical datasets is critical to advance scientific discoveries and their equitable translation to improve human health. However, data sharing remains challenging in the context of legacy datasets, evolving policies, multi-institutional consortium science, and international stakeholders. The NIH-funded Polygenic Risk Methods in Diverse Populations (PRIMED) Consortium was established to improve the performance of polygenic risk estimates for a broad range of health and disease outcomes with global impacts. Improving polygenic risk score performance across genetically diverse populations requires access to large, diverse cohorts. We report on the design and implementation of data sharing policies and procedures developed in PRIMED to aggregate and analyze data from multiple, heterogeneous sources while adhering to existing data sharing policies for each integrated dataset. We describe two primary data sharing mechanisms: coordinated dbGaP applications and a Consortium Data Sharing Agreement, as well as provide alternatives when individual-level data cannot be shared within the Consortium (e.g., federated analyses). We also describe technical implementation of Consortium data sharing in the NHGRI Analysis Visualization and Informatics Lab-space (AnVIL) cloud platform, to share derived individual-level data, genomic summary results, and methods workflows with appropriate permissions. As a Consortium making secondary use of pre-existing data sources, we also discuss challenges and propose solutions for release of individual- and summary-level data products to the broader scientific community. We make recommendations for ongoing and future policymaking with the goal of informing future consortia and other research activities.", "authors": ["Johanna L. Smith", "Quenna Wong", "Whitney Hornsby", "Matthew P. Conomos", "Benjamin D. Heavner", "Iftikhar J. Kullo", "Bruce M. Psaty", "Stephen S. Rich", "Bamidele Tayo", "Pradeep Natarajan", "Sarah C. Nelson", "Polygenic Risk Methods in Diverse Populations PRIMED Consortium Data Sharing Working Group", "Polygenic Risk Methods in Diverse Populations PRIMED Consortium"], "categories": ["q-bio.OT"], "fields_of_study": ["Medicine", "Biology"], "published_date": "2025-02-12", "url": "https://arxiv.org/abs/2502.09351", "pdf_url": "https://arxiv.org/pdf/2502.09351v1", "arxiv_id": "2502.09351", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "8445f46fcfd1196c6d17499866571afe34e2dff722cb8d1ea3282019c3322545", "sources": ["arxiv", "semantic_scholar"], "title": "OpenGrok: Enhancing SNS Data Processing with Distilled Knowledge and Mask-like Mechanisms", "abstract": "This report details Lumen Labs' novel approach to processing Social Networking Service (SNS) data. We leverage knowledge distillation, specifically a simple distillation method inspired by DeepSeek-R1's CoT acquisition, combined with prompt hacking, to extract valuable training data from the Grok model. This data is then used to fine-tune a Phi-3-mini model, augmented with a mask-like mechanism specifically designed for handling the nuances of SNS data. Our method demonstrates state-of-the-art (SOTA) performance on several SNS data processing tasks, outperforming existing models like Grok, Phi-3, and GPT-4. We provide a comprehensive analysis of our approach, including mathematical formulations, engineering details, ablation studies, and comparative evaluations.", "authors": ["Lumen AI", "Zaozhuang No. 28 Middle School", "Shihao Ji", "Zihui Song", "Fucheng Zhong", "Jisen Jia", "Zhaobo Wu", "Zheyi Cao", "Tianhao Xu"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-02-11", "url": "https://arxiv.org/abs/2502.07312", "pdf_url": "https://arxiv.org/pdf/2502.07312v1", "arxiv_id": "2502.07312", "doi": "10.48550/arXiv.2502.07312", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0573}
{"id": "4c4add5d130ed1be5201e3510def21854ba87db0ca527db1c33898ad903d9883", "sources": ["arxiv", "semantic_scholar"], "title": "Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model", "abstract": "Differentially private (DP) synthetic data, which closely resembles the original private data while maintaining strong privacy guarantees, has become a key tool for unlocking the value of private data without compromising privacy. Recently, Private Evolution (PE) has emerged as a promising method for generating DP synthetic data. Unlike other training-based approaches, PE only requires access to inference APIs from foundation models, enabling it to harness the power of state-of-the-art (SoTA) models. However, a suitable foundation model for a specific private data domain is not always available. In this paper, we discover that the PE framework is sufficiently general to allow APIs beyond foundation models. In particular, we demonstrate that many SoTA data synthesizers that do not rely on neural networks--such as computer graphics-based image generators, which we refer to as simulators--can be effectively integrated into PE. This insight significantly broadens PE's applicability and unlocks the potential of powerful simulators for DP data synthesis. We explore this approach, named Sim-PE, in the context of image synthesis. Across four diverse simulators, Sim-PE performs well, improving the downstream classification accuracy of PE by up to 3x, reducing FID by up to 80%, and offering much greater efficiency. We also show that simulators and foundation models can be easily leveraged together within PE to achieve further improvements. The code is open-sourced in the Private Evolution Python library: https://github.com/microsoft/DPSDA.", "authors": ["Zinan Lin", "Tadas Baltrusaitis", "Wenyu Wang", "Sergey Yekhanin"], "categories": ["cs.LG", "cs.CR", "cs.CV", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2025-02-08", "url": "https://arxiv.org/abs/2502.05505", "pdf_url": "https://arxiv.org/pdf/2502.05505v3", "arxiv_id": "2502.05505", "doi": "10.48550/arXiv.2502.05505", "citation_count": 12, "influential_citation_count": 2, "has_code": true, "code_url": "https://github.com/microsoft/DPSDA", "venue": "arXiv.org", "quality_score": 0.2785}
{"id": "6a6b830ec48d5c6d24fbd9ce61874d6563c2e43ad5c6689ea0db924c7b4c1f9f", "sources": ["arxiv", "semantic_scholar"], "title": "Knowledge Distillation from Large Language Models for Household Energy Modeling", "abstract": "Machine learning (ML) is increasingly vital for smart-grid research, yet restricted access to realistic, diverse data - often due to privacy concerns - slows progress and fuels doubts within the energy sector about adopting ML-based strategies. We propose integrating Large Language Models (LLMs) in energy modeling to generate realistic, culturally sensitive, and behavior-specific data for household energy usage across diverse geographies. In this study, we employ and compare five different LLMs to systematically produce family structures, weather patterns, and daily consumption profiles for households in six distinct countries. A four-stage methodology synthesizes contextual daily data, including culturally nuanced activities, realistic weather ranges, HVAC operations, and distinct `energy signatures' that capture unique consumption footprints. Additionally, we explore an alternative strategy where external weather datasets can be directly integrated, bypassing intermediate weather modeling stages while ensuring physically consistent data inputs. The resulting dataset provides insights into how cultural, climatic, and behavioral factors converge to shape carbon emissions, offering a cost-effective avenue for scenario-based energy optimization. This approach underscores how prompt engineering, combined with knowledge distillation, can advance sustainable energy research and climate mitigation efforts. Source code is available at https://github.com/Singularity-AI-Lab/LLM-Energy-Knowledge-Distillation .", "authors": ["Mohannad Takrouri", "Nicolás M. Cuadrado", "Martin Takáč"], "categories": ["cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-02-05", "url": "https://arxiv.org/abs/2502.03034", "pdf_url": "https://arxiv.org/pdf/2502.03034v1", "arxiv_id": "2502.03034", "doi": "10.48550/arXiv.2502.03034", "citation_count": 2, "influential_citation_count": 2, "has_code": true, "code_url": "https://github.com/Singularity-AI-Lab/LLM-Energy-Knowledge-Distillation", "venue": "arXiv.org", "quality_score": 0.2386}
{"id": "c0e4469b2730c21d6f0afe6588192345ca829d6fe22ce754da6735d80390df32", "sources": ["arxiv", "semantic_scholar"], "title": "LeaFi: Data Series Indexes on Steroids with Learned Filters", "abstract": "The ever-growing collections of data series create a pressing need for efficient similarity search, which serves as the backbone for various analytics pipelines. Recent studies have shown that tree-based series indexes excel in many scenarios. However, we observe a significant waste of effort during search, due to suboptimal pruning. To address this issue, we introduce LeaFi, a novel framework that uses machine learning models to boost pruning effectiveness of tree-based data series indexes. These models act as learned filters, which predict tight node-wise distance lower bounds that are used to make pruning decisions, thus, improving pruning effectiveness. We describe the LeaFi-enhanced index building algorithm, which selects leaf nodes and generates training data to insert and train machine learning models, as well as the LeaFi-enhanced search algorithm, which calibrates learned filters at query time to support the user-defined quality target of each query. Our experimental evaluation, using two different tree-based series indexes and five diverse datasets, demonstrates the advantages of the proposed approach. LeaFi-enhanced data-series indexes improve pruning ratio by up to 20x and search time by up to 32x, while maintaining a target recall of 99%.", "authors": ["Qitong Wang", "Ioana Ileana", "Themis Palpanas"], "categories": ["cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2025-02-03", "url": "https://arxiv.org/abs/2502.01836", "pdf_url": "https://arxiv.org/pdf/2502.01836v1", "arxiv_id": "2502.01836", "doi": "10.1145/3709701", "citation_count": 11, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Proc. ACM Manag. Data 3, N1 (SIGMOD), Article 51 (February 2025), 24 pages", "quality_score": 0.2698}
{"id": "234776c68e718f71756532cc93d52839147737de1742b91cef65d86b2e8eb54f", "sources": ["arxiv", "semantic_scholar"], "title": "Role of Mixup in Topological Persistence Based Knowledge Distillation for Wearable Sensor Data", "abstract": "The analysis of wearable sensor data has enabled many successes in several applications. To represent the high-sampling rate time-series with sufficient detail, the use of topological data analysis (TDA) has been considered, and it is found that TDA can complement other time-series features. Nonetheless, due to the large time consumption and high computational resource requirements of extracting topological features through TDA, it is difficult to deploy topological knowledge in various applications. To tackle this problem, knowledge distillation (KD) can be adopted, which is a technique facilitating model compression and transfer learning to generate a smaller model by transferring knowledge from a larger network. By leveraging multiple teachers in KD, both time-series and topological features can be transferred, and finally, a superior student using only time-series data is distilled. On the other hand, mixup has been popularly used as a robust data augmentation technique to enhance model performance during training. Mixup and KD employ similar learning strategies. In KD, the student model learns from the smoothed distribution generated by the teacher model, while mixup creates smoothed labels by blending two labels. Hence, this common smoothness serves as the connecting link that establishes a connection between these two methods. In this paper, we analyze the role of mixup in KD with time-series as well as topological persistence, employing multiple teachers. We present a comprehensive analysis of various methods in KD and mixup on wearable sensor data.", "authors": ["Eun Som Jeon", "Hongjun Choi", "Matthew P. Buman", "Pavan Turaga"], "categories": ["cs.LG", "cs.AI", "eess.SP"], "fields_of_study": ["Computer Science", "Engineering", "Medicine"], "published_date": "2025-02-02", "url": "https://arxiv.org/abs/2502.00779", "pdf_url": "https://arxiv.org/pdf/2502.00779v1", "arxiv_id": "2502.00779", "doi": "10.1109/JSEN.2024.3517653", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Sensors Journal", "quality_score": 0.047}
{"id": "9f3ad9e50de213415a604c4104b5d1982f7c33db4d886ff499185c89904ce057", "sources": ["arxiv", "semantic_scholar"], "title": "RADx Data Hub: A Cloud Platform for FAIR, Harmonized COVID-19 Data", "abstract": "The COVID-19 pandemic highlighted the urgent need for robust systems to enable rapid data collection, integration, and analysis for public health responses. Existing approaches often relied on disparate, non-interoperable systems, creating bottlenecks in comprehensive analyses and timely decision-making. To address these challenges, the U.S. National Institutes of Health (NIH) launched the Rapid Acceleration of Diagnostics (RADx) initiative in 2020, with the RADx Data Hub, a centralized repository for de-identified and curated COVID-19 data, as its cornerstone. The RADx Data Hub hosts diverse study data, including clinical data, testing results, smart sensor outputs, self-reported symptoms, and information on social determinants of health. Built on cloud infrastructure, the RADx Data Hub integrates metadata standards, interoperable formats, and ontology-based tools to adhere to the FAIR (Findable, Accessible, Interoperable, Reusable) principles for data sharing. Initially developed for COVID-19 research, its architecture and processes are adaptable to other scientific disciplines. This paper provides an overview of the data hosted by the RADx Data Hub and describes the platform's capabilities and architecture.", "authors": ["Marcos Martinez-Romero", "Matthew Horridge", "Nilesh Mistry", "Aubrie Weyhmiller", "Jimmy K. Yu", "Alissa Fujimoto", "Aria Henry", "Martin J. O'Connor", "Ashley Sier", "Stephanie Suber", "Mete U. Akdogan", "Yan Cao", "Somu Valliappan", "Joanna O. Mieczkowska", "the RADx Data Hub team", "Ashok Krishnamurthy", "Michael A. Keller", "Mark A. Musen"], "categories": ["cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2025-02-01", "url": "https://arxiv.org/abs/2502.00265", "pdf_url": "https://arxiv.org/pdf/2502.00265v3", "arxiv_id": "2502.00265", "doi": "10.48550/arXiv.2502.00265", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "b5ad8c876602d6328373b0e8da72baea38ce14ae10ce0aad30f951e3d11d9a23", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetic Data Generation for Augmenting Small Samples", "abstract": "Small datasets are common in health research. However, the generalization performance of machine learning models is suboptimal when the training datasets are small. To address this, data augmentation is one solution. Augmentation increases sample size and is seen as a form of regularization that increases the diversity of small datasets, leading them to perform better on unseen data. We found that augmentation improves prognostic performance for datasets that: have fewer observations, with smaller baseline AUC, have higher cardinality categorical variables, and have more balanced outcome variables. No specific generative model consistently outperformed the others. We developed a decision support model that can be used to inform analysts if augmentation would be useful. For seven small application datasets, augmenting the existing data results in an increase in AUC between 4.31% (AUC from 0.71 to 0.75) and 43.23% (AUC from 0.51 to 0.73), with an average 15.55% relative improvement, demonstrating the nontrivial impact of augmentation on small datasets (p=0.0078). Augmentation AUC was higher than resampling only AUC (p=0.016). The diversity of augmented datasets was higher than the diversity of resampled datasets (p=0.046).", "authors": ["Dan Liu", "Samer El Kababji", "Nicholas Mitsakakis", "Lisa Pilgram", "Thomas Walters", "Mark Clemons", "Greg Pond", "Alaa El-Hussuna", "Khaled El Emam"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2025-01-30", "url": "https://arxiv.org/abs/2501.18741", "pdf_url": "https://arxiv.org/pdf/2501.18741v1", "arxiv_id": "2501.18741", "doi": "10.48550/arXiv.2501.18741", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2386}
{"id": "9b0c5ec314f82812399d86824db309753b44a663edafa289038718abe5cecfd0", "sources": ["arxiv", "semantic_scholar"], "title": "How an AI-ready National Data Library would help UK science", "abstract": "In this paper, we provide a technical vision for key enabling elements for the architecture of the UK National Data Library (NDL) with a strong focus on building it as an AI-ready data infrastructure through standardised vocabularies, automated analysis tools, and interoperability services. We follow the ODI Multilayer Interoperability Framework (MIF) for data stewardship, covering central socio-technical aspects for the NDL including user-centric approaches to service design and governance.", "authors": ["Albert Meroño-Peñuela", "Joe Massey", "Andrew Newman", "Elena Simperl"], "categories": ["cs.DL"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-28", "url": "https://arxiv.org/abs/2501.17013", "pdf_url": "https://arxiv.org/pdf/2501.17013v1", "arxiv_id": "2501.17013", "doi": "10.48550/arXiv.2501.17013", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "5c85a73a5521a019b44beb133345b4a97e4d4ef6da2c8387d7e4f977460af558", "sources": ["arxiv", "semantic_scholar"], "title": "Descriptor: Five years of meteorological surface data at Oak Ridge Reserve in Tennessee", "abstract": "Access to continuous, quality assessed meteorological data is critical for understanding the climatology and atmospheric dynamics of a region. Research facilities like Oak Ridge National Laboratory (ORNL) rely on such data to assess site-specific climatology, model potential emissions, establish safety baselines, and prepare for emergency scenarios. To meet these needs, on-site towers at ORNL collect meteorological data at 15-minute and hourly intervals. However, data measurements from meteorological towers are affected by sensor sensitivity, degradation, lightning strikes, power fluctuations, glitching, and sensor failures, all of which can affect data quality. To address these challenges, we conducted a comprehensive quality assessment and processing of five years of meteorological data collected from ORNL at 15-minute intervals, including measurements of temperature, pressure, humidity, wind, and solar radiation. The time series of each variable was pre-processed and gap-filled using established meteorological data collection and cleaning techniques, i.e., the time series were subjected to structural standardization, data integrity testing, automated and manual outlier detection, and gap-filling. The data product and highly generalizable processing workflow developed in Python Jupyter notebooks are publicly accessible online. As a key contribution of this study, the evaluated 5-year data will be used to train atmospheric dispersion models that simulate dispersion dynamics across the complex ridge-and-valley topography of the Oak Ridge Reservation in East Tennessee.", "authors": ["Morgan R. Steckler", "Kevin R. Birdwell", "Haowen Xu", "Xiao-Ying Yu"], "categories": ["physics.ao-ph"], "fields_of_study": ["Physics"], "published_date": "2025-01-27", "url": "https://arxiv.org/abs/2502.05191", "pdf_url": "https://arxiv.org/pdf/2502.05191v1", "arxiv_id": "2502.05191", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0255}
{"id": "a8ebf6f2a906795d934cbfe491bd4683d30ab92af721b42cf162337a138973c6", "sources": ["arxiv", "semantic_scholar"], "title": "TrustDataFilter:Leveraging Trusted Knowledge Base Data for More Effective Filtering of Unknown Information", "abstract": "With the advancement of technology and changes in the market, the demand for the construction of domain-specific knowledge bases has been increasing, either to improve model performance or to promote enterprise innovation and competitiveness. The construction of domain-specific knowledge bases typically relies on web crawlers or existing industry databases, leading to problems with accuracy and consistency of the data. To address these challenges, we considered the characteristics of domain data, where internal knowledge is interconnected, and proposed the Self-Natural Language Inference Data Filtering (self-nli-TDF) framework. This framework compares trusted filtered knowledge with the data to be filtered, deducing the reasoning relationship between them, thus improving filtering performance. The framework uses plug-and-play large language models for trustworthiness assessment and employs the RoBERTa-MNLI model from the NLI domain for reasoning. We constructed three datasets in the domains of biology, radiation, and science, and conducted experiments using RoBERTa, GPT3.5, and the local Qwen2 model. The experimental results show that this framework improves filter quality, producing more consistent and reliable filtering results.", "authors": ["Jinghong Zhang", "Yidong Cui", "Weiling Wang", "Xianyou Cheng"], "categories": ["cs.IR", "cs.AI", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-25", "url": "https://arxiv.org/abs/2502.15714", "pdf_url": "https://arxiv.org/pdf/2502.15714v1", "arxiv_id": "2502.15714", "doi": "10.48550/arXiv.2502.15714", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0378}
{"id": "83d991e58cd68d2cf100b09ff4da9c79747fbee2c863b3bcc462453fae481244", "sources": ["arxiv", "semantic_scholar"], "title": "Experimentally Evaluating the Resource Efficiency of Big Data Autoscaling", "abstract": "Distributed dataflow systems like Spark and Flink enable data-parallel processing of large datasets on clusters. Yet, selecting appropriate computational resources for dataflow jobs is often challenging. For efficient execution, individual resource allocations, such as memory and CPU cores, must meet the specific resource requirements of the job. An alternative to selecting a static resource allocation for a job execution is autoscaling as implemented for example by Spark. In this paper, we evaluate the resource efficiency of autoscaling batch data processing jobs based on resource demand both conceptually and experimentally by analyzing a new dataset of Spark job executions on Google Dataproc Serverless. In our experimental evaluation, we show that there is no significant resource efficiency gain over static resource allocations. We found that the inherent conceptual limitations of such autoscaling approaches are the inelasticity of node size as well as the inelasticity of the ratio of memory to CPU cores.", "authors": ["Jonathan Will", "Nico Treide", "Lauritz Thamsen", "Odej Kao"], "categories": ["cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-24", "url": "https://arxiv.org/abs/2501.14456", "pdf_url": "https://arxiv.org/pdf/2501.14456v1", "arxiv_id": "2501.14456", "doi": "10.1109/BigData62323.2024.10825367", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "BigData Congress [Services Society]", "quality_score": 0.0753}
{"id": "03edbaf9a37b4f48fdc9d302c61f7a1cd02b2a25b71270094bc4dcfce4a4b03e", "sources": ["arxiv", "semantic_scholar"], "title": "Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement", "abstract": "This paper presents a new challenge that calls for zero-shot text-to-speech (TTS) systems to augment speech data for the downstream task, personalized speech enhancement (PSE), as part of the Generative Data Augmentation workshop at ICASSP 2025. Collecting high-quality personalized data is challenging due to privacy concerns and technical difficulties in recording audio from the test scene. To address these issues, synthetic data generation using generative models has gained significant attention. In this challenge, participants are tasked first with building zero-shot TTS systems to augment personalized data. Subsequently, PSE systems are asked to be trained with this augmented personalized dataset. Through this challenge, we aim to investigate how the quality of augmented data generated by zero-shot TTS models affects PSE model performance. We also provide baseline experiments using open-source zero-shot TTS models to encourage participation and benchmark advancements. Our baseline code implementation and checkpoints are available online.", "authors": ["Jae-Sung Bae", "Anastasia Kuznetsova", "Dinesh Manocha", "John Hershey", "Trausti Kristjansson", "Minje Kim"], "categories": ["eess.AS", "cs.AI"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2025-01-23", "url": "https://arxiv.org/abs/2501.13372", "pdf_url": "https://arxiv.org/pdf/2501.13372v1", "arxiv_id": "2501.13372", "doi": "10.1109/ICASSPW65056.2025.11011159", "citation_count": 6, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": null, "quality_score": 0.2113}
{"id": "aabf84c46346a3b946dae4f2673575876963f0333fbc91b24bddc907c438d89f", "sources": ["arxiv", "semantic_scholar"], "title": "AdEval: Alignment-based Dynamic Evaluation to Mitigate Data Contamination in Large Language Models", "abstract": "As Large Language Models (LLMs) are pre-trained on ultra-large-scale corpora, the problem of data contamination is becoming increasingly serious, and there is a risk that static evaluation benchmarks overestimate the performance of LLMs. To address this, this paper proposes a dynamic data evaluation method called AdEval (Alignment-based Dynamic Evaluation). AdEval first extracts knowledge points and main ideas from static datasets to achieve dynamic alignment with the core content of static benchmarks, and by avoiding direct reliance on static datasets, it inherently reduces the risk of data contamination from the source. It then obtains background information through online searches to generate detailed descriptions of the knowledge points. Finally, it designs questions based on Bloom's cognitive hierarchy across six dimensions-remembering, understanding, applying, analyzing, evaluating, and creating to enable multi-level cognitive assessment. Additionally, AdEval controls the complexity of dynamically generated datasets through iterative question reconstruction. Experimental results on multiple datasets show that AdEval effectively alleviates the impact of data contamination on evaluation results, solves the problems of insufficient complexity control and single-dimensional evaluation, and improves the fairness, reliability and diversity of LLMs evaluation.", "authors": ["Yang Fan"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-23", "url": "https://arxiv.org/abs/2501.13983", "pdf_url": "https://arxiv.org/pdf/2501.13983v5", "arxiv_id": "2501.13983", "doi": "10.48550/arXiv.2501.13983", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "2cecc43b66c190365c97373d208e9108ee28fdc6970c925da90e04997fe8ddeb", "sources": ["arxiv", "semantic_scholar"], "title": "Generative Data Augmentation Challenge: Synthesis of Room Acoustics for Speaker Distance Estimation", "abstract": "This paper describes the synthesis of the room acoustics challenge as a part of the generative data augmentation workshop at ICASSP 2025. The challenge defines a unique generative task that is designed to improve the quantity and diversity of the room impulse responses dataset so that it can be used for spatially sensitive downstream tasks: speaker distance estimation. The challenge identifies the technical difficulty in measuring or simulating many rooms' acoustic characteristics precisely. As a solution, it proposes generative data augmentation as an alternative that can potentially be used to improve various downstream tasks. The challenge website, dataset, and evaluation code are available at https://sites.google.com/view/genda2025.", "authors": ["Jackie Lin", "Georg Götz", "Hermes Sampedro Llopis", "Haukur Hafsteinsson", "Steinar Guðjónsson", "Daniel Gert Nielsen", "Finnur Pind", "Paris Smaragdis", "Dinesh Manocha", "John Hershey", "Trausti Kristjansson", "Minje Kim"], "categories": ["eess.AS", "cs.SD"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2025-01-22", "url": "https://arxiv.org/abs/2501.13250", "pdf_url": "https://arxiv.org/pdf/2501.13250v1", "arxiv_id": "2501.13250", "doi": "10.1109/ICASSPW65056.2025.11011109", "citation_count": 11, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2698}
{"id": "f2ceafaecc05b01e458073934e44a15bd45c221639a8e5139ad4a7ff9be93563", "sources": ["arxiv", "semantic_scholar"], "title": "Revisiting Data Augmentation for Ultrasound Images", "abstract": "Data augmentation is a widely used and effective technique to improve the generalization performance of deep neural networks. Yet, despite often facing limited data availability when working with medical images, it is frequently underutilized. This appears to come from a gap in our collective understanding of the efficacy of different augmentation techniques across different tasks and modalities. One modality where this is especially true is ultrasound imaging. This work addresses this gap by analyzing the effectiveness of different augmentation techniques at improving model performance across a wide range of ultrasound image analysis tasks. To achieve this, we introduce a new standardized benchmark of 14 ultrasound image classification and semantic segmentation tasks from 10 different sources and covering 11 body regions. Our results demonstrate that many of the augmentations commonly used for tasks on natural images are also effective on ultrasound images, even more so than augmentations developed specifically for ultrasound images in some cases. We also show that diverse augmentation using TrivialAugment, which is widely used for natural images, is also effective for ultrasound images. Moreover, our proposed methodology represents a structured approach for assessing various data augmentations that can be applied to other contexts and modalities.", "authors": ["Adam Tupper", "Christian Gagné"], "categories": ["eess.IV", "cs.CV"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2025-01-22", "url": "https://arxiv.org/abs/2501.13193", "pdf_url": "https://arxiv.org/pdf/2501.13193v2", "arxiv_id": "2501.13193", "doi": "10.48550/arXiv.2501.13193", "citation_count": 12, "influential_citation_count": 2, "has_code": true, "code_url": "https://github.com/adamtupper/ultrasound-augmentation", "venue": null, "quality_score": 0.2785}
{"id": "e5e96b6ba4670c649947083287ea7208c210f704a34df05206e978370b746c5f", "sources": ["arxiv", "semantic_scholar"], "title": "A systematic data characteristic understanding framework towards physical-sensor big data challenges", "abstract": "Big data present new opportunities for modern society while posing challenges for data scientists. Recent advancements in sensor networks and the widespread adoption of IoT have led to the collection of physical-sensor data on an enormous scale. However, significant challenges arise in high-quality big data analytics. To uncover big data challenges and enhance data quality, it is essential to quantitatively unveil data characteristics. Furthermore, the existing studies lack analysis of the specific time-related characteristics. Enhancing the efficiency and precision of data analytics through the big data lifecycle requires a comprehensive understanding of data characteristics to address the hidden big data challenges. To fill in the research gap, this paper proposes a systematic data characteristic framework based on a 6Vs model. The framework aims to unveil the data characteristics in terms of data volume, variety, velocity, veracity, value, and variability through a set of statistical indicators. This model improves the objectivity of data characteristic understanding by relying solely on data-driven indicators. The indicators related to time-related characteristics in physical-sensor data are also included. Furthermore, the big data challenges are linked to each dimension of the 6Vs model to gain a quantitative understanding of the data challenges. Finally, a pipeline is developed to implement the proposed framework, and two case studies are conducted to illustrate the process of understanding the physical-sensor data characteristics and making recommendations for data preprocessing to address the big data challenges. The proposed framework is able to analyze the characteristics of all physical-sensor data, therefore, identifying potential challenges in subsequent analytics, and providing recommendations for data preprocessing.", "authors": ["Zhipeng Ma", "Bo Nørregaard Jørgensen", "Zheng Grace Ma"], "categories": ["cs.IR"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-22", "url": "https://arxiv.org/abs/2501.12720", "pdf_url": "https://arxiv.org/pdf/2501.12720v1", "arxiv_id": "2501.12720", "doi": "10.1186/s40537-024-00942-5", "citation_count": 16, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Journal of Big Data", "quality_score": 0.3076}
{"id": "620f3e089cbec061ccbe35e56542c5a34881e7a7e307acda7bc6398e6afe35de", "sources": ["arxiv", "semantic_scholar"], "title": "How Does the Spatial Distribution of Pre-training Data Affect Geospatial Foundation Models?", "abstract": "Foundation models have made rapid advances in many domains including Earth observation, where Geospatial Foundation Models (GFMs) can help address global challenges such as climate change, agriculture, and disaster response. Previous work on GFMs focused on tailoring model architecture and pre-text tasks, and did not investigate the impact of pre-training data selection on model performance. However, recent works from other domains show that the pre-training data distribution is an important factor influencing the performance of the foundation models. With this motivation, our research explores how the geographic distribution of pre-training data affects the performance of GFMs. We evaluated several pre-training data distributions by sampling different compositions from a global data pool. Our experiments with two GFMs on downstream tasks indicate that balanced and globally representative data compositions often outperform region-specific sampling, highlighting the importance of diversity and global coverage in pre-training data. Our results suggest that the most appropriate data sampling technique may depend on the specific GFM architecture. These findings will support the development of robust GFMs by incorporating quality pre-training data distributions, ultimately improving machine learning solutions for Earth observation.", "authors": ["Mirali Purohit", "Gedeon Muhawenayo", "Esther Rolf", "Hannah Kerner"], "categories": ["cs.LG", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-21", "url": "https://arxiv.org/abs/2501.12535", "pdf_url": "https://arxiv.org/pdf/2501.12535v1", "arxiv_id": "2501.12535", "doi": "10.48550/arXiv.2501.12535", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2258}
{"id": "23840bba8b31d9d60f831c9c73e337217cbe238473bbfea702af0cfe7d84cc4a", "sources": ["arxiv", "semantic_scholar"], "title": "Multi-Modality Transformer for E-Commerce: Inferring User Purchase Intention to Bridge the Query-Product Gap", "abstract": "E-commerce click-stream data and product catalogs offer critical user behavior insights and product knowledge. This paper propose a multi-modal transformer termed as PINCER, that leverages the above data sources to transform initial user queries into pseudo-product representations. By tapping into these external data sources, our model can infer users' potential purchase intent from their limited queries and capture query relevant product features. We demonstrate our model's superior performance over state-of-the-art alternatives on e-commerce online retrieval in both controlled and real-world experiments. Our ablation studies confirm that the proposed transformer architecture and integrated learning strategies enable the mining of key data sources to infer purchase intent, extract product features, and enhance the transformation pipeline from queries to more accurate pseudo-product representations.", "authors": ["Srivatsa Mallapragada", "Ying Xie", "Varsha Rani Chawan", "Zeyad Hailat", "Yuanbo Wang"], "categories": ["cs.IR", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-21", "url": "https://arxiv.org/abs/2501.14826", "pdf_url": "https://arxiv.org/pdf/2501.14826v1", "arxiv_id": "2501.14826", "doi": "10.1109/BigData62323.2024.10826020", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "BigData Congress [Services Society]", "quality_score": 0.1505}
{"id": "47dacf21c3396cec5046357023024e5dbf0e6862d833081503eeaa805defda88", "sources": ["arxiv", "semantic_scholar"], "title": "Automating RT Planning at Scale: High Quality Data For AI Training", "abstract": "Radiotherapy (RT) planning is complex, subjective, and time-intensive. Advances with artificial intelligence (AI) promise to improve its precision and efficiency, but progress is often limited by the scarcity of large, standardized datasets. To address this, we introduce the Automated Iterative RT Planning (AIRTP) system, a scalable solution for generating high-quality treatment plans. This scalable solution is designed to generate substantial volumes of consistently high-quality treatment plans, overcoming a key obstacle in the advancement of AI-driven RT planning. Our AIRTP pipeline adheres to clinical guidelines and automates essential steps, including organ-at-risk (OAR) contouring, helper structure creation, beam setup, optimization, and plan quality improvement, using AI integrated with RT planning software like Varian Eclipse. Furthermore, a novel approach for determining optimization parameters to reproduce 3D dose distributions, i.e. a method to convert dose predictions to deliverable treatment plans constrained by machine limitations is proposed. A comparative analysis of plan quality reveals that our automated pipeline produces treatment plans of quality comparable to those generated manually, which traditionally require several hours of labor per plan. Committed to public research, the first data release of our AIRTP pipeline includes nine cohorts covering head-and-neck and lung cancer sites to support an AAPM 2025 challenge. To our best knowledge, this dataset features more than 10 times number of plans compared to the largest existing well-curated public dataset. Repo: https://github.com/RiqiangGao/GDP-HMM_AAPMChallenge.", "authors": ["Riqiang Gao", "Mamadou Diallo", "Han Liu", "Anthony Magliari", "Jonathan Sackett", "Wilko Verbakel", "Sandra Meyers", "Rafe Mcbeth", "Masoud Zarepisheh", "Simon Arberet", "Martin Kraus", "Florin C. Ghesu", "Ali Kamen"], "categories": ["cs.HC", "cs.LG", "cs.RO"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-21", "url": "https://arxiv.org/abs/2501.11803", "pdf_url": "https://arxiv.org/pdf/2501.11803v5", "arxiv_id": "2501.11803", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/RiqiangGao/GDP-HMM_AAPMChallenge", "venue": null, "quality_score": 0.0753}
{"id": "20d1ceef6be04271049cd8fa068c9bfe4153ac97674f15fe6174139796e0fc32", "sources": ["arxiv", "semantic_scholar"], "title": "Adaptive Hoeffding Tree with Transfer Learning for Streaming Synchrophasor Data Sets", "abstract": "Synchrophasor technology or phasor measurement units (PMUs) are known to detect multiple type of oscillations or faults better than Supervisory Control and Data Acquisition (SCADA) systems, but the volume of Bigdata (e.g., 30-120 samples per second on a single PMU) generated by these sensors at the aggregator level (e.g., several PMUs) requires special handling. Conventional machine learning or data mining methods are not suitable to handle such larger streaming realtime data. This is primarily due to latencies associated with cloud environments (e.g., at an aggregator or PDC level), and thus necessitates the need for local computing to move the data on the edge (or locally at the PMU level) for processing. This requires faster real-time streaming algorithms to be processed at the local level (e.g., typically by a Field Programmable Gate Array (FPGA) based controllers). This paper proposes a transfer learning-based hoeffding tree with ADWIN (THAT) method to detect anomalous synchrophasor signatures. The proposed algorithm is trained and tested with the OzaBag method. The preliminary results with transfer learning indicate that a computational time saving of 0.7ms is achieved with THAT algorithm (0.34ms) over Ozabag (1.04ms), while the accuracy of both methods in detecting fault events remains at 94% for four signatures.", "authors": ["Zakaria El Mrabet", "Daisy Flora Selvaraj", "Prakash Ranganathan"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-19", "url": "https://arxiv.org/abs/2501.16354", "pdf_url": "https://arxiv.org/pdf/2501.16354v1", "arxiv_id": "2501.16354", "doi": "10.1109/BigData47090.2019.9005720", "citation_count": 15, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "2019 IEEE International Conference on Big Data (Big Data)", "quality_score": 0.301}
{"id": "2fbfb7d7e9258344fa0622d2b2165d63f1e53a5d6707455c9d0918a4cef9db15", "sources": ["arxiv", "semantic_scholar"], "title": "Metrics for Inter-Dataset Similarity with Example Applications in Synthetic Data and Feature Selection Evaluation -- Extended Version", "abstract": "Measuring inter-dataset similarity is an important task in machine learning and data mining with various use cases and applications. Existing methods for measuring inter-dataset similarity are computationally expensive, limited, or sensitive to different entities and non-trivial choices for parameters. They also lack a holistic perspective on the entire dataset. In this paper, we propose two novel metrics for measuring inter-dataset similarity. We discuss the mathematical foundation and the theoretical basis of our proposed metrics. We demonstrate the effectiveness of the proposed metrics by investigating two applications in the evaluation of synthetic data and in the evaluation of feature selection methods. The theoretical and empirical studies conducted in this paper illustrate the effectiveness of the proposed metrics.", "authors": ["Muhammad Rajabinasab", "Anton D. Lautrup", "Arthur Zimek"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-16", "url": "https://arxiv.org/abs/2501.09591", "pdf_url": "https://arxiv.org/pdf/2501.09591v1", "arxiv_id": "2501.09591", "doi": "10.1137/1.9781611978520.57", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "SDM", "quality_score": 0.1747}
{"id": "0b806a5fc6f993790e24273c1f09b0cf70cd3fdbb406035f1bef2caac55f92b1", "sources": ["arxiv", "semantic_scholar"], "title": "Enhancing Data Integrity through Provenance Tracking in Semantic Web Frameworks", "abstract": "This paper explores the integration of provenance tracking systems within the context of Semantic Web technologies to enhance data integrity in diverse operational environments. SURROUND Australia Pty Ltd demonstrates innovative applica-tions of the PROV Data Model (PROV-DM) and its Semantic Web variant, PROV-O, to systematically record and manage provenance information across multiple data processing domains. By employing RDF and Knowledge Graphs, SURROUND ad-dresses the critical challenges of shared entity identification and provenance granularity. The paper highlights the company's architecture for capturing comprehensive provenance data, en-abling robust validation, traceability, and knowledge inference. Through the examination of two projects, we illustrate how provenance mechanisms not only improve data reliability but also facilitate seamless integration across heterogeneous systems. Our findings underscore the importance of sophisticated provenance solutions in maintaining data integrity, serving as a reference for industry peers and academics engaged in provenance research and implementation.", "authors": ["Nilesh Jain"], "categories": ["cs.CR", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-12", "url": "https://arxiv.org/abs/2501.09029", "pdf_url": "https://arxiv.org/pdf/2501.09029v1", "arxiv_id": "2501.09029", "doi": "10.48550/arXiv.2501.09029", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "822eeabcc40ffdcc8a533faad01aad2b2fc7e9642774793ca2cb9dfa6b3b962e", "sources": ["arxiv", "semantic_scholar"], "title": "Automating Date Format Detection for Data Visualization", "abstract": "Data preparation, specifically date parsing, is a significant bottleneck in analytic workflows. To address this, we present two algorithms, one based on minimum entropy and the other on natural language modeling that automatically derive date formats from string data. These algorithms achieve over 90% accuracy on a large corpus of data columns, streamlining the data preparation process within visualization environments. The minimal entropy approach is particularly fast, providing interactive feedback. Our methods simplify date format extraction, making them suitable for integration into data visualization tools and databases.", "authors": ["Zixuan Liang"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-10", "url": "https://arxiv.org/abs/2501.05640", "pdf_url": "https://arxiv.org/pdf/2501.05640v1", "arxiv_id": "2501.05640", "doi": "10.1109/AMLDS63918.2025.11159385", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2113}
{"id": "9d636ef2d3b99fc82dd510ce72df5323629d8dda026ddf28b5ebef443d30a37c", "sources": ["arxiv"], "title": "Inside Out: Externalizing Assumptions in Data Analysis as Validation Checks", "abstract": "In data analysis, unexpected results often prompt researchers to revisit their procedures to identify potential issues. While some researchers may struggle to identify the root causes, experienced researchers can often quickly diagnose problems by checking a few key assumptions. These checked assumptions, or expectations, are typically informal, difficult to trace, and rarely discussed in publications. In this paper, we introduce the term *analysis validation checks* to formalize and externalize these informal assumptions. We then introduce a procedure to identify a subset of checks that best predict the occurrence of unexpected outcomes, based on simulations of the original data. The checks are evaluated in terms of accuracy, determined by binary classification metrics, and independence, which measures the shared information among checks. We demonstrate this approach with a toy example using step count data and a generalized linear model example examining the effect of particulate matter air pollution on daily mortality.", "authors": ["H. Sherry Zhang", "Roger D. Peng"], "categories": ["stat.ME"], "fields_of_study": [], "published_date": "2025-01-08", "url": "https://arxiv.org/abs/2501.04296", "pdf_url": "https://arxiv.org/pdf/2501.04296v2", "arxiv_id": "2501.04296", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Journal of Data Science, 2026", "quality_score": 0.0183}
{"id": "0bbd3353a06d5152ba522677a6763aeb33a74078adc4b45136b306ce169ebdc8", "sources": ["arxiv", "semantic_scholar"], "title": "Can Synthetic Data be Fair and Private? A Comparative Study of Synthetic Data Generation and Fairness Algorithms", "abstract": "The increasing use of machine learning in learning analytics (LA) has raised significant concerns around algorithmic fairness and privacy. Synthetic data has emerged as a dual-purpose tool, enhancing privacy and improving fairness in LA models. However, prior research suggests an inverse relationship between fairness and privacy, making it challenging to optimize both. This study investigates which synthetic data generators can best balance privacy and fairness, and whether pre-processing fairness algorithms, typically applied to real datasets, are effective on synthetic data. Our results highlight that the DEbiasing CAusal Fairness (DECAF) algorithm achieves the best balance between privacy and fairness. However, DECAF suffers in utility, as reflected in its predictive accuracy. Notably, we found that applying pre-processing fairness algorithms to synthetic data improves fairness even more than when applied to real data. These findings suggest that combining synthetic data generation with fairness pre-processing offers a promising approach to creating fairer LA models.", "authors": ["Qinyi Liu", "Oscar Deho", "Sam Urmian", "Mohammad Khalil", "Srecko Joksimovic", "George Siemens"], "categories": ["cs.LG", "cs.AI", "cs.CY"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-03", "url": "https://arxiv.org/abs/2501.01785", "pdf_url": "https://arxiv.org/pdf/2501.01785v1", "arxiv_id": "2501.01785", "doi": "10.1145/3706468.3706546", "citation_count": 21, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Learning Analytics and Knowledge", "quality_score": 0.3356}
{"id": "6c3b3426add0097bac989a08af0231bb05aea548083879f51187cf726ab0c579", "sources": ["arxiv", "semantic_scholar"], "title": "A Novel Diffusion Model for Pairwise Geoscience Data Generation with Unbalanced Training Dataset", "abstract": "Recently, the advent of generative AI technologies has made transformational impacts on our daily lives, yet its application in scientific applications remains in its early stages. Data scarcity is a major, well-known barrier in data-driven scientific computing, so physics-guided generative AI holds significant promise. In scientific computing, most tasks study the conversion of multiple data modalities to describe physical phenomena, for example, spatial and waveform in seismic imaging, time and frequency in signal processing, and temporal and spectral in climate modeling; as such, multi-modal pairwise data generation is highly required instead of single-modal data generation, which is usually used in natural images (e.g., faces, scenery). Moreover, in real-world applications, the unbalance of available data in terms of modalities commonly exists; for example, the spatial data (i.e., velocity maps) in seismic imaging can be easily simulated, but real-world seismic waveform is largely lacking. While the most recent efforts enable the powerful diffusion model to generate multi-modal data, how to leverage the unbalanced available data is still unclear. In this work, we use seismic imaging in subsurface geophysics as a vehicle to present ``UB-Diff'', a novel diffusion model for multi-modal paired scientific data generation. One major innovation is a one-in-two-out encoder-decoder network structure, which can ensure pairwise data is obtained from a co-latent representation. Then, the co-latent representation will be used by the diffusion process for pairwise data generation. Experimental results on the OpenFWI dataset show that UB-Diff significantly outperforms existing techniques in terms of Fréchet Inception Distance (FID) score and pairwise evaluation, indicating the generation of reliable and useful multi-modal pairwise data.", "authors": ["Junhuan Yang", "Yuzhou Zhang", "Yi Sheng", "Youzuo Lin", "Lei Yang"], "categories": ["cs.LG", "cs.CV", "physics.geo-ph"], "fields_of_study": ["Computer Science", "Physics"], "published_date": "2025-01-01", "url": "https://arxiv.org/abs/2501.00941", "pdf_url": "https://arxiv.org/pdf/2501.00941v1", "arxiv_id": "2501.00941", "doi": "10.48550/arXiv.2501.00941", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "AAAI Conference on Artificial Intelligence", "quality_score": 0.2113}
{"id": "6a8d46789a1a89c0c66f3ca6259829b6c0c32a8f59f85d8c65780b2570b54157", "sources": ["arxiv", "semantic_scholar"], "title": "Addressing Challenges in Data Quality and Model Generalization for Malaria Detection", "abstract": "Malaria remains a significant global health burden, particularly in resource-limited regions where timely and accurate diagnosis is critical to effective treatment and control. Deep Learning (DL) has emerged as a transformative tool for automating malaria detection and it offers high accuracy and scalability. However, the effectiveness of these models is constrained by challenges in data quality and model generalization including imbalanced datasets, limited diversity and annotation variability. These issues reduce diagnostic reliability and hinder real-world applicability. This article provides a comprehensive analysis of these challenges and their implications for malaria detection performance. Key findings highlight the impact of data imbalances which can lead to a 20\\% drop in F1-score and regional biases which significantly hinder model generalization. Proposed solutions, such as GAN-based augmentation, improved accuracy by 15-20\\% by generating synthetic data to balance classes and enhance dataset diversity. Domain adaptation techniques, including transfer learning, further improved cross-domain robustness by up to 25\\% in sensitivity. Additionally, the development of diverse global datasets and collaborative data-sharing frameworks is emphasized as a cornerstone for equitable and reliable malaria diagnostics. The role of explainable AI techniques in improving clinical adoption and trustworthiness is also underscored. By addressing these challenges, this work advances the field of AI-driven malaria detection and provides actionable insights for researchers and practitioners. The proposed solutions aim to support the development of accessible and accurate diagnostic tools, particularly for resource-constrained populations.", "authors": ["Kiswendsida Kisito Kabore", "Desire Guel"], "categories": ["cs.LG", "eess.SP"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-12-31", "url": "https://arxiv.org/abs/2501.00464", "pdf_url": "https://arxiv.org/pdf/2501.00464v1", "arxiv_id": "2501.00464", "doi": "10.33140/JSNDC.04.03.09", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1747}
{"id": "3430e80829d4a7af5ea018aa9a5c0bd7d23409110be099c963fdb7884d6c98ad", "sources": ["arxiv", "semantic_scholar"], "title": "Data clustering: a fundamental method in data science and management", "abstract": "This paper explores the critical role of data clustering in data science, emphasizing its methodologies, tools, and diverse applications. Traditional techniques, such as partitional and hierarchical clustering, are analyzed alongside advanced approaches such as data stream, density-based, graph-based, and model-based clustering for handling complex structured datasets. The paper highlights key principles underpinning clustering, outlines widely used tools and frameworks, introduces the workflow of clustering in data science, discusses challenges in practical implementation, and examines various applications of clustering. By focusing on these foundations and applications, the discussion underscores clustering's transformative potential. The paper concludes with insights into future research directions, emphasizing clustering's role in driving innovation and enabling data-driven decision-making.", "authors": ["Tai Dinh", "Wong Hauchi", "Daniil Lisik", "Michal Koren", "Dat Tran", "Philip S. Yu", "Joaquín Torres-Sospedra"], "categories": ["cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-25", "url": "https://arxiv.org/abs/2412.18760", "pdf_url": "https://arxiv.org/pdf/2412.18760v3", "arxiv_id": "2412.18760", "doi": "10.1016/j.dsm.2025.08.001", "citation_count": 14, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.294}
{"id": "4dea02a0a5b28574ceaba66788d48c0e200b9c4973d927cc3fdef31e2d131ed4", "sources": ["arxiv", "semantic_scholar"], "title": "A Multimodal Fusion Framework for Bridge Defect Detection with Cross-Verification", "abstract": "This paper presents a pilot study introducing a multimodal fusion framework for the detection and analysis of bridge defects, integrating Non-Destructive Evaluation (NDE) techniques with advanced image processing to enable precise structural assessment. By combining data from Impact Echo (IE) and Ultrasonic Surface Waves (USW) methods, this preliminary investigation focuses on identifying defect-prone regions within concrete structures, emphasizing critical indicators such as delamination and debonding. Using geospatial analysis with alpha shapes, fusion of defect points, and unified lane boundaries, the proposed framework consolidates disparate data sources to enhance defect localization and facilitate the identification of overlapping defect regions. Cross-verification with adaptive image processing further validates detected defects by aligning their coordinates with visual data, utilizing advanced contour-based mapping and bounding box techniques for precise defect identification. The experimental results, with an F1 score of 0.83, demonstrate the potential efficacy of the approach in improving defect localization, reducing false positives, and enhancing detection accuracy, which provides a foundation for future research and larger-scale validation. This preliminary exploration establishes the framework as a promising tool for efficient bridge health assessment, with implications for proactive structural monitoring and maintenance.", "authors": ["Ravi Datta Rachuri", "Duoduo Liao", "Samhita Sarikonda", "Datha Vaishnavi Kondur"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-23", "url": "https://arxiv.org/abs/2412.17968", "pdf_url": "https://arxiv.org/pdf/2412.17968v1", "arxiv_id": "2412.17968", "doi": "10.1109/BigData62323.2024.10825867", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "BigData Congress [Services Society]", "quality_score": 0.1505}
{"id": "44674d97e9531b517861cf01aa49f9614106e1ef8d1fb8efdee2ea0a6fe799c2", "sources": ["arxiv", "semantic_scholar"], "title": "The Landscape of College-level Data Visualization Courses, and the Benefits of Incorporating Statistical Thinking", "abstract": "Data visualization is a core part of statistical practice and is ubiquitous in many fields. Although there are numerous books on data visualization, instructors in statistics and data science may be unsure how to teach data visualization, because it is such a broad discipline. To give guidance on teaching data visualization from a statistical perspective, we make two contributions. First, we conduct a survey of data visualization courses at top colleges and universities in the United States, in order to understand the landscape of data visualization courses. We find that most courses are not taught by statistics and data science departments and do not focus on statistical topics, especially those related to inference. Instead, most courses focus on visual storytelling, aesthetic design, dashboard design, and other topics specialized for other disciplines. Second, we outline three teaching principles for incorporating statistical inference in data visualization courses, and provide several examples that demonstrate how to follow these principles. The dataset from our survey allows others to explore the diversity of data visualization courses, and our teaching principles give guidance for encouraging statistical thinking when teaching data visualization.", "authors": ["Zach Branson", "Monica Paz Parra", "Ronald Yurko"], "categories": ["stat.OT", "cs.HC"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2024-12-20", "url": "https://arxiv.org/abs/2412.16402", "pdf_url": "https://arxiv.org/pdf/2412.16402v2", "arxiv_id": "2412.16402", "doi": "10.1080/26939169.2025.2537049", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Journal of Statistics and Data Science Education", "quality_score": 0.1193}
{"id": "a029c5ea93ccd1355cfc23aecf03239018960a4b813d51fe47c2fe78abff95a3", "sources": ["arxiv", "semantic_scholar"], "title": "Efficient Knowledge Injection in LLMs via Self-Distillation", "abstract": "In many practical applications, large language models (LLMs) need to acquire new knowledge not present in their pre-training data. Efficiently leveraging this knowledge usually relies on supervised fine-tuning or retrieval-augmented generation (RAG). Although RAG has emerged as the industry standard for knowledge injection, fine-tuning has not yet achieved comparable success. This paper proposes utilizing prompt distillation, a self-distillation-based method previously explored primarily for style alignment and instruction tuning, to internalize new factual knowledge from free-form documents. Unlike prior methods, our approach requires neither larger teacher models nor structured knowledge formats. Across multiple LLM sizes and model families, we show that prompt distillation outperforms standard supervised fine-tuning and can even surpass RAG. We analyze the key factors contributing to prompt distillation's effectiveness and examine how it scales.", "authors": ["Kalle Kujanpää", "Pekka Marttinen", "Harri Valpola", "Alexander Ilin"], "categories": ["cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-19", "url": "https://arxiv.org/abs/2412.14964", "pdf_url": "https://arxiv.org/pdf/2412.14964v2", "arxiv_id": "2412.14964", "doi": null, "citation_count": 20, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3306}
{"id": "6a1a5b9eaf20e7c52c2a79720a604e92c16a4c808eb17017e3bead68c07a8d3b", "sources": ["arxiv", "semantic_scholar"], "title": "AntiLeakBench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge", "abstract": "Data contamination hinders fair LLM evaluation by introducing test data into newer models' training sets. Existing studies solve this challenge by updating benchmarks with newly collected data. However, they fail to guarantee contamination-free evaluation as the newly collected data may contain pre-existing knowledge, and their benchmark updates rely on intensive human labor. To address these issues, we in this paper propose AntiLeak-Bench, an automated anti-leakage benchmarking framework. Instead of simply using newly collected data, we construct samples with explicitly new knowledge absent from LLMs' training sets, which thus ensures strictly contamination-free evaluation. We further design a fully automated workflow to build and update our benchmark without human labor. This significantly reduces the cost of benchmark maintenance to accommodate emerging LLMs. Through extensive experiments, we highlight that data contamination likely exists before LLMs' cutoff time and demonstrate AntiLeak-Bench effectively overcomes this challenge.", "authors": ["Xiaobao Wu", "Liangming Pan", "Yuxi Xie", "Ruiwen Zhou", "Shuai Zhao", "Yubo Ma", "Mingzhe Du", "Rui Mao", "Anh Tuan Luu", "William Yang Wang"], "categories": ["cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-18", "url": "https://arxiv.org/abs/2412.13670", "pdf_url": "https://arxiv.org/pdf/2412.13670v2", "arxiv_id": "2412.13670", "doi": "10.48550/arXiv.2412.13670", "citation_count": 40, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/bobxwu/AntiLeakBench", "venue": "Annual Meeting of the Association for Computational Linguistics", "quality_score": 0.4032}
{"id": "3c71af833ea3493c679b401b58ae31761131ec2cd61d7129e5b8e4f96bd47cb3", "sources": ["arxiv", "semantic_scholar"], "title": "Hybrid Data-Free Knowledge Distillation", "abstract": "Data-free knowledge distillation aims to learn a compact student network from a pre-trained large teacher network without using the original training data of the teacher network. Existing collection-based and generation-based methods train student networks by collecting massive real examples and generating synthetic examples, respectively. However, they inevitably become weak in practical scenarios due to the difficulties in gathering or emulating sufficient real-world data. To solve this problem, we propose a novel method called \\textbf{H}ybr\\textbf{i}d \\textbf{D}ata-\\textbf{F}ree \\textbf{D}istillation (HiDFD), which leverages only a small amount of collected data as well as generates sufficient examples for training student networks. Our HiDFD comprises two primary modules, \\textit{i.e.}, the teacher-guided generation and student distillation. The teacher-guided generation module guides a Generative Adversarial Network (GAN) by the teacher network to produce high-quality synthetic examples from very few real-world collected examples. Specifically, we design a feature integration mechanism to prevent the GAN from overfitting and facilitate the reliable representation learning from the teacher network. Meanwhile, we drive a category frequency smoothing technique via the teacher network to balance the generative training of each category. In the student distillation module, we explore a data inflation strategy to properly utilize a blend of real and synthetic data to train the student network via a classifier-sharing-based feature alignment technique. Intensive experiments across multiple benchmarks demonstrate that our HiDFD can achieve state-of-the-art performance using 120 times less collected data than existing methods. Code is available at https://github.com/tangjialiang97/HiDFD.", "authors": ["Jialiang Tang", "Shuo Chen", "Chen Gong"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-18", "url": "https://arxiv.org/abs/2412.13525", "pdf_url": "https://arxiv.org/pdf/2412.13525v1", "arxiv_id": "2412.13525", "doi": "10.48550/arXiv.2412.13525", "citation_count": 2, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/tangjialiang97/HiDFD", "venue": "AAAI Conference on Artificial Intelligence", "quality_score": 0.1193}
{"id": "e9339c863c06cbaaef8739e77932b8b3bb02d1035c38c4f26fec7aa9ad2702a1", "sources": ["arxiv", "semantic_scholar"], "title": "Low-Resource Fast Text Classification Based on Intra-Class and Inter-Class Distance Calculation", "abstract": "In recent years, text classification methods based on neural networks and pre-trained models have gained increasing attention and demonstrated excellent performance. However, these methods still have some limitations in practical applications: (1) They typically focus only on the matching similarity between sentences. However, there exists implicit high-value information both within sentences of the same class and across different classes, which is very crucial for classification tasks. (2) Existing methods such as pre-trained language models and graph-based approaches often consume substantial memory for training and text-graph construction. (3) Although some low-resource methods can achieve good performance, they often suffer from excessively long processing times. To address these challenges, we propose a low-resource and fast text classification model called LFTC. Our approach begins by constructing a compressor list for each class to fully mine the regularity information within intra-class data. We then remove redundant information irrelevant to the target classification to reduce processing time. Finally, we compute the similarity distance between text pairs for classification. We evaluate LFTC on 9 publicly available benchmark datasets, and the results demonstrate significant improvements in performance and processing time, especially under limited computational and data resources, highlighting its superior advantages.", "authors": ["Yanxu Mao", "Peipei Liu", "Tiehan Cui", "Congying Liu", "Datao You"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-13", "url": "https://arxiv.org/abs/2412.09922", "pdf_url": "https://arxiv.org/pdf/2412.09922v1", "arxiv_id": "2412.09922", "doi": "10.48550/arXiv.2412.09922", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1945}
{"id": "10f284108d368428f88f7a5d37d4b86d83eedbe9666cb760fb2a16d298348c39", "sources": ["arxiv", "semantic_scholar"], "title": "An Algorithm-Centered Approach To Model Streaming Data", "abstract": "Besides the classical offline setup of machine learning, stream learning constitutes a well-established setup where data arrives over time in potentially non-stationary environments. Concept drift, the phenomenon that the underlying distribution changes over time poses a significant challenge. Yet, despite high practical relevance, there is little to no foundational theory for learning in the drifting setup comparable to classical statistical learning theory in the offline setting. This can be attributed to the lack of an underlying object comparable to a probability distribution as in the classical setup. While there exist approaches to transfer ideas to the streaming setup, these start from a data perspective rather than an algorithmic one. In this work, we suggest a new model of data over time that is aimed at the algorithm's perspective. Instead of defining the setup using time points, we utilize a window-based approach that resembles the inner workings of most stream learning algorithms. We compare our framework to others from the literature on a theoretical basis, showing that in many cases both model the same situation. Furthermore, we perform a numerical evaluation and showcase an application in the domain of critical infrastructure.", "authors": ["Fabian Hinder", "Valerie Vaquet", "David Komnick", "Barbara Hammer"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-12", "url": "https://arxiv.org/abs/2412.09118", "pdf_url": "https://arxiv.org/pdf/2412.09118v1", "arxiv_id": "2412.09118", "doi": "10.48550/arXiv.2412.09118", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0}
{"id": "72b39362a9786b9910bcc9650644552d3940e1dd59fa5eb7c9f97b38e0511875", "sources": ["arxiv", "semantic_scholar"], "title": "Bootstrapping Language-Guided Navigation Learning with Self-Refining Data Flywheel", "abstract": "Creating high-quality data for training robust language-instructed agents is a long-lasting challenge in embodied AI. In this paper, we introduce a Self-Refining Data Flywheel (SRDF) that generates high-quality and large-scale navigational instruction-trajectory pairs by iteratively refining the data pool through the collaboration between two models, the instruction generator and the navigator, without any human-in-the-loop annotation. Specifically, SRDF starts with using a base generator to create an initial data pool for training a base navigator, followed by applying the trained navigator to filter the data pool. This leads to higher-fidelity data to train a better generator, which can, in turn, produce higher-quality data for training the next-round navigator. Such a flywheel establishes a data self-refining process, yielding a continuously improved and highly effective dataset for large-scale language-guided navigation learning. Our experiments demonstrate that after several flywheel rounds, the navigator elevates the performance boundary from 70% to 78% SPL on the classic R2R test set, surpassing human performance (76%) for the first time. Meanwhile, this process results in a superior generator, evidenced by a SPICE increase from 23.5 to 26.2, better than all previous VLN instruction generation methods. Finally, we demonstrate the scalability of our method through increasing environment and instruction diversity, and the generalization ability of our pre-trained navigator across various downstream navigation tasks, surpassing state-of-the-art methods by a large margin in all cases.", "authors": ["Zun Wang", "Jialu Li", "Yicong Hong", "Songze Li", "Kunchang Li", "Shoubin Yu", "Yi Wang", "Yu Qiao", "Yali Wang", "Mohit Bansal", "Limin Wang"], "categories": ["cs.CV", "cs.AI", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-11", "url": "https://arxiv.org/abs/2412.08467", "pdf_url": "https://arxiv.org/pdf/2412.08467v2", "arxiv_id": "2412.08467", "doi": "10.48550/arXiv.2412.08467", "citation_count": 18, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/wz0919/VLN-SRDF", "venue": "International Conference on Learning Representations", "quality_score": 0.3197}
{"id": "c9ead5c740863b46abc105231ff50ca6fb56742273767dd683da0e9b11cffb9f", "sources": ["arxiv", "semantic_scholar"], "title": "Diffusion-based Data Augmentation and Knowledge Distillation with Generated Soft Labels Solving Data Scarcity Problems of SAR Oil Spill Segmentation", "abstract": "Oil spills pose severe environmental risks, making early detection crucial for effective response and mitigation. As Synthetic Aperture Radar (SAR) images operate under all-weather conditions, SAR-based oil spill segmentation enables fast and robust monitoring. However, when using deep learning models, SAR oil spill segmentation often struggles in training due to the scarcity of labeled data. To address this limitation, we propose a diffusion-based data augmentation with knowledge transfer (DAKTer) strategy. Our DAKTer strategy enables a diffusion model to generate SAR oil spill images along with soft label pairs, which offer richer class probability distributions than segmentation masks (i.e. hard labels). Also, for reliable joint generation of high-quality SAR images and well-aligned soft labels, we introduce an SNR-based balancing factor aligning the noise corruption process of both modalilties in diffusion models. By leveraging the generated SAR images and soft labels, a student segmentation model can learn robust feature representations without teacher models trained for the same task, improving its ability to segment oil spill regions. Extensive experiments demonstrate that our DAKTer strategy effectively transfers the knowledge of per-pixel class probabilities to the student segmentation model to distinguish the oil spill regions from other look-alike regions in the SAR images. Our DAKTer strategy boosts various segmentation models to achieve superior performance with large margins compared to other generative data augmentation methods.", "authors": ["Jaeho Moon", "Jeonghwan Yun", "Jaehyun Kim", "Jaehyup Lee", "Munchurl Kim"], "categories": ["cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-11", "url": "https://arxiv.org/abs/2412.08116", "pdf_url": "https://arxiv.org/pdf/2412.08116v2", "arxiv_id": "2412.08116", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0753}
{"id": "e373e3a114b989391849eba90b87ad12375ca1ebd725222e16a9fe752b740f75", "sources": ["arxiv", "semantic_scholar"], "title": "Generative Modeling and Data Augmentation for Power System Production Simulation", "abstract": "As a key component of power system production simulation, load forecasting is critical for the stable operation of power systems. Machine learning methods prevail in this field. However, the limited training data can be a challenge. This paper proposes a generative model-assisted approach for load forecasting under small sample scenarios, consisting of two steps: expanding the dataset using a diffusion-based generative model and then training various machine learning regressors on the augmented dataset to identify the best performer. The expanded dataset significantly reduces forecasting errors compared to the original dataset, and the diffusion model outperforms the generative adversarial model by achieving about 200 times smaller errors and better alignment in latent data distributions.", "authors": ["Linna Xu", "Yongli Zhu"], "categories": ["eess.SY", "cs.AI", "cs.LG"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2024-12-10", "url": "https://arxiv.org/abs/2412.12146", "pdf_url": "https://arxiv.org/pdf/2412.12146v1", "arxiv_id": "2412.12146", "doi": "10.48550/arXiv.2412.12146", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1747}
{"id": "cc903eb8dc2576a91d08e4ea4ca54dc2cef613be233591a883cf879dfcfedb73", "sources": ["arxiv", "semantic_scholar"], "title": "Simplications: Why and how we should rethink data of/by/for the people in smart homes and its privacy implications", "abstract": "More and more smart devices enter our homes. Often these devices come with a variety of sensors, mostly simple sensors, e.g., for light, temperature, humidity or motion. And they all collect data. While it is data of the home environment it is also data of domestic life in the home. Thus it is data of the people and by the people in the home capturing their presence, arrival and departure, typical domestic activities, bad habits, health status etc. Based on previous as well as ongoing research we know that people are actually able to make sense of simple sensor data and that they will make use of it for their own purposes. Simple sensors, when critically reflected, are often only \"simple\" in a technical sense. The unreflected design and use of these sensors can easily lead to unintended implications, i.e. for privacy. However, it may not even need a Big Brother or data experts or AI to make the data of these sensors sensitive, e.g., if used for lateral surveillance within families. Often unintended but wicked implications emerge despite good intentions, such as improving efficiency or energy saving through collecting sensor data. Thus sensor data from the home is actually data of/by/for the people in the home. First, we explain how this might have relevance across scales of community of people - not only for the domain of the home but also in broader meaning. Second, we relate our previous as well as ongoing research in the domain of smart homes to this topic.", "authors": ["Albrecht Kurze", "Alexa Becker"], "categories": ["cs.HC"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-09", "url": "https://arxiv.org/abs/2412.06960", "pdf_url": "https://arxiv.org/pdf/2412.06960v1", "arxiv_id": "2412.06960", "doi": "10.48550/arXiv.2412.06960", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0}
{"id": "8565e9ef91e5326930098c2d4c9bd3ff0750d61ddcb2eda97e80ee3b2af68204", "sources": ["arxiv", "semantic_scholar"], "title": "Training and Evaluating Language Models with Template-based Data Generation", "abstract": "The rapid advancement of large language models (LLMs) such as GPT-3, PaLM, and Llama has significantly transformed natural language processing, showcasing remarkable capabilities in understanding and generating language. However, a fundamental bottleneck persists: these models often struggle with tasks requiring complex, multi-step reasoning, particularly in mathematical problem-solving. This deficiency stems from the critical scarcity of large-scale, high-quality, domain-specific datasets necessary for cultivating sophisticated reasoning abilities. To overcome this challenge, we introduce Template-based Data Generation (TDG), a novel and scalable paradigm that harnesses frontier LLMs (GPT-4) to automatically generate parameterized meta-templates, which in turn synthesize a virtually infinite stream of high-quality problems and solutions. Using this paradigm, we create TemplateMath Part I: TemplateGSM, a foundational dataset of over 7 million synthetically generated grade school math problems. Each problem is accompanied by a programmatically verifiable solution, offering an unprecedented level of quality at scale. This resource not only resolves the data scarcity issue for supervised fine-tuning but also provides a robust mechanism for model alignment through Reinforcement Learning with Verifiable Rewards (RLVR). Our approach elevates data augmentation by leveraging GPT-4 to generate meta-templates, ensuring diverse and complex problem structures. By providing a scalable solution to the data and verification bottleneck, TDG and TemplateGSM pave the way for a new generation of LLMs with powerful, reliable reasoning skills.", "authors": ["Yifan Zhang"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-27", "url": "https://arxiv.org/abs/2411.18104", "pdf_url": "https://arxiv.org/pdf/2411.18104v6", "arxiv_id": "2411.18104", "doi": "10.48550/arXiv.2411.18104", "citation_count": 8, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/iiis-ai/TemplateMath", "venue": "arXiv.org", "quality_score": 0.2386}
{"id": "0d3f486f31a651616818c1beb7419cb8dae6067b3505dc24e0c8d1503f427927", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification", "abstract": "Deep learning models need a sufficient amount of data in order to be able to find the hidden patterns in it. It is the purpose of generative modeling to learn the data distribution, thus allowing us to sample more data and augment the original dataset. In the context of physiological data, and more specifically electrocardiogram (ECG) data, given its sensitive nature and expensive data collection, we can exploit the benefits of generative models in order to enlarge existing datasets and improve downstream tasks, in our case, classification of heart rhythm. In this work, we explore the usefulness of synthetic data generated with different generative models from Deep Learning namely Diffweave, Time-Diffusion and Time-VQVAE in order to obtain better classification results for two open source multivariate ECG datasets. Moreover, we also investigate the effects of transfer learning, by fine-tuning a synthetically pre-trained model and then progressively adding increasing proportions of real data. We conclude that although the synthetic samples resemble the real ones, the classification improvement when simply augmenting the real dataset is barely noticeable on individual datasets, but when both datasets are merged the results show an increase across all metrics for the classifiers when using synthetic samples as augmented data. From the fine-tuning results the Time-VQVAE generative model has shown to be superior to the others but not powerful enough to achieve results close to a classifier trained with real data only. In addition, methods and metrics for measuring closeness between synthetic data and the real one have been explored as a side effect of the main research questions of this study.", "authors": ["José Fernando Núñez", "Jamie Arjona", "Javier Béjar"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-27", "url": "https://arxiv.org/abs/2411.18456", "pdf_url": "https://arxiv.org/pdf/2411.18456v1", "arxiv_id": "2411.18456", "doi": "10.48550/arXiv.2411.18456", "citation_count": 2, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "0b01645215f57a432b37d32a0a9042d8d0f3d89496ab7bab477a303cfd4a9c30", "sources": ["arxiv", "semantic_scholar"], "title": "FAIR Digital Objects for the Realization of Globally Aligned Data Spaces", "abstract": "The FAIR principles are globally accepted guidelines for improved data management practices with the potential to align data spaces on a global scale. In practice, this is only marginally achieved through the different ways in which organizations interpret and implement these principles. The concept of FAIR Digital Objects provides a way to realize a domain-independent abstraction layer that could solve this problem, but its specifications are currently diverse, contradictory, and restricted to semantic models. In this work, we introduce a rigorously formalized data model with a set of assertions using formal expressions to provide a common baseline for the implementation of FAIR Digital Objects. The model defines how these objects enable machine-actionable decisions based on the principles of abstraction, encapsulation, and entity relationship to fulfill FAIR criteria for the digital resources they represent. We provide implementation examples in the context of two use cases and explain how our model can facilitate the (re)use of data across domains. We also compare how our model assertions are met by FAIR Digital Objects as they have been described in other projects. Finally, we discuss our results' adoption criteria, limitations, and perspectives in the big data context. Overall, our work represents an important milestone for various communities working towards globally aligned data spaces through FAIRification.", "authors": ["Nicolas Blumenroehr", "Philipp-Joachim Ost", "Felix Kraus", "Achim Streit"], "categories": ["cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-27", "url": "https://arxiv.org/abs/2411.18663", "pdf_url": "https://arxiv.org/pdf/2411.18663v1", "arxiv_id": "2411.18663", "doi": "10.1109/BigData62323.2024.10825796", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "BigData Congress [Services Society]", "quality_score": 0.2113}
{"id": "b11e25ce625d672521d081de607bbacdf8d3d26679adf65aeac33cbc986f065f", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetic Data Generation with LLM for Improved Depression Prediction", "abstract": "Automatic detection of depression is a rapidly growing field of research at the intersection of psychology and machine learning. However, with its exponential interest comes a growing concern for data privacy and scarcity due to the sensitivity of such a topic. In this paper, we propose a pipeline for Large Language Models (LLMs) to generate synthetic data to improve the performance of depression prediction models. Starting from unstructured, naturalistic text data from recorded transcripts of clinical interviews, we utilize an open-source LLM to generate synthetic data through chain-of-thought prompting. This pipeline involves two key steps: the first step is the generation of the synopsis and sentiment analysis based on the original transcript and depression score, while the second is the generation of the synthetic synopsis/sentiment analysis based on the summaries generated in the first step and a new depression score. Not only was the synthetic data satisfactory in terms of fidelity and privacy-preserving metrics, it also balanced the distribution of severity in the training dataset, thereby significantly enhancing the model's capability in predicting the intensity of the patient's depression. By leveraging LLMs to generate synthetic data that can be augmented to limited and imbalanced real-world datasets, we demonstrate a novel approach to addressing data scarcity and privacy concerns commonly faced in automatic depression detection, all while maintaining the statistical integrity of the original dataset. This approach offers a robust framework for future mental health research and applications.", "authors": ["Andrea Kang", "Jun Yu Chen", "Zoe Lee-Youngzie", "Shuhao Fu"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-26", "url": "https://arxiv.org/abs/2411.17672", "pdf_url": "https://arxiv.org/pdf/2411.17672v1", "arxiv_id": "2411.17672", "doi": "10.48550/arXiv.2411.17672", "citation_count": 22, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3404}
{"id": "e922f34db7b5882c5579ba22b52bfef29a6edb41e4bc5e278a86a3016e0fe82b", "sources": ["arxiv", "semantic_scholar"], "title": "Large-Scale Data-Free Knowledge Distillation for ImageNet via Multi-Resolution Data Generation", "abstract": "Data-Free Knowledge Distillation (DFKD) is an advanced technique that enables knowledge transfer from a teacher model to a student model without relying on original training data. While DFKD methods have achieved success on smaller datasets like CIFAR10 and CIFAR100, they encounter challenges on larger, high-resolution datasets such as ImageNet. A primary issue with previous approaches is their generation of synthetic images at high resolutions (e.g., $224 \\times 224$) without leveraging information from real images, often resulting in noisy images that lack essential class-specific features in large datasets. Additionally, the computational cost of generating the extensive data needed for effective knowledge transfer can be prohibitive. In this paper, we introduce MUlti-reSolution data-freE (MUSE) to address these limitations. MUSE generates images at lower resolutions while using Class Activation Maps (CAMs) to ensure that the generated images retain critical, class-specific features. To further enhance model diversity, we propose multi-resolution generation and embedding diversity techniques that strengthen latent space representations, leading to significant performance improvements. Experimental results demonstrate that MUSE achieves state-of-the-art performance across both small- and large-scale datasets, with notable performance gains of up to two digits in nearly all ImageNet and subset experiments. Code is available at https://github.com/tmtuan1307/muse.", "authors": ["Minh-Tuan Tran", "Trung Le", "Xuan-May Le", "Jianfei Cai", "Mehrtash Harandi", "Dinh Phung"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-26", "url": "https://arxiv.org/abs/2411.17046", "pdf_url": "https://arxiv.org/pdf/2411.17046v1", "arxiv_id": "2411.17046", "doi": "10.48550/arXiv.2411.17046", "citation_count": 4, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/tmtuan1307/muse", "venue": "arXiv.org", "quality_score": 0.1747}
{"id": "8e9a81b15e5b992af5881839ecfba386b9bf81cc76615e676c57da13eb063f0e", "sources": ["arxiv", "semantic_scholar"], "title": "AeroGen: Enhancing Remote Sensing Object Detection with Diffusion-Driven Data Generation", "abstract": "Remote sensing image object detection (RSIOD) aims to identify and locate specific objects within satellite or aerial imagery. However, there is a scarcity of labeled data in current RSIOD datasets, which significantly limits the performance of current detection algorithms. Although existing techniques, e.g., data augmentation and semi-supervised learning, can mitigate this scarcity issue to some extent, they are heavily dependent on high-quality labeled data and perform worse in rare object classes. To address this issue, this paper proposes a layout-controllable diffusion generative model (i.e. AeroGen) tailored for RSIOD. To our knowledge, AeroGen is the first model to simultaneously support horizontal and rotated bounding box condition generation, thus enabling the generation of high-quality synthetic images that meet specific layout and object category requirements. Additionally, we propose an end-to-end data augmentation framework that integrates a diversity-conditioned generator and a filtering mechanism to enhance both the diversity and quality of generated data. Experimental results demonstrate that the synthetic data produced by our method are of high quality and diversity. Furthermore, the synthetic RSIOD data can significantly improve the detection performance of existing RSIOD models, i.e., the mAP metrics on DIOR, DIOR-R, and HRSC datasets are improved by 3.7%, 4.3%, and 2.43%, respectively. The code is available at https://github.com/Sonettoo/AeroGen.", "authors": ["Datao Tang", "Xiangyong Cao", "Xuan Wu", "Jialin Li", "Jing Yao", "Xueru Bai", "Dongsheng Jiang", "Yin Li", "Deyu Meng"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-23", "url": "https://arxiv.org/abs/2411.15497", "pdf_url": "https://arxiv.org/pdf/2411.15497v3", "arxiv_id": "2411.15497", "doi": "10.1109/CVPR52734.2025.00342", "citation_count": 56, "influential_citation_count": 5, "has_code": true, "code_url": "https://github.com/Sonettoo/AeroGen", "venue": "Computer Vision and Pattern Recognition", "quality_score": 0.439}
{"id": "714c789380bb38cb119ef5f342f06d985f5ba15247ca03515a2ce379b9838969", "sources": ["arxiv", "semantic_scholar"], "title": "Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai", "abstract": "We present a synthetic data approach for instruction-tuning large language models (LLMs) for low-resource languages in a data-efficient manner, specifically focusing on Thai. We identify three key properties that contribute to the effectiveness of instruction-tuning datasets: fluency, diversity, and cultural context. We propose a seed-data-free framework for generating synthetic instruction-tuning data that incorporates these essential properties. Our framework employs an LLM to generate diverse topics, retrieve relevant contexts from Wikipedia, and create instructions for various tasks, such as question answering, summarization, and conversation. The experimental results show that our best-performing synthetic dataset, which incorporates all three key properties, achieves competitive performance using only 5,000 instructions when compared to state-of-the-art Thai LLMs trained on hundreds of thousands of instructions. Our code and dataset are publicly available at https://github.com/parinzee/seed-free-synthetic-instruct.", "authors": ["Parinthapat Pengpun", "Can Udomcharoenchaikit", "Weerayut Buaphet", "Peerat Limkonchotiwat"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-23", "url": "https://arxiv.org/abs/2411.15484", "pdf_url": "https://arxiv.org/pdf/2411.15484v1", "arxiv_id": "2411.15484", "doi": "10.18653/v1/2024.acl-srw.38", "citation_count": 8, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/parinzee/seed-free-synthetic-instruct", "venue": "Annual Meeting of the Association for Computational Linguistics", "quality_score": 0.2386}
{"id": "e00a15af8551b8913d06612ad214c8b4837938b3a1ba3bf62cfbc88536cff563", "sources": ["arxiv", "semantic_scholar"], "title": "Data-to-Model Distillation: Data-Efficient Learning Framework", "abstract": "Dataset distillation aims to distill the knowledge of a large-scale real dataset into small yet informative synthetic data such that a model trained on it performs as well as a model trained on the full dataset. Despite recent progress, existing dataset distillation methods often struggle with computational efficiency, scalability to complex high-resolution datasets, and generalizability to deep architectures. These approaches typically require retraining when the distillation ratio changes, as knowledge is embedded in raw pixels. In this paper, we propose a novel framework called Data-to-Model Distillation (D2M) to distill the real dataset's knowledge into the learnable parameters of a pre-trained generative model by aligning rich representations extracted from real and generated images. The learned generative model can then produce informative training images for different distillation ratios and deep architectures. Extensive experiments on 15 datasets of varying resolutions show D2M's superior performance, re-distillation efficiency, and cross-architecture generalizability. Our method effectively scales up to high-resolution 128x128 ImageNet-1K. Furthermore, we verify D2M's practical benefits for downstream applications in neural architecture search.", "authors": ["Ahmad Sajedi", "Samir Khaki", "Lucy Z. Liu", "Ehsan Amjadian", "Yuri A. Lawryshyn", "Konstantinos N. Plataniotis"], "categories": ["cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-19", "url": "https://arxiv.org/abs/2411.12841", "pdf_url": "https://arxiv.org/pdf/2411.12841v1", "arxiv_id": "2411.12841", "doi": "10.1007/978-3-031-72775-7_25", "citation_count": 6, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "European Conference on Computer Vision", "quality_score": 0.2113}
{"id": "5f38e9eec82d47db4cfcf1a76755ebb83f4a64f7062a66c68af73dd4b7fb19fc", "sources": ["arxiv", "semantic_scholar"], "title": "Mitigating Sycophancy in Decoder-Only Transformer Architectures: Synthetic Data Intervention", "abstract": "To address the sycophancy problem caused by reinforcement learning from human feedback in large language models, this research applies synthetic data intervention technology to the decoder-only transformer architecture. Based on the research gaps in the existing literature, the researcher designed an experimental process to reduce the tendency of models to cater by generating diversified data, and used GPT4o as an experimental tool for verification. The experiment used 100 true and false questions, and compared the performance of the model trained with synthetic data intervention and the original untrained model on multiple indicators. The results show that the SDI training model supports the technology in terms of accuracy rate and sycophancy rate and has significant effectiveness in reducing sycophancy phenomena.", "authors": ["Libo Wang"], "categories": ["cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-15", "url": "https://arxiv.org/abs/2411.10156", "pdf_url": "https://arxiv.org/pdf/2411.10156v5", "arxiv_id": "2411.10156", "doi": "10.48550/arXiv.2411.10156", "citation_count": 5, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/brucewang123456789/GeniusTrail/tree/main/Synthetic%20Data%20Intervention", "venue": "arXiv.org", "quality_score": 0.1945}
{"id": "e36d4f52fa8269440b8c216fd8ee616b852508d6e91bc55e676af125b442d2b9", "sources": ["arxiv", "semantic_scholar"], "title": "Large Language Models as Robust Data Generators in Software Analytics: Are We There Yet?", "abstract": "Large Language Model (LLM)-generated data is increasingly used in software analytics, but it is unclear how this data compares to human-written data, particularly when models are exposed to adversarial scenarios. Adversarial attacks can compromise the reliability and security of software systems, so understanding how LLM-generated data performs under these conditions, compared to human-written data, which serves as the benchmark for model performance, can provide valuable insights into whether LLM-generated data offers similar robustness and effectiveness. To address this gap, we systematically evaluate and compare the quality of human-written and LLM-generated data for fine-tuning robust pre-trained models (PTMs) in the context of adversarial attacks. We evaluate the robustness of six widely used PTMs, fine-tuned on human-written and LLM-generated data, before and after adversarial attacks. This evaluation employs nine state-of-the-art (SOTA) adversarial attack techniques across three popular software analytics tasks: clone detection, code summarization, and sentiment analysis in code review discussions. Additionally, we analyze the quality of the generated adversarial examples using eleven similarity metrics. Our findings reveal that while PTMs fine-tuned on LLM-generated data perform competitively with those fine-tuned on human-written data, they exhibit less robustness against adversarial attacks in software analytics tasks. Our study underscores the need for further exploration into enhancing the quality of LLM-generated training data to develop models that are both high-performing and capable of withstanding adversarial attacks in software analytics.", "authors": ["Md. Abdul Awal", "Mrigank Rochan", "Chanchal K. Roy"], "categories": ["cs.SE"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-15", "url": "https://arxiv.org/abs/2411.10565", "pdf_url": "https://arxiv.org/pdf/2411.10565v3", "arxiv_id": "2411.10565", "doi": "10.1145/3756681.3756985", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Evaluation & Assessment in Software Engineering", "quality_score": 0.1747}
{"id": "8fad1e20c09a855ad5f57fe7a51828ecb08704b542908b636a979b1b3b2ad777", "sources": ["arxiv", "semantic_scholar"], "title": "FaaS and Furious: abstractions and differential caching for efficient data pre-processing", "abstract": "Data pre-processing pipelines are the bread and butter of any successful AI project. We introduce a novel programming model for pipelines in a data lakehouse, allowing users to interact declaratively with assets in object storage. Motivated by real-world industry usage patterns, we exploit these new abstractions with a columnar and differential cache to maximize iteration speed for data scientists, who spent most of their time in pre-processing - adding or removing features, restricting or relaxing time windows, wrangling current or older datasets. We show how the new cache works transparently across programming languages, schemas and time windows, and provide preliminary evidence on its efficiency on standard data workloads.", "authors": ["Jacopo Tagliabue", "Ryan Curtin", "Ciro Greco"], "categories": ["cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-12", "url": "https://arxiv.org/abs/2411.08203", "pdf_url": "https://arxiv.org/pdf/2411.08203v1", "arxiv_id": "2411.08203", "doi": "10.1109/BigData62323.2024.10825377", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "BigData Congress [Services Society]", "quality_score": 0.0753}
{"id": "9e5586b9ad870586db586ba6348a2c5dd56062ff9baa11d865c39f6ce5b68091", "sources": ["arxiv", "semantic_scholar"], "title": "Exploring the Landscape for Generative Sequence Models for Specialized Data Synthesis", "abstract": "Artificial Intelligence (AI) research often aims to develop models that can generalize reliably across complex datasets, yet this remains challenging in fields where data is scarce, intricate, or inaccessible. This paper introduces a novel approach that leverages three generative models of varying complexity to synthesize one of the most demanding structured datasets: Malicious Network Traffic. Our approach uniquely transforms numerical data into text, re-framing data generation as a language modeling task, which not only enhances data regularization but also significantly improves generalization and the quality of the synthetic data. Extensive statistical analyses demonstrate that our method surpasses state-of-the-art generative models in producing high-fidelity synthetic data. Additionally, we conduct a comprehensive study on synthetic data applications, effectiveness, and evaluation strategies, offering valuable insights into its role across various domains. Our code and pre-trained models are openly accessible at Github, enabling further exploration and application of our methodology. Index Terms: Data synthesis, machine learning, traffic generation, privacy preserving data, generative models.", "authors": ["Mohammad Zbeeb", "Mohammad Ghorayeb", "Mariam Salman"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-04", "url": "https://arxiv.org/abs/2411.01929", "pdf_url": "https://arxiv.org/pdf/2411.01929v2", "arxiv_id": "2411.01929", "doi": "10.33140/JDAEDM", "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Moe-Zbeeb/Exploring-the-landscape-for-generative-models-for-specialized-data-generation.git", "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "48ccbe3ccfd87b1574889586017d7ce8c88a187761dc089d0565d9a829440fb9", "sources": ["arxiv", "semantic_scholar"], "title": "Bi-Level Graph Structure Learning for Next POI Recommendation", "abstract": "Next point-of-interest (POI) recommendation aims to predict a user's next destination based on sequential check-in history and a set of POI candidates. Graph neural networks (GNNs) have demonstrated a remarkable capability in this endeavor by exploiting the extensive global collaborative signals present among POIs. However, most of the existing graph-based approaches construct graph structures based on pre-defined heuristics, failing to consider inherent hierarchical structures of POI features such as geographical locations and visiting peaks, or suffering from noisy and incomplete structures in graphs. To address the aforementioned issues, this paper presents a novel Bi-level Graph Structure Learning (BiGSL) for next POI recommendation. BiGSL first learns a hierarchical graph structure to capture the fine-to-coarse connectivity between POIs and prototypes, and then uses a pairwise learning module to dynamically infer relationships between POI pairs and prototype pairs. Based on the learned bi-level graphs, our model then employs a multi-relational graph network that considers both POI- and prototype-level neighbors, resulting in improved POI representations. Our bi-level structure learning scheme is more robust to data noise and incompleteness, and improves the exploration ability for recommendation by alleviating sparsity issues. Experimental results on three real-world datasets demonstrate the superiority of our model over existing state-of-the-art methods, with a significant improvement in recommendation accuracy and exploration performance.", "authors": ["Liang Wang", "Shu Wu", "Qiang Liu", "Yanqiao Zhu", "Xiang Tao", "Mengdi Zhang", "Liang Wang"], "categories": ["cs.LG", "cs.AI", "cs.SI"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-02", "url": "https://arxiv.org/abs/2411.01169", "pdf_url": "https://arxiv.org/pdf/2411.01169v1", "arxiv_id": "2411.01169", "doi": "10.1109/TKDE.2024.3397683", "citation_count": 24, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Knowledge and Data Engineering", "quality_score": 0.3495}
{"id": "ffc949c3ba61eedd9f86c01e74032464fe45d79f63d5a06a7b6269f41b37748a", "sources": ["arxiv", "semantic_scholar"], "title": "Capturing and Anticipating User Intents in Data Analytics via Knowledge Graphs", "abstract": "In today's data-driven world, the ability to extract meaningful information from data is becoming essential for businesses, organizations and researchers alike. For that purpose, a wide range of tools and systems exist addressing data-related tasks, from data integration, preprocessing and modeling, to the interpretation and evaluation of the results. As data continues to grow in volume, variety, and complexity, there is an increasing need for advanced but user-friendly tools, such as intelligent discovery assistants (IDAs) or automated machine learning (AutoML) systems, that facilitate the user's interaction with the data. This enables non-expert users, such as citizen data scientists, to leverage powerful data analytics techniques effectively. The assistance offered by IDAs or AutoML tools should not be guided only by the analytical problem's data but should also be tailored to each individual user. To this end, this work explores the usage of Knowledge Graphs (KG) as a basic framework for capturing in a human-centered manner complex analytics workflows, by storing information not only about the workflow's components, datasets and algorithms but also about the users, their intents and their feedback, among others. The data stored in the generated KG can then be exploited to provide assistance (e.g., recommendations) to the users interacting with these systems. To accomplish this objective, two methods are explored in this work. Initially, the usage of query templates to extract relevant information from the KG is studied. However, upon identifying its main limitations, the usage of link prediction with knowledge graph embeddings is explored, which enhances flexibility and allows leveraging the entire structure and components of the graph. The experiments show that the proposed method is able to capture the graph's structure and to produce sensible suggestions.", "authors": ["Gerard Pons", "Besim Bilalli", "Anna Queralt"], "categories": ["cs.LG", "cs.AI", "cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-01", "url": "https://arxiv.org/abs/2411.01023", "pdf_url": "https://arxiv.org/pdf/2411.01023v1", "arxiv_id": "2411.01023", "doi": "10.1016/j.knosys.2026.115835", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Knowledge-Based Systems", "quality_score": 0.0753}
{"id": "9caa44d9c1466c2b82a9af03e6156562004ff24b814f122ef6a4449e1c676402", "sources": ["arxiv", "semantic_scholar"], "title": "Knowledge Distillation Using Frontier Open-source LLMs: Generalizability and the Role of Synthetic Data", "abstract": "Leading open-source large language models (LLMs) such as Llama-3.1-Instruct-405B are extremely capable at generating text, answering questions, and solving a variety of natural language understanding tasks. However, they incur higher inference cost and latency compared to smaller LLMs. Knowledge distillation provides a way to use outputs from these large, capable teacher models to train smaller student models which can be used for inference at lower cost and latency, while retaining comparable accuracy. We investigate the efficacy of distillation using the Llama-3.1-405B-Instruct teacher and the smaller Llama-3.1-8B-Instruct and Llama-3.1-70B-Instruct student models. Contributions of this work include (a) We evaluate the generalizability of distillation with the above Llama-3.1 teacher-student pairs across different tasks and datasets (b) We show that using synthetic data during distillation significantly improves the accuracy of 8B and 70B models, and when used with reasoning chains, even matches or surpasses the zero-shot accuracy of 405B model on some datasets (c) We empirically show that distillation enables 8B and 70B models to internalize 405B's reasoning ability by using only standard fine-tuning (without customizing any loss function). This allows cost and latency-efficient student model inference. (d) We show pitfalls in evaluation of distillation, and present task-specific evaluation, including both human and LLM-grading, and ground-truth based traditional accuracy benchmarks. This methodical study brings out the fundamental importance of synthetic data quality in knowledge distillation, and of combining multiple, task-specific ways of accuracy and quality evaluation in assessing the effectiveness of distillation.", "authors": ["Anup Shirgaonkar", "Nikhil Pandey", "Nazmiye Ceren Abay", "Tolga Aktas", "Vijay Aski"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-24", "url": "https://arxiv.org/abs/2410.18588", "pdf_url": "https://arxiv.org/pdf/2410.18588v1", "arxiv_id": "2410.18588", "doi": "10.48550/arXiv.2410.18588", "citation_count": 17, "influential_citation_count": 1, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3138}
{"id": "87179c2e1637f3c845190a7a8615801b6a4a7eaabc674d4ae15406d64a455dab", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Effective Data-Free Knowledge Distillation via Diverse Diffusion Augmentation", "abstract": "Data-free knowledge distillation (DFKD) has emerged as a pivotal technique in the domain of model compression, substantially reducing the dependency on the original training data. Nonetheless, conventional DFKD methods that employ synthesized training data are prone to the limitations of inadequate diversity and discrepancies in distribution between the synthesized and original datasets. To address these challenges, this paper introduces an innovative approach to DFKD through diverse diffusion augmentation (DDA). Specifically, we revise the paradigm of common data synthesis in DFKD to a composite process through leveraging diffusion models subsequent to data synthesis for self-supervised augmentation, which generates a spectrum of data samples with similar distributions while retaining controlled variations. Furthermore, to mitigate excessive deviation in the embedding space, we introduce an image filtering technique grounded in cosine similarity to maintain fidelity during the knowledge distillation process. Comprehensive experiments conducted on CIFAR-10, CIFAR-100, and Tiny-ImageNet datasets showcase the superior performance of our method across various teacher-student network configurations, outperforming the contemporary state-of-the-art DFKD methods. Code will be available at:https://github.com/SLGSP/DDA.", "authors": ["Muquan Li", "Dongyang Zhang", "Tao He", "Xiurui Xie", "Yuan-Fang Li", "Ke Qin"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-23", "url": "https://arxiv.org/abs/2410.17606", "pdf_url": "https://arxiv.org/pdf/2410.17606v1", "arxiv_id": "2410.17606", "doi": "10.1145/3664647.3680711", "citation_count": 16, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/SLGSP/DDA", "venue": "ACM Multimedia", "quality_score": 0.3076}
{"id": "9d3eededb885986fc0bfcadaa2de67d64c5fb2815259d0c018a329776c13bfbb", "sources": ["arxiv", "semantic_scholar"], "title": "Masked Clinical Modelling: A Framework for Synthetic and Augmented Survival Data Generation", "abstract": "Access to real clinical data is often restricted due to privacy obligations, creating significant barriers for healthcare research. Synthetic datasets provide a promising solution, enabling secure data sharing and model development. However, most existing approaches focus on data realism rather than utility -- ensuring that models trained on synthetic data yield clinically meaningful insights comparable to those trained on real data. In this paper, we present Masked Clinical Modelling (MCM), a framework inspired by masked language modelling, designed for both data synthesis and conditional data augmentation. We evaluate this prototype on the WHAS500 dataset using Cox Proportional Hazards models, focusing on the preservation of hazard ratios as key clinical metrics. Our results show that data generated using the MCM framework improves both discrimination and calibration in survival analysis, outperforming existing methods. MCM demonstrates strong potential to support survival data analysis and broader healthcare applications.", "authors": ["Nicholas I-Hsien Kuo", "Blanca Gallego", "Louisa Jorm"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science", "Medicine"], "published_date": "2024-10-22", "url": "https://arxiv.org/abs/2410.16811", "pdf_url": "https://arxiv.org/pdf/2410.16811v2", "arxiv_id": "2410.16811", "doi": "10.48550/arXiv.2410.16811", "citation_count": 2, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Medinfo", "quality_score": 0.1505}
{"id": "0a65ffcd1e63e46ae142c50d8707f71814c6a5fb58cd48e0e953aad6dda5d69a", "sources": ["arxiv", "semantic_scholar"], "title": "Scaling Knowledge Graph Construction through Synthetic Data Generation and Distillation", "abstract": "Document-level knowledge graph (KG) construction faces a fundamental scaling challenge: existing methods either rely on expensive large language models (LLMs), making them economically nonviable for large-scale corpora, or employ smaller models that produce incomplete and inconsistent graphs. We find that this limitation stems not from model capabilities but from insufficient training on high-quality document-level KG data. To address this gap, we introduce SynthKG, a multi-step data synthesis pipeline that generates high-quality document-KG pairs through systematic chunking, decontextualization, and structured extraction using LLMs. By fine-tuning a smaller LLM on synthesized document-KG pairs, we streamline the multi-step process into a single-step KG generation approach called Distill-SynthKG. Furthermore, we repurpose existing question-answering datasets to construct KG evaluation datasets and introduce new evaluation metrics. Using KGs produced by Distill-SynthKG, we also design a novel graph-based retrieval framework for RAG. Experimental results demonstrate that Distill-SynthKG not only surpasses all baseline models in KG quality (including models up to eight times larger) but also consistently improves in retrieval and question-answering tasks. Additionally, our proposed graph retrieval framework outperforms all KG-retrieval methods across multiple benchmark datasets.", "authors": ["Prafulla Kumar Choubey", "Xin Su", "Man Luo", "Xiangyu Peng", "Caiming Xiong", "Tiep Le", "Shachar Rosenman", "Vasudev Lal", "Phil Mui", "Ricky Ho", "Phillip Howard", "Chien-Sheng Wu"], "categories": ["cs.CL", "cs.IR"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-22", "url": "https://arxiv.org/abs/2410.16597", "pdf_url": "https://arxiv.org/pdf/2410.16597v2", "arxiv_id": "2410.16597", "doi": null, "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1945}
{"id": "3f06b72713e15d0afde914ce97c7d0fda2f69926fb874e5eeb8b10960dabaade", "sources": ["arxiv", "semantic_scholar"], "title": "CK4Gen: A Knowledge Distillation Framework for Generating High-Utility Synthetic Survival Datasets in Healthcare", "abstract": "Access to real clinical data is heavily restricted by privacy regulations, hindering both healthcare research and education. These constraints slow progress in developing new treatments and data-driven healthcare solutions, while also limiting students' access to real-world datasets, leaving them without essential practical skills. High-utility synthetic datasets are therefore critical for advancing research and providing meaningful training material. However, current generative models -- such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) -- produce surface-level realism at the expense of healthcare utility, blending distinct patient profiles and producing synthetic data of limited practical relevance. To overcome these limitations, we introduce CK4Gen (Cox Knowledge for Generation), a novel framework that leverages knowledge distillation from Cox Proportional Hazards (CoxPH) models to create synthetic survival datasets that preserve key clinical characteristics, including hazard ratios and survival curves. CK4Gen avoids the interpolation issues seen in VAEs and GANs by maintaining distinct patient risk profiles, ensuring realistic and reliable outputs for research and educational use. Validated across four benchmark datasets -- GBSG2, ACTG320, WHAS500, and FLChain -- CK4Gen outperforms competing techniques by better aligning real and synthetic data, enhancing survival model performance in both discrimination and calibration via data augmentation. As CK4Gen is scalable across clinical conditions, and with code to be made publicly available, future researchers can apply it to their own datasets to generate synthetic versions suitable for open sharing.", "authors": ["Nicholas I-Hsien Kuo", "Blanca Gallego", "Louisa Jorm"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-22", "url": "https://arxiv.org/abs/2410.16872", "pdf_url": "https://arxiv.org/pdf/2410.16872v1", "arxiv_id": "2410.16872", "doi": "10.48550/arXiv.2410.16872", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "2da2cda396caec4eaca0eed67263eafaf0967f9b8febc2c98b09cdf45b73b55a", "sources": ["arxiv", "semantic_scholar"], "title": "Robust Variable Selection for High-dimensional Regression with Missing Data and Measurement Errors", "abstract": "In our paper, we focus on robust variable selection for missing data and measurement error. Missing data and measurement errors can lead to confusing data distribution. We propose an exponential loss function with a tuning parameter to apply to Missing and measurement errors data. By adjusting the parameter, the loss function can be better and more robust under various data distributions. We use inverse probability weighting and additive error models to address missing data and measurement errors. Also, we find that the Atan punishment method works better. We used Monte Carlo simulations to assess the validity of robust variable selection and validated our findings with the breast cancer dataset.", "authors": ["Zhenhao Zhang", "Yunquan Song"], "categories": ["stat.ME", "stat.ML"], "fields_of_study": ["Mathematics"], "published_date": "2024-10-22", "url": "https://arxiv.org/abs/2410.16722", "pdf_url": "https://arxiv.org/pdf/2410.16722v3", "arxiv_id": "2410.16722", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0}
{"id": "6f2262982fcd7790b7ad35792eef13046b5470ef9ce21564c6a1cd89db7858f9", "sources": ["arxiv", "semantic_scholar"], "title": "Montessori-Instruct: Generate Influential Training Data Tailored for Student Learning", "abstract": "Synthetic data has been widely used to train large language models, but their generative nature inevitably introduces noisy, non-informative, and misleading learning signals. In this paper, we propose Montessori-Instruct, a novel data synthesis framework that tailors the data synthesis ability of the teacher language model toward the student language model's learning process. Specifically, we utilize local data influence of synthetic training data points on students to characterize students' learning preferences. Then, we train the teacher model with Direct Preference Optimization (DPO) to generate synthetic data tailored toward student learning preferences. Experiments with Llama3-8B-Instruct (teacher) and Llama3-8B (student) on Alpaca Eval and MT-Bench demonstrate that Montessori-Instruct significantly outperforms standard synthesis methods by 18.35\\% and 46.24\\% relatively. Our method also beats data synthesized by a stronger teacher model, GPT-4o. Further analysis confirms the benefits of teacher's learning to generate more influential training data in the student's improved learning, the advantages of local data influence in accurately measuring student preferences, and the robustness of Montessori-Instruct across different student models. Our code and data are open-sourced at https://github.com/cxcscmu/Montessori-Instruct.", "authors": ["Xiaochuan Li", "Zichun Yu", "Chenyan Xiong"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-18", "url": "https://arxiv.org/abs/2410.14208", "pdf_url": "https://arxiv.org/pdf/2410.14208v1", "arxiv_id": "2410.14208", "doi": "10.48550/arXiv.2410.14208", "citation_count": 12, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/cxcscmu/Montessori-Instruct", "venue": "International Conference on Learning Representations", "quality_score": 0.2785}
{"id": "74365a3f300464ca47a3939e91102a9c92e1d558a18b2eb7192b58f924a10010", "sources": ["arxiv", "semantic_scholar"], "title": "Controlled Automatic Task-Specific Synthetic Data Generation for Hallucination Detection", "abstract": "We present a novel approach to automatically generate non-trivial task-specific synthetic datasets for hallucination detection. Our approach features a two-step generation-selection pipeline, using hallucination pattern guidance and a language style alignment during generation. Hallucination pattern guidance leverages the most important task-specific hallucination patterns while language style alignment aligns the style of the synthetic dataset with benchmark text. To obtain robust supervised detectors from synthetic datasets, we also adopt a data mixture strategy to improve performance robustness and generalization. Our results on three datasets show that our generated hallucination text is more closely aligned with non-hallucinated text versus baselines, to train hallucination detectors with better generalization. Our hallucination detectors trained on synthetic datasets outperform in-context-learning (ICL)-based detectors by a large margin of 32%. Our extensive experiments confirm the benefits of our approach with cross-task and cross-generator generalization. Our data-mixture-based training further improves the generalization and robustness of hallucination detection.", "authors": ["Yong Xie", "Karan Aggarwal", "Aitzaz Ahmad", "Stephen Lau"], "categories": ["cs.CV", "cs.AI", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-16", "url": "https://arxiv.org/abs/2410.12278", "pdf_url": "https://arxiv.org/pdf/2410.12278v2", "arxiv_id": "2410.12278", "doi": "10.48550/arXiv.2410.12278", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "6f30c19646ffdcc130d8d6e9aa5dd76d73a040d9807b6faec278d1049070923c", "sources": ["arxiv", "semantic_scholar"], "title": "Approaching Metaheuristic Deep Learning Combos for Automated Data Mining", "abstract": "Lack of data on which to perform experimentation is a recurring issue in many areas of research, particularly in machine learning. The inability of most automated data mining techniques to be generalized to all types of data is inherently related with their dependency on those types which deems them ineffective against anything slightly different. Meta-heuristics are algorithms which attempt to optimize some solution independently of the type of data used, whilst classifiers or neural networks focus on feature extrapolation and dimensionality reduction to fit some model onto data arranged in a particular way. These two algorithmic fields encompass a group of characteristics which when combined are seemingly capable of achieving data mining regardless of how it is arranged. To this end, this work proposes a means of combining meta-heuristic methods with conventional classifiers and neural networks in order to perform automated data mining. Experiments on the MNIST dataset for handwritten digit recognition were performed and it was empirically observed that using a ground truth labeled dataset's validation accuracy is inadequate for correcting labels of other previously unseen data instances.", "authors": ["Gustavo Assunção", "Paulo Menezes"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-16", "url": "https://arxiv.org/abs/2410.12435", "pdf_url": "https://arxiv.org/pdf/2410.12435v1", "arxiv_id": "2410.12435", "doi": "10.48550/arXiv.2410.12435", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0}
{"id": "650b03e3ddd3125db66e4bd793f00d1c7242101e6e0b4b57acd61f369cd62688", "sources": ["arxiv", "semantic_scholar"], "title": "Empirical Bayes estimation via data fission", "abstract": "We demonstrate how data fission, a method for creating synthetic replicates from single observations, can be applied to empirical Bayes estimation. This extends recent work on empirical Bayes with multiple replicates to the classical single-replicate setting. The key insight is that after data fission, empirical Bayes estimation can be cast as a general regression problem.", "authors": ["Nikolaos Ignatiadis", "Dennis L. Sun"], "categories": ["stat.ME"], "fields_of_study": ["Mathematics"], "published_date": "2024-10-15", "url": "https://arxiv.org/abs/2410.12117", "pdf_url": "https://arxiv.org/pdf/2410.12117v1", "arxiv_id": "2410.12117", "doi": "10.1080/01621459.2024.2421994", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Journal of the American Statistical Association", "quality_score": 0.0}
{"id": "bf50c1ac173abfb3cf644a47984dab458a94e1aeba3fd1dedc2f7673f86046f5", "sources": ["arxiv", "semantic_scholar"], "title": "Fake it till you predict it: data augmentation strategies to detect initiation and termination of oncology treatment", "abstract": "At the hospital, the dispersion of information regarding anti-cancer treatment makes it difficult to extract. We proposed a solution capable of identifying dates, drugs and their temporal relationship within free-text oncology reports with very few manual annotations. We used pattern recognition for dates, dictionaries for drugs and transformer language models for the relationship, combined with a data augmentation strategy. Our models achieved good prediction F1-scores, reaching 0.872. The performance of models with data augmentation outperforms those of models without. By inferring such models, we can now identify and structure thousands of previously unavailable treatment events to better apprehend solutions and patient response.", "authors": ["Valentin Pohyer", "Elizabeth Fabre", "Stéphane Oudard", "Laure Fournier", "Bastien Rance"], "categories": ["q-bio.QM"], "fields_of_study": ["Medicine", "Biology"], "published_date": "2024-10-14", "url": "https://arxiv.org/abs/2410.10271", "pdf_url": "https://arxiv.org/pdf/2410.10271v1", "arxiv_id": "2410.10271", "doi": "10.3233/SHTI250468", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Studies in Health Technology and Informatics", "quality_score": 0.0753}
{"id": "96d4b82f831ca7dcba2b2fa3fff51c74671bf2bd241dd46b65f9ed2ea72bd009", "sources": ["arxiv", "semantic_scholar"], "title": "An Evaluation of Large Pre-Trained Models for Gesture Recognition using Synthetic Videos", "abstract": "In this work, we explore the possibility of using synthetically generated data for video-based gesture recognition with large pre-trained models. We consider whether these models have sufficiently robust and expressive representation spaces to enable \"training-free\" classification. Specifically, we utilize various state-of-the-art video encoders to extract features for use in k-nearest neighbors classification, where the training data points are derived from synthetic videos only. We compare these results with another training-free approach -- zero-shot classification using text descriptions of each gesture. In our experiments with the RoCoG-v2 dataset, we find that using synthetic training videos yields significantly lower classification accuracy on real test videos compared to using a relatively small number of real training videos. We also observe that video backbones that were fine-tuned on classification tasks serve as superior feature extractors, and that the choice of fine-tuning data has a substantial impact on k-nearest neighbors performance. Lastly, we find that zero-shot text-based classification performs poorly on the gesture recognition task, as gestures are not easily described through natural language.", "authors": ["Arun Reddy", "Ketul Shah", "Corban Rivera", "William Paul", "Celso M. De Melo", "Rama Chellappa"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-10-03", "url": "https://arxiv.org/abs/2410.02152", "pdf_url": "https://arxiv.org/pdf/2410.02152v1", "arxiv_id": "2410.02152", "doi": "10.1117/12.3013530", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II. Vol. 13035. SPIE, 2024", "quality_score": 0.1193}
{"id": "4ef235b5813a323d4a2ec997085b0bcddeca9d3df9ac4867ea9122466647e4ec", "sources": ["arxiv", "semantic_scholar"], "title": "Analyzing black-hole ringdowns II: data conditioning", "abstract": "Time series data from observations of black hole ringdown gravitational waves are often analyzed in the time domain by using damped sinusoid models with acyclic boundary conditions. Data conditioning operations, including downsampling, filtering, and the choice of data segment duration, reduce the computational cost of such analyses and can improve numerical stability. Here we analyze simulated damped sinsuoid signals to illustrate how data conditioning operations, if not carefully applied, can undesirably alter the analysis' posterior distributions. We discuss how currently implemented downsampling and filtering methods, if applied too aggressively, can introduce systematic errors and skew tests of general relativity. These issues arise because current downsampling and filtering methods do not operate identically on the data and model. Alternative downsampling and filtering methods which identically operate on the data and model may be achievable, but we argue that the current operations can still be implemented safely. We also show that our preferred anti-alias filtering technique, which has an instantaneous frequency-domain response at its roll-off frequency, preserves the structure of posterior distributions better than other commonly used filters with transient frequency-domain responses. Lastly, we highlight that exceptionally long data segments may need to be analyzed in cases where thin lines in the noise power spectral density overlap with central signal frequencies. Our findings may be broadly applicable to any analysis of truncated time domain data with acyclic boundary conditions.", "authors": ["Harrison Siegel", "Maximiliano Isi", "Will M. Farr"], "categories": ["gr-qc", "astro-ph.IM", "physics.data-an"], "fields_of_study": ["Physics"], "published_date": "2024-10-03", "url": "https://arxiv.org/abs/2410.02704", "pdf_url": "https://arxiv.org/pdf/2410.02704v2", "arxiv_id": "2410.02704", "doi": "10.1103/PhysRevD.111.044070", "citation_count": 8, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/HarrisonS-Phys/ringdown-data-conditioning", "venue": "Phys. Rev. D 111, 044070 (2025)", "quality_score": 0.2386}
{"id": "78397b3bd8fdaaa71e6a77f11b8eed5fc7bb5d038fb7d98e5d75edabaee90936", "sources": ["arxiv", "semantic_scholar"], "title": "TAEGAN: Generating Synthetic Tabular Data For Data Augmentation", "abstract": "Synthetic tabular data generation has gained significant attention for its potential in data augmentation and privacy-preserving data sharing. While recent methods like diffusion and auto-regressive models (i.e., transformer) have advanced the field, generative adversarial networks (GANs) remain highly competitive due to their training efficiency and strong data generation capabilities. In this paper, we introduce Tabular Auto-Encoder Generative Adversarial Network (TAEGAN), a novel GAN-based framework that leverages a masked auto-encoder as the generator. TAEGAN is the first to incorporate self-supervised warmup training of generator into tabular GANs. It enhances GAN stability and exposes the generator to richer information beyond the discriminator's feedback. Additionally, we propose a novel sampling method tailored for imbalanced or skewed data and an improved loss function to better capture data distribution and correlations. We evaluate TAEGAN against seven state-of-the-art synthetic tabular data generation algorithms. Results from eight datasets show that TAEGAN outperforms all baselines on five datasets, achieving a 27% overall utility boost over the best-performing baseline while maintaining a model size less than 5% of the best-performing baseline model. Code is available at: https://github.com/BetterdataLabs/taegan.", "authors": ["Jiayu Li", "Zilong Zhao", "Kevin Yee", "Uzair Javaid", "Biplab Sikdar"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-02", "url": "https://arxiv.org/abs/2410.01933", "pdf_url": "https://arxiv.org/pdf/2410.01933v2", "arxiv_id": "2410.01933", "doi": null, "citation_count": 4, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/BetterdataLabs/taegan", "venue": null, "quality_score": 0.1747}
{"id": "4ba48b7a2cae960db8452f8859a57329909514af5c77f0cfde4ad60f10802ee8", "sources": ["arxiv", "semantic_scholar"], "title": "Fair4Free: Generating High-fidelity Fair Synthetic Samples using Data Free Distillation", "abstract": "This work presents Fair4Free, a novel generative model to generate synthetic fair data using data-free distillation in the latent space. Fair4Free can work on the situation when the data is private or inaccessible. In our approach, we first train a teacher model to create fair representation and then distil the knowledge to a student model (using a smaller architecture). The process of distilling the student model is data-free, i.e. the student model does not have access to the training dataset while distilling. After the distillation, we use the distilled model to generate fair synthetic samples. Our extensive experiments show that our synthetic samples outperform state-of-the-art models in all three criteria (fairness, utility and synthetic quality) with a performance increase of 5% for fairness, 8% for utility and 12% in synthetic quality for both tabular and image datasets.", "authors": ["Md Fahim Sikder", "Daniel de Leng", "Fredrik Heintz"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-02", "url": "https://arxiv.org/abs/2410.01423", "pdf_url": "https://arxiv.org/pdf/2410.01423v1", "arxiv_id": "2410.01423", "doi": "10.48550/arXiv.2410.01423", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "d536fd05e81fa42c236849dc45caf9f39c36c3189ea33db971152f38ee8d5600", "sources": ["arxiv", "semantic_scholar"], "title": "Synthio: Augmenting Small-Scale Audio Classification Datasets with Synthetic Data", "abstract": "We present Synthio, a novel approach for augmenting small-scale audio classification datasets with synthetic data. Our goal is to improve audio classification accuracy with limited labeled data. Traditional data augmentation techniques, which apply artificial transformations (e.g., adding random noise or masking segments), struggle to create data that captures the true diversity present in real-world audios. To address this shortcoming, we propose to augment the dataset with synthetic audio generated from text-to-audio (T2A) diffusion models. However, synthesizing effective augmentations is challenging because not only should the generated data be acoustically consistent with the underlying small-scale dataset, but they should also have sufficient compositional diversity. To overcome the first challenge, we align the generations of the T2A model with the small-scale dataset using preference optimization. This ensures that the acoustic characteristics of the generated data remain consistent with the small-scale dataset. To address the second challenge, we propose a novel caption generation technique that leverages the reasoning capabilities of Large Language Models to (1) generate diverse and meaningful audio captions and (2) iteratively refine their quality. The generated captions are then used to prompt the aligned T2A model. We extensively evaluate Synthio on ten datasets and four simulated limited-data settings. Results indicate our method consistently outperforms all baselines by 0.1%-39% using a T2A model trained only on weakly-captioned AudioSet.", "authors": ["Sreyan Ghosh", "Sonal Kumar", "Zhifeng Kong", "Rafael Valle", "Bryan Catanzaro", "Dinesh Manocha"], "categories": ["eess.AS", "cs.AI", "cs.CL"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-10-02", "url": "https://arxiv.org/abs/2410.02056", "pdf_url": "https://arxiv.org/pdf/2410.02056v2", "arxiv_id": "2410.02056", "doi": "10.48550/arXiv.2410.02056", "citation_count": 11, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Sreyan88/Synthio", "venue": "International Conference on Learning Representations", "quality_score": 0.2698}
{"id": "9d216eac9d6c4061a3e8ece1dce8c1604323c122dea7ba5457aa48b18b430f84", "sources": ["arxiv", "semantic_scholar"], "title": "Exploring Empty Spaces: Human-in-the-Loop Data Augmentation", "abstract": "Data augmentation is crucial to make machine learning models more robust and safe. However, augmenting data can be challenging as it requires generating diverse data points to rigorously evaluate model behavior on edge cases and mitigate potential harms. Creating high-quality augmentations that cover these \"unknown unknowns\" is a time- and creativity-intensive task. In this work, we introduce Amplio, an interactive tool to help practitioners navigate \"unknown unknowns\" in unstructured text datasets and improve data diversity by systematically identifying empty data spaces to explore. Amplio includes three human-in-the-loop data augmentation techniques: Augment With Concepts, Augment by Interpolation, and Augment with Large Language Model. In a user study with 18 professional red teamers, we demonstrate the utility of our augmentation methods in helping generate high-quality, diverse, and relevant model safety prompts. We find that Amplio enabled red teamers to augment data quickly and creatively, highlighting the transformative potential of interactive augmentation workflows.", "authors": ["Catherine Yeh", "Donghao Ren", "Yannick Assogba", "Dominik Moritz", "Fred Hohman"], "categories": ["cs.HC", "cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-01", "url": "https://arxiv.org/abs/2410.01088", "pdf_url": "https://arxiv.org/pdf/2410.01088v2", "arxiv_id": "2410.01088", "doi": "10.1145/3706598.3713491", "citation_count": 8, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/apple/ml-interactive-data-augmentation/", "venue": "International Conference on Human Factors in Computing Systems", "quality_score": 0.2386}
{"id": "2b4334160b271d859ddcebcfdab147119f4cef5cf4e108a5ffec983dee40a789", "sources": ["arxiv", "semantic_scholar"], "title": "Restoring Super-High Resolution GPS Mobility Data", "abstract": "This paper presents a novel system for reconstructing high-resolution GPS trajectory data from truncated or synthetic low-resolution inputs, addressing the critical challenge of balancing data utility with privacy preservation in mobility applications. The system integrates transformer-based encoder-decoder models with graph convolutional networks (GCNs) to effectively capture both the temporal dependencies of trajectory data and the spatial relationships in road networks. By combining these techniques, the system is able to recover fine-grained trajectory details that are lost through data truncation or rounding, a common practice to protect user privacy. We evaluate the system on the Beijing trajectory dataset, demonstrating its superior performance over traditional map-matching algorithms and LSTM-based synthetic data generation methods. The proposed model achieves an average Fréchet distance of 0.198 km, significantly outperforming map-matching algorithms (0.632 km) and synthetic trajectory models (0.498 km). The results show that the system is not only capable of accurately reconstructing real-world trajectories but also generalizes effectively to synthetic data. These findings suggest that the system can be deployed in urban mobility applications, providing both high accuracy and robust privacy protection.", "authors": ["Haruki Yonekura", "Ren Ozeki", "Hamada Rizk", "Hirozumi Yamaguchi"], "categories": ["eess.SP", "cs.LG"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2024-10-01", "url": "https://arxiv.org/abs/2410.12818", "pdf_url": "https://arxiv.org/pdf/2410.12818v1", "arxiv_id": "2410.12818", "doi": "10.1145/3681768.3698501", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Geo-Privacy and Data Utility for Smart Societies (GeoPrivacy '24), 2024, pp. 19-24", "quality_score": 0.0753}
{"id": "62b08f3a5566193a54a67608d3648a996f99092a306b61744016a20f4a0d9840", "sources": ["arxiv", "semantic_scholar"], "title": "Targeted synthetic data generation for tabular data via hardness characterization", "abstract": "Data augmentation via synthetic data generation has been shown to be effective in improving model performance and robustness in the context of scarce or low-quality data. Using the data valuation framework to statistically identify beneficial and detrimental observations, we introduce a simple augmentation pipeline that generates only high-value training points based on hardness characterization, in a computationally efficient manner. We first empirically demonstrate via benchmarks on real data that Shapley-based data valuation methods perform comparably with learning-based methods in hardness characterization tasks, while offering significant computational advantages. Then, we show that synthetic data generators trained on the hardest points outperform non-targeted data augmentation on a number of tabular datasets. Our approach improves the quality of out-of-sample predictions and it is computationally more efficient compared to non-targeted methods.", "authors": ["Tommaso Ferracci", "Leonie Tabea Goldmann", "Anton Hinel", "Francesco Sanna Passino"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2024-10-01", "url": "https://arxiv.org/abs/2410.00759", "pdf_url": "https://arxiv.org/pdf/2410.00759v2", "arxiv_id": "2410.00759", "doi": "10.48550/arXiv.2410.00759", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "a348c49b64baa38638b895f5fd02c9dc34a1fd642474cd5b30653e50fd69c27b", "sources": ["arxiv"], "title": "\"Hiding in Plain Sight\": Designing Synthetic Dialog Generation for Uncovering Socially Situated Norms", "abstract": "Naturally situated conversations encapsulate the social norms inherent to their context, reflecting both the relationships between interlocutors and the underlying communicative intent. In this paper, we propose a novel, multi-step framework for generating dialogues that automatically uncovers social norms from rich, context-laden interactions through a process of self-assessment and norm discovery, rather than relying on predefined norm labels. Leveraging this framework, we construct NormHint, a comprehensive synthetic dialogue dataset spanning a wide range of interlocutor attributes (e.g., age, profession, personality), relationship types, conversation topics, and conversational trajectories. NormHint is meticulously annotated with turn-level norm violation information, detailed participant descriptions, and remediation suggestions-including alternative trajectories achieved through early intervention. Human validation and automated analysis demonstrate that our dataset captures diverse conversational topics with high naturalness and realism. Moreover, we discovered that fine-tuning a model with our norm violation data significantly enhances its ability to detect and understand potential norm violations in conversations.", "authors": ["Chengfei Wu", "Dan Goldwasser"], "categories": ["cs.CL"], "fields_of_study": [], "published_date": "2024-10-01", "url": "https://arxiv.org/abs/2410.00998", "pdf_url": "https://arxiv.org/pdf/2410.00998v2", "arxiv_id": "2410.00998", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0}
{"id": "dd5cae53bcda19027c90d6bc8bbeb50dec5f421b7027ba40a6e930b27a31d808", "sources": ["arxiv", "semantic_scholar"], "title": "Enhancing Romanian Offensive Language Detection through Knowledge Distillation, Multi-Task Learning, and Data Augmentation", "abstract": "This paper highlights the significance of natural language processing (NLP) within artificial intelligence, underscoring its pivotal role in comprehending and modeling human language. Recent advancements in NLP, particularly in conversational bots, have garnered substantial attention and adoption among developers. This paper explores advanced methodologies for attaining smaller and more efficient NLP models. Specifically, we employ three key approaches: (1) training a Transformer-based neural network to detect offensive language, (2) employing data augmentation and knowledge distillation techniques to increase performance, and (3) incorporating multi-task learning with knowledge distillation and teacher annealing using diverse datasets to enhance efficiency. The culmination of these methods has yielded demonstrably improved outcomes.", "authors": ["Vlad-Cristian Matei", "Iulian-Marius Tăiatu", "Răzvan-Alexandru Smădu", "Dumitru-Clementin Cercel"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-30", "url": "https://arxiv.org/abs/2409.20498", "pdf_url": "https://arxiv.org/pdf/2409.20498v1", "arxiv_id": "2409.20498", "doi": "10.1007/978-3-031-70239-6_22", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Applications of Natural Language to Data Bases", "quality_score": 0.0753}
{"id": "e9334e272b45dc393ca62e4e7b7496a7fd3c45d30ff31869bb4c57116817b6ba", "sources": ["arxiv", "semantic_scholar"], "title": "Data-Centric Design: Introducing An Informatics Domain Model And Core Data Ontology For Computational Systems", "abstract": "The Core Data Ontology (CDO) and the Informatics Domain Model represent a transformative approach to computational systems, shifting from traditional node-centric designs to a data-centric paradigm. This paper introduces a framework where data is categorized into four modalities: objects, events, concepts, and actions. This quadrimodal structure enhances data security, semantic interoperability, and scalability across distributed data ecosystems. The CDO offers a comprehensive ontology that supports AI development, role-based access control, and multimodal data management. By focusing on the intrinsic value of data, the Informatics Domain Model redefines system architectures to prioritize data security, provenance, and auditability, addressing vulnerabilities in current models. The paper outlines the methodology for developing the CDO, explores its practical applications in fields such as AI, robotics, and legal compliance, and discusses future directions for scalable, decentralized, and interoperable data ecosystems.", "authors": ["Paul Knowles", "Bart Gajderowicz", "Keith Dugas"], "categories": ["cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-29", "url": "https://arxiv.org/abs/2409.19653", "pdf_url": "https://arxiv.org/pdf/2409.19653v2", "arxiv_id": "2409.19653", "doi": "10.5121/csit.2024.141720", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "071c60f6870d4f00d456c37affde359bd978c95c0f7611bf4fda8d386ebc6dec", "sources": ["arxiv", "semantic_scholar"], "title": "Balancing Cost and Effectiveness of Synthetic Data Generation Strategies for LLMs", "abstract": "As large language models (LLMs) are applied to more use cases, creating high quality, task-specific datasets for fine-tuning becomes a bottleneck for model improvement. Using high quality human data has been the most common approach to unlock model performance, but is prohibitively expensive in many scenarios. Several alternative methods have also emerged, such as generating synthetic or hybrid data, but the effectiveness of these approaches remain unclear, especially in resource-constrained scenarios and tasks that are not easily verified. To investigate this, we group various synthetic data generation strategies into three representative categories -- Answer Augmentation, Question Rephrase and New Question -- and study the performance of student LLMs trained under various constraints, namely seed instruction set size and query budget. We demonstrate that these strategies are not equally effective across settings. Notably, the optimal data generation strategy depends strongly on the ratio between the available teacher query budget and the size of the seed instruction set. When this ratio is low, generating new answers to existing questions proves most effective, but as this ratio increases, generating new questions becomes optimal. Across all tasks, we find that choice of augmentation method and other design choices matter substantially more in low to mid data regimes than in high data regimes. We provide a practical framework for selecting the appropriate augmentation method across settings, taking into account additional factors such as the scalability of each method, the importance of verifying synthetic data, and the use of different LLMs for synthetic data generation.", "authors": ["Yung-Chieh Chan", "George Pu", "Apaar Shanker", "Parth Suresh", "Penn Jenks", "John Heyer", "Sam Denton"], "categories": ["cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-29", "url": "https://arxiv.org/abs/2409.19759", "pdf_url": "https://arxiv.org/pdf/2409.19759v3", "arxiv_id": "2409.19759", "doi": "10.48550/arXiv.2409.19759", "citation_count": 21, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3356}
{"id": "b5bd5ca187b2900604b0c48c01923f15c2400baa491af0bd00f6140098bc8aed", "sources": ["arxiv", "semantic_scholar"], "title": "DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance Scaling", "abstract": "In this paper, we present an effective data augmentation framework leveraging the Large Language Model (LLM) and Diffusion Model (DM) to tackle the challenges inherent in data-scarce scenarios. Recently, DMs have opened up the possibility of generating synthetic images to complement a few training images. However, increasing the diversity of synthetic images also raises the risk of generating samples outside the target distribution. Our approach addresses this issue by embedding novel semantic information into text prompts via LLM and utilizing real images as visual prompts, thus generating semantically rich images. To ensure that the generated images remain within the target distribution, we dynamically adjust the guidance weight based on each image's CLIPScore to control the diversity. Experimental results show that our method produces synthetic images with enhanced diversity while maintaining adherence to the target distribution. Consequently, our approach proves to be more efficient in the few-shot setting on several benchmarks. Our code is available at https://github.com/kkyuhun94/dalda .", "authors": ["Kyuheon Jung", "Yongdeuk Seo", "Seongwoo Cho", "Jaeyoung Kim", "Hyun-seok Min", "Sungchul Choi"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-25", "url": "https://arxiv.org/abs/2409.16949", "pdf_url": "https://arxiv.org/pdf/2409.16949v1", "arxiv_id": "2409.16949", "doi": "10.48550/arXiv.2409.16949", "citation_count": 9, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/kkyuhun94/dalda", "venue": null, "quality_score": 0.25}
{"id": "9a6c6850f58664542f60cd513c055796d3d5a49be2adcc0d44f32ad644401308", "sources": ["arxiv", "semantic_scholar"], "title": "KIPPS: Knowledge infusion in Privacy Preserving Synthetic Data Generation", "abstract": "The integration of privacy measures, including differential privacy techniques, ensures a provable privacy guarantee for the synthetic data. However, challenges arise for Generative Deep Learning models when tasked with generating realistic data, especially in critical domains such as Cybersecurity and Healthcare. Generative Models optimized for continuous data struggle to model discrete and non-Gaussian features that have domain constraints. Challenges increase when the training datasets are limited and not diverse. In such cases, generative models create synthetic data that repeats sensitive features, which is a privacy risk. Moreover, generative models face difficulties comprehending attribute constraints in specialized domains. This leads to the generation of unrealistic data that impacts downstream accuracy. To address these issues, this paper proposes a novel model, KIPPS, that infuses Domain and Regulatory Knowledge from Knowledge Graphs into Generative Deep Learning models for enhanced Privacy Preserving Synthetic data generation. The novel framework augments the training of generative models with supplementary context about attribute values and enforces domain constraints during training. This added guidance enhances the model's capacity to generate realistic and domain-compliant synthetic data. The proposed model is evaluated on real-world datasets, specifically in the domains of Cybersecurity and Healthcare, where domain constraints and rules add to the complexity of the data. Our experiments evaluate the privacy resilience and downstream accuracy of the model against benchmark methods, demonstrating its effectiveness in addressing the balance between privacy preservation and data accuracy in complex domains.", "authors": ["Anantaa Kotal", "Anupam Joshi"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-25", "url": "https://arxiv.org/abs/2409.17315", "pdf_url": "https://arxiv.org/pdf/2409.17315v1", "arxiv_id": "2409.17315", "doi": "10.48550/arXiv.2409.17315", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "aa3dd8bd76071c5a24c2587d85e91f64bf305f5304abb7d483b7d598c3485b20", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Synthetic Data Generation for Improved Pain Recognition in Videos under Patient Constraints", "abstract": "Recognizing pain in video is crucial for improving patient-computer interaction systems, yet traditional data collection in this domain raises significant ethical and logistical challenges. This study introduces a novel approach that leverages synthetic data to enhance video-based pain recognition models, providing an ethical and scalable alternative. We present a pipeline that synthesizes realistic 3D facial models by capturing nuanced facial movements from a small participant pool, and mapping these onto diverse synthetic avatars. This process generates 8,600 synthetic faces, accurately reflecting genuine pain expressions from varied angles and perspectives. Utilizing advanced facial capture techniques, and leveraging public datasets like CelebV-HQ and FFHQ-UV for demographic diversity, our new synthetic dataset significantly enhances model training while ensuring privacy by anonymizing identities through facial replacements. Experimental results demonstrate that models trained on combinations of synthetic data paired with a small amount of real participants achieve superior performance in pain recognition, effectively bridging the gap between synthetic simulations and real-world applications. Our approach addresses data scarcity and ethical concerns, offering a new solution for pain detection and opening new avenues for research in privacy-preserving dataset generation. All resources are publicly available to encourage further innovation in this field.", "authors": ["Jonas Nasimzada", "Jens Kleesiek", "Ken Herrmann", "Alina Roitberg", "Constantin Seibold"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-24", "url": "https://arxiv.org/abs/2409.16382", "pdf_url": "https://arxiv.org/pdf/2409.16382v1", "arxiv_id": "2409.16382", "doi": "10.48550/arXiv.2409.16382", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "5c1301f5dff14e2ad54574b73da5ee9ea4313f8d74c2c9f74832611207ae0d5a", "sources": ["arxiv", "semantic_scholar"], "title": "HSIGene: A Foundation Model For Hyperspectral Image Generation", "abstract": "Hyperspectral image (HSI) plays a vital role in various fields such as agriculture and environmental monitoring. However, due to the expensive acquisition cost, the number of hyperspectral images is limited, degenerating the performance of downstream tasks. Although some recent studies have attempted to employ diffusion models to synthesize HSIs, they still struggle with the scarcity of HSIs, affecting the reliability and diversity of the generated images. Some studies propose to incorporate multi-modal data to enhance spatial diversity, but the spectral fidelity cannot be ensured. In addition, existing HSI synthesis models are typically uncontrollable or only support single-condition control, limiting their ability to generate accurate and reliable HSIs. To alleviate these issues, we propose HSIGene, a novel HSI generation foundation model which is based on latent diffusion and supports multi-condition control, allowing for more precise and reliable HSI generation. To enhance the spatial diversity of the training data while preserving spectral fidelity, we propose a new data augmentation method based on spatial super-resolution, in which HSIs are upscaled first, and thus abundant training patches could be obtained by cropping the high-resolution HSIs. In addition, to improve the perceptual quality of the augmented data, we introduce a novel two-stage HSI super-resolution framework, which first applies RGB bands super-resolution and then utilizes our proposed Rectangular Guided Attention Network (RGAN) for guided HSI super-resolution. Experiments demonstrate that the proposed model is capable of generating a vast quantity of realistic HSIs for downstream tasks such as denoising and super-resolution. The code and models are available at https://github.com/LiPang/HSIGene.", "authors": ["Li Pang", "Xiangyong Cao", "Datao Tang", "Shuang Xu", "Xueru Bai", "Feng Zhou", "Deyu Meng"], "categories": ["cs.CV", "eess.IV"], "fields_of_study": ["Computer Science", "Medicine", "Engineering"], "published_date": "2024-09-19", "url": "https://arxiv.org/abs/2409.12470", "pdf_url": "https://arxiv.org/pdf/2409.12470v2", "arxiv_id": "2409.12470", "doi": "10.1109/TPAMI.2025.3610927", "citation_count": 35, "influential_citation_count": 2, "has_code": true, "code_url": "https://github.com/LiPang/HSIGene", "venue": "IEEE Transactions on Pattern Analysis and Machine Intelligence", "quality_score": 0.3891}
{"id": "c1da214de810d21059756d6390b59e2d08f0dd79dfb938e1d64b0b28c24d5f4c", "sources": ["arxiv", "semantic_scholar"], "title": "SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problems", "abstract": "Stochastic optimization problems in large-scale multi-stakeholder networked systems (e.g., power grids and supply chains) rely on data-driven scenarios to encapsulate complex spatiotemporal interdependencies. However, centralized aggregation of stakeholder data is challenging due to the existence of data silos resulting from computational and logistical bottlenecks. In this paper, we present SplitVAEs, a decentralized scenario generation framework that leverages variational autoencoders to generate high-quality scenarios without moving stakeholder data. With the help of experiments on distributed memory systems, we demonstrate the broad applicability of SplitVAEs in a variety of domain areas that are dominated by a large number of stakeholders. Our experiments indicate that SplitVAEs can learn spatial and temporal interdependencies in large-scale networks to generate scenarios that match the joint historical distribution of stakeholder data in a decentralized manner. Our experiments show that SplitVAEs deliver robust performance compared to centralized, state-of-the-art benchmark methods while significantly reducing data transmission costs, leading to a scalable, privacy-enhancing alternative to scenario generation.", "authors": ["H M Mohaimanul Islam", "Huynh Q. N. Vo", "Paritosh Ramanan"], "categories": ["cs.LG", "math.OC", "stat.ME"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2024-09-18", "url": "https://arxiv.org/abs/2409.12328", "pdf_url": "https://arxiv.org/pdf/2409.12328v2", "arxiv_id": "2409.12328", "doi": "10.1109/BigData62323.2024.10826070", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "BigData Congress [Services Society]", "quality_score": 0.1193}
{"id": "02873c02d8af1aaf62148ae44662d0718d20abafedef1e071c95b8d0a8a3621e", "sources": ["arxiv", "semantic_scholar"], "title": "Towards a Unified Theory for Semiparametric Data Fusion with Individual-Level Data", "abstract": "We address the goal of conducting inference about a smooth finite-dimensional parameter by utilizing individual-level data from various independent sources. Recent advancements have led to the development of a comprehensive theory capable of handling scenarios where different data sources align with, possibly distinct subsets of, conditional distributions of a single factorization of the joint target distribution. While this theory proves effective in many significant contexts, it falls short in certain common data fusion problems, such as two-sample instrumental variable analysis, settings that integrate data from epidemiological studies with diverse designs (e.g., prospective cohorts and retrospective case-control studies), and studies with variables prone to measurement error that are supplemented by validation studies. In this paper, we extend the aforementioned comprehensive theory to allow for the fusion of individual-level data from sources aligned with conditional distributions that do not correspond to a single factorization of the target distribution. Assuming conditional and marginal distribution alignments, we provide universal results that characterize the class of all influence functions of regular asymptotically linear estimators and the efficient influence function of any pathwise differentiable parameter, irrespective of the number of data sources, the specific parameter of interest, or the statistical model for the target distribution. This theory paves the way for machine-learning debiased, semiparametric efficient estimation.", "authors": ["Ellen Graham", "Marco Carone", "Andrea Rotnitzky"], "categories": ["math.ST", "stat.ME", "stat.ML"], "fields_of_study": ["Mathematics"], "published_date": "2024-09-16", "url": "https://arxiv.org/abs/2409.09973", "pdf_url": "https://arxiv.org/pdf/2409.09973v3", "arxiv_id": "2409.09973", "doi": null, "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1945}
{"id": "49a5c7b9ba9579743f6d6e67c570754d64494cc8c51456f6cfc426fa4b656596", "sources": ["arxiv", "semantic_scholar"], "title": "Data-Centric Strategies for Overcoming PET/CT Heterogeneity: Insights from the AutoPET III Lesion Segmentation Challenge", "abstract": "The third autoPET challenge introduced a new data-centric task this year, shifting the focus from model development to improving metastatic lesion segmentation on PET/CT images through data quality and handling strategies. In response, we developed targeted methods to enhance segmentation performance tailored to the characteristics of PET/CT imaging. Our approach encompasses two key elements. First, to address potential alignment errors between CT and PET modalities as well as the prevalence of punctate lesions, we modified the baseline data augmentation scheme and extended it with misalignment augmentation. This adaptation aims to improve segmentation accuracy, particularly for tiny metastatic lesions. Second, to tackle the variability in image dimensions significantly affecting the prediction time, we implemented a dynamic ensembling and test-time augmentation (TTA) strategy. This method optimizes the use of ensembling and TTA within a 5-minute prediction time limit, effectively leveraging the generalization potential for both small and large images. Both of our solutions are designed to be robust across different tracers and institutional settings, offering a general, yet imaging-specific approach to the multi-tracer and multi-institutional challenges of the competition. We made the challenge repository with our modifications publicly available at \\url{https://github.com/MIC-DKFZ/miccai2024_autopet3_datacentric}.", "authors": ["Balint Kovacs", "Shuhan Xiao", "Maximilian Rokuss", "Constantin Ulrich", "Fabian Isensee", "Klaus H. Maier-Hein"], "categories": ["eess.IV", "cs.CV"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-09-16", "url": "https://arxiv.org/abs/2409.10120", "pdf_url": "https://arxiv.org/pdf/2409.10120v1", "arxiv_id": "2409.10120", "doi": "10.48550/arXiv.2409.10120", "citation_count": 2, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/MIC-DKFZ/miccai2024_autopet3_datacentric}", "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "169901fa4fbe63fb1c011df7795f3a216ee368154c86e5c1d9fda77b2e4beae3", "sources": ["arxiv", "semantic_scholar"], "title": "EchoDFKD: Data-Free Knowledge Distillation for Cardiac Ultrasound Segmentation using Synthetic Data", "abstract": "The application of machine learning to medical ultrasound videos of the heart, i.e., echocardiography, has recently gained traction with the availability of large public datasets. Traditional supervised tasks, such as ejection fraction regression, are now making way for approaches focusing more on the latent structure of data distributions, as well as generative methods. We propose a model trained exclusively by knowledge distillation, either on real or synthetical data, involving retrieving masks suggested by a teacher model. We achieve state-of-the-art (SOTA) values on the task of identifying end-diastolic and end-systolic frames. By training the model only on synthetic data, it reaches segmentation capabilities close to the performance when trained on real data with a significantly reduced number of weights. A comparison with the 5 main existing methods shows that our method outperforms the others in most cases. We also present a new evaluation method that does not require human annotation and instead relies on a large auxiliary model. We show that this method produces scores consistent with those obtained from human annotations. Relying on the integrated knowledge from a vast amount of records, this method overcomes certain inherent limitations of human annotator labeling. Code: https://github.com/GregoirePetit/EchoDFKD", "authors": ["Grégoire Petit", "Nathan Palluau", "Axel Bauer", "Clemens Dlaska"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-11", "url": "https://arxiv.org/abs/2409.07566", "pdf_url": "https://arxiv.org/pdf/2409.07566v2", "arxiv_id": "2409.07566", "doi": "10.1109/WACV61041.2025.00825", "citation_count": 2, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/GregoirePetit/EchoDFKD", "venue": "IEEE Workshop/Winter Conference on Applications of Computer Vision", "quality_score": 0.1193}
{"id": "942b81b27c7a86775b02f6a5219484b7288ad49c310ab8b32ab036fe4cc10e5a", "sources": ["arxiv", "semantic_scholar"], "title": "DKDM: Data-Free Knowledge Distillation for Diffusion Models with Any Architecture", "abstract": "Diffusion models (DMs) have demonstrated exceptional generative capabilities across various domains, including image, video, and so on. A key factor contributing to their effectiveness is the high quantity and quality of data used during training. However, mainstream DMs now consume increasingly large amounts of data. For example, training a Stable Diffusion model requires billions of image-text pairs. This enormous data requirement poses significant challenges for training large DMs due to high data acquisition costs and storage expenses. To alleviate this data burden, we propose a novel scenario: using existing DMs as data sources to train new DMs with any architecture. We refer to this scenario as Data-Free Knowledge Distillation for Diffusion Models (DKDM), where the generative ability of DMs is transferred to new ones in a data-free manner. To tackle this challenge, we make two main contributions. First, we introduce a DKDM objective that enables the training of new DMs via distillation, without requiring access to the data. Second, we develop a dynamic iterative distillation method that efficiently extracts time-domain knowledge from existing DMs, enabling direct retrieval of training data without the need for a prolonged generative process. To the best of our knowledge, we are the first to explore this scenario. Experimental results demonstrate that our data-free approach not only achieves competitive generative performance but also, in some instances, outperforms models trained with the entire dataset.", "authors": ["Qianlong Xiang", "Miao Zhang", "Yuzhang Shang", "Jianlong Wu", "Yan Yan", "Liqiang Nie"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-05", "url": "https://arxiv.org/abs/2409.03550", "pdf_url": "https://arxiv.org/pdf/2409.03550v2", "arxiv_id": "2409.03550", "doi": "10.1109/CVPR52734.2025.00281", "citation_count": 32, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Computer Vision and Pattern Recognition", "quality_score": 0.3796}
{"id": "4e78f13cd9dd84edae45d5328796364bbd92061fb7179f3ecc54bdfb11c2dc88", "sources": ["arxiv", "semantic_scholar"], "title": "The Impact of Balancing Real and Synthetic Data on Accuracy and Fairness in Face Recognition", "abstract": "Over the recent years, the advancements in deep face recognition have fueled an increasing demand for large and diverse datasets. Nevertheless, the authentic data acquired to create those datasets is typically sourced from the web, which, in many cases, can lead to significant privacy issues due to the lack of explicit user consent. Furthermore, obtaining a demographically balanced, large dataset is even more difficult because of the natural imbalance in the distribution of images from different demographic groups. In this paper, we investigate the impact of demographically balanced authentic and synthetic data, both individually and in combination, on the accuracy and fairness of face recognition models. Initially, several generative methods were used to balance the demographic representations of the corresponding synthetic datasets. Then a state-of-the-art face encoder was trained and evaluated using (combinations of) synthetic and authentic images. Our findings emphasized two main points: (i) the increased effectiveness of training data generated by diffusion-based models in enhancing accuracy, whether used alone or combined with subsets of authentic data, and (ii) the minimal impact of incorporating balanced data from pre-trained generative methods on fairness (in nearly all tested scenarios using combined datasets, fairness scores remained either unchanged or worsened, even when compared to unbalanced authentic datasets). Source code and data are available at \\url{https://cutt.ly/AeQy1K5G} for reproducibility.", "authors": ["Andrea Atzori", "Pietro Cosseddu", "Gianni Fenu", "Mirko Marras"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-04", "url": "https://arxiv.org/abs/2409.02867", "pdf_url": "https://arxiv.org/pdf/2409.02867v1", "arxiv_id": "2409.02867", "doi": "10.48550/arXiv.2409.02867", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2386}
{"id": "0a5da0f54ca2d85cf06d15f576030ed963ecab1b5ea46f1b6b48904fec9e1bcb", "sources": ["arxiv", "semantic_scholar"], "title": "How Knowledge Distillation Mitigates the Synthetic Gap in Fair Face Recognition", "abstract": "Leveraging the capabilities of Knowledge Distillation (KD) strategies, we devise a strategy to fight the recent retraction of face recognition datasets. Given a pretrained Teacher model trained on a real dataset, we show that carefully utilising synthetic datasets, or a mix between real and synthetic datasets to distil knowledge from this teacher to smaller students can yield surprising results. In this sense, we trained 33 different models with and without KD, on different datasets, with different architectures and losses. And our findings are consistent, using KD leads to performance gains across all ethnicities and decreased bias. In addition, it helps to mitigate the performance gap between real and synthetic datasets. This approach addresses the limitations of synthetic data training, improving both the accuracy and fairness of face recognition models.", "authors": ["Pedro C. Neto", "Ivona Colakovic", "Sašo Karakatič", "Ana F. Sequeira"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-30", "url": "https://arxiv.org/abs/2408.17399", "pdf_url": "https://arxiv.org/pdf/2408.17399v1", "arxiv_id": "2408.17399", "doi": "10.48550/arXiv.2408.17399", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1193}
{"id": "f6ed6891cf59c3470fd257e2d43fa24b85ff61e5c2869d5f2040b05232bf25b4", "sources": ["arxiv", "semantic_scholar"], "title": "Learning Tree-Structured Composition of Data Augmentation", "abstract": "Data augmentation is widely used for training a neural network given little labeled data. A common practice of augmentation training is applying a composition of multiple transformations sequentially to the data. Existing augmentation methods such as RandAugment randomly sample from a list of pre-selected transformations, while methods such as AutoAugment apply advanced search to optimize over an augmentation set of size $k^d$, which is the number of transformation sequences of length $d$, given a list of $k$ transformations. In this paper, we design efficient algorithms whose running time complexity is much faster than the worst-case complexity of $O(k^d)$, provably. We propose a new algorithm to search for a binary tree-structured composition of $k$ transformations, where each tree node corresponds to one transformation. The binary tree generalizes sequential augmentations, such as the SimCLR augmentation scheme for contrastive learning. Using a top-down, recursive search procedure, our algorithm achieves a runtime complexity of $O(2^d k)$, which is much faster than $O(k^d)$ as $k$ increases above $2$. We apply our algorithm to tackle data distributions with heterogeneous subpopulations by searching for one tree in each subpopulation and then learning a weighted combination, resulting in a forest of trees. We validate our proposed algorithms on numerous graph and image datasets, including a multi-label graph classification dataset we collected. The dataset exhibits significant variations in the sizes of graphs and their average degrees, making it ideal for studying data augmentation. We show that our approach can reduce the computation cost by 43% over existing search methods while improving performance by 4.3%. The tree structures can be used to interpret the relative importance of each transformation, such as identifying the important transformations on small vs. large graphs.", "authors": ["Dongyue Li", "Kailai Chen", "Predrag Radivojac", "Hongyang R. Zhang"], "categories": ["cs.LG", "cs.CV", "cs.DS"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-26", "url": "https://arxiv.org/abs/2408.14381", "pdf_url": "https://arxiv.org/pdf/2408.14381v1", "arxiv_id": "2408.14381", "doi": "10.48550/arXiv.2408.14381", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0753}
{"id": "6307c4682c9f30e82c38bbe2d36e5eaad2141fc858456ea3593d29b1186924b1", "sources": ["arxiv", "semantic_scholar"], "title": "Condensed Data Expansion Using Model Inversion for Knowledge Distillation", "abstract": "Condensed datasets offer a compact representation of larger datasets, but training models directly on them or using them to enhance model performance through knowledge distillation (KD) can result in suboptimal outcomes due to limited information. To address this, we propose a method that expands condensed datasets using model inversion, a technique for generating synthetic data based on the impressions of a pre-trained model on its training data. This approach is particularly well-suited for KD scenarios, as the teacher model is already pre-trained and retains knowledge of the original training data. By creating synthetic data that complements the condensed samples, we enrich the training set and better approximate the underlying data distribution, leading to improvements in student model accuracy during knowledge distillation. Our method demonstrates significant gains in KD accuracy compared to using condensed datasets alone and outperforms standard model inversion-based KD methods by up to 11.4% across various datasets and model architectures. Importantly, it remains effective even when using as few as one condensed sample per class, and can also enhance performance in few-shot scenarios where only limited real data samples are available.", "authors": ["Kuluhan Binici", "Shivam Aggarwal", "Cihan Acar", "Nam Trung Pham", "Karianto Leman", "Gim Hee Lee", "Tulika Mitra"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-25", "url": "https://arxiv.org/abs/2408.13850", "pdf_url": "https://arxiv.org/pdf/2408.13850v2", "arxiv_id": "2408.13850", "doi": "10.1609/aaai.v40i24.39057", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "AAAI Conference on Artificial Intelligence", "quality_score": 0.0753}
{"id": "9e3d14c5b1b102fa12ee8c0feccfad0d00f5a51b419fa59583f9b0a3e4dbcc1d", "sources": ["arxiv", "semantic_scholar"], "title": "Generating Synthetic Fair Syntax-agnostic Data by Learning and Distilling Fair Representation", "abstract": "Data Fairness is a crucial topic due to the recent wide usage of AI powered applications. Most of the real-world data is filled with human or machine biases and when those data are being used to train AI models, there is a chance that the model will reflect the bias in the training data. Existing bias-mitigating generative methods based on GANs, Diffusion models need in-processing fairness objectives and fail to consider computational overhead while choosing computationally-heavy architectures, which may lead to high computational demands, instability and poor optimization performance. To mitigate this issue, in this work, we present a fair data generation technique based on knowledge distillation, where we use a small architecture to distill the fair representation in the latent space. The idea of fair latent space distillation enables more flexible and stable training of Fair Generative Models (FGMs). We first learn a syntax-agnostic (for any data type) fair representation of the data, followed by distillation in the latent space into a smaller model. After distillation, we use the distilled fair latent space to generate high-fidelity fair synthetic data. While distilling, we employ quality loss (for fair distillation) and utility loss (for data utility) to ensure that the fairness and data utility characteristics remain in the distilled latent space. Our approaches show a 5%, 5% and 10% rise in performance in fairness, synthetic sample quality and data utility, respectively, than the state-of-the-art fair generative model.", "authors": ["Md Fahim Sikder", "Resmi Ramachandranpillai", "Daniel de Leng", "Fredrik Heintz"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-20", "url": "https://arxiv.org/abs/2408.10755", "pdf_url": "https://arxiv.org/pdf/2408.10755v1", "arxiv_id": "2408.10755", "doi": "10.48550/arXiv.2408.10755", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "53451059e191303f6f076a2be825c40e224e349940850b0252e4d857595245aa", "sources": ["arxiv", "semantic_scholar"], "title": "Data-Driven Pixel Control: Challenges and Prospects", "abstract": "Recent advancements in sensors have led to high resolution and high data throughput at the pixel level. Simultaneously, the adoption of increasingly large (deep) neural networks (NNs) has lead to significant progress in computer vision. Currently, visual intelligence comes at increasingly high computational complexity, energy, and latency. We study a data-driven system that combines dynamic sensing at the pixel level with computer vision analytics at the video level and propose a feedback control loop to minimize data movement between the sensor front-end and computational back-end without compromising detection and tracking precision. Our contributions are threefold: (1) We introduce anticipatory attention and show that it leads to high precision prediction with sparse activation of pixels; (2) Leveraging the feedback control, we show that the dimensionality of learned feature vectors can be significantly reduced with increased sparsity; and (3) We emulate analog design choices (such as varying RGB or Bayer pixel format and analog noise) and study their impact on the key metrics of the data-driven system. Comparative analysis with traditional pixel and deep learning models shows significant performance enhancements. Our system achieves a 10X reduction in bandwidth and a 15-30X improvement in Energy-Delay Product (EDP) when activating only 30% of pixels, with a minor reduction in object detection and tracking precision. Based on analog emulation, our system can achieve a throughput of 205 megapixels/sec (MP/s) with a power consumption of only 110 mW per MP, i.e., a theoretical improvement of ~30X in EDP.", "authors": ["Saurabh Farkya", "Zachary Alan Daniels", "Aswin Raghavan", "Gooitzen van der Wal", "Michael Isnardi", "Michael Piacentino", "David Zhang"], "categories": ["cs.CV", "cs.AI", "eess.SY"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-08-08", "url": "https://arxiv.org/abs/2408.04767", "pdf_url": "https://arxiv.org/pdf/2408.04767v1", "arxiv_id": "2408.04767", "doi": "10.48550/arXiv.2408.04767", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0}
{"id": "df021091eb4516324e3a437b9492d0756c84807731f90adc0e32c4bec2358544", "sources": ["arxiv", "semantic_scholar"], "title": "Is Child-Directed Speech Effective Training Data for Language Models?", "abstract": "While high-performing language models are typically trained on hundreds of billions of words, human children become fluent language users with a much smaller amount of data. What are the features of the data they receive, and how do these features support language modeling objectives? To investigate this question, we train GPT-2 and RoBERTa models on 29M words of English child-directed speech and a new matched, synthetic dataset (TinyDialogues), comparing to OpenSubtitles, Wikipedia, and a heterogeneous blend of datasets from the BabyLM challenge. We evaluate the syntactic and semantic knowledge of these models using developmentally-inspired evaluations. Through pretraining experiments, we test whether the global developmental ordering or the local discourse ordering of children's training data supports high performance relative to other datasets. The local properties of the data affect model results, but surprisingly, global properties do not. Further, child language input is not uniquely valuable for training language models. These findings support the hypothesis that, rather than proceeding from better data, the child's learning algorithm is substantially more data-efficient than current language modeling techniques.", "authors": ["Steven Y. Feng", "Noah D. Goodman", "Michael C. Frank"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-07", "url": "https://arxiv.org/abs/2408.03617", "pdf_url": "https://arxiv.org/pdf/2408.03617v2", "arxiv_id": "2408.03617", "doi": "10.48550/arXiv.2408.03617", "citation_count": 29, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/styfeng/TinyDialogues", "venue": "Conference on Empirical Methods in Natural Language Processing", "quality_score": 0.3693}
{"id": "f918d9b7e38331421369fb21070b9ea495393dd054d33bad345e41d5c2d7d18d", "sources": ["arxiv", "semantic_scholar"], "title": "Earth System Data Cubes: Avenues for advancing Earth system research", "abstract": "Recent advancements in Earth system science have been marked by the exponential increase in the availability of diverse, multivariate datasets characterised by moderate to high spatio-temporal resolutions. Earth System Data Cubes (ESDCs) have emerged as one suitable solution for transforming this flood of data into a simple yet robust data structure. ESDCs achieve this by organising data into an analysis-ready format aligned with a spatio-temporal grid, facilitating user-friendly analysis and diminishing the need for extensive technical data processing knowledge. Despite these significant benefits, the completion of the entire ESDC life cycle remains a challenging task. Obstacles are not only of a technical nature but also relate to domain-specific problems in Earth system research. There exist barriers to realising the full potential of data collections in light of novel cloud-based technologies, particularly in curating data tailored for specific application domains. These include transforming data to conform to a spatio-temporal grid with minimum distortions and managing complexities such as spatio-temporal autocorrelation issues. Addressing these challenges is pivotal for the effective application of Artificial Intelligence (AI) approaches. Furthermore, adhering to open science principles for data dissemination, reproducibility, visualisation, and reuse is crucial for fostering sustainable research. Overcoming these challenges offers a substantial opportunity to advance data-driven Earth system research, unlocking the full potential of an integrated, multidimensional view of Earth system processes. This is particularly true when such research is coupled with innovative research paradigms and technological progress.", "authors": ["David Montero", "Guido Kraemer", "Anca Anghelea", "César Aybar", "Gunnar Brandt", "Gustau Camps-Valls", "Felix Cremer", "Ida Flik", "Fabian Gans", "Sarah Habershon", "Chaonan Ji", "Teja Kattenborn", "Laura Martínez-Ferrer", "Francesco Martinuzzi", "Martin Reinhardt", "Maximilian Söchting", "Khalil Teber", "Miguel D. Mahecha"], "categories": ["cs.CV", "cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-05", "url": "https://arxiv.org/abs/2408.02348", "pdf_url": "https://arxiv.org/pdf/2408.02348v1", "arxiv_id": "2408.02348", "doi": "10.1017/eds.2024.22", "citation_count": 22, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Environmental Data Science", "quality_score": 0.3404}
{"id": "f5f1ad00ec3c0000804e3b9fb963a5b527521ee6b51fec1f1bc45ca82b252898", "sources": ["arxiv", "semantic_scholar"], "title": "One-Shot Collaborative Data Distillation", "abstract": "Large machine-learning training datasets can be distilled into small collections of informative synthetic data samples. These synthetic sets support efficient model learning and reduce the communication cost of data sharing. Thus, high-fidelity distilled data can support the efficient deployment of machine learning applications in distributed network environments. A naive way to construct a synthetic set in a distributed environment is to allow each client to perform local data distillation and to merge local distillations at a central server. However, the quality of the resulting set is impaired by heterogeneity in the distributions of the local data held by clients. To overcome this challenge, we introduce the first collaborative data distillation technique, called CollabDM, which captures the global distribution of the data and requires only a single round of communication between client and server. Our method outperforms the state-of-the-art one-shot learning method on skewed data in distributed learning environments. We also show the promising practical benefits of our method when applied to attack detection in 5G networks.", "authors": ["William Holland", "Chandra Thapa", "Sarah Ali Siddiqui", "Wei Shao", "Seyit Camtepe"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-05", "url": "https://arxiv.org/abs/2408.02266", "pdf_url": "https://arxiv.org/pdf/2408.02266v2", "arxiv_id": "2408.02266", "doi": "10.48550/arXiv.2408.02266", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "European Conference on Artificial Intelligence", "quality_score": 0.1193}
{"id": "716e1dda88be5b245fdc885fcab83be77a4099b34144436f5781ec66ec9ccb41", "sources": ["arxiv", "semantic_scholar"], "title": "Covariate-Adjusted Functional Data Analysis for Structural Health Monitoring", "abstract": "Structural Health Monitoring (SHM) is increasingly applied in civil engineering. One of its primary purposes is detecting and assessing changes in structure conditions to increase safety and reduce potential maintenance downtime. Recent advancements, especially in sensor technology, facilitate data measurements, collection, and process automation, leading to large data streams. We propose a function-on-function regression framework for (nonlinear) modeling the sensor data and adjusting for covariate-induced variation. Our approach is particularly suited for long-term monitoring when several months or years of training data are available. It combines highly flexible yet interpretable semi-parametric modeling with functional principal component analysis and uses the corresponding out-of-sample Phase-II scores for monitoring. The method proposed can also be described as a combination of an ``input-output'' and an ``output-only'' method.", "authors": ["Philipp Wittenberg", "Lizzie Neumann", "Alexander Mendler", "Jan Gertheiss"], "categories": ["stat.AP", "stat.ME"], "fields_of_study": ["Mathematics"], "published_date": "2024-08-04", "url": "https://arxiv.org/abs/2408.02106", "pdf_url": "https://arxiv.org/pdf/2408.02106v2", "arxiv_id": "2408.02106", "doi": "10.1017/dce.2025.18", "citation_count": 9, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Data-Centric Engineering", "quality_score": 0.25}
{"id": "c7e92020802c4574bceffb6a2527070750aa2a34f315643edfddc7de451929ae", "sources": ["arxiv", "semantic_scholar"], "title": "Exploring Urban Comfort through Novel Wearables and Environmental Surveys", "abstract": "This study presents a comprehensive dataset capturing indoor environmental parameters, physiological responses, and subjective perceptions across three global cities. Utilizing wearable sensors, including smart eyeglasses, and a modified Cozie app, environmental and physiological data were collected, along with pre-screening, onboarding, and recurring surveys. Peripheral cues facilitated participant engagement with micro-EMA surveys, minimizing disruption over a 5-day collection period. The dataset offers insights into urban comfort dynamics, highlighting the interplay between environmental conditions, physiological responses, and subjective perceptions. Researchers can utilize this dataset to deepen their understanding of indoor environmental quality and inform the design of healthier built environments. Access to this dataset can advance indoor environmental research and contribute to the creation of more comfortable and sustainable indoor spaces.", "authors": ["Patrick Chwalek", "Sailin Zhong", "Nathan Perry", "Tianqi Liu", "Clayton Miller", "Hamed Seiied Alavi", "Denis Lalanne", "Joseph A. Paradiso"], "categories": ["cs.HC"], "fields_of_study": ["Computer Science", "Medicine"], "published_date": "2024-08-01", "url": "https://arxiv.org/abs/2408.08323", "pdf_url": "https://arxiv.org/pdf/2408.08323v1", "arxiv_id": "2408.08323", "doi": "10.1038/s41597-024-04279-9", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Scientific Data", "quality_score": 0.2113}
{"id": "fc42ac1dfffe98b7cd35bdd2ca3ba548639d3ec703bc73b1db3f3b0e77596b50", "sources": ["arxiv", "semantic_scholar"], "title": "Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI", "abstract": "Machine learning (ML) on tabular data is ubiquitous, yet obtaining abundant high-quality tabular data for model training remains a significant obstacle. Numerous works have focused on tabular data augmentation (TDA) to enhance the original table with additional data, thereby improving downstream ML tasks. Recently, there has been a growing interest in leveraging the capabilities of generative AI for TDA. Therefore, we believe it is time to provide a comprehensive review of the progress and future prospects of TDA, with a particular emphasis on the trending generative AI. Specifically, we present an architectural view of the TDA pipeline, comprising three main procedures: pre-augmentation, augmentation, and post-augmentation. Pre-augmentation encompasses preparation tasks that facilitate subsequent TDA, including error handling, table annotation, table simplification, table representation, table indexing, table navigation, schema matching, and entity matching. Augmentation systematically analyzes current TDA methods, categorized into retrieval-based methods, which retrieve external data, and generation-based methods, which generate synthetic data. We further subdivide these methods based on the granularity of the augmentation process at the row, column, cell, and table levels. Post-augmentation focuses on the datasets, evaluation and optimization aspects of TDA. We also summarize current trends and future directions for TDA, highlighting promising opportunities in the era of generative AI. In addition, the accompanying papers and related resources are continuously updated and maintained in the GitHub repository at https://github.com/SuDIS-ZJU/awesome-tabular-data-augmentation to reflect ongoing advancements in the field.", "authors": ["Lingxi Cui", "Huan Li", "Ke Chen", "Lidan Shou", "Gang Chen"], "categories": ["cs.LG", "cs.AI", "cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-31", "url": "https://arxiv.org/abs/2407.21523", "pdf_url": "https://arxiv.org/pdf/2407.21523v1", "arxiv_id": "2407.21523", "doi": "10.1145/3808692", "citation_count": 31, "influential_citation_count": 2, "has_code": true, "code_url": "https://github.com/SuDIS-ZJU/awesome-tabular-data-augmentation", "venue": "ACM Computing Surveys", "quality_score": 0.3763}
{"id": "9ed398091d0824d19a599aaaaf8dbfcdfb5b03e6cbfcf8798e56f2f86e049bc2", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetic Simplicity: Unveiling Bias in Medical Data Augmentation", "abstract": "Synthetic data is becoming increasingly integral in data-scarce fields such as medical imaging, serving as a substitute for real data. However, its inherent statistical characteristics can significantly impact downstream tasks, potentially compromising deployment performance. In this study, we empirically investigate this issue and uncover a critical phenomenon: downstream neural networks often exploit spurious distinctions between real and synthetic data when there is a strong correlation between the data source and the task label. This exploitation manifests as \\textit{simplicity bias}, where models overly rely on superficial features rather than genuine task-related complexities. Through principled experiments, we demonstrate that the source of data (real vs.\\ synthetic) can introduce spurious correlating factors leading to poor performance during deployment when the correlation is absent. We first demonstrate this vulnerability on a digit classification task, where the model spuriously utilizes the source of data instead of the digit to provide an inference. We provide further evidence of this phenomenon in a medical imaging problem related to cardiac view classification in echocardiograms, particularly distinguishing between 2-chamber and 4-chamber views. Given the increasing role of utilizing synthetic datasets, we hope that our experiments serve as effective guidelines for the utilization of synthetic datasets in model training.", "authors": ["Krishan Agyakari Raja Babu", "Rachana Sathish", "Mrunal Pattanaik", "Rahul Venkataramani"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-31", "url": "https://arxiv.org/abs/2407.21674", "pdf_url": "https://arxiv.org/pdf/2407.21674v1", "arxiv_id": "2407.21674", "doi": "10.1007/978-3-031-73748-0_7", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "MICCAI Workshop on Data Engineering in Medical Imaging,LNCS,Vol 15265,(2024),pp 64-72", "quality_score": 0.1505}
{"id": "570e44a4e3984fd54936fa0c6dbd739045d5810876ba06911fab73de2575073e", "sources": ["arxiv", "semantic_scholar"], "title": "Data Contamination Report from the 2024 CONDA Shared Task", "abstract": "The 1st Workshop on Data Contamination (CONDA 2024) focuses on all relevant aspects of data contamination in natural language processing, where data contamination is understood as situations where evaluation data is included in pre-training corpora used to train large scale models, compromising evaluation results. The workshop fostered a shared task to collect evidence on data contamination in current available datasets and models. The goal of the shared task and associated database is to assist the community in understanding the extent of the problem and to assist researchers in avoiding reporting evaluation results on known contaminated resources. The shared task provides a structured, centralized public database for the collection of contamination evidence, open to contributions from the community via GitHub pool requests. This first compilation paper is based on 566 reported entries over 91 contaminated sources from a total of 23 contributors. The details of the individual contamination events are available in the platform. The platform continues to be online, open to contributions from the community.", "authors": ["Oscar Sainz", "Iker García-Ferrero", "Alon Jacovi", "Jon Ander Campos", "Yanai Elazar", "Eneko Agirre", "Yoav Goldberg", "Wei-Lin Chen", "Jenny Chim", "Leshem Choshen", "Luca D'Amico-Wong", "Melissa Dell", "Run-Ze Fan", "Shahriar Golchin", "Yucheng Li", "Pengfei Liu", "Bhavish Pahwa", "Ameya Prabhu", "Suryansh Sharma", "Emily Silcock", "Kateryna Solonko", "David Stap", "Mihai Surdeanu", "Yu-Min Tseng", "Vishaal Udandarao", "Zengzhi Wang", "Ruijie Xu", "Jinglin Yang"], "categories": ["cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-31", "url": "https://arxiv.org/abs/2407.21530", "pdf_url": "https://arxiv.org/pdf/2407.21530v2", "arxiv_id": "2407.21530", "doi": "10.48550/arXiv.2407.21530", "citation_count": 22, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3404}
{"id": "48a66a69c127057b724901e10e8017ae382c58be73e20165fc776990bf8a705c", "sources": ["arxiv", "semantic_scholar"], "title": "Human-Data Interaction Framework: A Comprehensive Model for a Future Driven by Data and Humans", "abstract": "In an age defined by rapid data expansion, the connection between individuals and their digital footprints has become more intricate. The Human-Data Interaction (HDI) framework has become an essential approach to tackling the challenges and ethical issues associated with data governance and utilization in the modern digital world. This paper outlines the fundamental steps required for organizations to seamlessly integrate HDI principles, emphasizing auditing, aligning, formulating considerations, and the need for continuous monitoring and adaptation. Through a thorough audit, organizations can critically assess their current data management practices, trace the data lifecycle from collection to disposal, and evaluate the effectiveness of existing policies, security protocols, and user interfaces. The next step involves aligning these practices with the main HDI principles, such as informed consent, data transparency, user control, algorithm transparency, and ethical data use, to identify gaps that need strategic action. Formulating preliminary considerations includes developing policies and technical solutions to close identified gaps, ensuring that these practices not only meet legal standards, but also promote fairness and accountability in data interactions. The final step, monitoring and adaptation, highlights the need for setting up continuous evaluation mechanisms and being responsive to technological, regulatory, and societal developments, ensuring HDI practices stay up-to-date and effective. Successful implementation of the HDI framework requires multi-disciplinary collaboration, incorporating insights from technology, law, ethics, and user experience design. The paper posits that this comprehensive approach is vital for building trust and legitimacy in digital environments, ultimately leading to more ethical, transparent, and user-centric data interactions.", "authors": ["Ivan Durango", "Jose A. Gallud", "Victor M. R. Penichet"], "categories": ["cs.CY", "cs.HC"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-30", "url": "https://arxiv.org/abs/2407.21010", "pdf_url": "https://arxiv.org/pdf/2407.21010v1", "arxiv_id": "2407.21010", "doi": "10.48550/arXiv.2407.21010", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1945}
{"id": "ed6b8fff93d9e42af5dcf0131c15cce8dde4a5bff358776298c89aefd52c7840", "sources": ["arxiv", "semantic_scholar"], "title": "A General Framework for Data-Use Auditing of ML Models", "abstract": "Auditing the use of data in training machine-learning (ML) models is an increasingly pressing challenge, as myriad ML practitioners routinely leverage the effort of content creators to train models without their permission. In this paper, we propose a general method to audit an ML model for the use of a data-owner's data in training, without prior knowledge of the ML task for which the data might be used. Our method leverages any existing black-box membership inference method, together with a sequential hypothesis test of our own design, to detect data use with a quantifiable, tunable false-detection rate. We show the effectiveness of our proposed framework by applying it to audit data use in two types of ML models, namely image classifiers and foundation models.", "authors": ["Zonghao Huang", "Neil Zhenqiang Gong", "Michael K. Reiter"], "categories": ["cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-21", "url": "https://arxiv.org/abs/2407.15100", "pdf_url": "https://arxiv.org/pdf/2407.15100v3", "arxiv_id": "2407.15100", "doi": "10.1145/3658644.3690226", "citation_count": 23, "influential_citation_count": 4, "has_code": false, "code_url": null, "venue": "Conference on Computer and Communications Security", "quality_score": 0.3495}
{"id": "07f58cbf84333b7fac270e105f6b12234610e66b0a4358c6e4672d4bf97d9b3b", "sources": ["arxiv", "semantic_scholar"], "title": "Honest Computing: Achieving demonstrable data lineage and provenance for driving data and process-sensitive policies", "abstract": "Data is the foundation of any scientific, industrial or commercial process. Its journey typically flows from collection to transport, storage, management and processing. While best practices and regulations guide data management and protection, recent events have underscored its vulnerability. Academic research and commercial data handling have been marred by scandals, revealing the brittleness of data management. Data, despite its importance, is susceptible to undue disclosures, leaks, losses, manipulation, or fabrication. These incidents often occur without visibility or accountability, necessitating a systematic structure for safe, honest, and auditable data management. In this paper, we introduce the concept of Honest Computing as the practice and approach that emphasizes transparency, integrity, and ethical behaviour within the realm of computing and technology. It ensures that computer systems and software operate honestly and reliably without hidden agendas, biases, or unethical practices. It enables privacy and confidentiality of data and code by design and by default. We also introduce a reference framework to achieve demonstrable data lineage and provenance, contrasting it with Secure Computing, a related but differently-orientated form of computing. At its core, Honest Computing leverages Trustless Computing, Confidential Computing, Distributed Computing, Cryptography and AAA security concepts. Honest Computing opens new ways of creating technology-based processes and workflows which permit the migration of regulatory frameworks for data protection from principle-based approaches to rule-based ones. Addressing use cases in many fields, from AI model protection and ethical layering to digital currency formation for finance and banking, trading, and healthcare, this foundational layer approach can help define new standards for appropriate data custody and processing.", "authors": ["Florian Guitton", "Axel Oehmichen", "Étienne Bossé", "Yike Guo"], "categories": ["cs.CY", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-19", "url": "https://arxiv.org/abs/2407.14390", "pdf_url": "https://arxiv.org/pdf/2407.14390v1", "arxiv_id": "2407.14390", "doi": "10.1017/dap.2024.68", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Data & Policy", "quality_score": 0.1193}
{"id": "0b033f08b2ef131b424f996844d4cd9c8def9d1cab471624055f1e127b048769", "sources": ["arxiv", "semantic_scholar"], "title": "Data-Centric Human Preference with Rationales for Direct Preference Alignment", "abstract": "Aligning language models with human preferences through reinforcement learning from human feedback is crucial for their safe and effective deployment. The human preference is typically represented through comparison where one response is chosen over another for a given prompt. However, standard preference datasets often lack explicit information on why a particular choice was made, presenting an ambiguity that can hinder efficient learning and robust alignment, especially given the high cost of acquiring extensive human annotations. While many studies focus on algorithmic improvements, this work adopts a data-centric perspective, exploring how to enhance learning from existing preference data. We propose augmenting standard preference pairs with rationales that explain the reasoning behind the human preference. Specifically, we introduce a simple and principled framework that leverages machine-generated rationales to enrich preference data for preference optimization algorithms. Our comprehensive analysis demonstrates that incorporating rationales improves learning efficiency. Extensive experiments reveal some advantages: rationale-augmented learning accelerates convergence and can achieve higher final model performance. Furthermore, this approach is versatile and compatible with various direct preference optimization algorithms. Our findings showcase the potential of thoughtful data design in preference learning, demonstrating that enriching existing datasets with explanatory rationales can help unlock improvements in model alignment and annotation efficiency.", "authors": ["Hoang Anh Just", "Ming Jin", "Anit Sahu", "Huy Phan", "Ruoxi Jia"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-19", "url": "https://arxiv.org/abs/2407.14477", "pdf_url": "https://arxiv.org/pdf/2407.14477v4", "arxiv_id": "2407.14477", "doi": null, "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1505}
{"id": "89e6dfb385dc473a04dd0e3613125891b3f73945bf0fab04ba69d3dbc2807398", "sources": ["arxiv", "semantic_scholar"], "title": "Leave No Knowledge Behind During Knowledge Distillation: Towards Practical and Effective Knowledge Distillation for Code-Switching ASR Using Realistic Data", "abstract": "Recent advances in automatic speech recognition (ASR) often rely on large speech foundation models for generating high-quality transcriptions. However, these models can be impractical due to limited computing resources. The situation is even more severe in terms of more realistic or difficult scenarios, such as code-switching ASR (CS-ASR). To address this, we present a framework for developing more efficient models for CS-ASR through knowledge distillation using realistic speech-only data. Our proposed method, Leave No Knowledge Behind During Knowledge Distillation (K$^2$D), leverages both the teacher model's knowledge and additional insights from a small auxiliary model. We evaluate our approach on two in-domain and two out-domain datasets, demonstrating that K$^2$D is effective. By conducting K$^2$D on the unlabeled realistic data, we have successfully obtained a 2-time smaller model with 5-time faster generation speed while outperforming the baseline methods and the teacher model on all the testing sets. We have made our model publicly available on Hugging Face (https://huggingface.co/andybi7676/k2d-whisper.zh-en).", "authors": ["Liang-Hsuan Tseng", "Zih-Ching Chen", "Wei-Shun Chang", "Cheng-Kuang Lee", "Tsung-Ren Huang", "Hung-yi Lee"], "categories": ["eess.AS", "cs.CL", "cs.SD"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-07-15", "url": "https://arxiv.org/abs/2407.10603", "pdf_url": "https://arxiv.org/pdf/2407.10603v1", "arxiv_id": "2407.10603", "doi": "10.1109/SLT61566.2024.10832290", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Spoken Language Technology Workshop", "quality_score": 0.2113}
{"id": "c631b5a51804a60b7cd0ef52a590859e2af92d31a558f900116cb9e5af2d72e6", "sources": ["arxiv", "semantic_scholar"], "title": "Heterogenous Multi-Source Data Fusion Through Input Mapping and Latent Variable Gaussian Process", "abstract": "Artificial intelligence and machine learning frameworks have served as computationally efficient mapping between inputs and outputs for engineering problems. These mappings have enabled optimization and analysis routines that have warranted superior designs, ingenious material systems and optimized manufacturing processes. A common occurrence in such modeling endeavors is the existence of multiple source of data, each differentiated by fidelity, operating conditions, experimental conditions, and more. Data fusion frameworks have opened the possibility of combining such differentiated sources into single unified models, enabling improved accuracy and knowledge transfer. However, these frameworks encounter limitations when the different sources are heterogeneous in nature, i.e., not sharing the same input parameter space. These heterogeneous input scenarios can occur when the domains differentiated by complexity, scale, and fidelity require different parametrizations. Towards addressing this void, a heterogeneous multi-source data fusion framework is proposed based on input mapping calibration (IMC) and latent variable Gaussian process (LVGP). In the first stage, the IMC algorithm is utilized to transform the heterogeneous input parameter spaces into a unified reference parameter space. In the second stage, a multi-source data fusion model enabled by LVGP is leveraged to build a single source-aware surrogate model on the transformed reference space. The proposed framework is demonstrated and analyzed on three engineering case studies (design of cantilever beam, design of ellipsoidal void and modeling properties of Ti6Al4V alloy). The results indicate that the proposed framework provides improved predictive accuracy over a single source model and transformed but source unaware model.", "authors": ["Yigitcan Comlek", "Sandipp Krishnan Ravi", "Piyush Pandita", "Sayan Ghosh", "Liping Wang", "Wei Chen"], "categories": ["stat.ML", "cs.CE", "cs.LG"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2024-07-15", "url": "https://arxiv.org/abs/2407.11268", "pdf_url": "https://arxiv.org/pdf/2407.11268v1", "arxiv_id": "2407.11268", "doi": "10.48550/arXiv.2407.11268", "citation_count": 3, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "92f2de52b15e854088927102ce3cb1e658540289363cc4d99d7defd1eecf43a6", "sources": ["arxiv", "semantic_scholar"], "title": "A Taxonomy for Data Contamination in Large Language Models", "abstract": "Large language models pretrained on extensive web corpora demonstrate remarkable performance across a wide range of downstream tasks. However, a growing concern is data contamination, where evaluation datasets may be contained in the pretraining corpus, inflating model performance. Decontamination, the process of detecting and removing such data, is a potential solution; yet these contaminants may originate from altered versions of the test set, evading detection during decontamination. How different types of contamination impact the performance of language models on downstream tasks is not fully understood. We present a taxonomy that categorizes the various types of contamination encountered by LLMs during the pretraining phase and identify which types pose the highest risk. We analyze the impact of contamination on two key NLP tasks -- summarization and question answering -- revealing how different types of contamination influence task performance during evaluation.", "authors": ["Medha Palavalli", "Amanda Bertsch", "Matthew R. Gormley"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-11", "url": "https://arxiv.org/abs/2407.08716", "pdf_url": "https://arxiv.org/pdf/2407.08716v1", "arxiv_id": "2407.08716", "doi": "10.48550/arXiv.2407.08716", "citation_count": 11, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2698}
{"id": "876b144401daf15e9aa53d469c331e78e4d4ffd3483c6046f7abafe867947196", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetic to Authentic: Transferring Realism to 3D Face Renderings for Boosting Face Recognition", "abstract": "In this paper, we investigate the potential of image-to-image translation (I2I) techniques for transferring realism to 3D-rendered facial images in the context of Face Recognition (FR) systems. The primary motivation for using 3D-rendered facial images lies in their ability to circumvent the challenges associated with collecting large real face datasets for training FR systems. These images are generated entirely by 3D rendering engines, facilitating the generation of synthetic identities. However, it has been observed that FR systems trained on such synthetic datasets underperform when compared to those trained on real datasets, on various FR benchmarks. In this work, we demonstrate that by transferring the realism to 3D-rendered images (i.e., making the 3D-rendered images look more real), we can boost the performance of FR systems trained on these more photorealistic images. This improvement is evident when these systems are evaluated against FR benchmarks utilizing real-world data, thereby paving new pathways for employing synthetic data in real-world applications.", "authors": ["Parsa Rahimi", "Behrooz Razeghi", "Sebastien Marcel"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-10", "url": "https://arxiv.org/abs/2407.07627", "pdf_url": "https://arxiv.org/pdf/2407.07627v2", "arxiv_id": "2407.07627", "doi": "10.48550/arXiv.2407.07627", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2258}
{"id": "c80f1d054ced1553de7512c3075e3f0cfd66ac617fbd762d636504f9dffa6d10", "sources": ["arxiv", "semantic_scholar"], "title": "Multimodal Self-Instruct: Synthetic Abstract Image and Visual Reasoning Instruction Using Language Model", "abstract": "Although most current large multimodal models (LMMs) can already understand photos of natural scenes and portraits, their understanding of abstract images, e.g., charts, maps, or layouts, and visual reasoning capabilities remains quite rudimentary. They often struggle with simple daily tasks, such as reading time from a clock, understanding a flowchart, or planning a route using a road map. In light of this, we design a multi-modal self-instruct, utilizing large language models and their code capabilities to synthesize massive abstract images and visual reasoning instructions across daily scenarios. Our strategy effortlessly creates a multimodal benchmark with 11,193 instructions for eight visual scenarios: charts, tables, simulated maps, dashboards, flowcharts, relation graphs, floor plans, and visual puzzles. \\textbf{This benchmark, constructed with simple lines and geometric elements, exposes the shortcomings of most advanced LMMs} like Claude-3.5-Sonnet and GPT-4o in abstract image understanding, spatial relations reasoning, and visual element induction. Besides, to verify the quality of our synthetic data, we fine-tune an LMM using 62,476 synthetic chart, table and road map instructions. The results demonstrate improved chart understanding and map navigation performance, and also demonstrate potential benefits for other visual reasoning tasks. Our code is available at: \\url{https://github.com/zwq2018/Multi-modal-Self-instruct}.", "authors": ["Wenqi Zhang", "Zhenglin Cheng", "Yuanyu He", "Mengna Wang", "Yongliang Shen", "Zeqi Tan", "Guiyang Hou", "Mingqian He", "Yanna Ma", "Weiming Lu", "Yueting Zhuang"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-09", "url": "https://arxiv.org/abs/2407.07053", "pdf_url": "https://arxiv.org/pdf/2407.07053v5", "arxiv_id": "2407.07053", "doi": "10.48550/arXiv.2407.07053", "citation_count": 28, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/zwq2018/Multi-modal-Self-instruct", "venue": "Conference on Empirical Methods in Natural Language Processing", "quality_score": 0.3656}
{"id": "04702829db9e372f788bb03e7e5ec8c64a8d7b508541441c5b63f409d12339ce", "sources": ["arxiv", "semantic_scholar"], "title": "Wastewater Treatment Plant Data for Nutrient Removal System", "abstract": "This paper introduces the Agtrup (BlueKolding) dataset, collected from Denmark's Agtrup wastewater treatment plant, specifically designed to enhance phosphorus removal via chemical and biological methods. This rich dataset is assembled through a high-frequency Supervisory Control and Data Acquisition (SCADA) system data collection process, which captures a wide range of variables related to the operational dynamics of nutrient removal. It comprises time-series data featuring measurements sampled to a frequency of two minutes across various control, process, and environmental variables. The comprehensive dataset aims to foster significant advancements in wastewater management by supporting the development of sophisticated predictive models and optimizing operational strategies. By providing detailed insights into the interactions and efficiencies of chemical and biological phosphorus removal processes, the dataset serves as a vital resource for environmental researchers and engineers focused on improving the sustainability and effectiveness of wastewater treatment operations. The ultimate goal of this dataset is to facilitate the creation of digital twins and the application of machine learning techniques, such as deep reinforcement learning, to predict and enhance system performance under varying operational conditions.", "authors": ["Esmaeel Mohammadi", "Anju Rani", "Mikkel Stokholm-Bjerregaard", "Daniel Ortiz-Arroyo", "Petar Durdevic"], "categories": ["eess.SY", "cs.CE", "eess.SP"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2024-07-07", "url": "https://arxiv.org/abs/2407.05346", "pdf_url": "https://arxiv.org/pdf/2407.05346v1", "arxiv_id": "2407.05346", "doi": "10.48550/arXiv.2407.05346", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "8a0b0b3c3f8bbc73b750e418ff496c78a19adb27690c2d0c2e918d8bbc48a620", "sources": ["arxiv", "semantic_scholar"], "title": "Quantifying Privacy Risks of Public Statistics to Residents of Subsidized Housing", "abstract": "As the U.S. Census Bureau implements its controversial new disclosure avoidance system, researchers and policymakers debate the necessity of new privacy protections for public statistics. With experiments on both public statistics and synthetic microdata, we explore a particular privacy concern: respondents in subsidized housing may deliberately not mention unauthorized children and other household members for fear of being discovered and evicted. By combining public statistics from the Decennial Census and the Department of Housing and Urban Development, we demonstrate a simple, inexpensive reconstruction attack that could identify subsidized households living in violation of occupancy guidelines in 2010. Experiments on synthetic data suggest that a random swapping mechanism similar to the Census Bureau's 2010 disclosure avoidance measures does not significantly reduce the precision of this attack, while a differentially private mechanism similar to the 2020 disclosure avoidance system does. Our results provide a valuable example for policymakers seeking trustworthy public statistics.", "authors": ["Ryan Steed", "Diana Qing", "Zhiwei Steven Wu"], "categories": ["cs.CY"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-05", "url": "https://arxiv.org/abs/2407.04776", "pdf_url": "https://arxiv.org/pdf/2407.04776v2", "arxiv_id": "2407.04776", "doi": "10.1162/99608f92.39d8bfa4", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Harvard data science review", "quality_score": 0.1193}
{"id": "83773d2507544a46d164e8078964d82b3c0fb47e039186d2e0921ea849f887a4", "sources": ["arxiv", "semantic_scholar"], "title": "MedPix 2.0: A Comprehensive Multimodal Biomedical Data set for Advanced AI Applications with Retrieval Augmented Generation and Knowledge Graphs", "abstract": "The increasing interest in developing Artificial Intelligence applications in the medical domain, suffers from the lack of high-quality data set, mainly due to privacy-related issues. In addition, the recent increase in Vision Language Models (VLM) leads to the need for multimodal medical data sets, where clinical reports and findings are attached to the corresponding medical scans. This paper illustrates the entire workflow for building the MedPix 2.0 data set. Starting with the well-known multimodal data set MedPix\\textsuperscript{\\textregistered}, mainly used by physicians, nurses, and healthcare students for Continuing Medical Education purposes, a semi-automatic pipeline was developed to extract visual and textual data followed by a manual curing procedure in which noisy samples were removed, thus creating a MongoDB database. Along with the data set, we developed a Graphical User Interface aimed at navigating efficiently the MongoDB instance and obtaining the raw data that can be easily used for training and/or fine-tuning VLMs. To enforce this point, in this work, we first recall DR-Minerva, a Retrieve Augmented Generation-based VLM model trained upon MedPix 2.0. DR-Minerva predicts the body part and the modality used to scan its input image. We also propose the extension of DR-Minerva with a Knowledge Graph that uses Llama 3.1 Instruct 8B, and leverages MedPix 2.0. The resulting architecture can be queried in a end-to-end manner, as a medical decision support system. MedPix 2.0 is available on GitHub.", "authors": ["Irene Siragusa", "Salvatore Contino", "Massimo La Ciura", "Rosario Alicata", "Roberto Pirrone"], "categories": ["cs.DB", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-03", "url": "https://arxiv.org/abs/2407.02994", "pdf_url": "https://arxiv.org/pdf/2407.02994v5", "arxiv_id": "2407.02994", "doi": "10.48550/arXiv.2407.02994", "citation_count": 8, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Data Science and Engineering", "quality_score": 0.2386}
{"id": "eb51cc06b22599caf349ce4d8db9244200c886d26e662fecb0fc30660597c7b9", "sources": ["arxiv", "semantic_scholar"], "title": "When big data actually are low-rank, or entrywise approximation of certain function-generated matrices", "abstract": "The article concerns low-rank approximation of matrices generated by sampling a smooth function of two $m$-dimensional variables. We identify several misconceptions surrounding a claim that, for a specific class of analytic functions, such $n \\times n$ matrices admit accurate entrywise approximation of rank that is independent of $m$ and grows as $\\log(n)$ -- colloquially known as ''big-data matrices are approximately low-rank''. We provide a theoretical explanation of the numerical results presented in support of this claim, describing three narrower classes of functions for which function-generated matrices can be approximated within an entrywise error of order $\\varepsilon$ with rank $\\mathcal{O}(\\log(n) \\varepsilon^{-2} \\log(\\varepsilon^{-1}))$ that is independent of the dimension $m$: (i) functions of the inner product of the two variables, (ii) functions of the Euclidean distance between the variables, and (iii) shift-invariant positive-definite kernels. We extend our argument to tensor-train approximation of tensors generated with functions of the ''higher-order inner product'' of their multiple variables. We discuss our results in the context of low-rank approximation of (a) growing datasets and (b) attention in transformer neural networks.", "authors": ["Stanislav Budzinskiy"], "categories": ["math.NA", "cs.LG"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2024-07-03", "url": "https://arxiv.org/abs/2407.03250", "pdf_url": "https://arxiv.org/pdf/2407.03250v4", "arxiv_id": "2407.03250", "doi": "10.1137/24M1687133", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "SIAM Journal on Mathematics of Data Science", "quality_score": 0.2113}
{"id": "effa87edf682a93c2cbee7392ccac4ce84fd12e4b787da2591cf343297e71d01", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Training Music Taggers on Synthetic Data", "abstract": "Most contemporary music tagging systems rely on large volumes of annotated data. As an alternative, we investigate the extent to which synthetically generated music excerpts can improve tagging systems when only small annotated collections are available. To this end, we release GTZAN-synth, a synthetic dataset that follows the taxonomy of the well-known GTZAN dataset while being ten times larger in data volume. We first observe that simply adding this synthetic dataset to the training split of GTZAN does not result into performance improvements. We then proceed to investigating domain adaptation, transfer learning and fine-tuning strategies for the task at hand and draw the conclusion that the last two options yield an increase in accuracy. Overall, the proposed approach can be considered as a first guide in a promising field for future research.", "authors": ["Nadine Kroher", "Steven Manangu", "Aggelos Pikrakis"], "categories": ["cs.SD", "cs.AI", "cs.IR", "cs.LG", "eess.AS"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-07-02", "url": "https://arxiv.org/abs/2407.02156", "pdf_url": "https://arxiv.org/pdf/2407.02156v1", "arxiv_id": "2407.02156", "doi": "10.1109/CBMI62980.2024.10859229", "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/NadineKroher/music-tagging-synthetic-data-cbmi-2024", "venue": "International Conference on Content-Based Multimedia Indexing", "quality_score": 0.0753}
{"id": "81e9bc8d94190f2ef18c0fbf878105bad46d4dda20d0591b2323df2ca795f388", "sources": ["arxiv", "semantic_scholar"], "title": "Diffusion Models for Tabular Data Imputation and Synthetic Data Generation", "abstract": "Data imputation and data generation have important applications for many domains, like healthcare and finance, where incomplete or missing data can hinder accurate analysis and decision-making. Diffusion models have emerged as powerful generative models capable of capturing complex data distributions across various data modalities such as image, audio, and time series data. Recently, they have been also adapted to generate tabular data. In this paper, we propose a diffusion model for tabular data that introduces three key enhancements: (1) a conditioning attention mechanism, (2) an encoder-decoder transformer as the denoising network, and (3) dynamic masking. The conditioning attention mechanism is designed to improve the model's ability to capture the relationship between the condition and synthetic data. The transformer layers help model interactions within the condition (encoder) or synthetic data (decoder), while dynamic masking enables our model to efficiently handle both missing data imputation and synthetic data generation tasks within a unified framework. We conduct a comprehensive evaluation by comparing the performance of diffusion models with transformer conditioning against state-of-the-art techniques, such as Variational Autoencoders, Generative Adversarial Networks and Diffusion Models, on benchmark datasets. Our evaluation focuses on the assessment of the generated samples with respect to three important criteria, namely: (1) Machine Learning efficiency, (2) statistical similarity, and (3) privacy risk mitigation. For the task of data imputation, we consider the efficiency of the generated samples across different levels of missing features.", "authors": ["Mario Villaizán-Vallelado", "Matteo Salvatori", "Carlos Segura", "Ioannis Arapakis"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-02", "url": "https://arxiv.org/abs/2407.02549", "pdf_url": "https://arxiv.org/pdf/2407.02549v2", "arxiv_id": "2407.02549", "doi": "10.1145/3742435", "citation_count": 32, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "ACM Transactions on Knowledge Discovery from Data", "quality_score": 0.3796}
{"id": "7a890fb9074637b51ebaba8edef1c76d725e2ed918ed39435763a502f78c48f5", "sources": ["arxiv", "semantic_scholar"], "title": "uDistil-Whisper: Label-Free Data Filtering for Knowledge Distillation in Low-Data Regimes", "abstract": "Recent work on distilling Whisper's knowledge into small models using pseudo-labels shows promising performance while reducing the size by up to 50%. This results in small, efficient, and dedicated models. However, a critical step of distillation using pseudo-labels involves filtering high-quality predictions and using only those during training. This step requires ground truth labels to compare with and filter low-quality examples, making the process dependent on human labels. Additionally, the distillation process requires a large amount of data thereby limiting its applicability in low-resource settings. To address this, we propose a distillation framework that does not require any labeled data. Through experimentation, we show that our best-distilled models outperform the teacher model by 5-7 WER points and are on par with or outperform similar supervised data filtering setups. When scaling the data, our models significantly outperform all zero-shot and supervised models. Our models are also 25-50% more compute- and memory-efficient while maintaining performance equal to or better than that of the teacher model. For more details about our models, dataset, and other resources, please visit our GitHub page: https://github.com/UBC-NLP/uDistilWhisper.", "authors": ["Abdul Waheed", "Karima Kadaoui", "Bhiksha Raj", "Muhammad Abdul-Mageed"], "categories": ["cs.CL", "cs.SD", "eess.AS"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-07-01", "url": "https://arxiv.org/abs/2407.01257", "pdf_url": "https://arxiv.org/pdf/2407.01257v5", "arxiv_id": "2407.01257", "doi": "10.18653/v1/2025.naacl-long.296", "citation_count": 5, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/UBC-NLP/uDistilWhisper", "venue": "North American Chapter of the Association for Computational Linguistics", "quality_score": 0.1945}
{"id": "7122210181a017fd05570c3d086de0c5469458fe145d5ca059e7cb4f00d5b1fc", "sources": ["arxiv", "semantic_scholar"], "title": "From Counting Stations to City-Wide Estimates: Data-Driven Bicycle Volume Extrapolation", "abstract": "Shifting to cycling in urban areas reduces greenhouse gas emissions and improves public health. Street-level bicycle volume information would aid cities in planning targeted infrastructure improvements to encourage cycling and provide civil society with evidence to advocate for cyclists' needs. Yet, the data currently available to cities and citizens often only comes from sparsely located counting stations. This paper extrapolates bicycle volume beyond these few locations to estimate bicycle volume for the entire city of Berlin. We predict daily and average annual daily street-level bicycle volumes using machine-learning techniques and various public data sources. These include app-based crowdsourced data, infrastructure, bike-sharing, motorized traffic, socioeconomic indicators, weather, and holiday data. Our analysis reveals that the best-performing model is XGBoost, and crowdsourced cycling and infrastructure data are most important for the prediction. We further simulate how collecting short-term counts at predicted locations improves performance. By providing ten days of such sample counts for each predicted location to the model, we are able to halve the error and greatly reduce the variability in performance among predicted locations.", "authors": ["Silke K. Kaiser", "Nadja Klein", "Lynn H. Kaack"], "categories": ["cs.CY", "stat.AP"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2024-06-26", "url": "https://arxiv.org/abs/2406.18454", "pdf_url": "https://arxiv.org/pdf/2406.18454v2", "arxiv_id": "2406.18454", "doi": "10.1017/eds.2025.5", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Environmental Data Science", "quality_score": 0.2386}
{"id": "1036c9e356c85f46b0aee377fb6c62136d8df33f1aa403c8b99a3d171734ab5f", "sources": ["arxiv", "semantic_scholar"], "title": "Research on Education Big Data for Students Academic Performance Analysis based on Machine Learning", "abstract": "The application of the Internet in the field of education is becoming more and more popular, and a large amount of educational data is generated in the process. How to effectively use these data has always been a key issue in the field of educational data mining. In this work, a machine learning model based on Long Short-Term Memory Network (LSTM) was used to conduct an in-depth analysis of educational big data to evaluate student performance. The LSTM model efficiently processes time series data, allowing us to capture time-dependent and long-term trends in students' learning activities. This approach is particularly useful for analyzing student progress, engagement, and other behavioral patterns to support personalized education. In an experimental analysis, we verified the effectiveness of the deep learning method in predicting student performance by comparing the performance of different models. Strict cross-validation techniques are used to ensure the accuracy and generalization of experimental results.", "authors": ["Chun Wang", "Jiexiao Chen", "Ziyang Xie", "Jianke Zou"], "categories": ["cs.CY", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-06-25", "url": "https://arxiv.org/abs/2407.16907", "pdf_url": "https://arxiv.org/pdf/2407.16907v1", "arxiv_id": "2407.16907", "doi": "10.1145/3686424.3686462", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2258}
{"id": "c17b6658ce4318db20f83624417f706853000af1c7e3fde58013638490a9da0d", "sources": ["arxiv", "semantic_scholar"], "title": "Improving Rehabilitative Assessment with Statistical and Shape Preserving Surrogate Data and Singular Spectrum Analysis", "abstract": "Time series data are collected in temporal order and are widely used to train systems for prediction, modeling and classification to name a few. These systems require large amounts of data to improve generalization and prevent over-fitting. However there is a comparative lack of time series data due to operational constraints. This situation is alleviated by synthesizing data which have a suitable spread of features yet retain the distinctive features of the original data. These would be its basic statistical properties and overall shape which are important for short time series such as in rehabilitative applications or in quickly changing portions of lengthy data. In our earlier work synthesized surrogate time series were used to augment rehabilitative data. This gave good results in classification but the resulting waveforms did not preserve the original signal shape. To remedy this, we use singular spectrum analysis (SSA) to separate a signal into trends and cycles to describe the shape of the signal and low level components. In a novel way we subject the low level component to randomizing processes then recombine this with the original trend and cycle components to form a synthetic time series. We compare our approach with other methods, using statistical and shape measures and demonstrate its effectiveness in classification.", "authors": ["T. K. M. Lee", "H. W. Chan", "K. H. Leo", "E. Chew", "Ling Zhao", "S. Sanei"], "categories": ["eess.SP"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2024-06-22", "url": "https://arxiv.org/abs/2406.16970", "pdf_url": "https://arxiv.org/pdf/2406.16970v1", "arxiv_id": "2406.16970", "doi": "10.23919/SPA53010.2022.9927805", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "2022 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), Poznan, Poland, 2022, pp. 58-63", "quality_score": 0.1505}
{"id": "b42f046e16c706268a415bdbda7f6c48dfef656538c2ec6389a5e29518198f34", "sources": ["arxiv", "semantic_scholar"], "title": "You can't handle the (dirty) truth: Data-centric insights improve pseudo-labeling", "abstract": "Pseudo-labeling is a popular semi-supervised learning technique to leverage unlabeled data when labeled samples are scarce. The generation and selection of pseudo-labels heavily rely on labeled data. Existing approaches implicitly assume that the labeled data is gold standard and 'perfect'. However, this can be violated in reality with issues such as mislabeling or ambiguity. We address this overlooked aspect and show the importance of investigating labeled data quality to improve any pseudo-labeling method. Specifically, we introduce a novel data characterization and selection framework called DIPS to extend pseudo-labeling. We select useful labeled and pseudo-labeled samples via analysis of learning dynamics. We demonstrate the applicability and impact of DIPS for various pseudo-labeling methods across an extensive range of real-world tabular and image datasets. Additionally, DIPS improves data efficiency and reduces the performance distinctions between different pseudo-labelers. Overall, we highlight the significant benefits of a data-centric rethinking of pseudo-labeling in real-world settings.", "authors": ["Nabeel Seedat", "Nicolas Huynh", "Fergus Imrie", "Mihaela van der Schaar"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-06-19", "url": "https://arxiv.org/abs/2406.13733", "pdf_url": "https://arxiv.org/pdf/2406.13733v1", "arxiv_id": "2406.13733", "doi": "10.48550/arXiv.2406.13733", "citation_count": 9, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.25}
{"id": "60f09589e1c2591583e9e041b2dc5fa5e8828482289c1c2a584108774331b3b1", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Bayesian Data Selection", "abstract": "A wide range of machine learning algorithms iteratively add data to the training sample. Examples include semi-supervised learning, active learning, multi-armed bandits, and Bayesian optimization. We embed this kind of data addition into decision theory by framing data selection as a decision problem. This paves the way for finding Bayes-optimal selections of data. For the illustrative case of self-training in semi-supervised learning, we derive the respective Bayes criterion. We further show that deploying this criterion mitigates the issue of confirmation bias by empirically assessing our method for generalized linear models, semi-parametric generalized additive models, and Bayesian neural networks on simulated and real-world data.", "authors": ["Julian Rodemann"], "categories": ["stat.ML", "cs.AI", "cs.LG", "math.ST"], "fields_of_study": ["Mathematics", "Computer Science"], "published_date": "2024-06-18", "url": "https://arxiv.org/abs/2406.12560", "pdf_url": "https://arxiv.org/pdf/2406.12560v2", "arxiv_id": "2406.12560", "doi": "10.48550/arXiv.2406.12560", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "a67b7fa027e072073400aa9b5fec8056cf1a6dd1d08c4babb0f0f7e908b45001", "sources": ["arxiv", "semantic_scholar"], "title": "On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey", "abstract": "Within the evolving landscape of deep learning, the dilemma of data quantity and quality has been a long-standing problem. The recent advent of Large Language Models (LLMs) offers a data-centric solution to alleviate the limitations of real-world data with synthetic data generation. However, current investigations into this field lack a unified framework and mostly stay on the surface. Therefore, this paper provides an organization of relevant studies based on a generic workflow of synthetic data generation. By doing so, we highlight the gaps within existing research and outline prospective avenues for future study. This work aims to shepherd the academic and industrial communities towards deeper, more methodical inquiries into the capabilities and applications of LLMs-driven synthetic data generation.", "authors": ["Lin Long", "Rui Wang", "Ruixuan Xiao", "Junbo Zhao", "Xiao Ding", "Gang Chen", "Haobo Wang"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-06-14", "url": "https://arxiv.org/abs/2406.15126", "pdf_url": "https://arxiv.org/pdf/2406.15126v1", "arxiv_id": "2406.15126", "doi": "10.48550/arXiv.2406.15126", "citation_count": 328, "influential_citation_count": 21, "has_code": false, "code_url": null, "venue": "Annual Meeting of the Association for Computational Linguistics", "quality_score": 0.6712}
{"id": "5b8a48b039914262ec29f13117fe36f5caf22609e2ae5339a2f8c41025951099", "sources": ["arxiv", "semantic_scholar"], "title": "Small Scale Data-Free Knowledge Distillation", "abstract": "Data-free knowledge distillation is able to utilize the knowledge learned by a large teacher network to augment the training of a smaller student network without accessing the original training data, avoiding privacy, security, and proprietary risks in real applications. In this line of research, existing methods typically follow an inversion-and-distillation paradigm in which a generative adversarial network on-the-fly trained with the guidance of the pre-trained teacher network is used to synthesize a large-scale sample set for knowledge distillation. In this paper, we reexamine this common data-free knowledge distillation paradigm, showing that there is considerable room to improve the overall training efficiency through a lens of ``small-scale inverted data for knowledge distillation\". In light of three empirical observations indicating the importance of how to balance class distributions in terms of synthetic sample diversity and difficulty during both data inversion and distillation processes, we propose Small Scale Data-free Knowledge Distillation SSD-KD. In formulation, SSD-KD introduces a modulating function to balance synthetic samples and a priority sampling function to select proper samples, facilitated by a dynamic replay buffer and a reinforcement learning strategy. As a result, SSD-KD can perform distillation training conditioned on an extremely small scale of synthetic samples (e.g., 10X less than the original training data scale), making the overall training efficiency one or two orders of magnitude faster than many mainstream methods while retaining superior or competitive model performance, as demonstrated on popular image classification and semantic segmentation benchmarks. The code is available at https://github.com/OSVAI/SSD-KD.", "authors": ["He Liu", "Yikai Wang", "Huaping Liu", "Fuchun Sun", "Anbang Yao"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-06-12", "url": "https://arxiv.org/abs/2406.07876", "pdf_url": "https://arxiv.org/pdf/2406.07876v1", "arxiv_id": "2406.07876", "doi": "10.1109/CVPR52733.2024.00574", "citation_count": 35, "influential_citation_count": 2, "has_code": true, "code_url": "https://github.com/OSVAI/SSD-KD", "venue": "Computer Vision and Pattern Recognition", "quality_score": 0.3891}
{"id": "8104b4727d729bc2f2074f58c71c546e703186c144ca3ef7deaa0b718e57dea8", "sources": ["arxiv", "semantic_scholar"], "title": "Enhancing Indoor Temperature Forecasting through Synthetic Data in Low-Data Environments", "abstract": "Forecasting indoor temperatures is important to achieve efficient control of HVAC systems. In this task, the limited data availability presents a challenge as most of the available data is acquired during standard operation where extreme scenarios and transitory regimes such as major temperature increases or decreases are de-facto excluded. Acquisition of such data requires significant energy consumption and a dedicated facility, hindering the quantity and diversity of available data. Cost related constraints however do not allow for continuous year-around acquisition. To address this, we investigate the efficacy of data augmentation techniques leveraging SoTA AI-based methods for synthetic data generation. Inspired by practical and experimental motivations, we explore fusion strategies of real and synthetic data to improve forecasting models. This approach alleviates the need for continuously acquiring extensive time series data, especially in contexts involving repetitive heating and cooling cycles in buildings. In our evaluation 1) we assess the performance of synthetic data generators independently, particularly focusing on SoTA AI-based methods; 2) we measure the utility of incorporating synthetically augmented data in a subsequent forecasting tasks where we employ a simple model in two distinct scenarios: 1) we first examine an augmentation technique that combines real and synthetically generated data to expand the training dataset, 2) we delve into utilizing synthetic data to tackle dataset imbalances. Our results highlight the potential of synthetic data augmentation in enhancing forecasting accuracy while mitigating training variance. Through empirical experiments, we show significant improvements achievable by integrating synthetic data, thereby paving the way for more robust forecasting models in low-data regime.", "authors": ["Zachari Thiry", "Massimiliano Ruocco", "Alessandro Nocente", "Michail Spitieris"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-06-07", "url": "https://arxiv.org/abs/2406.04890", "pdf_url": "https://arxiv.org/pdf/2406.04890v1", "arxiv_id": "2406.04890", "doi": "10.48550/arXiv.2406.04890", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "a7b0c0041bb3c83bc830bb28596d72decc39187fe05ac62cb26b1260742bd8da", "sources": ["arxiv", "semantic_scholar"], "title": "ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract Descriptions", "abstract": "We present ABEX, a novel and effective generative data augmentation methodology for low-resource Natural Language Understanding (NLU) tasks. ABEX is based on ABstract-and-EXpand, a novel paradigm for generating diverse forms of an input document -- we first convert a document into its concise, abstract description and then generate new documents based on expanding the resultant abstraction. To learn the task of expanding abstract descriptions, we first train BART on a large-scale synthetic dataset with abstract-document pairs. Next, to generate abstract descriptions for a document, we propose a simple, controllable, and training-free method based on editing AMR graphs. ABEX brings the best of both worlds: by expanding from abstract representations, it preserves the original semantic properties of the documents, like style and meaning, thereby maintaining alignment with the original label and data distribution. At the same time, the fundamental process of elaborating on abstract descriptions facilitates diverse generations. We demonstrate the effectiveness of ABEX on 4 NLU tasks spanning 12 datasets and 4 low-resource settings. ABEX outperforms all our baselines qualitatively with improvements of 0.04% - 38.8%. Qualitatively, ABEX outperforms all prior methods from literature in terms of context and length diversity.", "authors": ["Sreyan Ghosh", "Utkarsh Tyagi", "Sonal Kumar", "C. K. Evuru", "S Ramaneswaran", "S Sakshi", "Dinesh Manocha"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-06-06", "url": "https://arxiv.org/abs/2406.04286", "pdf_url": "https://arxiv.org/pdf/2406.04286v1", "arxiv_id": "2406.04286", "doi": "10.48550/arXiv.2406.04286", "citation_count": 13, "influential_citation_count": 2, "has_code": true, "code_url": "https://github.com/Sreyan88/ABEX", "venue": "Annual Meeting of the Association for Computational Linguistics", "quality_score": 0.2865}
{"id": "a23590a15a3853144e882957bfbbce6f638aceab6c6adfbc3484b45f147a6a70", "sources": ["arxiv", "semantic_scholar"], "title": "CMDBench: A Benchmark for Coarse-to-fine Multimodal Data Discovery in Compound AI Systems", "abstract": "Compound AI systems (CASs) that employ LLMs as agents to accomplish knowledge-intensive tasks via interactions with tools and data retrievers have garnered significant interest within database and AI communities. While these systems have the potential to supplement typical analysis workflows of data analysts in enterprise data platforms, unfortunately, CASs are subject to the same data discovery challenges that analysts have encountered over the years -- silos of multimodal data sources, created across teams and departments within an organization, make it difficult to identify appropriate data sources for accomplishing the task at hand. Existing data discovery benchmarks do not model such multimodality and multiplicity of data sources. Moreover, benchmarks of CASs prioritize only evaluating end-to-end task performance. To catalyze research on evaluating the data discovery performance of multimodal data retrievers in CASs within a real-world setting, we propose CMDBench, a benchmark modeling the complexity of enterprise data platforms. We adapt existing datasets and benchmarks in open-domain -- from question answering and complex reasoning tasks to natural language querying over structured data -- to evaluate coarse- and fine-grained data discovery and task execution performance. Our experiments reveal the impact of data retriever design on downstream task performance -- a 46% drop in task accuracy on average -- across various modalities, data sources, and task difficulty. The results indicate the need to develop optimization strategies to identify appropriate LLM agents and retrievers for efficient execution of CASs over enterprise data.", "authors": ["Yanlin Feng", "Sajjadur Rahman", "Aaron Feng", "Vincent Chen", "Eser Kandogan"], "categories": ["cs.DB", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-06-02", "url": "https://arxiv.org/abs/2406.00583", "pdf_url": "https://arxiv.org/pdf/2406.00583v1", "arxiv_id": "2406.00583", "doi": "10.1145/3665601.3669846", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2386}
{"id": "56f77440a985f02d7be9e570b0e4b080239b8e0ab75c3fdcc1dd63f1d90e5c2d", "sources": ["arxiv", "semantic_scholar"], "title": "Uncertainty Quantification for Deep Learning", "abstract": "We present a critical survey on the consistency of uncertainty quantification used in deep learning and highlight partial uncertainty coverage and many inconsistencies. We then provide a comprehensive and statistically consistent framework for uncertainty quantification in deep learning that accounts for all major sources of uncertainty: input data, training and testing data, neural network weights, and machine-learning model imperfections, targeting regression problems. We systematically quantify each source by applying Bayes' theorem and conditional probability densities and introduce a fast, practical implementation method. We demonstrate its effectiveness on a simple regression problem and a real-world application: predicting cloud autoconversion rates using a neural network trained on aircraft measurements from the Azores and guided by a two-moment bin model of the stochastic collection equation. In this application, uncertainty from the training and testing data dominates, followed by input data, neural network model, and weight variability. Finally, we highlight the practical advantages of this methodology, showing that explicitly modeling training data uncertainty improves robustness to new inputs that fall outside the training data, and enhances model reliability in real-world scenarios.", "authors": ["Peter Jan van Leeuwen", "J. Christine Chiu", "C. Kevin Yang"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2024-05-31", "url": "https://arxiv.org/abs/2405.20550", "pdf_url": "https://arxiv.org/pdf/2405.20550v2", "arxiv_id": "2405.20550", "doi": "10.1017/eds.2025.10027", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Environmental Data Science", "quality_score": 0.1193}
{"id": "69ddb106b43fcda02910998b37ffa675270488f553845a83903531fcb397d6d2", "sources": ["arxiv", "semantic_scholar"], "title": "Is Synthetic Data all We Need? Benchmarking the Robustness of Models Trained with Synthetic Images", "abstract": "A long-standing challenge in developing machine learning approaches has been the lack of high-quality labeled data. Recently, models trained with purely synthetic data, here termed synthetic clones, generated using large-scale pre-trained diffusion models have shown promising results in overcoming this annotation bottleneck. As these synthetic clone models progress, they are likely to be deployed in challenging real-world settings, yet their suitability remains understudied. Our work addresses this gap by providing the first benchmark for three classes of synthetic clone models, namely supervised, self-supervised, and multi-modal ones, across a range of robustness measures. We show that existing synthetic self-supervised and multi-modal clones are comparable to or outperform state-of-the-art real-image baselines for a range of robustness metrics - shape bias, background bias, calibration, etc. However, we also find that synthetic clones are much more susceptible to adversarial and real-world noise than models trained with real data. To address this, we find that combining both real and synthetic data further increases the robustness, and that the choice of prompt used for generating synthetic images plays an important part in the robustness of synthetic clones.", "authors": ["Krishnakant Singh", "Thanush Navaratnam", "Jannik Holmer", "Simone Schaub-Meyer", "Stefan Roth"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-30", "url": "https://arxiv.org/abs/2405.20469", "pdf_url": "https://arxiv.org/pdf/2405.20469v2", "arxiv_id": "2405.20469", "doi": "10.1109/CVPRW63382.2024.00257", "citation_count": 46, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.418}
{"id": "38eba3a2dfbbbccebaed76259307b78facab889e8513d57511b2fb18f2ef943f", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Data-Driven Electricity Management: Multi-Region Harmonized Data and Knowledge Graph", "abstract": "Due to growing population and technological advances, global electricity consumption, and consequently also CO2 emissions are increasing. The residential sector makes up 25% of global electricity consumption and has great potential to increase efficiency and reduce CO2 footprint without sacrificing comfort. However, a lack of uniform consumption data at the household level spanning multiple regions hinders large-scale studies and robust multi-region model development. This paper introduces a multi-region dataset compiled from publicly available sources and presented in a uniform format. This data enables machine learning tasks such as disaggregation, demand forecasting, appliance ON/OFF classification, etc. Furthermore, we develop an RDF knowledge graph that characterizes the electricity consumption of the households and contextualizes it with household related properties enabling semantic queries and interoperability with other open knowledge bases like Wikidata and DBpedia. This structured data can be utilized to inform various stakeholders towards data-driven policy and business development.", "authors": ["Vid Hanžel", "Blaž Bertalanič", "Carolina Fortuna"], "categories": ["cs.LG"], "fields_of_study": ["Medicine", "Computer Science"], "published_date": "2024-05-29", "url": "https://arxiv.org/abs/2405.18869", "pdf_url": "https://arxiv.org/pdf/2405.18869v1", "arxiv_id": "2405.18869", "doi": "10.1038/s41597-024-04310-z", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Scientific Data", "quality_score": 0.1945}
{"id": "f5563cecfb7988698c0a3a890acd483fe22681884f6635999d6e1e6ecfc4f512", "sources": ["arxiv", "semantic_scholar"], "title": "KiNETGAN: Enabling Distributed Network Intrusion Detection through Knowledge-Infused Synthetic Data Generation", "abstract": "In the realm of IoT/CPS systems connected over mobile networks, traditional intrusion detection methods analyze network traffic across multiple devices using anomaly detection techniques to flag potential security threats. However, these methods face significant privacy challenges, particularly with deep packet inspection and network communication analysis. This type of monitoring is highly intrusive, as it involves examining the content of data packets, which can include personal and sensitive information. Such data scrutiny is often governed by stringent laws and regulations, especially in environments like smart homes where data privacy is paramount. Synthetic data offers a promising solution by mimicking real network behavior without revealing sensitive details. Generative models such as Generative Adversarial Networks (GANs) can produce synthetic data, but they often struggle to generate realistic data in specialized domains like network activity. This limitation stems from insufficient training data, which impedes the model's ability to grasp the domain's rules and constraints adequately. Moreover, the scarcity of training data exacerbates the problem of class imbalance in intrusion detection methods. To address these challenges, we propose a Privacy-Driven framework that utilizes a knowledge-infused Generative Adversarial Network for generating synthetic network activity data (KiNETGAN). This approach enhances the resilience of distributed intrusion detection while addressing privacy concerns. Our Knowledge Guided GAN produces realistic representations of network activity, validated through rigorous experimentation. We demonstrate that KiNETGAN maintains minimal accuracy loss in downstream tasks, effectively balancing data privacy and utility.", "authors": ["Anantaa Kotal", "Brandon Luton", "Anupam Joshi"], "categories": ["cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-26", "url": "https://arxiv.org/abs/2405.16476", "pdf_url": "https://arxiv.org/pdf/2405.16476v1", "arxiv_id": "2405.16476", "doi": "10.1109/ICDCSW63686.2024.00026", "citation_count": 9, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Distributed Computing Systems Workshops", "quality_score": 0.25}
{"id": "3f8a4d008249cf87d9cebf9f51c97ed65634c315f42d354996cac3a0f7d89790", "sources": ["arxiv", "semantic_scholar"], "title": "CuckooGraph: A Scalable and Space-Time Efficient Data Structure for Large-Scale Dynamic Graphs", "abstract": "Graphs play an increasingly important role in various big data applications. However, existing graph data structures cannot simultaneously address the performance bottlenecks caused by the dynamic updates, large scale, and high query complexity of current graphs. This paper proposes a novel data structure for large-scale dynamic graphs called CuckooGraph. It does not require any prior knowledge of the upcoming graphs, and can adaptively resize to the most memory-efficient form while requiring few memory accesses for very fast graph data processing. The key techniques of CuckooGraph include TRANSFORMATION and DENYLIST. TRANSFORMATION fully utilizes the limited memory by designing related data structures that allow flexible space transformations to smoothly expand/tighten the required space depending on the number of incoming items. DENYLIST efficiently handles item insertion failures and further improves processing speed. Our experimental results show that compared with the most competitive solution Spruce, CuckooGraph achieves about $33\\times$ higher insertion throughput while requiring only about $68\\%$ of the memory space.", "authors": ["Zhuochen Fan", "Yalun Cai", "Zirui Liu", "Jiarui Guo", "Xin Fan", "Tong Yang", "Bin Cui"], "categories": ["cs.DB", "cs.DS"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-24", "url": "https://arxiv.org/abs/2405.15193", "pdf_url": "https://arxiv.org/pdf/2405.15193v3", "arxiv_id": "2405.15193", "doi": "10.1109/ICDE65448.2025.00099", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE International Conference on Data Engineering", "quality_score": 0.1945}
{"id": "1a30ae779f7f3d9d71a162200b1ad1352e789bfc9e900cb5193e83737c8c49b8", "sources": ["arxiv", "semantic_scholar"], "title": "JiuZhang3.0: Efficiently Improving Mathematical Reasoning by Training Small Data Synthesis Models", "abstract": "Mathematical reasoning is an important capability of large language models~(LLMs) for real-world applications. To enhance this capability, existing work either collects large-scale math-related texts for pre-training, or relies on stronger LLMs (\\eg GPT-4) to synthesize massive math problems. Both types of work generally lead to large costs in training or synthesis. To reduce the cost, based on open-source available texts, we propose an efficient way that trains a small LLM for math problem synthesis, to efficiently generate sufficient high-quality pre-training data. To achieve it, we create a dataset using GPT-4 to distill its data synthesis capability into the small LLM. Concretely, we craft a set of prompts based on human education stages to guide GPT-4, to synthesize problems covering diverse math knowledge and difficulty levels. Besides, we adopt the gradient-based influence estimation method to select the most valuable math-related texts. The both are fed into GPT-4 for creating the knowledge distillation dataset to train the small LLM. We leverage it to synthesize 6 million math problems for pre-training our JiuZhang3.0 model, which only needs to invoke GPT-4 API 9.3k times and pre-train on 4.6B data. Experimental results have shown that JiuZhang3.0 achieves state-of-the-art performance on several mathematical reasoning datasets, under both natural language reasoning and tool manipulation settings. Our code and data will be publicly released in \\url{https://github.com/RUCAIBox/JiuZhang3.0}.", "authors": ["Kun Zhou", "Beichen Zhang", "Jiapeng Wang", "Zhipeng Chen", "Wayne Xin Zhao", "Jing Sha", "Zhichao Sheng", "Shijin Wang", "Ji-Rong Wen"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-23", "url": "https://arxiv.org/abs/2405.14365", "pdf_url": "https://arxiv.org/pdf/2405.14365v1", "arxiv_id": "2405.14365", "doi": "10.48550/arXiv.2405.14365", "citation_count": 56, "influential_citation_count": 6, "has_code": true, "code_url": "https://github.com/RUCAIBox/JiuZhang3.0}", "venue": "Neural Information Processing Systems", "quality_score": 0.439}
{"id": "c1f179b38a28ed26dce1ca9f89280655c382cf023ac9e3701eeccdd707e0c444", "sources": ["arxiv", "semantic_scholar"], "title": "Token-wise Influential Training Data Retrieval for Large Language Models", "abstract": "Given a Large Language Model (LLM) generation, how can we identify which training data led to this generation? In this paper, we proposed RapidIn, a scalable framework adapting to LLMs for estimating the influence of each training data. The proposed framework consists of two stages: caching and retrieval. First, we compress the gradient vectors by over 200,000x, allowing them to be cached on disk or in GPU/CPU memory. Then, given a generation, RapidIn efficiently traverses the cached gradients to estimate the influence within minutes, achieving over a 6,326x speedup. Moreover, RapidIn supports multi-GPU parallelization to substantially accelerate caching and retrieval. Our empirical result confirms the efficiency and effectiveness of RapidIn.", "authors": ["Huawei Lin", "Jikai Long", "Zhaozhuo Xu", "Weijie Zhao"], "categories": ["cs.CL", "cs.AI", "cs.CR", "cs.IR"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-20", "url": "https://arxiv.org/abs/2405.11724", "pdf_url": "https://arxiv.org/pdf/2405.11724v2", "arxiv_id": "2405.11724", "doi": "10.48550/arXiv.2405.11724", "citation_count": 15, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Annual Meeting of the Association for Computational Linguistics", "quality_score": 0.301}
{"id": "073333e61d36792cb98e0d68805f9b96a0b635d41ba3741777113fb91898f961", "sources": ["arxiv", "semantic_scholar"], "title": "Densely Distilling Cumulative Knowledge for Continual Learning", "abstract": "Continual learning, involving sequential training on diverse tasks, often faces catastrophic forgetting. While knowledge distillation-based approaches exhibit notable success in preventing forgetting, we pinpoint a limitation in their ability to distill the cumulative knowledge of all the previous tasks. To remedy this, we propose Dense Knowledge Distillation (DKD). DKD uses a task pool to track the model's capabilities. It partitions the output logits of the model into dense groups, each corresponding to a task in the task pool. It then distills all tasks' knowledge using all groups. However, using all the groups can be computationally expensive, we also suggest random group selection in each optimization step. Moreover, we propose an adaptive weighting scheme, which balances the learning of new classes and the retention of old classes, based on the count and similarity of the classes. Our DKD outperforms recent state-of-the-art baselines across diverse benchmarks and scenarios. Empirical analysis underscores DKD's ability to enhance model stability, promote flatter minima for improved generalization, and remains robust across various memory budgets and task orders. Moreover, it seamlessly integrates with other CL methods to boost performance and proves versatile in offline scenarios like model compression.", "authors": ["Zenglin Shi", "Pei Liu", "Tong Su", "Yunpeng Wu", "Kuien Liu", "Yu Song", "Meng Wang"], "categories": ["cs.LG", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-16", "url": "https://arxiv.org/abs/2405.09820", "pdf_url": "https://arxiv.org/pdf/2405.09820v1", "arxiv_id": "2405.09820", "doi": "10.48550/arXiv.2405.09820", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "41d21a0e0860587ce5ea56130dffc5f970df5c1210e0682c56039871a2551fe8", "sources": ["arxiv", "semantic_scholar"], "title": "Flow Score Distillation for Diverse Text-to-3D Generation", "abstract": "Recent advancements in Text-to-3D generation have yielded remarkable progress, particularly through methods that rely on Score Distillation Sampling (SDS). While SDS exhibits the capability to create impressive 3D assets, it is hindered by its inherent maximum-likelihood-seeking essence, resulting in limited diversity in generation outcomes. In this paper, we discover that the Denoise Diffusion Implicit Models (DDIM) generation process (\\ie PF-ODE) can be succinctly expressed using an analogue of SDS loss. One step further, one can see SDS as a generalized DDIM generation process. Following this insight, we show that the noise sampling strategy in the noise addition stage significantly restricts the diversity of generation results. To address this limitation, we present an innovative noise sampling approach and introduce a novel text-to-3D method called Flow Score Distillation (FSD). Our validation experiments across various text-to-image Diffusion Models demonstrate that FSD substantially enhances generation diversity without compromising quality.", "authors": ["Runjie Yan", "Kailu Wu", "Kaisheng Ma"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-16", "url": "https://arxiv.org/abs/2405.10988", "pdf_url": "https://arxiv.org/pdf/2405.10988v2", "arxiv_id": "2405.10988", "doi": "10.48550/arXiv.2405.10988", "citation_count": 6, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2386}
{"id": "f2a04e9b90cec2926d6fdda33010450a85cead8fa6a644a3c7f305f751e98628", "sources": ["arxiv", "semantic_scholar"], "title": "Drag prediction of rough-wall turbulent flow using data-driven regression", "abstract": "Efficient tools for predicting the drag of rough walls in turbulent flows would have a tremendous impact. However, methods for drag prediction rely on experiments or numerical simulations which are costly and time-consuming. Data-driven regression methods have the potential to provide a prediction that is accurate and fast. We assess the performance and limitations of linear regression, kernel methods and neural networks for drag prediction using a database of 1000 homogeneous rough surfaces. Model performance is evaluated using the roughness function obtained at friction-scaled Reynolds number 500. With two trainable parameters, the kernel method can fully account for nonlinear relations between $ΔU^+$ and surface statistics (roughness height, effective slope, skewness, etc). In contrast, linear regression cannot account for nonlinear correlations and display large errors and high uncertainty. Multilayer perceptron and convolutional neural networks demonstrate performance on par with the kernel method but have orders of magnitude more trainable parameters. For the current database size, the networks' capacity cannot be fully exploited, resulting in reduced generalizability and reliability. Our study provides insight into the appropriateness of different regression models for drag prediction. We also discuss the remaining steps before data-driven methods emerge as useful tools in applications.", "authors": ["Zhaoyu Shi", "Seyed Morteza Habibi Khorasani", "Heesoo Shin", "Jiasheng Yang", "Sangseung Lee", "Shervin Bagheri"], "categories": ["physics.flu-dyn", "physics.data-an"], "fields_of_study": ["Physics"], "published_date": "2024-05-15", "url": "https://arxiv.org/abs/2405.09256", "pdf_url": "https://arxiv.org/pdf/2405.09256v1", "arxiv_id": "2405.09256", "doi": "10.1017/flo.2024.33", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Flow", "quality_score": 0.1945}
{"id": "821569c7abf4c4dab3ba18e17e7a188adf326c5f415eeb7891f588a9ef592f9e", "sources": ["arxiv", "semantic_scholar"], "title": "Certifying Robustness of Graph Convolutional Networks for Node Perturbation with Polyhedra Abstract Interpretation", "abstract": "Graph convolutional neural networks (GCNs) are powerful tools for learning graph-based knowledge representations from training data. However, they are vulnerable to small perturbations in the input graph, which makes them susceptible to input faults or adversarial attacks. This poses a significant problem for GCNs intended to be used in critical applications, which need to provide certifiably robust services even in the presence of adversarial perturbations. We propose an improved GCN robustness certification technique for node classification in the presence of node feature perturbations. We introduce a novel polyhedra-based abstract interpretation approach to tackle specific challenges of graph data and provide tight upper and lower bounds for the robustness of the GCN. Experiments show that our approach simultaneously improves the tightness of robustness bounds as well as the runtime performance of certification. Moreover, our method can be used during training to further improve the robustness of GCNs.", "authors": ["Boqi Chen", "Kristóf Marussy", "Oszkár Semeráth", "Gunter Mussbacher", "Dániel Varró"], "categories": ["cs.LG", "cs.FL"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-14", "url": "https://arxiv.org/abs/2405.08645", "pdf_url": "https://arxiv.org/pdf/2405.08645v2", "arxiv_id": "2405.08645", "doi": "10.1007/s10618-025-01180-w", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Data mining and knowledge discovery", "quality_score": 0.0}
{"id": "fefce6a6eedfff43fec84facda1e5730c215facecc18d8951cc9a8039e506cfe", "sources": ["arxiv", "semantic_scholar"], "title": "Adaptive-TMLE for the Average Treatment Effect based on Randomized Controlled Trial Augmented with Real-World Data", "abstract": "We consider the problem of estimating the average treatment effect (ATE) when both randomized control trial (RCT) data and external real-world data (RWD) are available. We decompose the ATE estimand as the difference between a pooled-ATE estimand that integrates RCT and RWD and a bias estimand that captures the conditional effect of RCT enrollment on the outcome. We introduce an adaptive targeted maximum likelihood estimation (A-TMLE) framework to estimate them. We prove that the A-TMLE estimator is root-n-consistent and asymptotically normal. Moreover, in finite sample, it achieves the super-efficiency one would obtain had one known the oracle model for the conditional effect of the RCT enrollment on the outcome. Consequently, the smaller and more parsimonious the working model of the bias induced by the RWD is, the greater our estimator's efficiency, while our estimator will always be at least as efficient as an efficient estimator that uses the RCT data only. A-TMLE outperforms existing methods in simulations by having smaller mean-squared-error and 95% confidence intervals. We apply A-TMLE to augment the DEVOTE trial with external data from the Optum Clinformatics Data Mart, demonstrating its potential to establish treatment superiority in noninferiority trials. A-TMLE could utilize external RWD to help improve the power of randomized trials without biasing the estimates of intervention effects. This approach could allow for smaller, faster trials, decreasing the time until patients can receive effective treatments.", "authors": ["Mark van der Laan", "Sky Qiu", "Jens Magelund Tarp", "Lars van der Laan"], "categories": ["stat.ME", "stat.ML"], "fields_of_study": ["Mathematics"], "published_date": "2024-05-12", "url": "https://arxiv.org/abs/2405.07186", "pdf_url": "https://arxiv.org/pdf/2405.07186v2", "arxiv_id": "2405.07186", "doi": "10.1515/jci-2024-0025", "citation_count": 20, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Journal of Causal Inference", "quality_score": 0.3306}
{"id": "de1dee15acf890f86f82841e2997e52c37abf90f1304706acaec8c718d843c78", "sources": ["arxiv", "semantic_scholar"], "title": "Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model Bias", "abstract": "Large language models (LLMs) are increasingly essential in processing natural languages, yet their application is frequently compromised by biases and inaccuracies originating in their training data. In this study, we introduce Cross-Care, the first benchmark framework dedicated to assessing biases and real world knowledge in LLMs, specifically focusing on the representation of disease prevalence across diverse demographic groups. We systematically evaluate how demographic biases embedded in pre-training corpora like $ThePile$ influence the outputs of LLMs. We expose and quantify discrepancies by juxtaposing these biases against actual disease prevalences in various U.S. demographic groups. Our results highlight substantial misalignment between LLM representation of disease prevalence and real disease prevalence rates across demographic subgroups, indicating a pronounced risk of bias propagation and a lack of real-world grounding for medical applications of LLMs. Furthermore, we observe that various alignment methods minimally resolve inconsistencies in the models' representation of disease prevalence across different languages. For further exploration and analysis, we make all data and a data visualization tool available at: www.crosscare.net.", "authors": ["Shan Chen", "Jack Gallifant", "Mingye Gao", "Pedro Moreira", "Nikolaj Munch", "Ajay Muthukkumar", "Arvind Rajan", "Jaya Kolluri", "Amelia Fiske", "Janna Hastings", "Hugo Aerts", "Brian Anthony", "Leo Anthony Celi", "William G. La Cava", "Danielle S. Bitterman"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science", "Medicine"], "published_date": "2024-05-09", "url": "https://arxiv.org/abs/2405.05506", "pdf_url": "https://arxiv.org/pdf/2405.05506v2", "arxiv_id": "2405.05506", "doi": "10.48550/arXiv.2405.05506", "citation_count": 25, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Neural Information Processing Systems", "quality_score": 0.3537}
{"id": "c5887c8001c433896a0d7d7e57cf0ddc13fad5f00ec36d8f5393a39c8cb7a8f7", "sources": ["arxiv", "semantic_scholar"], "title": "Predicting Cognitive Load Using Sensor Data in a Literacy Game", "abstract": "Educational games are being increasingly used to support self-paced learning. However, educators and system designers often face challenges in monitoring student affect and cognitive load. Existing assessments in game-based learning environments (GBLEs) tend to focus more on outcomes rather than processes, potentially overlooking key aspects of the learning journey that include learner affect and cognitive load. To address this issue, we collected data and trained a model to track learner cognitive load while they used an online literacy game for English. We collected affect-related physiological data and pupil data during gameplay to enable the development of models that identify these latent characteristics of learner processes. Our model indicates the feasibility of using these data to track cognitive load in GBLEs. Our multimodal model distinguished different levels of cognitive load, achieving the highest Kappa (.417) and accuracy (70%). Our model reveals the importance of including affect-related features (i.e., EDA and heart rate) when predicting cognitive load and extends recent findings suggesting the benefit of using multiple channels when modeling latent aspects of learner processes. Findings also suggest that cognitive load tracking could now be used to facilitate the creation of personalized learning experiences.", "authors": ["Minghao Cai", "Carrie Demmans Epp"], "categories": ["cs.HC"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-09", "url": "https://arxiv.org/abs/2405.05543", "pdf_url": "https://arxiv.org/pdf/2405.05543v1", "arxiv_id": "2405.05543", "doi": "10.48550/arXiv.2405.05543", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Educational Data Mining", "quality_score": 0.1193}
{"id": "1a09df68736c73a1c880a342ca8d7a12568997476492f3d93b9fc101b385753a", "sources": ["arxiv", "semantic_scholar"], "title": "Dynamic GNNs for Precise Seizure Detection and Classification from EEG Data", "abstract": "Diagnosing epilepsy requires accurate seizure detection and classification, but traditional manual EEG signal analysis is resource-intensive. Meanwhile, automated algorithms often overlook EEG's geometric and semantic properties critical for interpreting brain activity. This paper introduces NeuroGNN, a dynamic Graph Neural Network (GNN) framework that captures the dynamic interplay between the EEG electrode locations and the semantics of their corresponding brain regions. The specific brain region where an electrode is placed critically shapes the nature of captured EEG signals. Each brain region governs distinct cognitive functions, emotions, and sensory processing, influencing both the semantic and spatial relationships within the EEG data. Understanding and modeling these intricate brain relationships are essential for accurate and meaningful insights into brain activity. This is precisely where the proposed NeuroGNN framework excels by dynamically constructing a graph that encapsulates these evolving spatial, temporal, semantic, and taxonomic correlations to improve precision in seizure detection and classification. Our extensive experiments with real-world data demonstrate that NeuroGNN significantly outperforms existing state-of-the-art models.", "authors": ["Arash Hajisafi", "Haowen Lin", "Yao-Yi Chiang", "Cyrus Shahabi"], "categories": ["eess.SP", "cs.LG"], "fields_of_study": ["Engineering", "Computer Science", "Medicine"], "published_date": "2024-05-08", "url": "https://arxiv.org/abs/2405.09568", "pdf_url": "https://arxiv.org/pdf/2405.09568v1", "arxiv_id": "2405.09568", "doi": "10.1007/978-981-97-2238-9_16", "citation_count": 12, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "European Conference on Principles of Data Mining and Knowledge Discovery", "quality_score": 0.2785}
{"id": "3b90b632ea1dc1db4442af57b339143acb9c7fd594f9f72ea7f4acb0113330b9", "sources": ["arxiv", "semantic_scholar"], "title": "The Canadian VirusSeq Data Portal & Duotang: open resources for SARS-CoV-2 viral sequences and genomic epidemiology", "abstract": "The COVID-19 pandemic led to a large global effort to sequence SARS-CoV-2 genomes from patient samples to track viral evolution and inform public health response. Millions of SARS-CoV-2 genome sequences have been deposited in global public repositories. The Canadian COVID-19 Genomics Network (CanCOGeN - VirusSeq), a consortium tasked with coordinating expanded sequencing of SARS-CoV-2 genomes across Canada early in the pandemic, created the Canadian VirusSeq Data Portal, with associated data pipelines and procedures, to support these efforts. The goal of VirusSeq was to allow open access to Canadian SARS-CoV-2 genomic sequences and enhanced, standardized contextual data that were unavailable in other repositories and that meet FAIR standards (Findable, Accessible, Interoperable and Reusable). The Portal data submission pipeline contains data quality checking procedures and appropriate acknowledgement of data generators that encourages collaboration. Here we also highlight Duotang, a web platform that presents genomic epidemiology and modeling analyses on circulating and emerging SARS-CoV-2 variants in Canada. Duotang presents dynamic changes in variant composition of SARS-CoV-2 in Canada and by province, estimates variant growth, and displays complementary interactive visualizations, with a text overview of the current situation. The VirusSeq Data Portal and Duotang resources, alongside additional analyses and resources computed from the Portal (COVID-MVP, CoVizu), are all open-source and freely available. Together, they provide an updated picture of SARS-CoV-2 evolution to spur scientific discussions, inform public discourse, and support communication with and within public health authorities. They also serve as a framework for other jurisdictions interested in open, collaborative sequence data sharing and analyses.", "authors": ["Erin E. Gill", "Baofeng Jia", "Carmen Lia Murall", "Raphaël Poujol", "Muhammad Zohaib Anwar", "Nithu Sara John", "Justin Richardsson", "Ashley Hobb", "Abayomi S. Olabode", "Alexandru Lepsa", "Ana T. Duggan", "Andrea D. Tyler", "Arnaud N'Guessan", "Atul Kachru", "Brandon Chan", "Catherine Yoshida", "Christina K. Yung", "David Bujold", "Dusan Andric", "Edmund Su", "Emma J. Griffiths", "Gary Van Domselaar", "Gordon W. Jolly", "Heather K. E. Ward", "Henrich Feher", "Jared Baker", "Jared T. Simpson", "Jaser Uddin", "Jiannis Ragoussis", "Jon Eubank", "Jörg H. Fritz", "José Héctor Gálvez", "Karen Fang", "Kim Cullion", "Leonardo Rivera", "Linda Xiang", "Matthew A. Croxen", "Mitchell Shiell", "Natalie Prystajecky", "Pierre-Olivier Quirion", "Rosita Bajari", "Samantha Rich", "Samira Mubareka", "Sandrine Moreira", "Scott Cain", "Steven G. Sutcliffe", "Susanne A. Kraemer", "Yann Joly", "Yelizar Alturmessov", "CPHLN consortium", "CanCOGeN consortium", "VirusSeq Data Portal Academic", "Health network", "Marc Fiume", "Terrance P. Snutch", "Cindy Bell", "Catalina Lopez-Correa", "Julie G. Hussin", "Jeffrey B. Joy", "Caroline Colijn", "Paul M. K. Gordon", "William W. L. Hsiao", "Art F. Y. Poon", "Natalie C. Knox", "Mélanie Courtot", "Lincoln Stein", "Sarah P. Otto", "Guillaume Bourque", "B. Jesse Shapiro", "Fiona S. L. Brinkman"], "categories": ["q-bio.GN"], "fields_of_study": ["Medicine", "Biology"], "published_date": "2024-05-08", "url": "https://arxiv.org/abs/2405.04734", "pdf_url": "https://arxiv.org/pdf/2405.04734v1", "arxiv_id": "2405.04734", "doi": null, "citation_count": 2, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "d91fa14d38911471a98cd76bd8ebce79b36ebf9f6ddc47cdbf7440963213a47b", "sources": ["arxiv", "semantic_scholar"], "title": "SEED-Data-Edit Technical Report: A Hybrid Dataset for Instructional Image Editing", "abstract": "In this technical report, we introduce SEED-Data-Edit: a unique hybrid dataset for instruction-guided image editing, which aims to facilitate image manipulation using open-form language. SEED-Data-Edit is composed of three distinct types of data: (1) High-quality editing data produced by an automated pipeline, ensuring a substantial volume of diverse image editing pairs. (2) Real-world scenario data collected from the internet, which captures the intricacies of user intentions for promoting the practical application of image editing in the real world. (3) High-precision multi-turn editing data annotated by humans, which involves multiple rounds of edits for simulating iterative editing processes. The combination of these diverse data sources makes SEED-Data-Edit a comprehensive and versatile dataset for training language-guided image editing model. We fine-tune a pretrained Multimodal Large Language Model (MLLM) that unifies comprehension and generation with SEED-Data-Edit. The instruction tuned model demonstrates promising results, indicating the potential and effectiveness of SEED-Data-Edit in advancing the field of instructional image editing. The datasets are released in https://huggingface.co/datasets/AILab-CVC/SEED-Data-Edit.", "authors": ["Yuying Ge", "Sijie Zhao", "Chen Li", "Yixiao Ge", "Ying Shan"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-07", "url": "https://arxiv.org/abs/2405.04007", "pdf_url": "https://arxiv.org/pdf/2405.04007v1", "arxiv_id": "2405.04007", "doi": "10.48550/arXiv.2405.04007", "citation_count": 93, "influential_citation_count": 16, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.6152}
{"id": "2cc51afa85e85cfe61feb781f815547f11db9a8a79b1f952e89d227f1ae2de9a", "sources": ["arxiv", "semantic_scholar"], "title": "Provably Unlearnable Data Examples", "abstract": "The exploitation of publicly accessible data has led to escalating concerns regarding data privacy and intellectual property (IP) breaches in the age of artificial intelligence. To safeguard both data privacy and IP-related domain knowledge, efforts have been undertaken to render shared data unlearnable for unauthorized models in the wild. Existing methods apply empirically optimized perturbations to the data in the hope of disrupting the correlation between the inputs and the corresponding labels such that the data samples are converted into Unlearnable Examples (UEs). Nevertheless, the absence of mechanisms to verify the robustness of UEs against uncertainty in unauthorized models and their training procedures engenders several under-explored challenges. First, it is hard to quantify the unlearnability of UEs against unauthorized adversaries from different runs of training, leaving the soundness of the defense in obscurity. Particularly, as a prevailing evaluation metric, empirical test accuracy faces generalization errors and may not plausibly represent the quality of UEs. This also leaves room for attackers, as there is no rigid guarantee of the maximal test accuracy achievable by attackers. Furthermore, we find that a simple recovery attack can restore the clean-task performance of the classifiers trained on UEs by slightly perturbing the learned weights. To mitigate the aforementioned problems, in this paper, we propose a mechanism for certifying the so-called $(q, η)$-Learnability of an unlearnable dataset via parametric smoothing. A lower certified $(q, η)$-Learnability indicates a more robust and effective protection over the dataset. Concretely, we 1) improve the tightness of certified $(q, η)$-Learnability and 2) design Provably Unlearnable Examples (PUEs) which have reduced $(q, η)$-Learnability.", "authors": ["Derui Wang", "Minhui Xue", "Bo Li", "Seyit Camtepe", "Liming Zhu"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-06", "url": "https://arxiv.org/abs/2405.03316", "pdf_url": "https://arxiv.org/pdf/2405.03316v2", "arxiv_id": "2405.03316", "doi": "10.14722/ndss.2025.240886", "citation_count": 14, "influential_citation_count": 2, "has_code": true, "code_url": "https://github.com/NeuralSec/certified-data-learnability", "venue": "Network and Distributed System Security Symposium", "quality_score": 0.294}
{"id": "e105340ad5b96cae0ba69afd3f98e9b31ba5e1c2f2b4c335778c7dc27f9e892c", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Building Autonomous Data Services on Azure", "abstract": "Modern cloud has turned data services into easily accessible commodities. With just a few clicks, users are now able to access a catalog of data processing systems for a wide range of tasks. However, the cloud brings in both complexity and opportunity. While cloud users can quickly start an application by using various data services, it can be difficult to configure and optimize these services to gain the most value from them. For cloud providers, managing every aspect of an ever-increasing set of data services, while meeting customer SLAs and minimizing operational cost is becoming more challenging. Cloud technology enables the collection of significant amounts of workload traces and system telemetry. With the progress in data science (DS) and machine learning (ML), it is feasible and desirable to utilize a data-driven, ML-based approach to automate various aspects of data services, resulting in the creation of autonomous data services. This paper presents our perspectives and insights on creating autonomous data services on Azure. It also covers the future endeavors we plan to undertake and unresolved issues that still need attention.", "authors": ["Yiwen Zhu", "Yuanyuan Tian", "Joyce Cahoon", "Subru Krishnan", "Ankita Agarwal", "Rana Alotaibi", "Jesús Camacho-Rodríguez", "Bibin Chundatt", "Andrew Chung", "Niharika Dutta", "Andrew Fogarty", "Anja Gruenheid", "Brandon Haynes", "Matteo Interlandi", "Minu Iyer", "Nick Jurgens", "Sumeet Khushalani", "Brian Kroth", "Manoj Kumar", "Jyoti Leeka", "Sergiy Matusevych", "Minni Mittal", "Andreas Mueller", "Kartheek Muthyala", "Harsha Nagulapalli", "Yoonjae Park", "Hiren Patel", "Anna Pavlenko", "Olga Poppe", "Santhosh Ravindran", "Karla Saur", "Rathijit Sen", "Steve Suh", "Arijit Tarafdar", "Kunal Waghray", "Demin Wang", "Carlo Curino", "Raghu Ramakrishnan"], "categories": ["cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-03", "url": "https://arxiv.org/abs/2405.01813", "pdf_url": "https://arxiv.org/pdf/2405.01813v1", "arxiv_id": "2405.01813", "doi": "10.1145/3555041.3589674", "citation_count": 13, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2865}
{"id": "26694e7a7ccec3d276e6ef88e5481e2f83e7d91c9a56ef092987c3ed70183f83", "sources": ["arxiv", "semantic_scholar"], "title": "\"I'm in the Bluesky Tonight\": Insights from a Year Worth of Social Data", "abstract": "Pollution of online social spaces caused by rampaging d/misinformation is a growing societal concern. However, recent decisions to reduce access to social media APIs are causing a shortage of publicly available, recent, social media data, thus hindering the advancement of computational social science as a whole. We present a large, high-coverage dataset of social interactions and user-generated content from Bluesky Social to address this pressing issue. The dataset contains the complete post history of over 4M users (81% of all registered accounts), totalling 235M posts. We also make available social data covering follow, comment, repost, and quote interactions. Since Bluesky allows users to create and bookmark feed generators (i.e., content recommendation algorithms), we also release the full output of several popular algorithms available on the platform, along with their timestamped ``like'' interactions and time of bookmarking. This dataset allows unprecedented analysis of online behavior and human-machine engagement patterns. Notably, it provides ground-truth data for studying the effects of content exposure and self-selection and performing content virality and diffusion analysis.", "authors": ["Andrea Failla", "Giulio Rossetti"], "categories": ["cs.SI", "cs.CY"], "fields_of_study": ["Computer Science", "Medicine"], "published_date": "2024-04-29", "url": "https://arxiv.org/abs/2404.18984", "pdf_url": "https://arxiv.org/pdf/2404.18984v1", "arxiv_id": "2404.18984", "doi": "10.1371/journal.pone.0310330", "citation_count": 33, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "PLoS ONE", "quality_score": 0.3829}
{"id": "9df3c88687162df6dfab82633db581734f6e9440f35b7604fa33f9b4a14c524a", "sources": ["arxiv", "semantic_scholar"], "title": "Generative Dataset Distillation: Balancing Global Structure and Local Details", "abstract": "In this paper, we propose a new dataset distillation method that considers balancing global structure and local details when distilling the information from a large dataset into a generative model. Dataset distillation has been proposed to reduce the size of the required dataset when training models. The conventional dataset distillation methods face the problem of long redeployment time and poor cross-architecture performance. Moreover, previous methods focused too much on the high-level semantic attributes between the synthetic dataset and the original dataset while ignoring the local features such as texture and shape. Based on the above understanding, we propose a new method for distilling the original image dataset into a generative model. Our method involves using a conditional generative adversarial network to generate the distilled dataset. Subsequently, we ensure balancing global structure and local details in the distillation process, continuously optimizing the generator for more information-dense dataset generation.", "authors": ["Longzhen Li", "Guang Li", "Ren Togo", "Keisuke Maeda", "Takahiro Ogawa", "Miki Haseyama"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-26", "url": "https://arxiv.org/abs/2404.17732", "pdf_url": "https://arxiv.org/pdf/2404.17732v1", "arxiv_id": "2404.17732", "doi": "10.1109/CVPRW63382.2024.00762", "citation_count": 19, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3253}
{"id": "9f20087fe46a886ecc84d0ca9c1d35937a3528772a7181f6bf4e53cb73116ff0", "sources": ["arxiv", "semantic_scholar"], "title": "Integrating Heterogeneous Gene Expression Data through Knowledge Graphs for Improving Diabetes Prediction", "abstract": "Diabetes is a worldwide health issue affecting millions of people. Machine learning methods have shown promising results in improving diabetes prediction, particularly through the analysis of diverse data types, namely gene expression data. While gene expression data can provide valuable insights, challenges arise from the fact that the sample sizes in expression datasets are usually limited, and the data from different datasets with different gene expressions cannot be easily combined. This work proposes a novel approach to address these challenges by integrating multiple gene expression datasets and domain-specific knowledge using knowledge graphs, a unique tool for biomedical data integration. KG embedding methods are then employed to generate vector representations, serving as inputs for a classifier. Experiments demonstrated the efficacy of our approach, revealing improvements in diabetes prediction when integrating multiple gene expression datasets and domain-specific knowledge about protein functions and interactions.", "authors": ["Rita T. Sousa", "Heiko Paulheim"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-23", "url": "https://arxiv.org/abs/2404.14970", "pdf_url": "https://arxiv.org/pdf/2404.14970v1", "arxiv_id": "2404.14970", "doi": "10.48550/arXiv.2404.14970", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0753}
{"id": "852e4260699422c97731d9c27e40d3f37330124e234ef22529d106d5e25b1fd1", "sources": ["arxiv", "semantic_scholar"], "title": "A Stochastic Geo-spatiotemporal Bipartite Network to Optimize GCOOS Sensor Placement Strategies", "abstract": "This paper proposes two new measures applicable in a spatial bipartite network model: coverage and coverage robustness. The bipartite network must consist of observer nodes, observable nodes, and edges that connect observer nodes to observable nodes. The coverage and coverage robustness scores evaluate the effectiveness of the observer node placements. This measure is beneficial for stochastic data as it may be coupled with Monte Carlo simulations to identify optimal placements for new observer nodes. In this paper, we construct a Geo-SpatioTemporal Bipartite Network (GSTBN) within the stochastic and dynamical environment of the Gulf of Mexico. This GSTBN consists of GCOOS sensor nodes and HYCOM Region of Interest (RoI) event nodes. The goal is to identify optimal placements to expand GCOOS to improve the forecasting outcomes by the HYCOM ocean prediction model.", "authors": ["Ted Edward Holmberg", "Elias Ioup", "Mahdi Abdelguerfi"], "categories": ["cs.MA", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-22", "url": "https://arxiv.org/abs/2404.14357", "pdf_url": "https://arxiv.org/pdf/2404.14357v2", "arxiv_id": "2404.14357", "doi": "10.1109/BigData55660.2022.10020928", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan, 2022, pp. 3668-3674", "quality_score": 0.1505}
{"id": "530cb48571478635d6f2710f7092fe6c9d5ce9b7f4682354bf4f08d8a8fa1604", "sources": ["arxiv", "semantic_scholar"], "title": "Scalable Data Assimilation with Message Passing", "abstract": "Data assimilation is a core component of numerical weather prediction systems. The large quantity of data processed during assimilation requires the computation to be distributed across increasingly many compute nodes, yet existing approaches suffer from synchronisation overhead in this setting. In this paper, we exploit the formulation of data assimilation as a Bayesian inference problem and apply a message-passing algorithm to solve the spatial inference problem. Since message passing is inherently based on local computations, this approach lends itself to parallel and distributed computation. In combination with a GPU-accelerated implementation, we can scale the algorithm to very large grid sizes while retaining good accuracy and compute and memory requirements.", "authors": ["Oscar Key", "So Takao", "Daniel Giles", "Marc Peter Deisenroth"], "categories": ["cs.LG", "cs.DC", "stat.AP"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2024-04-19", "url": "https://arxiv.org/abs/2404.12968", "pdf_url": "https://arxiv.org/pdf/2404.12968v2", "arxiv_id": "2404.12968", "doi": "10.1017/eds.2024.47", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Environmental Data Science", "quality_score": 0.0753}
{"id": "829c09285643c08dccf54fbdb1cf69eb358391d569a8daa7d060aad6346f2b2c", "sources": ["arxiv", "semantic_scholar"], "title": "Efficient Parking Search using Shared Fleet Data", "abstract": "Finding an available on-street parking spot is a relevant problem of day-to-day life. In recent years, cities such as Melbourne and San Francisco deployed sensors that provide real-time information about the occupation of parking spots. Finding a free parking spot in such a smart environment can be modeled and solved as a Markov decision process (MDP). The problem has to consider uncertainty as available parking spots might not remain available until arrival due to other vehicles also claiming spots in the meantime. Knowing the parking intention of every vehicle in the environment would eliminate this uncertainty. Unfortunately, it does currently not seem realistic to have such data from all vehicles. In contrast, acquiring data from a subset of vehicles or a vehicle fleet appears feasible and has the potential to reduce uncertainty. In this paper, we examine the question of how useful sharing data within a vehicle fleet might be for the search times of particular drivers. We use fleet data to better estimate the availability of parking spots at arrival. Since optimal solutions for large scenarios are infeasible, we base our method on approximate solutions, which have been shown to perform well in single-agent settings. Our experiments are conducted on a simulation using real-world and synthetic data from the city of Melbourne. The results indicate that fleet data can significantly reduce search times for an available parking spot.", "authors": ["Niklas Strauß", "Lukas Rottkamp", "Sebatian Schmoll", "Matthias Schubert"], "categories": ["cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-16", "url": "https://arxiv.org/abs/2404.10646", "pdf_url": "https://arxiv.org/pdf/2404.10646v1", "arxiv_id": "2404.10646", "doi": "10.1109/MDM52706.2021.00026", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Mobile Data Management", "quality_score": 0.1193}
{"id": "8f12bed18ec2784e098fe216f5ab94d63aa3f28a4fda347aaf15f3904664188c", "sources": ["arxiv", "semantic_scholar"], "title": "Exploring Augmentation and Cognitive Strategies for AI based Synthetic Personae", "abstract": "Large language models (LLMs) hold potential for innovative HCI research, including the creation of synthetic personae. However, their black-box nature and propensity for hallucinations pose challenges. To address these limitations, this position paper advocates for using LLMs as data augmentation systems rather than zero-shot generators. We further propose the development of robust cognitive and memory frameworks to guide LLM responses. Initial explorations suggest that data enrichment, episodic memory, and self-reflection techniques can improve the reliability of synthetic personae and open up new avenues for HCI research.", "authors": ["Rafael Arias Gonzalez", "Steve DiPaola"], "categories": ["cs.AI", "cs.HC", "cs.IR"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-16", "url": "https://arxiv.org/abs/2404.10890", "pdf_url": "https://arxiv.org/pdf/2404.10890v1", "arxiv_id": "2404.10890", "doi": "10.48550/arXiv.2404.10890", "citation_count": 3, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "8d052591911bbdbe43ca221c0eca9d3aa243b1be6e84ba425ae4ceae6ff6540a", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetic Census Data Generation via Multidimensional Multiset Sum", "abstract": "The US Decennial Census provides valuable data for both research and policy purposes. Census data are subject to a variety of disclosure avoidance techniques prior to release in order to preserve respondent confidentiality. While many are interested in studying the impacts of disclosure avoidance methods on downstream analyses, particularly with the introduction of differential privacy in the 2020 Decennial Census, these efforts are limited by a critical lack of data: The underlying \"microdata,\" which serve as necessary input to disclosure avoidance methods, are kept confidential. In this work, we aim to address this limitation by providing tools to generate synthetic microdata solely from published Census statistics, which can then be used as input to any number of disclosure avoidance algorithms for the sake of evaluation and carrying out comparisons. We define a principled distribution over microdata given published Census statistics and design algorithms to sample from this distribution. We formulate synthetic data generation in this context as a knapsack-style combinatorial optimization problem and develop novel algorithms for this setting. While the problem we study is provably hard, we show empirically that our methods work well in practice, and we offer theoretical arguments to explain our performance. Finally, we verify that the data we produce are \"close\" to the desired ground truth.", "authors": ["Cynthia Dwork", "Kristjan Greenewald", "Manish Raghavan"], "categories": ["cs.CY", "cs.CR", "cs.DS"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-15", "url": "https://arxiv.org/abs/2404.10095", "pdf_url": "https://arxiv.org/pdf/2404.10095v2", "arxiv_id": "2404.10095", "doi": "10.29012/jpc.932", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Journal of Privacy and Confidentiality", "quality_score": 0.1505}
{"id": "2c8502c7b1c7ef2cb5fc883f68fbc2b759105cfeeaa7b4ea90ef29d0db66d6f9", "sources": ["arxiv", "semantic_scholar"], "title": "An evaluation framework for synthetic data generation models", "abstract": "Nowadays, the use of synthetic data has gained popularity as a cost-efficient strategy for enhancing data augmentation for improving machine learning models performance as well as addressing concerns related to sensitive data privacy. Therefore, the necessity of ensuring quality of generated synthetic data, in terms of accurate representation of real data, consists of primary importance. In this work, we present a new framework for evaluating synthetic data generation models' ability for developing high-quality synthetic data. The proposed approach is able to provide strong statistical and theoretical information about the evaluation framework and the compared models' ranking. Two use case scenarios demonstrate the applicability of the proposed framework for evaluating the ability of synthetic data generation models to generated high quality data. The implementation code can be found in https://github.com/novelcore/synthetic_data_evaluation_framework.", "authors": ["Ioannis E. Livieris", "Nikos Alimpertis", "George Domalis", "Dimitris Tsakalidis"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-13", "url": "https://arxiv.org/abs/2404.08866", "pdf_url": "https://arxiv.org/pdf/2404.08866v1", "arxiv_id": "2404.08866", "doi": "10.1007/978-3-031-63219-8_24", "citation_count": 19, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/novelcore/synthetic_data_evaluation_framework", "venue": "Artificial Intelligence Applications and Innovations", "quality_score": 0.3253}
{"id": "88bf9553b1163af7fae3b0ecd2f5c38c83dfe75ca4ead4c28e75ac283f60538a", "sources": ["arxiv", "semantic_scholar"], "title": "Using ChatGPT for Data Science Analyses", "abstract": "As a result of recent advancements in generative AI, the field of data science is prone to various changes. The way practitioners construct their data science workflows is now irreversibly shaped by recent advancements, particularly by tools like OpenAI's Data Analysis plugin. While it offers powerful support as a quantitative co-pilot, its limitations demand careful consideration in empirical analysis. This paper assesses the potential of ChatGPT for data science analyses, illustrating its capabilities for data exploration and visualization, as well as for commonly used supervised and unsupervised modeling tasks. While we focus here on how the Data Analysis plugin can serve as co-pilot for Data Science workflows, its broader potential for automation is implicit throughout.", "authors": ["Ozan Evkaya", "Miguel de Carvalho"], "categories": ["cs.LG", "cs.CL", "stat.CO"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2024-04-12", "url": "https://arxiv.org/abs/2404.08480", "pdf_url": "https://arxiv.org/pdf/2404.08480v2", "arxiv_id": "2404.08480", "doi": "10.1162/99608f92.c9429f07", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Harvard data science review", "quality_score": 0.1193}
{"id": "dc6bff24a60d03242916a123b3d83fbe3d672169e073642fe3b882b39abeeced", "sources": ["arxiv", "semantic_scholar"], "title": "An improved tabular data generator with VAE-GMM integration", "abstract": "The rising use of machine learning in various fields requires robust methods to create synthetic tabular data. Data should preserve key characteristics while addressing data scarcity challenges. Current approaches based on Generative Adversarial Networks, such as the state-of-the-art CTGAN model, struggle with the complex structures inherent in tabular data. These data often contain both continuous and discrete features with non-Gaussian distributions. Therefore, we propose a novel Variational Autoencoder (VAE)-based model that addresses these limitations. Inspired by the TVAE model, our approach incorporates a Bayesian Gaussian Mixture model (BGM) within the VAE architecture. This avoids the limitations imposed by assuming a strictly Gaussian latent space, allowing for a more accurate representation of the underlying data distribution during data generation. Furthermore, our model offers enhanced flexibility by allowing the use of various differentiable distributions for individual features, making it possible to handle both continuous and discrete data types. We thoroughly validate our model on three real-world datasets with mixed data types, including two medically relevant ones, based on their resemblance and utility. This evaluation demonstrates significant outperformance against CTGAN and TVAE, establishing its potential as a valuable tool for generating synthetic tabular data in various domains, particularly in healthcare.", "authors": ["Patricia A. Apellániz", "Juan Parras", "Santiago Zazo"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-12", "url": "https://arxiv.org/abs/2404.08434", "pdf_url": "https://arxiv.org/pdf/2404.08434v2", "arxiv_id": "2404.08434", "doi": "10.48550/arXiv.2404.08434", "citation_count": 24, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "European Signal Processing Conference", "quality_score": 0.3495}
{"id": "84301d665756a83777988f4a588a66ddbc6a66a62a8305f612254238a27c4b90", "sources": ["arxiv", "semantic_scholar"], "title": "GANsemble for Small and Imbalanced Data Sets: A Baseline for Synthetic Microplastics Data", "abstract": "Microplastic particle ingestion or inhalation by humans is a problem of growing concern. Unfortunately, current research methods that use machine learning to understand their potential harms are obstructed by a lack of available data. Deep learning techniques in particular are challenged by such domains where only small or imbalanced data sets are available. Overcoming this challenge often involves oversampling underrepresented classes or augmenting the existing data to improve model performance. This paper proposes GANsemble: a two-module framework connecting data augmentation with conditional generative adversarial networks (cGANs) to generate class-conditioned synthetic data. First, the data chooser module automates augmentation strategy selection by searching for the best data augmentation strategy. Next, the cGAN module uses this strategy to train a cGAN for generating enhanced synthetic data. We experiment with the GANsemble framework on a small and imbalanced microplastics data set. A Microplastic-cGAN (MPcGAN) algorithm is introduced, and baselines for synthetic microplastics (SYMP) data are established in terms of Frechet Inception Distance (FID) and Inception Scores (IS). We also provide a synthetic microplastics filter (SYMP-Filter) algorithm to increase the quality of generated SYMP. Additionally, we show the best amount of oversampling with augmentation to fix class imbalance in small microplastics data sets. To our knowledge, this study is the first application of generative AI to synthetically create microplastics data.", "authors": ["Daniel Platnick", "Sourena Khanzadeh", "Alireza Sadeghian", "Richard Anthony Valenzano"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-10", "url": "https://arxiv.org/abs/2404.07356", "pdf_url": "https://arxiv.org/pdf/2404.07356v2", "arxiv_id": "2404.07356", "doi": "10.48550/arXiv.2404.07356", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1193}
{"id": "730f15804b952939dcdaf0122ad4eca862637b60ad7b3c82f4fdb1849ad17968", "sources": ["arxiv", "semantic_scholar"], "title": "How to Craft Backdoors with Unlabeled Data Alone?", "abstract": "Relying only on unlabeled data, Self-supervised learning (SSL) can learn rich features in an economical and scalable way. As the drive-horse for building foundation models, SSL has received a lot of attention recently with wide applications, which also raises security concerns where backdoor attack is a major type of threat: if the released dataset is maliciously poisoned, backdoored SSL models can behave badly when triggers are injected to test samples. The goal of this work is to investigate this potential risk. We notice that existing backdoors all require a considerable amount of \\emph{labeled} data that may not be available for SSL. To circumvent this limitation, we explore a more restrictive setting called no-label backdoors, where we only have access to the unlabeled data alone, where the key challenge is how to select the proper poison set without using label information. We propose two strategies for poison selection: clustering-based selection using pseudolabels, and contrastive selection derived from the mutual information principle. Experiments on CIFAR-10 and ImageNet-100 show that both no-label backdoors are effective on many SSL methods and outperform random poisoning by a large margin. Code will be available at https://github.com/PKU-ML/nlb.", "authors": ["Yifei Wang", "Wenhan Ma", "Stefanie Jegelka", "Yisen Wang"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-10", "url": "https://arxiv.org/abs/2404.06694", "pdf_url": "https://arxiv.org/pdf/2404.06694v2", "arxiv_id": "2404.06694", "doi": "10.48550/arXiv.2404.06694", "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/PKU-ML/nlb", "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "c0442fc48992a9ee3775ec5a7f7183e63794fb98e094617c227fe7fff1a4ca85", "sources": ["arxiv", "semantic_scholar"], "title": "The Categorical Data Map: A Multidimensional Scaling-Based Approach", "abstract": "Categorical data does not have an intrinsic definition of distance or order, and therefore, established visualization techniques for categorical data only allow for a set-based or frequency-based analysis, e.g., through Euler diagrams or Parallel Sets, and do not support a similarity-based analysis. We present a novel dimensionality reduction-based visualization for categorical data, which is based on defining the distance of two data items as the number of varying attributes. Our technique enables users to pre-attentively detect groups of similar data items and observe the properties of the projection, such as attributes strongly influencing the embedding. Our prototype visually encodes data properties in an enhanced scatterplot-like visualization, encoding attributes in the background to show the distribution of categories. In addition, we propose two graph-based measures to quantify the plot's visual quality, which rank attributes according to their contribution to cluster cohesion. To demonstrate the capabilities of our similarity-based approach, we compare it to Euler diagrams and Parallel Sets regarding visual scalability and show its benefits through an expert study with five data scientists analyzing the Titanic and Mushroom datasets with up to 23 attributes and 8124 category combinations. Our results indicate that the Categorical Data Map offers an effective analysis method, especially for large datasets with a high number of category combinations.", "authors": ["Frederik L. Dennig", "Lucas Joos", "Patrick Paetzold", "Daniela Blumberg", "Oliver Deussen", "Daniel A. Keim", "Maximilian T. Fischer"], "categories": ["cs.HC", "cs.GR"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-04", "url": "https://arxiv.org/abs/2404.16044", "pdf_url": "https://arxiv.org/pdf/2404.16044v4", "arxiv_id": "2404.16044", "doi": "10.1109/VDS63897.2024.00008", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "2024 IEEE Visualization in Data Science (VDS)", "quality_score": 0.1747}
{"id": "48fe7225a8684678106df70a0c5adc139038bd21d5e26165d839129b76a16aa0", "sources": ["arxiv", "semantic_scholar"], "title": "SiloFuse: Cross-silo Synthetic Data Generation with Latent Tabular Diffusion Models", "abstract": "Synthetic tabular data is crucial for sharing and augmenting data across silos, especially for enterprises with proprietary data. However, existing synthesizers are designed for centrally stored data. Hence, they struggle with real-world scenarios where features are distributed across multiple silos, necessitating on-premise data storage. We introduce SiloFuse, a novel generative framework for high-quality synthesis from cross-silo tabular data. To ensure privacy, SiloFuse utilizes a distributed latent tabular diffusion architecture. Through autoencoders, latent representations are learned for each client's features, masking their actual values. We employ stacked distributed training to improve communication efficiency, reducing the number of rounds to a single step. Under SiloFuse, we prove the impossibility of data reconstruction for vertically partitioned synthesis and quantify privacy risks through three attacks using our benchmark framework. Experimental results on nine datasets showcase SiloFuse's competence against centralized diffusion-based synthesizers. Notably, SiloFuse achieves 43.8 and 29.8 higher percentage points over GANs in resemblance and utility. Experiments on communication show stacked training's fixed cost compared to the growing costs of end-to-end training as the number of training iterations increases. Additionally, SiloFuse proves robust to feature permutations and varying numbers of clients.", "authors": ["Aditya Shankar", "Hans Brouwer", "Rihan Hai", "Lydia Chen"], "categories": ["cs.LG", "cs.CR", "cs.DB", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-04", "url": "https://arxiv.org/abs/2404.03299", "pdf_url": "https://arxiv.org/pdf/2404.03299v1", "arxiv_id": "2404.03299", "doi": "10.1109/ICDE60146.2024.00016", "citation_count": 13, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "IEEE International Conference on Data Engineering", "quality_score": 0.2865}
{"id": "37cc1f19890626fd37034a97a9c4788851880d60716864d150bd14be849c7a74", "sources": ["arxiv", "semantic_scholar"], "title": "Semi-analytical covariance matrices for two-point correlation function for DESI 2024 data", "abstract": "We present an optimized way of producing the fast semi-analytical covariance matrices for the Legendre moments of the two-point correlation function, taking into account survey geometry and mimicking the non-Gaussian effects. We validate the approach on simulated (mock) catalogs for different galaxy types, representative of the Dark Energy Spectroscopic Instrument (DESI) Data Release 1, used in 2024 analyses. We find only a few percent differences between the mock sample covariance matrix and our results, which can be expected given the approximate nature of the mocks, although we do identify discrepancies between the shot-noise properties of the DESI fiber assignment algorithm and the faster approximation (emulator) used in the mocks. Importantly, we find a close agreement (<=8% relative differences) in the projected errorbars for distance scale parameters for the baryon acoustic oscillation measurements. This confirms our method as an attractive alternative to simulation-based covariance matrices, especially for non-standard models or galaxy sample selections, making it particularly relevant to the broad current and future analyses of DESI data.", "authors": ["M. Rashkovetskyi", "D. Forero-Sánchez", "A. de Mattia", "D. J. Eisenstein", "N. Padmanabhan", "H. Seo", "A. J. Ross", "J. Aguilar", "S. Ahlen", "O. Alves", "U. Andrade", "D. Brooks", "E. Burtin", "X. Chen", "T. Claybaugh", "S. Cole", "A. de la Macorra", "Z. Ding", "P. Doel", "K. Fanning", "S. Ferraro", "A. Font-Ribera", "J. E. Forero-Romero", "C. Garcia-Quintero", "H. Gil-Marín", "S. Gontcho A Gontcho", "A. X. Gonzalez-Morales", "G. Gutierrez", "K. Honscheid", "C. Howlett", "S. Juneau", "A. Kremin", "L. Le Guillou", "M. Manera", "L. Medina-Varela", "J. Mena-Fernández", "R. Miquel", "E. Mueller", "A. Muñoz-Gutiérrez", "A. D. Myers", "J. Nie", "G. Niz", "E. Paillas", "W. J. Percival", "C. Poppett", "A. Pérez-Fernández", "M. Rezaie", "A. Rosado-Marin", "G. Rossi", "R. Ruggeri", "E. Sanchez", "C. Saulder", "D. Schlegel", "M. Schubnell", "D. Sprayberry", "G. Tarlé", "B. A. Weaver", "J. Yu", "C. Zhao", "H. Zou"], "categories": ["astro-ph.CO", "physics.data-an"], "fields_of_study": ["Physics"], "published_date": "2024-04-03", "url": "https://arxiv.org/abs/2404.03007", "pdf_url": "https://arxiv.org/pdf/2404.03007v5", "arxiv_id": "2404.03007", "doi": "10.1088/1475-7516/2025/01/145", "citation_count": 29, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/oliverphilcox/RascalC", "venue": "Journal of Cosmology and Astroparticle Physics", "quality_score": 0.3693}
{"id": "9cb1b04c6a778e105d0603b730cbafb1ebf9ddf72a2769a97c716cb88210b1ff", "sources": ["arxiv", "semantic_scholar"], "title": "Improve Knowledge Distillation via Label Revision and Data Selection", "abstract": "Knowledge distillation (KD) has become a widely used technique in the field of model compression, which aims to transfer knowledge from a large teacher model to a lightweight student model for efficient network development. In addition to the supervision of ground truth, the vanilla KD method regards the predictions of the teacher as soft labels to supervise the training of the student model. Based on vanilla KD, various approaches have been developed to further improve the performance of the student model. However, few of these previous methods have considered the reliability of the supervision from teacher models. Supervision from erroneous predictions may mislead the training of the student model. This paper therefore proposes to tackle this problem from two aspects: Label Revision to rectify the incorrect supervision and Data Selection to select appropriate samples for distillation to reduce the impact of erroneous supervision. In the former, we propose to rectify the teacher's inaccurate predictions using the ground truth. In the latter, we introduce a data selection technique to choose suitable training samples to be supervised by the teacher, thereby reducing the impact of incorrect predictions to some extent. Experiment results demonstrate the effectiveness of our proposed method, and show that our method can be combined with other distillation approaches, improving their performance.", "authors": ["Weichao Lan", "Yiu-ming Cheung", "Qing Xu", "Buhua Liu", "Zhikai Hu", "Mengke Li", "Zhenghua Chen"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-03", "url": "https://arxiv.org/abs/2404.03693", "pdf_url": "https://arxiv.org/pdf/2404.03693v1", "arxiv_id": "2404.03693", "doi": "10.1109/TCDS.2025.3559881", "citation_count": 8, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Cognitive and Developmental Systems", "quality_score": 0.301}
{"id": "dface2a5a6f0c8b40e5e488dee40f40dce4671daba50e4de10524bf905a84fd8", "sources": ["arxiv", "semantic_scholar"], "title": "Muon Nuclear Data", "abstract": "We plan to develop a new nuclear database for muon-induced nuclear reactions (muon nuclear data). The database will consist of (1) energies and intensities of the muonic X rays, (2) lifetimes of the muonic atom, (3) production branching ratio of the residual nuclei by muon capture, (4) emission probabilities of the particles after muon capture, and (5) energy spectra of the emitted particles after muon capture. In this paper, we review the present status and current investigations for the muon nuclear data.", "authors": ["Megumi Niikura", "Shinichiro Abe", "Shoichiro Kawase", "Teiichiro Matsuzaki", "Futoshi Minato", "Rurie Mizuno", "Yukinobu Watanabe", "Yuji Yamaguchi"], "categories": ["nucl-ex"], "fields_of_study": ["Physics"], "published_date": "2024-03-29", "url": "https://arxiv.org/abs/2403.19965", "pdf_url": "https://arxiv.org/pdf/2403.19965v1", "arxiv_id": "2403.19965", "doi": null, "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1505}
{"id": "92ea0e6cdaf2417e4fbeeef82b952d32e0acf24588fbab28bff54e7c1b15c12f", "sources": ["arxiv", "semantic_scholar"], "title": "GOLD: Generalized Knowledge Distillation via Out-of-Distribution-Guided Language Data Generation", "abstract": "Knowledge distillation from LLMs is essential for the efficient deployment of language models. Prior works have proposed data generation using LLMs for preparing distilled models. We argue that generating data with LLMs is prone to sampling mainly from the center of original content distribution. This limitation hinders the distilled model from learning the true underlying data distribution and to forget the tails of the distributions (samples with lower probability). To this end, we propose GOLD, a task-agnostic data generation and knowledge distillation framework, which employs an iterative out-of-distribution-guided feedback mechanism for the LLM. As a result, the generated data improves the generalizability of distilled models. An energy-based OOD evaluation approach is also introduced to deal with noisy generated data. Our extensive experiments on 10 different classification and sequence-to-sequence tasks in NLP show that GOLD respectively outperforms prior arts and the LLM with an average improvement of 5% and 14%. We will also show that the proposed method is applicable to less explored and novel tasks. The code is available.", "authors": ["Mohsen Gholami", "Mohammad Akbari", "Cindy Hu", "Vaden Masrani", "Z. Jane Wang", "Yong Zhang"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-28", "url": "https://arxiv.org/abs/2403.19754", "pdf_url": "https://arxiv.org/pdf/2403.19754v1", "arxiv_id": "2403.19754", "doi": "10.48550/arXiv.2403.19754", "citation_count": 8, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": null, "quality_score": 0.2386}
{"id": "8f1fc706f48d456e19afc936e9891bd66ecc3ee8f5ebc9a7afeeb760e05aafe2", "sources": ["arxiv", "semantic_scholar"], "title": "De-confounded Data-free Knowledge Distillation for Handling Distribution Shifts", "abstract": "Data-Free Knowledge Distillation (DFKD) is a promising task to train high-performance small models to enhance actual deployment without relying on the original training data. Existing methods commonly avoid relying on private data by utilizing synthetic or sampled data. However, a long-overlooked issue is that the severe distribution shifts between their substitution and original data, which manifests as huge differences in the quality of images and class proportions. The harmful shifts are essentially the confounder that significantly causes performance bottlenecks. To tackle the issue, this paper proposes a novel perspective with causal inference to disentangle the student models from the impact of such shifts. By designing a customized causal graph, we first reveal the causalities among the variables in the DFKD task. Subsequently, we propose a Knowledge Distillation Causal Intervention (KDCI) framework based on the backdoor adjustment to de-confound the confounder. KDCI can be flexibly combined with most existing state-of-the-art baselines. Experiments in combination with six representative DFKD methods demonstrate the effectiveness of our KDCI, which can obviously help existing methods under almost all settings, \\textit{e.g.}, improving the baseline by up to 15.54\\% accuracy on the CIFAR-100 dataset.", "authors": ["Yuzheng Wang", "Dingkang Yang", "Zhaoyu Chen", "Yang Liu", "Siao Liu", "Wenqiang Zhang", "Lihua Zhang", "Lizhe Qi"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-28", "url": "https://arxiv.org/abs/2403.19539", "pdf_url": "https://arxiv.org/pdf/2403.19539v1", "arxiv_id": "2403.19539", "doi": "10.1109/CVPR52733.2024.01199", "citation_count": 19, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "Computer Vision and Pattern Recognition", "quality_score": 0.3253}
{"id": "3ec2d58d363a810dc941851f50925c95bdff3c35f95e617a1bf6965d10c7b63b", "sources": ["arxiv", "semantic_scholar"], "title": "Exploring the potential of prototype-based soft-labels data distillation for imbalanced data classification", "abstract": "Dataset distillation aims at synthesizing a dataset by a small number of artificially generated data items, which, when used as training data, reproduce or approximate a machine learning (ML) model as if it were trained on the entire original dataset. Consequently, data distillation methods are usually tied to a specific ML algorithm. While recent literature deals mainly with distillation of large collections of images in the context of neural network models, tabular data distillation is much less represented and mainly focused on a theoretical perspective. The current paper explores the potential of a simple distillation technique previously proposed in the context of Less-than-one shot learning. The main goal is to push further the performance of prototype-based soft-labels distillation in terms of classification accuracy, by integrating optimization steps in the distillation process. The analysis is performed on real-world data sets with various degrees of imbalance. Experimental studies trace the capability of the method to distill the data, but also the opportunity to act as an augmentation method, i.e. to generate new data that is able to increase model accuracy when used in conjunction with - as opposed to instead of - the original data.", "authors": ["Radu-Andrei Rosu", "Mihaela-Elena Breaban", "Henri Luchian"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-25", "url": "https://arxiv.org/abs/2403.17130", "pdf_url": "https://arxiv.org/pdf/2403.17130v1", "arxiv_id": "2403.17130", "doi": "10.1109/SYNASC57785.2022.00034", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Symposium on Symbolic and Numeric Algorithms for Scientific Computing", "quality_score": 0.0}
{"id": "490b5c1987d94ef6e7cd18dfefec26b58d0ef02d61dad77927f780eb563b62e1", "sources": ["arxiv", "semantic_scholar"], "title": "Six Levels of Privacy: A Framework for Financial Synthetic Data", "abstract": "Synthetic Data is increasingly important in financial applications. In addition to the benefits it provides, such as improved financial modeling and better testing procedures, it poses privacy risks as well. Such data may arise from client information, business information, or other proprietary sources that must be protected. Even though the process by which Synthetic Data is generated serves to obscure the original data to some degree, the extent to which privacy is preserved is hard to assess. Accordingly, we introduce a hierarchy of ``levels'' of privacy that are useful for categorizing Synthetic Data generation methods and the progressively improved protections they offer. While the six levels were devised in the context of financial applications, they may also be appropriate for other industries as well. Our paper includes: A brief overview of Financial Synthetic Data, how it can be used, how its value can be assessed, privacy risks, and privacy attacks. We close with details of the ``Six Levels'' that include defenses against those attacks.", "authors": ["Tucker Balch", "Vamsi K. Potluru", "Deepak Paramanand", "Manuela Veloso"], "categories": ["cs.CR", "cs.LG", "q-fin.ST"], "fields_of_study": ["Computer Science", "Economics"], "published_date": "2024-03-20", "url": "https://arxiv.org/abs/2403.14724", "pdf_url": "https://arxiv.org/pdf/2403.14724v1", "arxiv_id": "2403.14724", "doi": "10.48550/arXiv.2403.14724", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1945}
{"id": "f266f8c84875a13c77e24e20b19b3841ffe344e30f50b7cefd1c9f8b28c36673", "sources": ["arxiv", "semantic_scholar"], "title": "CRS-Diff: Controllable Remote Sensing Image Generation with Diffusion Model", "abstract": "The emergence of generative models has revolutionized the field of remote sensing (RS) image generation. Despite generating high-quality images, existing methods are limited in relying mainly on text control conditions, and thus do not always generate images accurately and stably. In this paper, we propose CRS-Diff, a new RS generative framework specifically tailored for RS image generation, leveraging the inherent advantages of diffusion models while integrating more advanced control mechanisms. Specifically, CRS-Diff can simultaneously support text-condition, metadata-condition, and image-condition control inputs, thus enabling more precise control to refine the generation process. To effectively integrate multiple condition control information, we introduce a new conditional control mechanism to achieve multi-scale feature fusion, thus enhancing the guiding effect of control conditions. To our knowledge, CRS-Diff is the first multiple-condition controllable RS generative model. Experimental results in single-condition and multiple-condition cases have demonstrated the superior ability of our CRS-Diff to generate RS images both quantitatively and qualitatively compared with previous methods. Additionally, our CRS-Diff can serve as a data engine that generates high-quality training data for downstream tasks, e.g., road extraction. The code is available at https://github.com/Sonettoo/CRS-Diff.", "authors": ["Datao Tang", "Xiangyong Cao", "Xingsong Hou", "Zhongyuan Jiang", "Junmin Liu", "Deyu Meng"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-18", "url": "https://arxiv.org/abs/2403.11614", "pdf_url": "https://arxiv.org/pdf/2403.11614v4", "arxiv_id": "2403.11614", "doi": "10.1109/TGRS.2024.3453414", "citation_count": 83, "influential_citation_count": 4, "has_code": true, "code_url": "https://github.com/Sonettoo/CRS-Diff", "venue": "IEEE Transactions on Geoscience and Remote Sensing", "quality_score": 0.4811}
{"id": "3e2dc94fbaecf481adf2e64a4aa42443d6ed26a8d37c73aec5faf8ba2fbc2bd1", "sources": ["arxiv", "semantic_scholar"], "title": "Data-Efficient Contrastive Language-Image Pretraining: Prioritizing Data Quality over Quantity", "abstract": "Contrastive Language-Image Pre-training (CLIP) on large-scale image-caption datasets learns representations that can achieve remarkable zero-shot generalization. However, such models require a massive amount of pre-training data. Improving the quality of the pre-training data has been shown to be much more effective in improving CLIP's performance than increasing its volume. Nevertheless, finding small subsets of training data that provably generalize the best has remained an open question. In this work, we propose the first theoretically rigorous data selection method for CLIP. We show that subsets that closely preserve the cross-covariance of the images and captions of the full data provably achieve a superior generalization performance. Our extensive experiments on ConceptualCaptions3M and ConceptualCaptions12M demonstrate that subsets found by \\method\\ achieve over 2.7x and 1.4x the accuracy of the next best baseline on ImageNet and its shifted versions. Moreover, we show that our subsets obtain 1.5x the average accuracy across 11 downstream datasets, of the next best baseline. The code is available at: https://github.com/BigML-CS-UCLA/clipcov-data-efficient-clip.", "authors": ["Siddharth Joshi", "Arnav Jain", "Ali Payani", "Baharan Mirzasoleiman"], "categories": ["cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-18", "url": "https://arxiv.org/abs/2403.12267", "pdf_url": "https://arxiv.org/pdf/2403.12267v2", "arxiv_id": "2403.12267", "doi": "10.48550/arXiv.2403.12267", "citation_count": 22, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/BigML-CS-UCLA/clipcov-data-efficient-clip", "venue": "International Conference on Artificial Intelligence and Statistics", "quality_score": 0.3404}
{"id": "a7bf54149308fdd024407b4f131056042ade2d15f15f919eedb725d86c2338d9", "sources": ["arxiv", "semantic_scholar"], "title": "A survey of synthetic data augmentation methods in computer vision", "abstract": "The standard approach to tackling computer vision problems is to train deep convolutional neural network (CNN) models using large-scale image datasets which are representative of the target task. However, in many scenarios, it is often challenging to obtain sufficient image data for the target task. Data augmentation is a way to mitigate this challenge. A common practice is to explicitly transform existing images in desired ways so as to create the required volume and variability of training data necessary to achieve good generalization performance. In situations where data for the target domain is not accessible, a viable workaround is to synthesize training data from scratch--i.e., synthetic data augmentation. This paper presents an extensive review of synthetic data augmentation techniques. It covers data synthesis approaches based on realistic 3D graphics modeling, neural style transfer (NST), differential neural rendering, and generative artificial intelligence (AI) techniques such as generative adversarial networks (GANs) and variational autoencoders (VAEs). For each of these classes of methods, we focus on the important data generation and augmentation techniques, general scope of application and specific use-cases, as well as existing limitations and possible workarounds. Additionally, we provide a summary of common synthetic datasets for training computer vision models, highlighting the main features, application domains and supported tasks. Finally, we discuss the effectiveness of synthetic data augmentation methods. Since this is the first paper to explore synthetic data augmentation methods in great detail, we are hoping to equip readers with the necessary background information and in-depth knowledge of existing methods and their attendant issues.", "authors": ["Alhassan Mumuni", "Fuseini Mumuni", "Nana Kobina Gerrar"], "categories": ["cs.CV", "cs.GR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-15", "url": "https://arxiv.org/abs/2403.10075", "pdf_url": "https://arxiv.org/pdf/2403.10075v2", "arxiv_id": "2403.10075", "doi": "10.1007/s11633-022-1411-7", "citation_count": 101, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Machine Intelligence Research", "quality_score": 0.5022}
{"id": "cf2a7ba4d8f4307f232ed248236fae2f84d999bd62bf1ea6d2150730dc8b45a1", "sources": ["arxiv", "semantic_scholar"], "title": "Analyzing Data Augmentation for Medical Images: A Case Study in Ultrasound Images", "abstract": "Data augmentation is one of the most effective techniques to improve the generalization performance of deep neural networks. Yet, despite often facing limited data availability in medical image analysis, it is frequently underutilized. This appears to be due to a gap in our collective understanding of the efficacy of different augmentation techniques across medical imaging tasks and modalities. One domain where this is especially true is breast ultrasound images. This work addresses this issue by analyzing the effectiveness of different augmentation techniques for the classification of breast lesions in ultrasound images. We assess the generalizability of our findings across several datasets, demonstrate that certain augmentations are far more effective than others, and show that their usage leads to significant performance gains.", "authors": ["Adam Tupper", "Christian Gagné"], "categories": ["eess.IV", "cs.CV"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2024-03-14", "url": "https://arxiv.org/abs/2403.09828", "pdf_url": "https://arxiv.org/pdf/2403.09828v1", "arxiv_id": "2403.09828", "doi": "10.48550/arXiv.2403.09828", "citation_count": 6, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/adamtupper/medical-image-augmentation", "venue": "arXiv.org", "quality_score": 0.2113}
{"id": "81614b18ac5348b5cf0d3a2188d2b1e9463938c80b759ccf23ba3d44941c8aaf", "sources": ["arxiv", "semantic_scholar"], "title": "Data-Efficient Sleep Staging with Synthetic Time Series Pretraining", "abstract": "Analyzing electroencephalographic (EEG) time series can be challenging, especially with deep neural networks, due to the large variability among human subjects and often small datasets. To address these challenges, various strategies, such as self-supervised learning, have been suggested, but they typically rely on extensive empirical datasets. Inspired by recent advances in computer vision, we propose a pretraining task termed \"frequency pretraining\" to pretrain a neural network for sleep staging by predicting the frequency content of randomly generated synthetic time series. Our experiments demonstrate that our method surpasses fully supervised learning in scenarios with limited data and few subjects, and matches its performance in regimes with many subjects. Furthermore, our results underline the relevance of frequency information for sleep stage scoring, while also demonstrating that deep neural networks utilize information beyond frequencies to enhance sleep staging performance, which is consistent with previous research. We anticipate that our approach will be advantageous across a broad spectrum of applications where EEG data is limited or derived from a small number of subjects, including the domain of brain-computer interfaces.", "authors": ["Niklas Grieger", "Siamak Mehrkanoon", "Stephan Bialonski"], "categories": ["cs.LG", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-03-13", "url": "https://arxiv.org/abs/2403.08592", "pdf_url": "https://arxiv.org/pdf/2403.08592v2", "arxiv_id": "2403.08592", "doi": "10.3390/a18090580", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Algorithms", "quality_score": 0.0753}
{"id": "71ccfb7a1942399c9a8c17a4a337c19e92ee996166702a5a3ae6cbac57f7f581", "sources": ["arxiv", "semantic_scholar"], "title": "Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods", "abstract": "Data augmentation is arguably the most important regularization technique commonly used to improve generalization performance of machine learning models. It primarily involves the application of appropriate data transformation operations to create new data samples with desired properties. Despite its effectiveness, the process is often challenging because of the time-consuming trial and error procedures for creating and testing different candidate augmentations and their hyperparameters manually. State-of-the-art approaches are increasingly relying on automated machine learning (AutoML) principles. This work presents a comprehensive survey of AutoML-based data augmentation techniques. We discuss various approaches for accomplishing data augmentation with AutoML, including data manipulation, data integration and data synthesis techniques. The focus of this work is on image data augmentation methods. Nonetheless, we cover other data modalities, especially in cases where the specific data augmentations techniques being discussed are more suitable for these other modalities. For instance, since automated data integration methods are more suitable for tabular data, we cover tabular data in the discussion of data integration methods. The work also presents extensive discussion of techniques for accomplishing each of the major subtasks of the image data augmentation process: search space design, hyperparameter optimization and model evaluation. Finally, we carried out an extensive comparison and analysis of the performance of automated data augmentation techniques and state-of-the-art methods based on classical augmentation approaches. The results show that AutoML methods for data augmentation currently outperform state-of-the-art techniques based on conventional approaches.", "authors": ["Alhassan Mumuni", "Fuseini Mumuni"], "categories": ["cs.LG", "cs.AI", "cs.CV", "cs.NE"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-13", "url": "https://arxiv.org/abs/2403.08352", "pdf_url": "https://arxiv.org/pdf/2403.08352v3", "arxiv_id": "2403.08352", "doi": "10.1007/s10115-025-02349-x", "citation_count": 31, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Knowledge and Information Systems", "quality_score": 0.3763}
{"id": "12ad39885b2ce04a408702d802f03467e4f759ff7756a3b59f75742a01374924", "sources": ["arxiv", "semantic_scholar"], "title": "Distilling the Knowledge in Data Pruning", "abstract": "With the increasing size of datasets used for training neural networks, data pruning becomes an attractive field of research. However, most current data pruning algorithms are limited in their ability to preserve accuracy compared to models trained on the full data, especially in high pruning regimes. In this paper we explore the application of data pruning while incorporating knowledge distillation (KD) when training on a pruned subset. That is, rather than relying solely on ground-truth labels, we also use the soft predictions from a teacher network pre-trained on the complete data. By integrating KD into training, we demonstrate significant improvement across datasets, pruning methods, and on all pruning fractions. We first establish a theoretical motivation for employing self-distillation to improve training on pruned data. Then, we empirically make a compelling and highly practical observation: using KD, simple random pruning is comparable or superior to sophisticated pruning methods across all pruning regimes. On ImageNet for example, we achieve superior accuracy despite training on a random subset of only 50% of the data. Additionally, we demonstrate a crucial connection between the pruning factor and the optimal knowledge distillation weight. This helps mitigate the impact of samples with noisy labels and low-quality images retained by typical pruning algorithms. Finally, we make an intriguing observation: when using lower pruning fractions, larger teachers lead to accuracy degradation, while surprisingly, employing teachers with a smaller capacity than the student's may improve results. Our code will be made available.", "authors": ["Emanuel Ben-Baruch", "Adam Botach", "Igor Kviatkovsky", "Manoj Aggarwal", "Gérard Medioni"], "categories": ["cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-12", "url": "https://arxiv.org/abs/2403.07854", "pdf_url": "https://arxiv.org/pdf/2403.07854v2", "arxiv_id": "2403.07854", "doi": "10.48550/arXiv.2403.07854", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Machine Learning", "quality_score": 0.2113}
{"id": "94e64bd4eddb2ab75d5da38cbd075750a66b1552f19e7fe041d29e68c6da0311", "sources": ["arxiv", "semantic_scholar"], "title": "Understanding data analysis aspects of TMS-EEG in clinical study: a mini review and a case study with open dataset", "abstract": "Concurrency of transcranial magnetic stimulation with electroencephalography (TMS-EEG) technique is a powerful and challenging methodology for basic research and clinical applications. Aspects considered in experiments for effective TMS-EEG recordings and analysis, including artifact management, data analysis and interpretation and protocols. mini review offers an extensive insight of TMS-EEG methodology in experimental and computational procedures. Case study aims to leverage an openly available, high-quality EEG dataset to delve into the alterations in cortical activity. By applying Intermittent theta-burst stimulation (iTBS) and continuous theta-burst stimulation (cTBS) to the left dorsolateral prefrontal cortex (DLPFC) in healthy individuals, we observe changes in oscillatory patterns within the EEG data. The dataset includes meticulously extracted resting-state EEG recordings, TMS-evoked potential data, and MRI scans. To process these data, we utilized Brainstorm, an open-source Matlab application, which facilitated noise reduction through independent component analysis and signal-space projection techniques. It allowed us to identify, visualize, and analyze TMS-evoked potentials (TEPs) and TMS-induced oscillations (TIOs). In addition, the study presents detailed plots of resting-state EEG power, local mean field power (LMFP), TMS-related spectral perturbation (TSRP), and inter-trial phase clustering (ITPC). Paired t-tests and cluster-based permutation tests have been performed for statistical analysis. The wealth and quality of this dataset make it ideal for examining the neuromodulatory impact of TBS on the prefrontal cortex. Brainstorm's extensive feature set greatly supports the exploration of such neurological data. Future research directions could concentrate on conducting source localization analyses and comparative group studies.", "authors": ["Hua Cheng"], "categories": ["q-bio.NC"], "fields_of_study": ["Biology"], "published_date": "2024-03-09", "url": "https://arxiv.org/abs/2403.09707", "pdf_url": "https://arxiv.org/pdf/2403.09707v1", "arxiv_id": "2403.09707", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": null, "quality_score": 0.0753}
{"id": "585bfceb66b943c2022facf1bed34eaa42f6c1d8b4c0e25af86a68e232d8b7d2", "sources": ["arxiv", "semantic_scholar"], "title": "Extracting Protein-Protein Interactions (PPIs) from Biomedical Literature using Attention-based Relational Context Information", "abstract": "Because protein-protein interactions (PPIs) are crucial to understand living systems, harvesting these data is essential to probe disease development and discern gene/protein functions and biological processes. Some curated datasets contain PPI data derived from the literature and other sources (e.g., IntAct, BioGrid, DIP, and HPRD). However, they are far from exhaustive, and their maintenance is a labor-intensive process. On the other hand, machine learning methods to automate PPI knowledge extraction from the scientific literature have been limited by a shortage of appropriate annotated data. This work presents a unified, multi-source PPI corpora with vetted interaction definitions augmented by binary interaction type labels and a Transformer-based deep learning method that exploits entities' relational context information for relation representation to improve relation classification performance. The model's performance is evaluated on four widely studied biomedical relation extraction datasets, as well as this work's target PPI datasets, to observe the effectiveness of the representation to relation extraction tasks in various data. Results show the model outperforms prior state-of-the-art models. The code and data are available at: https://github.com/BNLNLP/PPI-Relation-Extraction", "authors": ["Gilchan Park", "Sean McCorkle", "Carlos Soto", "Ian Blaby", "Shinjae Yoo"], "categories": ["q-bio.BM", "cs.CL", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-03-08", "url": "https://arxiv.org/abs/2403.05602", "pdf_url": "https://arxiv.org/pdf/2403.05602v1", "arxiv_id": "2403.05602", "doi": "10.1109/BigData55660.2022.10021099", "citation_count": 11, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/BNLNLP/PPI-Relation-Extraction", "venue": "In 2022 IEEE Big Data, pp. 2052-2061 (2022)", "quality_score": 0.2698}
{"id": "4e0eda7f4ce0105294cf19941899dd2e58fe1c5795901d9152ff6de163d4d132", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetic data generation for system identification: leveraging knowledge transfer from similar systems", "abstract": "This paper addresses the challenge of overfitting in the learning of dynamical systems by introducing a novel approach for the generation of synthetic data, aimed at enhancing model generalization and robustness in scenarios characterized by data scarcity. Central to the proposed methodology is the concept of knowledge transfer from systems within the same class. Specifically, synthetic data is generated through a pre-trained meta-model that describes a broad class of systems to which the system of interest is assumed to belong. Training data serves a dual purpose: firstly, as input to the pre-trained meta model to discern the system's dynamics, enabling the prediction of its behavior and thereby generating synthetic output sequences for new input sequences; secondly, in conjunction with synthetic data, to define the loss function used for model estimation. A validation dataset is used to tune a scalar hyper-parameter balancing the relative importance of training and synthetic data in the definition of the loss function. The same validation set can be also used for other purposes, such as early stopping during the training, fundamental to avoid overfitting in case of small-size training datasets. The efficacy of the approach is shown through a numerical example that highlights the advantages of integrating synthetic data into the system identification process.", "authors": ["Dario Piga", "Matteo Rufolo", "Gabriele Maroni", "Manas Mejari", "Marco Forgione"], "categories": ["cs.LG", "cs.AI", "eess.SY"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-03-08", "url": "https://arxiv.org/abs/2403.05164", "pdf_url": "https://arxiv.org/pdf/2403.05164v1", "arxiv_id": "2403.05164", "doi": "10.1109/CDC56724.2024.10886106", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Conference on Decision and Control", "quality_score": 0.2258}
{"id": "6b5f43a04a364110d20220dbd5e257c3a820898c30ee346d3eff5529842372d7", "sources": ["arxiv", "semantic_scholar"], "title": "Enhancing Data Quality in Federated Fine-Tuning of Foundation Models", "abstract": "In the current landscape of foundation model training, there is a significant reliance on public domain data, which is nearing exhaustion according to recent research. To further scale up, it is crucial to incorporate collaboration among multiple specialized and high-quality private domain data sources. However, the challenge of training models locally without sharing private data presents numerous obstacles in data quality control. To tackle this issue, we propose a data quality control pipeline for federated fine-tuning of foundation models. This pipeline computes scores reflecting the quality of training data and determines a global threshold for a unified standard, aiming for improved global performance. Our experiments show that the proposed quality control pipeline facilitates the effectiveness and reliability of the model training, leading to better performance.", "authors": ["Wanru Zhao", "Yaxin Du", "Nicholas Donald Lane", "Siheng Chen", "Yanfeng Wang"], "categories": ["cs.LG", "cs.AI", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-07", "url": "https://arxiv.org/abs/2403.04529", "pdf_url": "https://arxiv.org/pdf/2403.04529v1", "arxiv_id": "2403.04529", "doi": "10.48550/arXiv.2403.04529", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2113}
{"id": "5c4b95314140d5ec4176f4a4146a5b3a21eadc45c9db41b503796cf9cdff14e3", "sources": ["arxiv", "semantic_scholar"], "title": "Rapidly Developing High-quality Instruction Data and Evaluation Benchmark for Large Language Models with Minimal Human Effort: A Case Study on Japanese", "abstract": "The creation of instruction data and evaluation benchmarks for serving Large language models often involves enormous human annotation. This issue becomes particularly pronounced when rapidly developing such resources for a non-English language like Japanese. Instead of following the popular practice of directly translating existing English resources into Japanese (e.g., Japanese-Alpaca), we propose an efficient self-instruct method based on GPT-4. We first translate a small amount of English instructions into Japanese and post-edit them to obtain native-level quality. GPT-4 then utilizes them as demonstrations to automatically generate Japanese instruction data. We also construct an evaluation benchmark containing 80 questions across 8 categories, using GPT-4 to automatically assess the response quality of LLMs without human references. The empirical results suggest that the models fine-tuned on our GPT-4 self-instruct data significantly outperformed the Japanese-Alpaca across all three base pre-trained models. Our GPT-4 self-instruct data allowed the LLaMA 13B model to defeat GPT-3.5 (Davinci-003) with a 54.37\\% win-rate. The human evaluation exhibits the consistency between GPT-4's assessments and human preference. Our high-quality instruction data and evaluation benchmark have been released here.", "authors": ["Yikun Sun", "Zhen Wan", "Nobuhiro Ueda", "Sakiko Yahata", "Fei Cheng", "Chenhui Chu", "Sadao Kurohashi"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-06", "url": "https://arxiv.org/abs/2403.03690", "pdf_url": "https://arxiv.org/pdf/2403.03690v1", "arxiv_id": "2403.03690", "doi": "10.48550/arXiv.2403.03690", "citation_count": 7, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/hitoshizuku7/awesome-Ja-self-instruct}{self-instruct", "venue": "International Conference on Language Resources and Evaluation", "quality_score": 0.2258}
{"id": "12f284680a7deee4facf1fe10fd7e935af7a4a013db2582be40a700f8cf35a3e", "sources": ["arxiv", "semantic_scholar"], "title": "Marginalize, Rather than Impute: Probabilistic Wind Power Forecasting with Incomplete Data", "abstract": "Machine learning methods are widely and successfully used for probabilistic wind power forecasting, yet the pervasive issue of missing values (e.g., due to sensor faults or communication outages) has received limited attention. The prevailing practice is impute-then-predict, but conditioning on point imputations biases parameter estimates and fails to propagate uncertainty from missing features. Our approach treats missing features and forecast targets uniformly: we learn a joint generative model of features and targets from incomplete data and, at operational deployment, condition on the observed features and marginalize the unobserved ones to produce forecasts. This imputation-free procedure avoids error introduced by imputation and preserves uncertainty aroused from missing features. In experiments, it improves forecast quality in terms of continuous ranked probability score relative to impute-then-predict baselines while incurring substantially lower computational cost than common alternatives.", "authors": ["Honglin Wen", "Pierre Pinson", "Jie Gu", "Zhijian Jin"], "categories": ["cs.LG", "eess.SY"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-03-06", "url": "https://arxiv.org/abs/2403.03631", "pdf_url": "https://arxiv.org/pdf/2403.03631v2", "arxiv_id": "2403.03631", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0}
{"id": "fd338b0f49424d346eac300b26d44cadce3d53cbedca777eaf171995459487d7", "sources": ["arxiv", "semantic_scholar"], "title": "Quantum Data Management: From Theory to Opportunities", "abstract": "Quantum computing has emerged as a transformative tool for future data management. Classical problems in database domains, including query optimization, data integration, and transaction management, have recently been addressed using quantum computing techniques. This tutorial aims to establish the theoretical foundation essential for enhancing methodologies and practical implementations in this line of research. Moreover, this tutorial takes a forward-looking approach by delving into recent strides in quantum internet technologies and the nonlocality theory. We aim to shed light on the uncharted territory of future data systems tailored for the quantum internet.", "authors": ["Rihan Hai", "Shih-Han Hung", "Sebastian Feld"], "categories": ["cs.DB", "cs.NI"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-05", "url": "https://arxiv.org/abs/2403.02856", "pdf_url": "https://arxiv.org/pdf/2403.02856v1", "arxiv_id": "2403.02856", "doi": "10.1109/ICDE60146.2024.00410", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE International Conference on Data Engineering", "quality_score": 0.2386}
{"id": "59c5cab833247ee47fa2fc352852236eef5f096139693a60db50e7fdd2d35c0d", "sources": ["arxiv", "semantic_scholar"], "title": "DIVERSE: A Dataset of YouTube Video Comment Stances with a Data Programming Model", "abstract": "Public opinion of military organizations significantly influences their ability to recruit talented individuals. As recruitment efforts increasingly extend into digital spaces like social media, it becomes essential to assess the stance of social media users toward online military content. However, there is a notable lack of data for analyzing opinions on military recruiting efforts online, compounded by challenges in stance labeling, which is crucial for understanding public perceptions. Despite the importance of stance analysis for successful online military recruitment, creating human-annotated, in-domain stance labels is resource-intensive. In this paper, we address both the challenges of stance labeling and the scarcity of data on public opinions of online military recruitment by introducing and releasing the DIVERSE dataset: https://doi.org/10.5281/zenodo.10493803. This dataset comprises all comments from the U.S. Army's official YouTube Channel videos. We employed a state-of-the-art weak supervision approach, leveraging large language models to label the stance of each comment toward its respective video and the U.S. Army. Our findings indicate that the U.S. Army's videos began attracting a significant number of comments post-2021, with the stance distribution generally balanced among supportive, oppositional, and neutral comments, with a slight skew towards oppositional versus supportive comments.", "authors": ["Iain J. Cruickshank", "Amir Soofi", "Lynnette Hui Xian Ng"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-05", "url": "https://arxiv.org/abs/2403.03334", "pdf_url": "https://arxiv.org/pdf/2403.03334v3", "arxiv_id": "2403.03334", "doi": "10.1109/BigData62323.2024.10825480", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "BigData Congress [Services Society]", "quality_score": 0.1193}
{"id": "741b3f4f1fa0d4309609931b7962813d19ddfefbfecb4d686735563d88a87a04", "sources": ["arxiv", "semantic_scholar"], "title": "Model-Based Data-Centric AI: Bridging the Divide Between Academic Ideals and Industrial Pragmatism", "abstract": "This paper delves into the contrasting roles of data within academic and industrial spheres, highlighting the divergence between Data-Centric AI and Model-Agnostic AI approaches. We argue that while Data-Centric AI focuses on the primacy of high-quality data for model performance, Model-Agnostic AI prioritizes algorithmic flexibility, often at the expense of data quality considerations. This distinction reveals that academic standards for data quality frequently do not meet the rigorous demands of industrial applications, leading to potential pitfalls in deploying academic models in real-world settings. Through a comprehensive analysis, we address these disparities, presenting both the challenges they pose and strategies for bridging the gap. Furthermore, we propose a novel paradigm: Model-Based Data-Centric AI, which aims to reconcile these differences by integrating model considerations into data optimization processes. This approach underscores the necessity for evolving data requirements that are sensitive to the nuances of both academic research and industrial deployment. By exploring these discrepancies, we aim to foster a more nuanced understanding of data's role in AI development and encourage a convergence of academic and industrial standards to enhance AI's real-world applicability.", "authors": ["Chanjun Park", "Minsoo Khang", "Dahyun Kim"], "categories": ["cs.AI", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-04", "url": "https://arxiv.org/abs/2403.01832", "pdf_url": "https://arxiv.org/pdf/2403.01832v1", "arxiv_id": "2403.01832", "doi": "10.48550/arXiv.2403.01832", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "12975d2d6f98f13fd6e93a6d9b66f43a0a15d7eaa4588ef3492ff77a70c2063f", "sources": ["arxiv", "semantic_scholar"], "title": "Tunable correlation retention: A statistical method for generating synthetic data", "abstract": "We propose a method to generate statistically representative synthetic data from a given dataset. The main goal of our method is for the created data set to mimic the inter--feature correlations present in the original data, while also offering a tunable parameter to influence the privacy level. In particular, our method constructs a statistical map by using the empirical conditional distributions between the features of the original dataset. Part of the tunability is achieved by limiting the depths of conditional distributions that are being used. We describe in detail our algorithms used both in the construction of a statistical map and how to use this map to generate synthetic observations. This approach is tested in three different ways: with a hand calculated example; a manufactured dataset; and a real world energy-related dataset of consumption/production of households in Madeira Island. We evaluate the method by comparing the datasets using the Pearson correlation matrix with different levels of resolution and depths of correlation. These two considerations are being viewed as tunable parameters influencing the resulting datasets fidelity and privacy. The proposed methodology is general in the sense that it does not rely on the used test dataset. We expect it to be applicable in a much broader context than indicated here.", "authors": ["Nicklas Jävergård", "Rainey Lyons", "Adrian Muntean", "Jonas Forsman"], "categories": ["cs.LG", "math.PR", "physics.data-an"], "fields_of_study": ["Computer Science", "Mathematics", "Physics"], "published_date": "2024-03-03", "url": "https://arxiv.org/abs/2403.01471", "pdf_url": "https://arxiv.org/pdf/2403.01471v3", "arxiv_id": "2403.01471", "doi": null, "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1747}
{"id": "8af720ba505febb4626fb0995a57834e94206facafb6bc1c749ea64627303b8a", "sources": ["arxiv", "semantic_scholar"], "title": "Data Quality Assessment: Challenges and Opportunities", "abstract": "Data-oriented applications, their users, and even the law require data of high quality. Research has divided the rather vague notion of data quality into various dimensions, such as accuracy, consistency, and reputation. To achieve the goal of high data quality, many tools and techniques exist to clean and otherwise improve data. Yet, systematic research on actually assessing data quality in its dimensions is largely absent, and with it, the ability to gauge the success of any data cleaning effort. We propose five facets as ingredients to assess data quality: data, source, system, task, and human. Tapping each facet for data quality assessment poses its own challenges. We show how overcoming these challenges helps data quality assessment for those data quality dimensions mentioned in Europe's AI Act. Our work concludes with a proposal for a comprehensive data quality assessment framework.", "authors": ["Sedir Mohammed", "Lisa Ehrlinger", "Hazar Harmouch", "Felix Naumann", "Divesh Srivastava"], "categories": ["cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-01", "url": "https://arxiv.org/abs/2403.00526", "pdf_url": "https://arxiv.org/pdf/2403.00526v2", "arxiv_id": "2403.00526", "doi": "10.1145/3749116.3749120", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "SIGMOD record", "quality_score": 0.2386}
{"id": "82b05f37ae1554596a36128461eaa6730a67e555d8d16c67217c7e538f03cc2c", "sources": ["arxiv", "semantic_scholar"], "title": "Differentially Private Knowledge Distillation via Synthetic Text Generation", "abstract": "Large Language models (LLMs) are achieving state-of-the-art performance in many different downstream tasks. However, the increasing urgency of data privacy puts pressure on practitioners to train LLMs with Differential Privacy (DP) on private data. Concurrently, the exponential growth in parameter size of LLMs necessitates model compression before deployment of LLMs on resource-constrained devices or latency-sensitive applications. Differential privacy and model compression generally must trade off utility loss to achieve their objectives. Moreover, simultaneously applying both schemes can compound the utility degradation. To this end, we propose DistilDP: a novel differentially private knowledge distillation algorithm that exploits synthetic data generated by a differentially private teacher LLM. The knowledge of a teacher LLM is transferred onto the student in two ways: one way from the synthetic data itself -- the hard labels, and the other way by the output distribution of the teacher evaluated on the synthetic data -- the soft labels. Furthermore, if the teacher and student share a similar architectural structure, we can further distill knowledge by aligning the hidden representations between both. Our experimental results demonstrate that DistilDP can substantially improve the utility over existing baselines, at least $9.0$ PPL on the Big Patent dataset, with strong privacy parameters, $ε=2$. These promising results progress privacy-preserving compression of autoregressive LLMs. Our code can be accessed here: https://github.com/james-flemings/dp_compress.", "authors": ["James Flemings", "Murali Annavaram"], "categories": ["cs.LG", "cs.CL", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-01", "url": "https://arxiv.org/abs/2403.00932", "pdf_url": "https://arxiv.org/pdf/2403.00932v3", "arxiv_id": "2403.00932", "doi": "10.48550/arXiv.2403.00932", "citation_count": 20, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/james-flemings/dp_compress", "venue": "Annual Meeting of the Association for Computational Linguistics", "quality_score": 0.3306}
{"id": "8121a64dbc769d0da2e429549406f6eaa6671db33b9a938f2402f11e41aaa012", "sources": ["arxiv", "semantic_scholar"], "title": "ClickTree: A Tree-based Method for Predicting Math Students' Performance Based on Clickstream Data", "abstract": "The prediction of student performance and the analysis of students' learning behavior play an important role in enhancing online courses. By analysing a massive amount of clickstream data that captures student behavior, educators can gain valuable insights into the factors that influence academic outcomes and identify areas of improvement in courses. In this study, we developed ClickTree, a tree-based methodology, to predict student performance in mathematical assignments based on students' clickstream data. We extracted a set of features, including problem-level, assignment-level and student-level features, from the extensive clickstream data and trained a CatBoost tree to predict whether a student successfully answers a problem in an assignment. The developed method achieved an AUC of 0.78844 in the Educational Data Mining Cup 2023 and ranked second in the competition. Furthermore, our results indicate that students encounter more difficulties in the problem types that they must select a subset of answers from a given set as well as problem subjects of Algebra II. Additionally, students who performed well in answering end-unit assignment problems engaged more with in-unit assignments and answered more problems correctly, while those who struggled had higher tutoring request rate. The proposed method can be utilized to improve students' learning experiences, and the above insights can be integrated into mathematical courses to enhance students' learning outcomes.", "authors": ["Narjes Rohani", "Behnam Rohani", "Areti Manataki"], "categories": ["cs.CY", "cs.HC", "cs.LG", "stat.AP"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2024-03-01", "url": "https://arxiv.org/abs/2403.14664", "pdf_url": "https://arxiv.org/pdf/2403.14664v1", "arxiv_id": "2403.14664", "doi": "10.5281/zenodo.13627655", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "2d856d381054cbc487735b3d74941c73718679f7f83fa24f23583494e1053e81", "sources": ["arxiv", "semantic_scholar"], "title": "On the Convergence of Federated Learning Algorithms without Data Similarity", "abstract": "Data similarity assumptions have traditionally been relied upon to understand the convergence behaviors of federated learning methods. Unfortunately, this approach often demands fine-tuning step sizes based on the level of data similarity. When data similarity is low, these small step sizes result in an unacceptably slow convergence speed for federated methods. In this paper, we present a novel and unified framework for analyzing the convergence of federated learning algorithms without the need for data similarity conditions. Our analysis centers on an inequality that captures the influence of step sizes on algorithmic convergence performance. By applying our theorems to well-known federated algorithms, we derive precise expressions for three widely used step size schedules: fixed, diminishing, and step-decay step sizes, which are independent of data similarity conditions. Finally, we conduct comprehensive evaluations of the performance of these federated learning algorithms, employing the proposed step size strategies to train deep neural network models on benchmark datasets under varying data similarity conditions. Our findings demonstrate significant improvements in convergence speed and overall performance, marking a substantial advancement in federated learning research.", "authors": ["Ali Beikmohammadi", "Sarit Khirirat", "Sindri Magnússon"], "categories": ["cs.LG", "cs.GT"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-29", "url": "https://arxiv.org/abs/2403.02347", "pdf_url": "https://arxiv.org/pdf/2403.02347v2", "arxiv_id": "2403.02347", "doi": "10.1109/TBDATA.2024.3423693", "citation_count": 9, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Big Data", "quality_score": 0.25}
{"id": "e24f24b16fb397aae11872ea1efc0599f2634fa899c8550ceebd43e7638d345b", "sources": ["arxiv", "semantic_scholar"], "title": "Benchmarking Data Science Agents", "abstract": "In the era of data-driven decision-making, the complexity of data analysis necessitates advanced expertise and tools of data science, presenting significant challenges even for specialists. Large Language Models (LLMs) have emerged as promising aids as data science agents, assisting humans in data analysis and processing. Yet their practical efficacy remains constrained by the varied demands of real-world applications and complicated analytical process. In this paper, we introduce DSEval -- a novel evaluation paradigm, as well as a series of innovative benchmarks tailored for assessing the performance of these agents throughout the entire data science lifecycle. Incorporating a novel bootstrapped annotation method, we streamline dataset preparation, improve the evaluation coverage, and expand benchmarking comprehensiveness. Our findings uncover prevalent obstacles and provide critical insights to inform future advancements in the field.", "authors": ["Yuge Zhang", "Qiyang Jiang", "Xingyu Han", "Nan Chen", "Yuqing Yang", "Kan Ren"], "categories": ["cs.AI", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-27", "url": "https://arxiv.org/abs/2402.17168", "pdf_url": "https://arxiv.org/pdf/2402.17168v1", "arxiv_id": "2402.17168", "doi": "10.48550/arXiv.2402.17168", "citation_count": 42, "influential_citation_count": 4, "has_code": true, "code_url": "https://github.com/MetaCopilot/dseval", "venue": "Annual Meeting of the Association for Computational Linguistics", "quality_score": 0.4084}
{"id": "9238b8b6be7dc6c7021d61daf88fd8f4a78cc094ec22b7b3a9c7cb6fcc0202dc", "sources": ["arxiv", "semantic_scholar"], "title": "A Survey on Data Selection for Language Models", "abstract": "A major factor in the recent success of large language models is the use of enormous and ever-growing text datasets for unsupervised pre-training. However, naively training a model on all available data may not be optimal (or feasible), as the quality of available text data can vary. Filtering out data can also decrease the carbon footprint and financial costs of training models by reducing the amount of training required. Data selection methods aim to determine which candidate data points to include in the training dataset and how to appropriately sample from the selected data points. The promise of improved data selection methods has caused the volume of research in the area to rapidly expand. However, because deep learning is mostly driven by empirical evidence and experimentation on large-scale data is expensive, few organizations have the resources for extensive data selection research. Consequently, knowledge of effective data selection practices has become concentrated within a few organizations, many of which do not openly share their findings and methodologies. To narrow this gap in knowledge, we present a comprehensive review of existing literature on data selection methods and related research areas, providing a taxonomy of existing approaches. By describing the current landscape of research, this work aims to accelerate progress in data selection by establishing an entry point for new and established researchers. Additionally, throughout this review we draw attention to noticeable holes in the literature and conclude the paper by proposing promising avenues for future research.", "authors": ["Alon Albalak", "Yanai Elazar", "Sang Michael Xie", "Shayne Longpre", "Nathan Lambert", "Xinyi Wang", "Niklas Muennighoff", "Bairu Hou", "Liangming Pan", "Haewon Jeong", "Colin Raffel", "Shiyu Chang", "Tatsunori Hashimoto", "William Yang Wang"], "categories": ["cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-26", "url": "https://arxiv.org/abs/2402.16827", "pdf_url": "https://arxiv.org/pdf/2402.16827v3", "arxiv_id": "2402.16827", "doi": "10.48550/arXiv.2402.16827", "citation_count": 263, "influential_citation_count": 14, "has_code": true, "code_url": "https://github.com/alon-albalak/data-selection-survey", "venue": null, "quality_score": 0.6054}
{"id": "2be75bde83bb3e5bcfec27b9885ac360c821d82c2601303123a136d02cc59dd1", "sources": ["arxiv", "semantic_scholar"], "title": "Enhancement of 3D Camera Synthetic Training Data with Noise Models", "abstract": "The goal of this paper is to assess the impact of noise in 3D camera-captured data by modeling the noise of the imaging process and applying it on synthetic training data. We compiled a dataset of specifically constructed scenes to obtain a noise model. We specifically model lateral noise, affecting the position of captured points in the image plane, and axial noise, affecting the position along the axis perpendicular to the image plane. The estimated models can be used to emulate noise in synthetic training data. The added benefit of adding artificial noise is evaluated in an experiment with rendered data for object segmentation. We train a series of neural networks with varying levels of noise in the data and measure their ability to generalize on real data. The results show that using too little or too much noise can hurt the networks' performance indicating that obtaining a model of noise from real scanners is beneficial for synthetic data generation.", "authors": ["Katarína Osvaldová", "Lukáš Gajdošech", "Viktor Kocur", "Martin Madaras"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-26", "url": "https://arxiv.org/abs/2402.16514", "pdf_url": "https://arxiv.org/pdf/2402.16514v1", "arxiv_id": "2402.16514", "doi": "10.5281/zenodo.10694437", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "18f25c89027cac049b65d3e940d8ace0781f691b2c757c8fab09fcf6e06f2244", "sources": ["arxiv", "semantic_scholar"], "title": "LLM-based Privacy Data Augmentation Guided by Knowledge Distillation with a Distribution Tutor for Medical Text Classification", "abstract": "As sufficient data are not always publically accessible for model training, researchers exploit limited data with advanced learning algorithms or expand the dataset via data augmentation (DA). Conducting DA in private domain requires private protection approaches (i.e. anonymization and perturbation), but those methods cannot provide protection guarantees. Differential privacy (DP) learning methods theoretically bound the protection but are not skilled at generating pseudo text samples with large models. In this paper, we transfer DP-based pseudo sample generation task to DP-based generated samples discrimination task, where we propose a DP-based DA method with a LLM and a DP-based discriminator for text classification on private domains. We construct a knowledge distillation model as the DP-based discriminator: teacher models, accessing private data, teaches students how to select private samples with calibrated noise to achieve DP. To constrain the distribution of DA's generation, we propose a DP-based tutor that models the noised private distribution and controls samples' generation with a low privacy cost. We theoretically analyze our model's privacy protection and empirically verify our model.", "authors": ["Yiping Song", "Juhua Zhang", "Zhiliang Tian", "Yuxin Yang", "Minlie Huang", "Dongsheng Li"], "categories": ["cs.CL", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-26", "url": "https://arxiv.org/abs/2402.16515", "pdf_url": "https://arxiv.org/pdf/2402.16515v1", "arxiv_id": "2402.16515", "doi": "10.48550/arXiv.2402.16515", "citation_count": 19, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3253}
{"id": "4dee668a5f173ff4fdbe3876fa4c7402583c919666417fe138a41e30dfd6b7a1", "sources": ["arxiv", "semantic_scholar"], "title": "Comparative Analysis of Data Preprocessing Methods, Feature Selection Techniques and Machine Learning Models for Improved Classification and Regression Performance on Imbalanced Genetic Data", "abstract": "Rapid advancements in genome sequencing have led to the collection of vast amounts of genomics data. Researchers may be interested in using machine learning models on such data to predict the pathogenicity or clinical significance of a genetic mutation. However, many genetic datasets contain imbalanced target variables that pose challenges to machine learning models: observations are skewed/imbalanced in regression tasks or class-imbalanced in classification tasks. Genetic datasets are also often high-cardinal and contain skewed predictor variables, which poses further challenges. We aimed to investigate the effects of data preprocessing, feature selection techniques, and model selection on the performance of models trained on these datasets. We measured performance with 5-fold cross-validation and compared averaged r-squared and accuracy metrics across different combinations of techniques. We found that outliers/skew in predictor or target variables did not pose a challenge to regression models. We also found that class-imbalanced target variables and skewed predictors had little to no impact on classification performance. Random forest was the best model to use for imbalanced regression tasks. While our study uses a genetic dataset as an example of a real-world application, our findings can be generalized to any similar datasets.", "authors": ["Arshmeet Kaur", "Morteza Sarmadi"], "categories": ["q-bio.QM", "cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Biology", "Mathematics"], "published_date": "2024-02-22", "url": "https://arxiv.org/abs/2402.14980", "pdf_url": "https://arxiv.org/pdf/2402.14980v1", "arxiv_id": "2402.14980", "doi": "10.1007/s40745-024-00575-8", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2258}
{"id": "b20e0ca2aa1abf2686a4429cf42759f70e55117d74557f75a0a54e31ca83637e", "sources": ["arxiv", "semantic_scholar"], "title": "Closed-loop Data-Enabled Predictive Control and its equivalence with Closed-loop Subspace Predictive Control", "abstract": "Factors like improved data availability and increasing system complexity have sparked interest in data-driven predictive control (DDPC) methods like Data-enabled Predictive Control (DeePC). However, closed-loop identification bias arises in the presence of noise, which reduces the effectiveness of obtained control policies. In this paper we propose Closed-loop Data-enabled Predictive Control (CL-DeePC), a framework that unifies different approaches to address this challenge. To this end, CL-DeePC incorporates instrumental variables (IVs) to synthesize and sequentially apply consistent single or multi-step-ahead predictors. Furthermore, a computationally efficient CL-DeePC implementation is developed that reveals an equivalence with Closed-loop Subspace Predictive Control (CL-SPC). Compared to DeePC, CL-DeePC simulations demonstrate superior reference tracking, with a sensitivity study finding a 48% lower susceptibility to noise-induced reference tracking performance degradation.", "authors": ["Rogier Dinkla", "Sebastiaan Mulders", "Tom Oomen", "Jan-Willem van Wingerden"], "categories": ["eess.SY"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2024-02-22", "url": "https://arxiv.org/abs/2402.14374", "pdf_url": "https://arxiv.org/pdf/2402.14374v1", "arxiv_id": "2402.14374", "doi": "10.48550/arXiv.2402.14374", "citation_count": 9, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.25}
{"id": "42c1a1dde5eee7bbe0fe4981a947634cb72ffbe7c3d4d3f6449fca36944d9e32", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Spatially-Lucid AI Classification in Non-Euclidean Space: An Application for MxIF Oncology Data", "abstract": "Given multi-category point sets from different place-types, our goal is to develop a spatially-lucid classifier that can distinguish between two classes based on the arrangements of their points. This problem is important for many applications, such as oncology, for analyzing immune-tumor relationships and designing new immunotherapies. It is challenging due to spatial variability and interpretability needs. Previously proposed techniques require dense training data or have limited ability to handle significant spatial variability within a single place-type. Most importantly, these deep neural network (DNN) approaches are not designed to work in non-Euclidean space, particularly point sets. Existing non-Euclidean DNN methods are limited to one-size-fits-all approaches. We explore a spatial ensemble framework that explicitly uses different training strategies, including weighted-distance learning rate and spatial domain adaptation, on various place-types for spatially-lucid classification. Experimental results on real-world datasets (e.g., MxIF oncology data) show that the proposed framework provides higher prediction accuracy than baseline methods.", "authors": ["Majid Farhadloo", "Arun Sharma", "Jayant Gupta", "Alexey Leontovich", "Svetomir N. Markovic", "Shashi Shekhar"], "categories": ["eess.IV", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-02-22", "url": "https://arxiv.org/abs/2402.14974", "pdf_url": "https://arxiv.org/pdf/2402.14974v2", "arxiv_id": "2402.14974", "doi": "10.48550/arXiv.2402.14974", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "SDM", "quality_score": 0.2113}
{"id": "650378bcdff03b086238e161df8acd0ab54c962535758cccb1d30f1fa6882fbb", "sources": ["arxiv", "semantic_scholar"], "title": "Teacher as a Lenient Expert: Teacher-Agnostic Data-Free Knowledge Distillation", "abstract": "Data-free knowledge distillation (DFKD) aims to distill pretrained knowledge to a student model with the help of a generator without using original data. In such data-free scenarios, achieving stable performance of DFKD is essential due to the unavailability of validation data. Unfortunately, this paper has discovered that existing DFKD methods are quite sensitive to different teacher models, occasionally showing catastrophic failures of distillation, even when using well-trained teacher models. Our observation is that the generator in DFKD is not always guaranteed to produce precise yet diverse samples using the existing representative strategy of minimizing both class-prior and adversarial losses. Through our empirical study, we focus on the fact that class-prior not only decreases the diversity of generated samples, but also cannot completely address the problem of generating unexpectedly low-quality samples depending on teacher models. In this paper, we propose the teacher-agnostic data-free knowledge distillation (TA-DFKD) method, with the goal of more robust and stable performance regardless of teacher models. Our basic idea is to assign the teacher model a lenient expert role for evaluating samples, rather than a strict supervisor that enforces its class-prior on the generator. Specifically, we design a sample selection approach that takes only clean samples verified by the teacher model without imposing restrictions on the power of generating diverse samples. Through extensive experiments, we show that our method successfully achieves both robustness and training stability across various teacher models, while outperforming the existing DFKD methods.", "authors": ["Hyunjune Shin", "Dong-Wan Choi"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-18", "url": "https://arxiv.org/abs/2402.12406", "pdf_url": "https://arxiv.org/pdf/2402.12406v1", "arxiv_id": "2402.12406", "doi": "10.48550/arXiv.2402.12406", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "AAAI Conference on Artificial Intelligence", "quality_score": 0.2386}
{"id": "ae1434e4366415f1ce426a078c6c14c861806fb36b486793843e892c641a923d", "sources": ["arxiv", "semantic_scholar"], "title": "Learning-Augmented Search Data Structures", "abstract": "We study the integration of machine learning advice to improve upon traditional data structure designed for efficient search queries. Although there has been recent effort in improving the performance of binary search trees using machine learning advice, e.g., Lin et. al. (ICML 2022), the resulting constructions nevertheless suffer from inherent weaknesses of binary search trees, such as complexity of maintaining balance across multiple updates and the inability to handle partially-ordered or high-dimensional datasets. For these reasons, we focus on skip lists and KD trees in this work. Given access to a possibly erroneous oracle that outputs estimated fractional frequencies for search queries on a set of items, we construct skip lists and KD trees that provably provides the optimal expected search time, within nearly a factor of two. In fact, our learning-augmented skip lists and KD trees are still optimal up to a constant factor, even if the oracle is only accurate within a constant factor. We also demonstrate robustness by showing that our data structures achieves an expected search time that is within a constant factor of an oblivious skip list/KD tree construction even when the predictions are arbitrarily incorrect. Finally, we empirically show that our learning-augmented search data structures outperforms their corresponding traditional analogs on both synthetic and real-world datasets.", "authors": ["Chunkai Fu", "Brandon G. Nguyen", "Jung Hoon Seo", "Ryan Zesch", "Samson Zhou"], "categories": ["cs.DS", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-16", "url": "https://arxiv.org/abs/2402.10457", "pdf_url": "https://arxiv.org/pdf/2402.10457v2", "arxiv_id": "2402.10457", "doi": null, "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Learning Representations", "quality_score": 0.2386}
{"id": "c9a80d558beadd266198068bb750293bccd5df4d78b69296d6d729d6b68bb9b4", "sources": ["arxiv", "semantic_scholar"], "title": "FedD2S: Personalized Data-Free Federated Knowledge Distillation", "abstract": "This paper addresses the challenge of mitigating data heterogeneity among clients within a Federated Learning (FL) framework. The model-drift issue, arising from the noniid nature of client data, often results in suboptimal personalization of a global model compared to locally trained models for each client. To tackle this challenge, we propose a novel approach named FedD2S for Personalized Federated Learning (pFL), leveraging knowledge distillation. FedD2S incorporates a deep-to-shallow layer-dropping mechanism in the data-free knowledge distillation process to enhance local model personalization. Through extensive simulations on diverse image datasets-FEMNIST, CIFAR10, CINIC0, and CIFAR100-we compare FedD2S with state-of-the-art FL baselines. The proposed approach demonstrates superior performance, characterized by accelerated convergence and improved fairness among clients. The introduced layer-dropping technique effectively captures personalized knowledge, resulting in enhanced performance compared to alternative FL models. Moreover, we investigate the impact of key hyperparameters, such as the participation ratio and layer-dropping rate, providing valuable insights into the optimal configuration for FedD2S. The findings demonstrate the efficacy of adaptive layer-dropping in the knowledge distillation process to achieve enhanced personalization and performance across diverse datasets and tasks.", "authors": ["Kawa Atapour", "S. Jamal Seyedmohammadi", "Jamshid Abouei", "Arash Mohammadi", "Konstantinos N. Plataniotis"], "categories": ["cs.LG", "cs.AI", "cs.DC", "eess.IV"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-02-16", "url": "https://arxiv.org/abs/2402.10846", "pdf_url": "https://arxiv.org/pdf/2402.10846v1", "arxiv_id": "2402.10846", "doi": "10.48550/arXiv.2402.10846", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2258}
{"id": "e81aed328ea5adcff7301f32aa89d8cdd5a7195d5e737674a324d693f09bd02b", "sources": ["arxiv", "semantic_scholar"], "title": "Data Engineering for Scaling Language Models to 128K Context", "abstract": "We study the continual pretraining recipe for scaling language models' context lengths to 128K, with a focus on data engineering. We hypothesize that long context modeling, in particular \\textit{the ability to utilize information at arbitrary input locations}, is a capability that is mostly already acquired through large-scale pretraining, and that this capability can be readily extended to contexts substantially longer than seen during training~(e.g., 4K to 128K) through lightweight continual pretraining on appropriate data mixture. We investigate the \\textit{quantity} and \\textit{quality} of the data for continual pretraining: (1) for quantity, we show that 500 million to 5 billion tokens are enough to enable the model to retrieve information anywhere within the 128K context; (2) for quality, our results equally emphasize \\textit{domain balance} and \\textit{length upsampling}. Concretely, we find that naively upsampling longer data on certain domains like books, a common practice of existing work, gives suboptimal performance, and that a balanced domain mixture is important. We demonstrate that continual pretraining of the full model on 1B-5B tokens of such data is an effective and affordable strategy for scaling the context length of language models to 128K. Our recipe outperforms strong open-source long-context models and closes the gap to frontier models like GPT-4 128K.", "authors": ["Yao Fu", "Rameswar Panda", "Xinyao Niu", "Xiang Yue", "Hannaneh Hajishirzi", "Yoon Kim", "Hao Peng"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-15", "url": "https://arxiv.org/abs/2402.10171", "pdf_url": "https://arxiv.org/pdf/2402.10171v1", "arxiv_id": "2402.10171", "doi": "10.48550/arXiv.2402.10171", "citation_count": 211, "influential_citation_count": 21, "has_code": true, "code_url": "https://github.com/FranxYao/Long-Context-Data-Engineering", "venue": "International Conference on Machine Learning", "quality_score": 0.6712}
{"id": "08acdf6e25fa19cd340cce5b7872c502892fe7c9a27cb4036ef7cb9e9b5db766", "sources": ["arxiv", "semantic_scholar"], "title": "Data Analytics for Intermodal Freight Transportation Applications", "abstract": "With the growth of intermodal freight transportation, it is important that transportation planners and decision makers are knowledgeable about freight flow data to make informed decisions. This is particularly true with Intelligent Transportation Systems (ITS) offering new capabilities to intermodal freight transportation. Specifically, ITS enables access to multiple different data sources, but they have different formats, resolution, and time scales. Thus, knowledge of data science is essential to be successful in future ITS-enabled intermodal freight transportation system. This chapter discusses the commonly used descriptive and predictive data analytic techniques in intermodal freight transportation applications. These techniques cover the entire spectrum of univariate, bivariate, and multivariate analyses. In addition to illustrating how to apply these techniques through relatively simple examples, this chapter will also show how to apply them using the statistical software R. Additional exercises are provided for those who wish to apply the described techniques to more complex problems.", "authors": ["Nathan Huynh", "Majbah Uddin", "Chu Cong Minh"], "categories": ["stat.ME"], "fields_of_study": ["Mathematics", "Engineering"], "published_date": "2024-02-13", "url": "https://arxiv.org/abs/2402.08707", "pdf_url": "https://arxiv.org/pdf/2402.08707v1", "arxiv_id": "2402.08707", "doi": "10.1016/B978-0-12-809715-1.00010-9", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Data Analytics for intelligent transportation systems (pp. 241-262)", "quality_score": 0.2386}
{"id": "295571d84f3d452d1c16579988be7bd72c2775c8c1db9fe547b04eaf114ae1dc", "sources": ["arxiv", "semantic_scholar"], "title": "Online Differentially Private Synthetic Data Generation", "abstract": "We present a polynomial-time algorithm for online differentially private synthetic data generation. For a data stream within the hypercube $[0,1]^d$ and an infinite time horizon, we develop an online algorithm that generates a differentially private synthetic dataset at each time $t$. This algorithm achieves a near-optimal accuracy bound of $O(\\log(t)t^{-1/d})$ for $d\\geq 2$ and $O(\\log^{4.5}(t)t^{-1})$ for $d=1$ in the 1-Wasserstein distance. This result extends the previous work on the continual release model for counting queries to Lipschitz queries. Compared to the offline case, where the entire dataset is available at once, our approach requires only an extra polylog factor in the accuracy bound.", "authors": ["Yiyun He", "Roman Vershynin", "Yizhe Zhu"], "categories": ["math.ST", "cs.DS", "cs.LG", "math.PR"], "fields_of_study": ["Mathematics", "Computer Science"], "published_date": "2024-02-12", "url": "https://arxiv.org/abs/2402.08012", "pdf_url": "https://arxiv.org/pdf/2402.08012v3", "arxiv_id": "2402.08012", "doi": "10.1109/TP.2024.3486687", "citation_count": 7, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2258}
{"id": "cf3895a993275ac296588e9e331d258c9071ca745379b221d003c7c95892a71b", "sources": ["arxiv", "semantic_scholar"], "title": "Educational data mining and learning analytics: An updated survey", "abstract": "This survey is an updated and improved version of the previous one published in 2013 in this journal with the title data mining in education. It reviews in a comprehensible and very general way how Educational Data Mining and Learning Analytics have been applied over educational data. In the last decade, this research area has evolved enormously and a wide range of related terms are now used in the bibliography such as Academic Analytics, Institutional Analytics, Teaching Analytics, Data-Driven Education, Data-Driven Decision-Making in Education, Big Data in Education, and Educational Data Science. This paper provides the current state of the art by reviewing the main publications, the key milestones, the knowledge discovery cycle, the main educational environments, the specific tools, the free available datasets, the most used methods, the main objectives, and the future trends in this research area.", "authors": ["C. Romero", "S. Ventura"], "categories": ["cs.HC", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-10", "url": "https://arxiv.org/abs/2402.07956", "pdf_url": "https://arxiv.org/pdf/2402.07956v1", "arxiv_id": "2402.07956", "doi": "10.1002/widm.1355", "citation_count": 905, "influential_citation_count": 38, "has_code": false, "code_url": null, "venue": "Wiley interdisciplinary reviews: Data mining and knowledge discovery;2020; 10(3):e1355", "quality_score": 0.7955}
{"id": "c097d6399ace03fe94de251e5205ead32acef3483ef5a85f9e68e8545eacb9b6", "sources": ["arxiv", "semantic_scholar"], "title": "CounterCLR: Counterfactual Contrastive Learning with Non-random Missing Data in Recommendation", "abstract": "Recommender systems are designed to learn user preferences from observed feedback and comprise many fundamental tasks, such as rating prediction and post-click conversion rate (pCVR) prediction. However, the observed feedback usually suffer from two issues: selection bias and data sparsity, where biased and insufficient feedback seriously degrade the performance of recommender systems in terms of accuracy and ranking. Existing solutions for handling the issues, such as data imputation and inverse propensity score, are highly susceptible to additional trained imputation or propensity models. In this work, we propose a novel counterfactual contrastive learning framework for recommendation, named CounterCLR, to tackle the problem of non-random missing data by exploiting the advances in contrast learning. Specifically, the proposed CounterCLR employs a deep representation network, called CauNet, to infer non-random missing data in recommendations and perform user preference modeling by further introducing a self-supervised contrastive learning task. Our CounterCLR mitigates the selection bias problem without the need for additional models or estimators, while also enhancing the generalization ability in cases of sparse data. Experiments on real-world datasets demonstrate the effectiveness and superiority of our method.", "authors": ["Jun Wang", "Haoxuan Li", "Chi Zhang", "Dongxu Liang", "Enyun Yu", "Wenwu Ou", "Wenjia Wang"], "categories": ["cs.IR"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-08", "url": "https://arxiv.org/abs/2402.05740", "pdf_url": "https://arxiv.org/pdf/2402.05740v1", "arxiv_id": "2402.05740", "doi": "10.1109/ICDM58522.2023.00174", "citation_count": 20, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Industrial Conference on Data Mining", "quality_score": 0.3306}
{"id": "4dcc0429f26ec77732d4488e186455e22170b816b0f37db4b6073dd3e44314d1", "sources": ["arxiv", "semantic_scholar"], "title": "Enhancing Cross-Modal Contextual Congruence for Crowdfunding Success using Knowledge-infused Learning", "abstract": "The digital landscape continually evolves with multimodality, enriching the online experience for users. Creators and marketers aim to weave subtle contextual cues from various modalities into congruent content to engage users with a harmonious message. This interplay of multimodal cues is often a crucial factor in attracting users' attention. However, this richness of multimodality presents a challenge to computational modeling, as the semantic contextual cues spanning across modalities need to be unified to capture the true holistic meaning of the multimodal content. This contextual meaning is critical in attracting user engagement as it conveys the intended message of the brand or the organization. In this work, we incorporate external commonsense knowledge from knowledge graphs to enhance the representation of multimodal data using compact Visual Language Models (VLMs) and predict the success of multi-modal crowdfunding campaigns. Our results show that external knowledge commonsense bridges the semantic gap between text and image modalities, and the enhanced knowledge-infused representations improve the predictive performance of models for campaign success upon the baselines without knowledge. Our findings highlight the significance of contextual congruence in online multimodal content for engaging and successful crowdfunding campaigns.", "authors": ["Trilok Padhi", "Ugur Kursuncu", "Yaman Kumar", "Valerie L. Shalin", "Lane Peterson Fronczek"], "categories": ["cs.AI", "cs.CL", "cs.CV", "cs.CY", "cs.HC"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-06", "url": "https://arxiv.org/abs/2402.03607", "pdf_url": "https://arxiv.org/pdf/2402.03607v2", "arxiv_id": "2402.03607", "doi": "10.1109/BigData62323.2024.10825252", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "BigData Congress [Services Society]", "quality_score": 0.1193}
{"id": "6c90c12bd20f6d020f7cec7a56ddcc6cbc8738299d5a4c6318698bc8076c6311", "sources": ["arxiv", "semantic_scholar"], "title": "LESS: Selecting Influential Data for Targeted Instruction Tuning", "abstract": "Instruction tuning has unlocked powerful capabilities in large language models (LLMs), effectively using combined datasets to develop generalpurpose chatbots. However, real-world applications often require a specialized suite of skills (e.g., reasoning). The challenge lies in identifying the most relevant data from these extensive datasets to effectively develop specific capabilities, a setting we frame as targeted instruction tuning. We propose LESS, an optimizer-aware and practically efficient algorithm to effectively estimate data influences and perform Low-rank gradiEnt Similarity Search for instruction data selection. Crucially, LESS adapts existing influence formulations to work with the Adam optimizer and variable-length instruction data. LESS first constructs a highly reusable and transferable gradient datastore with low-dimensional gradient features and then selects examples based on their similarity to few-shot examples embodying a specific capability. Experiments show that training on a LESS-selected 5% of the data can often outperform training on the full dataset across diverse downstream tasks. Furthermore, the selected data is highly transferable: smaller models can be leveraged to select useful data for larger models and models from different families. Our qualitative analysis shows that our method goes beyond surface form cues to identify data that exemplifies the necessary reasoning skills for the intended downstream application.", "authors": ["Mengzhou Xia", "Sadhika Malladi", "Suchin Gururangan", "Sanjeev Arora", "Danqi Chen"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-06", "url": "https://arxiv.org/abs/2402.04333", "pdf_url": "https://arxiv.org/pdf/2402.04333v3", "arxiv_id": "2402.04333", "doi": "10.48550/arXiv.2402.04333", "citation_count": 515, "influential_citation_count": 102, "has_code": true, "code_url": "https://github.com/princeton-nlp/LESS", "venue": "International Conference on Machine Learning", "quality_score": 1.0}
{"id": "492995c017ebeb236b87656f70e0ab5cbefda60a1e05ed21bf04ab9cf6d8fc10", "sources": ["arxiv", "semantic_scholar"], "title": "From Data Creator to Data Reuser: Distance Matters", "abstract": "Sharing research data is necessary, but not sufficient, for data reuse. Open science policies focus more heavily on data sharing than on reuse, yet both are complex, labor-intensive, expensive, and require infrastructure investments by multiple stakeholders. The value of data reuse lies in relationships between creators and reusers. By addressing knowledge exchange, rather than mere transactions between stakeholders, investments in data management and knowledge infrastructures can be made more wisely. Drawing upon empirical studies of data sharing and reuse, we develop the metaphor of distance between data creator and data reuser, identifying six dimensions of distance that influence the ability to transfer knowledge effectively: domain, methods, collaboration, curation, purposes, and time and temporality. We explore how social and socio-technical aspects of these dimensions may decrease -- or increase -- distances to be traversed between creators and reusers. Our theoretical framing of the distance between data creators and prospective reusers leads to recommendations to four categories of stakeholders on how to make data sharing and reuse more effective: data creators, data reusers, data archivists, and funding agencies. 'It takes a village' to share research data -- and a village to reuse data. Our aim is to provoke new research questions, new research, and new investments in effective and efficient circulation of research data; and to identify criteria for investments at each stage of data and research life cycles.", "authors": ["Christine L. Borgman", "Paul T. Groth"], "categories": ["cs.HC", "cs.CY", "cs.DL", "cs.IR"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-05", "url": "https://arxiv.org/abs/2402.07926", "pdf_url": "https://arxiv.org/pdf/2402.07926v3", "arxiv_id": "2402.07926", "doi": "10.1162/99608f92.35d32cfc", "citation_count": 24, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Harvard data science review", "quality_score": 0.3495}
{"id": "7506328c5e00e2b9a00f64cbfc5ac64bc16559539e39777e83db1ac6ea8c59a0", "sources": ["arxiv", "semantic_scholar"], "title": "Nuclear mass table in deformed relativistic Hartree-Bogoliubov theory in continuum, II: Even-$Z$ nuclei", "abstract": "The mass table in the deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc) with the PC-PK1 density functional has been established for even-$Z$ nuclei with $8\\le Z\\le120$, extended from the previous work for even-even nuclei [Zhang $\\it{et.~al.}$ (DRHBc Mass Table Collaboration), At. Data Nucl. Data Tables 144, 101488 (2022)]. The calculated binding energies, two-nucleon and one-neutron separation energies, root-mean-square (rms) radii of neutron, proton, matter, and charge distributions, quadrupole deformations, and neutron and proton Fermi surfaces are tabulated and compared with available experimental data. A total of 4829 even-$Z$ nuclei are predicted to be bound, with an rms deviation of 1.477 MeV from the 1244 mass data. Good agreement with the available experimental odd-even mass differences, $α$ decay energies, and charge radii is also achieved. The description accuracy for nuclear masses and nucleon separation energies as well as the prediction for drip lines is compared with the results obtained from other relativistic and nonrelativistic density functional. The comparison shows that the DRHBc theory with PC-PK1 provides an excellent microscopic description for the masses of even-$Z$ nuclei. The systematics of the nucleon separation energies, odd-even mass differences, pairing energies, two-nucleon gaps, $α$ decay energies, rms radii, quadrupole deformations, potential energy curves, neutron density distributions, and neutron mean-field potentials are discussed.", "authors": [" DRHBc Mass Table Collaboration", "Peng Guo", "Xiaojie Cao", "Kangmin Chen", "Zhihui Chen", "Myung-Ki Cheoun", "Yong-Beom Choi", "Pak Chung Lam", "Wenmin Deng", "Jianmin Dong", "Pengxiang Du", "Xiaokai Du", "Kangda Duan", "Xiaohua Fan", "Wei Gao", "Lisheng Geng", "Eunja Ha", "Xiao-Tao He", "Jinniu Hu", "Jingke Huang", "Kun Huang", "Yanan Huang", "Zidan Huang", "Kim Da Hyung", "Hoi Yat Chan", "Xiaofei Jiang", "Seonghyun Kim", "Youngman Kim", "Chang-Hwan Lee", "Jenny Lee", "Jian Li", "Minglong Li", "Zhipan Li", "Zhengzheng Li", "Zhanjiang Lian", "Haozhao Liang", "Lang Liu", "Xiao Lu", "Zhi-Rui Liu", "Jie Meng", "Ziyan Meng", "Myeong-Hwan Mun", "Yifei Niu", "Zhongming Niu", "Cong Pan", "Jing Peng", "Xiaoying Qu", "Panagiota Papakonstantinou", "Tianshuai Shang", "Xinle Shang", "Caiwan Shen", "Guofang Shen", "Tingting Sun", "Xiang-Xiang Sun", "Sibo Wang", "Tianyu Wang", "Yiran Wang", "Yuanyuan Wang", "Jiawei Wu", "Liang Wu", "Xinhui Wu", "Xuewei Xia", "Huihui Xie", "Jiangming Yao", "Ip Kwan Yau Ip", "To Chung Yiu", "Jianghan Yu", "Yangyang Yu", "Kaiyuan Zhang", "Shijie Zhang", "Shuangquan Zhang", "Wei Zhang", "Xiaoyan Zhang", "Yanxin Zhang", "Ying Zhang", "Yingxun Zhang", "Zhenhua Zhang", "Qiang Zhao", "Yingchun Zhao", "Ruyou Zheng", "Chang Zhou", "Shan-Gui Zhou", "Lianjian Zou"], "categories": ["nucl-th", "astro-ph.SR", "nucl-ex"], "fields_of_study": ["Physics"], "published_date": "2024-02-05", "url": "https://arxiv.org/abs/2402.02935", "pdf_url": "https://arxiv.org/pdf/2402.02935v4", "arxiv_id": "2402.02935", "doi": "10.1016/j.adt.2024.101661", "citation_count": 52, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "Atomic Data and Nuclear Data Tables", "quality_score": 0.4311}
{"id": "0ad9e4895f8b83c27d6c2da1b37f261ad57ee11fa85d89c559ab9a4550be774d", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetic Data for the Mitigation of Demographic Biases in Face Recognition", "abstract": "This study investigates the possibility of mitigating the demographic biases that affect face recognition technologies through the use of synthetic data. Demographic biases have the potential to impact individuals from specific demographic groups, and can be identified by observing disparate performance of face recognition systems across demographic groups. They primarily arise from the unequal representations of demographic groups in the training data. In recent times, synthetic data have emerged as a solution to some problems that affect face recognition systems. In particular, during the generation process it is possible to specify the desired demographic and facial attributes of images, in order to control the demographic distribution of the synthesized dataset, and fairly represent the different demographic groups. We propose to fine-tune with synthetic data existing face recognition systems that present some demographic biases. We use synthetic datasets generated with GANDiffFace, a novel framework able to synthesize datasets for face recognition with controllable demographic distribution and realistic intra-class variations. We consider multiple datasets representing different demographic groups for training and evaluation. Also, we fine-tune different face recognition systems, and evaluate their demographic fairness with different metrics. Our results support the proposed approach and the use of synthetic data to mitigate demographic biases in face recognition.", "authors": ["Pietro Melzi", "Christian Rathgeb", "Ruben Tolosana", "Ruben Vera-Rodriguez", "Aythami Morales", "Dominik Lawatsch", "Florian Domin", "Maxim Schaubert"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-02", "url": "https://arxiv.org/abs/2402.01472", "pdf_url": "https://arxiv.org/pdf/2402.01472v1", "arxiv_id": "2402.01472", "doi": "10.1109/IJCB57857.2023.10449034", "citation_count": 24, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Proceedings of the International Joint Conference on Biometrics 2023, special session on \"Synthetic Data in Biometrics\"", "quality_score": 0.3495}
{"id": "def1d7da2e8b931ffae646bc42fe05ae1d7b7cd4c1acefa77b4946151ae0313a", "sources": ["arxiv", "semantic_scholar"], "title": "Optical Data Transmission ASICs for the High-Luminosity LHC (HL-LHC) Experiments", "abstract": "We present the design and test results of two optical data transmission ASICs for the High-Luminosity LHC (HL-LHC) experiments. These ASICs include a two-channel serializer (LOCs2) and a single-channel Vertical Cavity Surface Emitting Laser (VCSEL) driver (LOCld1V2). Both ASICs are fabricated in a commercial 0.25-um Silicon-on-Sapphire (SoS) CMOS technology and operate at a data rate up to 8 Gbps per channel. The power consumption of LOCs2 and LOCld1V2 are 1.25 W and 0.27 W at 8-Gbps data rate, respectively. LOCld1V2 has been verified meeting the radiation-tolerance requirements for HL-LHC experiments.", "authors": ["Xiaoting Li", "Gang Liu", "Jinghong Chen", "Binwei Deng", "Datao Gong", "Di Guo", "Mengxun He", "Suen Hou", "Guangming Huang", "Ge Jin", "Hao Liang", "Futian Liang", "Chonghan Liu", "Tiankuan Liu", "Xiangming Sun", "Ping-Kun Teng", "Annie C. Xiang", "Jingbo Ye", "Yang You", "Xiandong Zhao"], "categories": ["physics.ins-det"], "fields_of_study": ["Physics"], "published_date": "2024-01-30", "url": "https://arxiv.org/abs/2401.17471", "pdf_url": "https://arxiv.org/pdf/2401.17471v1", "arxiv_id": "2401.17471", "doi": "10.1088/1748-0221/9/03/C03007", "citation_count": 11, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2698}
{"id": "a5815815dd651ae630d9c24e1e6259fb0f4cacb04fe4741af5f9878621c51385", "sources": ["arxiv", "semantic_scholar"], "title": "Development of A 16:1 serializer for data transmission at 5 Gbps", "abstract": "Radiation tolerant, high speed and low power serializer ASIC is critical for optical link systems in particle physics experiments. Based on a commercial 0.25 um silicon-on-sapphire CMOS technology, we design a 16:1 serializer with 5 Gbps serial data rate. This ASIC has been submitted for fabrication. The post-layout simulation indicates the deterministic jitter is 54 ps (pk-pk) and random jitter is 3 ps (rms). The power consumption of the serializer is 500 mW. The design details and post layout simulation results are presented in this paper.", "authors": ["Datao Gong", "Suen Hou", "Zhihua Liang", "Chonghan Liu", "Tiankuan Liu", "Da-Shun Su", "Ping-Kun Teng", "Annie C. Xiang", "Jingbo Ye"], "categories": ["physics.ins-det"], "fields_of_study": ["Physics", "Computer Science"], "published_date": "2024-01-28", "url": "https://arxiv.org/abs/2401.15755", "pdf_url": "https://arxiv.org/pdf/2401.15755v1", "arxiv_id": "2401.15755", "doi": "10.5170/CERN-2009-006.481", "citation_count": 9, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.25}
{"id": "ad2501d14ec739d0ff52fbd4fdb4e280bcbfcd1da65cb77348c3f694b948e732", "sources": ["arxiv", "semantic_scholar"], "title": "High-Speed Serial Optical Link Test Bench Using FPGA with Embedded Transceivers", "abstract": "We develop a custom Bit Error Rate test bench based on Altera's Stratix II GX transceiver signal integrity development kit, demonstrate it on point-to-point serial optical link with data rate up to 5 Gbps, and compare it with commercial stand alone tester. The 8B/10B protocol is implemented and its effects studied. A variable optical attenuator is inserted in the fibre loop to induce transmission degradation and to measure receiver sensitivity. We report comparable receiver sensitivity results using the FPGA based tester and commercial tester. The results of the FPGA also shows that there are more one-to-zero bit flips than zero-to-one bit flips at lower error rate. In 8B/10B coded transmission, there are more word errors than bit flips, and the total error rate is less than two times that of non-coded transmission. Total error rate measured complies with simulation results, according to the protocol setup.", "authors": ["Annie C. Xiang", "Tingting Cao", "Datao Gong", "Suen Hou", "Chonghan Liu", "Tiankuan Liu", "Da-Shung Su", "Ping-Kun Teng", "Jingbo Ye"], "categories": ["physics.ins-det"], "fields_of_study": ["Physics", "Computer Science"], "published_date": "2024-01-28", "url": "https://arxiv.org/abs/2401.15754", "pdf_url": "https://arxiv.org/pdf/2401.15754v1", "arxiv_id": "2401.15754", "doi": "10.5170/CERN-2009-006.471", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2258}
{"id": "15f0493867831d8569659efb2c96a668cb1a0bd095c8f866cae023fa00354d85", "sources": ["arxiv", "semantic_scholar"], "title": "The VCSEL-based Array Optical Transmitter (ATx) Development Towards 120-Gbps Link for Collider Detector: Development Update", "abstract": "A compact radiation-tolerant array optical transmitter module (ATx) is developed to provide data transmission up to 10Gbps per channel with 12 parallel channels for collider detector applications. The ATx integrates a Vertical Cavity Surface-Emitting Laser (VCSEL) array and driver circuitry for electrical to optical conversion, an edge warp substrate for the electrical interface and a micro-lens array for the optical interface. This paper reports the continuing development of the ATx custom package. A simple, high-accuracy and reliable active-alignment method for the optical coupling is introduced. The radiation-resistance of the optoelectronic components is evaluated and the inclusion of a custom-designed array driver is discussed.", "authors": ["Di Guo", "Chonghan Liu", "Jinghong Chen", "John Chramowicz", "Datao Gong", "Suen Hou", "Deping Huang", "Ge Jin", "Xiaoting Li", "Tiankuan Liu", "Alan Prosser", "Ping-Kun Teng", "Jingbo Ye", "Yongzhao Zhou", "Yang You", "Annie C. Xiang", "Hao Liang"], "categories": ["physics.ins-det"], "fields_of_study": ["Physics"], "published_date": "2024-01-28", "url": "https://arxiv.org/abs/2401.15749", "pdf_url": "https://arxiv.org/pdf/2401.15749v1", "arxiv_id": "2401.15749", "doi": "10.1088/1748-0221/10/01/C01034", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2113}
{"id": "46f483780fc6c3f402dc1cafad0385d02977074fb14deb498ccb814f4c6805ad", "sources": ["arxiv", "semantic_scholar"], "title": "A Survey on Data Augmentation in Large Model Era", "abstract": "Large models, encompassing large language and diffusion models, have shown exceptional promise in approximating human-level intelligence, garnering significant interest from both academic and industrial spheres. However, the training of these large models necessitates vast quantities of high-quality data, and with continuous updates to these models, the existing reservoir of high-quality data may soon be depleted. This challenge has catalyzed a surge in research focused on data augmentation methods. Leveraging large models, these data augmentation techniques have outperformed traditional approaches. This paper offers an exhaustive review of large model-driven data augmentation methods, adopting a comprehensive perspective. We begin by establishing a classification of relevant studies into three main categories: image augmentation, text augmentation, and paired data augmentation. Following this, we delve into various data post-processing techniques pertinent to large model-based data augmentation. Our discussion then expands to encompass the array of applications for these data augmentation methods within natural language processing, computer vision, and audio signal processing. We proceed to evaluate the successes and limitations of large model-based data augmentation across different scenarios. Concluding our review, we highlight prospective challenges and avenues for future exploration in the field of data augmentation. Our objective is to furnish researchers with critical insights, ultimately contributing to the advancement of more sophisticated large models. We consistently maintain the related open-source materials at: https://github.com/MLGroup-JLU/LLM-data-aug-survey.", "authors": ["Yue Zhou", "Chenlu Guo", "Xu Wang", "Yi Chang", "Yuan Wu"], "categories": ["cs.LG", "cs.CL", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-01-27", "url": "https://arxiv.org/abs/2401.15422", "pdf_url": "https://arxiv.org/pdf/2401.15422v2", "arxiv_id": "2401.15422", "doi": "10.48550/arXiv.2401.15422", "citation_count": 55, "influential_citation_count": 3, "has_code": true, "code_url": "https://github.com/MLGroup-JLU/LLM-data-aug-survey", "venue": "arXiv.org", "quality_score": 0.437}
{"id": "a4364c3fdfd3f27f5e34a46d458b0b7bdf84e7335051d8b1f33b2dce6552a001", "sources": ["arxiv", "semantic_scholar"], "title": "A Semantic Approach for Big Data Exploration in Industry 4.0", "abstract": "The growing trends in automation, Internet of Things, big data and cloud computing technologies have led to the fourth industrial revolution (Industry 4.0), where it is possible to visualize and identify patterns and insights, which results in a better understanding of the data and can improve the manufacturing process. However, many times, the task of data exploration results difficult for manufacturing experts because they might be interested in analyzing also data that does not appear in pre-designed visualizations and therefore they must be assisted by Information Technology experts. In this paper, we present a proposal materialized in a semantic-based visual query system developed for a real Industry 4.0 scenario that allows domain experts to explore and visualize data in a friendly way. The main novelty of the system is the combined use that it makes of captured data that are semantically annotated first, and a 2D customized digital representation of a machine that is also linked with semantic descriptions. Those descriptions are expressed using terms of an ontology, where, among others, the sensors that are used to capture indicators about the performance of a machine that belongs to a Industry 4.0 scenario have been modeled. Moreover, this semantic description allows to: formulate queries at a higher level of abstraction, provide customized graphical visualizations of the results based on the format and nature of the data, and download enriched data enabling further types of analysis.", "authors": ["Idoia Berges", "Víctor Julio Ramírez-Durán", "Arantza Illarramendi"], "categories": ["cs.AI", "cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2024-01-18", "url": "https://arxiv.org/abs/2401.09789", "pdf_url": "https://arxiv.org/pdf/2401.09789v1", "arxiv_id": "2401.09789", "doi": "10.1016/j.bdr.2021.100222", "citation_count": 15, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Big Data Research", "quality_score": 0.301}
{"id": "d81e1d6dc228806da85d945028c959c1fdb100311c398e4c3c56410fc0aab5ae", "sources": ["arxiv", "semantic_scholar"], "title": "Improve Fidelity and Utility of Synthetic Credit Card Transaction Time Series from Data-centric Perspective", "abstract": "Exploring generative model training for synthetic tabular data, specifically in sequential contexts such as credit card transaction data, presents significant challenges. This paper addresses these challenges, focusing on attaining both high fidelity to actual data and optimal utility for machine learning tasks. We introduce five pre-processing schemas to enhance the training of the Conditional Probabilistic Auto-Regressive Model (CPAR), demonstrating incremental improvements in the synthetic data's fidelity and utility. Upon achieving satisfactory fidelity levels, our attention shifts to training fraud detection models tailored for time-series data, evaluating the utility of the synthetic data. Our findings offer valuable insights and practical guidelines for synthetic data practitioners in the finance sector, transitioning from real to synthetic datasets for training purposes, and illuminating broader methodologies for synthesizing credit card transaction time series.", "authors": ["Din-Yin Hsieh", "Chi-Hua Wang", "Guang Cheng"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-01-01", "url": "https://arxiv.org/abs/2401.00965", "pdf_url": "https://arxiv.org/pdf/2401.00965v1", "arxiv_id": "2401.00965", "doi": "10.48550/arXiv.2401.00965", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2113}
{"id": "9db03b35e1e173b57509dc1e2093a364fb28ac2ff16a8eb718af84807a4c4435", "sources": ["arxiv", "semantic_scholar"], "title": "Downstream Task-Oriented Generative Model Selections on Synthetic Data Training for Fraud Detection Models", "abstract": "Devising procedures for downstream task-oriented generative model selections is an unresolved problem of practical importance. Existing studies focused on the utility of a single family of generative models. They provided limited insights on how synthetic data practitioners select the best family generative models for synthetic training tasks given a specific combination of machine learning model class and performance metric. In this paper, we approach the downstream task-oriented generative model selections problem in the case of training fraud detection models and investigate the best practice given different combinations of model interpretability and model performance constraints. Our investigation supports that, while both Neural Network(NN)-based and Bayesian Network(BN)-based generative models are both good to complete synthetic training task under loose model interpretability constrain, the BN-based generative models is better than NN-based when synthetic training fraud detection model under strict model interpretability constrain. Our results provides practical guidance for machine learning practitioner who is interested in replacing their training dataset from real to synthetic, and shed lights on more general downstream task-oriented generative model selection problems.", "authors": ["Yinan Cheng", "Chi-Hua Wang", "Vamsi K. Potluru", "Tucker Balch", "Guang Cheng"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-01-01", "url": "https://arxiv.org/abs/2401.00974", "pdf_url": "https://arxiv.org/pdf/2401.00974v1", "arxiv_id": "2401.00974", "doi": "10.48550/arXiv.2401.00974", "citation_count": 10, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2603}
{"id": "cfd808aeedbfbc850e86545dc272ec98e7ee20af25035f78982bb64a8b3753b3", "sources": ["arxiv", "semantic_scholar"], "title": "LLMs with User-defined Prompts as Generic Data Operators for Reliable Data Processing", "abstract": "Data processing is one of the fundamental steps in machine learning pipelines to ensure data quality. Majority of the applications consider the user-defined function (UDF) design pattern for data processing in databases. Although the UDF design pattern introduces flexibility, reusability and scalability, the increasing demand on machine learning pipelines brings three new challenges to this design pattern -- not low-code, not dependency-free and not knowledge-aware. To address these challenges, we propose a new design pattern that large language models (LLMs) could work as a generic data operator (LLM-GDO) for reliable data cleansing, transformation and modeling with their human-compatible performance. In the LLM-GDO design pattern, user-defined prompts (UDPs) are used to represent the data processing logic rather than implementations with a specific programming language. LLMs can be centrally maintained so users don't have to manage the dependencies at the run-time. Fine-tuning LLMs with domain-specific data could enhance the performance on the domain-specific tasks which makes data processing knowledge-aware. We illustrate these advantages with examples in different data processing tasks. Furthermore, we summarize the challenges and opportunities introduced by LLMs to provide a complete view of this design pattern for more discussions.", "authors": ["Luyi Ma", "Nikhil Thakurdesai", "Jiao Chen", "Jianpeng Xu", "Evren Korpeoglu", "Sushant Kumar", "Kannan Achan"], "categories": ["cs.DB", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-12-26", "url": "https://arxiv.org/abs/2312.16351", "pdf_url": "https://arxiv.org/pdf/2312.16351v1", "arxiv_id": "2312.16351", "doi": "10.1109/BigData59044.2023.10386472", "citation_count": 8, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "BigData Congress [Services Society]", "quality_score": 0.2386}
{"id": "30a41ac56d4e27910aba7b8d0bc5a20af07cef545790ec68c1934288ba4e4ad7", "sources": ["arxiv", "semantic_scholar"], "title": "What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning", "abstract": "Instruction tuning is a standard technique employed to align large language models to end tasks and user preferences after the initial pretraining phase. Recent research indicates the critical role of data engineering in instruction tuning -- when appropriately selected, only limited data is necessary to achieve superior performance. However, we still lack a principled understanding of what makes good instruction tuning data for alignment, and how we should select data automatically and effectively. In this work, we delve deeply into automatic data selection strategies for alignment. We start with controlled studies to measure data across three dimensions: complexity, quality, and diversity, along which we examine existing methods and introduce novel techniques for enhanced data measurement. Subsequently, we propose a simple strategy to select data samples based on the measurement. We present deita (short for Data-Efficient Instruction Tuning for Alignment), a series of models fine-tuned from LLaMA and Mistral models using data samples automatically selected with our proposed approach. Empirically, deita performs better or on par with the state-of-the-art open-source alignment models with only 6K SFT training data samples -- over 10x less than the data used in the baselines. When further trained with direct preference optimization (DPO), deita-Mistral-7B + DPO trained with 6K SFT and 10K DPO samples achieve 7.55 MT-Bench and 90.06% AlpacaEval scores. We anticipate this work to provide tools on automatic data selection, facilitating data-efficient alignment. We release our models as well as the selected datasets for future researches to effectively align models more efficiently.", "authors": ["Wei Liu", "Weihao Zeng", "Keqing He", "Yong Jiang", "Junxian He"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-12-25", "url": "https://arxiv.org/abs/2312.15685", "pdf_url": "https://arxiv.org/pdf/2312.15685v2", "arxiv_id": "2312.15685", "doi": "10.48550/arXiv.2312.15685", "citation_count": 390, "influential_citation_count": 70, "has_code": true, "code_url": "https://github.com/hkust-nlp/deita", "venue": "International Conference on Learning Representations", "quality_score": 0.9256}
{"id": "531ae7c7431ceedde0fa7264456230d507ed13f0792e8a8da64abbb3902f112b", "sources": ["arxiv", "semantic_scholar"], "title": "WellFactor: Patient Profiling using Integrative Embedding of Healthcare Data", "abstract": "In the rapidly evolving healthcare industry, platforms now have access to not only traditional medical records, but also diverse data sets encompassing various patient interactions, such as those from healthcare web portals. To address this rich diversity of data, we introduce WellFactor: a method that derives patient profiles by integrating information from these sources. Central to our approach is the utilization of constrained low-rank approximation. WellFactor is optimized to handle the sparsity that is often inherent in healthcare data. Moreover, by incorporating task-specific label information, our method refines the embedding results, offering a more informed perspective on patients. One important feature of WellFactor is its ability to compute embeddings for new, previously unobserved patient data instantaneously, eliminating the need to revisit the entire data set or recomputing the embedding. Comprehensive evaluations on real-world healthcare data demonstrate WellFactor's effectiveness. It produces better results compared to other existing methods in classification performance, yields meaningful clustering of patients, and delivers consistent results in patient similarity searches and predictions.", "authors": ["Dongjin Choi", "Andy Xiang", "Ozgur Ozturk", "Deep Shrestha", "Barry Drake", "Hamid Haidarian", "Faizan Javed", "Haesun Park"], "categories": ["cs.LG", "cs.AI", "cs.IR"], "fields_of_study": ["Computer Science"], "published_date": "2023-12-21", "url": "https://arxiv.org/abs/2312.14129", "pdf_url": "https://arxiv.org/pdf/2312.14129v1", "arxiv_id": "2312.14129", "doi": "10.1109/BigData59044.2023.10386138", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "BigData Congress [Services Society]", "quality_score": 0.1505}
{"id": "e6ed16e74d02be57475c4f696f48b36a8917d167f5ca8c753948e8508acc1dab", "sources": ["arxiv", "semantic_scholar"], "title": "R2D2: Reducing Redundancy and Duplication in Data Lakes", "abstract": "Enterprise data lakes often suffer from substantial amounts of duplicate and redundant data, with data volumes ranging from terabytes to petabytes. This leads to both increased storage costs and unnecessarily high maintenance costs for these datasets. In this work, we focus on identifying and reducing redundancy in enterprise data lakes by addressing the problem of 'dataset containment'. To the best of our knowledge, this is one of the first works that addresses table-level containment at a large scale. We propose R2D2: a three-step hierarchical pipeline that efficiently identifies almost all instances of containment by progressively reducing the search space in the data lake. It first builds (i) a schema containment graph, followed by (ii) statistical min-max pruning, and finally, (iii) content level pruning. We further propose minimizing the total storage and access costs by optimally identifying redundant datasets that can be deleted (and reconstructed on demand) while respecting latency constraints. We implement our system on Azure Databricks clusters using Apache Spark for enterprise data stored in ADLS Gen2, and on AWS clusters for open-source data. In contrast to existing modified baselines that are inaccurate or take several days to run, our pipeline can process an enterprise customer data lake at the TB scale in approximately 5 hours with high accuracy. We present theoretical results as well as extensive empirical validation on both enterprise (scale of TBs) and open-source datasets (scale of MBs - GBs), which showcase the effectiveness of our pipeline.", "authors": ["Raunak Shah", "Koyel Mukherjee", "Atharv Tyagi", "Sai Keerthana Karnam", "Dhruv Joshi", "Shivam Bhosale", "Subrata Mitra"], "categories": ["cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2023-12-20", "url": "https://arxiv.org/abs/2312.13427", "pdf_url": "https://arxiv.org/pdf/2312.13427v1", "arxiv_id": "2312.13427", "doi": "10.1145/3626762", "citation_count": 7, "influential_citation_count": 2, "has_code": true, "code_url": null, "venue": "Proc. ACM Manag. Data 1, 4, Article 268 (December 2023), 25 pages", "quality_score": 0.2386}
{"id": "f9a66df00e2fc2a8270fe78352ca741a56fc8c77210c82dff3809d16700359bf", "sources": ["arxiv", "semantic_scholar"], "title": "A Weighted K-Center Algorithm for Data Subset Selection", "abstract": "The success of deep learning hinges on enormous data and large models, which require labor-intensive annotations and heavy computation costs. Subset selection is a fundamental problem that can play a key role in identifying smaller portions of the training data, which can then be used to produce similar models as the ones trained with full data. Two prior methods are shown to achieve impressive results: (1) margin sampling that focuses on selecting points with high uncertainty, and (2) core-sets or clustering methods such as k-center for informative and diverse subsets. We are not aware of any work that combines these methods in a principled manner. To this end, we develop a novel and efficient factor 3-approximation algorithm to compute subsets based on the weighted sum of both k-center and uncertainty sampling objective functions. To handle large datasets, we show a parallel algorithm to run on multiple machines with approximation guarantees. The proposed algorithm achieves similar or better performance compared to other strong baselines on vision datasets such as CIFAR-10, CIFAR-100, and ImageNet.", "authors": ["Srikumar Ramalingam", "Pranjal Awasthi", "Sanjiv Kumar"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2023-12-17", "url": "https://arxiv.org/abs/2312.10602", "pdf_url": "https://arxiv.org/pdf/2312.10602v1", "arxiv_id": "2312.10602", "doi": "10.48550/arXiv.2312.10602", "citation_count": 11, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2698}
{"id": "042d41daa5a709449679cf20b8f9f01b0e9cdc3e90c115f7e1120bac85167c2b", "sources": ["arxiv", "semantic_scholar"], "title": "Ocean Data Quality Assessment through Outlier Detection-enhanced Active Learning", "abstract": "Ocean and climate research benefits from global ocean observation initiatives such as Argo, GLOSS, and EMSO. The Argo network, dedicated to ocean profiling, generates a vast volume of observatory data. However, data quality issues from sensor malfunctions and transmission errors necessitate stringent quality assessment. Existing methods, including machine learning, fall short due to limited labeled data and imbalanced datasets. To address these challenges, we propose an ODEAL framework for ocean data quality assessment, employing AL to reduce human experts' workload in the quality assessment workflow and leveraging outlier detection algorithms for effective model initialization. We also conduct extensive experiments on five large-scale realistic Argo datasets to gain insights into our proposed method, including the effectiveness of AL query strategies and the initial set construction approach. The results suggest that our framework enhances quality assessment efficiency by up to 465.5% with the uncertainty-based query strategy compared to random sampling and minimizes overall annotation costs by up to 76.9% using the initial set built with outlier detectors.", "authors": ["Na Li", "Yiyang Qi", "Ruyue Xin", "Zhiming Zhao"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-12-17", "url": "https://arxiv.org/abs/2312.10817", "pdf_url": "https://arxiv.org/pdf/2312.10817v1", "arxiv_id": "2312.10817", "doi": "10.1109/BigData59044.2023.10386969", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "BigData Congress [Services Society]", "quality_score": 0.1505}
{"id": "f33eb2e98c3e133eff24f71e2451e9697a2cc53a203329cbdb53ff5a5dbd2868", "sources": ["arxiv", "semantic_scholar"], "title": "Data-Driven Socio-Economic Deprivation Prediction via Dimensionality Reduction: The Power of Diffusion Maps", "abstract": "This research proposes a model to predict the location of the most deprived areas in a city using data from the census. Census data is very high-dimensional and needs to be simplified. We use the diffusion map algorithm to reduce dimensionality and find patterns. Features are defined by eigenvectors of the Laplacian matrix that defines the diffusion map. The eigenvectors corresponding to the smallest eigenvalues indicate specific characteristics of the population. Previous work has found qualitatively that the second most important dimension for describing the census data in Bristol, UK is linked to deprivation. In this research, we analyse how good this dimension is as a model for predicting deprivation by comparing it with the recognised measures. The Pearson correlation coefficient was found to be greater than 0.7. The top 10 per cent of deprived areas in the UK, which are also located in Bristol, are extracted to test the accuracy of the model. There are 52 of the most deprived areas, and 38 areas are correctly identified by comparing them to the model. The influence of scores of IMD domains that do not correlate with the models and Eigenvector 2 entries of non-deprived Output Areas cause the model to fail the prediction of 14 deprived areas. The model demonstrates strong performance in predicting future deprivation in the project areas, which is expected to assist in government resource allocation and funding greatly. The codes can be accessed here: https://github.com/junegoo94/diffusion_maps", "authors": ["June Moh Goo"], "categories": ["cs.LG", "cs.CE"], "fields_of_study": ["Computer Science"], "published_date": "2023-12-15", "url": "https://arxiv.org/abs/2312.09830", "pdf_url": "https://arxiv.org/pdf/2312.09830v2", "arxiv_id": "2312.09830", "doi": "10.1109/BigData62323.2024.10825109", "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/junegoo94/diffusion_maps", "venue": "BigData Congress [Services Society]", "quality_score": 0.0753}
{"id": "b163b398382a65391e6d84864357e8a0764c34b6e3ead94643d82824b0cc7a28", "sources": ["arxiv", "semantic_scholar"], "title": "Deep Unsupervised Domain Adaptation for Time Series Classification: a Benchmark", "abstract": "Unsupervised Domain Adaptation (UDA) aims to harness labeled source data to train models for unlabeled target data. Despite extensive research in domains like computer vision and natural language processing, UDA remains underexplored for time series data, which has widespread real-world applications ranging from medicine and manufacturing to earth observation and human activity recognition. Our paper addresses this gap by introducing a comprehensive benchmark for evaluating UDA techniques for time series classification, with a focus on deep learning methods. We provide seven new benchmark datasets covering various domain shifts and temporal dynamics, facilitating fair and standardized UDA method assessments with state of the art neural network backbones (e.g. Inception) for time series data. This benchmark offers insights into the strengths and limitations of the evaluated approaches while preserving the unsupervised nature of domain adaptation, making it directly applicable to practical problems. Our paper serves as a vital resource for researchers and practitioners, advancing domain adaptation solutions for time series data and fostering innovation in this critical field. The implementation code of this benchmark is available at https://github.com/EricssonResearch/UDA-4-TSC.", "authors": ["Hassan Ismail Fawaz", "Ganesh Del Grosso", "Tanguy Kerdoncuff", "Aurelie Boisbunon", "Illyyne Saffar"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2023-12-15", "url": "https://arxiv.org/abs/2312.09857", "pdf_url": "https://arxiv.org/pdf/2312.09857v3", "arxiv_id": "2312.09857", "doi": "10.1007/s10618-025-01108-4", "citation_count": 5, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/EricssonResearch/UDA-4-TSC", "venue": "Data mining and knowledge discovery", "quality_score": 0.1945}
{"id": "89e4e4a4bb29f08082d1040492bfa39e69e4654f7d3b2b33d474c4f39f73681c", "sources": ["arxiv", "semantic_scholar"], "title": "Conformalised data synthesis", "abstract": "With the proliferation of increasingly complicated Deep Learning architectures, data synthesis is a highly promising technique to address the demand of data-hungry models. However, reliably assessing the quality of a 'synthesiser' model's output is an open research question with significant associated risks for high-stake domains. To address this challenge, we propose a unique synthesis algorithm that generates data from high-confidence feature space regions based on the Conformal Prediction framework. We support our proposed algorithm with a comprehensive exploration of the core parameter's influence, an in-depth discussion of practical advice, and an extensive empirical evaluation of five benchmark datasets. To show our approach's versatility on ubiquitous real-world challenges, the datasets were carefully selected for their variety of difficult characteristics: low sample count, class imbalance, and non-separability. In all trials, training sets extended with our confident synthesised data performed at least as well as the original set and frequently significantly improved Deep Learning performance by up to 61 percentage points F1-score.", "authors": ["Julia A. Meister", "Khuong An Nguyen"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2023-12-14", "url": "https://arxiv.org/abs/2312.08999", "pdf_url": "https://arxiv.org/pdf/2312.08999v2", "arxiv_id": "2312.08999", "doi": "10.1007/s10994-024-06701-0", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Machine-mediated learning", "quality_score": 0.0}
{"id": "bdb89b37272113fb4b0e473e78c6cc4067d8335bafc810847034bac3ebad4906", "sources": ["arxiv", "semantic_scholar"], "title": "Estimation of Dynamic Origin-Destination Matrices in a Railway Transportation Network integrating Ticket Sales and Passenger Count Data", "abstract": "Accurately estimating Origin-Destination (OD) matrices is a topic of increasing interest for efficient transportation network management and sustainable urban planning. Traditionally, travel surveys have supported this process; however, their availability and comprehensiveness can be limited. Moreover, the recent COVID-19 pandemic has triggered unprecedented shifts in mobility patterns, underscoring the urgency of accurate and dynamic mobility data supporting policies and decisions with data-driven evidence. In this study, we tackle these challenges by introducing an innovative pipeline for estimating dynamic OD matrices. The real motivating problem behind this is based on the Trenord railway transportation network in Lombardy, Italy. We apply a novel approach that integrates ticket and subscription sales data with passenger counts obtained from Automated Passenger Counting (APC) systems, making use of the Iterative Proportional Fitting (IPF) algorithm. Our work effectively addresses the complexities posed by incomplete and diverse data sources, showcasing the adaptability of our pipeline across various transportation contexts. Ultimately, this research bridges the gap between available data sources and the escalating need for precise OD matrices. The proposed pipeline fosters a comprehensive grasp of transportation network dynamics, providing a valuable tool for transportation operators, policymakers, and researchers. Indeed, to highlight the potentiality of dynamic OD matrices, we showcase some methods to perform anomaly detection of mobility trends in the network through such matrices and interpret them in light of events that happened in the last months of 2022.", "authors": ["Greta Galliani", "Piercesare Secchi", "Francesca Ieva"], "categories": ["stat.AP"], "fields_of_study": ["Mathematics"], "published_date": "2023-12-12", "url": "https://arxiv.org/abs/2312.07732", "pdf_url": "https://arxiv.org/pdf/2312.07732v1", "arxiv_id": "2312.07732", "doi": "10.1016/j.tra.2024.104246", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2113}
{"id": "a01218985fcc17214738ef49baaff47f51ce9229ec4aac9b838ebd35f4784a54", "sources": ["arxiv", "semantic_scholar"], "title": "A unified repository for pre-processed climate data weighted by gridded economic activity", "abstract": "Although high-resolution gridded climate variables are provided by multiple sources, the need for country and region-specific climate data weighted by indicators of economic activity is becoming increasingly common in environmental and economic research. We process available information from different climate data sources to provide spatially aggregated data with global coverage for both countries (GADM0 resolution) and regions (GADM1 resolution) and for a variety of climate indicators (average precipitations, average temperatures, average SPEI). We weigh gridded climate data by population density or by night light intensity -- both proxies of economic activity -- before aggregation. Climate variables are measured daily, monthly, and annually, covering (depending on the data source) a time window from 1900 (at the earliest) to 2023. We pipeline all the preprocessing procedures in a unified framework, which we share in the open-access Weighted Climate Data Repository web app. Finally, we validate our data through a systematic comparison with those employed in leading climate impact studies.", "authors": ["Marco Gortan", "Lorenzo Testa", "Giorgio Fagiolo", "Francesco Lamperti"], "categories": ["econ.GN", "stat.AP"], "fields_of_study": ["Medicine", "Economics", "Mathematics"], "published_date": "2023-12-10", "url": "https://arxiv.org/abs/2312.05971", "pdf_url": "https://arxiv.org/pdf/2312.05971v1", "arxiv_id": "2312.05971", "doi": "10.1038/s41597-024-03304-1", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Scientific Data", "quality_score": 0.2386}
{"id": "4e730669b562b385560e97c09fb970df03701f1b97e0853fedc8210950faa0ad", "sources": ["arxiv", "semantic_scholar"], "title": "Influence of initial conditions on data-driven model identification and information entropy for ideal mhd problems", "abstract": "Data-driven methods of model identification are able to discern governing dynamics of a system from data. Such methods are well suited to help us learn about systems with unpredictable evolution or systems with ambiguous governing dynamics given our current understanding. Many plasma problems of interest fall into these categories as there are a wide range of models that exist, however each model is only useful in a certain regime and often limited by computational complexity. To ensure data-driven methods align with theory, they must be consistent and predictable when acting on data whose governing dynamics are known. Weak Sparse Identification of Nonlinear Dynamics (WSINDy) is a recently developed data-driven method that has shown promise in learning governing dynamics from data with high noise levels [1]. This work examines how WSINDy acts on ideal MHD test problems as the initial conditions are varied and specifies limiting requirements for successful equation identification. It is hard to recover the governing dynamics from data that emphasize a single dominant behavior. In these low information cases, Shannon information entropy is able to pick up on the redundancies in the data that affect recoverability.", "authors": ["Gina Vasey", "Daniel Messenger", "David Bortz", "Andrew Christlieb", "Brian O'Shea"], "categories": ["physics.data-an", "physics.comp-ph", "physics.flu-dyn", "physics.plasm-ph"], "fields_of_study": ["Physics", "Computer Science"], "published_date": "2023-12-08", "url": "https://arxiv.org/abs/2312.05339", "pdf_url": "https://arxiv.org/pdf/2312.05339v2", "arxiv_id": "2312.05339", "doi": "10.1016/j.jcp.2025.113719", "citation_count": 15, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Journal of Computational Physics", "quality_score": 0.301}
{"id": "95b47d5d0252124796db1aa45653be08d52cfc02013d655eb5f82997c6058dde", "sources": ["arxiv", "semantic_scholar"], "title": "Image Synthesis-based Late Stage Cancer Augmentation and Semi-Supervised Segmentation for MRI Rectal Cancer Staging", "abstract": "Rectal cancer is one of the most common diseases and a major cause of mortality. For deciding rectal cancer treatment plans, T-staging is important. However, evaluating the index from preoperative MRI images requires high radiologists' skill and experience. Therefore, the aim of this study is to segment the mesorectum, rectum, and rectal cancer region so that the system can predict T-stage from segmentation results. Generally, shortage of large and diverse dataset and high quality annotation are known to be the bottlenecks in computer aided diagnostics development. Regarding rectal cancer, advanced cancer images are very rare, and per-pixel annotation requires high radiologists' skill and time. Therefore, it is not feasible to collect comprehensive disease patterns in a training dataset. To tackle this, we propose two kinds of approaches of image synthesis-based late stage cancer augmentation and semi-supervised learning which is designed for T-stage prediction. In the image synthesis data augmentation approach, we generated advanced cancer images from labels. The real cancer labels were deformed to resemble advanced cancer labels by artificial cancer progress simulation. Next, we introduce a T-staging loss which enables us to train segmentation models from per-image T-stage labels. The loss works to keep inclusion/invasion relationships between rectum and cancer region consistent to the ground truth T-stage. The verification tests show that the proposed method obtains the best sensitivity (0.76) and specificity (0.80) in distinguishing between over T3 stage and underT2. In the ablation studies, our semi-supervised learning approach with the T-staging loss improved specificity by 0.13. Adding the image synthesis-based data augmentation improved the DICE score of invasion cancer area by 0.08 from baseline.", "authors": ["Saeko Sasuga", "Akira Kudo", "Yoshiro Kitamura", "Satoshi Iizuka", "Edgar Simo-Serra", "Atsushi Hamabe", "Masayuki Ishii", "Ichiro Takemasa"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2023-12-08", "url": "https://arxiv.org/abs/2312.04779", "pdf_url": "https://arxiv.org/pdf/2312.04779v1", "arxiv_id": "2312.04779", "doi": "10.1007/978-3-031-17027-0_1", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0753}
{"id": "89ff09bbcc279dfad9480b3bc19ddfddb0f4182191ad7fe6451ca36c01f8a87c", "sources": ["arxiv", "semantic_scholar"], "title": "Jellyfish: A Large Language Model for Data Preprocessing", "abstract": "This paper explores the utilization of LLMs for data preprocessing (DP), a crucial step in the data mining pipeline that transforms raw data into a clean format conducive to easy processing. Whereas the use of LLMs has sparked interest in devising universal solutions to DP, recent initiatives in this domain typically rely on GPT APIs, raising inevitable data breach concerns. Unlike these approaches, we consider instruction-tuning local LLMs (7 -- 13B models) as universal DP task solvers that operate on a local, single, and low-priced GPU, ensuring data security and enabling further customization. We select a collection of datasets across four representative DP tasks and construct instruction tuning data using data configuration, knowledge injection, and reasoning data distillation techniques tailored to DP. By tuning Mistral-7B, Llama 3-8B, and OpenOrca-Platypus2-13B, our models, namely, Jellyfish-7B/8B/13B, deliver competitiveness compared to GPT-3.5/4 models and strong generalizability to unseen tasks while barely compromising the base models' abilities in NLP tasks. Meanwhile, Jellyfish offers enhanced reasoning capabilities compared to GPT-3.5. Our models are available at: https://huggingface.co/NECOUDBFM/Jellyfish . Our instruction dataset is available at: https://huggingface.co/datasets/NECOUDBFM/Jellyfish-Instruct .", "authors": ["Haochen Zhang", "Yuyang Dong", "Chuan Xiao", "Masafumi Oyamada"], "categories": ["cs.AI", "cs.CL", "cs.DB", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-12-04", "url": "https://arxiv.org/abs/2312.01678", "pdf_url": "https://arxiv.org/pdf/2312.01678v6", "arxiv_id": "2312.01678", "doi": "10.48550/arXiv.2312.01678", "citation_count": 47, "influential_citation_count": 6, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4225}
{"id": "e050dbc2d69497e69942b9883e86736f59ee5d1f278b9b964f21f2f761679176", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy Measurement in Tabular Synthetic Data: State of the Art and Future Research Directions", "abstract": "Synthetic data (SD) have garnered attention as a privacy enhancing technology. Unfortunately, there is no standard for quantifying their degree of privacy protection. In this paper, we discuss proposed quantification approaches. This contributes to the development of SD privacy standards; stimulates multi-disciplinary discussion; and helps SD researchers make informed modeling and evaluation decisions.", "authors": ["Alexander Boudewijn", "Andrea Filippo Ferraris", "Daniele Panfilo", "Vanessa Cocca", "Sabrina Zinutti", "Karel De Schepper", "Carlo Rossi Chauvenet"], "categories": ["cs.AI", "cs.CR", "cs.DB", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2023-11-29", "url": "https://arxiv.org/abs/2311.17453", "pdf_url": "https://arxiv.org/pdf/2311.17453v1", "arxiv_id": "2311.17453", "doi": "10.48550/arXiv.2311.17453", "citation_count": 9, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.25}
{"id": "2266bacdf0cf6fad4a50f03c962b024b8bebc08e6eef1d94d219ec5048f8c8b8", "sources": ["arxiv", "semantic_scholar"], "title": "Community recommendations on cryoEM data archiving and validation", "abstract": "In January 2020, a workshop was held at EMBL-EBI (Hinxton, UK) to discuss data requirements for deposition and validation of cryoEM structures, with a focus on single-particle analysis. The meeting was attended by 45 experts in data processing, model building and refinement, validation, and archiving of such structures. This report describes the workshop's motivation and history, the topics discussed, and consensus recommendations resulting from the workshop. Some challenges for future methods-development efforts in this area are also highlighted, as is the implementation to date of some of the recommendations.", "authors": ["Gerard J. Kleywegt", "Paul D. Adams", "Sarah J. Butcher", "Cathy Lawson", "Alexis Rohou", "Peter B. Rosenthal", "Sriram Subramaniam", "Maya Topf", "Sanja Abbott", "Philip R. Baldwin", "John M. Berrisford", "Gérard Bricogne", "Preeti Choudhary", "Tristan I. Croll", "Radostin Danev", "Sai J. Ganesan", "Timothy Grant", "Aleksandras Gutmanas", "Richard Henderson", "J. Bernard Heymann", "Juha T. Huiskonen", "Andrei Istrate", "Takayuki Kato", "Gabriel C. Lander", "Shee-Mei Lok", "Steven J. Ludtke", "Garib N. Murshudov", "Ryan Pye", "Grigore D. Pintilie", "Jane S. Richardson", "Carsten Sachse", "Osman Salih", "Sjors H. W. Scheres", "Gunnar F. Schroeder", "Carlos Oscar S. Sorzano", "Scott M. Stagg", "Zhe Wang", "Rangana Warshamanage", "John D. Westbrook", "Martyn D. Winn", "Jasmine Y. Young", "Stephen K. Burley", "Jeffrey C. Hoch", "Genji Kurisu", "Kyle Morris", "Ardan Patwardhan", "Sameer Velankar"], "categories": ["q-bio.BM"], "fields_of_study": ["Biology", "Medicine"], "published_date": "2023-11-29", "url": "https://arxiv.org/abs/2311.17640", "pdf_url": "https://arxiv.org/pdf/2311.17640v3", "arxiv_id": "2311.17640", "doi": "10.1107/S2052252524001246", "citation_count": 16, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "IUCrJ", "quality_score": 0.3076}
{"id": "f1d41c772f2119393bd789cb9442495395630b20e1e4ef5a97435bdd9f72e37a", "sources": ["arxiv", "semantic_scholar"], "title": "Grafite: Taming Adversarial Queries with Optimal Range Filters", "abstract": "Range filters allow checking whether a query range intersects a given set of keys with a chance of returning a false positive answer, thus generalising the functionality of Bloom filters from point to range queries. Existing practical range filters have addressed this problem heuristically, resulting in high false positive rates and query times when dealing with adversarial inputs, such as in the common scenario where queries are correlated with the keys. We introduce Grafite, a novel range filter that solves these issues with a simple design and clear theoretical guarantees that hold regardless of the input data and query distribution: given a fixed space budget of $B$ bits per key, the query time is $O(1)$, and the false positive probability is upper bounded by $\\ell/2^{B-2}$, where $\\ell$ is the query range size. Our experimental evaluation shows that Grafite is the only range filter to date to achieve robust and predictable false positive rates across all combinations of datasets, query workloads, and range sizes, while providing faster queries and construction times, and dominating all competitors in the case of correlated queries. As a further contribution, we introduce a very simple heuristic range filter whose performance on uncorrelated queries is very close to or better than the one achieved by the best heuristic range filters proposed in the literature so far.", "authors": ["Marco Costa", "Paolo Ferragina", "Giorgio Vinciguerra"], "categories": ["cs.DS", "cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2023-11-26", "url": "https://arxiv.org/abs/2311.15380", "pdf_url": "https://arxiv.org/pdf/2311.15380v2", "arxiv_id": "2311.15380", "doi": "10.1145/3639258", "citation_count": 13, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "Proceedings of the ACM on Management of Data, Volume 2, Issue 1 (2024), Article No. 3, pp 1-23", "quality_score": 0.2865}
{"id": "22d50323534ea178f49cb472e7d21ea50f343faaacc862a8d2366a78fb26f606", "sources": ["arxiv", "semantic_scholar"], "title": "Comparative Analysis of Transformers for Modeling Tabular Data: A Casestudy using Industry Scale Dataset", "abstract": "We perform a comparative analysis of transformer-based models designed for modeling tabular data, specifically on an industry-scale dataset. While earlier studies demonstrated promising outcomes on smaller public or synthetic datasets, the effectiveness did not extend to larger industry-scale datasets. The challenges identified include handling high-dimensional data, the necessity for efficient pre-processing of categorical and numerical features, and addressing substantial computational requirements. To overcome the identified challenges, the study conducts an extensive examination of various transformer-based models using both synthetic datasets and the default prediction Kaggle dataset (2022) from American Express. The paper presents crucial insights into optimal data pre-processing, compares pre-training and direct supervised learning methods, discusses strategies for managing categorical and numerical features, and highlights trade-offs between computational resources and performance. Focusing on temporal financial data modeling, the research aims to facilitate the systematic development and deployment of transformer-based models in real-world scenarios, emphasizing scalability.", "authors": ["Usneek Singh", "Piyush Arora", "Shamika Ganesan", "Mohit Kumar", "Siddhant Kulkarni", "Salil R. Joshi"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-11-24", "url": "https://arxiv.org/abs/2311.14335", "pdf_url": "https://arxiv.org/pdf/2311.14335v1", "arxiv_id": "2311.14335", "doi": "10.1145/3632410.3632456", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1505}
{"id": "4770bd043b12468b70a067c5d54ae54fb5fc95659dc61b78e0bed76707af8a15", "sources": ["arxiv", "semantic_scholar"], "title": "Coupled Twiss Parameters Estimation from Turn-by-Turn Data", "abstract": "Linear optics parameters are often estimated using readily available turn-by-turn (TbT) data. In-plane beta functions, which are the most common measurement objectives, can be estimated from the amplitudes or phases of TbT data, providing an overall characterization of the linear lattice. In addition to estimating uncoupled Twiss parameters, TbT data can also be utilized for characterizing coupled linear motion. We investigate several methods for constructing a full normalization matrix at each beam position monitor (BPM). BPMs provide information on beam centroid transverse coordinates, but direct observation of transverse momenta is not available. To estimate momenta, one can use a pair of BPMs or fit data obtained from several BPMs. By using both coordinates and momenta, it is possible to fit the one-turn matrix or its power at each BPM, allowing for the computation of coupled Twiss parameters. Another approach involves the minimization of peak coupling amplitudes in the spectrum of normalized complex coordinates. Similarly, the normalization matrix can be estimated by fitting linear coupled invariants. In this study, we derive and utilize a special form of the normalization matrix, which remains non-singular in the zero coupling limit. Coupled Twiss parameters can be obtained from the normalization matrix. The paper presents the results of applying and comparing these methods to both modeled and measured VEPP-4M TbT data, demonstrating effective estimation of coupled Twiss parameters.", "authors": ["I. Morozov", "Yu. Maltseva"], "categories": ["physics.acc-ph"], "fields_of_study": ["Physics"], "published_date": "2023-11-24", "url": "https://arxiv.org/abs/2311.14287", "pdf_url": "https://arxiv.org/pdf/2311.14287v3", "arxiv_id": "2311.14287", "doi": "10.1016/j.nima.2024.169646", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Nuclear Instruments and Methods in Physics Research Section A : Accelerators, Spectrometers, Detectors and Associated Equipment", "quality_score": 0.0753}
{"id": "4c7cf127e58f1dec17df65eb18c80fb2e7b7be4db0cba97b4555e5fd8824e764", "sources": ["arxiv", "semantic_scholar"], "title": "Robustness-Reinforced Knowledge Distillation with Correlation Distance and Network Pruning", "abstract": "The improvement in the performance of efficient and lightweight models (i.e., the student model) is achieved through knowledge distillation (KD), which involves transferring knowledge from more complex models (i.e., the teacher model). However, most existing KD techniques rely on Kullback-Leibler (KL) divergence, which has certain limitations. First, if the teacher distribution has high entropy, the KL divergence's mode-averaging nature hinders the transfer of sufficient target information. Second, when the teacher distribution has low entropy, the KL divergence tends to excessively focus on specific modes, which fails to convey an abundant amount of valuable knowledge to the student. Consequently, when dealing with datasets that contain numerous confounding or challenging samples, student models may struggle to acquire sufficient knowledge, resulting in subpar performance. Furthermore, in previous KD approaches, we observed that data augmentation, a technique aimed at enhancing a model's generalization, can have an adverse impact. Therefore, we propose a Robustness-Reinforced Knowledge Distillation (R2KD) that leverages correlation distance and network pruning. This approach enables KD to effectively incorporate data augmentation for performance improvement. Extensive experiments on various datasets, including CIFAR-100, FGVR, TinyImagenet, and ImageNet, demonstrate our method's superiority over current state-of-the-art methods.", "authors": ["Seonghak Kim", "Gyeongdo Ham", "Yucheol Cho", "Daeshik Kim"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2023-11-23", "url": "https://arxiv.org/abs/2311.13934", "pdf_url": "https://arxiv.org/pdf/2311.13934v2", "arxiv_id": "2311.13934", "doi": "10.1109/TKDE.2024.3438074", "citation_count": 9, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Knowledge and Data Engineering", "quality_score": 0.25}
{"id": "c543449e9387a79806c8f24c6b8c21781cdcab9d7224a4eb5d276fcad61ea237", "sources": ["arxiv", "semantic_scholar"], "title": "Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval", "abstract": "There has been limited success for dense retrieval models in multilingual retrieval, due to uneven and scarce training data available across multiple languages. Synthetic training data generation is promising (e.g., InPars or Promptagator), but has been investigated only for English. Therefore, to study model capabilities across both cross-lingual and monolingual retrieval tasks, we develop SWIM-IR, a synthetic retrieval training dataset containing 33 (high to very-low resource) languages for fine-tuning multilingual dense retrievers without requiring any human supervision. To construct SWIM-IR, we propose SAP (summarize-then-ask prompting), where the large language model (LLM) generates a textual summary prior to the query generation step. SAP assists the LLM in generating informative queries in the target language. Using SWIM-IR, we explore synthetic fine-tuning of multilingual dense retrieval models and evaluate them robustly on three retrieval benchmarks: XOR-Retrieve (cross-lingual), MIRACL (monolingual) and XTREME-UP (cross-lingual). Our models, called SWIM-X, are competitive with human-supervised dense retrieval models, e.g., mContriever-X, finding that SWIM-IR can cheaply substitute for expensive human-labeled retrieval training data. SWIM-IR dataset and SWIM-X models are available at https://github.com/google-research-datasets/SWIM-IR.", "authors": ["Nandan Thakur", "Jianmo Ni", "Gustavo Hernández Ábrego", "John Wieting", "Jimmy Lin", "Daniel Cer"], "categories": ["cs.IR", "cs.AI", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2023-11-10", "url": "https://arxiv.org/abs/2311.05800", "pdf_url": "https://arxiv.org/pdf/2311.05800v2", "arxiv_id": "2311.05800", "doi": "10.48550/arXiv.2311.05800", "citation_count": 30, "influential_citation_count": 3, "has_code": true, "code_url": "https://github.com/google-research-datasets/swim-ir", "venue": "North American Chapter of the Association for Computational Linguistics", "quality_score": 0.3728}
{"id": "ede85c90ac926f6ec047f905843e8bffc40fb5ba22791cec3975893514b5423d", "sources": ["arxiv", "semantic_scholar"], "title": "A Practical Approach to Novel Class Discovery in Tabular Data", "abstract": "The problem of Novel Class Discovery (NCD) consists in extracting knowledge from a labeled set of known classes to accurately partition an unlabeled set of novel classes. While NCD has recently received a lot of attention from the community, it is often solved on computer vision problems and under unrealistic conditions. In particular, the number of novel classes is usually assumed to be known in advance, and their labels are sometimes used to tune hyperparameters. Methods that rely on these assumptions are not applicable in real-world scenarios. In this work, we focus on solving NCD in tabular data when no prior knowledge of the novel classes is available. To this end, we propose to tune the hyperparameters of NCD methods by adapting the $k$-fold cross-validation process and hiding some of the known classes in each fold. Since we have found that methods with too many hyperparameters are likely to overfit these hidden classes, we define a simple deep NCD model. This method is composed of only the essential elements necessary for the NCD problem and performs impressively well under realistic conditions. Furthermore, we find that the latent space of this method can be used to reliably estimate the number of novel classes. Additionally, we adapt two unsupervised clustering algorithms ($k$-means and Spectral Clustering) to leverage the knowledge of the known classes. Extensive experiments are conducted on 7 tabular datasets and demonstrate the effectiveness of the proposed method and hyperparameter tuning process, and show that the NCD problem can be solved without relying on knowledge from the novel classes.", "authors": ["Colin Troisemaine", "Alexandre Reiffers-Masson", "Stéphane Gosselin", "Vincent Lemaire", "Sandrine Vaton"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-11-09", "url": "https://arxiv.org/abs/2311.05440", "pdf_url": "https://arxiv.org/pdf/2311.05440v3", "arxiv_id": "2311.05440", "doi": "10.1007/s10618-024-01025-y", "citation_count": 4, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Data mining and knowledge discovery", "quality_score": 0.1747}
{"id": "93af6e9ca1499d12daf36633a7fac55a15abf1e6f7d225e4445d741665e8c444", "sources": ["arxiv", "semantic_scholar"], "title": "MixUp-MIL: A Study on Linear & Multilinear Interpolation-Based Data Augmentation for Whole Slide Image Classification", "abstract": "For classifying digital whole slide images in the absence of pixel level annotation, typically multiple instance learning methods are applied. Due to the generic applicability, such methods are currently of very high interest in the research community, however, the issue of data augmentation in this context is rarely explored. Here we investigate linear and multilinear interpolation between feature vectors, a data augmentation technique, which proved to be capable of improving the generalization performance classification networks and also for multiple instance learning. Experiments, however, have been performed on only two rather small data sets and one specific feature extraction approach so far and a strong dependence on the data set has been identified. Here we conduct a large study incorporating 10 different data set configurations, two different feature extraction approaches (supervised and self-supervised), stain normalization and two multiple instance learning architectures. The results showed an extraordinarily high variability in the effect of the method. We identified several interesting aspects to bring light into the darkness and identified novel promising fields of research.", "authors": ["Michael Gadermayr", "Lukas Koller", "Maximilian Tschuchnig", "Lea Maria Stangassinger", "Christina Kreutzer", "Sebastien Couillard-Despres", "Gertie Janneke Oostingh", "Anton Hittmair"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2023-11-06", "url": "https://arxiv.org/abs/2311.03052", "pdf_url": "https://arxiv.org/pdf/2311.03052v2", "arxiv_id": "2311.03052", "doi": "10.48550/arXiv.2311.03052", "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "f8b5e08c0edf8424c50e36c91d5412f558a3e5945cbee47fb4d6e688a620c8ff", "sources": ["arxiv", "semantic_scholar"], "title": "A Simple and Efficient Baseline for Data Attribution on Images", "abstract": "Data attribution methods play a crucial role in understanding machine learning models, providing insight into which training data points are most responsible for model outputs during deployment. However, current state-of-the-art approaches require a large ensemble of as many as 300,000 models to accurately attribute model predictions. These approaches therefore come at a high computational cost, are memory intensive, and are hard to scale to large models or datasets. In this work, we focus on a minimalist baseline, utilizing the feature space of a backbone pretrained via self-supervised learning to perform data attribution. Our method is model-agnostic and scales easily to large datasets. We show results on CIFAR-10 and ImageNet, achieving strong performance that rivals or outperforms state-of-the-art approaches at a fraction of the compute or memory cost. Contrary to prior work, our results reinforce the intuition that a model's prediction on one image is most impacted by visually similar training samples. Our approach serves as a simple and efficient baseline for data attribution on images.", "authors": ["Vasu Singla", "Pedro Sandoval-Segura", "Micah Goldblum", "Jonas Geiping", "Tom Goldstein"], "categories": ["cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-11-03", "url": "https://arxiv.org/abs/2311.03386", "pdf_url": "https://arxiv.org/pdf/2311.03386v1", "arxiv_id": "2311.03386", "doi": "10.48550/arXiv.2311.03386", "citation_count": 7, "influential_citation_count": 2, "has_code": true, "code_url": "https://github.com/vasusingla/simple-data-attribution", "venue": "arXiv.org", "quality_score": 0.2386}
{"id": "5ab493f0788f7321e9adc96c8021b016ae6049e0931d928995a228692cbfc0cd", "sources": ["arxiv", "semantic_scholar"], "title": "C2C: Cough to COVID-19 Detection in BHI 2023 Data Challenge", "abstract": "This report describes our submission to BHI 2023 Data Competition: Sensor challenge. Our Audio Alchemists team designed an acoustic-based COVID-19 diagnosis system, Cough to COVID-19 (C2C), and won the 1st place in the challenge. C2C involves three key contributions: pre-processing of input signals, cough-related representation extraction leveraging Wav2vec2.0, and data augmentation. Through experimental findings, we demonstrate C2C's promising potential to enhance the diagnostic accuracy of COVID-19 via cough signals. Our proposed model achieves a ROC-AUC value of 0.7810 in the context of COVID-19 diagnosis. The implementation details and the python code can be found in the following link: https://github.com/Woo-jin-Chung/BHI_2023_challenge_Audio_Alchemists", "authors": ["Woo-Jin Chung", "Miseul Kim", "Hong-Goo Kang"], "categories": ["eess.AS", "cs.SD", "physics.bio-ph"], "fields_of_study": ["Computer Science", "Engineering", "Physics"], "published_date": "2023-11-01", "url": "https://arxiv.org/abs/2311.00364", "pdf_url": "https://arxiv.org/pdf/2311.00364v1", "arxiv_id": "2311.00364", "doi": "10.48550/arXiv.2311.00364", "citation_count": 1, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/Woo-jin-Chung/BHI_2023_challenge_Audio_Alchemists", "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "f14a6a6144d30c4e8ed48848d03fadba1fd82520bdded1d703931689e0fbe907", "sources": ["arxiv", "semantic_scholar"], "title": "Thermal-Infrared Remote Target Detection System for Maritime Rescue based on Data Augmentation with 3D Synthetic Data", "abstract": "This paper proposes a thermal-infrared (TIR) remote target detection system for maritime rescue using deep learning and data augmentation. We established a self-collected TIR dataset consisting of multiple scenes imitating human rescue situations using a TIR camera (FLIR). Additionally, to address dataset scarcity and improve model robustness, a synthetic dataset from a 3D game (ARMA3) to augment the data is further collected. However, a significant domain gap exists between synthetic TIR and real TIR images. Hence, a proper domain adaptation algorithm is essential to overcome the gap. Therefore, we suggest a domain adaptation algorithm in a target-background separated manner from 3D game-to-real, based on a generative model, to address this issue. Furthermore, a segmentation network with fixed-weight kernels at the head is proposed to improve the signal-to-noise ratio (SNR) and provide weak attention, as remote TIR targets inherently suffer from unclear boundaries. Experiment results reveal that the network trained on augmented data consisting of translated synthetic and real TIR data outperforms that trained on only real TIR data by a large margin. Furthermore, the proposed segmentation model surpasses the performance of state-of-the-art segmentation methods.", "authors": ["Sungjin Cheong", "Wonho Jung", "Yoon Seop Lim", "Yong-Hwa Park"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2023-10-31", "url": "https://arxiv.org/abs/2310.20412", "pdf_url": "https://arxiv.org/pdf/2310.20412v1", "arxiv_id": "2310.20412", "doi": "10.48550/arXiv.2310.20412", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "76a6ecd8b771619acaebf7f89183b1f3269dabc517707cf7da85c8069313b8f7", "sources": ["arxiv", "semantic_scholar"], "title": "Generating Medical Prescriptions with Conditional Transformer", "abstract": "Access to real-world medication prescriptions is essential for medical research and healthcare quality improvement. However, access to real medication prescriptions is often limited due to the sensitive nature of the information expressed. Additionally, manually labelling these instructions for training and fine-tuning Natural Language Processing (NLP) models can be tedious and expensive. We introduce a novel task-specific model architecture, Label-To-Text-Transformer (\\textbf{LT3}), tailored to generate synthetic medication prescriptions based on provided labels, such as a vocabulary list of medications and their attributes. LT3 is trained on a set of around 2K lines of medication prescriptions extracted from the MIMIC-III database, allowing the model to produce valuable synthetic medication prescriptions. We evaluate LT3's performance by contrasting it with a state-of-the-art Pre-trained Language Model (PLM), T5, analysing the quality and diversity of generated texts. We deploy the generated synthetic data to train the SpacyNER model for the Named Entity Recognition (NER) task over the n2c2-2018 dataset. The experiments show that the model trained on synthetic data can achieve a 96-98\\% F1 score at Label Recognition on Drug, Frequency, Route, Strength, and Form. LT3 codes and data will be shared at \\url{https://github.com/HECTA-UoM/Label-To-Text-Transformer}", "authors": ["Samuel Belkadi", "Nicolo Micheletti", "Lifeng Han", "Warren Del-Pinto", "Goran Nenadic"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-10-30", "url": "https://arxiv.org/abs/2310.19727", "pdf_url": "https://arxiv.org/pdf/2310.19727v2", "arxiv_id": "2310.19727", "doi": "10.18653/v1/2025.cl4health-1.17", "citation_count": 7, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/HECTA-UoM/Label-To-Text-Transformer}", "venue": null, "quality_score": 0.2258}
{"id": "5bd4670ecdbee1f694fae814cbe0ad1d3a64b9a1f15eee4250243da74b56c6f3", "sources": ["arxiv", "semantic_scholar"], "title": "MCRAGE: Synthetic Healthcare Data for Fairness", "abstract": "In the field of healthcare, electronic health records (EHR) serve as crucial training data for developing machine learning models for diagnosis, treatment, and the management of healthcare resources. However, medical datasets are often imbalanced in terms of sensitive attributes such as race/ethnicity, gender, and age. Machine learning models trained on class-imbalanced EHR datasets perform significantly worse in deployment for individuals of the minority classes compared to those from majority classes, which may lead to inequitable healthcare outcomes for minority groups. To address this challenge, we propose Minority Class Rebalancing through Augmentation by Generative modeling (MCRAGE), a novel approach to augment imbalanced datasets using samples generated by a deep generative model. The MCRAGE process involves training a Conditional Denoising Diffusion Probabilistic Model (CDDPM) capable of generating high-quality synthetic EHR samples from underrepresented classes. We use this synthetic data to augment the existing imbalanced dataset, resulting in a more balanced distribution across all classes, which can be used to train less biased downstream models. We measure the performance of MCRAGE versus alternative approaches using Accuracy, F1 score and AUROC of these downstream models. We provide theoretical justification for our method in terms of recent convergence results for DDPMs.", "authors": ["Keira Behal", "Jiayi Chen", "Caleb Fikes", "Sophia Xiao"], "categories": ["stat.ML", "cs.LG"], "fields_of_study": ["Mathematics", "Computer Science"], "published_date": "2023-10-27", "url": "https://arxiv.org/abs/2310.18430", "pdf_url": "https://arxiv.org/pdf/2310.18430v3", "arxiv_id": "2310.18430", "doi": "10.48550/arXiv.2310.18430", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "SIAM Undergraduate Research Online", "quality_score": 0.1193}
{"id": "7b5716c760cf40b7aee8f43f6bc80632495d755e90ea4715c73da82e0037ffc7", "sources": ["arxiv", "semantic_scholar"], "title": "Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark", "abstract": "Synthetic data serves as an alternative in training machine learning models, particularly when real-world data is limited or inaccessible. However, ensuring that synthetic data mirrors the complex nuances of real-world data is a challenging task. This paper addresses this issue by exploring the potential of integrating data-centric AI techniques which profile the data to guide the synthetic data generation process. Moreover, we shed light on the often ignored consequences of neglecting these data profiles during synthetic data generation -- despite seemingly high statistical fidelity. Subsequently, we propose a novel framework to evaluate the integration of data profiles to guide the creation of more representative synthetic data. In an empirical study, we evaluate the performance of five state-of-the-art models for tabular data generation on eleven distinct tabular datasets. The findings offer critical insights into the successes and limitations of current synthetic data generation techniques. Finally, we provide practical recommendations for integrating data-centric insights into the synthetic data generation process, with a specific focus on classification performance, model selection, and feature selection. This study aims to reevaluate conventional approaches to synthetic data generation and promote the application of data-centric AI techniques in improving the quality and effectiveness of synthetic data.", "authors": ["Lasse Hansen", "Nabeel Seedat", "Mihaela van der Schaar", "Andrija Petrovic"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-10-25", "url": "https://arxiv.org/abs/2310.16981", "pdf_url": "https://arxiv.org/pdf/2310.16981v1", "arxiv_id": "2310.16981", "doi": "10.48550/arXiv.2310.16981", "citation_count": 37, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Neural Information Processing Systems", "quality_score": 0.3949}
{"id": "e5e686326da9467fa78384544340cfa1f787e213443bf39b090ccc604962f75f", "sources": ["arxiv", "semantic_scholar"], "title": "Data-Free Distillation Improves Efficiency and Privacy in Federated Thorax Disease Analysis", "abstract": "Thorax disease analysis in large-scale, multi-centre, and multi-scanner settings is often limited by strict privacy policies. Federated learning (FL) offers a potential solution, while traditional parameter-based FL can be limited by issues such as high communication costs, data leakage, and heterogeneity. Distillation-based FL can improve efficiency, but it relies on a proxy dataset, which is often impractical in clinical practice. To address these challenges, we introduce a data-free distillation-based FL approach FedKDF. In FedKDF, the server employs a lightweight generator to aggregate knowledge from different clients without requiring access to their private data or a proxy dataset. FedKDF combines the predictors from clients into a single, unified predictor, which is further optimized using the learned knowledge in the lightweight generator. Our empirical experiments demonstrate that FedKDF offers a robust solution for efficient, privacy-preserving federated thorax disease analysis.", "authors": ["Ming Li", "Guang Yang"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2023-10-22", "url": "https://arxiv.org/abs/2310.18346", "pdf_url": "https://arxiv.org/pdf/2310.18346v2", "arxiv_id": "2310.18346", "doi": "10.1109/IEEECONF58974.2023.10405205", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1505}
{"id": "79d0cbe37c452ab1f15a6d9d01fd2aa1119b076aaf542c3700401527b7d97707", "sources": ["arxiv", "semantic_scholar"], "title": "Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs", "abstract": "Using in-context learning (ICL) for data generation, techniques such as Self-Instruct (Wang et al., 2023) or the follow-up Alpaca (Taori et al., 2023) can train strong conversational agents with only a small amount of human supervision. One limitation of these approaches is that they resort to very large language models (around 175B parameters) that are also proprietary and non-public. Here we explore the application of such techniques to language models that are much smaller (around 10B--40B parameters) and have permissive licenses. We find the Self-Instruct approach to be less effective at these sizes and propose new ICL methods that draw on two main ideas: (a) Categorization and simplification of the ICL templates to make prompt learning easier for the LM, and (b) Ensembling over multiple LM outputs to help select high-quality synthetic examples. Our algorithm leverages the 175 Self-Instruct seed tasks and employs separate pipelines for instructions that require an input and instructions that do not. Empirical investigations with different LMs show that: (1) Our proposed method yields higher-quality instruction tuning data than Self-Instruct, (2) It improves performances of both vanilla and instruction-tuned LMs by significant margins, and (3) Smaller instruction-tuned LMs generate more useful outputs than their larger un-tuned counterparts. Our codebase is available at https://github.com/IBM/ensemble-instruct.", "authors": ["Young-Suk Lee", "Md Arafat Sultan", "Yousef El-Kurdi", "Tahira Naseem Asim Munawar", "Radu Florian", "Salim Roukos", "Ramón Fernandez Astudillo"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-10-21", "url": "https://arxiv.org/abs/2310.13961", "pdf_url": "https://arxiv.org/pdf/2310.13961v1", "arxiv_id": "2310.13961", "doi": "10.48550/arXiv.2310.13961", "citation_count": 7, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/IBM/ensemble-instruct", "venue": "arXiv.org", "quality_score": 0.2258}
{"id": "d63de48295339c5ce4f09a46064814eb9bd8224f7733663690aebe1ecea5e816", "sources": ["arxiv", "semantic_scholar"], "title": "Deep-Learning-based Change Detection with Spaceborne Hyperspectral PRISMA data", "abstract": "Change detection (CD) methods have been applied to optical data for decades, while the use of hyperspectral data with a fine spectral resolution has been rarely explored. CD is applied in several sectors, such as environmental monitoring and disaster management. Thanks to the PRecursore IperSpettrale della Missione operativA (PRISMA), hyperspectral-from-space CD is now possible. In this work, we apply standard and deep-learning (DL) CD methods to different targets, from natural to urban areas. We propose a pipeline starting from coregistration, followed by CD with a full-spectrum algorithm and by a DL network developed for optical data. We find that changes in vegetation and built environments are well captured. The spectral information is valuable to identify subtle changes and the DL methods are less affected by noise compared to the statistical method, but atmospheric effects and the lack of reliable ground truth represent a major challenge to hyperspectral CD.", "authors": ["J. F. Amieva", "A. Austoni", "M. A. Brovelli", "L. Ansalone", "P. Naylor", "F. Serva", "B. Le Saux"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2023-10-20", "url": "https://arxiv.org/abs/2310.13627", "pdf_url": "https://arxiv.org/pdf/2310.13627v1", "arxiv_id": "2310.13627", "doi": "10.48550/arXiv.2310.13627", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1747}
{"id": "cfdc6322d53763eaea6869ab8964f5ad58129cff45fddd8e3c188f2b835dd4a3", "sources": ["arxiv", "semantic_scholar"], "title": "Data-Free Knowledge Distillation Using Adversarially Perturbed OpenGL Shader Images", "abstract": "Knowledge distillation (KD) has been a popular and effective method for model compression. One important assumption of KD is that the original training dataset is always available. However, this is not always the case due to privacy concerns and more. In recent years, \"data-free\" KD has emerged as a growing research topic which focuses on the scenario of performing KD when no data is provided. Many methods rely on a generator network to synthesize examples for distillation (which can be difficult to train) and can frequently produce images that are visually similar to the original dataset, which raises questions surrounding whether privacy is completely preserved. In this work, we propose a new approach to data-free KD that utilizes unnatural OpenGL images, combined with large amounts of data augmentation and adversarial attacks, to train a student network. We demonstrate that our approach achieves state-of-the-art results for a variety of datasets/networks and is more stable than existing generator-based data-free KD methods. Source code will be available in the future.", "authors": ["Logan Frank", "Jim Davis"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2023-10-20", "url": "https://arxiv.org/abs/2310.13782", "pdf_url": "https://arxiv.org/pdf/2310.13782v1", "arxiv_id": "2310.13782", "doi": "10.48550/arXiv.2310.13782", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "13371644688190b1af74bcd96424ccc531c43ceb6b825280178300a6ff5a24ac", "sources": ["arxiv", "semantic_scholar"], "title": "Real-Fake: Effective Training Data Synthesis Through Distribution Matching", "abstract": "Synthetic training data has gained prominence in numerous learning tasks and scenarios, offering advantages such as dataset augmentation, generalization evaluation, and privacy preservation. Despite these benefits, the efficiency of synthetic data generated by current methodologies remains inferior when training advanced deep models exclusively, limiting its practical utility. To address this challenge, we analyze the principles underlying training data synthesis for supervised learning and elucidate a principled theoretical framework from the distribution-matching perspective that explicates the mechanisms governing synthesis efficacy. Through extensive experiments, we demonstrate the effectiveness of our synthetic data across diverse image classification tasks, both as a replacement for and augmentation to real datasets, while also benefits such as out-of-distribution generalization, privacy preservation, and scalability. Specifically, we achieve 70.9% top1 classification accuracy on ImageNet1K when training solely with synthetic data equivalent to 1 X the original real data size, which increases to 76.0% when scaling up to 10 X synthetic data.", "authors": ["Jianhao Yuan", "Jie Zhang", "Shuyang Sun", "Philip Torr", "Bo Zhao"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-10-16", "url": "https://arxiv.org/abs/2310.10402", "pdf_url": "https://arxiv.org/pdf/2310.10402v2", "arxiv_id": "2310.10402", "doi": "10.48550/arXiv.2310.10402", "citation_count": 48, "influential_citation_count": 5, "has_code": true, "code_url": "https://github.com/BAAI-DCAI/Training-Data-Synthesis", "venue": "International Conference on Learning Representations", "quality_score": 0.4225}
{"id": "8287896891c90a4b81e7442210f17f3d52d24ec231e4f992158673167e8b22b8", "sources": ["arxiv", "semantic_scholar"], "title": "Farzi Data: Autoregressive Data Distillation", "abstract": "We study data distillation for auto-regressive machine learning tasks, where the input and output have a strict left-to-right causal structure. More specifically, we propose Farzi, which summarizes an event sequence dataset into a small number of synthetic sequences -- Farzi Data -- which are optimized to maintain (if not improve) model performance compared to training on the full dataset. Under the hood, Farzi conducts memory-efficient data distillation by (i) deriving efficient reverse-mode differentiation of the Adam optimizer by leveraging Hessian-Vector Products; and (ii) factorizing the high-dimensional discrete event-space into a latent-space which provably promotes implicit regularization. Empirically, for sequential recommendation and language modeling tasks, we are able to achieve 98-120% of downstream full-data performance when training state-of-the-art models on Farzi Data of size as little as 0.1% of the original dataset. Notably, being able to train better models with significantly less data sheds light on the design of future large auto-regressive models, and opens up new opportunities to further scale up model and data sizes.", "authors": ["Noveen Sachdeva", "Zexue He", "Wang-Cheng Kang", "Jianmo Ni", "Derek Zhiyuan Cheng", "Julian McAuley"], "categories": ["cs.LG", "cs.AI", "cs.CL", "cs.IR"], "fields_of_study": ["Computer Science"], "published_date": "2023-10-15", "url": "https://arxiv.org/abs/2310.09983", "pdf_url": "https://arxiv.org/pdf/2310.09983v1", "arxiv_id": "2310.09983", "doi": "10.48550/arXiv.2310.09983", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1747}
{"id": "f548a461bfaab87f44ae25ad922015479539c4266617ef451c90f5913f6cf7de", "sources": ["arxiv", "semantic_scholar"], "title": "Validating Synthetic Usage Data in Living Lab Environments", "abstract": "Evaluating retrieval performance without editorial relevance judgments is challenging, but instead, user interactions can be used as relevance signals. Living labs offer a way for small-scale platforms to validate information retrieval systems with real users. If enough user interaction data are available, click models can be parameterized from historical sessions to evaluate systems before exposing users to experimental rankings. However, interaction data are sparse in living labs, and little is studied about how click models can be validated for reliable user simulations when click data are available in moderate amounts. This work introduces an evaluation approach for validating synthetic usage data generated by click models in data-sparse human-in-the-loop environments like living labs. We ground our methodology on the click model's estimates about a system ranking compared to a reference ranking for which the relative performance is known. Our experiments compare different click models and their reliability and robustness as more session log data becomes available. In our setup, simple click models can reliably determine the relative system performance with already 20 logged sessions for 50 queries. In contrast, more complex click models require more session data for reliable estimates, but they are a better choice in simulated interleaving experiments when enough session data are available. While it is easier for click models to distinguish between more diverse systems, it is harder to reproduce the system ranking based on the same retrieval algorithm with different interpolation weights. Our setup is entirely open, and we share the code to reproduce the experiments.", "authors": ["Timo Breuer", "Norbert Fuhr", "Philipp Schaer"], "categories": ["cs.IR"], "fields_of_study": ["Computer Science"], "published_date": "2023-10-11", "url": "https://arxiv.org/abs/2310.07142", "pdf_url": "https://arxiv.org/pdf/2310.07142v1", "arxiv_id": "2310.07142", "doi": "10.1145/3623640", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "ACM Journal of Data and Information Quality", "quality_score": 0.1747}
{"id": "44976574a2b09386904458d5f92732ddde47c23800fb8c2c7739a2c0fce5b087", "sources": ["arxiv", "semantic_scholar"], "title": "Utilizing Synthetic Data for Medical Vision-Language Pre-training: Bypassing the Need for Real Images", "abstract": "Medical Vision-Language Pre-training (VLP) learns representations jointly from medical images and paired radiology reports. It typically requires large-scale paired image-text datasets to achieve effective pre-training for both the image encoder and text encoder. The advent of text-guided generative models raises a compelling question: Can VLP be implemented solely with synthetic images generated from genuine radiology reports, thereby mitigating the need for extensively pairing and curating image-text datasets? In this work, we scrutinize this very question by examining the feasibility and effectiveness of employing synthetic images for medical VLP. We replace real medical images with their synthetic equivalents, generated from authentic medical reports. Utilizing three state-of-the-art VLP algorithms, we exclusively train on these synthetic samples. Our empirical evaluation across three subsequent tasks, namely image classification, semantic segmentation and object detection, reveals that the performance achieved through synthetic data is on par with or even exceeds that obtained with real images. As a pioneering contribution to this domain, we introduce a large-scale synthetic medical image dataset, paired with anonymized real radiology reports. This alleviates the need of sharing medical images, which are not easy to curate and share in practice. The code and the dataset can be found in \\href{https://github.com/cheliu-computation/MedSyn-RepLearn/tree/main}{https://github.com/cheliu-computation/MedSyn-RepLearn/tree/main}.", "authors": ["Che Liu", "Anand Shah", "Wenjia Bai", "Rossella Arcucci"], "categories": ["cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-10-10", "url": "https://arxiv.org/abs/2310.07027", "pdf_url": "https://arxiv.org/pdf/2310.07027v2", "arxiv_id": "2310.07027", "doi": "10.48550/arXiv.2310.07027", "citation_count": 23, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/cheliu-computation/MedSyn-RepLearn/tree/main}{https://github.com/cheliu-computation/MedSyn-RepLearn/tree/main}", "venue": "arXiv.org", "quality_score": 0.3451}
{"id": "8c9bfc3d2557ebbb80ee899a6a8bb3e6b0bba629c5500fbe77970d66df7c480f", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching", "abstract": "The ultimate goal of Dataset Distillation is to synthesize a small synthetic dataset such that a model trained on this synthetic set will perform equally well as a model trained on the full, real dataset. Until now, no method of Dataset Distillation has reached this completely lossless goal, in part due to the fact that previous methods only remain effective when the total number of synthetic samples is extremely small. Since only so much information can be contained in such a small number of samples, it seems that to achieve truly loss dataset distillation, we must develop a distillation method that remains effective as the size of the synthetic dataset grows. In this work, we present such an algorithm and elucidate why existing methods fail to generate larger, high-quality synthetic sets. Current state-of-the-art methods rely on trajectory-matching, or optimizing the synthetic data to induce similar long-term training dynamics as the real data. We empirically find that the training stage of the trajectories we choose to match (i.e., early or late) greatly affects the effectiveness of the distilled dataset. Specifically, early trajectories (where the teacher network learns easy patterns) work well for a low-cardinality synthetic set since there are fewer examples wherein to distribute the necessary information. Conversely, late trajectories (where the teacher network learns hard patterns) provide better signals for larger synthetic sets since there are now enough samples to represent the necessary complex patterns. Based on our findings, we propose to align the difficulty of the generated patterns with the size of the synthetic dataset. In doing so, we successfully scale trajectory matching-based methods to larger synthetic datasets, achieving lossless dataset distillation for the very first time. Code and distilled datasets are available at https://gzyaftermath.github.io/DATM.", "authors": ["Ziyao Guo", "Kai Wang", "George Cazenavette", "Hui Li", "Kaipeng Zhang", "Yang You"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2023-10-09", "url": "https://arxiv.org/abs/2310.05773", "pdf_url": "https://arxiv.org/pdf/2310.05773v2", "arxiv_id": "2310.05773", "doi": "10.48550/arXiv.2310.05773", "citation_count": 149, "influential_citation_count": 36, "has_code": false, "code_url": null, "venue": "International Conference on Learning Representations", "quality_score": 0.7841}
{"id": "a0a0590d9d8ec3a8a8452d21bc0b6c4b8f4f7367f3665480a9c83e465de8d06c", "sources": ["arxiv", "semantic_scholar"], "title": "Data-centric Graph Learning: A Survey", "abstract": "The history of artificial intelligence (AI) has witnessed the significant impact of high-quality data on various deep learning models, such as ImageNet for AlexNet and ResNet. Recently, instead of designing more complex neural architectures as model-centric approaches, the attention of AI community has shifted to data-centric ones, which focuses on better processing data to strengthen the ability of neural models. Graph learning, which operates on ubiquitous topological data, also plays an important role in the era of deep learning. In this survey, we comprehensively review graph learning approaches from the data-centric perspective, and aim to answer three crucial questions: (1) when to modify graph data, (2) what part of the graph data needs modification to unlock the potential of various graph models, and (3) how to safeguard graph models from problematic data influence. Accordingly, we propose a novel taxonomy based on the stages in the graph learning pipeline, and highlight the processing methods for different data structures in the graph data, i.e., topology, feature and label. Furthermore, we analyze some potential problems embedded in graph data and discuss how to solve them in a data-centric manner. Finally, we provide some promising future directions for data-centric graph learning.", "authors": ["Yuxin Guo", "Deyu Bo", "Cheng Yang", "Zhiyuan Lu", "Zhongjian Zhang", "Jixi Liu", "Yufei Peng", "Chuan Shi"], "categories": ["cs.LG", "cs.SI"], "fields_of_study": ["Computer Science"], "published_date": "2023-10-08", "url": "https://arxiv.org/abs/2310.04987", "pdf_url": "https://arxiv.org/pdf/2310.04987v3", "arxiv_id": "2310.04987", "doi": "10.1109/TBDATA.2024.3489412", "citation_count": 34, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Big Data", "quality_score": 0.386}
{"id": "fc967c698e96367b43cf3e32545c6447ecd3a3bffec32718547047ca73a7d462", "sources": ["arxiv", "semantic_scholar"], "title": "Pre-training with Synthetic Data Helps Offline Reinforcement Learning", "abstract": "Recently, it has been shown that for offline deep reinforcement learning (DRL), pre-training Decision Transformer with a large language corpus can improve downstream performance (Reid et al., 2022). A natural question to ask is whether this performance gain can only be achieved with language pre-training, or can be achieved with simpler pre-training schemes which do not involve language. In this paper, we first show that language is not essential for improved performance, and indeed pre-training with synthetic IID data for a small number of updates can match the performance gains from pre-training with a large language corpus; moreover, pre-training with data generated by a one-step Markov chain can further improve the performance. Inspired by these experimental results, we then consider pre-training Conservative Q-Learning (CQL), a popular offline DRL algorithm, which is Q-learning-based and typically employs a Multi-Layer Perceptron (MLP) backbone. Surprisingly, pre-training with simple synthetic data for a small number of updates can also improve CQL, providing consistent performance improvement on D4RL Gym locomotion datasets. The results of this paper not only illustrate the importance of pre-training for offline DRL but also show that the pre-training data can be synthetic and generated with remarkably simple mechanisms.", "authors": ["Zecheng Wang", "Che Wang", "Zixuan Dong", "Keith Ross"], "categories": ["cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-10-01", "url": "https://arxiv.org/abs/2310.00771", "pdf_url": "https://arxiv.org/pdf/2310.00771v4", "arxiv_id": "2310.00771", "doi": "10.48550/arXiv.2310.00771", "citation_count": 11, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Victor-wang-902/synthetic-pretrain-rl", "venue": "International Conference on Learning Representations", "quality_score": 0.2698}
{"id": "b2c9e16fb251142f02afa90aced1fa544577c90da5ec63f64a0f6b307d82454d", "sources": ["arxiv", "semantic_scholar"], "title": "A Unified Framework for Generative Data Augmentation: A Comprehensive Survey", "abstract": "Generative data augmentation (GDA) has emerged as a promising technique to alleviate data scarcity in machine learning applications. This thesis presents a comprehensive survey and unified framework of the GDA landscape. We first provide an overview of GDA, discussing its motivation, taxonomy, and key distinctions from synthetic data generation. We then systematically analyze the critical aspects of GDA - selection of generative models, techniques to utilize them, data selection methodologies, validation approaches, and diverse applications. Our proposed unified framework categorizes the extensive GDA literature, revealing gaps such as the lack of universal benchmarks. The thesis summarises promising research directions, including , effective data selection, theoretical development for large-scale models' application in GDA and establishing a benchmark for GDA. By laying a structured foundation, this thesis aims to nurture more cohesive development and accelerate progress in the vital arena of generative data augmentation.", "authors": ["Yunhao Chen", "Zihui Yan", "Yunjie Zhu"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-09-30", "url": "https://arxiv.org/abs/2310.00277", "pdf_url": "https://arxiv.org/pdf/2310.00277v2", "arxiv_id": "2310.00277", "doi": "10.48550/arXiv.2310.00277", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1945}
{"id": "cadeadda4bba81027d554086f0928465ecf2ec1b004f9df19ba93d6ab116e475", "sources": ["arxiv", "semantic_scholar"], "title": "Decoding the Workplace & EOR: An Employee Survey Analysis by Data Science Techniques and Visualization", "abstract": "This research study explores the new dynamics of employee-organi-zation relationships (EOR) [6] using advanced data science methodologies and presents findings through accessible visualizations. Leveraging a dataset pro-cured from a comprehensive nationwide big employee survey, this study employs innovative strategy for theoretical researcher by using our state-of-the-art visual-ization. The results present insightful visualizations encapsulating demographic analysis, workforce satisfaction, work environment scrutiny, and the employee's view via word cloud interpretations and burnout predictions. The study underscores the profound implications of data science across various management sectors, enhancing understanding of workplace dynamics and pro-moting mutual growth and satisfaction. This multifaceted approach caters to a diverse array of readers, from researchers in sociology and management to firms seeking detailed understanding of their workforce's satisfaction, emphasizing on practicality and interpretability. The research encourages proactive measures to improve workplace environ-ments, boost employee satisfaction, and foster healthier, more productive organ-izations. It serves as a resourceful tool for those committed to these objectives, manifesting the transformative potential of data science in driving insightful nar-ratives about workplace dynamics and employee-organization relationships. In essence, this research unearths valuable insights to aid management, HR profes-sionals, and companies", "authors": ["Kishankumar Bhimani", "Khushbu Saradva"], "categories": ["cs.IR", "cs.HC", "math.NA"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2023-09-28", "url": "https://arxiv.org/abs/2309.16329", "pdf_url": "https://arxiv.org/pdf/2309.16329v1", "arxiv_id": "2309.16329", "doi": "10.48550/arXiv.2309.16329", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "b467cca9f1b9725bbdd2bb503d1dcfdcefc70e0d31691fba5ab809d2b10235a9", "sources": ["arxiv", "semantic_scholar"], "title": "Approximate Message Passing with Rigorous Guarantees for Pooled Data and Quantitative Group Testing", "abstract": "In the pooled data problem, the goal is to identify the categories associated with a large collection of items via a sequence of pooled tests. Each pooled test reveals the number of items of each category within the pool. We study an approximate message passing (AMP) algorithm for estimating the categories and rigorously characterize its performance, in both the noiseless and noisy settings. For the noiseless setting, we show that the AMP algorithm is equivalent to one recently proposed by El Alaoui et al. Our results provide a rigorous version of their performance guarantees, previously obtained via non-rigorous techniques. For the case of pooled data with two categories, known as quantitative group testing (QGT), we use the AMP guarantees to compute precise limiting values of the false positive rate and the false negative rate. Though the pooled data problem and QGT are both instances of estimation in a linear model, existing AMP theory cannot be directly applied since the design matrices are binary valued. The key technical ingredient in our analysis is a rigorous asymptotic characterization of AMP for generalized linear models defined via generalized white noise design matrices. This result, established using a recent universality result of Wang et al., is of independent interest. Our theoretical results are validated by numerical simulations. For comparison, we propose estimators based on convex relaxation and iterative thresholding, without providing theoretical guarantees. The simulations indicate that AMP consistently outperforms these estimators.", "authors": ["Nelvin Tan", "Pablo Pascual Cobo", "Jonathan Scarlett", "Ramji Venkataramanan"], "categories": ["cs.IT", "eess.SP"], "fields_of_study": ["Computer Science", "Engineering", "Mathematics"], "published_date": "2023-09-27", "url": "https://arxiv.org/abs/2309.15507", "pdf_url": "https://arxiv.org/pdf/2309.15507v4", "arxiv_id": "2309.15507", "doi": "10.1137/23M1604928", "citation_count": 10, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "SIAM Journal on Mathematics of Data Science", "quality_score": 0.2603}
{"id": "d43b7cb746b17743151acde5d89b0045ad7a8e23f48fec64545e9f4bfd353456", "sources": ["arxiv", "semantic_scholar"], "title": "Data Upcycling Knowledge Distillation for Image Super-Resolution", "abstract": "Knowledge distillation (KD) compresses deep neural networks by transferring task-related knowledge from cumbersome pre-trained teacher models to compact student models. However, current KD methods for super-resolution (SR) networks overlook the nature of SR task that the outputs of the teacher model are noisy approximations to the ground-truth distribution of high-quality images (GT), which shades the teacher model's knowledge to result in limited KD effects. To utilize the teacher model beyond the GT upper-bound, we present the Data Upcycling Knowledge Distillation (DUKD), to transfer the teacher model's knowledge to the student model through the upcycled in-domain data derived from training data. Besides, we impose label consistency regularization to KD for SR by the paired invertible augmentations to improve the student model's performance and robustness. Comprehensive experiments demonstrate that the DUKD method significantly outperforms previous arts on several SR tasks.", "authors": ["Yun Zhang", "Wei Li", "Simiao Li", "Hanting Chen", "Zhijun Tu", "Wenjia Wang", "Bingyi Jing", "Shaohui Lin", "Jie Hu"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-09-25", "url": "https://arxiv.org/abs/2309.14162", "pdf_url": "https://arxiv.org/pdf/2309.14162v4", "arxiv_id": "2309.14162", "doi": "10.48550/arXiv.2309.14162", "citation_count": 8, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2386}
{"id": "3b46013d87405823a19ee164b803fff8f006e137327f7dbea55c3d77f0a158dc", "sources": ["arxiv", "semantic_scholar"], "title": "The Effect of Smoothing on the Interpretation of Time Series Data: A COVID-19 Case Study", "abstract": "We conduct a controlled crowd-sourced experiment of COVID-19 case data visualization to study if and how different plotting methods, time windows, and the nature of the data influence people's interpretation of real-world COVID-19 data and people's prediction of how the data will evolve in the future. We find that a 7-day backward average smoothed line successfully reduces the distraction of periodic data patterns compared to just unsmoothed bar data. Additionally, we find that the presence of a smoothed line helps readers form a consensus on how the data will evolve in the future. We also find that the fixed 7-day smoothing window size leads to different amounts of perceived recurring patterns in the data depending on the time period plotted -- this suggests that varying the smoothing window size together with the plot window size might be a promising strategy to influence the perception of spurious patterns in the plot.", "authors": ["Oded Stein", "Alec Jacobson", "Fanny Chevalier"], "categories": ["cs.HC", "cs.GR"], "fields_of_study": ["Computer Science"], "published_date": "2023-09-14", "url": "https://arxiv.org/abs/2309.08018", "pdf_url": "https://arxiv.org/pdf/2309.08018v1", "arxiv_id": "2309.08018", "doi": "10.48550/arXiv.2309.08018", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "e9b480128ab1605e9700a4b9e912a6f1a426adf09c661ca50633590ce98980ff", "sources": ["arxiv", "semantic_scholar"], "title": "Quantum Data Center: Perspectives", "abstract": "A quantum version of data centers might be significant in the quantum era. In this paper, we introduce Quantum Data Center (QDC), a quantum version of existing classical data centers, with a specific emphasis on combining Quantum Random Access Memory (QRAM) and quantum networks. We argue that QDC will provide significant benefits to customers in terms of efficiency, security, and precision, and will be helpful for quantum computing, communication, and sensing. We investigate potential scientific and business opportunities along this novel research direction through hardware realization and possible specific applications. We show the possible impacts of QDCs in business and science, especially the machine learning and big data industries.", "authors": ["Junyu Liu", "Liang Jiang"], "categories": ["quant-ph", "cs.AI", "cs.ET", "cs.LG", "stat.ML"], "fields_of_study": ["Physics", "Computer Science", "Mathematics"], "published_date": "2023-09-12", "url": "https://arxiv.org/abs/2309.06641", "pdf_url": "https://arxiv.org/pdf/2309.06641v1", "arxiv_id": "2309.06641", "doi": "10.1109/MNET.2024.3397836", "citation_count": 23, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Network", "quality_score": 0.3451}
{"id": "7209f253449a1a562361581822ae33c58d7b3ca32c7bdaa7db3b210eac282abd", "sources": ["arxiv", "semantic_scholar"], "title": "Distributional Data Augmentation Methods for Low Resource Language", "abstract": "Text augmentation is a technique for constructing synthetic data from an under-resourced corpus to improve predictive performance. Synthetic data generation is common in numerous domains. However, recently text augmentation has emerged in natural language processing (NLP) to improve downstream tasks. One of the current state-of-the-art text augmentation techniques is easy data augmentation (EDA), which augments the training data by injecting and replacing synonyms and randomly permuting sentences. One major obstacle with EDA is the need for versatile and complete synonym dictionaries, which cannot be easily found in low-resource languages. To improve the utility of EDA, we propose two extensions, easy distributional data augmentation (EDDA) and type specific similar word replacement (TSSR), which uses semantic word context information and part-of-speech tags for word replacement and augmentation. In an extensive empirical evaluation, we show the utility of the proposed methods, measured by F1 score, on two representative datasets in Swedish as an example of a low-resource language. With the proposed methods, we show that augmented data improve classification performances in low-resource settings.", "authors": ["Mosleh Mahamud", "Zed Lee", "Isak Samsten"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-09-09", "url": "https://arxiv.org/abs/2309.04862", "pdf_url": "https://arxiv.org/pdf/2309.04862v1", "arxiv_id": "2309.04862", "doi": "10.48550/arXiv.2309.04862", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2258}
{"id": "a4d58a7468e838a8cde8d54625c6a20f9a62a8b265fc085c515c946b08d7988e", "sources": ["arxiv", "semantic_scholar"], "title": "Value-Compressed Sparse Column (VCSC): Sparse Matrix Storage for Redundant Data", "abstract": "Compressed Sparse Column (CSC) and Coordinate (COO) are popular compression formats for sparse matrices. However, both CSC and COO are general purpose and cannot take advantage of any of the properties of the data other than sparsity, such as data redundancy. Highly redundant sparse data is common in many machine learning applications, such as genomics, and is often too large for in-core computation using conventional sparse storage formats. In this paper, we present two extensions to CSC: (1) Value-Compressed Sparse Column (VCSC) and (2) Index- and Value-Compressed Sparse Column (IVCSC). VCSC takes advantage of high redundancy within a column to further compress data up to 3-fold over COO and 2.25-fold over CSC, without significant negative impact to performance characteristics. IVCSC extends VCSC by compressing index arrays through delta encoding and byte-packing, achieving a 10-fold decrease in memory usage over COO and 7.5-fold decrease over CSC. Our benchmarks on simulated and real data show that VCSC and IVCSC can be read in compressed form with little added computational cost. These two novel compression formats offer a broadly useful solution to encoding and reading redundant sparse data.", "authors": ["Skyler Ruiter", "Seth Wolfgang", "Marc Tunnell", "Timothy Triche", "Erin Carrier", "Zachary DeBruine"], "categories": ["cs.DS", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-09-08", "url": "https://arxiv.org/abs/2309.04355", "pdf_url": "https://arxiv.org/pdf/2309.04355v2", "arxiv_id": "2309.04355", "doi": "10.1109/BigData62323.2024.10825091", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "BigData Congress [Services Society]", "quality_score": 0.1193}
{"id": "f85bfc73dc4402a0c4e5da50367f49252fa5367d158f88d149ed6f01ffc31568", "sources": ["arxiv", "semantic_scholar"], "title": "Data-Juicer: A One-Stop Data Processing System for Large Language Models", "abstract": "The immense evolution in Large Language Models (LLMs) has underscored the importance of massive, heterogeneous, and high-quality data. A data recipe is a mixture of data from different sources for training LLMs, which plays a vital role in LLMs' performance. Existing open-source tools for LLM data processing are mostly tailored for specific data recipes. To continuously uncover the potential of LLMs, incorporate data from new sources, and improve LLMs' performance, we build a new system named Data-Juicer, with which we can efficiently generate diverse data recipes, explore different possibilities in forming data mixtures, and evaluate their effects on model performance. Different from traditional data-analytics pipelines, Data-Juicer faces some unique challenges. Firstly, the possible data sources for forming data recipes are truly heterogeneous and massive with various qualities. Secondly, it is extremely expensive to precisely evaluate data recipes' impact on LLMs' performance. Thirdly, the end users of Data-Juicer, model developers, need sufficient flexibility to configure and evaluate different data recipes. Data-Juicer features a fine-grained abstraction of pipelines for constructing data recipes, with over 50 built-in operators for easy composition and extension. By incorporating visualization and auto-evaluation capabilities, Data-Juicer enables a timely feedback loop for both LLM pre-training and fine-tuning. Further, Data-Juicer is optimized and integrated with ecosystems for LLM training, evaluation, and distributed computing. The data recipes derived with Data-Juicer gain notable improvements on state-of-the-art LLMs, by up to 7.45% increase in averaged score across 16 LLM benchmarks and 17.5% higher win rate in pair-wise GPT-4 evaluations. Our system, data recipes, and tutorials are released, calling for broader data-centric research on training and understanding LLMs.", "authors": ["Daoyuan Chen", "Yilun Huang", "Zhijian Ma", "Hesen Chen", "Xuchen Pan", "Ce Ge", "Dawei Gao", "Yuexiang Xie", "Zhaoyang Liu", "Jinyang Gao", "Yaliang Li", "Bolin Ding", "Jingren Zhou"], "categories": ["cs.LG", "cs.DB", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2023-09-05", "url": "https://arxiv.org/abs/2309.02033", "pdf_url": "https://arxiv.org/pdf/2309.02033v3", "arxiv_id": "2309.02033", "doi": "10.1145/3626246.3653385", "citation_count": 83, "influential_citation_count": 5, "has_code": true, "code_url": "https://github.com/alibaba/data-juicer", "venue": null, "quality_score": 0.4811}
{"id": "895b60a864b6d4a578729ddb3ace28ddcc9ad9157605482ec728c2b17cb3c8b2", "sources": ["arxiv", "semantic_scholar"], "title": "Automatic Data Transformation Using Large Language Model: An Experimental Study on Building Energy Data", "abstract": "Existing approaches to automatic data transformation are insufficient to meet the requirements in many real-world scenarios, such as the building sector. First, there is no convenient interface for domain experts to provide domain knowledge easily. Second, they require significant training data collection overheads. Third, the accuracy suffers from complicated schema changes. To bridge this gap, we present a novel approach that leverages the unique capabilities of large language models (LLMs) in coding, complex reasoning, and zero-shot learning to generate SQL code that transforms the source datasets into the target datasets. We demonstrate the viability of this approach by designing an LLM-based framework, termed SQLMorpher, which comprises a prompt generator that integrates the initial prompt with optional domain knowledge and historical patterns in external databases. It also implements an iterative prompt optimization mechanism that automatically improves the prompt based on flaw detection. The key contributions of this work include (1) pioneering an end-to-end LLM-based solution for data transformation, (2) developing a benchmark dataset of 105 real-world building energy data transformation problems, and (3) conducting an extensive empirical evaluation where our approach achieved 96% accuracy in all 105 problems. SQLMorpher demonstrates the effectiveness of utilizing LLMs in complex, domain-specific challenges, highlighting the potential of their potential to drive sustainable solutions.", "authors": ["Ankita Sharma", "Xuanmao Li", "Hong Guan", "Guoxin Sun", "Liang Zhang", "Lanjun Wang", "Kesheng Wu", "Lei Cao", "Erkang Zhu", "Alexander Sim", "Teresa Wu", "Jia Zou"], "categories": ["cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2023-09-05", "url": "https://arxiv.org/abs/2309.01957", "pdf_url": "https://arxiv.org/pdf/2309.01957v2", "arxiv_id": "2309.01957", "doi": "10.1109/BigData59044.2023.10386931", "citation_count": 29, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "BigData Congress [Services Society]", "quality_score": 0.3693}
{"id": "b68624b590a4a2e76897c5a68d0dbb10ec33e0ca221c0062132326a4b90f257b", "sources": ["arxiv", "semantic_scholar"], "title": "No Data Augmentation? Alternative Regularizations for Effective Training on Small Datasets", "abstract": "Solving image classification tasks given small training datasets remains an open challenge for modern computer vision. Aggressive data augmentation and generative models are among the most straightforward approaches to overcoming the lack of data. However, the first fails to be agnostic to varying image domains, while the latter requires additional compute and careful design. In this work, we study alternative regularization strategies to push the limits of supervised learning on small image classification datasets. In particular, along with the model size and training schedule scaling, we employ a heuristic to select (semi) optimal learning rate and weight decay couples via the norm of model parameters. By training on only 1% of the original CIFAR-10 training set (i.e., 50 images per class) and testing on ciFAIR-10, a variant of the original CIFAR without duplicated images, we reach a test accuracy of 66.5%, on par with the best state-of-the-art methods.", "authors": ["Lorenzo Brigato", "Stavroula Mougiakakou"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-09-04", "url": "https://arxiv.org/abs/2309.01694", "pdf_url": "https://arxiv.org/pdf/2309.01694v1", "arxiv_id": "2309.01694", "doi": "10.1109/ICCVW60793.2023.00021", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1945}
{"id": "e14200f7f2743d4148d9d5b2811be58648947a575faf361d0a6b8a907a9bfbd8", "sources": ["arxiv", "semantic_scholar"], "title": "Learning Sequential Information in Task-based fMRI for Synthetic Data Augmentation", "abstract": "Insufficiency of training data is a persistent issue in medical image analysis, especially for task-based functional magnetic resonance images (fMRI) with spatio-temporal imaging data acquired using specific cognitive tasks. In this paper, we propose an approach for generating synthetic fMRI sequences that can then be used to create augmented training datasets in downstream learning tasks. To synthesize high-resolution task-specific fMRI, we adapt the $α$-GAN structure, leveraging advantages of both GAN and variational autoencoder models, and propose different alternatives in aggregating temporal information. The synthetic images are evaluated from multiple perspectives including visualizations and an autism spectrum disorder (ASD) classification task. The results show that the synthetic task-based fMRI can provide effective data augmentation in learning the ASD classification task.", "authors": ["Jiyao Wang", "Nicha C. Dvornek", "Lawrence H. Staib", "James S. Duncan"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "fields_of_study": ["Medicine", "Computer Science", "Engineering"], "published_date": "2023-08-29", "url": "https://arxiv.org/abs/2308.15564", "pdf_url": "https://arxiv.org/pdf/2308.15564v1", "arxiv_id": "2308.15564", "doi": "10.48550/arXiv.2308.15564", "citation_count": 4, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1747}
{"id": "84828810d1544538c49d4b4fc36faff7fe1fb6bf54cda475804dfd94aabe0b58", "sources": ["arxiv", "semantic_scholar"], "title": "SynthDistill: Face Recognition with Knowledge Distillation from Synthetic Data", "abstract": "State-of-the-art face recognition networks are often computationally expensive and cannot be used for mobile applications. Training lightweight face recognition models also requires large identity-labeled datasets. Meanwhile, there are privacy and ethical concerns with collecting and using large face recognition datasets. While generating synthetic datasets for training face recognition models is an alternative option, it is challenging to generate synthetic data with sufficient intra-class variations. In addition, there is still a considerable gap between the performance of models trained on real and synthetic data. In this paper, we propose a new framework (named SynthDistill) to train lightweight face recognition models by distilling the knowledge of a pretrained teacher face recognition model using synthetic data. We use a pretrained face generator network to generate synthetic face images and use the synthesized images to learn a lightweight student network. We use synthetic face images without identity labels, mitigating the problems in the intra-class variation generation of synthetic datasets. Instead, we propose a novel dynamic sampling strategy from the intermediate latent space of the face generator network to include new variations of the challenging images while further exploring new face images in the training batch. The results on five different face recognition datasets demonstrate the superiority of our lightweight model compared to models trained on previous synthetic datasets, achieving a verification accuracy of 99.52% on the LFW dataset with a lightweight network. The results also show that our proposed framework significantly reduces the gap between training with real and synthetic data. The source code for replicating the experiments is publicly released.", "authors": ["Hatef Otroshi Shahreza", "Anjith George", "Sébastien Marcel"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2023-08-28", "url": "https://arxiv.org/abs/2308.14852", "pdf_url": "https://arxiv.org/pdf/2308.14852v1", "arxiv_id": "2308.14852", "doi": "10.1109/IJCB57857.2023.10448642", "citation_count": 17, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3138}
{"id": "0d9fc0968a632a550b9d77521299d7ce0980a3c9a062b26ee969ea3523ab07ed", "sources": ["arxiv", "semantic_scholar"], "title": "A Measurement of Gravitational Lensing of the Cosmic Microwave Background Using SPT-3G 2018 Data", "abstract": "We present a measurement of gravitational lensing over 1500 deg$^2$ of the Southern sky using SPT-3G temperature data at 95 and 150 GHz taken in 2018. The lensing amplitude relative to a fiducial Planck 2018 $Λ$CDM cosmology is found to be $1.020\\pm0.060$, excluding instrumental and astrophysical systematic uncertainties. We conduct extensive systematic and null tests to check the robustness of the lensing measurements, and report a minimum-variance combined lensing power spectrum over angular multipoles of $50<L<2000$, which we use to constrain cosmological models. When analyzed alone and jointly with primary cosmic microwave background (CMB) spectra within the $Λ$CDM model, our lensing amplitude measurements are consistent with measurements from SPT-SZ, SPTpol, ACT, and Planck. Incorporating loose priors on the baryon density and other parameters including uncertainties on a foreground bias template, we obtain a $1σ$ constraint on $σ_8 Ω_{\\rm m}^{0.25}=0.595 \\pm 0.026$ using the SPT-3G 2018 lensing data alone, where $σ_8$ is a common measure of the amplitude of structure today and $Ω_{\\rm m}$ is the matter density parameter. Combining SPT-3G 2018 lensing measurements with baryon acoustic oscillation (BAO) data, we derive parameter constraints of $σ_8 = 0.810 \\pm 0.033$, $S_8 \\equiv σ_8(Ω_{\\rm m}/0.3)^{0.5}= 0.836 \\pm 0.039$, and Hubble constant $H_0 =68.8^{+1.3}_{-1.6}$ km s$^{-1}$ Mpc$^{-1}$. Using CMB anisotropy and lensing measurements from SPT-3G only, we provide independent constraints on the spatial curvature of $Ω_{K} = 0.014^{+0.023}_{-0.026}$ (95% C.L.) and the dark energy density of $Ω_Λ= 0.722^{+0.031}_{-0.026}$ (68% C.L.). When combining SPT-3G lensing data with SPT-3G CMB anisotropy and BAO data, we find an upper limit on the sum of the neutrino masses of $\\sum m_ν< 0.30$ eV (95% C.L.).", "authors": ["Z. Pan", "F. Bianchini", "W. L. K. Wu", "P. A. R. Ade", "Z. Ahmed", "E. Anderes", "A. J. Anderson", "B. Ansarinejad", "M. Archipley", "K. Aylor", "L. Balkenhol", "P. S. Barry", "R. Basu Thakur", "K. Benabed", "A. N. Bender", "B. A. Benson", "L. E. Bleem", "F. R. Bouchet", "L. Bryant", "K. Byrum", "E. Camphuis", "J. E. Carlstrom", "F. W. Carter", "T. W. Cecil", "C. L. Chang", "P. Chaubal", "G. Chen", "P. M. Chichura", "H. -M. Cho", "T. -L. Chou", "J. -F. Cliche", "A. Coerver", "T. M. Crawford", "A. Cukierman", "C. Daley", "T. de Haan", "E. V. Denison", "K. R. Dibert", "J. Ding", "M. A. Dobbs", "A. Doussot", "D. Dutcher", "W. Everett", "C. Feng", "K. R. Ferguson", "K. Fichman", "A. Foster", "J. Fu", "S. Galli", "A. E. Gambrel", "R. W. Gardner", "F. Ge", "N. Goeckner-Wald", "R. Gualtieri", "F. Guidi", "S. Guns", "N. Gupta", "N. W. Halverson", "A. H. Harke-Hosemann", "N. L. Harrington", "J. W. Henning", "G. C. Hilton", "E. Hivon", "G. P. Holder", "W. L. Holzapfel", "J. C. Hood", "D. Howe", "N. Huang", "K. D. Irwin", "O. Jeong", "M. Jonas", "A. Jones", "F. Kéruzoré", "T. S. Khaire", "L. Knox", "A. M. Kofman", "M. Korman", "D. L. Kubik", "S. Kuhlmann", "C. -L. Kuo", "A. T. Lee", "E. M. Leitch", "K. Levy", "A. E. Lowitz", "C. Lu", "A. Maniyar", "F. Menanteau", "S. S. Meyer", "D. Michalik", "M. Millea", "J. Montgomery", "A. Nadolski", "Y. Nakato", "T. Natoli", "H. Nguyen", "G. I. Noble", "V. Novosad", "Y. Omori", "S. Padin", "P. Paschos", "J. Pearson", "C. M. Posada", "K. Prabhu", "W. Quan", "S. Raghunathan", "M. Rahimi", "A. Rahlin", "C. L. Reichardt", "D. Riebel", "B. Riedel", "J. E. Ruhl", "J. T. Sayre", "E. Schiappucci", "E. Shirokoff", "G. Smecher", "J. A. Sobrin", "A. A. Stark", "J. Stephen", "K. T. Story", "A. Suzuki", "S. Takakura", "C. Tandoi", "K. L. Thompson", "B. Thorne", "C. Trendafilova", "C. Tucker", "C. Umilta", "L. R. Vale", "K. Vanderlinde", "J. D. Vieira", "G. Wang", "N. Whitehorn", "V. Yefremenko", "K. W. Yoon", "M. R. Young", "J. A. Zebrowski"], "categories": ["astro-ph.CO"], "fields_of_study": ["Physics"], "published_date": "2023-08-22", "url": "https://arxiv.org/abs/2308.11608", "pdf_url": "https://arxiv.org/pdf/2308.11608v2", "arxiv_id": "2308.11608", "doi": "10.1103/PhysRevD.108.122005", "citation_count": 37, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Physical Review D 108.12 (2023): 122005", "quality_score": 0.3949}
{"id": "27468a371c45710c2edf80c2c444bcb92c7bebd95c6610cfa2a7eddcfa529462", "sources": ["arxiv", "semantic_scholar"], "title": "Classification of modular data up to rank 12", "abstract": "We use the computer algebra system GAP to classify modular data up to rank 12. This extends the previously obtained classification of modular data up to rank 6. Our classification includes all the modular data from modular tensor categories up to rank 12, with a few possible exceptions at rank 12 and levels $5,7$ and $14$. Those exceptions are eliminated up to a certain bound by an extensive finite search in place of required infinite search. Our list contains a few potential unitary modular data which are not known to correspond to any unitary modular tensor categories (such as those from Kac-Moody algebra, twisted quantum doubles of finite group, as well as their Abelian anyon condensations). It remains to be shown if those potential modular data can be realized by modular tensor categories or not. We provide some evidence that all may be constructed from centers of near-group categories or gauging group symmetries of known modular tensor categories, with the exception of a total of five cases at rank 11 (with $D^2 =1964.590$) and 12 (with $D^2 =3926.660$). The classification of modular data corresponds to a classification of modular tensor categories (up to modular isotopes which are not expected to be present at low ranks). The classification of modular tensor categories leads to a classification of gapped quantum phases of matter in 2-dimensional space for bosonic lattice systems with no symmetry, as well as a classification of generalized symmetries in 1-dimensional space.", "authors": ["Siu-Hung Ng", "Eric C. Rowell", "Xiao-Gang Wen"], "categories": ["math.QA", "cond-mat.str-el"], "fields_of_study": ["Mathematics", "Physics"], "published_date": "2023-08-18", "url": "https://arxiv.org/abs/2308.09670", "pdf_url": "https://arxiv.org/pdf/2308.09670v2", "arxiv_id": "2308.09670", "doi": null, "citation_count": 19, "influential_citation_count": 4, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3495}
{"id": "2c6138095a65447bc29535501d87d8491a24b3cf207f4239ff0a647e5d78a125", "sources": ["arxiv", "semantic_scholar"], "title": "Microscopy Image Segmentation via Point and Shape Regularized Data Synthesis", "abstract": "Current deep learning-based approaches for the segmentation of microscopy images heavily rely on large amount of training data with dense annotation, which is highly costly and laborious in practice. Compared to full annotation where the complete contour of objects is depicted, point annotations, specifically object centroids, are much easier to acquire and still provide crucial information about the objects for subsequent segmentation. In this paper, we assume access to point annotations only during training and develop a unified pipeline for microscopy image segmentation using synthetically generated training data. Our framework includes three stages: (1) it takes point annotations and samples a pseudo dense segmentation mask constrained with shape priors; (2) with an image generative model trained in an unpaired manner, it translates the mask to a realistic microscopy image regularized by object level consistency; (3) the pseudo masks along with the synthetic images then constitute a pairwise dataset for training an ad-hoc segmentation model. On the public MoNuSeg dataset, our synthesis pipeline produces more diverse and realistic images than baseline models while maintaining high coherence between input masks and generated images. When using the identical segmentation backbones, the models trained on our synthetic dataset significantly outperform those trained with pseudo-labels or baseline-generated images. Moreover, our framework achieves comparable results to models trained on authentic microscopy images with dense labels, demonstrating its potential as a reliable and highly efficient alternative to labor-intensive manual pixel-wise annotations in microscopy image segmentation. The code is available.", "authors": ["Shijie Li", "Mengwei Ren", "Thomas Ach", "Guido Gerig"], "categories": ["cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-08-18", "url": "https://arxiv.org/abs/2308.09835", "pdf_url": "https://arxiv.org/pdf/2308.09835v3", "arxiv_id": "2308.09835", "doi": "10.48550/arXiv.2308.09835", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": null, "quality_score": 0.0}
{"id": "2b46c998090e0e6fd5f0d0208d1ced8524e21ad214b6f39390c6fcf5d2cfe164", "sources": ["arxiv", "semantic_scholar"], "title": "The Mastery Rubric for Statistics and Data Science: promoting coherence and consistency in data science education and training", "abstract": "Consensus based publications of both competencies and undergraduate curriculum guidance documents targeting data science instruction for higher education have recently been published. Recommendations for curriculum features from diverse sources may not result in consistent training across programs. A Mastery Rubric was developed that prioritizes the promotion and documentation of formal growth as well as the development of independence needed for the 13 requisite knowledge, skills, and abilities for professional practice in statistics and data science, SDS. The Mastery Rubric, MR, driven curriculum can emphasize computation, statistics, or a third discipline in which the other would be deployed or, all three can be featured. The MR SDS supports each of these program structures while promoting consistency with international, consensus based, curricular recommendations for statistics and data science, and allows 'statistics', 'data science', and 'statistics and data science' curricula to consistently educate students with a focus on increasing learners independence. The Mastery Rubric construct integrates findings from the learning sciences, cognitive and educational psychology, to support teachers and students through the learning enterprise. The MR SDS will support higher education as well as the interests of business, government, and academic work force development, bringing a consistent framework to address challenges that exist for a domain that is claimed to be both an independent discipline and part of other disciplines, including computer science, engineering, and statistics. The MR-SDS can be used for development or revision of an evaluable curriculum that will reliably support the preparation of early e.g., undergraduate degree programs, middle e.g., upskilling and training programs, and late e.g., doctoral level training practitioners.", "authors": ["Rochelle E. Tractenberg", "Donna LaLonde", "Suzanne Thornton"], "categories": ["stat.OT"], "fields_of_study": ["Mathematics"], "published_date": "2023-08-15", "url": "https://arxiv.org/abs/2308.08004", "pdf_url": "https://arxiv.org/pdf/2308.08004v1", "arxiv_id": "2308.08004", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0753}
{"id": "c6136d6f08070e53190fb625f650e6c3992c21683663ba8fc7c97bd2a3fd6992", "sources": ["arxiv", "semantic_scholar"], "title": "Character-Oriented Design for Visual Data Storytelling", "abstract": "When telling a data story, an author has an intention they seek to convey to an audience. This intention can be of many forms such as to persuade, to educate, to inform, or even to entertain. In addition to expressing their intention, the story plot must balance being consumable and enjoyable while preserving scientific integrity. In data stories, numerous methods have been identified for constructing and presenting a plot. However, there is an opportunity to expand how we think and create the visual elements that present the story. Stories are brought to life by characters; often they are what make a story captivating, enjoyable, memorable, and facilitate following the plot until the end. Through the analysis of 160 existing data stories, we systematically investigate and identify distinguishable features of characters in data stories, and we illustrate how they feed into the broader concept of \"character-oriented design\". We identify the roles and visual representations data characters assume as well as the types of relationships these roles have with one another. We identify characteristics of antagonists as well as define conflict in data stories. We find the need for an identifiable central character that the audience latches on to in order to follow the narrative and identify their visual representations. We then illustrate \"character-oriented design\" by showing how to develop data characters with common data story plots. With this work, we present a framework for data characters derived from our analysis; we then offer our extension to the data storytelling process using character-oriented design. To access our supplemental materials please visit https://chaorientdesignds.github.io/", "authors": ["Keshav Dasu", "Yun-Hsin Kuo", "Kwan-Liu Ma"], "categories": ["cs.HC"], "fields_of_study": ["Computer Science", "Medicine"], "published_date": "2023-08-15", "url": "https://arxiv.org/abs/2308.07557", "pdf_url": "https://arxiv.org/pdf/2308.07557v1", "arxiv_id": "2308.07557", "doi": "10.1109/TVCG.2023.3326578", "citation_count": 17, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Visualization and Computer Graphics", "quality_score": 0.3138}
{"id": "bff215b18a4130421f47fe051b8af998cf406c88a8b91e7b9e9959daed63dfaa", "sources": ["arxiv", "semantic_scholar"], "title": "Building a serverless Data Lakehouse from spare parts", "abstract": "The recently proposed Data Lakehouse architecture is built on open file formats, performance, and first-class support for data transformation, BI and data science: while the vision stresses the importance of lowering the barrier for data work, existing implementations often struggle to live up to user expectations. At Bauplan, we decided to build a new serverless platform to fulfill the Lakehouse vision. Since building from scratch is a challenge unfit for a startup, we started by re-using (sometimes unconventionally) existing projects, and then investing in improving the areas that would give us the highest marginal gains for the developer experience. In this work, we review user experience, high-level architecture and tooling decisions, and conclude by sharing plans for future development.", "authors": ["Jacopo Tagliabue", "Ciro Greco", "Luca Bigon"], "categories": ["cs.DB", "cs.DC", "cs.SE"], "fields_of_study": ["Computer Science"], "published_date": "2023-08-10", "url": "https://arxiv.org/abs/2308.05368", "pdf_url": "https://arxiv.org/pdf/2308.05368v1", "arxiv_id": "2308.05368", "doi": "10.48550/arXiv.2308.05368", "citation_count": 11, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2698}
{"id": "7bd0c9a055e5933f81e672295921449ed4da57fbfcc2fe19893cb4d0019b77df", "sources": ["arxiv", "semantic_scholar"], "title": "Why Data Science Projects Fail", "abstract": "Data Science is a modern Data Intelligence practice, which is the core of many businesses and helps businesses build smart strategies around to deal with businesses challenges more efficiently. Data Science practice also helps in automating business processes using the algorithm, and it has several other benefits, which also deliver in a non-profitable framework. In regards to data science, three key components primarily influence the effective outcome of a data science project. Those are 1.Availability of Data 2.Algorithm 3.Processing power or infrastructure", "authors": ["Balaram Panda"], "categories": ["cs.LG", "cs.CY", "cs.DB", "stat.ME"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2023-08-08", "url": "https://arxiv.org/abs/2308.04896", "pdf_url": "https://arxiv.org/pdf/2308.04896v1", "arxiv_id": "2308.04896", "doi": "10.48550/arXiv.2308.04896", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "81cc29710848517ec97c9a1ef5a622a2f08d041509dc3e2a2c7237c074bfaa5e", "sources": ["arxiv", "semantic_scholar"], "title": "Semi-supervised Cooperative Learning for Multiomics Data Fusion", "abstract": "Multiomics data fusion integrates diverse data modalities, ranging from transcriptomics to proteomics, to gain a comprehensive understanding of biological systems and enhance predictions on outcomes of interest related to disease phenotypes and treatment responses. Cooperative learning, a recently proposed method, unifies the commonly-used fusion approaches, including early and late fusion, and offers a systematic framework for leveraging the shared underlying relationships across omics to strengthen signals. However, the challenge of acquiring large-scale labeled data remains, and there are cases where multiomics data are available but in the absence of annotated labels. To harness the potential of unlabeled multiomcis data, we introduce semi-supervised cooperative learning. By utilizing an \"agreement penalty\", our method incorporates the additional unlabeled data in the learning process and achieves consistently superior predictive performance on simulated data and a real multiomics study of aging. It offers an effective solution to multiomics data fusion in settings with both labeled and unlabeled data and maximizes the utility of available data resources, with the potential of significantly improving predictive models for diagnostics and therapeutics in an increasingly multiomics world.", "authors": ["Daisy Yi Ding", "Xiaotao Shen", "Michael Snyder", "Robert Tibshirani"], "categories": ["q-bio.QM", "q-bio.GN", "stat.AP"], "fields_of_study": ["Biology", "Mathematics", "Computer Science"], "published_date": "2023-08-02", "url": "https://arxiv.org/abs/2308.01458", "pdf_url": "https://arxiv.org/pdf/2308.01458v1", "arxiv_id": "2308.01458", "doi": "10.1007/978-3-031-47679-2_5", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0753}
{"id": "c16cc484512bfda8e1c76ad20f42cfdaef6e77ccd9c9040c12f2539f8dbee77c", "sources": ["arxiv", "semantic_scholar"], "title": "Sampling to Distill: Knowledge Transfer from Open-World Data", "abstract": "Data-Free Knowledge Distillation (DFKD) is a novel task that aims to train high-performance student models using only the pre-trained teacher network without original training data. Most of the existing DFKD methods rely heavily on additional generation modules to synthesize the substitution data resulting in high computational costs and ignoring the massive amounts of easily accessible, low-cost, unlabeled open-world data. Meanwhile, existing methods ignore the domain shift issue between the substitution data and the original data, resulting in knowledge from teachers not always trustworthy and structured knowledge from data becoming a crucial supplement. To tackle the issue, we propose a novel Open-world Data Sampling Distillation (ODSD) method for the DFKD task without the redundant generation process. First, we try to sample open-world data close to the original data's distribution by an adaptive sampling module and introduce a low-noise representation to alleviate the domain shift issue. Then, we build structured relationships of multiple data examples to exploit data knowledge through the student model itself and the teacher's structured representation. Extensive experiments on CIFAR-10, CIFAR-100, NYUv2, and ImageNet show that our ODSD method achieves state-of-the-art performance with lower FLOPs and parameters. Especially, we improve 1.50\\%-9.59\\% accuracy on the ImageNet dataset and avoid training the separate generator for each class.", "authors": ["Yuzheng Wang", "Zhaoyu Chen", "Jie Zhang", "Dingkang Yang", "Zuhao Ge", "Yang Liu", "Siao Liu", "Yunquan Sun", "Wenqiang Zhang", "Lizhe Qi"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2023-07-31", "url": "https://arxiv.org/abs/2307.16601", "pdf_url": "https://arxiv.org/pdf/2307.16601v2", "arxiv_id": "2307.16601", "doi": "10.1145/3664647.3680618", "citation_count": 14, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "ACM Multimedia", "quality_score": 0.294}
{"id": "da6362782f7fa76d3e5569c7f9ae988248fe7ca80c49c718d3cbbd41db106608", "sources": ["arxiv", "semantic_scholar"], "title": "Interpolation-Split: a data-centric deep learning approach with big interpolated data to boost airway segmentation performance", "abstract": "The morphology and distribution of airway tree abnormalities enables diagnosis and disease characterisation across a variety of chronic respiratory conditions. In this regard, airway segmentation plays a critical role in the production of the outline of the entire airway tree to enable estimation of disease extent and severity. In this study, we propose a data-centric deep learning technique to segment the airway tree. The proposed technique utilises interpolation and image split to improve data usefulness and quality. Then, an ensemble learning strategy is implemented to aggregate the segmented airway trees at different scales. In terms of segmentation performance (dice similarity coefficient), our method outperforms the baseline model by 2.5% on average when a combined loss is used. Further, our proposed technique has a low GPU usage and high flexibility enabling it to be deployed on any 2D deep learning model.", "authors": ["Wing Keung Cheung", "Ashkan Pakzad", "Nesrin Mogulkoc", "Sarah Needleman", "Bojidar Rangelov", "Eyjolfur Gudmundsson", "An Zhao", "Mariam Abbas", "Davina McLaverty", "Dimitrios Asimakopoulos", "Robert Chapman", "Recep Savas", "Sam M Janes", "Yipeng Hu", "Daniel C. Alexander", "John R Hurst", "Joseph Jacob"], "categories": ["eess.IV", "cs.LG"], "fields_of_study": ["Engineering", "Computer Science", "Medicine"], "published_date": "2023-07-29", "url": "https://arxiv.org/abs/2308.00008", "pdf_url": "https://arxiv.org/pdf/2308.00008v2", "arxiv_id": "2308.00008", "doi": "10.1186/s40537-024-00974-x", "citation_count": 14, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Journal of Big Data", "quality_score": 0.294}
{"id": "c1b887b11c932eae3f49789c5c266c935ff8e279ee337b002eab86bfd3cf4b8a", "sources": ["arxiv", "semantic_scholar"], "title": "Cos R-CNN for Online Few-shot Object Detection", "abstract": "We propose Cos R-CNN, a simple exemplar-based R-CNN formulation that is designed for online few-shot object detection. That is, it is able to localise and classify novel object categories in images with few examples without fine-tuning. Cos R-CNN frames detection as a learning-to-compare task: unseen classes are represented as exemplar images, and objects are detected based on their similarity to these exemplars. The cosine-based classification head allows for dynamic adaptation of classification parameters to the exemplar embedding, and encourages the clustering of similar classes in embedding space without the need for manual tuning of distance-metric hyperparameters. This simple formulation achieves best results on the recently proposed 5-way ImageNet few-shot detection benchmark, beating the online 1/5/10-shot scenarios by more than 8/3/1%, as well as performing up to 20% better in online 20-way few-shot VOC across all shots on novel classes.", "authors": ["Gratianus Wesley Putra Data", "Henry Howard-Jenkins", "David Murray", "Victor Prisacariu"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2023-07-25", "url": "https://arxiv.org/abs/2307.13485", "pdf_url": "https://arxiv.org/pdf/2307.13485v1", "arxiv_id": "2307.13485", "doi": "10.48550/arXiv.2307.13485", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "1a0ea25b3d3e4a7ee831aad2f6a9c89a8148467d540961e538b3fe27ef570282", "sources": ["arxiv", "semantic_scholar"], "title": "Fermi Large Area Telescope Fourth Source Catalog Data Release 4 (4FGL-DR4)", "abstract": "We present an incremental version (4FGL-DR4, for Data Release 4) of the fourth Fermi-LAT catalog containing 7194 gamma-ray sources. Based on the first 14 years of science data in the energy range from 50 MeV to 1 TeV, it uses the same analysis methods as the 4FGL-DR3 catalog did for 12 years of data, with only a few improvements. The spectral parameters, spectral energy distributions, light curves and associations are updated for all sources. We add four new extended sources and modify two existing ones. Among the 6658 4FGL-DR3 sources, we delete 14 and change the localization of 10, while 32 are newly associated, eleven associations are changed and three associations are discarded. We add 546 point sources, among which 8 are considered identified and 229 have a plausible counterpart at other wavelengths. Most are just above the detection threshold, and 14 are transient sources below the detection threshold that can affect the light curves of nearby sources.", "authors": ["J. Ballet", "P. Bruel", "T. H. Burnett", "B. Lott", "The Fermi-LAT collaboration"], "categories": ["astro-ph.HE"], "fields_of_study": ["Physics"], "published_date": "2023-07-24", "url": "https://arxiv.org/abs/2307.12546", "pdf_url": "https://arxiv.org/pdf/2307.12546v4", "arxiv_id": "2307.12546", "doi": null, "citation_count": 48, "influential_citation_count": 4, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.4225}
{"id": "696b22024cdfd6f157451bc2503fbf6b21202a3d10bb9cfeb57e68cd2eeba45c", "sources": ["arxiv", "semantic_scholar"], "title": "Two-layer Space-oriented Partitioning for Non-point Data", "abstract": "Non-point spatial objects (e.g., polygons, linestrings, etc.) are ubiquitous. We study the problem of indexing non-point objects in memory for range queries and spatial intersection joins. We propose a secondary partitioning technique for space-oriented partitioning indices (e.g., grids), which improves their performance significantly, by avoiding the generation and elimination of duplicate results. Our approach is easy to implement and can be used by any space-partitioning index to significantly reduce the cost of range queries and intersection joins. In addition, the secondary partitions can be processed independently, which makes our method appropriate for distributed and parallel indexing. Experiments on real datasets confirm the advantage of our approach against alternative duplicate elimination techniques and data-oriented state-of-the-art spatial indices. We also show that our partitioning technique, paired with optimized partition-to-partition join algorithms, typically reduces the cost of spatial joins by around 50%.", "authors": ["Dimitrios Tsitsigkos", "Panagiotis Bouros", "Konstantinos Lampropoulos", "Nikos Mamoulis", "Manolis Terrovitis"], "categories": ["cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2023-07-18", "url": "https://arxiv.org/abs/2307.09256", "pdf_url": "https://arxiv.org/pdf/2307.09256v1", "arxiv_id": "2307.09256", "doi": "10.1109/TKDE.2023.3297975", "citation_count": 4, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Knowledge and Data Engineering", "quality_score": 0.1747}
{"id": "057b9e05cc8badd206b1f3ec78707eccbe528bf9d7f5782848f0669ebe748945", "sources": ["arxiv", "semantic_scholar"], "title": "Cross-Lingual NER for Financial Transaction Data in Low-Resource Languages", "abstract": "We propose an efficient modeling framework for cross-lingual named entity recognition in semi-structured text data. Our approach relies on both knowledge distillation and consistency training. The modeling framework leverages knowledge from a large language model (XLMRoBERTa) pre-trained on the source language, with a student-teacher relationship (knowledge distillation). The student model incorporates unsupervised consistency training (with KL divergence loss) on the low-resource target language. We employ two independent datasets of SMSs in English and Arabic, each carrying semi-structured banking transaction information, and focus on exhibiting the transfer of knowledge from English to Arabic. With access to only 30 labeled samples, our model can generalize the recognition of merchants, amounts, and other fields from English to Arabic. We show that our modeling approach, while efficient, performs best overall when compared to state-of-the-art approaches like DistilBERT pre-trained on the target language or a supervised model directly trained on labeled data in the target language. Our experiments show that it is enough to learn to recognize entities in English to reach reasonable performance in a low-resource language in the presence of a few labeled samples of semi-structured data. The proposed framework has implications for developing multi-lingual applications, especially in geographies where digital endeavors rely on both English and one or more low-resource language(s), sometimes mixed with English or employed singly.", "authors": ["Sunisth Kumar", "Davide Liu", "Alexandre Boulenger"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2023-07-16", "url": "https://arxiv.org/abs/2307.08714", "pdf_url": "https://arxiv.org/pdf/2307.08714v1", "arxiv_id": "2307.08714", "doi": "10.48550/arXiv.2307.08714", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "9afa68b8534c010a0fdcf4a5b795f8344a7f6ff68a617c7dc166a0a59bd2a01d", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Ordinal Data Science", "abstract": "Order is one of the main instruments to measure the relationship between objects in (empirical) data. However, compared to methods that use numerical properties of objects, the amount of ordinal methods developed is rather small. One reason for this is the limited availability of computational resources in the last century that would have been required for ordinal computations. Another reason -- particularly important for this line of research -- is that order-based methods are often seen as too mathematically rigorous for applying them to real-world data. In this paper, we will therefore discuss different means for measuring and 'calculating' with ordinal structures -- a specific class of directed graphs -- and show how to infer knowledge from them. Our aim is to establish Ordinal Data Science as a fundamentally new research agenda. Besides cross-fertilization with other cornerstone machine learning and knowledge representation methods, a broad range of disciplines will benefit from this endeavor, including, psychology, sociology, economics, web science, knowledge engineering, scientometrics.", "authors": ["Gerd Stumme", "Dominik Dürrschnabel", "Tom Hanika"], "categories": ["cs.AI", "cs.DM", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-07-13", "url": "https://arxiv.org/abs/2307.09477", "pdf_url": "https://arxiv.org/pdf/2307.09477v2", "arxiv_id": "2307.09477", "doi": "10.4230/TGDK.1.1.6", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "TGDK 1(1): 6:1-6:39 (2023)", "quality_score": 0.1193}
{"id": "58158d38e7cdf5155d999190012126b660d725074ec68320006d698f843f4434", "sources": ["arxiv", "semantic_scholar"], "title": "Customizing Synthetic Data for Data-Free Student Learning", "abstract": "Data-free knowledge distillation (DFKD) aims to obtain a lightweight student model without original training data. Existing works generally synthesize data from the pre-trained teacher model to replace the original training data for student learning. To more effectively train the student model, the synthetic data shall be customized to the current student learning ability. However, this is ignored in the existing DFKD methods and thus negatively affects the student training. To address this issue, we propose Customizing Synthetic Data for Data-Free Student Learning (CSD) in this paper, which achieves adaptive data synthesis using a self-supervised augmented auxiliary task to estimate the student learning ability. Specifically, data synthesis is dynamically adjusted to enlarge the cross entropy between the labels and the predictions from the self-supervised augmented task, thus generating hard samples for the student model. The experiments on various datasets and teacher-student models show the effectiveness of our proposed method. Code is available at: $\\href{https://github.com/luoshiya/CSD}{https://github.com/luoshiya/CSD}$", "authors": ["Shiya Luo", "Defang Chen", "Can Wang"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2023-07-10", "url": "https://arxiv.org/abs/2307.04542", "pdf_url": "https://arxiv.org/pdf/2307.04542v1", "arxiv_id": "2307.04542", "doi": "10.1109/ICME55011.2023.00312", "citation_count": 2, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/luoshiya/CSD}{https://github.com/luoshiya/CSD}$", "venue": "IEEE International Conference on Multimedia and Expo", "quality_score": 0.1193}
{"id": "478983a204a6863323312973cd49f626085b40e28a0d3751176dcc3bfeae8ac2", "sources": ["arxiv", "semantic_scholar"], "title": "DIG: The Data Interface Grammar", "abstract": "Building interactive data interfaces is hard because the design of an interface depends on the data processing needs for the underlying analysis task, yet we do not have a good representation for analysis tasks. To fill this gap, this paper advocates for a Data Interface Grammar (DIG) as an intermediate representation of analysis tasks. We show that DIG is compatible with existing data engineering practices, compact to represent any analysis, simple to translate into an interface design, and amenable to offline analysis. We further illustrate the potential benefits of this abstraction, such as automatic interface generation, automatic interface backend optimization, tutorial generation, and workload generation.", "authors": ["Yiru Chen", "Jeffery Tao", "Eugene Wu"], "categories": ["cs.DB", "cs.HC"], "fields_of_study": ["Computer Science"], "published_date": "2023-07-07", "url": "https://arxiv.org/abs/2307.03336", "pdf_url": "https://arxiv.org/pdf/2307.03336v2", "arxiv_id": "2307.03336", "doi": "10.1145/3597465.3605223", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2113}
{"id": "1b631932b758a14dfe37d81713af9389a15aaf69b9589b7fcc0b6ca935ca7b49", "sources": ["arxiv", "semantic_scholar"], "title": "CuTS: Customizable Tabular Synthetic Data Generation", "abstract": "Privacy, data quality, and data sharing concerns pose a key limitation for tabular data applications. While generating synthetic data resembling the original distribution addresses some of these issues, most applications would benefit from additional customization on the generated data. However, existing synthetic data approaches are limited to particular constraints, e.g., differential privacy (DP) or fairness. In this work, we introduce CuTS, the first customizable synthetic tabular data generation framework. Customization in CuTS is achieved via declarative statistical and logical expressions, supporting a wide range of requirements (e.g., DP or fairness, among others). To ensure high synthetic data quality in the presence of custom specifications, CuTS is pre-trained on the original dataset and fine-tuned on a differentiable loss automatically derived from the provided specifications using novel relaxations. We evaluate CuTS over four datasets and on numerous custom specifications, outperforming state-of-the-art specialized approaches on several tasks while being more general. In particular, at the same fairness level, we achieve 2.3% higher downstream accuracy than the state-of-the-art in fair synthetic data generation on the Adult dataset.", "authors": ["Mark Vero", "Mislav Balunović", "Martin Vechev"], "categories": ["cs.LG", "cs.DB", "cs.PL"], "fields_of_study": ["Computer Science"], "published_date": "2023-07-07", "url": "https://arxiv.org/abs/2307.03577", "pdf_url": "https://arxiv.org/pdf/2307.03577v4", "arxiv_id": "2307.03577", "doi": null, "citation_count": 13, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "International Conference on Machine Learning", "quality_score": 0.2865}
{"id": "505f273db8f9de583cedd2a4421f2c56966109a66f8ff497020e86999e1cf1f3", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetic is all you need: removing the auxiliary data assumption for membership inference attacks against synthetic data", "abstract": "Synthetic data is emerging as one of the most promising solutions to share individual-level data while safeguarding privacy. While membership inference attacks (MIAs), based on shadow modeling, have become the standard to evaluate the privacy of synthetic data, they currently assume the attacker to have access to an auxiliary dataset sampled from a similar distribution as the training dataset. This is often seen as a very strong assumption in practice, especially as the proposed main use cases for synthetic tabular data (e.g. medical data, financial transactions) are very specific and don't have any reference datasets directly available. We here show how this assumption can be removed, allowing for MIAs to be performed using only the synthetic data. Specifically, we developed three different scenarios: (S1) Black-box access to the generator, (S2) only access to the released synthetic dataset and (S3) a theoretical setup as upper bound for the attack performance using only synthetic data. Our results show that MIAs are still successful, across two real-world datasets and two synthetic data generators. These results show how the strong hypothesis made when auditing synthetic data releases - access to an auxiliary dataset - can be relaxed, making the attacks more realistic in practice.", "authors": ["Florent Guépin", "Matthieu Meeus", "Ana-Maria Cretu", "Yves-Alexandre de Montjoye"], "categories": ["cs.CR", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-07-04", "url": "https://arxiv.org/abs/2307.01701", "pdf_url": "https://arxiv.org/pdf/2307.01701v2", "arxiv_id": "2307.01701", "doi": "10.48550/arXiv.2307.01701", "citation_count": 25, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "ESORICS 2023 workshop Data Privacy Management (DPM) 2023", "quality_score": 0.3537}
{"id": "1140e136299a8ba1352f3da26da287959e29cfa8dda12fbc10868e7c286a1666", "sources": ["arxiv", "semantic_scholar"], "title": "FFPDG: Fast, Fair and Private Data Generation", "abstract": "Generative modeling has been used frequently in synthetic data generation. Fairness and privacy are two big concerns for synthetic data. Although Recent GAN [\\cite{goodfellow2014generative}] based methods show good results in preserving privacy, the generated data may be more biased. At the same time, these methods require high computation resources. In this work, we design a fast, fair, flexible and private data generation method. We show the effectiveness of our method theoretically and empirically. We show that models trained on data generated by the proposed method can perform well (in inference stage) on real application scenarios.", "authors": ["Weijie Xu", "Jinjin Zhao", "Francis Iannacci", "Bo Wang"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-06-30", "url": "https://arxiv.org/abs/2307.00161", "pdf_url": "https://arxiv.org/pdf/2307.00161v1", "arxiv_id": "2307.00161", "doi": "10.48550/arXiv.2307.00161", "citation_count": 12, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2785}
{"id": "316b37d2e195783f87073ca2255d37e8ca0bc2519b0c295e3890d0d2e2ac837a", "sources": ["arxiv", "semantic_scholar"], "title": "On the Usefulness of Synthetic Tabular Data Generation", "abstract": "Despite recent advances in synthetic data generation, the scientific community still lacks a unified consensus on its usefulness. It is commonly believed that synthetic data can be used for both data exchange and boosting machine learning (ML) training. Privacy-preserving synthetic data generation can accelerate data exchange for downstream tasks, but there is not enough evidence to show how or why synthetic data can boost ML training. In this study, we benchmarked ML performance using synthetic tabular data for four use cases: data sharing, data augmentation, class balancing, and data summarization. We observed marginal improvements for the balancing use case on some datasets. However, we conclude that there is not enough evidence to claim that synthetic tabular data is useful for ML training.", "authors": ["Dionysis Manousakas", "Sergül Aydöre"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-06-27", "url": "https://arxiv.org/abs/2306.15636", "pdf_url": "https://arxiv.org/pdf/2306.15636v1", "arxiv_id": "2306.15636", "doi": "10.48550/arXiv.2306.15636", "citation_count": 20, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3306}
{"id": "8997073b5f6c908cf2670b03298be65540f6103b3eee9df2e27b31b1b5d98861", "sources": ["arxiv", "semantic_scholar"], "title": "Data-Driven Approach for Formality-Sensitive Machine Translation: Language-Specific Handling and Synthetic Data Generation", "abstract": "In this paper, we introduce a data-driven approach for Formality-Sensitive Machine Translation (FSMT) that caters to the unique linguistic properties of four target languages. Our methodology centers on two core strategies: 1) language-specific data handling, and 2) synthetic data generation using large-scale language models and empirical prompt engineering. This approach demonstrates a considerable improvement over the baseline, highlighting the effectiveness of data-centric techniques. Our prompt engineering strategy further improves performance by producing superior synthetic translation examples.", "authors": ["Seugnjun Lee", "Hyeonseok Moon", "Chanjun Park", "Heuiseok Lim"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-06-26", "url": "https://arxiv.org/abs/2306.14514", "pdf_url": "https://arxiv.org/pdf/2306.14514v2", "arxiv_id": "2306.14514", "doi": "10.48550/arXiv.2306.14514", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0}
{"id": "eeff5e0f200c08ff539837d9ea593725357bd1b8fc5f06f15124e1a6eceb6332", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetic Alone: Exploring the Dark Side of Synthetic Data for Grammatical Error Correction", "abstract": "Data-centric AI approach aims to enhance the model performance without modifying the model and has been shown to impact model performance positively. While recent attention has been given to data-centric AI based on synthetic data, due to its potential for performance improvement, data-centric AI has long been exclusively validated using real-world data and publicly available benchmark datasets. In respect of this, data-centric AI still highly depends on real-world data, and the verification of models using synthetic data has not yet been thoroughly carried out. Given the challenges above, we ask the question: Does data quality control (noise injection and balanced data), a data-centric AI methodology acclaimed to have a positive impact, exhibit the same positive impact in models trained solely with synthetic data? To address this question, we conducted comparative analyses between models trained on synthetic and real-world data based on grammatical error correction (GEC) task. Our experimental results reveal that the data quality control method has a positive impact on models trained with real-world data, as previously reported in existing studies, while a negative impact is observed in models trained solely on synthetic data.", "authors": ["Chanjun Park", "Seonmin Koo", "Seolhwa Lee", "Jaehyung Seo", "Sugyeong Eo", "Hyeonseok Moon", "Heuiseok Lim"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-06-26", "url": "https://arxiv.org/abs/2306.14377", "pdf_url": "https://arxiv.org/pdf/2306.14377v1", "arxiv_id": "2306.14377", "doi": "10.48550/arXiv.2306.14377", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0}
{"id": "41d435f6170599cca3ea5cf9d38e27b3e6f4f7760a3f0784a4837e47b7f6105b", "sources": ["arxiv", "semantic_scholar"], "title": "Transcending Traditional Boundaries: Leveraging Inter-Annotator Agreement (IAA) for Enhancing Data Management Operations (DMOps)", "abstract": "This paper presents a novel approach of leveraging Inter-Annotator Agreement (IAA), traditionally used for assessing labeling consistency, to optimize Data Management Operations (DMOps). We advocate for the use of IAA in predicting the labeling quality of individual annotators, leading to cost and time efficiency in data production. Additionally, our work highlights the potential of IAA in forecasting document difficulty, thereby boosting the data construction process's overall efficiency. This research underscores IAA's broader application potential in data-driven research optimization and holds significant implications for large-scale data projects prioritizing efficiency, cost reduction, and high-quality data.", "authors": ["Damrin Kim", "NamHyeok Kim", "Chanjun Park", "Harksoo Kim"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-06-26", "url": "https://arxiv.org/abs/2306.14374", "pdf_url": "https://arxiv.org/pdf/2306.14374v1", "arxiv_id": "2306.14374", "doi": "10.48550/arXiv.2306.14374", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "75b160555fe29e46c3a1128b62d196ccd7e044c8d148dbb9c349b215b9e40390", "sources": ["arxiv", "semantic_scholar"], "title": "High Spectral Spatial Resolution Synthetic HyperSpectral Dataset form multi-source fusion", "abstract": "This research paper introduces a synthetic hyperspectral dataset that combines high spectral and spatial resolution imaging to achieve a comprehensive, accurate, and detailed representation of observed scenes or objects. Obtaining such desirable qualities is challenging when relying on a single camera. The proposed dataset addresses this limitation by leveraging three modalities: RGB, push-broom visible hyperspectral camera, and snapshot infrared hyperspectral camera, each offering distinct spatial and spectral resolutions. Different camera systems exhibit varying photometric properties, resulting in a trade-off between spatial and spectral resolution. RGB cameras typically offer high spatial resolution but limited spectral resolution, while hyperspectral cameras possess high spectral resolution at the expense of spatial resolution. Moreover, hyperspectral cameras themselves employ different capturing techniques and spectral ranges, further complicating the acquisition of comprehensive data. By integrating the photometric properties of these modalities, a single synthetic hyperspectral image can be generated, facilitating the exploration of broader spectral-spatial relationships for improved analysis, monitoring, and decision-making across various fields. This paper emphasizes the importance of multi-modal fusion in producing a high-quality synthetic hyperspectral dataset with consistent spectral intervals between bands.", "authors": ["Yajie Sun", "Ali Zia", "Jun Zhou"], "categories": ["cs.CV", "cs.LG", "eess.IV"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2023-06-25", "url": "https://arxiv.org/abs/2309.00005", "pdf_url": "https://arxiv.org/pdf/2309.00005v1", "arxiv_id": "2309.00005", "doi": "10.48550/arXiv.2309.00005", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "602b130f0c2411bbe70e2585671dbbd81cf269a3852aec0c505cfb3ea5768fb0", "sources": ["arxiv", "semantic_scholar"], "title": "Lumbar spine segmentation in MR images: a dataset and a public benchmark", "abstract": "This paper presents a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset includes 447 sagittal T1 and T2 MRI series from 218 patients with a history of low back pain and was collected from four different hospitals. An iterative data annotation approach was used by training a segmentation algorithm on a small part of the dataset, enabling semi-automatic segmentation of the remaining images. The algorithm provided an initial segmentation, which was subsequently reviewed, manually corrected, and added to the training data. We provide reference performance values for this baseline algorithm and nnU-Net, which performed comparably. Performance values were computed on a sequestered set of 39 studies with 97 series, which were additionally used to set up a continuous segmentation challenge that allows for a fair comparison of different segmentation algorithms. This study may encourage wider collaboration in the field of spine segmentation and improve the diagnostic value of lumbar spine MRI.", "authors": ["Jasper W. van der Graaf", "Miranda L. van Hooff", "Constantinus F. M. Buckens", "Matthieu Rutten", "Job L. C. van Susante", "Robert Jan Kroeze", "Marinus de Kleuver", "Bram van Ginneken", "Nikolas Lessmann"], "categories": ["eess.IV", "cs.CV"], "fields_of_study": ["Computer Science", "Medicine", "Engineering"], "published_date": "2023-06-21", "url": "https://arxiv.org/abs/2306.12217", "pdf_url": "https://arxiv.org/pdf/2306.12217v3", "arxiv_id": "2306.12217", "doi": "10.1038/s41597-024-03090-w", "citation_count": 89, "influential_citation_count": 5, "has_code": false, "code_url": null, "venue": "Scientific Data", "quality_score": 0.4886}
{"id": "ef6e1cb60d5a75c2f0f8feecad24995898e63684f0388a9c7e5b6c168109eeab", "sources": ["arxiv", "semantic_scholar"], "title": "Categories of Response-Based, Feature-Based, and Relation-Based Knowledge Distillation", "abstract": "Deep neural networks have achieved remarkable performance for artificial intelligence tasks. The success behind intelligent systems often relies on large-scale models with high computational complexity and storage costs. The over-parameterized networks are often easy to optimize and can achieve better performance. However, it is challenging to deploy them over resource-limited edge-devices. Knowledge Distillation (KD) aims to optimize a lightweight network from the perspective of over-parameterized training. The traditional offline KD transfers knowledge from a cumbersome teacher to a small and fast student network. When a sizeable pre-trained teacher network is unavailable, online KD can improve a group of models by collaborative or mutual learning. Without needing extra models, Self-KD boosts the network itself using attached auxiliary architectures. KD mainly involves knowledge extraction and distillation strategies these two aspects. Beyond KD schemes, various KD algorithms are widely used in practical applications, such as multi-teacher KD, cross-modal KD, attention-based KD, data-free KD and adversarial KD. This paper provides a comprehensive KD survey, including knowledge categories, distillation schemes and algorithms, as well as some empirical studies on performance comparison. Finally, we discuss the open challenges of existing KD works and prospect the future directions.", "authors": ["Chuanguang Yang", "Xinqiang Yu", "Zhulin An", "Yongjun Xu"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2023-06-19", "url": "https://arxiv.org/abs/2306.10687", "pdf_url": "https://arxiv.org/pdf/2306.10687v1", "arxiv_id": "2306.10687", "doi": "10.48550/arXiv.2306.10687", "citation_count": 43, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4109}
{"id": "b11add55d6bc499b5de6b598006028f9dae9bb7fc2fc6327da7bf3c39e2ae021", "sources": ["arxiv", "semantic_scholar"], "title": "Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation", "abstract": "Graph anomaly detection (GAD) is a vital task since even a few anomalies can pose huge threats to benign users. Recent semi-supervised GAD methods, which can effectively leverage the available labels as prior knowledge, have achieved superior performances than unsupervised methods. In practice, people usually need to identify anomalies on new (sub)graphs to secure their business, but they may lack labels to train an effective detection model. One natural idea is to directly adopt a trained GAD model to the new (sub)graph for testing. However, we find that existing semi-supervised GAD methods suffer from poor generalization issue, i.e., well-trained models could not perform well on an unseen area (i.e., not accessible in training) of the same graph. It may cause great troubles. In this paper, we base on the phenomenon and propose a general and novel research problem of generalized graph anomaly detection that aims to effectively identify anomalies on both the training-domain graph and unseen testing graph to eliminate potential dangers. Nevertheless, it is a challenging task since only limited labels are available, and the normal background may differ between training and testing data. Accordingly, we propose a data augmentation method named \\textit{AugAN} (\\uline{Aug}mentation for \\uline{A}nomaly and \\uline{N}ormal distributions) to enrich training data and boost the generalizability of GAD models. Experiments verify the effectiveness of our method in improving model generalizability.", "authors": ["Shuang Zhou", "Xiao Huang", "Ninghao Liu", "Huachi Zhou", "Fu-Lai Chung", "Long-Kai Huang"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-06-18", "url": "https://arxiv.org/abs/2306.10534", "pdf_url": "https://arxiv.org/pdf/2306.10534v1", "arxiv_id": "2306.10534", "doi": "10.1109/TKDE.2023.3271771", "citation_count": 40, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Knowledge and Data Engineering", "quality_score": 0.4032}
{"id": "c879e0d577aa0bb97a310df333f63e6591d6788c23b2b9c3a830cb1f8b1583b2", "sources": ["arxiv", "semantic_scholar"], "title": "Unifying Large Language Models and Knowledge Graphs: A Roadmap", "abstract": "Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the field of natural language processing and artificial intelligence, due to their emergent ability and generalizability. However, LLMs are black-box models, which often fall short of capturing and accessing factual knowledge. In contrast, Knowledge Graphs (KGs), Wikipedia and Huapu for example, are structured knowledge models that explicitly store rich factual knowledge. KGs can enhance LLMs by providing external knowledge for inference and interpretability. Meanwhile, KGs are difficult to construct and evolving by nature, which challenges the existing methods in KGs to generate new facts and represent unseen knowledge. Therefore, it is complementary to unify LLMs and KGs together and simultaneously leverage their advantages. In this article, we present a forward-looking roadmap for the unification of LLMs and KGs. Our roadmap consists of three general frameworks, namely, 1) KG-enhanced LLMs, which incorporate KGs during the pre-training and inference phases of LLMs, or for the purpose of enhancing understanding of the knowledge learned by LLMs; 2) LLM-augmented KGs, that leverage LLMs for different KG tasks such as embedding, completion, construction, graph-to-text generation, and question answering; and 3) Synergized LLMs + KGs, in which LLMs and KGs play equal roles and work in a mutually beneficial way to enhance both LLMs and KGs for bidirectional reasoning driven by both data and knowledge. We review and summarize existing efforts within these three frameworks in our roadmap and pinpoint their future research directions.", "authors": ["Shirui Pan", "Linhao Luo", "Yufei Wang", "Chen Chen", "Jiapu Wang", "Xindong Wu"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-06-14", "url": "https://arxiv.org/abs/2306.08302", "pdf_url": "https://arxiv.org/pdf/2306.08302v3", "arxiv_id": "2306.08302", "doi": "10.1109/TKDE.2024.3352100", "citation_count": 1478, "influential_citation_count": 60, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Knowledge and Data Engineering", "quality_score": 0.8927}
{"id": "8aa234d7084ce6089923d0b5796af1b378efcedfa9cf36579a6fd30dbfc1eea2", "sources": ["arxiv", "semantic_scholar"], "title": "Continual Release of Differentially Private Synthetic Data from Longitudinal Data Collections", "abstract": "Motivated by privacy concerns in long-term longitudinal studies in medical and social science research, we study the problem of continually releasing differentially private synthetic data from longitudinal data collections. We introduce a model where, in every time step, each individual reports a new data element, and the goal of the synthesizer is to incrementally update a synthetic dataset in a consistent way to capture a rich class of statistical properties. We give continual synthetic data generation algorithms that preserve two basic types of queries: fixed time window queries and cumulative time queries. We show nearly tight upper bounds on the error rates of these algorithms and demonstrate their empirical performance on realistically sized datasets from the U.S. Census Bureau's Survey of Income and Program Participation.", "authors": ["Mark Bun", "Marco Gaboardi", "Marcel Neunhoeffer", "Wanrong Zhang"], "categories": ["cs.DS", "cs.CR", "cs.CY", "stat.AP"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2023-06-13", "url": "https://arxiv.org/abs/2306.07884", "pdf_url": "https://arxiv.org/pdf/2306.07884v2", "arxiv_id": "2306.07884", "doi": "10.1145/3651595", "citation_count": 14, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Proc. ACM Manag. Data 2, 2, Article 94 (May 2024), 26 pages (2024)", "quality_score": 0.294}
{"id": "9cb11694a3f5d7507f599c1c079ed93156b16aa99f2909a9fad3cb2d48e21134", "sources": ["arxiv", "semantic_scholar"], "title": "Between-Sample Relationship in Learning Tabular Data Using Graph and Attention Networks", "abstract": "Traditional machine learning assumes samples in tabular data to be independent and identically distributed (i.i.d). This assumption may miss useful information within and between sample relationships in representation learning. This paper relaxes the i.i.d assumption to learn tabular data representations by incorporating between-sample relationships for the first time using graph neural networks (GNN). We investigate our hypothesis using several GNNs and state-of-the-art (SOTA) deep attention models to learn the between-sample relationship on ten tabular data sets by comparing them to traditional machine learning methods. GNN methods show the best performance on tabular data with large feature-to-sample ratios. Our results reveal that attention-based GNN methods outperform traditional machine learning on five data sets and SOTA deep tabular learning methods on three data sets. Between-sample learning via GNN and deep attention methods yield the best classification accuracy on seven of the ten data sets. This suggests that the i.i.d assumption may not always hold for most tabular data sets.", "authors": ["Shourav B. Rabbani", "Manar D. Samad"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-06-11", "url": "https://arxiv.org/abs/2306.06772", "pdf_url": "https://arxiv.org/pdf/2306.06772v1", "arxiv_id": "2306.06772", "doi": "10.1109/CSCE60160.2023.00247", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1747}
{"id": "a3935c41c48bb232eeede3cf7e4e167a9a3cdb9328979587d501274ee1ff8988", "sources": ["arxiv", "semantic_scholar"], "title": "Validation of semi-analytical, semi-empirical covariance matrices for two-point correlation function for Early DESI data", "abstract": "We present an extended validation of semi-analytical, semi-empirical covariance matrices for the two-point correlation function (2PCF) on simulated catalogs representative of Luminous Red Galaxies (LRG) data collected during the initial two months of operations of the Stage-IV ground-based Dark Energy Spectroscopic Instrument (DESI). We run the pipeline on multiple effective Zel'dovich (EZ) mock galaxy catalogs with the corresponding cuts applied and compare the results with the mock sample covariance to assess the accuracy and its fluctuations. We propose an extension of the previously developed formalism for catalogs processed with standard reconstruction algorithms. We consider methods for comparing covariance matrices in detail, highlighting their interpretation and statistical properties caused by sample variance, in particular, nontrivial expectation values of certain metrics even when the external covariance estimate is perfect. With improved mocks and validation techniques, we confirm a good agreement between our predictions and sample covariance. This allows one to generate covariance matrices for comparable datasets without the need to create numerous mock galaxy catalogs with matching clustering, only requiring 2PCF measurements from the data itself. The code used in this paper is publicly available at https://github.com/oliverphilcox/RascalC.", "authors": ["Michael Rashkovetskyi", "Daniel J. Eisenstein", "Jessica Nicole Aguilar", "David Brooks", "Todd Claybaugh", "Shaun Cole", "Kyle Dawson", "Axel de la Macorra", "Peter Doel", "Kevin Fanning", "Andreu Font-Ribera", "Jaime E. Forero-Romero", "Satya Gontcho A Gontcho", "ChangHoon Hahn", "Klaus Honscheid", "Robert Kehoe", "Theodore Kisner", "Martin Landriau", "Michael Levi", "Marc Manera", "Ramon Miquel", "Jeongin Moon", "Seshadri Nadathur", "Jundan Nie", "Claire Poppett", "Ashley J. Ross", "Graziano Rossi", "Eusebio Sanchez", "Christoph Saulder", "Michael Schubnell", "Hee-Jong Seo", "Gregory Tarle", "David Valcin", "Benjamin Alan Weaver", "Cheng Zhao", "Zhimin Zhou", "Hu Zou"], "categories": ["astro-ph.CO", "math.ST", "physics.data-an"], "fields_of_study": ["Physics", "Mathematics"], "published_date": "2023-06-09", "url": "https://arxiv.org/abs/2306.06320", "pdf_url": "https://arxiv.org/pdf/2306.06320v4", "arxiv_id": "2306.06320", "doi": "10.1093/mnras/stad2078", "citation_count": 17, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/oliverphilcox/RascalC", "venue": "Monthly notices of the Royal Astronomical Society", "quality_score": 0.3138}
{"id": "afbe9523b2903380f350b1ce63c1bd0f7214c732a6e3862a4021a7b9ac640f77", "sources": ["arxiv", "semantic_scholar"], "title": "Using Auxiliary Data to Boost Precision in the Analysis of A/B Tests on an Online Educational Platform: New Data and New Results", "abstract": "Randomized A/B tests within online learning platforms represent an exciting direction in learning sciences. With minimal assumptions, they allow causal effect estimation without confounding bias and exact statistical inference even in small samples. However, often experimental samples and/or treatment effects are small, A/B tests are underpowered, and effect estimates are overly imprecise. Recent methodological advances have shown that power and statistical precision can be substantially boosted by coupling design-based causal estimation to machine-learning models of rich log data from historical users who were not in the experiment. Estimates using these techniques remain unbiased and inference remains exact without any additional assumptions. This paper reviews those methods and applies them to a new dataset including over 250 randomized A/B comparisons conducted within ASSISTments, an online learning platform. We compare results across experiments using four novel deep-learning models of auxiliary data and show that incorporating auxiliary data into causal estimates is roughly equivalent to increasing the sample size by 20\\% on average, or as much as 50-80\\% in some cases, relative to t-tests, and by about 10\\% on average, or as much as 30-50\\%, compared to cutting-edge machine learning unbiased estimates that use only data from the experiments. We show that the gains can be even larger for estimating subgroup effects, hold even when the remnant is unrepresentative of the A/B test sample, and extend to post-stratification population effects estimators.", "authors": ["Adam C. Sales", "Ethan B. Prihar", "Johann A. Gagnon-Bartsch", "Neil T. Heffernan"], "categories": ["stat.ME", "stat.ML"], "fields_of_study": ["Mathematics"], "published_date": "2023-06-09", "url": "https://arxiv.org/abs/2306.06273", "pdf_url": "https://arxiv.org/pdf/2306.06273v1", "arxiv_id": "2306.06273", "doi": null, "citation_count": 4, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.301}
{"id": "af5e3d395b45ab0fefce648f4c02158f5d3efc62a9331378867d53f0b754d6ea", "sources": ["arxiv", "semantic_scholar"], "title": "Robust Explainer Recommendation for Time Series Classification", "abstract": "Time series classification is a task which deals with temporal sequences, a prevalent data type common in domains such as human activity recognition, sports analytics and general sensing. In this area, interest in explainability has been growing as explanation is key to understand the data and the model better. Recently, a great variety of techniques have been proposed and adapted for time series to provide explanation in the form of saliency maps, where the importance of each data point in the time series is quantified with a numerical value. However, the saliency maps can and often disagree, so it is unclear which one to use. This paper provides a novel framework to quantitatively evaluate and rank explanation methods for time series classification. We show how to robustly evaluate the informativeness of a given explanation method (i.e., relevance for the classification task), and how to compare explanations side-by-side. The goal is to recommend the best explainer for a given time series classification dataset. We propose AMEE, a Model-Agnostic Explanation Evaluation framework, for recommending saliency-based explanations for time series classification. In this approach, data perturbation is added to the input time series guided by each explanation. Our results show that perturbing discriminative parts of the time series leads to significant changes in classification accuracy, which can be used to evaluate each explanation. To be robust to different types of perturbations and different types of classifiers, we aggregate the accuracy loss across perturbations and classifiers. This novel approach allows us to recommend the best explainer among a set of different explainers, including random and oracle explainers. We provide a quantitative and qualitative analysis for synthetic datasets, a variety of timeseries datasets, as well as a real-world case study with known expert ground truth.", "authors": ["Thu Trang Nguyen", "Thach Le Nguyen", "Georgiana Ifrim"], "categories": ["cs.LG"], "fields_of_study": ["Medicine", "Computer Science"], "published_date": "2023-06-08", "url": "https://arxiv.org/abs/2306.05501", "pdf_url": "https://arxiv.org/pdf/2306.05501v4", "arxiv_id": "2306.05501", "doi": "10.1007/s10618-024-01045-8", "citation_count": 15, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Data mining and knowledge discovery", "quality_score": 0.301}
{"id": "93967b910e0998b10fae37d1ecde546b01ae26bb9d339609e906c5ec0486f4bf", "sources": ["arxiv", "semantic_scholar"], "title": "Revisiting Data-Free Knowledge Distillation with Poisoned Teachers", "abstract": "Data-free knowledge distillation (KD) helps transfer knowledge from a pre-trained model (known as the teacher model) to a smaller model (known as the student model) without access to the original training data used for training the teacher model. However, the security of the synthetic or out-of-distribution (OOD) data required in data-free KD is largely unknown and under-explored. In this work, we make the first effort to uncover the security risk of data-free KD w.r.t. untrusted pre-trained models. We then propose Anti-Backdoor Data-Free KD (ABD), the first plug-in defensive method for data-free KD methods to mitigate the chance of potential backdoors being transferred. We empirically evaluate the effectiveness of our proposed ABD in diminishing transferred backdoor knowledge while maintaining compatible downstream performances as the vanilla KD. We envision this work as a milestone for alarming and mitigating the potential backdoors in data-free KD. Codes are released at https://github.com/illidanlab/ABD.", "authors": ["Junyuan Hong", "Yi Zeng", "Shuyang Yu", "Lingjuan Lyu", "Ruoxi Jia", "Jiayu Zhou"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2023-06-04", "url": "https://arxiv.org/abs/2306.02368", "pdf_url": "https://arxiv.org/pdf/2306.02368v1", "arxiv_id": "2306.02368", "doi": "10.48550/arXiv.2306.02368", "citation_count": 16, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/illidanlab/ABD", "venue": "International Conference on Machine Learning", "quality_score": 0.3076}
{"id": "98c13b7d0b7f58d078cecafd6a9465e6e6caaa34e27c2457ca7c16a73bdfedef", "sources": ["arxiv", "semantic_scholar"], "title": "Generative Adversarial Networks for Data Augmentation", "abstract": "One way to expand the available dataset for training AI models in the medical field is through the use of Generative Adversarial Networks (GANs) for data augmentation. GANs work by employing a generator network to create new data samples that are then assessed by a discriminator network to determine their similarity to real samples. The discriminator network is taught to differentiate between actual and synthetic samples, while the generator system is trained to generate data that closely resemble real ones. The process is repeated until the generator network can produce synthetic data that is indistinguishable from genuine data. GANs have been utilized in medical image analysis for various tasks, including data augmentation, image creation, and domain adaptation. They can generate synthetic samples that can be used to increase the available dataset, especially in cases where obtaining large amounts of genuine data is difficult or unethical. However, it is essential to note that the use of GANs in medical imaging is still an active area of research to ensure that the produced images are of high quality and suitable for use in clinical settings.", "authors": ["Angona Biswas", "MD Abdullah Al Nasim", "Al Imran", "Anika Tabassum Sejuty", "Fabliha Fairooz", "Sai Puppala", "Sajedul Talukder"], "categories": ["cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-06-03", "url": "https://arxiv.org/abs/2306.02019", "pdf_url": "https://arxiv.org/pdf/2306.02019v2", "arxiv_id": "2306.02019", "doi": "10.48550/arXiv.2306.02019", "citation_count": 49, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4247}
{"id": "f9538c0606e042e205c63a21f1e40ef2586adec284624a454f27154a761109c8", "sources": ["arxiv", "semantic_scholar"], "title": "Differentially Private Low-dimensional Synthetic Data from High-dimensional Datasets", "abstract": "Differentially private synthetic data provide a powerful mechanism to enable data analysis while protecting sensitive information about individuals. However, when the data lie in a high-dimensional space, the accuracy of the synthetic data suffers from the curse of dimensionality. In this paper, we propose a differentially private algorithm to generate low-dimensional synthetic data efficiently from a high-dimensional dataset with a utility guarantee with respect to the Wasserstein distance. A key step of our algorithm is a private principal component analysis (PCA) procedure with a near-optimal accuracy bound that circumvents the curse of dimensionality. Unlike the standard perturbation analysis, our analysis of private PCA works without assuming the spectral gap for the covariance matrix.", "authors": ["Yiyun He", "Thomas Strohmer", "Roman Vershynin", "Yizhe Zhu"], "categories": ["cs.LG", "cs.CR", "cs.DS", "math.PR", "math.ST"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2023-05-26", "url": "https://arxiv.org/abs/2305.17148", "pdf_url": "https://arxiv.org/pdf/2305.17148v3", "arxiv_id": "2305.17148", "doi": null, "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1747}
{"id": "c7c89b39df20f0686532dc56d2bf8d245ecd34e3b4e4a99c20601ab46b9aafa2", "sources": ["arxiv", "semantic_scholar"], "title": "Dynosaur: A Dynamic Growth Paradigm for Instruction-Tuning Data Curation", "abstract": "Instruction tuning has emerged to enhance the capabilities of large language models (LLMs) to comprehend instructions and generate appropriate responses. Existing methods either manually annotate or employ LLM (e.g., GPT-series) to generate data for instruction tuning. However, they often overlook associating instructions with existing annotated datasets. In this paper, we propose Dynosaur, a dynamic growth paradigm for the automatic curation of instruction-tuning data. Based on the metadata of existing datasets, we use LLMs to automatically construct instruction-tuning data by identifying relevant data fields and generating appropriate instructions. By leveraging the existing annotated datasets, Dynosaur offers several advantages: 1) it reduces the API cost for generating instructions (e.g., it costs less than $12 USD by calling GPT-3.5-turbo for generating 800K instruction tuning samples; 2) it provides high-quality data for instruction tuning (e.g., it performs better than Alpaca and Flan on Super-NI and Longform with comparable data sizes); and 3) it supports the continuous improvement of models by generating instruction-tuning data when a new annotated dataset becomes available. We further investigate a continual learning scheme for learning with the ever-growing instruction-tuning dataset, and demonstrate that replaying tasks with diverse instruction embeddings not only helps mitigate forgetting issues but generalizes to unseen tasks better. Code and data are available at https://github.com/WadeYin9712/Dynosaur.", "authors": ["Da Yin", "Xiao Liu", "Fan Yin", "Ming Zhong", "Hritik Bansal", "Jiawei Han", "Kai-Wei Chang"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-05-23", "url": "https://arxiv.org/abs/2305.14327", "pdf_url": "https://arxiv.org/pdf/2305.14327v2", "arxiv_id": "2305.14327", "doi": "10.48550/arXiv.2305.14327", "citation_count": 51, "influential_citation_count": 3, "has_code": true, "code_url": "https://github.com/WadeYin9712/Dynosaur", "venue": "Conference on Empirical Methods in Natural Language Processing", "quality_score": 0.429}
{"id": "0a565b05d0e57897e2eaded1815554fc71dc7083ec0c2101b638d71eaf711ccd", "sources": ["arxiv", "semantic_scholar"], "title": "Is Synthetic Data From Diffusion Models Ready for Knowledge Distillation?", "abstract": "Diffusion models have recently achieved astonishing performance in generating high-fidelity photo-realistic images. Given their huge success, it is still unclear whether synthetic images are applicable for knowledge distillation when real images are unavailable. In this paper, we extensively study whether and how synthetic images produced from state-of-the-art diffusion models can be used for knowledge distillation without access to real images, and obtain three key conclusions: (1) synthetic data from diffusion models can easily lead to state-of-the-art performance among existing synthesis-based distillation methods, (2) low-fidelity synthetic images are better teaching materials, and (3) relatively weak classifiers are better teachers. Code is available at https://github.com/zhengli97/DM-KD.", "authors": ["Zheng Li", "Yuxuan Li", "Penghai Zhao", "Renjie Song", "Xiang Li", "Jian Yang"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2023-05-22", "url": "https://arxiv.org/abs/2305.12954", "pdf_url": "https://arxiv.org/pdf/2305.12954v1", "arxiv_id": "2305.12954", "doi": "10.48550/arXiv.2305.12954", "citation_count": 26, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/zhengli97/DM-KD", "venue": "arXiv.org", "quality_score": 0.3578}
{"id": "ddd12e2f0d1e0e7c5144a936b7b59c13f5d9973e910a991833799886faca9c76", "sources": ["arxiv", "semantic_scholar"], "title": "Text Generation with Speech Synthesis for ASR Data Augmentation", "abstract": "Aiming at reducing the reliance on expensive human annotations, data synthesis for Automatic Speech Recognition (ASR) has remained an active area of research. While prior work mainly focuses on synthetic speech generation for ASR data augmentation, its combination with text generation methods is considerably less explored. In this work, we explore text augmentation for ASR using large-scale pre-trained neural networks, and systematically compare those to traditional text augmentation methods. The generated synthetic texts are then converted to synthetic speech using a text-to-speech (TTS) system and added to the ASR training data. In experiments conducted on three datasets, we find that neural models achieve 9%-15% relative WER improvement and outperform traditional methods. We conclude that text augmentation, particularly through modern neural approaches, is a viable tool for improving the accuracy of ASR systems.", "authors": ["Zhuangqun Huang", "Gil Keren", "Ziran Jiang", "Shashank Jain", "David Goss-Grubbs", "Nelson Cheng", "Farnaz Abtahi", "Duc Le", "David Zhang", "Antony D'Avirro", "Ethan Campbell-Taylor", "Jessie Salas", "Irina-Elena Veliche", "Xi Chen"], "categories": ["cs.CL", "cs.AI", "cs.LG", "eess.AS"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2023-05-22", "url": "https://arxiv.org/abs/2305.16333", "pdf_url": "https://arxiv.org/pdf/2305.16333v1", "arxiv_id": "2305.16333", "doi": "10.48550/arXiv.2305.16333", "citation_count": 10, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2603}
{"id": "c26f8efbf65ced46906b088320e640ac33dd5741739f32e579d7083356c77f47", "sources": ["arxiv", "semantic_scholar"], "title": "Understanding the Effect of Data Augmentation on Knowledge Distillation", "abstract": "Knowledge distillation (KD) requires sufficient data to transfer knowledge from large-scale teacher models to small-scale student models. Therefore, data augmentation has been widely used to mitigate the shortage of data under specific scenarios. Classic data augmentation techniques, such as synonym replacement and k-nearest-neighbors, are initially designed for fine-tuning. To avoid severe semantic shifts and preserve task-specific labels, those methods prefer to change only a small proportion of tokens (e.g., changing 10% tokens is generally the best option for fine-tuning). However, such data augmentation methods are sub-optimal for knowledge distillation since the teacher model could provide label distributions and is more tolerant to semantic shifts. We first observe that KD prefers as much data as possible, which is different from fine-tuning that too much data will not gain more performance. Since changing more tokens leads to more semantic shifts, we use the proportion of changed tokens to reflect semantic shift degrees. Then we find that KD prefers augmented data with a larger semantic shift degree (e.g., changing 30% tokens is generally the best option for KD) than fine-tuning (changing 10% tokens). Besides, our findings show that smaller datasets prefer larger degrees until the out-of-distribution problem occurs (e.g., datasets with less than 10k inputs may prefer the 50% degree, and datasets with more than 100k inputs may prefer the 10% degree). Our work sheds light on the preference difference in data augmentation between fine-tuning and knowledge distillation and encourages the community to explore KD-specific data augmentation methods.", "authors": ["Ziqi Wang", "Chi Han", "Wenxuan Bao", "Heng Ji"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2023-05-21", "url": "https://arxiv.org/abs/2305.12565", "pdf_url": "https://arxiv.org/pdf/2305.12565v1", "arxiv_id": "2305.12565", "doi": "10.48550/arXiv.2305.12565", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1747}
{"id": "d6d2a13796ec53fd8c1fac83d1af531c9597374779a9f666a9aca7fa60119702", "sources": ["arxiv", "semantic_scholar"], "title": "BioAug: Conditional Generation based Data Augmentation for Low-Resource Biomedical NER", "abstract": "Biomedical Named Entity Recognition (BioNER) is the fundamental task of identifying named entities from biomedical text. However, BioNER suffers from severe data scarcity and lacks high-quality labeled data due to the highly specialized and expert knowledge required for annotation. Though data augmentation has shown to be highly effective for low-resource NER in general, existing data augmentation techniques fail to produce factual and diverse augmentations for BioNER. In this paper, we present BioAug, a novel data augmentation framework for low-resource BioNER. BioAug, built on BART, is trained to solve a novel text reconstruction task based on selective masking and knowledge augmentation. Post training, we perform conditional generation and generate diverse augmentations conditioning BioAug on selectively corrupted text similar to the training stage. We demonstrate the effectiveness of BioAug on 5 benchmark BioNER datasets and show that BioAug outperforms all our baselines by a significant margin (1.5%-21.5% absolute improvement) and is able to generate augmentations that are both more factual and diverse. Code: https://github.com/Sreyan88/BioAug.", "authors": ["Sreyan Ghosh", "Utkarsh Tyagi", "Sonal Kumar", "Dinesh Manocha"], "categories": ["cs.CL", "cs.AI", "cs.IR"], "fields_of_study": ["Computer Science"], "published_date": "2023-05-18", "url": "https://arxiv.org/abs/2305.10647", "pdf_url": "https://arxiv.org/pdf/2305.10647v1", "arxiv_id": "2305.10647", "doi": "10.1145/3539618.3591957", "citation_count": 17, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Sreyan88/BioAug", "venue": "Annual International ACM SIGIR Conference on Research and Development in Information Retrieval", "quality_score": 0.3138}
{"id": "01db9a24a2ee3192b87a5d81a106659f806c0dcf171228fc7ea5ccb6aa3ca7cb", "sources": ["arxiv", "semantic_scholar"], "title": "Utility Theory of Synthetic Data Generation", "abstract": "Synthetic data algorithms are widely employed in industries to generate artificial data for downstream learning tasks. While existing research primarily focuses on empirically evaluating utility of synthetic data, its theoretical understanding is largely lacking. This paper bridges the practice-theory gap by establishing relevant utility theory in a statistical learning framework. It considers two utility metrics: generalization and ranking of models trained on synthetic data. The former is defined as the generalization difference between models trained on synthetic and on real data. By deriving analytical bounds for this utility metric, we demonstrate that the synthetic feature distribution does not need to be similar as that of real data for ensuring comparable generalization of synthetic models, provided proper model specifications in downstream learning tasks. The latter utility metric studies the relative performance of models trained on synthetic data. In particular, we discover that the distribution of synthetic data is not necessarily similar as the real one to ensure consistent model comparison. Interestingly, consistent model comparison is still achievable even when synthetic responses are not well generated, as long as downstream models are separable by a generalization gap. Finally, extensive experiments on non-parametric models and deep neural networks have been conducted to validate these theoretical findings.", "authors": ["Shirong Xu", "Will Wei Sun", "Guang Cheng"], "categories": ["stat.ML", "cs.LG"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2023-05-17", "url": "https://arxiv.org/abs/2305.10015", "pdf_url": "https://arxiv.org/pdf/2305.10015v4", "arxiv_id": "2305.10015", "doi": "10.48550/arXiv.2305.10015", "citation_count": 8, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2386}
{"id": "cbc87ea235660fa0e8e1ed7df623f5fd0113b08b8ba454da7646fda31cf840c0", "sources": ["arxiv", "semantic_scholar"], "title": "StructGPT: A General Framework for Large Language Model to Reason over Structured Data", "abstract": "In this paper, we study how to improve the zero-shot reasoning ability of large language models~(LLMs) over structured data in a unified way. Inspired by the study on tool augmentation for LLMs, we develop an \\emph{Iterative Reading-then-Reasoning~(IRR)} approach for solving question answering tasks based on structured data, called \\textbf{StructGPT}. In our approach, we construct the specialized function to collect relevant evidence from structured data (\\ie \\emph{reading}), and let LLMs concentrate the reasoning task based on the collected information (\\ie \\emph{reasoning}). Specially, we propose an \\emph{invoking-linearization-generation} procedure to support LLMs in reasoning on the structured data with the help of the external interfaces. By iterating this procedures with provided interfaces, our approach can gradually approach the target answer to a given query. Extensive experiments conducted on three types of structured data demonstrate the effectiveness of our approach, which can significantly boost the performance of ChatGPT and achieve comparable performance against the full-data supervised-tuning baselines. Our codes and data are publicly available at~\\url{https://github.com/RUCAIBox/StructGPT}.", "authors": ["Jinhao Jiang", "Kun Zhou", "Zican Dong", "Keming Ye", "Wayne Xin Zhao", "Ji-Rong Wen"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2023-05-16", "url": "https://arxiv.org/abs/2305.09645", "pdf_url": "https://arxiv.org/pdf/2305.09645v2", "arxiv_id": "2305.09645", "doi": "10.48550/arXiv.2305.09645", "citation_count": 545, "influential_citation_count": 70, "has_code": true, "code_url": "https://github.com/RUCAIBox/StructGPT}", "venue": "Conference on Empirical Methods in Natural Language Processing", "quality_score": 0.9256}
{"id": "44fd54d41972550b7c5d7594f01f82f53a1e667755507152376e7ccd23e03b8e", "sources": ["arxiv", "semantic_scholar"], "title": "AdoptODE: Fusion of data and expert knowledge for modeling dynamical systems", "abstract": "Building a representative model of a complex system remains a highly challenging problem. While by now there is basic understanding of most physical domains, model design is often hindered by lack of detail, for example concerning model dimensions or its relevant constraints. Here we present a novel model-building approach -- adoptODE -- augmenting basic system descriptions, based on expert knowledge in the form of ordinary differential equations, with continuous adjoint sensitivity analysis related to artificial neural network principles, based on observable data. With this we have created a general tool, that can be applied to any physical system described by ordinary differential equations. AdoptODE allows validating or extending the initial description, for example with different variables and constraints. This way one arrives at a better-optimised, representative low-dimensional model, which can fit existing data and predict novel experimental outcomes. We validate our method on five, quite different problem domains. (1) Kolmogorov model: Lotka Volterra model where we show the application of adoptODE to continuous-time Markov processes and the performance of adoptODE when working with noisy data. (2) Particle model: Interactive N-body, is a demonstration of the scalability of adoptODE and that even interactive system with a high number of elements can be reconstructed with high precision. (3) Excitable media (heart dynamics) by the Bueno\\-Orovio\\-Cherry\\-Fenton model: AdoptODE can reconstruct the parameters of a high-dimensional model and fields for diffusion driven system for chaotic behaviour. (4) Fluid dynamics: Rayleigh-Bénard Convection where a complete unknown field, the temperature, can be extracted from only velocity data. (5) New experimental data of Zebrafish embryogenesis: This is a case where we extend existing models with new variables.", "authors": ["Leon Lettermann", "Alejandro Jurado", "Timo Betz", "Florentin Wörgötter", "Sebastian Herzog"], "categories": ["physics.data-an", "nlin.CD", "physics.bio-ph", "physics.flu-dyn"], "fields_of_study": ["Physics"], "published_date": "2023-05-16", "url": "https://arxiv.org/abs/2305.09325", "pdf_url": "https://arxiv.org/pdf/2305.09325v2", "arxiv_id": "2305.09325", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0}
{"id": "37735165474814cf7958f9509bb2c9fd581993ddd9244720aeb4724c52cf7235", "sources": ["arxiv", "semantic_scholar"], "title": "Data-Driven Dystopia: an uninterrupted breach of ethics", "abstract": "This article discusses the risks and complexities associated with the exponential rise in data and the misuse of data by large corporations. The article presents instances of data breaches and data harvesting practices that violate user privacy. It also explores the concept of \"Weapons Of Math Destruction\" (WMDs), which refers to big data models that perpetuate inequality and discrimination. The article highlights the need for companies to take responsibility for safeguarding user information and the ethical use of data models, AI, and ML. The article also emphasises the significance of data privacy for individuals in their daily lives and the need for a more conscious and responsible approach towards data management.", "authors": ["Shreyansh Padarha"], "categories": ["cs.CY", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2023-05-13", "url": "https://arxiv.org/abs/2305.07934", "pdf_url": "https://arxiv.org/pdf/2305.07934v1", "arxiv_id": "2305.07934", "doi": "10.48550/arXiv.2305.07934", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "b37b1241d864fb26fdd5244cee4ec211294c61305bfdf903136f7380a466a2cd", "sources": ["arxiv", "semantic_scholar"], "title": "Novel Synthetic Data Tool for Data-Driven Cardboard Box Localization", "abstract": "Application of neural networks in industrial settings, such as automated factories with bin-picking solutions requires costly production of large labeled data-sets. This paper presents an automatic data generation tool with a procedural model of a cardboard box. We briefly demonstrate the capabilities of the system, its various parameters and empirically prove the usefulness of the generated synthetic data by training a simple neural network. We make sample synthetic data generated by the tool publicly available.", "authors": ["Lukáš Gajdošech", "Peter Kravár"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2023-05-09", "url": "https://arxiv.org/abs/2305.05215", "pdf_url": "https://arxiv.org/pdf/2305.05215v2", "arxiv_id": "2305.05215", "doi": "10.1007/978-3-031-44207-0_50", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Artificial Neural Networks", "quality_score": 0.0}
{"id": "e6576578bf1be2a0976797eda2bd8df51e0c2f753d9daed3bf40410278621ffb", "sources": ["arxiv", "semantic_scholar"], "title": "Self-supervised Activity Representation Learning with Incremental Data: An Empirical Study", "abstract": "In the context of mobile sensing environments, various sensors on mobile devices continually generate a vast amount of data. Analyzing this ever-increasing data presents several challenges, including limited access to annotated data and a constantly changing environment. Recent advancements in self-supervised learning have been utilized as a pre-training step to enhance the performance of conventional supervised models to address the absence of labelled datasets. This research examines the impact of using a self-supervised representation learning model for time series classification tasks in which data is incrementally available. We proposed and evaluated a workflow in which a model learns to extract informative features using a corpus of unlabeled time series data and then conducts classification on labelled data using features extracted by the model. We analyzed the effect of varying the size, distribution, and source of the unlabeled data on the final classification performance across four public datasets, including various types of sensors in diverse applications.", "authors": ["Jason Liu", "Shohreh Deldari", "Hao Xue", "Van Nguyen", "Flora D. Salim"], "categories": ["cs.LG", "eess.SP"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2023-05-01", "url": "https://arxiv.org/abs/2305.00619", "pdf_url": "https://arxiv.org/pdf/2305.00619v1", "arxiv_id": "2305.00619", "doi": "10.1109/MDM58254.2023.00019", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Mobile Data Management", "quality_score": 0.1505}
{"id": "1a1d84588f2fa2920e5de6469ed07aec0c37597094b16459e729e857219fb146", "sources": ["arxiv", "semantic_scholar"], "title": "ZIRCON: Zero-watermarking-based approach for data integrity and secure provenance in IoT networks", "abstract": "The Internet of Things (IoT) is integrating the Internet and smart devices in almost every domain such as home automation, e-healthcare systems, vehicular networks, industrial control and military applications. In these sectors, sensory data, which is collected from multiple sources and managed through intermediate processing by multiple nodes, is used for decision-making processes. Ensuring data integrity and keeping track of data provenance is a core requirement in such a highly dynamic context, since data provenance is an important tool for the assurance of data trustworthiness. Dealing with such requirements is challenging due to the limited computational and energy resources in IoT networks. This requires addressing several challenges such as processing overhead, secure provenance, bandwidth consumption and storage efficiency. In this paper, we propose ZIRCON, a novel zero-watermarking approach to establish end-to-end data trustworthiness in an IoT network. In ZIRCON, provenance information is stored in a tamper-proof centralized network database through watermarks, generated at source node before transmission. We provide an extensive security analysis showing the resilience of our scheme against passive and active attacks. We also compare our scheme with existing works based on performance metrics such as computational time, energy utilization and cost analysis. The results show that ZIRCON is robust against several attacks, lightweight, storage efficient, and better in energy utilization and bandwidth consumption, compared to prior art.", "authors": ["Omair Faraj", "David Megías", "Joaquin Garcia-Alfaro"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2023-04-29", "url": "https://arxiv.org/abs/2305.00266", "pdf_url": "https://arxiv.org/pdf/2305.00266v1", "arxiv_id": "2305.00266", "doi": "10.48550/arXiv.2305.00266", "citation_count": 8, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Journal of Information Security and Applications", "quality_score": 0.2386}
{"id": "525dd0ad0433afa76796ec89c755dc43a7bd6d80c72daf1686b8b801cf06ad4a", "sources": ["arxiv", "semantic_scholar"], "title": "Subdata selection for big data regression: an improved approach", "abstract": "In the big data era researchers face a series of problems. Even standard approaches/methodologies, like linear regression, can be difficult or problematic with huge volumes of data. Traditional approaches for regression in big datasets may suffer due to the large sample size, since they involve inverting huge data matrices or even because the data cannot fit to the memory. Proposed approaches are based on selecting representative subdata to run the regression. Existing approaches select the subdata using information criteria and/or properties from orthogonal arrays. In the present paper we improve existing algorithms providing a new algorithm that is based on D-optimality approach. We provide simulation evidence for its performance. Evidence about the parameters of the proposed algorithm is also provided in order to clarify the trade-offs between execution time and information gain. Real data applications are also provided.", "authors": ["Vasilis Chasiotis", "Dimitris Karlis"], "categories": ["stat.ME", "stat.AP"], "fields_of_study": ["Mathematics", "Computer Science"], "published_date": "2023-04-29", "url": "https://arxiv.org/abs/2305.00218", "pdf_url": "https://arxiv.org/pdf/2305.00218v2", "arxiv_id": "2305.00218", "doi": "10.52933/jdssv.v4i3.78", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Journal of Data Science Statistics and Visualisation", "quality_score": 0.2113}
{"id": "5f6da2720936e38d2dfb5e7be4db631c56e545031442c41fca952322630f679c", "sources": ["arxiv", "semantic_scholar"], "title": "Heterogeneous Domain Adaptation with Positive and Unlabeled Data", "abstract": "Heterogeneous unsupervised domain adaptation (HUDA) is the most challenging domain adaptation setting where the feature spaces of source and target domains are heterogeneous, and the target domain has only unlabeled data. Existing HUDA methods assume that both positive and negative examples are available in the source domain, which may not be satisfied in some real applications. This paper addresses a new challenging setting called positive and unlabeled heterogeneous unsupervised domain adaptation (PU-HUDA), a HUDA setting where the source domain only has positives. PU-HUDA can also be viewed as an extension of PU learning where the positive and unlabeled examples are sampled from different domains. A naive combination of existing HUDA and PU learning methods is ineffective in PU-HUDA due to the gap in label distribution between the source and target domains. To overcome this issue, we propose a novel method, predictive adversarial domain adaptation (PADA), which can predict likely positive examples from the unlabeled target data and simultaneously align the feature spaces to reduce the distribution divergence between the whole source data and the likely positive target data. PADA achieves this by a unified adversarial training framework for learning a classifier to predict positive examples and a feature transformer to transform the target feature space to that of the source. Specifically, they are both trained to fool a common discriminator that determines whether the likely positive examples are from the target or source domain. We experimentally show that PADA outperforms several baseline methods, such as the naive combination of HUDA and PU learning.", "authors": ["Junki Mori", "Ryo Furukawa", "Isamu Teranishi", "Jun Sakuma"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-04-17", "url": "https://arxiv.org/abs/2304.07955", "pdf_url": "https://arxiv.org/pdf/2304.07955v2", "arxiv_id": "2304.07955", "doi": "10.1109/BigData59044.2023.10386193", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "BigData Congress [Services Society]", "quality_score": 0.0}
{"id": "581f3d61836ddce7581338e3c8fe78bc25111690fa7b601f8a9035ed79af7b9e", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetically Generating Human-like Data for Sequential Decision Making Tasks via Reward-Shaped Imitation Learning", "abstract": "We consider the problem of synthetically generating data that can closely resemble human decisions made in the context of an interactive human-AI system like a computer game. We propose a novel algorithm that can generate synthetic, human-like, decision making data while starting from a very small set of decision making data collected from humans. Our proposed algorithm integrates the concept of reward shaping with an imitation learning algorithm to generate the synthetic data. We have validated our synthetic data generation technique by using the synthetically generated data as a surrogate for human interaction data to solve three sequential decision making tasks of increasing complexity within a small computer game-like setup. Different empirical and statistical analyses of our results show that the synthetically generated data can substitute the human data and perform the game-playing tasks almost indistinguishably, with very low divergence, from a human performing the same tasks.", "authors": ["Bryan Brandt", "Prithviraj Dasgupta"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2023-04-14", "url": "https://arxiv.org/abs/2304.07280", "pdf_url": "https://arxiv.org/pdf/2304.07280v1", "arxiv_id": "2304.07280", "doi": "10.1117/12.2664109", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1193}
{"id": "a4e43c2c6098e430309daf8218c75fb9461e94a57d2eacfc415644357beafafc", "sources": ["arxiv", "semantic_scholar"], "title": "On Extend-Only Directed Posets and Derived Byzantine-Tolerant Replicated Data Types (Extended Version)", "abstract": "We uncover the extend-only directed posets (EDP) structure as a unification of recently discussed DAG-based Byzantine-tolerant conflict-free replicated data types (CRDT). We also show how a key-value map model can be derived from the EDP formulation, and give an outlook on an EDP-based systemic access control CRDT as a formalization of the CRDT used in the Matrix messaging system.", "authors": ["Florian Jacob", "Hannes Hartenstein"], "categories": ["cs.DC", "cs.DS"], "fields_of_study": ["Computer Science"], "published_date": "2023-04-09", "url": "https://arxiv.org/abs/2304.04318", "pdf_url": "https://arxiv.org/pdf/2304.04318v1", "arxiv_id": "2304.04318", "doi": "10.1145/3578358.3591333", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1505}
{"id": "9f9830dfb705b0de10ebff84e99e77f9a8e3526778c010c3ba357d2ca2ade8b3", "sources": ["arxiv", "semantic_scholar"], "title": "A Guide for Practical Use of ADMG Causal Data Augmentation", "abstract": "Data augmentation is essential when applying Machine Learning in small-data regimes. It generates new samples following the observed data distribution while increasing their diversity and variability to help researchers and practitioners improve their models' robustness and, thus, deploy them in the real world. Nevertheless, its usage in tabular data still needs to be improved, as prior knowledge about the underlying data mechanism is seldom considered, limiting the fidelity and diversity of the generated data. Causal data augmentation strategies have been pointed out as a solution to handle these challenges by relying on conditional independence encoded in a causal graph. In this context, this paper experimentally analyzed the ADMG causal augmentation method considering different settings to support researchers and practitioners in understanding under which conditions prior knowledge helps generate new data points and, consequently, enhances the robustness of their models. The results highlighted that the studied method (a) is independent of the underlying model mechanism, (b) requires a minimal number of observations that may be challenging in a small-data regime to improve an ML model's accuracy, (c) propagates outliers to the augmented set degrading the performance of the model, and (d) is sensitive to its hyperparameter's value.", "authors": ["Audrey Poinsot", "Alessandro Leite"], "categories": ["cs.LG", "stat.ME"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2023-04-03", "url": "https://arxiv.org/abs/2304.01237", "pdf_url": "https://arxiv.org/pdf/2304.01237v1", "arxiv_id": "2304.01237", "doi": "10.48550/arXiv.2304.01237", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "61f6c2fa94381f9a3999f4befeeba58408470264bebd26ff25440e3d7a4f6c1a", "sources": ["arxiv", "semantic_scholar"], "title": "Going Beyond Nouns With Vision & Language Models Using Synthetic Data", "abstract": "Large-scale pre-trained Vision & Language (VL) models have shown remarkable performance in many applications, enabling replacing a fixed set of supported classes with zero-shot open vocabulary reasoning over (almost arbitrary) natural language prompts. However, recent works have uncovered a fundamental weakness of these models. For example, their difficulty to understand Visual Language Concepts (VLC) that go 'beyond nouns' such as the meaning of non-object words (e.g., attributes, actions, relations, states, etc.), or difficulty in performing compositional reasoning such as understanding the significance of the order of the words in a sentence. In this work, we investigate to which extent purely synthetic data could be leveraged to teach these models to overcome such shortcomings without compromising their zero-shot capabilities. We contribute Synthetic Visual Concepts (SyViC) - a million-scale synthetic dataset and data generation codebase allowing to generate additional suitable data to improve VLC understanding and compositional reasoning of VL models. Additionally, we propose a general VL finetuning strategy for effectively leveraging SyViC towards achieving these improvements. Our extensive experiments and ablations on VL-Checklist, Winoground, and ARO benchmarks demonstrate that it is possible to adapt strong pre-trained VL models with synthetic data significantly enhancing their VLC understanding (e.g. by 9.9% on ARO and 4.3% on VL-Checklist) with under 1% drop in their zero-shot accuracy.", "authors": ["Paola Cascante-Bonilla", "Khaled Shehada", "James Seale Smith", "Sivan Doveh", "Donghyun Kim", "Rameswar Panda", "Gül Varol", "Aude Oliva", "Vicente Ordonez", "Rogerio Feris", "Leonid Karlinsky"], "categories": ["cs.CV", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2023-03-30", "url": "https://arxiv.org/abs/2303.17590", "pdf_url": "https://arxiv.org/pdf/2303.17590v2", "arxiv_id": "2303.17590", "doi": "10.1109/ICCV51070.2023.01844", "citation_count": 49, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "IEEE International Conference on Computer Vision", "quality_score": 0.4247}
{"id": "32677e6aa7ef9944b33fc2a9cd02d845ffdf4ecfbb6dd53ae6d580ece1a46831", "sources": ["arxiv", "semantic_scholar"], "title": "KD-DLGAN: Data Limited Image Generation via Knowledge Distillation", "abstract": "Generative Adversarial Networks (GANs) rely heavily on large-scale training data for training high-quality image generation models. With limited training data, the GAN discriminator often suffers from severe overfitting which directly leads to degraded generation especially in generation diversity. Inspired by the recent advances in knowledge distillation (KD), we propose KD-DLGAN, a knowledge-distillation based generation framework that introduces pre-trained vision-language models for training effective data-limited generation models. KD-DLGAN consists of two innovative designs. The first is aggregated generative KD that mitigates the discriminator overfitting by challenging the discriminator with harder learning tasks and distilling more generalizable knowledge from the pre-trained models. The second is correlated generative KD that improves the generation diversity by distilling and preserving the diverse image-text correlation within the pre-trained models. Extensive experiments over multiple benchmarks show that KD-DLGAN achieves superior image generation with limited training data. In addition, KD-DLGAN complements the state-of-the-art with consistent and substantial performance gains.", "authors": ["Kaiwen Cui", "Yingchen Yu", "Fangneng Zhan", "Shengcai Liao", "Shijian Lu1", "Eric Xing"], "categories": ["cs.CV", "eess.IV"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2023-03-30", "url": "https://arxiv.org/abs/2303.17158", "pdf_url": "https://arxiv.org/pdf/2303.17158v1", "arxiv_id": "2303.17158", "doi": "10.1109/CVPR52729.2023.00377", "citation_count": 30, "influential_citation_count": 7, "has_code": false, "code_url": null, "venue": "Computer Vision and Pattern Recognition", "quality_score": 0.4515}
{"id": "55fefd12d471246f3717ad02851a3c32b1e4a29444a43615fd3b5fc3c3c50d9e", "sources": ["arxiv", "semantic_scholar"], "title": "PENTACET data -- 23 Million Contextual Code Comments and 250,000 SATD comments", "abstract": "Most Self-Admitted Technical Debt (SATD) research utilizes explicit SATD features such as 'TODO' and 'FIXME' for SATD detection. A closer look reveals several SATD research uses simple SATD ('Easy to Find') code comments without the contextual data (preceding and succeeding source code context). This work addresses this gap through PENTACET (or 5C dataset) data. PENTACET is a large Curated Contextual Code Comments per Contributor and the most extensive SATD data. We mine 9,096 Open Source Software Java projects with a total of 435 million LOC. The outcome is a dataset with 23 million code comments, preceding and succeeding source code context for each comment, and more than 250,000 comments labeled as SATD, including both 'Easy to Find' and 'Hard to Find' SATD. We believe PENTACET data will further SATD research using Artificial Intelligence techniques.", "authors": ["Murali Sridharan", "Leevi Rantala", "Mika Mäntylä"], "categories": ["cs.SE", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-03-24", "url": "https://arxiv.org/abs/2303.14029", "pdf_url": "https://arxiv.org/pdf/2303.14029v2", "arxiv_id": "2303.14029", "doi": "10.1109/MSR59073.2023.00063", "citation_count": 9, "influential_citation_count": 1, "has_code": true, "code_url": null, "venue": "IEEE Working Conference on Mining Software Repositories", "quality_score": 0.25}
{"id": "79a5cfc3dca79982b9a9b368d984a16c0e8ed7d7624bfb05f93c953b98794d91", "sources": ["arxiv", "semantic_scholar"], "title": "Human Behavior in the Time of COVID-19: Learning from Big Data", "abstract": "Since the World Health Organization (WHO) characterized COVID-19 as a pandemic in March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths as of October 2022. The relationship between the COVID-19 pandemic and human behavior is complicated. On one hand, human behavior is found to shape the spread of the disease. On the other hand, the pandemic has impacted and even changed human behavior in almost every aspect. To provide a holistic understanding of the complex interplay between human behavior and the COVID-19 pandemic, researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning. In this study, we present an overview of the existing studies on using big data techniques to study human behavior in the time of the COVID-19 pandemic. In particular, we categorize these studies into three groups - using big data to measure, model, and leverage human behavior, respectively. The related tasks, data, and methods are summarized accordingly. To provide more insights into how to fight the COVID-19 pandemic and future global catastrophes, we further discuss challenges and potential opportunities.", "authors": ["Hanjia Lyu", "Arsal Imtiaz", "Yufei Zhao", "Jiebo Luo"], "categories": ["cs.CY", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-03-23", "url": "https://arxiv.org/abs/2303.13452", "pdf_url": "https://arxiv.org/pdf/2303.13452v1", "arxiv_id": "2303.13452", "doi": "10.3389/fdata.2023.1099182", "citation_count": 10, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Frontiers in Big Data", "quality_score": 0.2603}
{"id": "7d91799d2f62bcf60112f561309946d5942ced1de0fa431e8f7924b06daba461", "sources": ["arxiv", "semantic_scholar"], "title": "CH-Go: Online Go System Based on Chunk Data Storage", "abstract": "The training and running of an online Go system require the support of effective data management systems to deal with vast data, such as the initial Go game records, the feature data set obtained by representation learning, the experience data set of self-play, the randomly sampled Monte Carlo tree, and so on. Previous work has rarely mentioned this problem, but the ability and efficiency of data management systems determine the accuracy and speed of the Go system. To tackle this issue, we propose an online Go game system based on the chunk data storage method (CH-Go), which processes the format of 160k Go game data released by Kiseido Go Server (KGS) and designs a Go encoder with 11 planes, a parallel processor and generator for better memory performance. Specifically, we store the data in chunks, take the chunk size of 1024 as a batch, and save the features and labels of each chunk as binary files. Then a small set of data is randomly sampled each time for the neural network training, which is accessed by batch through yield method. The training part of the prototype includes three modules: supervised learning module, reinforcement learning module, and an online module. Firstly, we apply Zobrist-guided hash coding to speed up the Go board construction. Then we train a supervised learning policy network to initialize the self-play for generation of experience data with 160k Go game data released by KGS. Finally, we conduct reinforcement learning based on REINFORCE algorithm. Experiments show that the training accuracy of CH- Go in the sampled 150 games is 99.14%, and the accuracy in the test set is as high as 98.82%. Under the condition of limited local computing power and time, we have achieved a better level of intelligence. Given the current situation that classical systems such as GOLAXY are not free and open, CH-Go has realized and maintained complete Internet openness.", "authors": ["H. Lu", "C. Li", "Y. Yang", "C. Li", "A. Islam"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-03-22", "url": "https://arxiv.org/abs/2303.13553", "pdf_url": "https://arxiv.org/pdf/2303.13553v1", "arxiv_id": "2303.13553", "doi": "10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00325", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0}
{"id": "33c436763a54d9bb46ee5098adeb8a17fd05e6c9229cafd4518176392b6a5ca9", "sources": ["arxiv", "semantic_scholar"], "title": "Understanding the Role of the Projector in Knowledge Distillation", "abstract": "In this paper we revisit the efficacy of knowledge distillation as a function matching and metric learning problem. In doing so we verify three important design decisions, namely the normalisation, soft maximum function, and projection layers as key ingredients. We theoretically show that the projector implicitly encodes information on past examples, enabling relational gradients for the student. We then show that the normalisation of representations is tightly coupled with the training dynamics of this projector, which can have a large impact on the students performance. Finally, we show that a simple soft maximum function can be used to address any significant capacity gap problems. Experimental results on various benchmark datasets demonstrate that using these insights can lead to superior or comparable performance to state-of-the-art knowledge distillation techniques, despite being much more computationally efficient. In particular, we obtain these results across image classification (CIFAR100 and ImageNet), object detection (COCO2017), and on more difficult distillation objectives, such as training data efficient transformers, whereby we attain a 77.2% top-1 accuracy with DeiT-Ti on ImageNet. Code and models are publicly available.", "authors": ["Roy Miles", "Krystian Mikolajczyk"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-03-20", "url": "https://arxiv.org/abs/2303.11098", "pdf_url": "https://arxiv.org/pdf/2303.11098v5", "arxiv_id": "2303.11098", "doi": "10.1609/aaai.v38i5.28219", "citation_count": 62, "influential_citation_count": 2, "has_code": true, "code_url": "https://github.com/roymiles/Simple-Recipe-Distillation", "venue": "AAAI Conference on Artificial Intelligence", "quality_score": 0.4498}
{"id": "486ac22492e3d1b96581b6c4a1eadee5d85e09eae7ae09bd0271e5dbfa718cca", "sources": ["arxiv", "semantic_scholar"], "title": "Forecasting COVID-19 Case Counts Based on 2020 Ontario Data", "abstract": "Objective: To develop machine learning models that can predict the number of COVID-19 cases per day given the last 14 days of environmental and mobility data. Approach: COVID-19 data from four counties around Toronto, Ontario, were used. Data were prepared into daily records containing the number of new COVID case counts, patient demographic data, outdoor weather variables, indoor environment factors, and human movement based on cell mobility and public health restrictions. This data was analyzed to determine the most important variables and their interactions. Predictive models were developed using CNN and LSTM deep neural network approaches. A 5-fold chronological cross-validation approach used these methods to develop predictive models using data from Mar 1 to Oct 14 2020, and test them on data covering Oct 15 to Dec 24 2020. Results: The best LSTM models forecasted tomorrow's daily COVID case counts with 90.7% accuracy, and the 7-day rolling average COVID case counts with 98.1% accuracy using independent test data. The best models to forecast the next 7 days of daily COVID case counts did so with 79.4% accuracy over all days. Models forecasting the 7-day rolling average case counts had a mean accuracy of 83.6% on the same test set. Conclusions: Our findings point to the importance of indoor humidity for the transmission of a virus such as COVID-19. During the coldest portions of the year, when humans spend greater amounts of time indoors or in vehicles, air quality drops within buildings, most significantly indoor relative humidity levels. Moderate to high indoor temperatures coupled with low IRH (below 20%) create conditions where viral transmission is more likely because water vapour ejected from an infected person's mouth can remain longer in the air because of evaporation and dry skin conditions, particularly in a recipient's airway, promotes transmission.", "authors": ["Daniel L. Silver", "Rinda Digamarthi"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-03-18", "url": "https://arxiv.org/abs/2303.10294", "pdf_url": "https://arxiv.org/pdf/2303.10294v1", "arxiv_id": "2303.10294", "doi": "10.48550/arXiv.2303.10294", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0}
{"id": "3927bfe22a112062b2143b7fc9b7638c82026e9377ecba58c294691a3cea145d", "sources": ["arxiv", "semantic_scholar"], "title": "Data-centric Artificial Intelligence: A Survey", "abstract": "Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler of its great success is the availability of abundant and high-quality data for building machine learning models. Recently, the role of data in AI has been significantly magnified, giving rise to the emerging concept of data-centric AI. The attention of researchers and practitioners has gradually shifted from advancing model design to enhancing the quality and quantity of the data. In this survey, we discuss the necessity of data-centric AI, followed by a holistic view of three general data-centric goals (training data development, inference data development, and data maintenance) and the representative methods. We also organize the existing literature from automation and collaboration perspectives, discuss the challenges, and tabulate the benchmarks for various tasks. We believe this is the first comprehensive survey that provides a global view of a spectrum of tasks across various stages of the data lifecycle. We hope it can help the readers efficiently grasp a broad picture of this field, and equip them with the techniques and further research ideas to systematically engineer data for building AI systems. A companion list of data-centric AI resources will be regularly updated on https://github.com/daochenzha/data-centric-AI", "authors": ["Daochen Zha", "Zaid Pervaiz Bhat", "Kwei-Herng Lai", "Fan Yang", "Zhimeng Jiang", "Shaochen Zhong", "Xia Hu"], "categories": ["cs.LG", "cs.AI", "cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2023-03-17", "url": "https://arxiv.org/abs/2303.10158", "pdf_url": "https://arxiv.org/pdf/2303.10158v3", "arxiv_id": "2303.10158", "doi": "10.1145/3711118", "citation_count": 433, "influential_citation_count": 8, "has_code": true, "code_url": "https://github.com/daochenzha/data-centric-AI", "venue": "ACM Computing Surveys", "quality_score": 0.6594}
{"id": "1cb7e379cd8c79f78889cd073803d89c6d092ad2e9cd5f4849da9b72d8e6bbda", "sources": ["arxiv", "semantic_scholar"], "title": "Learning towards Selective Data Augmentation for Dialogue Generation", "abstract": "As it is cumbersome and expensive to acquire a huge amount of data for training neural dialog models, data augmentation is proposed to effectively utilize existing training samples. However, current data augmentation techniques on the dialog generation task mostly augment all cases in the training dataset without considering the intrinsic attributes between different cases. We argue that not all cases are beneficial for augmentation task, and the cases suitable for augmentation should obey the following two attributes: (1) low-quality (the dialog model cannot generate a high-quality response for the case), (2) representative (the case should represent the property of the whole dataset). Herein, we explore this idea by proposing a Selective Data Augmentation framework (SDA) for the response generation task. SDA employs a dual adversarial network to select the lowest quality and most representative data points for augmentation in one stage. Extensive experiments conducted on two publicly available datasets, i.e., DailyDialog and OpenSubtitles, show that our framework can improve the response generation performance with respect to various metrics.", "authors": ["Xiuying Chen", "Mingzhe Li", "Jiayi Zhang", "Xiaoqiang Xia", "Chen Wei", "Jianwei Cui", "Xin Gao", "Xiangliang Zhang", "Rui Yan"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2023-03-17", "url": "https://arxiv.org/abs/2303.09719", "pdf_url": "https://arxiv.org/pdf/2303.09719v1", "arxiv_id": "2303.09719", "doi": "10.48550/arXiv.2303.09719", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "AAAI Conference on Artificial Intelligence", "quality_score": 0.2258}
{"id": "22469782d574871efd14227f6146f9cec58d01f5f63bb5552af6ef56b7e20459", "sources": ["arxiv", "semantic_scholar"], "title": "Resonant inelastic x-ray scattering data for Ruddlesden-Popper and reduced Ruddlesden-Popper nickelates", "abstract": "Ruddlesden-Popper and reduced Ruddlesden-Popper nickelates are intriguing candidates for mimicking the properties of high-temperature superconducting cuprates. The degree of similarity between these nickelates and cuprates has been the subject of considerable debate. Resonant inelastic x-ray scattering (RIXS) has played an important role in exploring their electronic and magnetic excitations, but these efforts have been stymied by inconsistencies between different samples and the lack of publicly available data for detailed comparison. To address this issue, we present open RIXS data on La4Ni3O10 and La4Ni3O8.", "authors": ["G. Fabbris", "D. Meyers", "Y. Shen", "V. Bisogni", "J. Zhang", "J. F. Mitchell", "M. R. Norman", "S. Johnston", "J. Feng", "G. S. Chiuzbaian", "A. Nicolaou", "N. Jaouen", "M. P. M. Dean"], "categories": ["cond-mat.str-el"], "fields_of_study": ["Physics", "Medicine"], "published_date": "2023-03-16", "url": "https://arxiv.org/abs/2303.09507", "pdf_url": "https://arxiv.org/pdf/2303.09507v1", "arxiv_id": "2303.09507", "doi": "10.1038/s41597-023-02079-1", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Scientific Data", "quality_score": 0.1747}
{"id": "3ba9ad251b13d64c3faba9d68a457a91c843d34848bc8550c0cabe443b90103f", "sources": ["arxiv", "semantic_scholar"], "title": "Data management and execution systems for the Rubin Observatory Science Pipelines", "abstract": "We present the Rubin Observatory system for data storage/retrieval and pipelined code execution. The layer for data storage and retrieval is named the Butler. It consists of a relational database, known as the registry, to keep track of metadata and relations, and a system to manage where the data is located, named the datastore. Together these systems create an abstraction layer that science algorithms can be written against. This abstraction layer manages the complexities of the large data volumes expected and allows algorithms to be written independently, yet be tied together automatically into a coherent processing pipeline. This system consists of tools which execute these pipelines by transforming them into execution graphs which contain concrete data stored in the Butler. The pipeline infrastructure is designed to be scalable in nature, allowing execution on environments ranging from a laptop all the way up to multi-facility data centers. This presentation will focus on the data management aspects as well as an overview on the creation of pipelines and the corresponding execution graphs.", "authors": ["Nate B. Lust", "Tim Jenness", "James F. Bosch", "Andrei Salnikov", "Nathan M. Pease", "Michelle Gower", "Mikolaj Kowalik", "Gregory P. Dubois-Felsmann", "Fritz Mueller", "Pim Schellart"], "categories": ["astro-ph.IM", "cs.DC"], "fields_of_study": ["Computer Science", "Physics"], "published_date": "2023-03-06", "url": "https://arxiv.org/abs/2303.03313", "pdf_url": "https://arxiv.org/pdf/2303.03313v1", "arxiv_id": "2303.03313", "doi": "10.48550/arXiv.2303.03313", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "79cf09a6c16953efde97ee3fbb1de24a866aa6315a5a867a6061a6d2ed21d744", "sources": ["arxiv", "semantic_scholar"], "title": "Benchmark of Data Preprocessing Methods for Imbalanced Classification", "abstract": "Severe class imbalance is one of the main conditions that make machine learning in cybersecurity difficult. A variety of dataset preprocessing methods have been introduced over the years. These methods modify the training dataset by oversampling, undersampling or a combination of both to improve the predictive performance of classifiers trained on this dataset. Although these methods are used in cybersecurity occasionally, a comprehensive, unbiased benchmark comparing their performance over a variety of cybersecurity problems is missing. This paper presents a benchmark of 16 preprocessing methods on six cybersecurity datasets together with 17 public imbalanced datasets from other domains. We test the methods under multiple hyperparameter configurations and use an AutoML system to train classifiers on the preprocessed datasets, which reduces potential bias from specific hyperparameter or classifier choices. Special consideration is also given to evaluating the methods using appropriate performance measures that are good proxies for practical performance in real-world cybersecurity systems. The main findings of our study are: 1) Most of the time, a data preprocessing method that improves classification performance exists. 2) Baseline approach of doing nothing outperformed a large portion of methods in the benchmark. 3) Oversampling methods generally outperform undersampling methods. 4) The most significant performance gains are brought by the standard SMOTE algorithm and more complicated methods provide mainly incremental improvements at the cost of often worse computational performance.", "authors": ["Radovan Haluška", "Jan Brabec", "Tomáš Komárek"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-03-06", "url": "https://arxiv.org/abs/2303.03094", "pdf_url": "https://arxiv.org/pdf/2303.03094v1", "arxiv_id": "2303.03094", "doi": "10.1109/BigData55660.2022.10021118", "citation_count": 3, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1505}
{"id": "f63a24afa081df5a2129a4251d5fd167a5d23324d968c2a02d3d79846316f718", "sources": ["arxiv", "semantic_scholar"], "title": "Data Augmentation for Generating Synthetic Electrogastrogram Time Series", "abstract": "To address an emerging need for large number of diverse datasets for rigor evaluation of signal processing techniques, we developed and evaluated a new method for generating synthetic electrogastrogram time series. We used electrogastrography (EGG) data from an open database to set model parameters and statistical tests to evaluate synthesized data. Additionally, we illustrated method customization for generating artificial EGG time series alterations caused by the simulator sickness. Proposed data augmentation method generates synthetic EGG data with specified duration, sampling frequency, recording state (postprandial or fasting state), overall noise and breathing artifact injection, and pauses in the gastric rhythm (arrhythmia occurrence) with statistically significant difference between postprandial and fasting states in > 70% cases while not accounting for individual differences. Features obtained from the synthetic EGG signal resembling simulator sickness occurrence displayed expected trends. The code for generation of synthetic EGG time series is not only freely available and can be further customized to assess signal processing algorithms but also may be used to increase data diversity for training artificial intelligence (AI) algorithms. The proposed approach is customized for EGG data synthesis but can be easily utilized for other biosignals with similar nature such as electroencephalogram.", "authors": ["Nadica Miljković", "Nikola Milenić", "Nenad B. Popović", "Jaka Sodnik"], "categories": ["eess.SP"], "fields_of_study": ["Medicine", "Engineering", "Computer Science"], "published_date": "2023-03-04", "url": "https://arxiv.org/abs/2303.02408", "pdf_url": "https://arxiv.org/pdf/2303.02408v5", "arxiv_id": "2303.02408", "doi": "10.1007/s11517-024-03112-0", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Medical and Biological Engineering and Computing", "quality_score": 0.1747}
{"id": "fbe0a7e0e7da7d566c1f43e27d8bad9312a9408f77f613c93a11fcb83a088b81", "sources": ["arxiv", "semantic_scholar"], "title": "Interoperability-oriented Quality Assessment for Czech Open Data", "abstract": "With the rapid increase of published open datasets, it is crucial to support the open data progress in smart cities while considering the open data quality. In the Czech Republic, and its National Open Data Catalogue (NODC), the open datasets are usually evaluated based on their metadata only, while leaving the content and the adherence to the recommended data structure to the sole responsibility of the data providers. The interoperability of open datasets remains unknown. This paper therefore aims to propose a novel content-aware quality evaluation framework that assesses the quality of open datasets based on five data quality dimensions. With the proposed framework, we provide a fundamental view on the interoperability-oriented data quality of Czech open datasets, which are published in NODC. Our evaluations find that domain-specific open data quality assessments are able to detect data quality issues beyond traditional heuristics used for determining Czech open data quality, increase their interoperability, and thus increase their potential to bring value for the society. The findings of this research are beneficial not only for the case of the Czech Republic, but also can be applied in other countries that intend to enhance their open data quality evaluation processes.", "authors": ["Dasa Kusnirakova", "Mouzhi Ge", "Leonard Walletzky", "Barbora Buhnova"], "categories": ["cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2023-03-03", "url": "https://arxiv.org/abs/2303.01950", "pdf_url": "https://arxiv.org/pdf/2303.01950v1", "arxiv_id": "2303.01950", "doi": "10.5220/0011291900003269", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Data Technologies and Applications", "quality_score": 0.0753}
{"id": "58cd952ba1894abcbdce1e0e899549ab3fd95aae676019f759d409101bacfba0", "sources": ["arxiv", "semantic_scholar"], "title": "Continual Causal Inference with Incremental Observational Data", "abstract": "The era of big data has witnessed an increasing availability of observational data from mobile and social networking, online advertising, web mining, healthcare, education, public policy, marketing campaigns, and so on, which facilitates the development of causal effect estimation. Although significant advances have been made to overcome the challenges in the academic area, such as missing counterfactual outcomes and selection bias, they only focus on source-specific and stationary observational data, which is unrealistic in most industrial applications. In this paper, we investigate a new industrial problem of causal effect estimation from incrementally available observational data and present three new evaluation criteria accordingly, including extensibility, adaptability, and accessibility. We propose a Continual Causal Effect Representation Learning method for estimating causal effects with observational data, which are incrementally available from non-stationary data distributions. Instead of having access to all seen observational data, our method only stores a limited subset of feature representations learned from previous data. Combining selective and balanced representation learning, feature representation distillation, and feature transformation, our method achieves the continual causal effect estimation for new data without compromising the estimation capability for original data. Extensive experiments demonstrate the significance of continual causal effect estimation and the effectiveness of our method.", "authors": ["Zhixuan Chu", "Ruopeng Li", "Stephen Rathbun", "Sheng Li"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2023-03-03", "url": "https://arxiv.org/abs/2303.01775", "pdf_url": "https://arxiv.org/pdf/2303.01775v1", "arxiv_id": "2303.01775", "doi": "10.1109/ICDE55515.2023.00263", "citation_count": 23, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "IEEE International Conference on Data Engineering", "quality_score": 0.3451}
{"id": "acd5d5400fa76d945358d25fef6b561d9a6a11133a18866e467301f36d592448", "sources": ["arxiv", "semantic_scholar"], "title": "WEARDA: Recording Wearable Sensor Data for Human Activity Monitoring", "abstract": "We present WEARDA, the open source WEARable sensor Data Acquisition software package. WEARDA facilitates the acquisition of human activity data with smartwatches and is primarily aimed at researchers who require transparency, full control, and access to raw sensor data. It provides functionality to simultaneously record raw data from four sensors -- tri-axis accelerometer, tri-axis gyroscope, barometer, and GPS -- which should enable researchers to, for example, estimate energy expenditure and mine movement trajectories. A Samsung smartwatch running the Tizen OS was chosen because of 1) the required functionalities of the smartwatch software API, 2) the availability of software development tools and accessible documentation, 3) having the required sensors, and 4) the requirements on case design for acceptance by the target user group. WEARDA addresses five practical challenges concerning preparation, measurement, logistics, privacy preservation, and reproducibility to ensure efficient and errorless data collection. The software package was initially created for the project \"Dementia back at the heart of the community\", and has been successfully used in that context.", "authors": ["Richard M. K. van Dijk", "Daniela Gawehns", "Matthijs van Leeuwen"], "categories": ["cs.HC", "cs.CY"], "fields_of_study": ["Computer Science"], "published_date": "2023-02-28", "url": "https://arxiv.org/abs/2303.00064", "pdf_url": "https://arxiv.org/pdf/2303.00064v2", "arxiv_id": "2303.00064", "doi": "10.5334/jors.454", "citation_count": 5, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "Journal of Open Research Software", "quality_score": 0.1945}
{"id": "e5079ea9da2c52cda9118ebaa09ce96e14ff12b974436b77d693fd80d00c4845", "sources": ["arxiv", "semantic_scholar"], "title": "Standardizing Paediatric Clinical Data: The Development of the conect4children (c4c) Cross Cutting Paediatric Data Dictionary", "abstract": "Standardization of data items collected in paediatric clinical trials is an important but challenging issue. The Clinical Data Interchange Standards Consortium (CDISC) data standards are well understood by the pharmaceutical industry but lack the implementation of some paediatric specific concepts. When a paediatric concept is absent within CDISC standards, companies and research institutions take multiple approaches in the collection of paediatric data, leading to different implementations of standards and potentially limited utility for reuse. To overcome these challenges, the conect4children consortium has developed a cross-cutting paediatric data dictionary (CCPDD). The dictionary was built over three phases - scoping (including a survey sent out to ten industrial and 34 academic partners to gauge interest), creation of a longlist and consensus building for the final set of terms. The dictionary was finalized during a workshop with attendees from academia, hospitals, industry and CDISC. The attendees held detailed discussions on each data item and participated in the final vote on the inclusion of the item in the CCPDD. Nine industrial and 34 academic partners responded to the survey, which showed overall interest in the development of the CCPDD. Following the final vote on 27 data items, three were rejected, six were deferred to the next version and a final opinion was sought from CDISC. The first version of the CCPDD with 25 data items was released in August 2019. The continued use of the dictionary has the potential to ensure the collection of standardized data that is interoperable and can later be pooled and reused for other applications. The dictionary is already being used for case report form creation in three clinical trials. The CCPDD will also serve as one of the inputs to the Paediatric User Guide, which is being developed by CDISC.", "authors": ["Anando Sen", "Victoria Hedley", "John Owen", "Ronald Cornet", "Dipak Kalra", "Corinna Engel", "Avril Palmeri", "Joanne Lee", "Jean-Christophe Roze", "Joseph F Standing", "Adilia Warris", "Claudia Pansieri", "Rebecca Leary", "Mark Turner", "Volker Straub"], "categories": ["q-bio.OT", "cs.DL"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2023-02-26", "url": "https://arxiv.org/abs/2302.13340", "pdf_url": "https://arxiv.org/pdf/2302.13340v1", "arxiv_id": "2302.13340", "doi": "10.47912/jscdm.218", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Journal of the Society for Clinical Data Management", "quality_score": 0.1505}
{"id": "89bddccf838708793446a74cc01e0b46bf5cdd0c32b59740238b55838d81c38b", "sources": ["arxiv", "semantic_scholar"], "title": "Spatio-Temporal Denoising Graph Autoencoders with Data Augmentation for Photovoltaic Timeseries Data Imputation", "abstract": "The integration of the global Photovoltaic (PV) market with real time data-loggers has enabled large scale PV data analytical pipelines for power forecasting and long-term reliability assessment of PV fleets. Nevertheless, the performance of PV data analysis heavily depends on the quality of PV timeseries data. This paper proposes a novel Spatio-Temporal Denoising Graph Autoencoder (STD-GAE) framework to impute missing PV Power Data. STD-GAE exploits temporal correlation, spatial coherence, and value dependencies from domain knowledge to recover missing data. Experimental results show that STD-GAE can achieve a gain of 43.14% in imputation accuracy and remains less sensitive to missing rate, different seasons, and missing scenarios, compared with state-of-the-art data imputation methods such as MIDA and LRTC-TNN.", "authors": ["Yangxin Fan", "Xuanji Yu", "Raymond Wieser", "David Meakin", "Avishai Shaton", "Jean-Nicolas Jaubert", "Robert Flottemesch", "Michael Howell", "Jennifer Braid", "Laura S. Bruckman", "Roger French", "Yinghui Wu"], "categories": ["cs.LG", "stat.AP"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2023-02-21", "url": "https://arxiv.org/abs/2302.10860", "pdf_url": "https://arxiv.org/pdf/2302.10860v1", "arxiv_id": "2302.10860", "doi": "10.48550/arXiv.2302.10860", "citation_count": 7, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2258}
{"id": "85eb05e2f588c49dbfa15703e4e09cd9a20ad46d2c4ad73450fe0b7c7e088732", "sources": ["arxiv", "semantic_scholar"], "title": "An overview of differentiable particle filters for data-adaptive sequential Bayesian inference", "abstract": "By approximating posterior distributions with weighted samples, particle filters (PFs) provide an efficient mechanism for solving non-linear sequential state estimation problems. While the effectiveness of particle filters has been recognised in various applications, their performance relies on the knowledge of dynamic models and measurement models, as well as the construction of effective proposal distributions. An emerging trend involves constructing components of particle filters using neural networks and optimising them by gradient descent, and such data-adaptive particle filtering approaches are often called differentiable particle filters. Due to the expressiveness of neural networks, differentiable particle filters are a promising computational tool for performing inference on sequential data in complex, high-dimensional tasks, such as vision-based robot localisation. In this paper, we review recent advances in differentiable particle filters and their applications. We place special emphasis on different design choices for key components of differentiable particle filters, including dynamic models, measurement models, proposal distributions, optimisation objectives, and differentiable resampling techniques.", "authors": ["Xiongjie Chen", "Yunpeng Li"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-02-19", "url": "https://arxiv.org/abs/2302.09639", "pdf_url": "https://arxiv.org/pdf/2302.09639v2", "arxiv_id": "2302.09639", "doi": "10.48550/arXiv.2302.09639", "citation_count": 35, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Foundations of Data Science", "quality_score": 0.3891}
{"id": "8730ad32fb192c9b99295aff8667ff62db2d67163c7610dca5fa6adc33e305de", "sources": ["arxiv", "semantic_scholar"], "title": "Explicit and Implicit Knowledge Distillation via Unlabeled Data", "abstract": "Data-free knowledge distillation is a challenging model lightweight task for scenarios in which the original dataset is not available. Previous methods require a lot of extra computational costs to update one or more generators and their naive imitate-learning lead to lower distillation efficiency. Based on these observations, we first propose an efficient unlabeled sample selection method to replace high computational generators and focus on improving the training efficiency of the selected samples. Then, a class-dropping mechanism is designed to suppress the label noise caused by the data domain shifts. Finally, we propose a distillation method that incorporates explicit features and implicit structured relations to improve the effect of distillation. Experimental results show that our method can quickly converge and obtain higher accuracy than other state-of-the-art methods.", "authors": ["Yuzheng Wang", "Zuhao Ge", "Zhaoyu Chen", "Xian Liu", "Chuangjia Ma", "Yunquan Sun", "Lizhe Qi"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2023-02-17", "url": "https://arxiv.org/abs/2302.08771", "pdf_url": "https://arxiv.org/pdf/2302.08771v2", "arxiv_id": "2302.08771", "doi": "10.1109/ICASSP49357.2023.10095175", "citation_count": 11, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "IEEE International Conference on Acoustics, Speech, and Signal Processing", "quality_score": 0.2698}
{"id": "263805f16ce9ce33ac7d5f498f6c3d49e9c845db363c1b3d5121e8054742b8e1", "sources": ["arxiv", "semantic_scholar"], "title": "Home-to-school pedestrian mobility GPS data from a citizen science experiment in the Barcelona area", "abstract": "The analysis of pedestrian GPS datasets is fundamental to further advance on the study and the design of walkable cities. The highest resolution GPS data can characterize micro-mobility patterns and pedestrians' micro-motives in relation to a small-scale urban context. Purposed-based recurrent mobility data inside people's neighborhoods is an important source in these sorts of studies. However, micro-mobility around people's homes is generally unavailable, and if data exists, it is generally not shareable often due to privacy issues. Citizen science and its public involvement practices in scientific research are valid options to circumvent these challenges and provide meaningful datasets for walkable cities. The study presents GPS records from single-day home-to-school pedestrian mobility of 10 schools in the Barcelona Metropolitan area (Spain). The research provides pedestrian mobility from an age-homogeneous group of people. The study shares processed records with specific filtering, cleaning, and interpolation procedures that can facilitate and accelerate data usage. Citizen science practices during the whole research process are reported to offer a complete perspective of the data collected.", "authors": ["Ferran Larroya", "Ofelia Díaz", "Oleguer Segarra", "Pol Colomer Simón", "Salva Ferré", "Esteban Moro", "Josep Perelló"], "categories": ["physics.soc-ph"], "fields_of_study": ["Physics", "Medicine"], "published_date": "2023-02-15", "url": "https://arxiv.org/abs/2302.07585", "pdf_url": "https://arxiv.org/pdf/2302.07585v2", "arxiv_id": "2302.07585", "doi": "10.1038/s41597-023-02328-3", "citation_count": 10, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Scientific Data", "quality_score": 0.2603}
{"id": "91d6dab06800ddf7656747595e638ab6802c37f1efa0623529002d6154ef87b5", "sources": ["arxiv", "semantic_scholar"], "title": "Multi-teacher knowledge distillation as an effective method for compressing ensembles of neural networks", "abstract": "Deep learning has contributed greatly to many successes in artificial intelligence in recent years. Today, it is possible to train models that have thousands of layers and hundreds of billions of parameters. Large-scale deep models have achieved great success, but the enormous computational complexity and gigantic storage requirements make it extremely difficult to implement them in real-time applications. On the other hand, the size of the dataset is still a real problem in many domains. Data are often missing, too expensive, or impossible to obtain for other reasons. Ensemble learning is partially a solution to the problem of small datasets and overfitting. However, ensemble learning in its basic version is associated with a linear increase in computational complexity. We analyzed the impact of the ensemble decision-fusion mechanism and checked various methods of sharing the decisions including voting algorithms. We used the modified knowledge distillation framework as a decision-fusion mechanism which allows in addition compressing of the entire ensemble model into a weight space of a single model. We showed that knowledge distillation can aggregate knowledge from multiple teachers in only one student model and, with the same computational complexity, obtain a better-performing model compared to a model trained in the standard manner. We have developed our own method for mimicking the responses of all teachers at the same time, simultaneously. We tested these solutions on several benchmark datasets. In the end, we presented a wide application use of the efficient multi-teacher knowledge distillation framework. In the first example, we used knowledge distillation to develop models that could automate corrosion detection on aircraft fuselage. The second example describes detection of smoke on observation cameras in order to counteract wildfires in forests.", "authors": ["Konrad Zuchniak"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-02-14", "url": "https://arxiv.org/abs/2302.07215", "pdf_url": "https://arxiv.org/pdf/2302.07215v1", "arxiv_id": "2302.07215", "doi": "10.48550/arXiv.2302.07215", "citation_count": 9, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.25}
{"id": "8ded626ee585f44e23f950ca7e28f4a239012545db5b6b2c5f433059adf6fd6b", "sources": ["arxiv", "semantic_scholar"], "title": "Estimation of Average Annual Daily Bicycle Count Using Bike-Share GPS Data and Bike Counter Data for an Urban Active Transportation Network", "abstract": "In 2018, the City of Kelowna entered into a license agreement with Dropbike to operate a dockless bike-share pilot in and around the downtown core. The bikes were tracked by the user's cell phone GPS through the Dropbike app. The City's Active Transportation team recognized that this GPS data could help understand the routes used by cyclists which would then inform decision-making for infrastructure improvements. Using OSMnx and NetworkX, the map of Kelowna was converted into a graph network to map inaccurate, infrequent GPS points to the nearest street intersection, calculate the potential paths taken by cyclists and count the number of trips by street segment though the comparison of different path-finding models. Combined with the data from four counters around downtown, a mixed effects statistical model and a least squares optimization were used to estimate a relationship between the different traffic patterns of the bike-share and counter data. Using this relationship based on sparse data input from physical counting stations and bike share data, estimations and visualizations of the annual daily bicycle volume in downtown Kelowna were produced. The analysis, modelling and visualization helped to better understand how the bike network was being used in the urban center, including non-traditional routes such as laneways and highway crossings.", "authors": ["Marzi Rafieenia", "Liza Wood", "Mohsen Zardadi", "Scott Fazackerley", "Ramon Lawrence"], "categories": ["cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2023-02-13", "url": "https://arxiv.org/abs/2302.06715", "pdf_url": "https://arxiv.org/pdf/2302.06715v1", "arxiv_id": "2302.06715", "doi": "10.48550/arXiv.2302.06715", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "ba278f102f2fcdd1591c05919a712660a114ac05aeeee1c2cf82649374a80aaf", "sources": ["arxiv", "semantic_scholar"], "title": "Algorithmically Effective Differentially Private Synthetic Data", "abstract": "We present a highly effective algorithmic approach for generating $\\varepsilon$-differentially private synthetic data in a bounded metric space with near-optimal utility guarantees under the 1-Wasserstein distance. In particular, for a dataset $X$ in the hypercube $[0,1]^d$, our algorithm generates synthetic dataset $Y$ such that the expected 1-Wasserstein distance between the empirical measure of $X$ and $Y$ is $O((\\varepsilon n)^{-1/d})$ for $d\\geq 2$, and is $O(\\log^2(\\varepsilon n)(\\varepsilon n)^{-1})$ for $d=1$. The accuracy guarantee is optimal up to a constant factor for $d\\geq 2$, and up to a logarithmic factor for $d=1$. Our algorithm has a fast running time of $O(\\varepsilon dn)$ for all $d\\geq 1$ and demonstrates improved accuracy compared to the method in (Boedihardjo et al., 2022) for $d\\geq 2$.", "authors": ["Yiyun He", "Roman Vershynin", "Yizhe Zhu"], "categories": ["cs.DS", "cs.CR", "math.PR", "math.ST"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2023-02-11", "url": "https://arxiv.org/abs/2302.05552", "pdf_url": "https://arxiv.org/pdf/2302.05552v3", "arxiv_id": "2302.05552", "doi": "10.48550/arXiv.2302.05552", "citation_count": 12, "influential_citation_count": 6, "has_code": false, "code_url": null, "venue": "Annual Conference Computational Learning Theory", "quality_score": 0.4225}
{"id": "76746c23990b3c67b251c92eac60df5ff55136d012d1c67fd7253c029af3c717", "sources": ["arxiv", "semantic_scholar"], "title": "Machine Learning for Synthetic Data Generation: A Review", "abstract": "Machine learning heavily relies on data, but real-world applications often encounter various data-related issues. These include data of poor quality, insufficient data points leading to under-fitting of machine learning models, and difficulties in data access due to concerns surrounding privacy, safety, and regulations. In light of these challenges, the concept of synthetic data generation emerges as a promising alternative that allows for data sharing and utilization in ways that real-world data cannot facilitate. This paper presents a comprehensive systematic review of existing studies that employ machine learning models for the purpose of generating synthetic data. The review encompasses various perspectives, starting with the applications of synthetic data generation, spanning computer vision, speech, natural language processing, healthcare, and business domains. Additionally, it explores different machine learning methods, with particular emphasis on neural network architectures and deep generative models. The paper also addresses the crucial aspects of privacy and fairness concerns related to synthetic data generation. Furthermore, this study identifies the challenges and opportunities prevalent in this emerging field, shedding light on the potential avenues for future research. By delving into the intricacies of synthetic data generation, this paper aims to contribute to the advancement of knowledge and inspire further exploration in synthetic data generation.", "authors": ["Yingzhou Lu", "Lulu Chen", "Yuanyuan Zhang", "Minjie Shen", "Huazheng Wang", "Xiao Wang", "Capucine van Rechem", "Tianfan Fu", "Wenqi Wei"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-02-08", "url": "https://arxiv.org/abs/2302.04062", "pdf_url": "https://arxiv.org/pdf/2302.04062v10", "arxiv_id": "2302.04062", "doi": "10.48550/arXiv.2302.04062", "citation_count": 285, "influential_citation_count": 14, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.6141}
{"id": "94968fc0dbeb508e6c9494e9e564676be6bcd43c40efe34b2521a513313bfbea", "sources": ["arxiv", "semantic_scholar"], "title": "Anderson Acceleration For Bioinformatics-Based Machine Learning", "abstract": "Anderson acceleration (AA) is a well-known method for accelerating the convergence of iterative algorithms, with applications in various fields including deep learning and optimization. Despite its popularity in these areas, the effectiveness of AA in classical machine learning classifiers has not been thoroughly studied. Tabular data, in particular, presents a unique challenge for deep learning models, and classical machine learning models are known to perform better in these scenarios. However, the convergence analysis of these models has received limited attention. To address this gap in research, we implement a support vector machine (SVM) classifier variant that incorporates AA to speed up convergence. We evaluate the performance of our SVM with and without Anderson acceleration on several datasets from the biology domain and demonstrate that the use of AA significantly improves convergence and reduces the training loss as the number of iterations increases. Our findings provide a promising perspective on the potential of Anderson acceleration in the training of simple machine learning classifiers and underscore the importance of further research in this area. By showing the effectiveness of AA in this setting, we aim to inspire more studies that explore the applications of AA in classical machine learning.", "authors": ["Sarwan Ali", "Prakash Chourasia", "Murray Patterson"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-02-01", "url": "https://arxiv.org/abs/2302.00347", "pdf_url": "https://arxiv.org/pdf/2302.00347v2", "arxiv_id": "2302.00347", "doi": "10.48550/arXiv.2302.00347", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "KDH-2023: Knowledge Discovery in Healthcare Data (IJCAI Workshop)", "quality_score": 0.1193}
{"id": "f51809ee3712f6065a10940712bc2c62a76f7fe73af4432f10b16455b2d449b4", "sources": ["arxiv", "semantic_scholar"], "title": "Generating High Fidelity Synthetic Data via Coreset selection and Entropic Regularization", "abstract": "Generative models have the ability to synthesize data points drawn from the data distribution, however, not all generated samples are high quality. In this paper, we propose using a combination of coresets selection methods and ``entropic regularization'' to select the highest fidelity samples. We leverage an Energy-Based Model which resembles a variational auto-encoder with an inference and generator model for which the latent prior is complexified by an energy-based model. In a semi-supervised learning scenario, we show that augmenting the labeled data-set, by adding our selected subset of samples, leads to better accuracy improvement rather than using all the synthetic samples.", "authors": ["Omead Pooladzandi", "Pasha Khosravi", "Erik Nijkamp", "Baharan Mirzasoleiman"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-01-31", "url": "https://arxiv.org/abs/2302.00138", "pdf_url": "https://arxiv.org/pdf/2302.00138v1", "arxiv_id": "2302.00138", "doi": "10.48550/arXiv.2302.00138", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "e1a6b1ddf1077f53b171a507c97e2e180947ace9c70014aac4659c9d18e5ad1b", "sources": ["arxiv", "semantic_scholar"], "title": "Deep Generative Neural Embeddings for High Dimensional Data Visualization", "abstract": "We propose a visualization technique that utilizes neural network embeddings and a generative network to reconstruct original data. This method allows for independent manipulation of individual image embeddings through its non-parametric structure, providing more flexibility than traditional autoencoder approaches. We have evaluated the effectiveness of this technique in data visualization and compared it to t-SNE and VAE methods. Furthermore, we have demonstrated the scalability of our method through visualizations on the ImageNet dataset. Our technique has potential applications in human-in-the-loop training, as it allows for independent editing of embedding locations without affecting the optimization process.", "authors": ["Halid Ziya Yerebakan", "Gerardo Hermosillo Valadez"], "categories": ["cs.LG", "cs.CV", "cs.HC"], "fields_of_study": ["Computer Science"], "published_date": "2023-01-25", "url": "https://arxiv.org/abs/2302.10801", "pdf_url": "https://arxiv.org/pdf/2302.10801v1", "arxiv_id": "2302.10801", "doi": "10.48550/arXiv.2302.10801", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0}
{"id": "5ad2133d754705c29f5f3bd84ca26855cdd8936e91ffe1eb04e6c309814b3046", "sources": ["arxiv", "semantic_scholar"], "title": "ScaDLES: Scalable Deep Learning over Streaming data at the Edge", "abstract": "Distributed deep learning (DDL) training systems are designed for cloud and data-center environments that assumes homogeneous compute resources, high network bandwidth, sufficient memory and storage, as well as independent and identically distributed (IID) data across all nodes. However, these assumptions don't necessarily apply on the edge, especially when training neural networks on streaming data in an online manner. Computing on the edge suffers from both systems and statistical heterogeneity. Systems heterogeneity is attributed to differences in compute resources and bandwidth specific to each device, while statistical heterogeneity comes from unbalanced and skewed data on the edge. Different streaming-rates among devices can be another source of heterogeneity when dealing with streaming data. If the streaming rate is lower than training batch-size, device needs to wait until enough samples have streamed in before performing a single iteration of stochastic gradient descent (SGD). Thus, low-volume streams act like stragglers slowing down devices with high-volume streams in synchronous training. On the other hand, data can accumulate quickly in the buffer if the streaming rate is too high and the devices can't train at line-rate. In this paper, we introduce ScaDLES to efficiently train on streaming data at the edge in an online fashion, while also addressing the challenges of limited bandwidth and training with non-IID data. We empirically show that ScaDLES converges up to 3.29 times faster compared to conventional distributed SGD.", "authors": ["Sahil Tyagi", "Martin Swany"], "categories": ["cs.DC", "cs.LG", "cs.NI"], "fields_of_study": ["Computer Science"], "published_date": "2023-01-21", "url": "https://arxiv.org/abs/2301.08897", "pdf_url": "https://arxiv.org/pdf/2301.08897v2", "arxiv_id": "2301.08897", "doi": "10.1109/BigData55660.2022.10020597", "citation_count": 9, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Tyagi, S., & Swany, M. (2022). ScaDLES: Scalable Deep Learning over Streaming data at the Edge. 2022 IEEE International Conference on Big Data (Big Data), 2113-2122", "quality_score": 0.25}
{"id": "b7d53d86afbb575524ae24dcdf29427ed8e9165c801d5c96da74a2e25f9e9541", "sources": ["arxiv", "semantic_scholar"], "title": "A data science and machine learning approach to continuous analysis of Shakespeare's plays", "abstract": "The availability of quantitative text analysis methods has provided new ways of analyzing literature in a manner that was not available in the pre-information era. Here we apply comprehensive machine learning analysis to the work of William Shakespeare. The analysis shows clear changes in the style of writing over time, with the most significant changes in the sentence length, frequency of adjectives and adverbs, and the sentiments expressed in the text. Applying machine learning to make a stylometric prediction of the year of the play shows a Pearson correlation of 0.71 between the actual and predicted year, indicating that Shakespeare's writing style as reflected by the quantitative measurements changed over time. Additionally, it shows that the stylometrics of some of the plays is more similar to plays written either before or after the year they were written. For instance, Romeo and Juliet is dated 1596, but is more similar in stylometrics to plays written by Shakespeare after 1600. The source code for the analysis is available for free download.", "authors": ["Charles Swisher", "Lior Shamir"], "categories": ["cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-01-15", "url": "https://arxiv.org/abs/2301.06024", "pdf_url": "https://arxiv.org/pdf/2301.06024v3", "arxiv_id": "2301.06024", "doi": "10.46298/jdmdh.10829", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Journal of Data Mining and Digital Humanities", "quality_score": 0.1945}
{"id": "ee0d058faeeee3e96c4124cba9cabc04d5ccaca744d59034d386d87be34fbf69", "sources": ["arxiv", "semantic_scholar"], "title": "Understanding Concept Identification as Consistent Data Clustering Across Multiple Feature Spaces", "abstract": "Identifying meaningful concepts in large data sets can provide valuable insights into engineering design problems. Concept identification aims at identifying non-overlapping groups of design instances that are similar in a joint space of all features, but which are also similar when considering only subsets of features. These subsets usually comprise features that characterize a design with respect to one specific context, for example, constructive design parameters, performance values, or operation modes. It is desirable to evaluate the quality of design concepts by considering several of these feature subsets in isolation. In particular, meaningful concepts should not only identify dense, well separated groups of data instances, but also provide non-overlapping groups of data that persist when considering pre-defined feature subsets separately. In this work, we propose to view concept identification as a special form of clustering algorithm with a broad range of potential applications beyond engineering design. To illustrate the differences between concept identification and classical clustering algorithms, we apply a recently proposed concept identification algorithm to two synthetic data sets and show the differences in identified solutions. In addition, we introduce the mutual information measure as a metric to evaluate whether solutions return consistent clusters across relevant subsets. To support the novel understanding of concept identification, we consider a simulated data set from a decision-making problem in the energy management domain and show that the identified clusters are more interpretable with respect to relevant feature subsets than clusters found by common clustering algorithms and are thus more suitable to support a decision maker.", "authors": ["Felix Lanfermann", "Sebastian Schmitt", "Patricia Wollstadt"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-01-13", "url": "https://arxiv.org/abs/2301.05525", "pdf_url": "https://arxiv.org/pdf/2301.05525v2", "arxiv_id": "2301.05525", "doi": "10.1109/ICDMW58026.2022.00032", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "2022 IEEE International Conference on Data Mining Workshops (ICDMW)", "quality_score": 0.1193}
{"id": "f2bb348a75af18232b2fd60b66f2ad8d71bbc6b97d22c85d0767ebe54689337f", "sources": ["arxiv", "semantic_scholar"], "title": "Data-centric AI: Perspectives and Challenges", "abstract": "The role of data in building AI systems has recently been significantly magnified by the emerging concept of data-centric AI (DCAI), which advocates a fundamental shift from model advancements to ensuring data quality and reliability. Although our community has continuously invested efforts into enhancing data in different aspects, they are often isolated initiatives on specific tasks. To facilitate the collective initiative in our community and push forward DCAI, we draw a big picture and bring together three general missions: training data development, inference data development, and data maintenance. We provide a top-level discussion on representative DCAI tasks and share perspectives. Finally, we list open challenges. More resources are summarized at https://github.com/daochenzha/data-centric-AI", "authors": ["Daochen Zha", "Zaid Pervaiz Bhat", "Kwei-Herng Lai", "Fan Yang", "Xia Hu"], "categories": ["cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-01-12", "url": "https://arxiv.org/abs/2301.04819", "pdf_url": "https://arxiv.org/pdf/2301.04819v3", "arxiv_id": "2301.04819", "doi": "10.48550/arXiv.2301.04819", "citation_count": 98, "influential_citation_count": 4, "has_code": true, "code_url": "https://github.com/daochenzha/data-centric-AI", "venue": "SDM", "quality_score": 0.4989}
{"id": "5840f3868a3dd2738bd42e742679c436ebda30bed51985705ab5bb691fbea008", "sources": ["arxiv", "semantic_scholar"], "title": "Synthetic data generation method for data-free knowledge distillation in regression neural networks", "abstract": "Knowledge distillation is the technique of compressing a larger neural network, known as the teacher, into a smaller neural network, known as the student, while still trying to maintain the performance of the larger neural network as much as possible. Existing methods of knowledge distillation are mostly applicable for classification tasks. Many of them also require access to the data used to train the teacher model. To address the problem of knowledge distillation for regression tasks under the absence of original training data, previous work has proposed a data-free knowledge distillation method where synthetic data are generated using a generator model trained adversarially against the student model. These synthetic data and their labels predicted by the teacher model are then used to train the student model. In this study, we investigate the behavior of various synthetic data generation methods and propose a new synthetic data generation strategy that directly optimizes for a large but bounded difference between the student and teacher model. Our results on benchmark and case study experiments demonstrate that the proposed strategy allows the student model to learn better and emulate the performance of the teacher model more closely.", "authors": ["Tianxun Zhou", "Keng-Hwee Chiam"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-01-11", "url": "https://arxiv.org/abs/2301.04338", "pdf_url": "https://arxiv.org/pdf/2301.04338v2", "arxiv_id": "2301.04338", "doi": "10.1016/j.eswa.2023.120327", "citation_count": 12, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Expert systems with applications", "quality_score": 0.2785}
{"id": "33ebd1df276c6e9711db55496124688110b2ecb068d98bdc3baf1792a4925c35", "sources": ["arxiv", "semantic_scholar"], "title": "A review of clustering models in educational data science towards fairness-aware learning", "abstract": "Ensuring fairness is essential for every education system. Machine learning is increasingly supporting the education system and educational data science (EDS) domain, from decision support to educational activities and learning analytics. However, the machine learning-based decisions can be biased because the algorithms may generate the results based on students' protected attributes such as race or gender. Clustering is an important machine learning technique to explore student data in order to support the decision-maker, as well as support educational activities, such as group assignments. Therefore, ensuring high-quality clustering models along with satisfying fairness constraints are important requirements. This chapter comprehensively surveys clustering models and their fairness in EDS. We especially focus on investigating the fair clustering models applied in educational activities. These models are believed to be practical tools for analyzing students' data and ensuring fairness in EDS.", "authors": ["Tai Le Quy", "Gunnar Friege", "Eirini Ntoutsi"], "categories": ["cs.LG", "cs.CY"], "fields_of_study": ["Computer Science"], "published_date": "2023-01-09", "url": "https://arxiv.org/abs/2301.03421", "pdf_url": "https://arxiv.org/pdf/2301.03421v1", "arxiv_id": "2301.03421", "doi": "10.1007/978-981-99-0026-8_2", "citation_count": 26, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3578}
{"id": "a053f058072ba4e5f7244c91a9a2fbbe059082b8e385fe3c4120f0ffcc49ad42", "sources": ["arxiv", "semantic_scholar"], "title": "Significant Digits: Using Large-Scale Blockchain Data to Predict Fraudulent Addresses", "abstract": "Blockchain systems and cryptocurrencies have exploded in popularity over the past decade, and with this growing user base, the number of cryptocurrency scams has also surged. Given the graphical structure of blockchain networks and the abundance of data generated on these networks, we use graph mining techniques to extract essential information on transactions and apply Benford's Law to extract distributional information on address transactions. We then apply a gradient-boosting tree model to predict fraudulent addresses. Our results show that our method can detect scams with reasonable accuracy and that the features generated based on Benford's Law are the most significant features.", "authors": ["Jared Gridley", "Oshani Seneviratne"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2023-01-03", "url": "https://arxiv.org/abs/2301.01809", "pdf_url": "https://arxiv.org/pdf/2301.01809v1", "arxiv_id": "2301.01809", "doi": "10.1109/BigData55660.2022.10020971", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1505}
{"id": "2c3cf67ca28e7d7bff3df096e202d2d707c11d2956a236d693bb6992c364b843", "sources": ["arxiv", "semantic_scholar"], "title": "Algorithms for Massive Data -- Lecture Notes", "abstract": "These are the lecture notes for the course CM0622 - Algorithms for Massive Data, Ca' Foscari University of Venice. The goal of this course is to introduce algorithmic techniques for dealing with massive data: data so large that it does not fit in the computer's memory. There are two main solutions to deal with massive data: (lossless) compressed data structures and (lossy) data sketches. These notes cover both topics: compressed suffix arrays, probabilistic filters, sketching under various metrics, Locality Sensitive Hashing, nearest neighbour search, algorithms on streams.", "authors": ["Nicola Prezza"], "categories": ["cs.DS"], "fields_of_study": ["Computer Science"], "published_date": "2023-01-02", "url": "https://arxiv.org/abs/2301.00754", "pdf_url": "https://arxiv.org/pdf/2301.00754v15", "arxiv_id": "2301.00754", "doi": "10.48550/arXiv.2301.00754", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0}
{"id": "f4c2f1f42a7add03e4ae5ab2025bffe09b21a0dd612c6823d1323b4d6913d5cf", "sources": ["arxiv", "semantic_scholar"], "title": "DMOps: Data Management Operation and Recipes", "abstract": "Data-centric AI has shed light on the significance of data within the machine learning (ML) pipeline. Recognizing its significance, academia, industry, and government departments have suggested various NLP data research initiatives. While the ability to utilize existing data is essential, the ability to build a dataset has become more critical than ever, especially in the industry. In consideration of this trend, we propose a \"Data Management Operations and Recipes\" to guide the industry in optimizing the building of datasets for NLP products. This paper presents the concept of DMOps which is derived from real-world experiences with NLP data management and aims to streamline data operations by offering a baseline.", "authors": ["Eujeong Choi", "Chanjun Park"], "categories": ["cs.DB", "cs.LG", "stat.ME"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2023-01-02", "url": "https://arxiv.org/abs/2301.01228", "pdf_url": "https://arxiv.org/pdf/2301.01228v3", "arxiv_id": "2301.01228", "doi": "10.48550/arXiv.2301.01228", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2258}
{"id": "d2b2fdb548db254591fdc27e200d2a9ab59dcd6e2ffde53cffcd6a49f5c19f61", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Assessing Data Bias in Clinical Trials", "abstract": "Algorithms and technologies are essential tools that pervade all aspects of our daily lives. In the last decades, health care research benefited from new computer-based recruiting methods, the use of federated architectures for data storage, the introduction of innovative analyses of datasets, and so on. Nevertheless, health care datasets can still be affected by data bias. Due to data bias, they provide a distorted view of reality, leading to wrong analysis results and, consequently, decisions. For example, in a clinical trial that studied the risk of cardiovascular diseases, predictions were wrong due to the lack of data on ethnic minorities. It is, therefore, of paramount importance for researchers to acknowledge data bias that may be present in the datasets they use, eventually adopt techniques to mitigate them and control if and how analyses results are impacted. This paper proposes a method to address bias in datasets that: (i) defines the types of data bias that may be present in the dataset, (ii) characterizes and quantifies data bias with adequate metrics, (iii) provides guidelines to identify, measure, and mitigate data bias for different data sources. The method we propose is applicable both for prospective and retrospective clinical trials. We evaluate our proposal both through theoretical considerations and through interviews with researchers in the health care environment.", "authors": ["Chiara Criscuolo", "Tommaso Dolci", "Mattia Salnitri"], "categories": ["cs.CY", "cs.AI", "cs.LG", "stat.ME"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2022-12-19", "url": "https://arxiv.org/abs/2212.09633", "pdf_url": "https://arxiv.org/pdf/2212.09633v1", "arxiv_id": "2212.09633", "doi": "10.1007/978-3-031-23905-2_5", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1505}
{"id": "5a4f3cc70c165d712d69d1253bfb86e811af897b2ea7564ccfbe8636d96f0c95", "sources": ["arxiv", "semantic_scholar"], "title": "Seeing through Things: Exploring the Design Space of Privacy-Aware Data-Enabled Objects", "abstract": "Increasing amounts of sensor-augmented research objects have been used in design research. We call these objects Data-Enabled Objects, which can be integrated into daily activities capturing data about people's detailed whereabouts, behaviours and routines. These objects provide data perspectives on everyday life for contextual design research. However, data-enabled objects are still computational devices with limited privacy awareness and nuanced data sharing. To better design data-enabled objects, we explore privacy design spaces by inviting 18 teams of undergraduate design students to re-design the same type of sensor-enabled home research camera. We developed the Connected Peekaboo Toolkit (CPT) to support the design teams in designing, building, and directly deploying their prototypes in real home studies. We conducted Thematic Analysis to analyse their outcomes which led us to interpret that privacy is not just an obstacle but can be a driver by unfolding an exploration of possible design spaces for data-enabled objects.", "authors": ["Yu-Ting Cheng", "Mathias Funk", "Rung-Huei Liang", "Lin-Lin Chen"], "categories": ["cs.HC"], "fields_of_study": ["Computer Science"], "published_date": "2022-12-16", "url": "https://arxiv.org/abs/2212.08278", "pdf_url": "https://arxiv.org/pdf/2212.08278v1", "arxiv_id": "2212.08278", "doi": "10.1145/3577012", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "ACM Trans. Comput.-Hum. Interact. 1, 1, Article 1 (January 2022)", "quality_score": 0.1945}
{"id": "ce911bc489eec5105f6d6e3eadbaf5cd4d5168a894fcee8d4ebd21e0fe3a9fa3", "sources": ["arxiv", "semantic_scholar"], "title": "GenSyn: A Multi-stage Framework for Generating Synthetic Microdata using Macro Data Sources", "abstract": "Individual-level data (microdata) that characterizes a population, is essential for studying many real-world problems. However, acquiring such data is not straightforward due to cost and privacy constraints, and access is often limited to aggregated data (macro data) sources. In this study, we examine synthetic data generation as a tool to extrapolate difficult-to-obtain high-resolution data by combining information from multiple easier-to-obtain lower-resolution data sources. In particular, we introduce a framework that uses a combination of univariate and multivariate frequency tables from a given target geographical location in combination with frequency tables from other auxiliary locations to generate synthetic microdata for individuals in the target location. Our method combines the estimation of a dependency graph and conditional probabilities from the target location with the use of a Gaussian copula to leverage the available information from the auxiliary locations. We perform extensive testing on two real-world datasets and demonstrate that our approach outperforms prior approaches in preserving the overall dependency structure of the data while also satisfying the constraints defined on the different variables.", "authors": ["Angeela Acharya", "Siddhartha Sikdar", "Sanmay Das", "Huzefa Rangwala"], "categories": ["cs.LG", "cs.CE"], "fields_of_study": ["Computer Science"], "published_date": "2022-12-08", "url": "https://arxiv.org/abs/2212.05975", "pdf_url": "https://arxiv.org/pdf/2212.05975v1", "arxiv_id": "2212.05975", "doi": "10.1109/BigData55660.2022.10021001", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2113}
{"id": "b723f2282f35ee80b8f2654b206c34dd63365198fda2da57a76a498e034add37", "sources": ["arxiv", "semantic_scholar"], "title": "VISEM-Tracking, a human spermatozoa tracking dataset", "abstract": "A manual assessment of sperm motility requires microscopy observation, which is challenging due to the fast-moving spermatozoa in the field of view. To obtain correct results, manual evaluation requires extensive training. Therefore, computer-assisted sperm analysis (CASA) has become increasingly used in clinics. Despite this, more data is needed to train supervised machine learning approaches in order to improve accuracy and reliability in the assessment of sperm motility and kinematics. In this regard, we provide a dataset called VISEM-Tracking with 20 video recordings of 30 seconds (comprising 29,196 frames) of wet sperm preparations with manually annotated bounding-box coordinates and a set of sperm characteristics analyzed by experts in the domain. In addition to the annotated data, we provide unlabeled video clips for easy-to-use access and analysis of the data via methods such as self- or unsupervised learning. As part of this paper, we present baseline sperm detection performances using the YOLOv5 deep learning (DL) model trained on the VISEM-Tracking dataset. As a result, we show that the dataset can be used to train complex DL models to analyze spermatozoa.", "authors": ["Vajira Thambawita", "Steven A. Hicks", "Andrea M. Storås", "Thu Nguyen", "Jorunn M. Andersen", "Oliwia Witczak", "Trine B. Haugen", "Hugo L. Hammer", "Pål Halvorsen", "Michael A. Riegler"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "fields_of_study": ["Medicine", "Computer Science"], "published_date": "2022-12-06", "url": "https://arxiv.org/abs/2212.02842", "pdf_url": "https://arxiv.org/pdf/2212.02842v5", "arxiv_id": "2212.02842", "doi": "10.1038/s41597-023-02173-4", "citation_count": 52, "influential_citation_count": 5, "has_code": false, "code_url": null, "venue": "Scientific Data", "quality_score": 0.4311}
{"id": "ff534b9e72dd076e88495f5e2a39e98e0e673d9f42f1273f7a9b73622ffb5ba7", "sources": ["arxiv", "semantic_scholar"], "title": "CLIP: Train Faster with Less Data", "abstract": "Deep learning models require an enormous amount of data for training. However, recently there is a shift in machine learning from model-centric to data-centric approaches. In data-centric approaches, the focus is to refine and improve the quality of the data to improve the learning performance of the models rather than redesigning model architectures. In this paper, we propose CLIP i.e., Curriculum Learning with Iterative data Pruning. CLIP combines two data-centric approaches i.e., curriculum learning and dataset pruning to improve the model learning accuracy and convergence speed. The proposed scheme applies loss-aware dataset pruning to iteratively remove the least significant samples and progressively reduces the size of the effective dataset in the curriculum learning training. Extensive experiments performed on crowd density estimation models validate the notion behind combining the two approaches by reducing the convergence time and improving generalization. To our knowledge, the idea of data pruning as an embedded process in curriculum learning is novel.", "authors": ["Muhammad Asif Khan", "Ridha Hamila", "Hamid Menouar"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2022-12-02", "url": "https://arxiv.org/abs/2212.01452", "pdf_url": "https://arxiv.org/pdf/2212.01452v3", "arxiv_id": "2212.01452", "doi": "10.1109/BigComp57234.2023.00014", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Big Data and Smart Computing", "quality_score": 0.2386}
{"id": "ebb57f2f77df995b5245102b8885e000a564a7ec0bf281750cfcb3e0145e6cd8", "sources": ["arxiv", "semantic_scholar"], "title": "Noisy Label Classification using Label Noise Selection with Test-Time Augmentation Cross-Entropy and NoiseMix Learning", "abstract": "As the size of the dataset used in deep learning tasks increases, the noisy label problem, which is a task of making deep learning robust to the incorrectly labeled data, has become an important task. In this paper, we propose a method of learning noisy label data using the label noise selection with test-time augmentation (TTA) cross-entropy and classifier learning with the NoiseMix method. In the label noise selection, we propose TTA cross-entropy by measuring the cross-entropy to predict the test-time augmented training data. In the classifier learning, we propose the NoiseMix method based on MixUp and BalancedMix methods by mixing the samples from the noisy and the clean label data. In experiments on the ISIC-18 public skin lesion diagnosis dataset, the proposed TTA cross-entropy outperformed the conventional cross-entropy and the TTA uncertainty in detecting label noise data in the label noise selection process. Moreover, the proposed NoiseMix not only outperformed the state-of-the-art methods in the classification performance but also showed the most robustness to the label noise in the classifier learning.", "authors": ["Hansang Lee", "Haeil Lee", "Helen Hong", "Junmo Kim"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2022-12-01", "url": "https://arxiv.org/abs/2212.00479", "pdf_url": "https://arxiv.org/pdf/2212.00479v2", "arxiv_id": "2212.00479", "doi": "10.1007/978-3-031-17027-0_8", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0753}
{"id": "1792490ec74a2330ef3927c639b12a4272ca0137898be72846645b3baecf4fc6", "sources": ["arxiv", "semantic_scholar"], "title": "Generating Realistic Synthetic Relational Data through Graph Variational Autoencoders", "abstract": "Synthetic data generation has recently gained widespread attention as a more reliable alternative to traditional data anonymization. The involved methods are originally developed for image synthesis. Hence, their application to the typically tabular and relational datasets from healthcare, finance and other industries is non-trivial. While substantial research has been devoted to the generation of realistic tabular datasets, the study of synthetic relational databases is still in its infancy. In this paper, we combine the variational autoencoder framework with graph neural networks to generate realistic synthetic relational databases. We then apply the obtained method to two publicly available databases in computational experiments. The results indicate that real databases' structures are accurately preserved in the resulting synthetic datasets, even for large datasets with advanced data types.", "authors": ["Ciro Antonio Mami", "Andrea Coser", "Eric Medvet", "Alexander T. P. Boudewijn", "Marco Volpe", "Michael Whitworth", "Borut Svara", "Gabriele Sgroi", "Daniele Panfilo", "Sebastiano Saccani"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2022-11-30", "url": "https://arxiv.org/abs/2211.16889", "pdf_url": "https://arxiv.org/pdf/2211.16889v1", "arxiv_id": "2211.16889", "doi": "10.48550/arXiv.2211.16889", "citation_count": 11, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2698}
{"id": "0afe5d5885609b9bd7fcc98a73074eee8e15ac8413c0656bd7c7efa8437414d1", "sources": ["arxiv", "semantic_scholar"], "title": "Evaluating Synthetic Tabular Data Generated To Augment Small Sample Datasets", "abstract": "This work proposes a method to evaluate synthetic tabular data generated to augment small sample datasets. While data augmentation techniques can increase sample counts for machine learning applications, traditional validation approaches fail when applied to extremely limited sample sizes. Our experiments across four datasets reveal significant inconsistencies between global metrics and topological measures, with statistical tests producing unreliable significance values due to insufficient sample sizes. We demonstrate that common metrics like propensity scoring and MMD often suggest similarity where fundamental topological differences exist. Our proposed normalized Bottleneck distance based metric provides complementary insights but suffers from high variability across experimental runs and occasional values exceeding theoretical bounds, showing inherent instability in topological approaches for very small datasets. These findings highlight the critical need for multi-faceted evaluation methodologies when validating synthetic data generated from limited samples, as no single metric reliably captures both distributional and structural similarity.", "authors": ["Javier Marin"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2022-11-19", "url": "https://arxiv.org/abs/2211.10760", "pdf_url": "https://arxiv.org/pdf/2211.10760v5", "arxiv_id": "2211.10760", "doi": null, "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1505}
{"id": "20d937c97c325f71d86e6fb48324ce891931240d73fbeaa34542f4383b1d7332", "sources": ["arxiv", "semantic_scholar"], "title": "Data Privacy in Multi-Cloud: An Enhanced Data Fragmentation Framework", "abstract": "Data splitting preserves privacy by partitioning data into various fragments to be stored remotely and shared. It supports most data operations because data can be stored in clear as opposed to methods that rely on cryptography. However, majority of existing data splitting techniques do not consider data already in the multi-cloud. This leads to unnecessary use of resources to re-split data into fragments. This work proposes a data splitting framework that leverages on existing data in the multi-cloud. It improves data splitting mechanisms by reducing the number of splitting operations and resulting fragments. Therefore, decreasing the number of storage locations a data owner manages. Broadcasts queries locate third-party data fragments to avoid costly operations when splitting data. This work examines considerations for the use of third-party fragments and application to existing data splitting techniques. The proposed framework was also applied to an existing data splitting mechanism to complement its capabilities.", "authors": ["Randolph Loh", "Vrizlynn L. L. Thing"], "categories": ["cs.CR", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2022-11-18", "url": "https://arxiv.org/abs/2211.11577", "pdf_url": "https://arxiv.org/pdf/2211.11577v1", "arxiv_id": "2211.11577", "doi": "10.1109/PST52912.2021.9647746", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Conference on Privacy, Security and Trust", "quality_score": 0.1505}
{"id": "4f3a6e15b07f65a754209c03b6b5245c4bd6a7bc48cf0d92ece92a5de1c7f5d5", "sources": ["arxiv", "semantic_scholar"], "title": "Permutation-Invariant Tabular Data Synthesis", "abstract": "Tabular data synthesis is an emerging approach to circumvent strict regulations on data privacy while discovering knowledge through big data. Although state-of-the-art AI-based tabular data synthesizers, e.g., table-GAN, CTGAN, TVAE, and CTAB-GAN, are effective at generating synthetic tabular data, their training is sensitive to column permutations of input data. In this paper, we first conduct an extensive empirical study to disclose such a property of permutation invariance and an in-depth analysis of the existing synthesizers. We show that changing the input column order worsens the statistical difference between real and synthetic data by up to 38.67% due to the encoding of tabular data and the network architectures. To fully unleash the potential of big synthetic tabular data, we propose two solutions: (i) AE-GAN, a synthesizer that uses an autoencoder network to represent the tabular data and GAN networks to synthesize the latent representation, and (ii) a feature sorting algorithm to find the suitable column order of input data for CNN-based synthesizers. We evaluate the proposed solutions on five datasets in terms of the sensitivity to the column permutation, the quality of synthetic data, and the utility in downstream analyses. Our results show that we enhance the property of permutation-invariance when training synthesizers and further improve the quality and utility of synthetic data, up to 22%, compared to the existing synthesizers.", "authors": ["Yujin Zhu", "Zilong Zhao", "Robert Birke", "Lydia Y. Chen"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-11-17", "url": "https://arxiv.org/abs/2211.09286", "pdf_url": "https://arxiv.org/pdf/2211.09286v1", "arxiv_id": "2211.09286", "doi": "10.1109/BigData55660.2022.10020639", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2386}
{"id": "72989091a9b7dc83a281563d7d467a57d4e526193cd7864c8322a13fcfc5c955", "sources": ["arxiv", "semantic_scholar"], "title": "Execution-based Evaluation for Data Science Code Generation Models", "abstract": "Code generation models can benefit data scientists' productivity by automatically generating code from context and text descriptions. An important measure of the modeling progress is whether a model can generate code that can correctly execute to solve the task. However, due to the lack of an evaluation dataset that directly supports execution-based model evaluation, existing work relies on code surface form similarity metrics (e.g., BLEU, CodeBLEU) for model selection, which can be inaccurate. To remedy this, we introduce ExeDS, an evaluation dataset for execution evaluation for data science code generation tasks. ExeDS contains a set of 534 problems from Jupyter Notebooks, each consisting of code context, task description, reference program, and the desired execution output. With ExeDS, we evaluate the execution performance of five state-of-the-art code generation models that have achieved high surface-form evaluation scores. Our experiments show that models with high surface-form scores do not necessarily perform well on execution metrics, and execution-based metrics can better capture model code generation errors. Source code and data can be found at https://github.com/Jun-jie-Huang/ExeDS", "authors": ["Junjie Huang", "Chenglong Wang", "Jipeng Zhang", "Cong Yan", "Haotian Cui", "Jeevana Priya Inala", "Colin Clement", "Nan Duan", "Jianfeng Gao"], "categories": ["cs.SE", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2022-11-17", "url": "https://arxiv.org/abs/2211.09374", "pdf_url": "https://arxiv.org/pdf/2211.09374v1", "arxiv_id": "2211.09374", "doi": "10.48550/arXiv.2211.09374", "citation_count": 43, "influential_citation_count": 5, "has_code": true, "code_url": "https://github.com/Jun-jie-Huang/ExeDS", "venue": null, "quality_score": 0.4109}
{"id": "a9a30532dc89c3b458ed3a865beea7b9eb0915632d18da268dac94d95a9c7675", "sources": ["arxiv", "semantic_scholar"], "title": "Scalable Modular Synthetic Data Generation for Advancing Aerial Autonomy", "abstract": "One major barrier to advancing aerial autonomy has been collecting large-scale aerial datasets for training machine learning models. Due to costly and time-consuming real-world data collection through deploying drones, there has been an increasing shift towards using synthetic data for training models in drone applications. However, to increase widespread generalization and transferring models to real-world, increasing the diversity of simulation environments to train a model over all the varieties and augmenting the training data, has been proved to be essential. Current synthetic aerial data generation tools either lack data augmentation or rely heavily on manual workload or real samples for configuring and generating diverse realistic simulation scenes for data collection. These dependencies limit scalability of the data generation workflow. Accordingly, there is a major challenge in balancing generalizability and scalability in synthetic data generation. To address these gaps, we introduce a scalable Aerial Synthetic Data Augmentation (ASDA) framework tailored to aerial autonomy applications. ASDA extends a central data collection engine with two scriptable pipelines that automatically perform scene and data augmentations to generate diverse aerial datasets for different training tasks. ASDA improves data generation workflow efficiency by providing a unified prompt-based interface over integrated pipelines for flexible control. The procedural generative approach of our data augmentation is performant and adaptable to different simulation environments, training tasks and data collection needs. We demonstrate the effectiveness of our method in automatically generating diverse datasets and show its potential for downstream performance optimization.", "authors": ["Mehrnaz Sabet", "Praveen Palanisamy", "Sakshi Mishra"], "categories": ["cs.CV", "cs.AI", "cs.GR", "cs.LG", "cs.RO"], "fields_of_study": ["Computer Science"], "published_date": "2022-11-10", "url": "https://arxiv.org/abs/2211.05335", "pdf_url": "https://arxiv.org/pdf/2211.05335v2", "arxiv_id": "2211.05335", "doi": "10.1016/j.robot.2023.104464", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1747}
{"id": "ce03027564c1891836b1947682a49c7c56deee56eefa1be55ca3fc5c7d7b9141", "sources": ["arxiv", "semantic_scholar"], "title": "Adaptive Data Depth via Multi-Armed Bandits", "abstract": "Data depth, introduced by Tukey (1975), is an important tool in data science, robust statistics, and computational geometry. One chief barrier to its broader practical utility is that many common measures of depth are computationally intensive, requiring on the order of $n^d$ operations to exactly compute the depth of a single point within a data set of $n$ points in $d$-dimensional space. Often however, we are not directly interested in the absolute depths of the points, but rather in their relative ordering. For example, we may want to find the most central point in a data set (a generalized median), or to identify and remove all outliers (points on the fringe of the data set with low depth). With this observation, we develop a novel and instance-adaptive algorithm for adaptive data depth computation by reducing the problem of exactly computing $n$ depths to an $n$-armed stochastic multi-armed bandit problem which we can efficiently solve. We focus our exposition on simplicial depth, developed by Liu (1990), which has emerged as a promising notion of depth due to its interpretability and asymptotic properties. We provide general instance-dependent theoretical guarantees for our proposed algorithms, which readily extend to many other common measures of data depth including majority depth, Oja depth, and likelihood depth. When specialized to the case where the gaps in the data follow a power law distribution with parameter $α<2$, we show that we can reduce the complexity of identifying the deepest point in the data set (the simplicial median) from $O(n^d)$ to $\\tilde{O}(n^{d-(d-1)α/2})$, where $\\tilde{O}$ suppresses logarithmic factors. We corroborate our theoretical results with numerical experiments on synthetic data, showing the practical utility of our proposed methods.", "authors": ["Tavor Z. Baharav", "Tze Leung Lai"], "categories": ["stat.CO", "cs.LG"], "fields_of_study": ["Mathematics", "Computer Science"], "published_date": "2022-11-08", "url": "https://arxiv.org/abs/2211.03985", "pdf_url": "https://arxiv.org/pdf/2211.03985v2", "arxiv_id": "2211.03985", "doi": "10.48550/arXiv.2211.03985", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Journal of machine learning research", "quality_score": 0.1193}
{"id": "7c3d4999903db3ea7a234440a6406e8285e4b049a777fc47f02a446b396ff7b4", "sources": ["arxiv", "semantic_scholar"], "title": "Efficacy of MRI data harmonization in the age of machine learning. A multicenter study across 36 datasets", "abstract": "Pooling publicly-available MRI data from multiple sites allows to assemble extensive groups of subjects, increase statistical power, and promote data reuse with machine learning techniques. The harmonization of multicenter data is necessary to reduce the confounding effect associated with non-biological sources of variability in the data. However, when applied to the entire dataset before machine learning, the harmonization leads to data leakage, because information outside the training set may affect model building, and potentially falsely overestimate performance. We propose a 1) measurement of the efficacy of data harmonization; 2) harmonizer transformer, i.e., an implementation of the ComBat harmonization allowing its encapsulation among the preprocessing steps of a machine learning pipeline, avoiding data leakage. We tested these tools using brain T1-weighted MRI data from 1740 healthy subjects acquired at 36 sites. After harmonization, the site effect was removed or reduced, and we showed the data leakage effect in predicting individual age from MRI data, highlighting that introducing the harmonizer transformer into a machine learning pipeline allows for avoiding data leakage.", "authors": ["Chiara Marzi", "Marco Giannelli", "Andrea Barucci", "Carlo Tessa", "Mario Mascalchi", "Stefano Diciotti"], "categories": ["cs.LG", "eess.IV", "q-bio.QM"], "fields_of_study": ["Medicine", "Computer Science", "Engineering", "Biology"], "published_date": "2022-11-08", "url": "https://arxiv.org/abs/2211.04125", "pdf_url": "https://arxiv.org/pdf/2211.04125v4", "arxiv_id": "2211.04125", "doi": "10.1038/s41597-023-02421-7", "citation_count": 54, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "Scientific Data", "quality_score": 0.4351}
{"id": "173fac9d4894446e9c157c206d250481b79be9cc31b75930c7a6ae28c2d6ee39", "sources": ["arxiv", "semantic_scholar"], "title": "Proceedings of Principle and practice of data and Knowledge Acquisition Workshop 2022 (PKAW 2022)", "abstract": "Over the past two decades, PKAW has provided a forum for researchers and practitioners to discuss the state-of-the-arts in the area of knowledge acquisition and machine intelligence (MI, also Artificial Intelligence, AI). PKAW2022 will continue the above focus and welcome the contributions on the multi-disciplinary approach of human and big data-driven knowledge acquisition, as well as AI techniques and applications.", "authors": ["Qing Liu", "Wenli Yang", "Shiqing Wu"], "categories": ["cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2022-11-07", "url": "https://arxiv.org/abs/2211.03888", "pdf_url": "https://arxiv.org/pdf/2211.03888v2", "arxiv_id": "2211.03888", "doi": "10.48550/arXiv.2211.03888", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0}
{"id": "eaae80c3a4e8be95760356a38024cdf4cc76836ea7f0f48491ce75a0fd34ebcf", "sources": ["arxiv", "semantic_scholar"], "title": "DatChain -- Blockchain implementation in Data transfer for IoT Devices", "abstract": "Currently, the IoT ecosystem is comprised of fully connected smart devices that exchange data to provide more automated, precise, and fast decisions. This idealised situation can only be accomplished if a system for data transactions is processed efficiently and security is ensured with high scalability and practicability. The integrity of data must be maintained during the exchange or transfer of data between entities. We propose to make a application called DatChain that responds to the above situation. The application stores data sensed by the Iot sensors in the backend after encrypting it and when the data is required for any purpose it can be exchanged using a suitable blockchain network that can keep up with the transfer rate even at high traffic in a secure environment.", "authors": ["Om Rajput", "Suyash Nigam", "M. J. Chowdhury", "Kayalvizhi Jayavel"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2022-11-04", "url": "https://arxiv.org/abs/2211.02246", "pdf_url": "https://arxiv.org/pdf/2211.02246v1", "arxiv_id": "2211.02246", "doi": "10.48550/arXiv.2211.02246", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0}
{"id": "a67a5f0625f165a32e98b215ee3b207df642f47e4cd94a7c5058cdabc185fc91", "sources": ["arxiv", "semantic_scholar"], "title": "The Tensor Data Platform: Towards an AI-centric Database System", "abstract": "Database engines have historically absorbed many of the innovations in data processing, adding features to process graph data, XML, object oriented, and text among many others. In this paper, we make the case that it is time to do the same for AI -- but with a twist! While existing approaches have tried to achieve this by integrating databases with external ML tools, in this paper we claim that achieving a truly AI-centric database requires moving the DBMS engine, at its core, from a relational to a tensor abstraction. This allows us to: (1) support multi-modal data processing such as images, videos, audio, text as well as relational; (2) leverage the wellspring of innovation in HW and runtimes for tensor computation; and (3) exploit automatic differentiation to enable a novel class of \"trainable\" queries that can learn to perform a task. To support the above scenarios, we introduce TDP: a system that builds upon our prior work mapping relational queries to tensors. Thanks to a tighter integration with the tensor runtime, TDP is able to provide a broader coverage of new emerging scenarios requiring access to multi-modal data and automatic differentiation.", "authors": ["Apurva Gandhi", "Yuki Asada", "Victor Fu", "Advitya Gemawat", "Lihao Zhang", "Rathijit Sen", "Carlo Curino", "Jesús Camacho-Rodríguez", "Matteo Interlandi"], "categories": ["cs.DB", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-11-04", "url": "https://arxiv.org/abs/2211.02753", "pdf_url": "https://arxiv.org/pdf/2211.02753v1", "arxiv_id": "2211.02753", "doi": "10.48550/arXiv.2211.02753", "citation_count": 22, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Conference on Innovative Data Systems Research", "quality_score": 0.3404}
{"id": "54c2f2742407c28693338b27827913c8bc2012d96c1f0ca41e912ccd60b41ae9", "sources": ["arxiv", "semantic_scholar"], "title": "Haven't I Seen You Before? Assessing Identity Leakage in Synthetic Irises", "abstract": "Generative Adversarial Networks (GANs) have proven to be a preferred method of synthesizing fake images of objects, such as faces, animals, and automobiles. It is not surprising these models can also generate ISO-compliant, yet synthetic iris images, which can be used to augment training data for iris matchers and liveness detectors. In this work, we trained one of the most recent GAN models (StyleGAN3) to generate fake iris images with two primary goals: (i) to understand the GAN's ability to produce \"never-before-seen\" irises, and (ii) to investigate the phenomenon of identity leakage as a function of the GAN's training time. Previous work has shown that personal biometric data can inadvertently flow from training data into synthetic samples, raising a privacy concern for subjects who accidentally appear in the training dataset. This paper presents analysis for three different iris matchers at varying points in the GAN training process to diagnose where and when authentic training samples are in jeopardy of leaking through the generative process. Our results show that while most synthetic samples do not show signs of identity leakage, a handful of generated samples match authentic (training) samples nearly perfectly, with consensus across all matchers. In order to prioritize privacy, security, and trust in the machine learning model development process, the research community must strike a delicate balance between the benefits of using synthetic data and the corresponding threats against privacy from potential identity leakage.", "authors": ["Patrick Tinsley", "Adam Czajka", "Patrick Flynn"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2022-11-03", "url": "https://arxiv.org/abs/2211.05629", "pdf_url": "https://arxiv.org/pdf/2211.05629v1", "arxiv_id": "2211.05629", "doi": "10.1109/IJCB54206.2022.10007948", "citation_count": 9, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.25}
{"id": "92daa8575ba03cdeb4c89ae2c5163f5b71b5b38f2512d9f4818a85629e4eb145", "sources": ["arxiv", "semantic_scholar"], "title": "Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifiers", "abstract": "Analysis of the fairness of machine learning (ML) algorithms recently attracted many researchers' interest. Most ML methods show bias toward protected groups, which limits the applicability of ML models in many applications like crime rate prediction etc. Since the data may have missing values which, if not appropriately handled, are known to further harmfully affect fairness. Many imputation methods are proposed to deal with missing data. However, the effect of missing data imputation on fairness is not studied well. In this paper, we analyze the effect on fairness in the context of graph data (node attributes) imputation using different embedding and neural network methods. Extensive experiments on six datasets demonstrate severe fairness issues in missing data imputation under graph node classification. We also find that the choice of the imputation method affects both fairness and accuracy. Our results provide valuable insights into graph data fairness and how to handle missingness in graphs efficiently. This work also provides directions regarding theoretical studies on fairness in graph data.", "authors": ["Haris Mansoor", "Sarwan Ali", "Shafiq Alam", "Muhammad Asad Khan", "Umair ul Hassan", "Imdadullah Khan"], "categories": ["cs.LG", "cs.CY", "cs.SI"], "fields_of_study": ["Computer Science"], "published_date": "2022-11-01", "url": "https://arxiv.org/abs/2211.00783", "pdf_url": "https://arxiv.org/pdf/2211.00783v1", "arxiv_id": "2211.00783", "doi": "10.1109/BigData55660.2022.10020694", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2113}
{"id": "6140c8ef15fedf6abb45b9c9bcbccc325cd0bb363b01f5a9100fe68143ee4804", "sources": ["arxiv", "semantic_scholar"], "title": "AI Assistants: A Framework for Semi-Automated Data Wrangling", "abstract": "Data wrangling tasks such as obtaining and linking data from various sources, transforming data formats, and correcting erroneous records, can constitute up to 80% of typical data engineering work. Despite the rise of machine learning and artificial intelligence, data wrangling remains a tedious and manual task. We introduce AI assistants, a class of semi-automatic interactive tools to streamline data wrangling. An AI assistant guides the analyst through a specific data wrangling task by recommending a suitable data transformation that respects the constraints obtained through interaction with the analyst. We formally define the structure of AI assistants and describe how existing tools that treat data cleaning as an optimization problem fit the definition. We implement AI assistants for four common data wrangling tasks and make AI assistants easily accessible to data analysts in an open-source notebook environment for data science, by leveraging the common structure they follow. We evaluate our AI assistants both quantitatively and qualitatively through three example scenarios. We show that the unified and interactive design makes it easy to perform tasks that would be difficult to do manually or with a fully automatic tool.", "authors": ["Tomas Petricek", "Gerrit J. J. van den Burg", "Alfredo Nazábal", "Taha Ceritli", "Ernesto Jiménez-Ruiz", "Christopher K. I. Williams"], "categories": ["cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2022-10-31", "url": "https://arxiv.org/abs/2211.00192", "pdf_url": "https://arxiv.org/pdf/2211.00192v1", "arxiv_id": "2211.00192", "doi": "10.1109/TKDE.2022.3222538", "citation_count": 14, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "IEEE Transactions on Knowledge and Data Engineering", "quality_score": 0.294}
{"id": "8061336ce876609753cd318058f4465a6eec235601f8af0afcc37814bca9cf8d", "sources": ["arxiv", "semantic_scholar"], "title": "Application of Knowledge Distillation to Multi-task Speech Representation Learning", "abstract": "Model architectures such as wav2vec 2.0 and HuBERT have been proposed to learn speech representations from audio waveforms in a self-supervised manner. When they are combined with downstream tasks such as keyword spotting and speaker verification, they provide state-of-the-art performance. However, these models use a large number of parameters, the smallest version of which has 95 million parameters. This constitutes a challenge for edge AI device deployments. In this paper, we investigate the application of knowledge distillation to speech representation learning (SRL) models followed by joint fine-tuning with multiple downstream voice-activated tasks. In our experiments on two such tasks, our approach results in nearly 75% reduction in model size while suffering only 0.1% accuracy and 0.9% equal error rate degradation compared to the full-size model. In addition, we show that fine-tuning the SRL models results in a significant performance boost compared to using frozen SRL models.", "authors": ["Mine Kerpicci", "Van Nguyen", "Shuhua Zhang", "Erik Visser"], "categories": ["eess.AS", "cs.CL", "cs.SD"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2022-10-29", "url": "https://arxiv.org/abs/2210.16611", "pdf_url": "https://arxiv.org/pdf/2210.16611v2", "arxiv_id": "2210.16611", "doi": "10.48550/arXiv.2210.16611", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Interspeech", "quality_score": 0.0}
{"id": "9647d5f4551f6b63076300eda39d795e4e2c86eb7c01e2a0d7063252b4683fc7", "sources": ["arxiv", "semantic_scholar"], "title": "Mitigating Health Data Poverty: Generative Approaches versus Resampling for Time-series Clinical Data", "abstract": "Several approaches have been developed to mitigate algorithmic bias stemming from health data poverty, where minority groups are underrepresented in training datasets. Augmenting the minority class using resampling (such as SMOTE) is a widely used approach due to the simplicity of the algorithms. However, these algorithms decrease data variability and may introduce correlations between samples, giving rise to the use of generative approaches based on GAN. Generation of high-dimensional, time-series, authentic data that provides a wide distribution coverage of the real data, remains a challenging task for both resampling and GAN-based approaches. In this work we propose CA-GAN architecture that addresses some of the shortcomings of the current approaches, where we provide a detailed comparison with both SMOTE and WGAN-GP*, using a high-dimensional, time-series, real dataset of 3343 hypotensive Caucasian and Black patients. We show that our approach is better at both generating authentic data of the minority class and remaining within the original distribution of the real data.", "authors": ["Raffaele Marchesi", "Nicolo Micheletti", "Giuseppe Jurman", "Venet Osmani"], "categories": ["cs.LG", "cs.CY", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2022-10-25", "url": "https://arxiv.org/abs/2210.13958", "pdf_url": "https://arxiv.org/pdf/2210.13958v2", "arxiv_id": "2210.13958", "doi": "10.48550/arXiv.2210.13958", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1945}
{"id": "b80e4f8a734762752096b0654298644e39ef54610eeb13149908da9bf07faad3", "sources": ["arxiv", "semantic_scholar"], "title": "Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings", "abstract": "In this paper, we introduce the Vehicle Claims dataset, consisting of fraudulent insurance claims for automotive repairs. The data belongs to the more broad category of Auditing data, which includes also Journals and Network Intrusion data. Insurance claim data are distinctively different from other auditing data (such as network intrusion data) in their high number of categorical attributes. We tackle the common problem of missing benchmark datasets for anomaly detection: datasets are mostly confidential, and the public tabular datasets do not contain relevant and sufficient categorical attributes. Therefore, a large-sized dataset is created for this purpose and referred to as Vehicle Claims (VC) dataset. The dataset is evaluated on shallow and deep learning methods. Due to the introduction of categorical attributes, we encounter the challenge of encoding them for the large dataset. As One Hot encoding of high cardinal dataset invokes the \"curse of dimensionality\", we experiment with GEL encoding and embedding layer for representing categorical attributes. Our work compares competitive learning, reconstruction-error, density estimation and contrastive learning approaches for Label, One Hot, GEL encoding and embedding layer to handle categorical values.", "authors": ["Ajay Chawda", "Stefanie Grimm", "Marius Kloft"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2022-10-25", "url": "https://arxiv.org/abs/2210.14056", "pdf_url": "https://arxiv.org/pdf/2210.14056v2", "arxiv_id": "2210.14056", "doi": "10.48550/arXiv.2210.14056", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2258}
{"id": "38dd9f4ea0596e5946fa3944ac89f983f5699f3ea5995b6916fe55edbb49b0a5", "sources": ["arxiv", "semantic_scholar"], "title": "Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation", "abstract": "Knowledge distillation is one of the primary methods of transferring knowledge from large to small models. However, it requires massive task-specific data, which may not be plausible in many real-world applications. Data augmentation methods such as representation interpolation, token replacement, or augmentation with models are applied to tackle this problem. However, these data augmentation methods either potentially cause shifts in decision boundaries (representation interpolation), are not expressive enough (token replacement), or introduce too much computational overhead (augmentation with models). To this end, we propose AugPro (Augmentation with Projection), an effective and efficient data augmentation method for distillation. Our method builds on top of representation interpolation augmentation methods to maintain the diversity of expressions and converts the augmented data to tokens to avoid shifting decision boundaries. It uses simple operations that come with little computational overhead. The results on multiple GLUE tasks show that our methods can improve distillation performance by a large margin at a low time cost. Codes are available at https://github.com/google-research/google-research/tree/master/augpro.", "authors": ["Ziqi Wang", "Yuexin Wu", "Frederick Liu", "Daogao Liu", "Le Hou", "Hongkun Yu", "Jing Li", "Heng Ji"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2022-10-21", "url": "https://arxiv.org/abs/2210.11768", "pdf_url": "https://arxiv.org/pdf/2210.11768v2", "arxiv_id": "2210.11768", "doi": "10.48550/arXiv.2210.11768", "citation_count": 6, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/google-research/google-research/tree/master/augpro", "venue": "International Conference on Learning Representations", "quality_score": 0.2113}
{"id": "7c3dc2f9413bd413fd35dea707e7cbccbdf3c24e7d01a1cb11f64bc6fae68a14", "sources": ["arxiv", "semantic_scholar"], "title": "Nuclear data activities for medium mass and heavy nuclei at Los Alamos", "abstract": "Nuclear data is critical for many modern applications from stockpile stewardship to cutting edge scientific research. Central to these pursuits is a robust pipeline for nuclear modeling as well as data assimilation and dissemination. We summarize a small portion of the ongoing nuclear data efforts at Los Alamos for medium mass to heavy nuclei. We begin with an overview of the NEXUS framework and show how one of its modules can be used for model parameter optimization using Bayesian techniques. The mathematical framework affords the combination of different measured data in determining model parameters and their associated correlations. It also has the advantage of being able to quantify outliers in data. We exemplify the power of this procedure by highlighting the recently evaluated 239-Pu cross section. We further showcase the success of our tools and pipeline by covering the insight gained from incorporating the latest nuclear modeling and data in astrophysical simulations as part of the Fission In R-process Elements (FIRE) collaboration.", "authors": ["M. R. Mumpower", "T. M Sprouse", "T. Kawano", "M. W. Herman", "A. E. Lovell", "G. W. Misch", "D. Neudecker", "H. Sasaki", "I. Stetcu", "P. Talou"], "categories": ["nucl-th", "astro-ph.SR"], "fields_of_study": ["Physics"], "published_date": "2022-10-21", "url": "https://arxiv.org/abs/2210.12136", "pdf_url": "https://arxiv.org/pdf/2210.12136v1", "arxiv_id": "2210.12136", "doi": "10.1051/epjconf/202328412001", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "EPJ Web of Conferences", "quality_score": 0.0}
{"id": "0fd4572a60ab659114ba4b0669be68e447378f5ee3802104e4621000f4a37981", "sources": ["arxiv", "semantic_scholar"], "title": "Causal Structural Hypothesis Testing and Data Generation Models", "abstract": "A vast amount of expert and domain knowledge is captured by causal structural priors, yet there has been little research on testing such priors for generalization and data synthesis purposes. We propose a novel model architecture, Causal Structural Hypothesis Testing, that can use nonparametric, structural causal knowledge and approximate a causal model's functional relationships using deep neural networks. We use these architectures for comparing structural priors, akin to hypothesis testing, using a deliberate (non-random) split of training and testing data. Extensive simulations demonstrate the effectiveness of out-of-distribution generalization error as a proxy for causal structural prior hypothesis testing and offers a statistical baseline for interpreting results. We show that the variational version of the architecture, Causal Structural Variational Hypothesis Testing can improve performance in low SNR regimes. Due to the simplicity and low parameter count of the models, practitioners can test and compare structural prior hypotheses on small dataset and use the priors with the best generalization capacity to synthesize much larger, causally-informed datasets. Finally, we validate our methods on a synthetic pendulum dataset, and show a use-case on a real-world trauma surgery ground-level falls dataset.", "authors": ["Jeffrey Jiang", "Omead Pooladzandi", "Sunay Bhat", "Gregory Pottie"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2022-10-20", "url": "https://arxiv.org/abs/2210.11275", "pdf_url": "https://arxiv.org/pdf/2210.11275v2", "arxiv_id": "2210.11275", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0753}
{"id": "2a207a010f061adfbbce9109f0af1b39182b186f46e7701fcf79fdd7c72f6407", "sources": ["arxiv", "semantic_scholar"], "title": "DAGAD: Data Augmentation for Graph Anomaly Detection", "abstract": "Graph anomaly detection in this paper aims to distinguish abnormal nodes that behave differently from the benign ones accounting for the majority of graph-structured instances. Receiving increasing attention from both academia and industry, yet existing research on this task still suffers from two critical issues when learning informative anomalous behavior from graph data. For one thing, anomalies are usually hard to capture because of their subtle abnormal behavior and the shortage of background knowledge about them, which causes severe anomalous sample scarcity. Meanwhile, the overwhelming majority of objects in real-world graphs are normal, bringing the class imbalance problem as well. To bridge the gaps, this paper devises a novel Data Augmentation-based Graph Anomaly Detection (DAGAD) framework for attributed graphs, equipped with three specially designed modules: 1) an information fusion module employing graph neural network encoders to learn representations, 2) a graph data augmentation module that fertilizes the training set with generated samples, and 3) an imbalance-tailored learning module to discriminate the distributions of the minority (anomalous) and majority (normal) classes. A series of experiments on three datasets prove that DAGAD outperforms ten state-of-the-art baseline detectors concerning various mostly-used metrics, together with an extensive ablation study validating the strength of our proposed modules.", "authors": ["Fanzhen Liu", "Xiaoxiao Ma", "Jia Wu", "Jian Yang", "Shan Xue", "Amin Beheshti", "Chuan Zhou", "Hao Peng", "Quan Z. Sheng", "Charu C. Aggarwal"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-10-18", "url": "https://arxiv.org/abs/2210.09766", "pdf_url": "https://arxiv.org/pdf/2210.09766v1", "arxiv_id": "2210.09766", "doi": "10.1109/ICDM54844.2022.00036", "citation_count": 60, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "Industrial Conference on Data Mining", "quality_score": 0.4463}
{"id": "2915861959e79abff1eed612adcd6be212e1d0024972dbd8b8de5c89067e4ab6", "sources": ["arxiv", "semantic_scholar"], "title": "From Play to Policy: Conditional Behavior Generation from Uncurated Robot Data", "abstract": "While large-scale sequence modeling from offline data has led to impressive performance gains in natural language and image generation, directly translating such ideas to robotics has been challenging. One critical reason for this is that uncurated robot demonstration data, i.e. play data, collected from non-expert human demonstrators are often noisy, diverse, and distributionally multi-modal. This makes extracting useful, task-centric behaviors from such data a difficult generative modeling problem. In this work, we present Conditional Behavior Transformers (C-BeT), a method that combines the multi-modal generation ability of Behavior Transformer with future-conditioned goal specification. On a suite of simulated benchmark tasks, we find that C-BeT improves upon prior state-of-the-art work in learning from play data by an average of 45.7%. Further, we demonstrate for the first time that useful task-centric behaviors can be learned on a real-world robot purely from play data without any task labels or reward information. Robot videos are best viewed on our project website: https://play-to-policy.github.io", "authors": ["Zichen Jeff Cui", "Yibin Wang", "Nur Muhammad Mahi Shafiullah", "Lerrel Pinto"], "categories": ["cs.RO", "cs.AI", "cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-10-18", "url": "https://arxiv.org/abs/2210.10047", "pdf_url": "https://arxiv.org/pdf/2210.10047v3", "arxiv_id": "2210.10047", "doi": "10.48550/arXiv.2210.10047", "citation_count": 141, "influential_citation_count": 28, "has_code": false, "code_url": null, "venue": "International Conference on Learning Representations", "quality_score": 0.7312}
{"id": "17b9fdb92f03e6fa291b798212fd362078ee3765e5e1cf30fb2e2b9137ba511f", "sources": ["arxiv", "semantic_scholar"], "title": "Dynamic Topological Data Analysis of Functional Human Brain Networks", "abstract": "Developing reliable methods to discriminate different transient brain states that change over time is a key neuroscientific challenge in brain imaging studies. Topological data analysis (TDA), a novel framework based on algebraic topology, can handle such a challenge. However, existing TDA has been somewhat limited to capturing the static summary of dynamically changing brain networks. We propose a novel dynamic-TDA framework that builds persistent homology over a time series of brain networks. We construct a Wasserstein distance based inference procedure to discriminate between time series of networks. The method is applied to the resting-state functional magnetic resonance images of human brain. We demonstrate that our proposed dynamic-TDA approach can distinctly discriminate between the topological patterns of male and female brain networks. MATLAB code for implementing this method is available at https://github.com/laplcebeltrami/PH-STAT.", "authors": ["Moo K. Chung", "Soumya Das", "Hernando Ombao"], "categories": ["q-bio.NC", "nlin.CD"], "fields_of_study": ["Biology", "Physics"], "published_date": "2022-10-17", "url": "https://arxiv.org/abs/2210.09092", "pdf_url": "https://arxiv.org/pdf/2210.09092v4", "arxiv_id": "2210.09092", "doi": "10.3934/fods.2023013", "citation_count": 12, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/laplcebeltrami/PH-STAT", "venue": "Foundations of Data Science", "quality_score": 0.2785}
{"id": "cce045f0060804dd62bcffd32e8dd1f15ff76f0f795e874c59d9928715c41ea5", "sources": ["arxiv", "semantic_scholar"], "title": "AMC 12 atomic mass compilation data extrapolated for atomic masses of nuclei far from the valley of stability", "abstract": "The experimental mass data from the Atomic Mass Compilation - 2012 (AMC12) has been analyzed for two-neutron separation energies (S$_{2n}$), two-proton separation energies (S$_{2p}$), double-beta decay energies (Q$_{2β^-}$), and four-beta decay energies (Q$_{4β^-}$) and plotted against neutron number and mass number, respectively. A new weighted slope method of extrapolation, tested for known and new mass measurements, has been used to obtain the extrapolated mass values with better precision for more than 1100 nuclei far from the valley of stability, out of which more than 100 are being reported for the first time. A comparison has been made with five of the popular mass models with reference to experimental extrapolated masses from the present work and the Atomic Mass Evaluation 2016 (AME16). The extrapolated experimental atomic mass data will be very useful for both experimentalists and mass-model theoreticians, as well as in simulations of astrophysical r-processes.", "authors": ["K. Venkataramaniah", "Shreesha Rao D. S.", "C. Scheidenberger"], "categories": ["physics.atom-ph"], "fields_of_study": ["Physics", "Medicine"], "published_date": "2022-10-16", "url": "https://arxiv.org/abs/2210.08605", "pdf_url": "https://arxiv.org/pdf/2210.08605v1", "arxiv_id": "2210.08605", "doi": "10.1038/s41597-022-01628-4", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Scientific Data", "quality_score": 0.1505}
{"id": "c6b941f6f645ee7a38bdc38153e35b4178b8734d659c9a97055f70ff954e860c", "sources": ["arxiv", "semantic_scholar"], "title": "Data-Efficient Augmentation for Training Neural Networks", "abstract": "Data augmentation is essential to achieve state-of-the-art performance in many deep learning applications. However, the most effective augmentation techniques become computationally prohibitive for even medium-sized datasets. To address this, we propose a rigorous technique to select subsets of data points that when augmented, closely capture the training dynamics of full data augmentation. We first show that data augmentation, modeled as additive perturbations, improves learning and generalization by relatively enlarging and perturbing the smaller singular values of the network Jacobian, while preserving its prominent directions. This prevents overfitting and enhances learning the harder to learn information. Then, we propose a framework to iteratively extract small subsets of training data that when augmented, closely capture the alignment of the fully augmented Jacobian with labels/residuals. We prove that stochastic gradient descent applied to the augmented subsets found by our approach has similar training dynamics to that of fully augmented data. Our experiments demonstrate that our method achieves 6.3x speedup on CIFAR10 and 2.2x speedup on SVHN, and outperforms the baselines by up to 10% across various subset sizes. Similarly, on TinyImageNet and ImageNet, our method beats the baselines by up to 8%, while achieving up to 3.3x speedup across various subset sizes. Finally, training on and augmenting 50% subsets using our method on a version of CIFAR10 corrupted with label noise even outperforms using the full dataset. Our code is available at: https://github.com/tianyu139/data-efficient-augmentation", "authors": ["Tian Yu Liu", "Baharan Mirzasoleiman"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-10-15", "url": "https://arxiv.org/abs/2210.08363", "pdf_url": "https://arxiv.org/pdf/2210.08363v3", "arxiv_id": "2210.08363", "doi": "10.48550/arXiv.2210.08363", "citation_count": 11, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/tianyu139/data-efficient-augmentation", "venue": "Neural Information Processing Systems", "quality_score": 0.2698}
{"id": "2cbc867f8efb1901a20aa710c58332b460f4200ceb45b3aa8413d5b0ec7922ad", "sources": ["arxiv", "semantic_scholar"], "title": "Data augmentation on-the-fly and active learning in data stream classification", "abstract": "There is an emerging need for predictive models to be trained on-the-fly, since in numerous machine learning applications data are arriving in an online fashion. A critical challenge encountered is that of limited availability of ground truth information (e.g., labels in classification tasks) as new data are observed one-by-one online, while another significant challenge is that of class imbalance. This work introduces the novel Augmented Queues method, which addresses the dual-problem by combining in a synergistic manner online active learning, data augmentation, and a multi-queue memory to maintain separate and balanced queues for each class. We perform an extensive experimental study using image and time-series augmentations, in which we examine the roles of the active learning budget, memory size, imbalance level, and neural network type. We demonstrate two major advantages of Augmented Queues. First, it does not reserve additional memory space as the generation of synthetic data occurs only at training times. Second, learning models have access to more labelled data without the need to increase the active learning budget and / or the original memory size. Learning on-the-fly poses major challenges which, typically, hinder the deployment of learning models. Augmented Queues significantly improves the performance in terms of learning quality and speed. Our code is made publicly available.", "authors": ["Kleanthis Malialis", "Dimitris Papatheodoulou", "Stylianos Filippou", "Christos G. Panayiotou", "Marios M. Polycarpou"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-10-13", "url": "https://arxiv.org/abs/2210.06873", "pdf_url": "https://arxiv.org/pdf/2210.06873v1", "arxiv_id": "2210.06873", "doi": "10.1109/SSCI51031.2022.10022133", "citation_count": 10, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Symposium Series on Computational Intelligence", "quality_score": 0.2603}
{"id": "f42b639e6238971bd60f0750d0e2f68e1effcbd505bf9b97d20374543bd11aa3", "sources": ["arxiv", "semantic_scholar"], "title": "Non-fungible token transactions: data and challenges", "abstract": "Non-fungible tokens (NFT) have recently emerged as a novel blockchain hosted financial asset class that has attracted major transaction volumes. Investment decisions rely on data and adequate preprocessing and application of analytics to them. Both owing to the non-fungible nature of the tokens and to a blockchain being the primary data source, NFT transaction data pose several challenges not commonly encountered in traditional financial data. Using data that consist of the transaction history of eight highly valued NFT collections, a selection of such challenges is illustrated. These are: price differentiation by token traits, the possible existence of lateral swaps and wash trades in the transaction history and finally, severe volatility. While this paper merely scratches the surface of how data analytics can be applied in this context, the data and challenges laid out here may present opportunities for future research on the topic.", "authors": ["Jason B. Cho", "Sven Serneels", "David S. Matteson"], "categories": ["q-fin.ST", "stat.AP"], "fields_of_study": ["Economics", "Mathematics"], "published_date": "2022-10-13", "url": "https://arxiv.org/abs/2210.07393", "pdf_url": "https://arxiv.org/pdf/2210.07393v1", "arxiv_id": "2210.07393", "doi": "10.1080/26941899.2022.2151950", "citation_count": 20, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Data Science in Science", "quality_score": 0.3306}
{"id": "aec99593db775a6f873e4690ad863399174fb5a5202202112baac8ac0924031b", "sources": ["arxiv", "semantic_scholar"], "title": "Secure Multiparty Computation for Synthetic Data Generation from Distributed Data", "abstract": "Legal and ethical restrictions on accessing relevant data inhibit data science research in critical domains such as health, finance, and education. Synthetic data generation algorithms with privacy guarantees are emerging as a paradigm to break this data logjam. Existing approaches, however, assume that the data holders supply their raw data to a trusted curator, who uses it as fuel for synthetic data generation. This severely limits the applicability, as much of the valuable data in the world is locked up in silos, controlled by entities who cannot show their data to each other or a central aggregator without raising privacy concerns. To overcome this roadblock, we propose the first solution in which data holders only share encrypted data for differentially private synthetic data generation. Data holders send shares to servers who perform Secure Multiparty Computation (MPC) computations while the original data stays encrypted. We instantiate this idea in an MPC protocol for the Multiplicative Weights with Exponential Mechanism (MWEM) algorithm to generate synthetic data based on real data originating from many data holders without reliance on a single point of failure.", "authors": ["Mayana Pereira", "Sikha Pentyala", "Anderson Nascimento", "Rafael T. de Sousa", "Martine De Cock"], "categories": ["cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-10-13", "url": "https://arxiv.org/abs/2210.07332", "pdf_url": "https://arxiv.org/pdf/2210.07332v2", "arxiv_id": "2210.07332", "doi": "10.48550/arXiv.2210.07332", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2386}
{"id": "ae44ce7a5b647fcadb6a3137cbddb59e48d350f360be5d48d89b632809723267", "sources": ["arxiv", "semantic_scholar"], "title": "How Do Data Science Workers Communicate Intermediate Results?", "abstract": "Data science workers increasingly collaborate on large-scale projects before communicating insights to a broader audience in the form of visualization. While prior work has modeled how data science teams, oftentimes with distinct roles and work processes, communicate knowledge to outside stakeholders, we have little knowledge of how data science workers communicate intermediately before delivering the final products. In this work, we contribute a nuanced description of the intermediate communication process within data science teams. By analyzing interview data with 8 self-identified data science workers, we characterized the data science intermediate communication process with four factors, including the types of audience, communication goals, shared artifacts, and mode of communication. We also identified overarching challenges in the current communication process. We also discussed design implications that might inform better tools that facilitate intermediate communication within data science teams.", "authors": ["Rock Yuren Pang", "Ruotong Wang", "Joely Nelson", "Leilani Battle"], "categories": ["cs.HC"], "fields_of_study": ["Computer Science"], "published_date": "2022-10-07", "url": "https://arxiv.org/abs/2210.03305", "pdf_url": "https://arxiv.org/pdf/2210.03305v1", "arxiv_id": "2210.03305", "doi": "10.1109/VDS57266.2022.00010", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2113}
{"id": "73b62dcefd5a6b653d61d5a6cd3db182a4a9cf1379b33db04c86144736b5a36b", "sources": ["arxiv", "semantic_scholar"], "title": "Increasing Data Equity Through Accessibility", "abstract": "This position statement is a response to the Office of Science and Technology Policy's Request for Information on \"Equitable Data Engagement and Accountability.\" This response considers data equity specifically for people with disabilities. The RFI asks \"how Federal agencies can better support collaboration with other levels of government, civil society, and the research community around the production and use of equitable data.\" We argue that one critically underserved community in the context of data equity is people with disabilities. Today's tools make it extremely difficult for disabled people to (1) interact with data and data visualizations and (2) take jobs that involve working with and visualizing data. Yet access to such data is increasingly critical, and integral, to engaging with government and civil society. We must change the standards and expectations around data practices to include disabled people and support the research necessary to achieve those goals.", "authors": ["Frank Elavsky", "Jennifer Mankoff", "Arvind Satyanarayan"], "categories": ["cs.HC", "cs.CY"], "fields_of_study": ["Computer Science"], "published_date": "2022-10-04", "url": "https://arxiv.org/abs/2210.01902", "pdf_url": "https://arxiv.org/pdf/2210.01902v1", "arxiv_id": "2210.01902", "doi": "10.48550/arXiv.2210.01902", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "6ac429d4d5d45827eaf849e5b57912763c3aa2a80c6552637f37470718c00c4f", "sources": ["arxiv", "semantic_scholar"], "title": "Nonstationary data stream classification with online active learning and siamese neural networks", "abstract": "We have witnessed in recent years an ever-growing volume of information becoming available in a streaming manner in various application areas. As a result, there is an emerging need for online learning methods that train predictive models on-the-fly. A series of open challenges, however, hinder their deployment in practice. These are, learning as data arrive in real-time one-by-one, learning from data with limited ground truth information, learning from nonstationary data, and learning from severely imbalanced data, while occupying a limited amount of memory for data storage. We propose the ActiSiamese algorithm, which addresses these challenges by combining online active learning, siamese networks, and a multi-queue memory. It develops a new density-based active learning strategy which considers similarity in the latent (rather than the input) space. We conduct an extensive study that compares the role of different active learning budgets and strategies, the performance with/without memory, the performance with/without ensembling, in both synthetic and real-world datasets, under different data nonstationarity characteristics and class imbalance levels. ActiSiamese outperforms baseline and state-of-the-art algorithms, and is effective under severe imbalance, even only when a fraction of the arriving instances' labels is available. We publicly release our code to the community.", "authors": ["Kleanthis Malialis", "Christos G. Panayiotou", "Marios M. Polycarpou"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-10-03", "url": "https://arxiv.org/abs/2210.01090", "pdf_url": "https://arxiv.org/pdf/2210.01090v1", "arxiv_id": "2210.01090", "doi": "10.1016/j.neucom.2022.09.065", "citation_count": 39, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Neurocomputing", "quality_score": 0.4005}
{"id": "4a38ad059891178983a2e5b9271599c02a69112e28cc2b318ee3b9f97c634afa", "sources": ["arxiv", "semantic_scholar"], "title": "The current state of single-cell proteomics data analysis", "abstract": "Sound data analysis is essential to retrieve meaningful biological information from single-cell proteomics experiments. This analysis is carried out by computational methods that are assembled into workflows, and their implementations influence the conclusions that can be drawn from the data. In this work, we explore and compare the computational workflows that have been used over the last four years and identify a profound lack of consensus on how to analyze single-cell proteomics data. We highlight the need for benchmarking of computational workflows, standardization of computational tools and data, as well as carefully designed experiments. Finally, we cover the current standardization efforts that aim to fill the gap and list the remaining missing pieces, and conclude with lessons learned from the replication of published single-cell proteomics analyses.", "authors": ["Christophe Vanderaa", "Laurent Gatto"], "categories": ["q-bio.QM"], "fields_of_study": ["Medicine", "Biology"], "published_date": "2022-10-03", "url": "https://arxiv.org/abs/2210.01020", "pdf_url": "https://arxiv.org/pdf/2210.01020v2", "arxiv_id": "2210.01020", "doi": "10.1002/cpz1.658", "citation_count": 23, "influential_citation_count": 2, "has_code": true, "code_url": "https://github.com/UCLouvain-CBIO/2022-scp-data-analysis", "venue": "Current Protocols", "quality_score": 0.3451}
{"id": "802195d31dfd98151768fc2024ef69146d771879616805a8e6e5e746e8795f92", "sources": ["arxiv", "semantic_scholar"], "title": "FAIR for AI: An interdisciplinary and international community building perspective", "abstract": "A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been re-interpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022.", "authors": ["E. A. Huerta", "Ben Blaiszik", "L. Catherine Brinson", "Kristofer E. Bouchard", "Daniel Diaz", "Caterina Doglioni", "Javier M. Duarte", "Murali Emani", "Ian Foster", "Geoffrey Fox", "Philip Harris", "Lukas Heinrich", "Shantenu Jha", "Daniel S. Katz", "Volodymyr Kindratenko", "Christine R. Kirkpatrick", "Kati Lassila-Perini", "Ravi K. Madduri", "Mark S. Neubauer", "Fotis E. Psomopoulos", "Avik Roy", "Oliver Rübel", "Zhizhen Zhao", "Ruike Zhu"], "categories": ["cs.CY", "cs.HC", "cs.LG", "hep-ex"], "fields_of_study": ["Computer Science", "Physics", "Medicine"], "published_date": "2022-09-30", "url": "https://arxiv.org/abs/2210.08973", "pdf_url": "https://arxiv.org/pdf/2210.08973v2", "arxiv_id": "2210.08973", "doi": "10.1038/s41597-023-02298-6", "citation_count": 93, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Scientific Data", "quality_score": 0.4933}
{"id": "d5d1d343e274cb523aa71b1891dc39ed1f1d382d95afdc24a662ddf84eb4cef8", "sources": ["arxiv", "semantic_scholar"], "title": "Label driven Knowledge Distillation for Federated Learning with non-IID Data", "abstract": "In real-world applications, Federated Learning (FL) meets two challenges: (1) scalability, especially when applied to massive IoT networks; and (2) how to be robust against an environment with heterogeneous data. Realizing the first problem, we aim to design a novel FL framework named Full-stack FL (F2L). More specifically, F2L utilizes a hierarchical network architecture, making extending the FL network accessible without reconstructing the whole network system. Moreover, leveraging the advantages of hierarchical network design, we propose a new label-driven knowledge distillation (LKD) technique at the global server to address the second problem. As opposed to current knowledge distillation techniques, LKD is capable of training a student model, which consists of good knowledge from all teachers' models. Therefore, our proposed algorithm can effectively extract the knowledge of the regions' data distribution (i.e., the regional aggregated models) to reduce the divergence between clients' models when operating under the FL system with non-independent identically distributed data. Extensive experiment results reveal that: (i) our F2L method can significantly improve the overall FL efficiency in all global distillations, and (ii) F2L rapidly achieves convergence as global distillation stages occur instead of increasing on each communication cycle.", "authors": ["Minh-Duong Nguyen", "Quoc-Viet Pham", "Dinh Thai Hoang", "Long Tran-Thanh", "Diep N. Nguyen", "Won-Joo Hwang"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2022-09-29", "url": "https://arxiv.org/abs/2209.14520", "pdf_url": "https://arxiv.org/pdf/2209.14520v2", "arxiv_id": "2209.14520", "doi": null, "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1945}
{"id": "b3301ac5fec8aa263506607d597fd94bc6b7eaf16a1d34fa06469e86df051bf7", "sources": ["arxiv", "semantic_scholar"], "title": "Benchmarking Apache Spark and Hadoop MapReduce on Big Data Classification", "abstract": "Most of the popular Big Data analytics tools evolved to adapt their working environment to extract valuable information from a vast amount of unstructured data. The ability of data mining techniques to filter this helpful information from Big Data led to the term Big Data Mining. Shifting the scope of data from small-size, structured, and stable data to huge volume, unstructured, and quickly changing data brings many data management challenges. Different tools cope with these challenges in their own way due to their architectural limitations. There are numerous parameters to take into consideration when choosing the right data management framework based on the task at hand. In this paper, we present a comprehensive benchmark for two widely used Big Data analytics tools, namely Apache Spark and Hadoop MapReduce, on a common data mining task, i.e., classification. We employ several evaluation metrics to compare the performance of the benchmarked frameworks, such as execution time, accuracy, and scalability. These metrics are specialized to measure the performance for classification task. To the best of our knowledge, there is no previous study in the literature that employs all these metrics while taking into consideration task-specific concerns. We show that Spark is 5 times faster than MapReduce on training the model. Nevertheless, the performance of Spark degrades when the input workload gets larger. Scaling the environment by additional clusters significantly improves the performance of Spark. However, similar enhancement is not observed in Hadoop. Machine learning utility of MapReduce tend to have better accuracy scores than that of Spark, like around 3%, even in small size data sets.", "authors": ["Taha Tekdogan", "Ali Cakmak"], "categories": ["cs.DC", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-09-21", "url": "https://arxiv.org/abs/2209.10637", "pdf_url": "https://arxiv.org/pdf/2209.10637v1", "arxiv_id": "2209.10637", "doi": "10.1145/3481646.3481649", "citation_count": 11, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Cloud and Big Data Computing", "quality_score": 0.2698}
{"id": "f07fbde919430cfac6a512b146f37914ba837280095b411f9479a1dfaa3e7e76", "sources": ["arxiv", "semantic_scholar"], "title": "Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation", "abstract": "Data-free Knowledge Distillation (DFKD) has attracted attention recently thanks to its appealing capability of transferring knowledge from a teacher network to a student network without using training data. The main idea is to use a generator to synthesize data for training the student. As the generator gets updated, the distribution of synthetic data will change. Such distribution shift could be large if the generator and the student are trained adversarially, causing the student to forget the knowledge it acquired at previous steps. To alleviate this problem, we propose a simple yet effective method called Momentum Adversarial Distillation (MAD) which maintains an exponential moving average (EMA) copy of the generator and uses synthetic samples from both the generator and the EMA generator to train the student. Since the EMA generator can be considered as an ensemble of the generator's old versions and often undergoes a smaller change in updates compared to the generator, training on its synthetic samples can help the student recall the past knowledge and prevent the student from adapting too quickly to new updates of the generator. Our experiments on six benchmark datasets including big datasets like ImageNet and Places365 demonstrate the superior performance of MAD over competing methods for handling the large distribution shift problem. Our method also compares favorably to existing DFKD methods and even achieves state-of-the-art results in some cases.", "authors": ["Kien Do", "Hung Le", "Dung Nguyen", "Dang Nguyen", "Haripriya Harikumar", "Truyen Tran", "Santu Rana", "Svetha Venkatesh"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2022-09-21", "url": "https://arxiv.org/abs/2209.10359", "pdf_url": "https://arxiv.org/pdf/2209.10359v1", "arxiv_id": "2209.10359", "doi": "10.48550/arXiv.2209.10359", "citation_count": 46, "influential_citation_count": 7, "has_code": false, "code_url": null, "venue": "Neural Information Processing Systems", "quality_score": 0.4515}
{"id": "dc6a3aa5de6f00c20cab53d145fb6b442fad63e68f8d45b9e3e2dad94c2b8ff1", "sources": ["arxiv", "semantic_scholar"], "title": "Exploring Inconsistent Knowledge Distillation for Object Detection with Data Augmentation", "abstract": "Knowledge Distillation (KD) for object detection aims to train a compact detector by transferring knowledge from a teacher model. Since the teacher model perceives data in a way different from humans, existing KD methods only distill knowledge that is consistent with labels annotated by human expert while neglecting knowledge that is not consistent with human perception, which results in insufficient distillation and sub-optimal performance. In this paper, we propose inconsistent knowledge distillation (IKD), which aims to distill knowledge inherent in the teacher model's counter-intuitive perceptions. We start by considering the teacher model's counter-intuitive perceptions of frequency and non-robust features. Unlike previous works that exploit fine-grained features or introduce additional regularizations, we extract inconsistent knowledge by providing diverse input using data augmentation. Specifically, we propose a sample-specific data augmentation to transfer the teacher model's ability in capturing distinct frequency components and suggest an adversarial feature augmentation to extract the teacher model's perceptions of non-robust features in the data. Extensive experiments demonstrate the effectiveness of our method which outperforms state-of-the-art KD baselines on one-stage, two-stage and anchor-free object detectors (at most +1.0 mAP). Our codes will be made available at \\url{https://github.com/JWLiang007/IKD.git}.", "authors": ["Jiawei Liang", "Siyuan Liang", "Aishan Liu", "Ke Ma", "Jingzhi Li", "Xiaochun Cao"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2022-09-20", "url": "https://arxiv.org/abs/2209.09841", "pdf_url": "https://arxiv.org/pdf/2209.09841v3", "arxiv_id": "2209.09841", "doi": "10.1145/3581783.3612281", "citation_count": 23, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/JWLiang007/IKD.git}", "venue": "ACM Multimedia", "quality_score": 0.3451}
{"id": "fb879eeb7fd6673e96fdddc5eac8b12766e7991d2949e3993df687d8fc21b87d", "sources": ["arxiv", "semantic_scholar"], "title": "How does Twitter account moderation work? Dynamics of account creation and suspension on Twitter during major geopolitical events", "abstract": "Social media moderation policies are often at the center of public debate, and their implementation and enactment are sometimes surrounded by a veil of mystery. Unsurprisingly, due to limited platform transparency and data access, relatively little research has been devoted to characterizing moderation dynamics, especially in the context of controversial events and the platform activity associated with them. Here, we study the dynamics of account creation and suspension on Twitter during two global political events: Russia's invasion of Ukraine and the 2022 French Presidential election. Leveraging a large-scale dataset of 270M tweets shared by 16M users in multiple languages over several months, we identify peaks of suspicious account creation and suspension, and we characterize behaviours that more frequently lead to account suspension. We show how large numbers of accounts get suspended within days from their creation. Suspended accounts tend to mostly interact with legitimate users, as opposed to other suspicious accounts, often making unwarranted and excessive use of reply and mention features, and predominantly sharing spam and harmful content. While we are only able to speculate about the specific causes leading to a given account suspension, our findings shed light on patterns of platform abuse and subsequent moderation during major events.", "authors": ["Francesco Pierri", "Luca Luceri", "Emily Chen", "Emilio Ferrara"], "categories": ["cs.SI", "cs.HC"], "fields_of_study": ["Computer Science", "Medicine"], "published_date": "2022-09-15", "url": "https://arxiv.org/abs/2209.07614", "pdf_url": "https://arxiv.org/pdf/2209.07614v4", "arxiv_id": "2209.07614", "doi": "10.1140/epjds/s13688-023-00420-7", "citation_count": 30, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "EPJ Data Science", "quality_score": 0.3728}
{"id": "f50f35fcce7e0ee4ce0d352242154c9038ed3dd2ba4fb44cf2d5cfd3928084d1", "sources": ["arxiv", "semantic_scholar"], "title": "Rule-adhering synthetic data -- the lingua franca of learning", "abstract": "AI-generated synthetic data allows to distill the general patterns of existing data, that can then be shared safely as granular-level representative, yet novel data samples within the original semantics. In this work we explore approaches of incorporating domain expertise into the data synthesis, to have the statistical properties as well as pre-existing domain knowledge of rules be represented. The resulting synthetic data generator, that can be probed for any number of new samples, can then serve as a common source of intelligence, as a lingua franca of learning, consumable by humans and machines alike. We demonstrate the concept for a publicly available data set, and evaluate its benefits via descriptive analysis as well as a downstream ML model.", "authors": ["Michael Platzer", "Ivona Krchova"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-09-12", "url": "https://arxiv.org/abs/2209.06679", "pdf_url": "https://arxiv.org/pdf/2209.06679v1", "arxiv_id": "2209.06679", "doi": "10.48550/arXiv.2209.06679", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "76cf60ccd72db25ce3fb35e44aa76e85f3babfaab84c2a0fac633fee28587ba5", "sources": ["arxiv", "semantic_scholar"], "title": "Online Continual Learning via the Meta-learning Update with Multi-scale Knowledge Distillation and Data Augmentation", "abstract": "Continual learning aims to rapidly and continually learn the current task from a sequence of tasks. Compared to other kinds of methods, the methods based on experience replay have shown great advantages to overcome catastrophic forgetting. One common limitation of this method is the data imbalance between the previous and current tasks, which would further aggravate forgetting. Moreover, how to effectively address the stability-plasticity dilemma in this setting is also an urgent problem to be solved. In this paper, we overcome these challenges by proposing a novel framework called Meta-learning update via Multi-scale Knowledge Distillation and Data Augmentation (MMKDDA). Specifically, we apply multiscale knowledge distillation to grasp the evolution of long-range and short-range spatial relationships at different feature levels to alleviate the problem of data imbalance. Besides, our method mixes the samples from the episodic memory and current task in the online continual training procedure, thus alleviating the side influence due to the change of probability distribution. Moreover, we optimize our model via the meta-learning update resorting to the number of tasks seen previously, which is helpful to keep a better balance between stability and plasticity. Finally, our experimental evaluation on four benchmark datasets shows the effectiveness of the proposed MMKDDA framework against other popular baselines, and ablation studies are also conducted to further analyze the role of each component in our framework.", "authors": ["Ya-nan Han", "Jian-wei Liu"], "categories": ["cs.LG", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2022-09-12", "url": "https://arxiv.org/abs/2209.06107", "pdf_url": "https://arxiv.org/pdf/2209.06107v1", "arxiv_id": "2209.06107", "doi": "10.1016/j.engappai.2022.104966", "citation_count": 16, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Engineering applications of artificial intelligence", "quality_score": 0.3076}
{"id": "661d415a42ded0e72e63b1eb4ab38333464ffa35d1299310342c0c30d9cc44b6", "sources": ["arxiv", "semantic_scholar"], "title": "Online Updating Huber Robust Regression for Big Data Streams", "abstract": "Big data streams are grasping increasing attention with the development of modern science and information technology. Due to the incompatibility of limited computer memory to high volume of streaming data, real-time methods without historical data storage is worth investigating. Moreover, outliers may occur with high velocity data streams generating, calling for more robust analysis. Motivated by these concerns, a novel Online Updating Huber Robust Regression algorithm is proposed in this paper. By extracting key features of new data subsets, it obtains a computational efficient online updating estimator without historical data storage. Meanwhile, by integrating Huber regression into the framework, the estimator is robust to contaminated data streams, such as heavy-tailed or heterogeneous distributed ones as well as cases with outliers. Moreover, the proposed online updating estimator is asymptotically equivalent to Oracle estimator obtained by the entire data and has a lower computation complexity. Extensive numerical simulations and a real data analysis are also conducted to evaluate the estimation and calculation efficiency of the proposed method.", "authors": ["Chunbai Tao", "Shanshan Wang"], "categories": ["stat.ME"], "fields_of_study": ["Mathematics"], "published_date": "2022-09-05", "url": "https://arxiv.org/abs/2209.01718", "pdf_url": "https://arxiv.org/pdf/2209.01718v2", "arxiv_id": "2209.01718", "doi": "10.1080/02331888.2024.2398057", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Statistics (Berlin)", "quality_score": 0.1945}
{"id": "f3f9c5ba978bae2aba72c7bf8b3df3fdc220637e0fcffba6cc492a50d0386113", "sources": ["arxiv", "semantic_scholar"], "title": "Data Augmentation for Deep Receivers", "abstract": "Deep neural networks (DNNs) allow digital receivers to learn to operate in complex environments. To do so, DNNs should preferably be trained using large labeled data sets with a similar statistical relationship as the one under which they are to infer. For DNN-aided receivers, obtaining labeled data conventionally involves pilot signalling at the cost of reduced spectral efficiency, typically resulting in access to limited data sets. In this paper, we study how one can enrich a small set of labeled pilots data into a larger data set for training deep receivers. Motivated by the widespread use of data augmentation techniques for enriching visual and text data, we propose dedicated augmentation schemes that exploits the characteristics of digital communication data. We identify the key considerations in data augmentations for deep receivers as the need for domain orientation, class (constellation) diversity, and low complexity. Following these guidelines, we devise three complementing augmentations that exploit the geometric properties of digital constellations. Our combined augmentation approach builds on the merits of these different augmentations to synthesize reliable data from a momentary channel distribution, to be used for training deep receivers. Furthermore, we exploit previous channel realizations to increase the reliability of the augmented samples.", "authors": ["Tomer Raviv", "Nir Shlezinger"], "categories": ["cs.IT"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2022-09-03", "url": "https://arxiv.org/abs/2209.01362", "pdf_url": "https://arxiv.org/pdf/2209.01362v1", "arxiv_id": "2209.01362", "doi": "10.1109/TWC.2023.3261782", "citation_count": 28, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/tomerraviv95/data-augmentations-for-receivers", "venue": "IEEE Transactions on Wireless Communications", "quality_score": 0.3656}
{"id": "5b77b91817ac4462207aa496f92b0a23e871201a03b612531d405b5f65b6b150", "sources": ["arxiv", "semantic_scholar"], "title": "FAKD: Feature Augmented Knowledge Distillation for Semantic Segmentation", "abstract": "In this work, we explore data augmentations for knowledge distillation on semantic segmentation. To avoid over-fitting to the noise in the teacher network, a large number of training examples is essential for knowledge distillation. Imagelevel argumentation techniques like flipping, translation or rotation are widely used in previous knowledge distillation framework. Inspired by the recent progress on semantic directions on feature-space, we propose to include augmentations in feature space for efficient distillation. Specifically, given a semantic direction, an infinite number of augmentations can be obtained for the student in the feature space. Furthermore, the analysis shows that those augmentations can be optimized simultaneously by minimizing an upper bound for the losses defined by augmentations. Based on the observation, a new algorithm is developed for knowledge distillation in semantic segmentation. Extensive experiments on four semantic segmentation benchmarks demonstrate that the proposed method can boost the performance of current knowledge distillation methods without any significant overhead. Code is available at: https://github.com/jianlong-yuan/FAKD.", "authors": ["Jianlong Yuan", "Qian Qi", "Fei Du", "Zhibin Wang", "Fan Wang", "Yifan Liu"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2022-08-30", "url": "https://arxiv.org/abs/2208.14143", "pdf_url": "https://arxiv.org/pdf/2208.14143v1", "arxiv_id": "2208.14143", "doi": "10.1109/WACV57701.2024.00065", "citation_count": 27, "influential_citation_count": 2, "has_code": true, "code_url": "https://github.com/jianlong-yuan/FAKD", "venue": "IEEE Workshop/Winter Conference on Applications of Computer Vision", "quality_score": 0.3618}
{"id": "099c64097f142ea82305477b3487e37c39c0d6705eda11a642a0e7fb7ba28a98", "sources": ["arxiv", "semantic_scholar"], "title": "Rethinking Cost-sensitive Classification in Deep Learning via Adversarial Data Augmentation", "abstract": "Cost-sensitive classification is critical in applications where misclassification errors widely vary in cost. However, over-parameterization poses fundamental challenges to the cost-sensitive modeling of deep neural networks (DNNs). The ability of a DNN to fully interpolate a training dataset can render a DNN, evaluated purely on the training set, ineffective in distinguishing a cost-sensitive solution from its overall accuracy maximization counterpart. This necessitates rethinking cost-sensitive classification in DNNs. To address this challenge, this paper proposes a cost-sensitive adversarial data augmentation (CSADA) framework to make over-parameterized models cost-sensitive. The overarching idea is to generate targeted adversarial examples that push the decision boundary in cost-aware directions. These targeted adversarial samples are generated by maximizing the probability of critical misclassifications and used to train a model with more conservative decisions on costly pairs. Experiments on well-known datasets and a pharmacy medication image (PMI) dataset made publicly available show that our method can effectively minimize the overall cost and reduce critical errors, while achieving comparable performance in terms of overall accuracy.", "authors": ["Qiyuan Chen", "Raed Al Kontar", "Maher Nouiehed", "Jessie Yang", "Corey Lester"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2022-08-24", "url": "https://arxiv.org/abs/2208.11739", "pdf_url": "https://arxiv.org/pdf/2208.11739v1", "arxiv_id": "2208.11739", "doi": "10.1287/ijds.2022.0033", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "INFORMS Journal on Data Science", "quality_score": 0.1505}
{"id": "a0fc01cc6baa25a5a5ed5c7ae37c7f1a3b58c55eead692856e82a69ab1be35f0", "sources": ["arxiv", "semantic_scholar"], "title": "Generating Synthetic Clinical Data that Capture Class Imbalanced Distributions with Generative Adversarial Networks: Example using Antiretroviral Therapy for HIV", "abstract": "Clinical data usually cannot be freely distributed due to their highly confidential nature and this hampers the development of machine learning in the healthcare domain. One way to mitigate this problem is by generating realistic synthetic datasets using generative adversarial networks (GANs). However, GANs are known to suffer from mode collapse thus creating outputs of low diversity. This lowers the quality of the synthetic healthcare data, and may cause it to omit patients of minority demographics or neglect less common clinical practices. In this paper, we extend the classic GAN setup with an additional variational autoencoder (VAE) and include an external memory to replay latent features observed from the real samples to the GAN generator. Using antiretroviral therapy for human immunodeficiency virus (ART for HIV) as a case study, we show that our extended setup overcomes mode collapse and generates a synthetic dataset that accurately describes severely imbalanced class distributions commonly found in real-world clinical variables. In addition, we demonstrate that our synthetic dataset is associated with a very low patient disclosure risk, and that it retains a high level of utility from the ground truth dataset to support the development of downstream machine learning algorithms.", "authors": ["Nicholas I-Hsien Kuo", "Federico Garcia", "Anders Sönnerborg", "Maurizio Zazzi", "Michael Böhm", "Rolf Kaiser", "Mark Polizzotto", "Louisa Jorm", "Sebastiano Barbieri"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science", "Medicine"], "published_date": "2022-08-18", "url": "https://arxiv.org/abs/2208.08655", "pdf_url": "https://arxiv.org/pdf/2208.08655v2", "arxiv_id": "2208.08655", "doi": "10.48550/arXiv.2208.08655", "citation_count": 44, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Journal of Biomedical Informatics", "quality_score": 0.4133}
{"id": "6eb4bc803bc2ea3ca422176a2a2bb65a55888dd548e2385d5c9f2e12eca941c5", "sources": ["arxiv", "semantic_scholar"], "title": "TexPrax: A Messaging Application for Ethical, Real-time Data Collection and Annotation", "abstract": "Collecting and annotating task-oriented dialog data is difficult, especially for highly specific domains that require expert knowledge. At the same time, informal communication channels such as instant messengers are increasingly being used at work. This has led to a lot of work-relevant information that is disseminated through those channels and needs to be post-processed manually by the employees. To alleviate this problem, we present TexPrax, a messaging system to collect and annotate problems, causes, and solutions that occur in work-related chats. TexPrax uses a chatbot to directly engage the employees to provide lightweight annotations on their conversation and ease their documentation work. To comply with data privacy and security regulations, we use an end-to-end message encryption and give our users full control over their data which has various advantages over conventional annotation tools. We evaluate TexPrax in a user-study with German factory employees who ask their colleagues for solutions on problems that arise during their daily work. Overall, we collect 202 task-oriented German dialogues containing 1,027 sentences with sentence-level expert annotations. Our data analysis also reveals that real-world conversations frequently contain instances with code-switching, varying abbreviations for the same entity, and dialects which NLP systems should be able to handle.", "authors": ["Lorenz Stangier", "Ji-Ung Lee", "Yuxi Wang", "Marvin Müller", "Nicholas Frick", "Joachim Metternich", "Iryna Gurevych"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2022-08-16", "url": "https://arxiv.org/abs/2208.07846", "pdf_url": "https://arxiv.org/pdf/2208.07846v2", "arxiv_id": "2208.07846", "doi": "10.48550/arXiv.2208.07846", "citation_count": 3, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/UKPLab/TexPrax", "venue": null, "quality_score": 0.1505}
{"id": "edff8bb1db1c144d7e8684b23d03e45fbb0c651356a6c5755ffa4ea779f992ad", "sources": ["arxiv", "semantic_scholar"], "title": "Local Differentially Private Fuzzy Counting in Stream Data using Probabilistic Data Structure", "abstract": "Privacy-preserving estimation of counts of items in streaming data finds applications in several real-world scenarios including word auto-correction and traffic management applications. Recent works of RAPPOR and Apple's count-mean sketch (CMS) algorithm propose privacy preserving mechanisms for count estimation in large volumes of data using probabilistic data structures like counting Bloom filter and CMS. However, these existing methods fall short in providing a sound solution for real-time streaming data applications. In this work, we propose a novel (local) Differentially private mechanism that provides high utility for the streaming data count estimation problem with similar or even lower privacy budgets while providing: a) fuzzy counting to report counts of related or similar items (for instance to account for typing errors and data variations), and b) improved querying efficiency to reduce the response time for real-time querying of counts. We provide formal proofs for privacy and utility guarantees and present extensive experimental evaluation of our algorithm using real and synthetic English words datasets for both the exact and fuzzy counting scenarios. Our privacy preserving mechanism substantially outperforms the prior work in terms of lower querying time, significantly higher utility (accuracy of count estimation) under similar or lower privacy guarantees, at the cost of communication overhead.", "authors": ["Dinusha Vatsalan", "Raghav Bhaskar", "Mohamed Ali Kaafar"], "categories": ["cs.DS", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2022-08-10", "url": "https://arxiv.org/abs/2208.05264", "pdf_url": "https://arxiv.org/pdf/2208.05264v2", "arxiv_id": "2208.05264", "doi": "10.1109/TKDE.2022.3198478", "citation_count": 5, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Knowledge and Data Engineering", "quality_score": 0.1945}
{"id": "29dc80c30b990322879e5ffb87f97d626592f49e59a9c40c8b4c9a2aa227a70f", "sources": ["arxiv", "semantic_scholar"], "title": "Cryptoeconomic Security for Data Availability Committees", "abstract": "Layer 2 systems have received increasing attention due to their potential to scale the throughput of L1 blockchains. To avoid the cost of putting data on chain, these systems increasingly turn to off-chain data availability solutions such as data availability committees (DACs). However, placing trust on DACs conflicts with the goal of obtaining an L2 architecture whose security relies solely on the L1 chain. To eliminate such trust assumptions, we propose a DAC protocol that provides financial incentives to deter the DAC nodes from adversarial behavior such as withholding data upon request. We then analyze the interaction of rational DAC nodes and clients as a dynamic game, with a Byzantine adversary that can corrupt and bribe the participants. We also define a notion of optimality for the DAC protocols, inspired by fairness and economic feasibility. Our main result shows that our protocol is optimal and guarantees security with the highest possible probability under reasonable assumptions on the adversary.", "authors": ["Ertem Nusret Tas", "Dan Boneh"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2022-08-05", "url": "https://arxiv.org/abs/2208.02999", "pdf_url": "https://arxiv.org/pdf/2208.02999v3", "arxiv_id": "2208.02999", "doi": "10.48550/arXiv.2208.02999", "citation_count": 14, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Financial Cryptography", "quality_score": 0.294}
{"id": "334a0f48dcfcf844ff06419427e1055a2114e29d80eb674902d563c494ae011c", "sources": ["arxiv", "semantic_scholar"], "title": "Teaching Visual Accessibility in Introductory Data Science Classes with Multi-Modal Data Representations", "abstract": "Although there are various ways to represent data patterns and models, visualization has been primarily taught in many data science courses for its efficiency. Such vision-dependent output may cause critical barriers against those who are blind and visually impaired and people with learning disabilities. We argue that instructors need to teach multiple data representation methods so that all students can produce data products that are more accessible. In this paper, we argue that accessibility should be taught as early as the introductory course as part of the data science curriculum so that regardless of whether learners major in data science or not, they can have foundational exposure to accessibility. As data science educators who teach accessibility as part of our lower-division courses in two different institutions, we share specific examples that can be utilized by other data science instructors.", "authors": ["JooYoung Seo", "Mine Dogucu"], "categories": ["cs.HC", "cs.CY", "stat.OT"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2022-08-04", "url": "https://arxiv.org/abs/2208.02565", "pdf_url": "https://arxiv.org/pdf/2208.02565v1", "arxiv_id": "2208.02565", "doi": "10.6339/23-JDS1095", "citation_count": 10, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Journal of Data Science", "quality_score": 0.2603}
{"id": "e4d734bb47a40d9a695ca9cdb6448be263e2c4fb9504c84835e792a8ecbbfc83", "sources": ["arxiv", "semantic_scholar"], "title": "Investigation of robustness and numerical stability of multiple regression and PCA in modeling world development data", "abstract": "Popular methods for modeling data both labelled and unlabeled, multiple regression and PCA has been used in research for a vast number of datasets. In this investigation, we attempt to push the limits of these two methods by running a fit on world development data, a set notorious for its complexity and high dimensionality. We assess the robustness and numerical stability of both methods using their matrix condition number and ability to capture variance in the dataset. The result indicates poor performance from both methods from a numerical standpoint, yet certain qualitative insights can still be captured.", "authors": ["Chen Ye Gan"], "categories": ["stat.ME", "stat.AP", "stat.CO"], "fields_of_study": ["Mathematics"], "published_date": "2022-07-30", "url": "https://arxiv.org/abs/2208.01549", "pdf_url": "https://arxiv.org/pdf/2208.01549v1", "arxiv_id": "2208.01549", "doi": "10.54097/hset.v16i.2503", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Highlights in Science Engineering and Technology", "quality_score": 0.0}
{"id": "d42a07f5ecc3407437deda8d954c7f30e3a8cdacac82e8bafc530d0870b5d96c", "sources": ["arxiv", "semantic_scholar"], "title": "Model based clustering of multinomial count data", "abstract": "We consider the problem of inferring an unknown number of clusters in replicated multinomial data. Under a model based clustering point of view, this task can be treated by estimating finite mixtures of multinomial distributions with or without covariates. Both Maximum Likelihood (ML) as well as Bayesian estimation are taken into account. Under a Maximum Likelihood approach, we provide an Expectation--Maximization (EM) algorithm which exploits a careful initialization procedure combined with a ridge--stabilized implementation of the Newton--Raphson method in the M--step. Under a Bayesian setup, a stochastic gradient Markov chain Monte Carlo (MCMC) algorithm embedded within a prior parallel tempering scheme is devised. The number of clusters is selected according to the Integrated Completed Likelihood criterion in the ML approach and estimating the number of non-empty components in overfitting mixture models in the Bayesian case. Our method is illustrated in simulated data and applied to two real datasets. An R package is available at https://github.com/mqbssppe/multinomialLogitMix.", "authors": ["Panagiotis Papastamoulis"], "categories": ["stat.ME", "stat.CO"], "fields_of_study": ["Mathematics"], "published_date": "2022-07-28", "url": "https://arxiv.org/abs/2207.13984", "pdf_url": "https://arxiv.org/pdf/2207.13984v2", "arxiv_id": "2207.13984", "doi": "10.1007/s11634-023-00547-5", "citation_count": 7, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/mqbssppe/multinomialLogitMix", "venue": "Advances in Data Analysis and Classification", "quality_score": 0.2258}
{"id": "28996e0e31f701d01c66a6b2b2f84839a1b1ffc5881588ed3261ce81ed5c425f", "sources": ["arxiv", "semantic_scholar"], "title": "Neural modal ordinary differential equations: Integrating physics-based modeling with neural ordinary differential equations for modeling high-dimensional monitored structures", "abstract": "The order/dimension of models derived on the basis of data is commonly restricted by the number of observations, or in the context of monitored systems, sensing nodes. This is particularly true for structural systems (e.g., civil or mechanical structures), which are typically high-dimensional in nature. In the scope of physics-informed machine learning, this paper proposes a framework -- termed Neural Modal ODEs -- to integrate physics-based modeling with deep learning for modeling the dynamics of monitored and high-dimensional engineered systems. Neural Ordinary Differential Equations -- Neural ODEs are exploited as the deep learning operator. In this initiating exploration, we restrict ourselves to linear or mildly nonlinear systems. We propose an architecture that couples a dynamic version of variational autoencoders with physics-informed Neural ODEs (Pi-Neural ODEs). An encoder, as a part of the autoencoder, learns the abstract mappings from the first few items of observational data to the initial values of the latent variables, which drive the learning of embedded dynamics via physics-informed Neural ODEs, imposing a modal model structure on that latent space. The decoder of the proposed model adopts the eigenmodes derived from an eigen-analysis applied to the linearized portion of a physics-based model: a process implicitly carrying the spatial relationship between degrees-of-freedom (DOFs). The framework is validated on a numerical example, and an experimental dataset of a scaled cable-stayed bridge, where the learned hybrid model is shown to outperform a purely physics-based approach to modeling. We further show the functionality of the proposed scheme within the context of virtual sensing, i.e., the recovery of generalized response quantities in unmeasured DOFs from spatially sparse data.", "authors": ["Zhilu Lai", "Wei Liu", "Xudong Jian", "Kiran Bacsa", "Limin Sun", "Eleni Chatzi"], "categories": ["cs.LG", "cs.CE", "physics.data-an"], "fields_of_study": ["Computer Science", "Physics"], "published_date": "2022-07-16", "url": "https://arxiv.org/abs/2207.07883", "pdf_url": "https://arxiv.org/pdf/2207.07883v2", "arxiv_id": "2207.07883", "doi": "10.1017/dce.2022.35", "citation_count": 35, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Data-Centric Engineering", "quality_score": 0.3891}
{"id": "7325e8a391297b1000f3b5687fbe9570fd91beddcb2ff4e5dc9ef9f72e7f329f", "sources": ["arxiv", "semantic_scholar"], "title": "Beyond Hard Labels: Investigating data label distributions", "abstract": "High-quality data is a key aspect of modern machine learning. However, labels generated by humans suffer from issues like label noise and class ambiguities. We raise the question of whether hard labels are sufficient to represent the underlying ground truth distribution in the presence of these inherent imprecision. Therefore, we compare the disparity of learning with hard and soft labels quantitatively and qualitatively for a synthetic and a real-world dataset. We show that the application of soft labels leads to improved performance and yields a more regular structure of the internal feature space.", "authors": ["Vasco Grossmann", "Lars Schmarje", "Reinhard Koch"], "categories": ["cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-07-13", "url": "https://arxiv.org/abs/2207.06224", "pdf_url": "https://arxiv.org/pdf/2207.06224v2", "arxiv_id": "2207.06224", "doi": "10.48550/arXiv.2207.06224", "citation_count": 12, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2785}
{"id": "b1c728c14b3be5900ad354e470ceb9b7ed2a7f2154730f497a9fcb787a22ac30", "sources": ["arxiv", "semantic_scholar"], "title": "A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy", "abstract": "A distinguishing characteristic of federated learning is that the (local) client data could have statistical heterogeneity. This heterogeneity has motivated the design of personalized learning, where individual (personalized) models are trained, through collaboration. There have been various personalization methods proposed in literature, with seemingly very different forms and methods ranging from use of a single global model for local regularization and model interpolation, to use of multiple global models for personalized clustering, etc. In this work, we begin with a generative framework that could potentially unify several different algorithms as well as suggest new algorithms. We apply our generative framework to personalized estimation, and connect it to the classical empirical Bayes' methodology. We develop private personalized estimation under this framework. We then use our generative framework for learning, which unifies several known personalized FL algorithms and also suggests new ones; we propose and study a new algorithm AdaPeD based on a Knowledge Distillation, which numerically outperforms several known algorithms. We also develop privacy for personalized learning methods with guarantees for user-level privacy and composition. We numerically evaluate the performance as well as the privacy for both the estimation and learning problems, demonstrating the advantages of our proposed methods.", "authors": ["Kaan Ozkara", "Antonious M. Girgis", "Deepesh Data", "Suhas Diggavi"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2022-07-05", "url": "https://arxiv.org/abs/2207.01771", "pdf_url": "https://arxiv.org/pdf/2207.01771v1", "arxiv_id": "2207.01771", "doi": "10.48550/arXiv.2207.01771", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1747}
{"id": "cb6122e03e3699edf1fac3ce9b7fb8a2acc2938e8b453cee602e839b07d8f28c", "sources": ["arxiv", "semantic_scholar"], "title": "Gaia Data Release 3: Cross-match of Gaia sources with variable objects from the literature", "abstract": "Context. In the current ever increasing data volumes of astronomical surveys, automated methods are essential. Objects of known classes from the literature are necessary for training supervised machine learning algorithms, as well as for verification/validation of their results. Aims.The primary goal of this work is to provide a comprehensive data set of known variable objects from the literature cross-matched with \\textit{Gaia}~DR3 sources, including a large number of both variability types and representatives, in order to cover as much as possible sky regions and magnitude ranges relevant to each class. In addition, non-variable objects from selected surveys are targeted to probe their variability in \\textit{Gaia} and possible use as standards. This data set can be the base for a training set applicable in variability detection, classification, and validation. MethodsA statistical method that employed both astrometry (position and proper motion) and photometry (mean magnitude) was applied to selected literature catalogues in order to identify the correct counterparts of the known objects in the \\textit{Gaia} data. The cross-match strategy was adapted to the properties of each catalogue and the verification of results excluded dubious matches. Results.Our catalogue gathers 7\\,841\\,723 \\textit{Gaia} sources among which 1.2~million non-variable objects and 1.7~million galaxies, in addition to 4.9~million variable sources representing over 100~variability (sub)types. Conclusions.This data set served the requirements of \\textit{Gaia}'s variability pipeline for its third data release (DR3), from classifier training to result validation, and it is expected to be a useful resource for the scientific community that is interested in the analysis of variability in the \\textit{Gaia} data and other surveys.", "authors": ["P. Gavras", "L. Rimoldini", "K. Nienartowicz", "G. Jevardat de Fombelle", "B. Holl", "P. Ábrahám", "M. Audard", "M. Carnerero", "G. Clementini", "J. De Ridder", "E. Distefano", "P. Garcia-Lario", "A. Garofalo", "Á. Kóspál", "K. Kruszyńska", "M. Kun", "I. Lecoeur-Taïbi", "G. Marton", "T. Mazeh", "N. Mowlavi", "C. Raiteri", "V. Ripepi", "L. Szabados", "S. Zucker", "L. Eyer"], "categories": ["astro-ph.IM", "astro-ph.GA", "astro-ph.SR"], "fields_of_study": ["Physics"], "published_date": "2022-07-05", "url": "https://arxiv.org/abs/2207.01946", "pdf_url": "https://arxiv.org/pdf/2207.01946v1", "arxiv_id": "2207.01946", "doi": "10.1051/0004-6361/202244367", "citation_count": 22, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3404}
{"id": "edf3683f529af564141783267680b13064f76dc1c0f4b9fb7bf4d520112d9b9e", "sources": ["arxiv", "semantic_scholar"], "title": "Proteus: A Self-Designing Range Filter", "abstract": "We introduce Proteus, a novel self-designing approximate range filter, which configures itself based on sampled data in order to optimize its false positive rate (FPR) for a given space requirement. Proteus unifies the probabilistic and deterministic design spaces of state-of-the-art range filters to achieve robust performance across a larger variety of use cases. At the core of Proteus lies our Contextual Prefix FPR (CPFPR) model - a formal framework for the FPR of prefix-based filters across their design spaces. We empirically demonstrate the accuracy of our model and Proteus' ability to optimize over both synthetic workloads and real-world datasets. We further evaluate Proteus in RocksDB and show that it is able to improve end-to-end performance by as much as 5.3x over more brittle state-of-the-art methods such as SuRF and Rosetta. Our experiments also indicate that the cost of modeling is not significant compared to the end-to-end performance gains and that Proteus is robust to workload shifts.", "authors": ["Eric R. Knorr", "Baptiste Lemaire", "Andrew Lim", "Siqiang Luo", "Huanchen Zhang", "Stratos Idreos", "Michael Mitzenmacher"], "categories": ["cs.DB", "cs.DS", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-06-30", "url": "https://arxiv.org/abs/2207.01503", "pdf_url": "https://arxiv.org/pdf/2207.01503v1", "arxiv_id": "2207.01503", "doi": "10.1145/3514221.3526167", "citation_count": 36, "influential_citation_count": 4, "has_code": false, "code_url": null, "venue": "SIGMOD'22: Proceedings of the 2022 International Conference on Management of Data, June 2022, 1670-1684", "quality_score": 0.3921}
{"id": "a9d0217a478ab7fa27c961d560e39bef7992e4294181911e9ea22ca1a29a2c92", "sources": ["arxiv", "semantic_scholar"], "title": "Protoformer: Embedding Prototypes for Transformers", "abstract": "Transformers have been widely applied in text classification. Unfortunately, real-world data contain anomalies and noisy labels that cause challenges for state-of-art Transformers. This paper proposes Protoformer, a novel self-learning framework for Transformers that can leverage problematic samples for text classification. Protoformer features a selection mechanism for embedding samples that allows us to efficiently extract and utilize anomalies prototypes and difficult class prototypes. We demonstrated such capabilities on datasets with diverse textual structures (e.g., Twitter, IMDB, ArXiv). We also applied the framework to several models. The results indicate that Protoformer can improve current Transformers in various empirical settings.", "authors": ["Ashkan Farhangi", "Ning Sui", "Nan Hua", "Haiyan Bai", "Arthur Huang", "Zhishan Guo"], "categories": ["cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-06-25", "url": "https://arxiv.org/abs/2206.12710", "pdf_url": "https://arxiv.org/pdf/2206.12710v1", "arxiv_id": "2206.12710", "doi": "10.1007/978-3-031-05933-9_35", "citation_count": 11, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "Pacific-Asia Conference on Knowledge Discovery and Data Mining", "quality_score": 0.2698}
{"id": "8a054d6057b446c1e4ca547f35df784ea984a87cc2688370d47dc86ccf30ceb7", "sources": ["arxiv", "semantic_scholar"], "title": "KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLP", "abstract": "This paper focuses on the data augmentation for low-resource NLP tasks where the training set is limited. The existing solutions either leverage task-independent heuristic rules (e.g., Synonym Replacement) or fine-tune general-purpose pre-trained language models (e.g., GPT2) using the limited training instances to produce new synthetic data. Consequently, they have trivial task-specific knowledge and are limited to yielding low-quality synthetic data. To combat this issue, we propose Knowledge Mixture Data Augmentation Model (KnowDA) which is an Seq2Seq language model pre-trained on a mixture of diverse NLP tasks under a novel framework of Knowledge Mixture Training (KoMT). The goal of KoMT is to condense diverse NLP task-specific knowledge into the single KnowDA model (i.e., all-in-one) such that KnowDA could utilize these knowledge to quickly grasp the inherent synthesis law of the target task through limited training instances. Specifically, KoMT reformulates input examples from various heterogeneous NLP tasks into a unified text-to-text format, and employs denoising training objectives in different granularity to learn to reconstruct partial or complete samples. To the best of our knowledge, we are the first attempt to apply 100+ NLP multi-task training for data augmentation. Extensive experiments show that i) the synthetic data produced by KnowDA successfully improves performance of the strong pre-trained language models (i.e., Bert, ALBert and Deberta) by a large margin on the low-resource NLP benchmark FewGLUE, CoNLL'03 and WikiAnn; ii) KnowDA successfully transfers the task knowledge to NLP tasks whose types are seen and unseen in KoMT.", "authors": ["Yufei Wang", "Jiayi Zheng", "Can Xu", "Xiubo Geng", "Tao Shen", "Chongyang Tao", "Daxin Jiang"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2022-06-21", "url": "https://arxiv.org/abs/2206.10265", "pdf_url": "https://arxiv.org/pdf/2206.10265v2", "arxiv_id": "2206.10265", "doi": null, "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Learning Representations", "quality_score": 0.1505}
{"id": "6e6682528e853548095191ce3e0f52aba49ec17999fc35037eef43e5e3e37c57", "sources": ["arxiv", "semantic_scholar"], "title": "Augmented Imagefication: A Data-driven Fault Detection Method for Aircraft Air Data Sensors", "abstract": "In this paper, a novel data-driven approach named Augmented Imagefication for Fault detection (FD) of aircraft air data sensors (ADS) is proposed. Exemplifying the FD problem of aircraft air data sensors, an online FD scheme on edge device based on deep neural network (DNN) is developed. First, the aircraft inertial reference unit measurements is adopted as equivalent inputs, which is scalable to different aircraft/flight cases. Data associated with 6 different aircraft/flight conditions are collected to provide diversity (scalability) in the training/testing database. Then Augmented Imagefication is proposed for the DNN-based prediction of flying conditions. The raw data are reshaped as a grayscale image for convolutional operation, and the necessity of augmentation is analyzed and pointed out. Different kinds of augmented method, i.e. Flip, Repeat, Tile and their combinations are discussed, the result shows that the All Repeat operation in both axes of image matrix leads to the best performance of DNN. The interpretability of DNN is studied based on Grad-CAM, which provide a better understanding and further solidifies the robustness of DNN. Next the DNN model, VGG-16 with augmented imagefication data is optimized for mobile hardware deployment. After pruning of DNN, a lightweight model (98.79% smaller than original VGG-16) with high accuracy (slightly up by 0.27%) and fast speed (time delay is reduced by 87.54%) is obtained. And the hyperparameters optimization of DNN based on TPE is implemented and the best combination of hyperparameters is determined (learning rate 0.001, iterative epochs 600, and batch size 100 yields the highest accuracy at 0.987). Finally, a online FD deployment based on edge device, Jetson Nano, is developed and the real time monitoring of aircraft is achieved. We believe that this method is instructive for addressing the FD problems in other similar fields.", "authors": ["Hang Zhao", "Jinyi Ma", "Zhongzhi Li", "Yiqun Dong", "Jianliang Ai"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2022-06-18", "url": "https://arxiv.org/abs/2206.09055", "pdf_url": "https://arxiv.org/pdf/2206.09055v2", "arxiv_id": "2206.09055", "doi": "10.48550/arXiv.2206.09055", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0}
{"id": "b135e3dca4620fa443212269302fb523111bfde7475a226d5b5f6b0a38f515e7", "sources": ["arxiv", "semantic_scholar"], "title": "Gaia Data Release 3: Catalogue Validation", "abstract": "The third gaia data release (DR3) provides a wealth of new data products. The early part of the release, Gaia EDR3, already provided the astrometric and photometric data for nearly two billion sources. The full release now adds improved parameters compared to Gaia DR2 for radial velocities, astrophysical parameters, variability information, light curves, and orbits for Solar System objects. The improvements are in terms of the number of sources, the variety of parameter information, precision, and accuracy. For the first time, Gaia DR3 also provides a sample of spectrophotometry and spectra obtained with the Radial Velocity Spectrometer, binary star solutions, and a characterisation of extragalactic object candidates. Before the publication of the catalogue, these data have undergone a dedicated transversal validation process. The aim of this paper is to highlight limitations of the data that were found during this process and to provide recommendations for the usage of the catalogue. The validation was obtained through a statistical analysis of the data, a confirmation of the internal consistency of different products, and a comparison of the values to external data or models. Gaia DR3 is a new major step forward in terms of the number, diversity, precision, and accuracy of the Gaia products. As always in such a large and complex catalogue, however, issues and limitations have also been found. Detailed examples of the scientific quality of the Gaia DR3 release can be found in the accompanying data-processing papers as well as in the performance verification papers. Here we focus only on the caveats that the user should be aware of to scientifically exploit the data.", "authors": ["C. Babusiaux", "C. Fabricius", "S. Khanna", "T. Muraveva", "C. Reylé", "F. Spoto", "A. Vallenari", "X. Luri", "F. Arenou", "M. A. Alvarez", "F. Anders", "T. Antoja", "E. Balbinot", "C. Barache", "N. Bauchet", "D. Bossini", "D. Busonero", "T. Cantat-Gaudin", "J. M. Carrasco", "C. Dafonte", "S. Diakite", "F. Figueras", "A. Garcia-Gutierrez", "A. Garofalo", "A. Helmi", "O. Jimenez-Arranz", "C. Jordi", "P. Kervella", "Z. Kostrzewa-Rutkowska", "N. Leclerc", "E. Licata", "M. Manteiga", "A. Masip", "M. Monguio", "P. Ramos", "N. Robichon", "A. C. Robin", "M. Romero-Gomez", "A. Saez", "R. Santovena", "L. Spina", "G. Torralba Elipe", "M. Weiler"], "categories": ["astro-ph.SR", "astro-ph.EP", "astro-ph.GA", "astro-ph.IM"], "fields_of_study": ["Physics"], "published_date": "2022-06-13", "url": "https://arxiv.org/abs/2206.05989", "pdf_url": "https://arxiv.org/pdf/2206.05989v1", "arxiv_id": "2206.05989", "doi": "10.1051/0004-6361/202243790", "citation_count": 484, "influential_citation_count": 106, "has_code": false, "code_url": null, "venue": "A&A 674, A32 (2023)", "quality_score": 1.0}
{"id": "3de973caa4df45f81cb6322c4c8703b8831b6b062eb91d33776b9c47d9dacd4c", "sources": ["arxiv", "semantic_scholar"], "title": "Data Augmentation for Intent Classification", "abstract": "Training accurate intent classifiers requires labeled data, which can be costly to obtain. Data augmentation methods may ameliorate this issue, but the quality of the generated data varies significantly across techniques. We study the process of systematically producing pseudo-labeled data given a small seed set using a wide variety of data augmentation techniques, including mixing methods together. We find that while certain methods dramatically improve qualitative and quantitative performance, other methods have minimal or even negative impact. We also analyze key considerations when implementing data augmentation methods in production.", "authors": ["Derek Chen", "Claire Yin"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2022-06-12", "url": "https://arxiv.org/abs/2206.05790", "pdf_url": "https://arxiv.org/pdf/2206.05790v1", "arxiv_id": "2206.05790", "doi": "10.48550/arXiv.2206.05790", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "3edc0b808209ed1e6017278a5c5e62f2ee4454ebe081307dbafd00d4b83d7f01", "sources": ["arxiv", "semantic_scholar"], "title": "Federated singular value decomposition for high dimensional data", "abstract": "Federated learning (FL) is emerging as a privacy-aware alternative to classical cloud-based machine learning. In FL, the sensitive data remains in data silos and only aggregated parameters are exchanged. Hospitals and research institutions which are not willing to share their data can join a federated study without breaching confidentiality. In addition to the extreme sensitivity of biomedical data, the high dimensionality poses a challenge in the context of federated genome-wide association studies (GWAS). In this article, we present a federated singular value decomposition (SVD) algorithm, suitable for the privacy-related and computational requirements of GWAS. Notably, the algorithm has a transmission cost independent of the number of samples and is only weakly dependent on the number of features, because the singular vectors associated with the samples are never exchanged and the vectors associated with the features only for a fixed number of iterations. Although motivated by GWAS, the algorithm is generically applicable for both horizontally and vertically partitioned data.", "authors": ["Anne Hartebrodt", "Richard Röttger", "David B. Blumenthal"], "categories": ["cs.LG", "cs.DS"], "fields_of_study": ["Computer Science"], "published_date": "2022-05-24", "url": "https://arxiv.org/abs/2205.12109", "pdf_url": "https://arxiv.org/pdf/2205.12109v1", "arxiv_id": "2205.12109", "doi": "10.1007/s10618-023-00983-z", "citation_count": 18, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Data mining and knowledge discovery", "quality_score": 0.3197}
{"id": "10e4d3a999152ac3c3f944b1118bfb9b926157c5a56e15c558e3bd83ca8f80a2", "sources": ["arxiv", "semantic_scholar"], "title": "COVID-19: An exploration of consecutive systemic barriers to pathogen-related data sharing during a pandemic", "abstract": "In 2020, the COVID-19 pandemic resulted in a rapid response from governments and researchers worldwide. As of late 2023, over millions have died as a result of COVID-19, with many COVID-19 survivors going on to experience long-term effects weeks, months, or years after their illness. Despite this staggering toll, those who work with pandemic-relevant data often face significant systemic barriers to accessing, sharing or re-using this data. In this paper we report results of a study, where we interviewed data professionals working with COVID-19-relevant data types including social media, mobility, viral genome, testing, infection, hospital admission, and deaths. These data types are variously used for pandemic spread modelling, healthcare system strain awareness, and devising therapeutic treatments for COVID-19. Barriers to data access, sharing and re-use include the cost of access to data (primarily certain healthcare sources and mobility data from mobile phone carriers), human throughput bottlenecks, unclear pathways to request access to data, unnecessarily strict access controls and data re-use policies, unclear data provenance, inability to link separate data sources that could collectively create a more complete picture, poor adherence to metadata standards, and a lack of computer-suitable data formats.", "authors": ["Yo Yehudi", "Lukas Hughes-Noehrer", "Carole Goble", "Caroline Jay"], "categories": ["cs.CY"], "fields_of_study": ["Computer Science"], "published_date": "2022-05-24", "url": "https://arxiv.org/abs/2205.12098", "pdf_url": "https://arxiv.org/pdf/2205.12098v3", "arxiv_id": "2205.12098", "doi": "10.1017/dap.2024.79", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Data & Policy", "quality_score": 0.2258}
{"id": "88de76ebc65bb29f243b1764480eb854539568458d7e2e0e3bca01eb1f21039c", "sources": ["arxiv", "semantic_scholar"], "title": "[Re] Distilling Knowledge via Knowledge Review", "abstract": "This effort aims to reproduce the results of experiments and analyze the robustness of the review framework for knowledge distillation introduced in the CVPR '21 paper 'Distilling Knowledge via Knowledge Review' by Chen et al. Previous works in knowledge distillation only studied connections paths between the same levels of the student and the teacher, and cross-level connection paths had not been considered. Chen et al. propose a new residual learning framework to train a single student layer using multiple teacher layers. They also design a novel fusion module to condense feature maps across levels and a loss function to compare feature information stored across different levels to improve performance. In this work, we consistently verify the improvements in test accuracy across student models as reported in the original paper and study the effectiveness of the novel modules introduced by conducting ablation studies and new experiments.", "authors": ["Apoorva Verma", "Pranjal Gulati", "Sarthak Gupta"], "categories": ["cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-05-18", "url": "https://arxiv.org/abs/2205.11246", "pdf_url": "https://arxiv.org/pdf/2205.11246v1", "arxiv_id": "2205.11246", "doi": "10.48550/arXiv.2205.11246", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "52b55e4df5bb6ad09c62acc76a464289077283848820e527055a7d1b2ef35361", "sources": ["arxiv", "semantic_scholar"], "title": "Prompting to Distill: Boosting Data-Free Knowledge Distillation via Reinforced Prompt", "abstract": "Data-free knowledge distillation (DFKD) conducts knowledge distillation via eliminating the dependence of original training data, and has recently achieved impressive results in accelerating pre-trained language models. At the heart of DFKD is to reconstruct a synthetic dataset by inverting the parameters of the uncompressed model. Prior DFKD approaches, however, have largely relied on hand-crafted priors of the target data distribution for the reconstruction, which can be inevitably biased and often incompetent to capture the intrinsic distributions. To address this problem, we propose a prompt-based method, termed as PromptDFD, that allows us to take advantage of learned language priors, which effectively harmonizes the synthetic sentences to be semantically and grammatically correct. Specifically, PromptDFD leverages a pre-trained generative model to provide language priors and introduces a reinforced topic prompter to control data synthesis, making the generated samples thematically relevant and semantically plausible, and thus friendly to downstream tasks. As shown in our experiments, the proposed method substantially improves the synthesis quality and achieves considerable improvements on distillation performance. In some cases, PromptDFD even gives rise to results on par with those from the data-driven knowledge distillation with access to the original training data.", "authors": ["Xinyin Ma", "Xinchao Wang", "Gongfan Fang", "Yongliang Shen", "Weiming Lu"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2022-05-16", "url": "https://arxiv.org/abs/2205.07523", "pdf_url": "https://arxiv.org/pdf/2205.07523v1", "arxiv_id": "2205.07523", "doi": "10.48550/arXiv.2205.07523", "citation_count": 13, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "International Joint Conference on Artificial Intelligence", "quality_score": 0.2865}
{"id": "9fb988a552b9634ffde548a38f363726fa6221ed979cbb3f11ed92d99065e28f", "sources": ["arxiv", "semantic_scholar"], "title": "Over seven decades of solar microwave data obtained with Toyokawa and Nobeyama Radio Polarimeters", "abstract": "Monitoring observations of solar microwave fluxes and their polarization began in Japan during the 1950s at Toyokawa and Mitaka. At present (April 2022), monitoring observations continue with the Nobeyama Radio Polarimeters (NoRP) at the Nobeyama campus of the National Astronomical Observatory of Japan (NAOJ). In this paper, we present a brief history of the solar microwave monitoring observations preceding those now carried out by NoRP. We then review the solar microwave obtained at Toyokawa and Nobeyama and their metadata. The datasets are publicly provided by the Solar Data Archive System (SDAS) operated by the Astronomy Data Center of the NAOJ, via http (https://solar.nro.nao.ac.jp/norp/) and FTP (ftp://solar-pub.nao.ac.jp/pub/nsro/norp/) protocols.", "authors": ["Masumi Shimojo", "Kazumasa Iwai"], "categories": ["astro-ph.IM", "astro-ph.SR"], "fields_of_study": ["Physics"], "published_date": "2022-05-16", "url": "https://arxiv.org/abs/2205.07454", "pdf_url": "https://arxiv.org/pdf/2205.07454v1", "arxiv_id": "2205.07454", "doi": "10.1002/gdj3.165", "citation_count": 12, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Geoscience Data Journal", "quality_score": 0.2785}
{"id": "603e380afa117297871aaff1190a707e5d4f7283edd3f3f74bf5325ff2efa2c3", "sources": ["arxiv", "semantic_scholar"], "title": "ParaCotta: Synthetic Multilingual Paraphrase Corpora from the Most Diverse Translation Sample Pair", "abstract": "We release our synthetic parallel paraphrase corpus across 17 languages: Arabic, Catalan, Czech, German, English, Spanish, Estonian, French, Hindi, Indonesian, Italian, Dutch, Romanian, Russian, Swedish, Vietnamese, and Chinese. Our method relies only on monolingual data and a neural machine translation system to generate paraphrases, hence simple to apply. We generate multiple translation samples using beam search and choose the most lexically diverse pair according to their sentence BLEU. We compare our generated corpus with the \\texttt{ParaBank2}. According to our evaluation, our synthetic paraphrase pairs are semantically similar and lexically diverse.", "authors": ["Alham Fikri Aji", "Tirana Noor Fatyanosa", "Radityo Eko Prasojo", "Philip Arthur", "Suci Fitriany", "Salma Qonitah", "Nadhifa Zulfa", "Tomi Santoso", "Mahendra Data"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2022-05-10", "url": "https://arxiv.org/abs/2205.04651", "pdf_url": "https://arxiv.org/pdf/2205.04651v1", "arxiv_id": "2205.04651", "doi": "10.48550/arXiv.2205.04651", "citation_count": 14, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "Pacific Asia Conference on Language, Information and Computation", "quality_score": 0.301}
{"id": "abf7576ccf3e0545cf2ccd76947ed853203add9f66ee1a8abd07c618a75764d2", "sources": ["arxiv", "semantic_scholar"], "title": "Ratatouille: A tool for Novel Recipe Generation", "abstract": "Due to availability of a large amount of cooking recipes online, there is a growing interest in using this as data to create novel recipes. Novel Recipe Generation is a problem in the field of Natural Language Processing in which our main interest is to generate realistic, novel cooking recipes. To come up with such novel recipes, we trained various Deep Learning models such as LSTMs and GPT-2 with a large amount of recipe data. We present Ratatouille (https://cosylab.iiitd.edu.in/ratatouille2/), a web based application to generate novel recipes.", "authors": ["Mansi Goel", "Pallab Chakraborty", "Vijay Ponnaganti", "Minnet Khan", "Sritanaya Tatipamala", "Aakanksha Saini", "Ganesh Bagler"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2022-05-10", "url": "https://arxiv.org/abs/2206.08267", "pdf_url": "https://arxiv.org/pdf/2206.08267v1", "arxiv_id": "2206.08267", "doi": "10.1109/ICDEW55742.2022.00022", "citation_count": 13, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "2022 IEEE 38th International Conference on Data Engineering Workshops (ICDEW)", "quality_score": 0.2865}
{"id": "66b5baf7c0f358af71f644f9a2d0cd32806b3dac2e5fe0aa0a7e9e7929a7f0f8", "sources": ["arxiv", "semantic_scholar"], "title": "Data-Free Adversarial Knowledge Distillation for Graph Neural Networks", "abstract": "Graph neural networks (GNNs) have been widely used in modeling graph structured data, owing to its impressive performance in a wide range of practical applications. Recently, knowledge distillation (KD) for GNNs has enabled remarkable progress in graph model compression and knowledge transfer. However, most of the existing KD methods require a large volume of real data, which are not readily available in practice, and may preclude their applicability in scenarios where the teacher model is trained on rare or hard to acquire datasets. To address this problem, we propose the first end-to-end framework for data-free adversarial knowledge distillation on graph structured data (DFAD-GNN). To be specific, our DFAD-GNN employs a generative adversarial network, which mainly consists of three components: a pre-trained teacher model and a student model are regarded as two discriminators, and a generator is utilized for deriving training graphs to distill knowledge from the teacher model into the student model. Extensive experiments on various benchmark models and six representative datasets demonstrate that our DFAD-GNN significantly surpasses state-of-the-art data-free baselines in the graph classification task.", "authors": ["Yuanxin Zhuang", "Lingjuan Lyu", "Chuan Shi", "Carl Yang", "Lichao Sun"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-05-08", "url": "https://arxiv.org/abs/2205.03811", "pdf_url": "https://arxiv.org/pdf/2205.03811v2", "arxiv_id": "2205.03811", "doi": "10.48550/arXiv.2205.03811", "citation_count": 20, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "International Joint Conference on Artificial Intelligence", "quality_score": 0.3306}
{"id": "a075a750563368163ab5d2e3259c8bd317168c3d135f3c06436276cd25a69603", "sources": ["arxiv", "semantic_scholar"], "title": "Data Augmentation for Manipulation", "abstract": "The success of deep learning depends heavily on the availability of large datasets, but in robotic manipulation there are many learning problems for which such datasets do not exist. Collecting these datasets is time-consuming and expensive, and therefore learning from small datasets is an important open problem. Within computer vision, a common approach to a lack of data is data augmentation. Data augmentation is the process of creating additional training examples by modifying existing ones. However, because the types of tasks and data differ, the methods used in computer vision cannot be easily adapted to manipulation. Therefore, we propose a data augmentation method for robotic manipulation. We argue that augmentations should be valid, relevant, and diverse. We use these principles to formalize augmentation as an optimization problem, with the objective function derived from physics and knowledge of the manipulation domain. This method applies rigid body transformations to trajectories of geometric state and action data. We test our method in two scenarios: 1) learning the dynamics of planar pushing of rigid cylinders, and 2) learning a constraint checker for rope manipulation. These two scenarios have different data and label types, yet in both scenarios, training on our augmented data significantly improves performance on downstream tasks. We also show how our augmentation method can be used on real-robot data to enable more data-efficient online learning.", "authors": ["Peter Mitrano", "Dmitry Berenson"], "categories": ["cs.RO"], "fields_of_study": ["Computer Science"], "published_date": "2022-05-05", "url": "https://arxiv.org/abs/2205.02886", "pdf_url": "https://arxiv.org/pdf/2205.02886v4", "arxiv_id": "2205.02886", "doi": "10.48550/arXiv.2205.02886", "citation_count": 28, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3656}
{"id": "b8d4e2fea15cd30a325b2f1047af7e27ffc5fd9ff067c23f369d0b887d8503bb", "sources": ["arxiv", "semantic_scholar"], "title": "Data-driven emotional body language generation for social robotics", "abstract": "In social robotics, endowing humanoid robots with the ability to generate bodily expressions of affect can improve human-robot interaction and collaboration, since humans attribute, and perhaps subconsciously anticipate, such traces to perceive an agent as engaging, trustworthy, and socially present. Robotic emotional body language needs to be believable, nuanced and relevant to the context. We implemented a deep learning data-driven framework that learns from a few hand-designed robotic bodily expressions and can generate numerous new ones of similar believability and lifelikeness. The framework uses the Conditional Variational Autoencoder model and a sampling approach based on the geometric properties of the model's latent space to condition the generative process on targeted levels of valence and arousal. The evaluation study found that the anthropomorphism and animacy of the generated expressions are not perceived differently from the hand-designed ones, and the emotional conditioning was adequately differentiable between most levels except the pairs of neutral-positive valence and low-medium arousal. Furthermore, an exploratory analysis of the results reveals a possible impact of the conditioning on the perceived dominance of the robot, as well as on the participants' attention.", "authors": ["Mina Marmpena", "Fernando Garcia", "Angelica Lim", "Nikolas Hemion", "Thomas Wennekers"], "categories": ["cs.RO", "cs.AI", "cs.HC", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-05-02", "url": "https://arxiv.org/abs/2205.00763", "pdf_url": "https://arxiv.org/pdf/2205.00763v1", "arxiv_id": "2205.00763", "doi": "10.48550/arXiv.2205.00763", "citation_count": 4, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/minamar/rebl-pepper-data", "venue": "arXiv.org", "quality_score": 0.1747}
{"id": "e9d70a30a30e7967321d29fa0ef5bf13229c8ebe5d2928c7945fb605d469a781", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Feature Distribution Alignment and Diversity Enhancement for Data-Free Quantization", "abstract": "To obtain lower inference latency and less memory footprint of deep neural networks, model quantization has been widely employed in deep model deployment, by converting the floating points to low-precision integers. However, previous methods (such as quantization aware training and post training quantization) require original data for the fine-tuning or calibration of quantized model, which makes them inapplicable to the cases that original data are not accessed due to privacy or security. This gives birth to the data-free quantization method with synthetic data generation. While current data-free quantization methods still suffer from severe performance degradation when quantizing a model into lower bit, caused by the low inter-class separability of semantic features. To this end, we propose a new and effective data-free quantization method termed ClusterQ, which utilizes the feature distribution alignment for synthetic data generation. To obtain high inter-class separability of semantic features, we cluster and align the feature distribution statistics to imitate the distribution of real data, so that the performance degradation is alleviated. Moreover, we incorporate the diversity enhancement to solve class-wise mode collapse. We also employ the exponential moving average to update the centroid of each cluster for further feature distribution improvement. Extensive experiments based on different deep models (e.g., ResNet-18 and MobileNet-V2) over the ImageNet dataset demonstrate that our proposed ClusterQ model obtains state-of-the-art performance.", "authors": ["Yangcheng Gao", "Zhao Zhang", "Richang Hong", "Haijun Zhang", "Jicong Fan", "Shuicheng Yan"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2022-04-30", "url": "https://arxiv.org/abs/2205.00179", "pdf_url": "https://arxiv.org/pdf/2205.00179v2", "arxiv_id": "2205.00179", "doi": "10.1109/ICDM54844.2022.00024", "citation_count": 11, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Industrial Conference on Data Mining", "quality_score": 0.2698}
{"id": "343a75b6ec030f805459f77eb91bc016b66357056a210efeb9ab7b0939ad3409", "sources": ["arxiv", "semantic_scholar"], "title": "DearKD: Data-Efficient Early Knowledge Distillation for Vision Transformers", "abstract": "Transformers are successfully applied to computer vision due to their powerful modeling capacity with self-attention. However, the excellent performance of transformers heavily depends on enormous training images. Thus, a data-efficient transformer solution is urgently needed. In this work, we propose an early knowledge distillation framework, which is termed as DearKD, to improve the data efficiency required by transformers. Our DearKD is a two-stage framework that first distills the inductive biases from the early intermediate layers of a CNN and then gives the transformer full play by training without distillation. Further, our DearKD can be readily applied to the extreme data-free case where no real images are available. In this case, we propose a boundary-preserving intra-divergence loss based on DeepInversion to further close the performance gap against the full-data counterpart. Extensive experiments on ImageNet, partial ImageNet, data-free setting and other downstream tasks prove the superiority of DearKD over its baselines and state-of-the-art methods.", "authors": ["Xianing Chen", "Qiong Cao", "Yujie Zhong", "Jing Zhang", "Shenghua Gao", "Dacheng Tao"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2022-04-27", "url": "https://arxiv.org/abs/2204.12997", "pdf_url": "https://arxiv.org/pdf/2204.12997v2", "arxiv_id": "2204.12997", "doi": "10.1109/CVPR52688.2022.01174", "citation_count": 115, "influential_citation_count": 7, "has_code": false, "code_url": null, "venue": "Computer Vision and Pattern Recognition", "quality_score": 0.5161}
{"id": "695feb9719b621787673d810b4836831230cbb0b72aea52394ffe506dbea42d5", "sources": ["arxiv", "semantic_scholar"], "title": "Why we should respect analysis results as data", "abstract": "The development and approval of new treatments generates large volumes of results, such as summaries of efficacy and safety. However, it is commonly overlooked that analyzing clinical study data also produces data in the form of results. For example, descriptive statistics and model predictions are data. Although integrating and putting findings into context is a cornerstone of scientific work, analysis results are often neglected as a data source. Results end up stored as \"data products\" such as PDF documents that are not machine readable or amenable to future analysis. We propose a solution to \"calculate once, use many times\" by combining analysis results standards with a common data model. This analysis results data model re-frames the target of analyses from static representations of the results (e.g., tables and figures) to a data model with applications in various contexts, including knowledge discovery. Further, we provide a working proof of concept detailing how to approach analyses standardization and construct a schema to store and query analysis results.", "authors": ["Joana M Barros", "Lukas A Widmer", "Mark Baillie", "Simon Wandel"], "categories": ["cs.CY"], "fields_of_study": ["Computer Science"], "published_date": "2022-04-21", "url": "https://arxiv.org/abs/2204.09959", "pdf_url": "https://arxiv.org/pdf/2204.09959v1", "arxiv_id": "2204.09959", "doi": "10.48550/arXiv.2204.09959", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2258}
{"id": "4a228b29c14a1d78b8b00c911fb1df9a212358908186987b92492ab5e2e8dbd5", "sources": ["arxiv", "semantic_scholar"], "title": "In-vitro Major Arterial Cardiovascular Simulator to generate Benchmark Data Sets for in-silico Model Validation", "abstract": "A deeper understanding of the influence of common cardiovascular diseases like stenosis, aneurysm or atherosclerosis on the circulatory mechanism is required, to establish new methods for early diagnosis. Different types of simulators were developed in the past to simulate healthy and pathological conditions of blood flow, often based on computational models, which allow to generate large data sets. However, since computational models often lack some aspects of real world data, hardware simulators are used to close this gap and generate data for model validation. The aim of this study is the development and validation of a hardware simulator to generate benchmark data sets of healthy and pathological conditions. The in-vitro hardware simulator in this study includes the major 33 arteries and is driven by a ventricular assist device generating a parametrised input condition at the heart node. Physiologic flow conditions including heart rate, systolic/diastolic pressure, peripheral resistance and compliance are adjustable in a wide range. The pressure and flow waves at 17+1 different locations are measured by inverted fluid resistant pressure transducers and one ultrasound flow transducer supporting a detailed analysis of the measurement data. The pressure and flow waves show physiological conditions. Furthermore, the influence of stenoses degree and location on blood pressure and flow was investigated. The results indicate decreasing translesional pressure and flow with increasing degree of stenosis, as expected. The benchmark data set is made available to the research community, with the purpose to validate and compare in-silico models of different type.", "authors": ["Michelle Wisotzki", "Alexander Mair", "Paul Schlett", "Bernhard Lindner", "Max Oberhardt", "Stefan Bernhard"], "categories": ["physics.med-ph"], "fields_of_study": ["Computer Science", "Physics"], "published_date": "2022-04-21", "url": "https://arxiv.org/abs/2204.10005", "pdf_url": "https://arxiv.org/pdf/2204.10005v1", "arxiv_id": "2204.10005", "doi": "10.3390/data7110145", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Data Technologies and Applications", "quality_score": 0.1505}
{"id": "1d9660b5df2e9913abc46502921e398bd9918de3b5400814c09700fe9b52b4a5", "sources": ["arxiv", "semantic_scholar"], "title": "Councils in Action: Automating the Curation of Municipal Governance Data for Research", "abstract": "Large scale comparative research into municipal governance is often prohibitively difficult due to a lack of high-quality data. But, recent advances in speech-to-text algorithms and natural language processing has made it possible to more easily collect and analyze data about municipal governments. In this paper, we introduce an open-source platform, the Council Data Project (CDP), to curate novel datasets for research into municipal governance. The contribution of this work is two-fold: 1. We demonstrate that CDP, as an infrastructure, can be used to assemble reliable comparative data on municipal governance; 2. We provide exploratory analysis of three municipalities to show how CDP data can be used to gain insight into how municipal governments perform over time. We conclude by describing future directions for research on and with CDP such as the development of machine learning models for speaker annotation, outline generation, and named entity recognition for improved linked data.", "authors": ["Eva Maxfield Brown", "Nicholas Weber"], "categories": ["cs.DL"], "fields_of_study": ["Computer Science"], "published_date": "2022-04-19", "url": "https://arxiv.org/abs/2204.09110", "pdf_url": "https://arxiv.org/pdf/2204.09110v3", "arxiv_id": "2204.09110", "doi": "10.1002/pra2.601", "citation_count": 9, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "ASIS&T Annual Meeting", "quality_score": 0.25}
{"id": "c87e40581e2a230c03b8babf683b8bbefe132144ca246117c86604b8b50a585b", "sources": ["arxiv", "semantic_scholar"], "title": "CILDA: Contrastive Data Augmentation using Intermediate Layer Knowledge Distillation", "abstract": "Knowledge distillation (KD) is an efficient framework for compressing large-scale pre-trained language models. Recent years have seen a surge of research aiming to improve KD by leveraging Contrastive Learning, Intermediate Layer Distillation, Data Augmentation, and Adversarial Training. In this work, we propose a learning based data augmentation technique tailored for knowledge distillation, called CILDA. To the best of our knowledge, this is the first time that intermediate layer representations of the main task are used in improving the quality of augmented samples. More precisely, we introduce an augmentation technique for KD based on intermediate layer matching using contrastive loss to improve masked adversarial data augmentation. CILDA outperforms existing state-of-the-art KD approaches on the GLUE benchmark, as well as in an out-of-domain evaluation.", "authors": ["Md Akmal Haidar", "Mehdi Rezagholizadeh", "Abbas Ghaddar", "Khalil Bibi", "Philippe Langlais", "Pascal Poupart"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2022-04-15", "url": "https://arxiv.org/abs/2204.07674", "pdf_url": "https://arxiv.org/pdf/2204.07674v1", "arxiv_id": "2204.07674", "doi": "10.48550/arXiv.2204.07674", "citation_count": 7, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "International Conference on Computational Linguistics", "quality_score": 0.2258}
{"id": "85d2986d53267d7736611c64f9b60a56c6d5ce6d689415353595d1ca0df88e08", "sources": ["arxiv", "semantic_scholar"], "title": "AABAC -- Automated Attribute Based Access Control for Genomics Data", "abstract": "The COVID-19 crisis has demonstrated the potential of cutting-edge genomics research. However, privacy of these sensitive pieces of information is an area of significant concern for genomics researchers. The current security models makes it difficult to create flexible and automated data sharing frameworks. These models also increases the complexity of adding or revoking access without contacting the data publisher. In this work, we investigate an automated attribute-based access control (AABAC) model for genomics data over Named Data Networking (NDN). AABAC secures the data itself rather than the storage location or transmission channel, provides automated data invalidation, and automates key retrieval and data validation while maintaining the ability to control access. We show that AABC when combined with NDN provide a secure and flexible combination for work with genomics research.", "authors": ["David Reddick", "Justin Presley", "F. Alex Feltus", "Susmit Shannigrahi"], "categories": ["cs.CR", "cs.NI"], "fields_of_study": ["Computer Science"], "published_date": "2022-04-10", "url": "https://arxiv.org/abs/2204.04591", "pdf_url": "https://arxiv.org/pdf/2204.04591v2", "arxiv_id": "2204.04591", "doi": "10.1145/3532105.3535037", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "ACM Symposium on Access Control Models and Technologies", "quality_score": 0.2386}
{"id": "c1cd8fa189517d3e843b03f406cdc187d95cc226bc0e70afd3597c0dcec64b85", "sources": ["arxiv", "semantic_scholar"], "title": "DAGAM: Data Augmentation with Generation And Modification", "abstract": "Text classification is a representative downstream task of natural language processing, and has exhibited excellent performance since the advent of pre-trained language models based on Transformer architecture. However, in pre-trained language models, under-fitting often occurs due to the size of the model being very large compared to the amount of available training data. Along with significant importance of data collection in modern machine learning paradigm, studies have been actively conducted for natural language data augmentation. In light of this, we introduce three data augmentation schemes that help reduce underfitting problems of large-scale language models. Primarily we use a generation model for data augmentation, which is defined as Data Augmentation with Generation (DAG). Next, we augment data using text modification techniques such as corruption and word order change (Data Augmentation with Modification, DAM). Finally, we propose Data Augmentation with Generation And Modification (DAGAM), which combines DAG and DAM techniques for a boosted performance. We conduct data augmentation for six benchmark datasets of text classification task, and verify the usefulness of DAG, DAM, and DAGAM through BERT-based fine-tuning and evaluation, deriving better results compared to the performance with original datasets.", "authors": ["Byeong-Cheol Jo", "Tak-Sung Heo", "Yeongjoon Park", "Yongmin Yoo", "Won Ik Cho", "Kyungsun Kim"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2022-04-06", "url": "https://arxiv.org/abs/2204.02633", "pdf_url": "https://arxiv.org/pdf/2204.02633v1", "arxiv_id": "2204.02633", "doi": "10.48550/arXiv.2204.02633", "citation_count": 2, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "1106034994394cc300d916d433136331ee0e6037b95230ae2919954df2b52171", "sources": ["arxiv", "semantic_scholar"], "title": "Using Explainable Boosting Machine to Compare Idiographic and Nomothetic Approaches for Ecological Momentary Assessment Data", "abstract": "Previous research on EMA data of mental disorders was mainly focused on multivariate regression-based approaches modeling each individual separately. This paper goes a step further towards exploring the use of non-linear interpretable machine learning (ML) models in classification problems. ML models can enhance the ability to accurately predict the occurrence of different behaviors by recognizing complicated patterns between variables in data. To evaluate this, the performance of various ensembles of trees are compared to linear models using imbalanced synthetic and real-world datasets. After examining the distributions of AUC scores in all cases, non-linear models appear to be superior to baseline linear models. Moreover, apart from personalized approaches, group-level prediction models are also likely to offer an enhanced performance. According to this, two different nomothetic approaches to integrate data of more than one individuals are examined, one using directly all data during training and one based on knowledge distillation. Interestingly, it is observed that in one of the two real-world datasets, knowledge distillation method achieves improved AUC scores (mean relative change of +17\\% compared to personalized) showing how it can benefit EMA data classification and performance.", "authors": ["Mandani Ntekouli", "Gerasimos Spanakis", "Lourens Waldorp", "Anne Roefs"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-04-04", "url": "https://arxiv.org/abs/2204.01689", "pdf_url": "https://arxiv.org/pdf/2204.01689v1", "arxiv_id": "2204.01689", "doi": "10.48550/arXiv.2204.01689", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Symposium on Intelligent Data Analysis", "quality_score": 0.2386}
{"id": "0a310fe71f37f7832fb41fc8a18d62882ec68bb9e92563eafbbbf040446b7ebf", "sources": ["arxiv", "semantic_scholar"], "title": "Empirical Analysis of Lifelog Data using Optimal Feature Selection based Unsupervised Logistic Regression (OFS-ULR) Model with Spark Streaming", "abstract": "Recent advancement in the field of pervasive healthcare monitoring systems causes the generation of a huge amount of lifelog data in real-time. Chronic diseases are one of the most serious health challenges in developing and developed countries. According to WHO, this accounts for 73% of all deaths and 60% of the global burden of diseases. Chronic disease classification models are now harnessing the potential of lifelog data to explore better healthcare practices. This paper is to construct an optimal feature selection-based unsupervised logistic regression model (OFS-ULR) to classify chronic diseases. Since lifelog data analysis is crucial due to its sensitive nature; thus the conventional classification models show limited performance. Therefore, designing new classifiers for the classification of chronic diseases using lifelog data is the need of the age. The vital part of building a good model depends on pre-processing of the dataset, identifying important features, and then training a learning algorithm with suitable hyper parameters for better performance. The proposed approach improves the performance of existing methods using a series of steps such as (i) removing redundant or invalid instances, (ii) making the data labelled using clustering and partitioning the data into classes, (iii) identifying the suitable subset of features by applying either some domain knowledge or selection algorithm, (iv) hyper parameter tuning for models to get best results, and (v) performance evaluation using Spark streaming environment. For this purpose, two-time series datasets are used in the experiment to compute the accuracy, recall, precision, and f1-score. The experimental analysis proves the suitability of the proposed approach as compared to the conventional classifiers and our newly constructed model achieved highest accuracy and reduced training complexity among all among all.", "authors": ["Sadhana Tiwari", "Sonali Agarwal"], "categories": ["cs.LG", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2022-04-04", "url": "https://arxiv.org/abs/2204.01281", "pdf_url": "https://arxiv.org/pdf/2204.01281v2", "arxiv_id": "2204.01281", "doi": "10.48550/arXiv.2204.01281", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0}
{"id": "0b14c2ee98fbc8df01f4271ddbc71a8bfa16abd6277b2500c7718e83c7e46d83", "sources": ["arxiv", "semantic_scholar"], "title": "Benchmarking distance-based partitioning methods for mixed-type data", "abstract": "Clustering mixed-type data, that is, observation by variable data that consist of both continuous and categorical variables poses novel challenges. Foremost among these challenges is the choice of the most appropriate clustering method for the data. This paper presents a benchmarking study comparing eight distance-based partitioning methods for mixed-type data in terms of cluster recovery performance. A series of simulations carried out by a full factorial design are presented that examined the effect of a variety of factors on cluster recovery. The amount of cluster overlap, the percentage of categorical variables in the data set, the number of clusters and the number of observations had the largest effects on cluster recovery and in most of the tested scenarios. KAMILA, K-Prototypes and sequential Factor Analysis and K-Means clustering typically performed better than other methods. The study can be a useful reference for practitioners in the choice of the most appropriate method.", "authors": ["Efthymios Costa", "Ioanna Papatsouma", "Angelos Markos"], "categories": ["stat.ME"], "fields_of_study": ["Mathematics", "Computer Science"], "published_date": "2022-03-30", "url": "https://arxiv.org/abs/2203.16287", "pdf_url": "https://arxiv.org/pdf/2203.16287v3", "arxiv_id": "2203.16287", "doi": "10.1007/s11634-022-00521-7", "citation_count": 21, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Advances in Data Analysis and Classification", "quality_score": 0.3356}
{"id": "49b2d70571e7803326638e1ed8520ec61aaf39a3768d20efa0de6a6cdca5c48e", "sources": ["arxiv", "semantic_scholar"], "title": "Dynamic Model Tree for Interpretable Data Stream Learning", "abstract": "Data streams are ubiquitous in modern business and society. In practice, data streams may evolve over time and cannot be stored indefinitely. Effective and transparent machine learning on data streams is thus often challenging. Hoeffding Trees have emerged as a state-of-the art for online predictive modelling. They are easy to train and provide meaningful convergence guarantees under a stationary process. Yet, at the same time, Hoeffding Trees often require heuristic and costly extensions to adjust to distributional change, which may considerably impair their interpretability. In this work, we revisit Model Trees for machine learning in evolving data streams. Model Trees are able to maintain more flexible and locally robust representations of the active data concept, making them a natural fit for data stream applications. Our novel framework, called Dynamic Model Tree, satisfies desirable consistency and minimality properties. In experiments with synthetic and real-world tabular streaming data sets, we show that the proposed framework can drastically reduce the number of splits required by existing incremental decision trees. At the same time, our framework often outperforms state-of-the-art models in terms of predictive quality -- especially when concept drift is involved. Dynamic Model Trees are thus a powerful online learning framework that contributes to more lightweight and interpretable machine learning in data streams.", "authors": ["Johannes Haug", "Klaus Broelemann", "Gjergji Kasneci"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2022-03-30", "url": "https://arxiv.org/abs/2203.16181", "pdf_url": "https://arxiv.org/pdf/2203.16181v1", "arxiv_id": "2203.16181", "doi": "10.1109/ICDE53745.2022.00237", "citation_count": 9, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "IEEE International Conference on Data Engineering", "quality_score": 0.25}
{"id": "ae116671f6b8af13af5050825c92a7c8694eca8da4860c91c975f8fd5f8c548a", "sources": ["arxiv", "semantic_scholar"], "title": "Biolink Model: A Universal Schema for Knowledge Graphs in Clinical, Biomedical, and Translational Science", "abstract": "Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness between core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these \"knowledge graphs\" (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally-accepted, open-access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object-oriented classification and graph-oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates), representing biomedical entities such as gene, disease, chemical, anatomical structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.", "authors": ["Deepak R. Unni", "Sierra A. T. Moxon", "Michael Bada", "Matthew Brush", "Richard Bruskiewich", "Paul Clemons", "Vlado Dancik", "Michel Dumontier", "Karamarie Fecho", "Gustavo Glusman", "Jennifer J. Hadlock", "Nomi L. Harris", "Arpita Joshi", "Tim Putman", "Guangrong Qin", "Stephen A. Ramsey", "Kent A. Shefchek", "Harold Solbrig", "Karthik Soman", "Anne T. Thessen", "Melissa A. Haendel", "Chris Bizon", "Christopher J. Mungall", "the Biomedical Data Translator Consortium"], "categories": ["cs.DB"], "fields_of_study": ["Computer Science", "Medicine"], "published_date": "2022-03-25", "url": "https://arxiv.org/abs/2203.13906", "pdf_url": "https://arxiv.org/pdf/2203.13906v1", "arxiv_id": "2203.13906", "doi": "10.1111/cts.13302", "citation_count": 95, "influential_citation_count": 6, "has_code": true, "code_url": null, "venue": "Clinical and Translational Science", "quality_score": 0.4956}
{"id": "acad837a4665f4249411217f411ec4111f9e057aeeb1f890accf69f6d1ca0f7f", "sources": ["arxiv", "semantic_scholar"], "title": "BigBird: Big Data Storage and Analytics at Scale in Hybrid Cloud", "abstract": "Implementing big data storage at scale is a complex and arduous task that requires an advanced infrastructure. With the rise of public cloud computing, various big data management services can be readily leveraged. As a critical part of Twitter's \"Project Partly Cloudy\", the cold storage data and analytics systems are being moved to the public cloud. This paper showcases our approach in designing a scalable big data storage and analytics management framework using BigQuery in Google Cloud Platform while ensuring security, privacy, and data protection. The paper also discusses the limitations on the public cloud resources and how they can be effectively overcome when designing a big data storage and analytics solution at scale. Although the paper discusses the framework implementation in Google Cloud Platform, it can easily be applied to all major cloud providers.", "authors": ["Saurabh Deochake", "Vrushali Channapattan", "Gary Steelman"], "categories": ["cs.DC", "cs.DB", "cs.LG", "cs.NI"], "fields_of_study": ["Computer Science"], "published_date": "2022-03-22", "url": "https://arxiv.org/abs/2203.11472", "pdf_url": "https://arxiv.org/pdf/2203.11472v1", "arxiv_id": "2203.11472", "doi": "10.48550/arXiv.2203.11472", "citation_count": 9, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.25}
{"id": "ee4c7ee993a418d0cee3104c2b0ea39c10c5663289b405ee87f28c47e9d9bae0", "sources": ["arxiv", "semantic_scholar"], "title": "Dataset Distillation by Matching Training Trajectories", "abstract": "Dataset distillation is the task of synthesizing a small dataset such that a model trained on the synthetic set will match the test accuracy of the model trained on the full dataset. In this paper, we propose a new formulation that optimizes our distilled data to guide networks to a similar state as those trained on real data across many training steps. Given a network, we train it for several iterations on our distilled data and optimize the distilled data with respect to the distance between the synthetically trained parameters and the parameters trained on real data. To efficiently obtain the initial and target network parameters for large-scale datasets, we pre-compute and store training trajectories of expert networks trained on the real dataset. Our method handily outperforms existing methods and also allows us to distill higher-resolution visual data.", "authors": ["George Cazenavette", "Tongzhou Wang", "Antonio Torralba", "Alexei A. Efros", "Jun-Yan Zhu"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-03-22", "url": "https://arxiv.org/abs/2203.11932", "pdf_url": "https://arxiv.org/pdf/2203.11932v1", "arxiv_id": "2203.11932", "doi": "10.1109/CVPR52688.2022.01045", "citation_count": 581, "influential_citation_count": 166, "has_code": true, "code_url": "https://github.com/GeorgeCazenavette/mtt-distillation", "venue": "Computer Vision and Pattern Recognition", "quality_score": 1.0}
{"id": "327646a001132193b9829f6723491a3f839476fed72ea3305e382cb9e4e565de", "sources": ["arxiv", "semantic_scholar"], "title": "ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data", "abstract": "Unsupervised domain adaptation methods aim to generalize well on unlabeled test data that may have a different (shifted) distribution from the training data. Such methods are typically developed on image data, and their application to time series data is less explored. Existing works on time series domain adaptation suffer from inconsistencies in evaluation schemes, datasets, and backbone neural network architectures. Moreover, labeled target data are often used for model selection, which violates the fundamental assumption of unsupervised domain adaptation. To address these issues, we develop a benchmarking evaluation suite (AdaTime) to systematically and fairly evaluate different domain adaptation methods on time series data. Specifically, we standardize the backbone neural network architectures and benchmarking datasets, while also exploring more realistic model selection approaches that can work with no labeled data or just a few labeled samples. Our evaluation includes adapting state-of-the-art visual domain adaptation methods to time series data as well as the recent methods specifically developed for time series data. We conduct extensive experiments to evaluate 11 state-of-the-art methods on five representative datasets spanning 50 cross-domain scenarios. Our results suggest that with careful selection of hyper-parameters, visual domain adaptation methods are competitive with methods proposed for time series domain adaptation. In addition, we find that hyper-parameters could be selected based on realistic model selection approaches. Our work unveils practical insights for applying domain adaptation methods on time series data and builds a solid foundation for future works in the field. The code is available at \\href{https://github.com/emadeldeen24/AdaTime}{github.com/emadeldeen24/AdaTime}.", "authors": ["Mohamed Ragab", "Emadeldeen Eldele", "Wee Ling Tan", "Chuan-Sheng Foo", "Zhenghua Chen", "Min Wu", "Chee-Keong Kwoh", "Xiaoli Li"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-03-15", "url": "https://arxiv.org/abs/2203.08321", "pdf_url": "https://arxiv.org/pdf/2203.08321v2", "arxiv_id": "2203.08321", "doi": "10.1145/3587937", "citation_count": 99, "influential_citation_count": 12, "has_code": true, "code_url": "https://github.com/emadeldeen24/AdaTime}{github.com/emadeldeen24/AdaTime}", "venue": "ACM Transactions on Knowledge Discovery from Data", "quality_score": 0.557}
{"id": "7f66baaeb61067775a0426dcdfe084198bdbd04dded306ffc528bc62f3d8a32b", "sources": ["arxiv", "semantic_scholar"], "title": "CEKD:Cross Ensemble Knowledge Distillation for Augmented Fine-grained Data", "abstract": "Data augmentation has been proved effective in training deep models. Existing data augmentation methods tackle the fine-grained problem by blending image pairs and fusing corresponding labels according to the statistics of mixed pixels, which produces additional noise harmful to the performance of networks. Motivated by this, we present a simple yet effective cross ensemble knowledge distillation (CEKD) model for fine-grained feature learning. We innovatively propose a cross distillation module to provide additional supervision to alleviate the noise problem, and propose a collaborative ensemble module to overcome the target conflict problem. The proposed model can be trained in an end-to-end manner, and only requires image-level label supervision. Extensive experiments on widely used fine-grained benchmarks demonstrate the effectiveness of our proposed model. Specifically, with the backbone of ResNet-101, CEKD obtains the accuracy of 89.59%, 95.96% and 94.56% in three datasets respectively, outperforming state-of-the-art API-Net by 0.99%, 1.06% and 1.16%.", "authors": ["Ke Zhang", "Jin Fan", "Shaoli Huang", "Yongliang Qiao", "Xiaofeng Yu", "Feiwei Qin"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2022-03-13", "url": "https://arxiv.org/abs/2203.06551", "pdf_url": "https://arxiv.org/pdf/2203.06551v1", "arxiv_id": "2203.06551", "doi": "10.1007/s10489-022-03355-0", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1945}
{"id": "ce066d334cdfb99c0b7bc3e48fec472c7d80b0cefaae603214e9d309c19553cb", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Efficient Data-Centric Robust Machine Learning with Noise-based Augmentation", "abstract": "The data-centric machine learning aims to find effective ways to build appropriate datasets which can improve the performance of AI models. In this paper, we mainly focus on designing an efficient data-centric scheme to improve robustness for models towards unforeseen malicious inputs in the black-box test settings. Specifically, we introduce a noised-based data augmentation method which is composed of Gaussian Noise, Salt-and-Pepper noise, and the PGD adversarial perturbations. The proposed method is built on lightweight algorithms and proved highly effective based on comprehensive evaluations, showing good efficiency on computation cost and robustness enhancement. In addition, we share our insights about the data-centric robust machine learning gained from our experiments.", "authors": ["Xiaogeng Liu", "Haoyu Wang", "Yechao Zhang", "Fangzhou Wu", "Shengshan Hu"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2022-03-08", "url": "https://arxiv.org/abs/2203.03810", "pdf_url": "https://arxiv.org/pdf/2203.03810v1", "arxiv_id": "2203.03810", "doi": "10.48550/arXiv.2203.03810", "citation_count": 14, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.294}
{"id": "aa3507906540c1bf336ae0e3bb172e128994f95b9f6f9cafa0dffe65e53c0125", "sources": ["arxiv", "semantic_scholar"], "title": "Estimation of a Factor-Augmented Linear Model with Applications Using Student Achievement Data", "abstract": "In many longitudinal settings, economic theory does not guide practitioners on the type of restrictions that must be imposed to solve the rotational indeterminacy of factor-augmented linear models. We study this problem and offer several novel results on identification using internally generated instruments. We propose a new class of estimators and establish large sample results using recent developments on clustered samples and high-dimensional models. We carry out simulation studies which show that the proposed approaches improve the performance of existing methods on the estimation of unknown factors. Lastly, we consider three empirical applications using administrative data of students clustered in different subjects in elementary school, high school and college.", "authors": ["Matthew Harding", "Carlos Lamarche", "Chris Muris"], "categories": ["econ.EM", "stat.ME"], "fields_of_study": ["Economics", "Mathematics"], "published_date": "2022-03-06", "url": "https://arxiv.org/abs/2203.03051", "pdf_url": "https://arxiv.org/pdf/2203.03051v1", "arxiv_id": "2203.03051", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0}
{"id": "90613c9712c4718c191ab7bb59d6af05659c32ff2ad97ecea24243c5c6dff992", "sources": ["arxiv", "semantic_scholar"], "title": "On generating parametrised structural data using conditional generative adversarial networks", "abstract": "A powerful approach, and one of the most common ones in structural health monitoring (SHM), is to use data-driven models to make predictions and inferences about structures and their condition. Such methods almost exclusively rely on the quality of the data. Within the SHM discipline, data do not always suffice to build models with satisfactory accuracy for given tasks. Even worse, data may be completely missing from one's dataset, regarding the behaviour of a structure under different environmental conditions. In the current work, with a view to confronting such issues, the generation of artificial data using a variation of the generative adversarial network (GAN) algorithm, is used. The aforementioned variation is that of the conditional GAN or cGAN. The algorithm is not only used to generate artificial data, but also to learn transformations of manifolds according to some known parameters. Assuming that the structure's response is represented by points in a manifold, part of the space will be formed due to variations in external conditions affecting the structure. This idea proves efficient in SHM, as it is exploited to generate structural data for specific values of environmental coefficients. The scheme is applied here on a simulated structure which operates under different temperature and humidity conditions. The cGAN is trained on data for some discrete values of the temperature within some range, and is able to generate data for every temperature in this range with satisfactory accuracy. The novelty, compared to classic regression in similar problems, is that the cGAN allows unknown environmental parameters to affect the structure and can generate whole manifolds of data for every value of the known parameters, while the unknown ones vary within the generated manifolds.", "authors": ["G. Tsialiamanis", "D. J. Wagg", "N. Dervilis", "K. Worden"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-03-03", "url": "https://arxiv.org/abs/2203.01641", "pdf_url": "https://arxiv.org/pdf/2203.01641v1", "arxiv_id": "2203.01641", "doi": "10.1007/978-3-030-76004-5_6", "citation_count": 2, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Data Science in Engineering, Volume 9 pp 35-46, 2021", "quality_score": 0.1505}
{"id": "697aee6515367b8a308fce4df6d6141741bc2a9562a41732f67a558246b35014", "sources": ["arxiv", "semantic_scholar"], "title": "Graph Neural Networks for Multimodal Single-Cell Data Integration", "abstract": "Recent advances in multimodal single-cell technologies have enabled simultaneous acquisitions of multiple omics data from the same cell, providing deeper insights into cellular states and dynamics. However, it is challenging to learn the joint representations from the multimodal data, model the relationship between modalities, and, more importantly, incorporate the vast amount of single-modality datasets into the downstream analyses. To address these challenges and correspondingly facilitate multimodal single-cell data analyses, three key tasks have been introduced: $\\textit{modality prediction}$, $\\textit{modality matching}$ and $\\textit{joint embedding}$. In this work, we present a general Graph Neural Network framework $\\textit{scMoGNN}$ to tackle these three tasks and show that $\\textit{scMoGNN}$ demonstrates superior results in all three tasks compared with the state-of-the-art and conventional approaches. Our method is an official winner in the overall ranking of $\\textit{Modality prediction}$ from NeurIPS 2021 Competition, and all implementations of our methods have been integrated into DANCE package~\\url{https://github.com/OmicsML/dance}.", "authors": ["Hongzhi Wen", "Jiayuan Ding", "Wei Jin", "Yiqi Wang", "Yuying Xie", "Jiliang Tang"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2022-03-03", "url": "https://arxiv.org/abs/2203.01884", "pdf_url": "https://arxiv.org/pdf/2203.01884v3", "arxiv_id": "2203.01884", "doi": "10.1145/3534678.3539213", "citation_count": 83, "influential_citation_count": 3, "has_code": true, "code_url": "https://github.com/OmicsML/dance}", "venue": "Knowledge Discovery and Data Mining", "quality_score": 0.4811}
{"id": "b6a704dd4545d8206545091baf33debfcd4f9fe9f7ecb1add94d4df417824bd4", "sources": ["arxiv", "semantic_scholar"], "title": "Learning Infomax and Domain-Independent Representations for Causal Effect Inference with Real-World Data", "abstract": "The foremost challenge to causal inference with real-world data is to handle the imbalance in the covariates with respect to different treatment options, caused by treatment selection bias. To address this issue, recent literature has explored domain-invariant representation learning based on different domain divergence metrics (e.g., Wasserstein distance, maximum mean discrepancy, position-dependent metric, and domain overlap). In this paper, we reveal the weaknesses of these strategies, i.e., they lead to the loss of predictive information when enforcing the domain invariance; and the treatment effect estimation performance is unstable, which heavily relies on the characteristics of the domain distributions and the choice of domain divergence metrics. Motivated by information theory, we propose to learn the Infomax and Domain-Independent Representations to solve the above puzzles. Our method utilizes the mutual information between the global feature representations and individual feature representations, and the mutual information between feature representations and treatment assignment predictions, in order to maximally capture the common predictive information for both treatment and control groups. Moreover, our method filters out the influence of instrumental and irrelevant variables, and thus it effectively increases the predictive ability of potential outcomes. Experimental results on both the synthetic and real-world datasets show that our method achieves state-of-the-art performance on causal effect inference. Moreover, our method exhibits reliable prediction performances when facing data with different characteristics of data distributions, complicated variable types, and severe covariate imbalance.", "authors": ["Zhixuan Chu", "Stephen Rathbun", "Sheng Li"], "categories": ["stat.ML", "cs.LG"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2022-02-22", "url": "https://arxiv.org/abs/2202.10885", "pdf_url": "https://arxiv.org/pdf/2202.10885v1", "arxiv_id": "2202.10885", "doi": "10.1137/1.9781611977172.49", "citation_count": 14, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "SDM", "quality_score": 0.294}
{"id": "cdc606296c2e343066f97fc20ea17b6a07ff233d9087b1834b27d8f3f42e1d19", "sources": ["arxiv", "semantic_scholar"], "title": "Inflation of test accuracy due to data leakage in deep learning-based classification of OCT images", "abstract": "In the application of deep learning on optical coherence tomography (OCT) data, it is common to train classification networks using 2D images originating from volumetric data. Given the micrometer resolution of OCT systems, consecutive images are often very similar in both visible structures and noise. Thus, an inappropriate data split can result in overlap between the training and testing sets, with a large portion of the literature overlooking this aspect. In this study, the effect of improper dataset splitting on model evaluation is demonstrated for three classification tasks using three OCT open-access datasets extensively used, Kermany's and Srinivasan's ophthalmology datasets, and AIIMS breast tissue dataset. Results show that the classification performance is inflated by 0.07 up to 0.43 in terms of Matthews Correlation Coefficient (accuracy: 5% to 30%) for models tested on datasets with improper splitting, highlighting the considerable effect of dataset handling on model evaluation. This study intends to raise awareness on the importance of dataset splitting given the increased research interest in implementing deep learning on OCT data.", "authors": ["Iulian Emil Tampu", "Anders Eklund", "Neda Haj-Hosseini"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "fields_of_study": ["Computer Science", "Engineering", "Medicine"], "published_date": "2022-02-21", "url": "https://arxiv.org/abs/2202.12267", "pdf_url": "https://arxiv.org/pdf/2202.12267v2", "arxiv_id": "2202.12267", "doi": "10.1038/s41597-022-01618-6", "citation_count": 77, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "Scientific Data", "quality_score": 0.473}
{"id": "93b44155ad4540de425197225db871bfa318e22ee791cb3d41987c84b5eb811a", "sources": ["arxiv", "semantic_scholar"], "title": "Gaussian and Non-Gaussian Universality of Data Augmentation", "abstract": "We provide universality results that quantify how data augmentation affects the variance and limiting distribution of estimates through simple surrogates, and analyze several specific models in detail. The results confirm some observations made in machine learning practice, but also lead to unexpected findings: Data augmentation may increase rather than decrease the uncertainty of estimates, such as the empirical prediction risk. It can act as a regularizer, but fails to do so in certain high-dimensional problems, and it may shift the double-descent peak of an empirical risk. Overall, the analysis shows that several properties data augmentation has been attributed with are not either true or false, but rather depend on a combination of factors -- notably the data distribution, the properties of the estimator, and the interplay of sample size, number of augmentations, and dimension. As our main theoretical tool, we develop an adaptation of Lindeberg's technique for block dependence. The resulting universality regime may be Gaussian or non-Gaussian.", "authors": ["Kevin Han Huang", "Peter Orbanz", "Morgane Austern"], "categories": ["cs.LG", "math.ST", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2022-02-18", "url": "https://arxiv.org/abs/2202.09134", "pdf_url": "https://arxiv.org/pdf/2202.09134v5", "arxiv_id": "2202.09134", "doi": null, "citation_count": 5, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1945}
{"id": "07e7c59706965db757e19c4409e10591a0b1ca2539a8c124129fcb7e36e0e08c", "sources": ["arxiv", "semantic_scholar"], "title": "Data Augmentation for Deep Graph Learning: A Survey", "abstract": "Graph neural networks, a powerful deep learning tool to model graph-structured data, have demonstrated remarkable performance on numerous graph learning tasks. To address the data noise and data scarcity issues in deep graph learning, the research on graph data augmentation has intensified lately. However, conventional data augmentation methods can hardly handle graph-structured data which is defined in non-Euclidean space with multi-modality. In this survey, we formally formulate the problem of graph data augmentation and further review the representative techniques and their applications in different deep graph learning problems. Specifically, we first propose a taxonomy for graph data augmentation techniques and then provide a structured review by categorizing the related work based on the augmented information modalities. Moreover, we summarize the applications of graph data augmentation in two representative problems in data-centric deep graph learning: (1) reliable graph learning which focuses on enhancing the utility of input graph as well as the model capacity via graph data augmentation; and (2) low-resource graph learning which targets on enlarging the labeled training data scale through graph data augmentation. For each problem, we also provide a hierarchical problem taxonomy and review the existing literature related to graph data augmentation. Finally, we point out promising research directions and the challenges in future research.", "authors": ["Kaize Ding", "Zhe Xu", "Hanghang Tong", "Huan Liu"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-02-16", "url": "https://arxiv.org/abs/2202.08235", "pdf_url": "https://arxiv.org/pdf/2202.08235v3", "arxiv_id": "2202.08235", "doi": "10.1145/3575637.3575646", "citation_count": 289, "influential_citation_count": 7, "has_code": true, "code_url": "https://github.com/kaize0409/awesome-graph-data-augmentaion", "venue": "SIGKDD Explorations", "quality_score": 0.6156}
{"id": "cf268131d42a1b5c51c9f2449f3209fef60a58d8f2e45e8516492b042ec85faf", "sources": ["arxiv", "semantic_scholar"], "title": "Cryogenic digital data links for the liquid argon time projection chamber", "abstract": "In this paper we present the cryogenic functionality of the components of data links for the Liquid Argon Time Projection Chamber (LArTPC), a potential far site detector technology of the Long-Baseline Neutrino Experiment (LBNE). We have confirmed that an LVDS driver can drive a 20-meter CAT5E twisted pair up to 1 gigabit per second at the liquid nitrogen temperature (77 K). We have verified that a commercial-off-the-shelf (COTS) serializer, a laser diode driver, laser diodes, optical fibers and connectors, and field-programming gate arrays (FPGA's) continue to function at 77 K. A variety of COTS resistors and capacitors have been tested at 77 K. All tests we have conducted show that the cryogenic digital data links for the liquid argon time projection chamber are promising.", "authors": ["Tiankuan Liu", "Datao Gong", "Suen Hou", "Chonghan Liu", "Da-Shung Su", "Ping-kun Teng", "Annie C. Xiang", "Jingbo Ye"], "categories": ["physics.ins-det"], "fields_of_study": ["Materials Science", "Physics"], "published_date": "2022-02-10", "url": "https://arxiv.org/abs/2202.05103", "pdf_url": "https://arxiv.org/pdf/2202.05103v1", "arxiv_id": "2202.05103", "doi": "10.1088/1748-0221/7/01/C01091", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "JINST 7 C01091 (2012)", "quality_score": 0.2258}
{"id": "161603b48916cb66cbcef0329f6948c748bea341e5012302c0d1b8dee4d5072b", "sources": ["arxiv", "semantic_scholar"], "title": "A line code with quick-resynchronization capability and low latency for the optical data links of LHC experiments", "abstract": "We propose a line code that has fast resynchronization capability and low latency. Both the encoder and decoder have been implemented in FPGAs. The encoder has also been implemented in an ASIC. The latency of the whole optical link (not including the optical fiber) is estimated to be less than 73.9 ns. In the case of radiation-induced link synchronization loss, the decoder can recover the synchronization in 25 ns. The line code will be used in the ATLAS liquid argon calorimeter Phase-I trigger upgrade and can also be potentially used in other LHC experiments.", "authors": ["Binwei Deng", "Mengxun He", "Jinghong Chen", "Di Guo", "Suen Hou", "Xiaoting Li", "Chonghan Liu", "Ping-Kun Teng", "Annie C. Xiang", "Yang You", "Jingbo Ye", "Datao Gong", "Tiankuan Liu"], "categories": ["physics.ins-det"], "fields_of_study": ["Physics", "Computer Science"], "published_date": "2022-02-09", "url": "https://arxiv.org/abs/2202.04288", "pdf_url": "https://arxiv.org/pdf/2202.04288v1", "arxiv_id": "2202.04288", "doi": "10.1088/1748-0221/9/07/P07020", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2386}
{"id": "bdc9d4cdc9ba123a4e7c06b9f05519115c98f06baa8a6f967aff35e154304466", "sources": ["arxiv", "semantic_scholar"], "title": "Component Prototypes towards a Low-Latency, Small-form-factor Optical Link for the ATLAS Liquid Argon Calorimeter Phase-I Trigger Upgrade", "abstract": "This paper presents several component prototypes towards a low-latency, small-form-factor optical link designed for the ATLAS Liquid Argon Calorimeter Phase-I trigger upgrade. A prototype of the custom-made dual-channel optical transmitter module, the Miniature optical Transmitter (MTx), with separate transmitter optical sub-assemblies (TOSAs) has been demonstrated at data rates up to 8 Gbps per channel. A Vertical-Cavity Surface-Emitting Laser (VCSEL) driver ASIC has been developed and is used in the current MTx prototypes. A serializer ASIC prototype, operating at up to 8 Gbps per channel, has been designed and tested. A low-latency, low-overhead encoder ASIC prototype has been designed and tested. The latency of the whole link, including the transmitter latency and the receiver latency but not the latency of the fiber, is estimated to be less than 57.9 ns. The size of the MTx is 45 mm x 15 mm x 6 mm.", "authors": ["Binwei Deng", "Mengxun He", "Jinghong Chen", "Datao Gong", "Di Guo", "Suen Hou", "Xiaoting Li", "Futian Liang", "Chonghan Liu", "Gang Liu", "Ping-Kun Teng", "Annie C Xiang", "Tongye Xu", "You Yang", "Jingbo Ye", "Xiandong Zhao", "Tiankuan Liu"], "categories": ["physics.ins-det"], "fields_of_study": ["Physics"], "published_date": "2022-02-09", "url": "https://arxiv.org/abs/2202.04285", "pdf_url": "https://arxiv.org/pdf/2202.04285v1", "arxiv_id": "2202.04285", "doi": "10.1109/TNS.2014.2373362", "citation_count": 10, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Nuclear Science", "quality_score": 0.2603}
{"id": "55c28d2b861c525d03cb2b9f935cf6c458ab1f0e45911c95863bb6547eef6225", "sources": ["arxiv", "semantic_scholar"], "title": "Optimal Ratio for Data Splitting", "abstract": "It is common to split a dataset into training and testing sets before fitting a statistical or machine learning model. However, there is no clear guidance on how much data should be used for training and testing. In this article we show that the optimal splitting ratio is $\\sqrt{p}:1$, where $p$ is the number of parameters in a linear regression model that explains the data well.", "authors": ["V. Roshan Joseph"], "categories": ["stat.ML", "cs.LG"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2022-02-07", "url": "https://arxiv.org/abs/2202.03326", "pdf_url": "https://arxiv.org/pdf/2202.03326v1", "arxiv_id": "2202.03326", "doi": "10.1002/sam.11583", "citation_count": 670, "influential_citation_count": 9, "has_code": false, "code_url": null, "venue": "Statistical analysis and data mining", "quality_score": 0.7067}
{"id": "01a55050f1738e16370e7c907518cac0732d8ac8a11faa051fe713bd35eb3ad3", "sources": ["arxiv", "semantic_scholar"], "title": "Locally Differentially Private Distributed Deep Learning via Knowledge Distillation", "abstract": "Deep learning often requires a large amount of data. In real-world applications, e.g., healthcare applications, the data collected by a single organization (e.g., hospital) is often limited, and the majority of massive and diverse data is often segregated across multiple organizations. As such, it motivates the researchers to conduct distributed deep learning, where the data user would like to build DL models using the data segregated across multiple different data owners. However, this could lead to severe privacy concerns due to the sensitive nature of the data, thus the data owners would be hesitant and reluctant to participate. We propose LDP-DL, a privacy-preserving distributed deep learning framework via local differential privacy and knowledge distillation, where each data owner learns a teacher model using its own (local) private dataset, and the data user learns a student model to mimic the output of the ensemble of the teacher models. In the experimental evaluation, a comprehensive comparison has been made among our proposed approach (i.e., LDP-DL), DP-SGD, PATE and DP-FL, using three popular deep learning benchmark datasets (i.e., CIFAR10, MNIST and FashionMNIST). The experimental results show that LDP-DL consistently outperforms the other competitors in terms of privacy budget and model accuracy.", "authors": ["Di Zhuang", "Mingchen Li", "J. Morris Chang"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2022-02-07", "url": "https://arxiv.org/abs/2202.02971", "pdf_url": "https://arxiv.org/pdf/2202.02971v1", "arxiv_id": "2202.02971", "doi": null, "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "ad1b8569ecc1907a28903a73e77abb4309865bdf148dfa77284c75344c6345f2", "sources": ["arxiv", "semantic_scholar"], "title": "State-of-the-Art Methods for Exposure-Health Studies: results from the Exposome Data Challenge Event", "abstract": "The exposome recognizes that individuals are exposed simultaneously to a multitude of different environmental factors and takes a holistic approach to the discovery of etiological factors for disease. However, challenges arise when trying to quantify the health effects of complex exposure mixtures. Analytical challenges include dealing with high dimensionality, studying the combined effects of these exposures and their interactions, integrating causal pathways, and integrating omics layers. To tackle these challenges, ISGlobal Exposome Hub held a data challenge event open to researchers from all over the world and from all expertises. Analysts had a chance to compete and apply state-of-the-art methods on a common partially simulated exposome dataset (based on real case data from the HELIX project) with multiple correlated exposure variables (P>100) arising from general and personal environments at different time points, biological molecular data (multi-omics: DNA methylation, gene expression, proteins, metabolomics) and multiple clinical phenotypes in 1301 mother-child pairs. Most of the methods presented included feature selection or feature reduction to deal with the high dimensionality of the exposome dataset. Several approaches explicitly searched for combined effects of exposures and/or their interactions using linear index models or response surface methods, including Bayesian methods. Other methods dealt with the multi-omics dataset in mediation analyses using multiple-step approaches. Here we discuss the statistical models and provide the data and codes used, so that analysts have examples of implementation and can learn how to use these methods. Overall, the exposome data challenge presented a unique opportunity for researchers from different disciplines to create and share methods, setting a new standard for open science in the exposome and environmental health field.", "authors": ["Léa Maitre", "Jean-Baptiste Guimbaud", "Charline Warembourg", "Nuria Güil-Oumrait", "The Exposome Data Challenge Participant Consortium", "Paula Marcela Petrone", "Marc Chadeau-Hyam", "Martine Vrijheid", "Juan R. Gonzalez", "Xavier Basagaña"], "categories": ["stat.AP"], "fields_of_study": ["Medicine", "Mathematics"], "published_date": "2022-02-03", "url": "https://arxiv.org/abs/2202.01680", "pdf_url": "https://arxiv.org/pdf/2202.01680v1", "arxiv_id": "2202.01680", "doi": "10.1016/j.envint.2022.107422", "citation_count": 66, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Environment International", "quality_score": 0.4565}
{"id": "c8978d61b17a7729a611af40058ed03d675bc0c7ed20ed00f3cfcc509c981b08", "sources": ["arxiv", "semantic_scholar"], "title": "Astronomical data organization, management and access in Scientific Data Lakes", "abstract": "The data volumes stored in telescope archives is constantly increasing due to the development and improvements in the instrumentation. Often the archives need to be stored over a distributed storage architecture, provided by independent compute centres. Such a distributed data archive requires overarching data management orchestration. Such orchestration comprises of tools which handle data storage and cataloguing, and steering transfers integrating different storage systems and protocols, while being aware of data policies and locality. In addition, it needs a common Authorisation and Authentication Infrastructure (AAI) layer which is perceived as a single entity by end users and provides transparent data access. The scientific domain of particle physics also uses complex and distributed data management systems. The experiments at the Large Hadron Collider\\,(LHC) accelerator at CERN generate several hundred petabytes of data per year. This data is globally distributed to partner sites and users using national compute facilities. Several innovative tools were developed to successfully address the distributed computing challenges in the context of the Worldwide LHC Computing Grid (WLCG). The work being carried out in the ESCAPE project and in the Data Infrastructure for Open Science (DIOS) work package is to prototype a Scientific Data Lake using the tools developed in the context of the WLCG, harnessing different physics scientific disciplines addressing FAIR standards and Open Data. We present how the Scientific Data Lake prototype is applied to address astronomical data use cases. We introduce the software stack and also discuss some of the differences between the domains.", "authors": ["Y. G. Grange", "V. N. Pandey", "X. Espinal", "R. Di Maria", "A. P. Millar"], "categories": ["astro-ph.IM", "cs.DC"], "fields_of_study": ["Computer Science", "Physics"], "published_date": "2022-02-03", "url": "https://arxiv.org/abs/2202.01828", "pdf_url": "https://arxiv.org/pdf/2202.01828v1", "arxiv_id": "2202.01828", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0}
{"id": "6f6ee7e850148478e6f89ccb6eac7bd4f7f318ed2ddefc705bae2c42bea5adff", "sources": ["arxiv", "semantic_scholar"], "title": "Ranking with Confidence for Large Scale Comparison Data", "abstract": "In this work, we leverage a generative data model considering comparison noise to develop a fast, precise, and informative ranking algorithm from pairwise comparisons that produces a measure of confidence on each comparison. The problem of ranking a large number of items from noisy and sparse pairwise comparison data arises in diverse applications, like ranking players in online games, document retrieval or ranking human perceptions. Although different algorithms are available, we need fast, large-scale algorithms whose accuracy degrades gracefully when the number of comparisons is too small. Fitting our proposed model entails solving a non-convex optimization problem, which we tightly approximate by a sum of quasi-convex functions and a regularization term. Resorting to an iterative reweighted minimization and the Primal-Dual Hybrid Gradient method, we obtain PD-Rank, achieving a Kendall tau 0.1 higher than all comparing methods, even for 10\\% of wrong comparisons in simulated data matching our data model, and leading in accuracy if data is generated according to the Bradley-Terry model, in both cases faster by one order of magnitude, in seconds. In real data, PD-Rank requires less computational time to achieve the same Kendall tau than active learning methods.", "authors": ["Filipa Valdeira", "Cláudia Soares"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-02-03", "url": "https://arxiv.org/abs/2202.01670", "pdf_url": "https://arxiv.org/pdf/2202.01670v2", "arxiv_id": "2202.01670", "doi": "10.1137/1.9781611978520.21", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "SDM", "quality_score": 0.1193}
{"id": "86716c9e6dfe11291b47242df7bf15aa17f420b84c50aa21fcac0026084db738", "sources": ["arxiv", "semantic_scholar"], "title": "A Nonlinear Hierarchical Model for Longitudinal Data on Manifolds", "abstract": "Large longitudinal studies provide lots of valuable information, especially in medical applications. A problem which must be taken care of in order to utilize their full potential is that of correlation between intra-subject measurements taken at different times. For data in Euclidean space this can be done with hierarchical models, that is, models that consider intra-subject and between-subject variability in two different stages. Nevertheless, data from medical studies often takes values in nonlinear manifolds. Here, as a first step, geodesic hierarchical models have been developed that generalize the linear ansatz by assuming that time-induced intra-subject variations occur along a generalized straight line in the manifold. However, this is often not the case (e.g., periodic motion or processes with saturation). We propose a hierarchical model for manifold-valued data that extends this to include trends along higher-order curves, namely Bézier splines in the manifold. To this end, we present a principled way of comparing shape trends in terms of a functional-based Riemannian metric. Remarkably, this metric allows efficient, yet simple computations by virtue of a variational time discretization requiring only the solution of regression problems. We validate our model on longitudinal data from the osteoarthritis initiative, including classification of disease progression.", "authors": ["Martin Hanik", "Hans-Christian Hege", "Christoph von Tycowicz"], "categories": ["stat.ME", "math.DG", "math.ST"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2022-02-01", "url": "https://arxiv.org/abs/2202.01180", "pdf_url": "https://arxiv.org/pdf/2202.01180v2", "arxiv_id": "2202.01180", "doi": "10.1109/ISBI52829.2022.9761465", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE International Symposium on Biomedical Imaging", "quality_score": 0.2113}
{"id": "d6fb02442ddabf18972f8ec73da45b60b84c045042487b23a643e189b3d29fb0", "sources": ["arxiv", "semantic_scholar"], "title": "Wicked Implications for Human Interaction with IoT Sensor Data", "abstract": "Human data interaction with sensor data from smart homes can cause some implications when it comes to human sensemaking of this data. With our data-driven method Guess the Data for individual and collective data work we revealed in previous work a number of potential pitfalls when interacting with this type of data. We introduce some of the identified, often wicked implications for further discussion.", "authors": ["Albrecht Kurze", "Andreas Bischof"], "categories": ["cs.HC"], "fields_of_study": ["Computer Science"], "published_date": "2022-01-25", "url": "https://arxiv.org/abs/2201.10470", "pdf_url": "https://arxiv.org/pdf/2201.10470v1", "arxiv_id": "2201.10470", "doi": null, "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2113}
{"id": "28a52f2220ced9ab3509bca20e4a5c9613849a13ac80b942200771021121874c", "sources": ["arxiv", "semantic_scholar"], "title": "The critical O(N) CFT: Methods and conformal data", "abstract": "The critical $O(N)$ CFT in spacetime dimensions $2 < d < 4$ is one of the most important examples of a conformal field theory, with the Ising CFT at $N=1$, $2 \\leq d < 4$, as a notable special case. Apart from numerous physical applications, it serves frequently as a concrete testing ground for new approaches and techniques based on conformal symmetry. In the perturbative limits - the $4-\\varepsilon$ expansion, the large $N$ expansion and the $2+\\tildeε$ expansion - a lot of conformal data have been computed over the years. In this report, we give an overview of the critical $O(N)$ CFT, including some methods to study it, and present a large collection of conformal data. The data, extracted from the literature and supplemented by many additional computations of order $\\varepsilon$ anomalous dimensions, are made available through an ancillary data file.", "authors": ["Johan Henriksson"], "categories": ["hep-th", "cond-mat.stat-mech"], "fields_of_study": ["Physics"], "published_date": "2022-01-24", "url": "https://arxiv.org/abs/2201.09520", "pdf_url": "https://arxiv.org/pdf/2201.09520v4", "arxiv_id": "2201.09520", "doi": "10.1016/j.physrep.2022.12.002", "citation_count": 80, "influential_citation_count": 9, "has_code": false, "code_url": null, "venue": "Physics reports", "quality_score": 0.5}
{"id": "a43c82bdb944a49751dd456ebda6880f20ff44d591b9c389b19497cd8504a65f", "sources": ["arxiv", "semantic_scholar"], "title": "Sample Size Considerations for Bayesian Multilevel Hidden Markov Models: A Simulation Study on Multivariate Continuous Data with highly overlapping Component Distributions based on Sleep Data", "abstract": "Spurred in part by the ever-growing number of sensors and web-based methods of collecting data, the use of Intensive Longitudinal Data (ILD) is becoming more common in the social and behavioural sciences. The ILD collected in this field are often hypothesised to be the result of latent states (e.g. behaviour, emotions), and the promise of ILD lies in its ability to capture the dynamics of these states as they unfold in time. In particular, by collecting data for multiple subjects, researchers can observe how such dynamics differ between subjects. The Bayesian Multilevel Hidden Markov Model (mHMM) is a relatively novel model that is suited to model the ILD of this kind while taking into account heterogeneity between subjects. While the mHMM has been applied in a variety of settings, large-scale studies that examine the required sample size for this model are lacking. In this paper, we address this research gap by conducting a simulation study to evaluate the effect of changing (1) the number of subjects, (2) the number of occasions, and (3) the between subjects variability on parameter estimates obtained by the mHMM. We frame this simulation study in the context of sleep research, which consists of multivariate continuous data that displays considerable overlap in the state dependent component distributions. In addition, we generate a set of baseline scenarios with more general data properties. Overall, the number of subjects has the largest effect on model performance. However, the number of occasions is important to adequately model latent state transitions. We discuss how the characteristics of the data influence parameter estimation and provide recommendations to researchers seeking to apply the mHMM to their own data.", "authors": ["Jasper Ginn", "Sebastian Mildiner Moraga", "Emmeke Aarts"], "categories": ["stat.ME", "stat.CO"], "fields_of_study": ["Mathematics"], "published_date": "2022-01-22", "url": "https://arxiv.org/abs/2201.09033", "pdf_url": "https://arxiv.org/pdf/2201.09033v1", "arxiv_id": "2201.09033", "doi": null, "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1193}
{"id": "e86011392e59c6bf7cbb4a57b10eadc154f9bbe164b952914dac0030d988c5cb", "sources": ["arxiv", "semantic_scholar"], "title": "Diversifying the Genomic Data Science Research Community", "abstract": "Over the last 20 years, there has been an explosion of genomic data collected for disease association, functional analyses, and other large-scale discoveries. At the same time, there have been revolutions in cloud computing that enable computational and data science research, while making data accessible to anyone with a web browser and an internet connection. However, students at institutions with limited resources have received relatively little exposure to curricula or professional development opportunities that lead to careers in genomic data science. To broaden participation in genomics research, the scientific community needs to support students, faculty, and administrators at Underserved Institutions (UIs) including Community Colleges, Historically Black Colleges and Universities, Hispanic-Serving Institutions, and Tribal Colleges and Universities in taking advantage of these tools in local educational and research programs. We have formed the Genomic Data Science Community Network (http://www.gdscn.org/) to identify opportunities and support broadening access to cloud-enabled genomic data science. Here, we provide a summary of the priorities for faculty members at UIs, as well as administrators, funders, and R1 researchers to consider as we create a more diverse genomic data science community.", "authors": ["The Genomic Data Science Community Network", "Rosa Alcazar", "Maria Alvarez", "Rachel Arnold", "Mentewab Ayalew", "Lyle G. Best", "Michael C. Campbell", "Kamal Chowdhury", "Katherine E. L. Cox", "Christina Daulton", "Youping Deng", "Carla Easter", "Karla Fuller", "Shazia Tabassum Hakim", "Ava M. Hoffman", "Natalie Kucher", "Andrew Lee", "Joslynn Lee", "Jeffrey T. Leek", "Robert Meller", "Loyda B. Méndez", "Miguel P. Méndez-González", "Stephen Mosher", "Michele Nishiguchi", "Siddharth Pratap", "Tiffany Rolle", "Sourav Roy", "Rachel Saidi", "Michael C. Schatz", "Shurjo Sen", "James Sniezek", "Edu Suarez Martinez", "Frederick Tan", "Jennifer Vessio", "Karriem Watson", "Wendy Westbroek", "Joseph Wilcox", "Xianfa Xie"], "categories": ["q-bio.OT", "cs.CY"], "fields_of_study": ["Biology", "Computer Science", "Medicine"], "published_date": "2022-01-20", "url": "https://arxiv.org/abs/2201.08443", "pdf_url": "https://arxiv.org/pdf/2201.08443v2", "arxiv_id": "2201.08443", "doi": "10.1101/gr.276496.121", "citation_count": 10, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Genome Research", "quality_score": 0.2603}
{"id": "f51a9760da8515d5aafb34ebe70399c74e7d9de2e6e27504228709bd63db6196", "sources": ["arxiv", "semantic_scholar"], "title": "WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data", "abstract": "Detecting relevant changes in dynamic time series data in a timely manner is crucially important for many data analysis tasks in real-world settings. Change point detection methods have the ability to discover changes in an unsupervised fashion, which represents a desirable property in the analysis of unbounded and unlabeled data streams. However, one limitation of most of the existing approaches is represented by their limited ability to handle multivariate and high-dimensional data, which is frequently observed in modern applications such as traffic flow prediction, human activity recognition, and smart grids monitoring. In this paper, we attempt to fill this gap by proposing WATCH, a novel Wasserstein distance-based change point detection approach that models an initial distribution and monitors its behavior while processing new data points, providing accurate and robust detection of change points in dynamic high-dimensional data. An extensive experimental evaluation involving a large number of benchmark datasets shows that WATCH is capable of accurately identifying change points and outperforming state-of-the-art methods.", "authors": ["Kamil Faber", "Roberto Corizzo", "Bartlomiej Sniezynski", "Michael Baron", "Nathalie Japkowicz"], "categories": ["cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2022-01-18", "url": "https://arxiv.org/abs/2201.07125", "pdf_url": "https://arxiv.org/pdf/2201.07125v1", "arxiv_id": "2201.07125", "doi": "10.1109/BigData52589.2021.9671962", "citation_count": 27, "influential_citation_count": 4, "has_code": false, "code_url": null, "venue": "2021 IEEE International Conference on Big Data (Big Data)", "quality_score": 0.3618}
{"id": "0947832854200ca46c250e18391e41379ed942c17c9efb160afd7a3f99d121a4", "sources": ["arxiv", "semantic_scholar"], "title": "A survey study of success factors in data science projects", "abstract": "In recent years, the data science community has pursued excellence and made significant research efforts to develop advanced analytics, focusing on solving technical problems at the expense of organizational and socio-technical challenges. According to previous surveys on the state of data science project management, there is a significant gap between technical and organizational processes. In this article we present new empirical data from a survey to 237 data science professionals on the use of project management methodologies for data science. We provide additional profiling of the survey respondents' roles and their priorities when executing data science projects. Based on this survey study, the main findings are: (1) Agile data science lifecycle is the most widely used framework, but only 25% of the survey participants state to follow a data science project methodology. (2) The most important success factors are precisely describing stakeholders' needs, communicating the results to end-users, and team collaboration and coordination. (3) Professionals who adhere to a project methodology place greater emphasis on the project's potential risks and pitfalls, version control, the deployment pipeline to production, and data security and privacy.", "authors": ["Iñigo Martinez", "Elisabeth Viles", "Igor G. Olaizola"], "categories": ["cs.DB", "cs.GL", "cs.LG", "cs.SE"], "fields_of_study": ["Computer Science"], "published_date": "2022-01-17", "url": "https://arxiv.org/abs/2201.06310", "pdf_url": "https://arxiv.org/pdf/2201.06310v1", "arxiv_id": "2201.06310", "doi": "10.1109/BigData52589.2021.9671588", "citation_count": 14, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "2021 IEEE International Conference on Big Data, pages 2313-2318", "quality_score": 0.294}
{"id": "834f062051cba03158f07e2cf3efbb096fd9db38b0c1ac572ffe638c7dcba52f", "sources": ["arxiv", "semantic_scholar"], "title": "A Formal Category Theoretical Framework for Multi-model Data Transformations", "abstract": "Data integration and migration processes in polystores and multi-model database management systems highly benefit from data and schema transformations. Rigorous modeling of transformations is a complex problem. The data and schema transformation field is scattered with multiple different transformation frameworks, tools, and mappings. These are usually domain-specific and lack solid theoretical foundations. Our first goal is to define category theoretical foundations for relational, graph, and hierarchical data models and instances. Each data instance is represented as a category theoretical mapping called a functor. We formalize data and schema transformations as Kan lifts utilizing the functorial representation for the instances. A Kan lift is a category theoretical construction consisting of two mappings satisfying a certain universal property. In this work, the two mappings correspond to schema transformation and data transformation.", "authors": ["Valter Uotila", "Jiaheng Lu"], "categories": ["cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2022-01-13", "url": "https://arxiv.org/abs/2201.04905", "pdf_url": "https://arxiv.org/pdf/2201.04905v1", "arxiv_id": "2201.04905", "doi": "10.1007/978-3-030-93663-1_2", "citation_count": 10, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Rezig E.K. et al. (eds) Heterogeneous Data Management, Polystores, and Analytics for Healthcare. DMAH 2021, Poly 2021. Lecture Notes in Computer Science, vol 12921. Pages 14-28. Springer, Cham", "quality_score": 0.2603}
{"id": "ebb4c25c22040ee59f5249f0073b853769251d3072d64f9dc1ebfc36fa99dedd", "sources": ["arxiv", "semantic_scholar"], "title": "Fantastic Data and How to Query Them", "abstract": "It is commonly acknowledged that the availability of the huge amount of (training) data is one of the most important factors for many recent advances in Artificial Intelligence (AI). However, datasets are often designed for specific tasks in narrow AI sub areas and there is no unified way to manage and access them. This not only creates unnecessary overheads when training or deploying Machine Learning models but also limits the understanding of the data, which is very important for data-centric AI. In this paper, we present our vision about a unified framework for different datasets so that they can be integrated and queried easily, e.g., using standard query languages. We demonstrate this in our ongoing work to create a framework for datasets in Computer Vision and show its advantages in different scenarios. Our demonstration is available at https://vision.semkg.org.", "authors": ["Trung-Kien Tran", "Anh Le-Tuan", "Manh Nguyen-Duc", "Jicheng Yuan", "Danh Le-Phuoc"], "categories": ["cs.AI", "cs.CV", "cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2022-01-13", "url": "https://arxiv.org/abs/2201.05026", "pdf_url": "https://arxiv.org/pdf/2201.05026v1", "arxiv_id": "2201.05026", "doi": null, "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1747}
{"id": "00496ba65b2cd5a2c420abd3cb8ab9d8280166452b378f89451ffc668c5e32ac", "sources": ["arxiv", "semantic_scholar"], "title": "Data augmentation through multivariate scenario forecasting in Data Centers using Generative Adversarial Networks", "abstract": "The Cloud paradigm is at a critical point in which the existing energy-efficiency techniques are reaching a plateau, while the computing resources demand at Data Center facilities continues to increase exponentially. The main challenge in achieving a global energy efficiency strategy based on Artificial Intelligence is that we need massive amounts of data to feed the algorithms. This paper proposes a time-series data augmentation methodology based on synthetic scenario forecasting within the Data Center. For this purpose, we will implement a powerful generative algorithm: Generative Adversarial Networks (GANs). Specifically, our work combines the disciplines of GAN-based data augmentation and scenario forecasting, filling the gap in the generation of synthetic data in DCs. Furthermore, we propose a methodology to increase the variability and heterogeneity of the generated data by introducing on-demand anomalies without additional effort or expert knowledge. We also suggest the use of Kullback-Leibler Divergence and Mean Squared Error as new metrics in the validation of synthetic time series generation, as they provide a better overall comparison of multivariate data distributions. We validate our approach using real data collected in an operating Data Center, successfully generating synthetic data helpful for prediction and optimization models. Our research will help optimize the energy consumed in Data Centers, although the proposed methodology can be employed in any similar time-series-like problem.", "authors": ["Jaime Pérez", "Patricia Arroba", "José M. Moya"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2022-01-12", "url": "https://arxiv.org/abs/2201.06147", "pdf_url": "https://arxiv.org/pdf/2201.06147v2", "arxiv_id": "2201.06147", "doi": "10.1007/s10489-022-03557-6", "citation_count": 24, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Appl Intell 53, 1469-1486 (2023)", "quality_score": 0.3495}
{"id": "f73c1a323aa8aaddaf3533b38b0de5fda71435ae3eb0f8c611b211279e4d4415", "sources": ["arxiv", "semantic_scholar"], "title": "Finding Your Way Through the Jungle of Big Data Architectures", "abstract": "This paper presents a systematic review of common analytical data architectures based on DAMA-DMBOK and ArchiMate. The paper is work in progress and provides a first view on Gartner's Logical Data Warehouse paradigm, Data Fabric and Dehghani's Data Mesh proposal as well as their interdependencies. It furthermore sketches the way forward how this work can be extended by covering more architecture paradigms (incl. classic Data Warehouse, Data Vault, Data Lake, Lambda and Kappa architectures) and introducing a template with among others \"context\", \"problem\" and \"solution\" descriptions, leading ultimately to a pattern system providing guidance for choosing the right architecture paradigm for the right situation.", "authors": ["Torsten Priebe", "Sebastian Neumaier", "Stefan Markus"], "categories": ["cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2022-01-11", "url": "https://arxiv.org/abs/2201.04233", "pdf_url": "https://arxiv.org/pdf/2201.04233v1", "arxiv_id": "2201.04233", "doi": "10.1109/BigData52589.2021.9671862", "citation_count": 14, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "2021 IEEE International Conference on Big Data (IEEE BigData 2021)", "quality_score": 0.294}
{"id": "33a579b7ac3c83613fdbd9d1344a4474cb1eaa1d8f359bdeb548d45af47d9dc7", "sources": ["arxiv", "semantic_scholar"], "title": "FedDTG:Federated Data-Free Knowledge Distillation via Three-Player Generative Adversarial Networks", "abstract": "While existing federated learning approaches primarily focus on aggregating local models to construct a global model, in realistic settings, some clients may be reluctant to share their private models due to the inclusion of privacy-sensitive information. Knowledge distillation, which can extract model knowledge without accessing model parameters, is well-suited for this federated scenario. However, most distillation methods in federated learning (federated distillation) require a proxy dataset, which is difficult to obtain in the real world. Therefore, in this paper, we introduce a distributed three-player Generative Adversarial Network (GAN) to implement data-free mutual distillation and propose an effective method called FedDTG. We confirmed that the fake samples generated by GAN can make federated distillation more efficient and robust. Additionally, the distillation process between clients can deliver good individual client performance while simultaneously acquiring global knowledge and protecting data privacy. Our extensive experiments on benchmark vision datasets demonstrate that our method outperforms other federated distillation algorithms in terms of generalization.", "authors": ["Lingzhi Gao", "Zhenyuan Zhang", "Chao Wu"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-01-10", "url": "https://arxiv.org/abs/2201.03169", "pdf_url": "https://arxiv.org/pdf/2201.03169v5", "arxiv_id": "2201.03169", "doi": null, "citation_count": 22, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3404}
{"id": "05563d12b065f39062fd0a35a898b833700c444a9ab54b754fb9be264f5d5530", "sources": ["arxiv", "semantic_scholar"], "title": "Robust and Resource-Efficient Data-Free Knowledge Distillation by Generative Pseudo Replay", "abstract": "Data-Free Knowledge Distillation (KD) allows knowledge transfer from a trained neural network (teacher) to a more compact one (student) in the absence of original training data. Existing works use a validation set to monitor the accuracy of the student over real data and report the highest performance throughout the entire process. However, validation data may not be available at distillation time either, making it infeasible to record the student snapshot that achieved the peak accuracy. Therefore, a practical data-free KD method should be robust and ideally provide monotonically increasing student accuracy during distillation. This is challenging because the student experiences knowledge degradation due to the distribution shift of the synthetic data. A straightforward approach to overcome this issue is to store and rehearse the generated samples periodically, which increases the memory footprint and creates privacy concerns. We propose to model the distribution of the previously observed synthetic samples with a generative network. In particular, we design a Variational Autoencoder (VAE) with a training objective that is customized to learn the synthetic data representations optimally. The student is rehearsed by the generative pseudo replay technique, with samples produced by the VAE. Hence knowledge degradation can be prevented without storing any samples. Experiments on image classification benchmarks show that our method optimizes the expected value of the distilled model accuracy while eliminating the large memory overhead incurred by the sample-storing methods.", "authors": ["Kuluhan Binici", "Shivam Aggarwal", "Nam Trung Pham", "Karianto Leman", "Tulika Mitra"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2022-01-09", "url": "https://arxiv.org/abs/2201.03019", "pdf_url": "https://arxiv.org/pdf/2201.03019v3", "arxiv_id": "2201.03019", "doi": "10.1609/aaai.v36i6.20556", "citation_count": 60, "influential_citation_count": 6, "has_code": false, "code_url": null, "venue": "AAAI Conference on Artificial Intelligence", "quality_score": 0.4463}
{"id": "8c6fdeeffa4cf8188360883be71ff71c617cb09f80df267a0b45d2843149c6d2", "sources": ["arxiv", "semantic_scholar"], "title": "Data-driven Model Generalizability in Crosslinguistic Low-resource Morphological Segmentation", "abstract": "Common designs of model evaluation typically focus on monolingual settings, where different models are compared according to their performance on a single data set that is assumed to be representative of all possible data for the task at hand. While this may be reasonable for a large data set, this assumption is difficult to maintain in low-resource scenarios, where artifacts of the data collection can yield data sets that are outliers, potentially making conclusions about model performance coincidental. To address these concerns, we investigate model generalizability in crosslinguistic low-resource scenarios. Using morphological segmentation as the test case, we compare three broad classes of models with different parameterizations, taking data from 11 languages across 6 language families. In each experimental setting, we evaluate all models on a first data set, then examine their performance consistency when introducing new randomly sampled data sets with the same size and when applying the trained models to unseen test sets of varying sizes. The results demonstrate that the extent of model generalization depends on the characteristics of the data set, and does not necessarily rely heavily on the data set size. Among the characteristics that we studied, the ratio of morpheme overlap and that of the average number of morphemes per word between the training and test sets are the two most prominent factors. Our findings suggest that future work should adopt random sampling to construct data sets with different sizes in order to make more responsible claims about model evaluation.", "authors": ["Zoey Liu", "Emily Prud'hommeaux"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2022-01-05", "url": "https://arxiv.org/abs/2201.01845", "pdf_url": "https://arxiv.org/pdf/2201.01845v2", "arxiv_id": "2201.01845", "doi": "10.1162/tacl_a_00467", "citation_count": 10, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Transactions of the Association for Computational Linguistics", "quality_score": 0.2603}
{"id": "64a3992690391a3818f60ae3f6fda2de89a20aaee8bca4626b99bf31df30be3a", "sources": ["arxiv", "semantic_scholar"], "title": "Role of Data Augmentation Strategies in Knowledge Distillation for Wearable Sensor Data", "abstract": "Deep neural networks are parametrized by several thousands or millions of parameters, and have shown tremendous success in many classification problems. However, the large number of parameters makes it difficult to integrate these models into edge devices such as smartphones and wearable devices. To address this problem, knowledge distillation (KD) has been widely employed, that uses a pre-trained high capacity network to train a much smaller network, suitable for edge devices. In this paper, for the first time, we study the applicability and challenges of using KD for time-series data for wearable devices. Successful application of KD requires specific choices of data augmentation methods during training. However, it is not yet known if there exists a coherent strategy for choosing an augmentation approach during KD. In this paper, we report the results of a detailed study that compares and contrasts various common choices and some hybrid data augmentation strategies in KD based human activity analysis. Research in this area is often limited as there are not many comprehensive databases available in the public domain from wearable devices. Our study considers databases from small scale publicly available to one derived from a large scale interventional study into human activity and sedentary behavior. We find that the choice of data augmentation techniques during KD have a variable level of impact on end performance, and find that the optimal network choice as well as data augmentation strategies are specific to a dataset at hand. However, we also conclude with a general set of recommendations that can provide a strong baseline performance across databases.", "authors": ["Eun Som Jeon", "Anirudh Som", "Ankita Shukla", "Kristina Hasanaj", "Matthew P. Buman", "Pavan Turaga"], "categories": ["cs.LG", "cs.HC", "eess.SP"], "fields_of_study": ["Computer Science", "Medicine", "Engineering"], "published_date": "2022-01-01", "url": "https://arxiv.org/abs/2201.00111", "pdf_url": "https://arxiv.org/pdf/2201.00111v1", "arxiv_id": "2201.00111", "doi": "10.1109/JIOT.2021.3139038", "citation_count": 18, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Internet of Things Journal", "quality_score": 0.3197}