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.

Feifan Wang
Tengfei Song
Minggui He
Chang Su
Zhanglin Wu
Hao Yang
Wenming Zheng
Osamu Yoshie


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.

Lihan Zha
Apurva Badithela
Michael Zhang
Justin Lidard
Jeremy Bao
Emily Zhou
David Snyder
Allen Z. Ren
Dhruv Shah
Anirudha Majumdar


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.


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

Shashank Agnihotri
David Schader
Nico Sharei
Mehmet Ege Kaçar
Margret Keuper


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.


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


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.

Shengjie Ma
Xuhui Jiang
Chengjin Xu
Cehao Yang
Liyu Zhang
Jian Guo


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.

Zherui Zhang
Changwei Wang
Rongtao Xu
Wenhao Xu
Shibiao Xu
Yu Zhang
Li Guo


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.

Dayananda Herurkar
Jörn Hees
Vesselin Tzvetkov
Andreas Dengel


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.

Dayananda Herurkar
Ahmad Ali
Andreas Dengel


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.

Meng Xiao
Xunxin Cai
Qingqing Long
Chengrui Wang
Yuanchun Zhou
Hengshu Zhu


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.

Ruxue Shi
Yili Wang
Mengnan Du
Xu Shen
Yi Chang
Xin Wang


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.

Gideon Vos
Maryam Ebrahimpour
Liza van Eijk
Zoltan Sarnyai
Mostafa Rahimi Azghadi


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.

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


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

Prasanna Reddy Pulakurthi
Majid Rabbani
Celso M. de Melo
Sohail A. Dianat
Raghuveer M. Rao

Proc. SPIE 13459, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications III, 134590I (2025)


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.

Samuel Maddock
Shripad Gade
Graham Cormode
Will Bullock


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).

Rong Yao
Hailin Hu
Yifei Fu
Hanting Chen
Wenyi Fang
Fanyi Du
Kai Han
Yunhe Wang


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.

Sonia Laguna
Alberto Garcia-Garcia
Marie-Julie Rakotosaona
Stylianos Moschoglou
Leonhard Helminger
Sergio Orts-Escolano


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.

Meenatchi Sundaram Muthu Selva Annamalai


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.


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.

Yuan Yuan
Yuheng Zhang
Jingtao Ding
Yong Li


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.


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.

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


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.

Stefano Cirillo
Domenico Desiato
Giuseppe Polese
Monica Maria Lucia Sebillo
Giandomenico Solimando


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.

Waris Gill
Justin Cechmanek
Tyler Hutcherson
Srijith Rajamohan
Jen Agarwal
Muhammad Ali Gulzar
Manvinder Singh
Benoit Dion


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.

Tiago Vasconcelos Afonso
Florian Heinrichs

Engineering
Computer Science
Mathematics


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.

Xiaohua Qi
Renda Li
Long Peng
Qiang Ling
Jun Yu
Ziyi Chen
Peng Chang
Mei Han
Jing Xiao

IEEE International Conference on Multimedia and Expo


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.

Florian Zimmer
Janosch Haber
Mayuko Kaneko

International Conference on Data Technologies and Applications


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.

Zhengyi Zhao
Shubo Zhang
Bin Liang
Binyang Li
Kam-Fai Wong


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.

Jiahao Li
Yiqiang Chen
Yunbing Xing
Yang Gu
Xiangyuan Lan


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.


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.

Fei Zhu
Yujing Liu
Wenzhuo Liu
Zhaoxiang Zhang


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.

Tung Sum Thomas Kwok
Chi-Hua Wang
Guang Cheng


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.

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


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.

Bowen Tan
Zheng Xu
Eric Xing
Zhiting Hu
Shanshan Wu

International Conference on Machine Learning


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.


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.


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.

IEEE Conference on Technologies for Sustainability


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.

G. Charbel N. Kindji
Elisa Fromont
Lina Maria Rojas-Barahona
Tanguy Urvoy

International Symposium on Intelligent Data Analysis


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.


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.

Lucy D'Agostino McGowan
Shannon Tass
Sam Tyner
HaiYing Wang
Jun Yan

The New England Journal of Statistics in Data Science


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.

C. Basalti
G. Caldoni
S. Coppini
B. Gualandi
M. Marino
F. Masini
S. Peroni


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.


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.

Zhong Li
Qi Huang
Lincen Yang
Jiayang Shi
Zhao Yang
Niki van Stein
Thomas Bäck
Matthijs van Leeuwen


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.

Afonso Lourenço
João Gama
Eric P. Xing
Goreti Marreiros


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.

Marco Arazzi
Mert Cihangiroglu
Serena Nicolazzo
Antonino Nocera


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.

Sepanta Zeighami
Yiming Lin
Shreya Shankar
Aditya Parameswaran


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.

International Conference on Learning Representations


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.


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.

Naoki Chihara
Yasuko Matsubara
Ren Fujiwara
Yasushi Sakurai


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.

Polygenic Risk Methods in Diverse Populations PRIMED Consortium Data Sharing Working Group


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.

Lumen AI
Zaozhuang No. 28 Middle School
Shihao Ji
Zihui Song
Fucheng Zhong
Jisen Jia
Zhaobo Wu
Zheyi Cao
Tianhao Xu


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.

Zinan Lin
Tadas Baltrusaitis
Wenyu Wang
Sergey Yekhanin


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 .

Mohannad Takrouri
Nicolás M. Cuadrado
Martin Takáč


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%.

Qitong Wang
Ioana Ileana
Themis Palpanas

Proc. ACM Manag. Data 3, N1 (SIGMOD), Article 51 (February 2025), 24 pages


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.

Eun Som Jeon
Hongjun Choi
Matthew P. Buman
Pavan Turaga


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.

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


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).

Dan Liu
Samer El Kababji
Nicholas Mitsakakis
Lisa Pilgram
Thomas Walters
Mark Clemons
Greg Pond
Alaa El-Hussuna
Khaled El Emam


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.

Albert Meroño-Peñuela
Joe Massey
Andrew Newman
Elena Simperl


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.

Morgan R. Steckler
Kevin R. Birdwell
Haowen Xu
Xiao-Ying Yu


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.

Jinghong Zhang
Yidong Cui
Weiling Wang
Xianyou Cheng


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.

Jonathan Will
Nico Treide
Lauritz Thamsen
Odej Kao


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.

Jae-Sung Bae
Anastasia Kuznetsova
Dinesh Manocha
John Hershey
Trausti Kristjansson
Minje Kim


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.


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.

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