id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.10781 | Crowd: A Social Network Simulation Framework | [
"cs.SI"
] | To observe how individual behavior shapes a larger community's actions, agent-based modeling and simulation (ABMS) has been widely adopted by researchers in social sciences, economics, and epidemiology. While simulations can be run on general-purpose ABMS frameworks, these tools are not specifically designed for social... | {
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2412.10782 | ANaGRAM: A Natural Gradient Relative to Adapted Model for efficient
PINNs learning | [
"cs.LG",
"cs.AI",
"cs.NA",
"math.NA",
"math.OC"
] | In the recent years, Physics Informed Neural Networks (PINNs) have received strong interest as a method to solve PDE driven systems, in particular for data assimilation purpose. This method is still in its infancy, with many shortcomings and failures that remain not properly understood. In this paper we propose a natur... | {
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2412.10783 | Video Diffusion Transformers are In-Context Learners | [
"cs.CV"
] | This paper investigates a solution for enabling in-context capabilities of video diffusion transformers, with minimal tuning required for activation. Specifically, we propose a simple pipeline to leverage in-context generation: ($\textbf{i}$) concatenate videos along spacial or time dimension, ($\textbf{ii}$) jointly c... | {
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2412.10785 | StyleDiT: A Unified Framework for Diverse Child and Partner Faces
Synthesis with Style Latent Diffusion Transformer | [
"cs.CV"
] | Kinship face synthesis is a challenging problem due to the scarcity and low quality of the available kinship data. Existing methods often struggle to generate descendants with both high diversity and fidelity while precisely controlling facial attributes such as age and gender. To address these issues, we propose the S... | {
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2412.10786 | Optimizing Few-Step Sampler for Diffusion Probabilistic Model | [
"cs.CV",
"cs.AI"
] | Diffusion Probabilistic Models (DPMs) have demonstrated exceptional capability of generating high-quality and diverse images, but their practical application is hindered by the intensive computational cost during inference. The DPM generation process requires solving a Probability-Flow Ordinary Differential Equation (P... | {
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2412.10787 | Why Not Together? A Multiple-Round Recommender System for Queries and
Items | [
"cs.IR"
] | A fundamental technique of recommender systems involves modeling user preferences, where queries and items are widely used as symbolic representations of user interests. Queries delineate user needs at an abstract level, providing a high-level description, whereas items operate on a more specific and concrete level, re... | {
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2412.10789 | Scaling Up Graph Propagation Computation on Large Graphs: A Local
Chebyshev Approximation Approach | [
"cs.LG",
"cs.DS"
] | Graph propagation (GP) computation plays a crucial role in graph data analysis, supporting various applications such as graph node similarity queries, graph node ranking, graph clustering, and graph neural networks. Existing methods, mainly relying on power iteration or push computation frameworks, often face challenge... | {
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2412.10792 | Audio-based Anomaly Detection in Industrial Machines Using Deep
One-Class Support Vector Data Description | [
"cs.SD",
"cs.LG",
"eess.AS"
] | The frequent breakdowns and malfunctions of industrial equipment have driven increasing interest in utilizing cost-effective and easy-to-deploy sensors, such as microphones, for effective condition monitoring of machinery. Microphones offer a low-cost alternative to widely used condition monitoring sensors with their h... | {
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2412.10795 | Reliable and superior elliptic Fourier descriptor normalization and its
application software ElliShape with efficient image processing | [
"cs.CV",
"q-bio.QM"
] | Elliptic Fourier analysis (EFA) is a powerful tool for shape analysis, which is often employed in geometric morphometrics. However, the normalization of elliptic Fourier descriptors has persistently posed challenges in obtaining unique results in basic contour transformations, requiring extensive manual alignment. Addi... | {
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2412.10798 | AuctionNet: A Novel Benchmark for Decision-Making in Large-Scale Games | [
"cs.AI",
"cs.LG"
] | Decision-making in large-scale games is an essential research area in artificial intelligence (AI) with significant real-world impact. However, the limited access to realistic large-scale game environments has hindered research progress in this area. In this paper, we present AuctionNet, a benchmark for bid decision-ma... | {
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2412.10804 | Medical Manifestation-Aware De-Identification | [
"cs.CV",
"cs.AI"
] | Face de-identification (DeID) has been widely studied for common scenes, but remains under-researched for medical scenes, mostly due to the lack of large-scale patient face datasets. In this paper, we release MeMa, consisting of over 40,000 photo-realistic patient faces. MeMa is re-generated from massive real patient p... | {
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2412.10805 | Are Language Models Agnostic to Linguistically Grounded Perturbations? A
Case Study of Indic Languages | [
"cs.CL"
] | Pre-trained language models (PLMs) are known to be susceptible to perturbations to the input text, but existing works do not explicitly focus on linguistically grounded attacks, which are subtle and more prevalent in nature. In this paper, we study whether PLMs are agnostic to linguistically grounded attacks or not. To... | {
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2412.10809 | Affine EKF: Exploring and Utilizing Sufficient and Necessary Conditions
for Observability Maintenance to Improve EKF Consistency | [
"cs.RO"
] | Inconsistency issue is one crucial challenge for the performance of extended Kalman filter (EKF) based methods for state estimation problems, which is mainly affected by the discrepancy of observability between the EKF model and the underlying dynamic system. In this work, some sufficient and necessary conditions for o... | {
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2412.10814 | Diffusion-based Method for Satellite Pattern-of-Life Identification | [
"cs.LG",
"cs.CE"
] | Satellite pattern-of-life (PoL) identification is crucial for space safety and satellite monitoring, involving the analysis of typical satellite behaviors such as station-keeping, drift, etc. However, existing PoL identification methods remain underdeveloped due to the complexity of aerospace systems, variability in sa... | {
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2412.10816 | Hyper-Fusion Network for Semi-Automatic Segmentation of Skin Lesions | [
"cs.CV"
] | Automatic skin lesion segmentation methods based on fully convolutional networks (FCNs) are regarded as the state-of-the-art for accuracy. When there are, however, insufficient training data to cover all the variations in skin lesions, where lesions from different patients may have major differences in size/shape/textu... | {
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2412.10817 | Enhance Vision-Language Alignment with Noise | [
"cs.CV",
"cs.AI"
] | With the advancement of pre-trained vision-language (VL) models, enhancing the alignment between visual and linguistic modalities in downstream tasks has emerged as a critical challenge. Different from existing fine-tuning methods that add extra modules to these two modalities, we investigate whether the frozen model c... | {
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2412.10820 | Inertia-aware Unit Commitment and Remuneration Methods for Decarbonized
Power System | [
"eess.SY",
"cs.SY"
] | To maintain frequency stability in decarbonized power systems, inertia services from synchronous generators (SGs) and inverter-based resources must be procured. However, designing an inertia-aware system operation poses significant challenges in considering the variability and uncertainty of renewable energy sources (R... | {
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2412.10821 | Graph Attention Hamiltonian Neural Networks: A Lattice System Analysis
Model Based on Structural Learning | [
"hep-lat",
"cond-mat.mtrl-sci",
"cs.LG",
"math.DS",
"physics.chem-ph"
] | A deep understanding of the intricate interactions between particles within a system is a key approach to revealing the essential characteristics of the system, whether it is an in-depth analysis of molecular properties in the field of chemistry or the design of new materials for specific performance requirements in ma... | {
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2412.10822 | Automated Driving with Evolution Capability: A Reinforcement Learning
Method with Monotonic Performance Enhancement | [
"eess.SY",
"cs.SY"
] | Reinforcement Learning (RL) offers a promising solution to enable evolutionary automated driving. However, the conventional RL method is always concerned with risk performance. The updated policy may not obtain a performance enhancement, even leading to performance deterioration. To address this challenge, this researc... | {
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2412.10823 | FinGPT: Enhancing Sentiment-Based Stock Movement Prediction with
Dissemination-Aware and Context-Enriched LLMs | [
"cs.CL",
"cs.LG",
"q-fin.CP",
"q-fin.TR"
] | Financial sentiment analysis is crucial for understanding the influence of news on stock prices. Recently, large language models (LLMs) have been widely adopted for this purpose due to their advanced text analysis capabilities. However, these models often only consider the news content itself, ignoring its disseminatio... | {
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2412.10824 | Diffusion Model from Scratch | [
"cs.CV",
"cs.LG"
] | Diffusion generative models are currently the most popular generative models. However, their underlying modeling process is quite complex, and starting directly with the seminal paper Denoising Diffusion Probability Model (DDPM) can be challenging. This paper aims to assist readers in building a foundational understand... | {
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2412.10826 | Generative AI: A Pix2pix-GAN-Based Machine Learning Approach for Robust
and Efficient Lung Segmentation | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Chest radiography is climacteric in identifying different pulmonary diseases, yet radiologist workload and inefficiency can lead to misdiagnoses. Automatic, accurate, and efficient segmentation of lung from X-ray images of chest is paramount for early disease detection. This study develops a deep learning framework usi... | {
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2412.10827 | Rethinking Chain-of-Thought from the Perspective of Self-Training | [
"cs.CL",
"cs.AI"
] | Chain-of-thought (CoT) reasoning has emerged as an effective approach for activating latent capabilities in LLMs. Interestingly, we observe that both CoT reasoning and self-training share the core objective: iteratively leveraging model-generated information to progressively reduce prediction uncertainty. Building on t... | {
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2412.10831 | Unbiased General Annotated Dataset Generation | [
"cs.CV"
] | Pre-training backbone networks on a general annotated dataset (e.g., ImageNet) that comprises numerous manually collected images with category annotations has proven to be indispensable for enhancing the generalization capacity of downstream visual tasks. However, those manually collected images often exhibit bias, whi... | {
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2412.10834 | SegACIL: Solving the Stability-Plasticity Dilemma in Class-Incremental
Semantic Segmentation | [
"cs.CV"
] | While deep learning has made remarkable progress in recent years, models continue to struggle with catastrophic forgetting when processing continuously incoming data. This issue is particularly critical in continual learning, where the balance between retaining prior knowledge and adapting to new information-known as t... | {
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2412.10837 | A Diagrammatic Approach to Improve Computational Efficiency in Group
Equivariant Neural Networks | [
"cs.LG",
"math.CO",
"math.RT",
"stat.ML"
] | Group equivariant neural networks are growing in importance owing to their ability to generalise well in applications where the data has known underlying symmetries. Recent characterisations of a class of these networks that use high-order tensor power spaces as their layers suggest that they have significant potential... | {
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2412.10838 | Deep Learning Models for Colloidal Nanocrystal Synthesis | [
"cond-mat.mtrl-sci",
"cs.AI",
"physics.app-ph"
] | Colloidal synthesis of nanocrystals usually includes complex chemical reactions and multi-step crystallization processes. Despite the great success in the past 30 years, it remains challenging to clarify the correlations between synthetic parameters of chemical reaction and physical properties of nanocrystals. Here, we... | {
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2412.10840 | Attention-driven GUI Grounding: Leveraging Pretrained Multimodal Large
Language Models without Fine-Tuning | [
"cs.CV"
] | Recent advancements in Multimodal Large Language Models (MLLMs) have generated significant interest in their ability to autonomously interact with and interpret Graphical User Interfaces (GUIs). A major challenge in these systems is grounding-accurately identifying critical GUI components such as text or icons based on... | {
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2412.10843 | Learning Semantic-Aware Representation in Visual-Language Models for
Multi-Label Recognition with Partial Labels | [
"cs.CV"
] | Multi-label recognition with partial labels (MLR-PL), in which only some labels are known while others are unknown for each image, is a practical task in computer vision, since collecting large-scale and complete multi-label datasets is difficult in real application scenarios. Recently, vision language models (e.g. CLI... | {
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2412.10844 | Lyapunov-based reinforcement learning for distributed control with
stability guarantee | [
"eess.SY",
"cs.SY"
] | In this paper, we propose a Lyapunov-based reinforcement learning method for distributed control of nonlinear systems comprising interacting subsystems with guaranteed closed-loop stability. Specifically, we conduct a detailed stability analysis and derive sufficient conditions that ensure closed-loop stability under a... | {
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2412.10846 | Detecting Activities of Daily Living in Egocentric Video to
Contextualize Hand Use at Home in Outpatient Neurorehabilitation Settings | [
"cs.CV",
"cs.HC"
] | Wearable egocentric cameras and machine learning have the potential to provide clinicians with a more nuanced understanding of patient hand use at home after stroke and spinal cord injury (SCI). However, they require detailed contextual information (i.e., activities and object interactions) to effectively interpret met... | {
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2412.10848 | Large Language Models for Medical Forecasting -- Foresight 2 | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Foresight 2 (FS2) is a large language model fine-tuned on hospital data for modelling patient timelines (GitHub 'removed for anon'). It can understand patients' clinical notes and predict SNOMED codes for a wide range of biomedical use cases, including diagnosis suggestions, risk forecasting, and procedure and medicati... | {
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2412.10849 | Superhuman performance of a large language model on the reasoning tasks
of a physician | [
"cs.AI",
"cs.CL"
] | Performance of large language models (LLMs) on medical tasks has traditionally been evaluated using multiple choice question benchmarks. However, such benchmarks are highly constrained, saturated with repeated impressive performance by LLMs, and have an unclear relationship to performance in real clinical scenarios. Cl... | {
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2412.10850 | Improving Graph Neural Networks via Adversarial Robustness Evaluation | [
"cs.LG",
"stat.ML"
] | Graph Neural Networks (GNNs) are currently one of the most powerful types of neural network architectures. Their advantage lies in the ability to leverage both the graph topology, which represents the relationships between samples, and the features of the samples themselves. However, the given graph topology often cont... | {
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2412.10851 | Economic MPC with an Online Reference Trajectory for Battery Scheduling
Considering Demand Charge Management | [
"eess.SY",
"cs.SY"
] | Monthly demand charges form a significant portion of the electric bill for microgrids with variable renewable energy generation. A battery energy storage system (BESS) is commonly used to manage these demand charges. Economic model predictive control (EMPC) with a reference trajectory can be used to dispatch the BESS t... | {
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2412.10853 | SEW: Self-calibration Enhanced Whole Slide Pathology Image Analysis | [
"cs.CV"
] | Pathology images are considered the ``gold standard" for cancer diagnosis and treatment, with gigapixel images providing extensive tissue and cellular information. Existing methods fail to simultaneously extract global structural and local detail features for comprehensive pathology image analysis efficiently. To addre... | {
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2412.10855 | Fast and Robust Visuomotor Riemannian Flow Matching Policy | [
"cs.RO",
"cs.LG"
] | Diffusion-based visuomotor policies excel at learning complex robotic tasks by effectively combining visual data with high-dimensional, multi-modal action distributions. However, diffusion models often suffer from slow inference due to costly denoising processes or require complex sequential training arising from recen... | {
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2412.10856 | RWKV-Lite: Deeply Compressed RWKV for Resource-Constrained Devices | [
"cs.LG",
"cs.PF"
] | To deploy LLMs on resource-contained platforms such as mobile robots and smartphones, non-transformers LLMs have achieved major breakthroughs. Recently, a novel RNN-based LLM family, Repentance Weighted Key Value (RWKV) has shown strong computational efficiency; nevertheless, RWKV models still have high parameter count... | {
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2412.10857 | Robust Persian Digit Recognition in Noisy Environments Using Hybrid
CNN-BiGRU Model | [
"cs.SD",
"cs.CV",
"eess.AS"
] | Artificial intelligence (AI) has significantly advanced speech recognition applications. However, many existing neural network-based methods struggle with noise, reducing accuracy in real-world environments. This study addresses isolated spoken Persian digit recognition (zero to nine) under noisy conditions, particular... | {
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2412.10858 | CRENER: A Character Relation Enhanced Chinese NER Model | [
"cs.CL",
"cs.IR"
] | Chinese Named Entity Recognition (NER) is an important task in information extraction, which has a significant impact on downstream applications. Due to the lack of natural separators in Chinese, previous NER methods mostly relied on external dictionaries to enrich the semantic and boundary information of Chinese words... | {
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2412.10859 | DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting | [
"cs.LG",
"stat.ML"
] | Multivariate time series forecasting is crucial for various applications, such as financial investment, energy management, weather forecasting, and traffic optimization. However, accurate forecasting is challenging due to two main factors. First, real-world time series often show heterogeneous temporal patterns caused ... | {
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2412.10860 | Classification of Financial Data Using Quantum Support Vector Machine | [
"quant-ph",
"cs.LG",
"q-fin.ST"
] | Quantum Support Vector Machine is a kernel-based approach to classification problems. We study the applicability of quantum kernels to financial data, specifically our self-curated Dhaka Stock Exchange (DSEx) Broad Index dataset. To the best of our knowledge, this is the very first systematic research work on this data... | {
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2412.10861 | Heterogeneous Graph Transformer for Multiple Tiny Object Tracking in
RGB-T Videos | [
"cs.CV",
"cs.AI"
] | Tracking multiple tiny objects is highly challenging due to their weak appearance and limited features. Existing multi-object tracking algorithms generally focus on single-modality scenes, and overlook the complementary characteristics of tiny objects captured by multiple remote sensors. To enhance tracking performance... | {
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2412.10869 | TinySubNets: An efficient and low capacity continual learning strategy | [
"cs.LG",
"cs.AI"
] | Continual Learning (CL) is a highly relevant setting gaining traction in recent machine learning research. Among CL works, architectural and hybrid strategies are particularly effective due to their potential to adapt the model architecture as new tasks are presented. However, many existing solutions do not efficiently... | {
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2412.10870 | A Novel End-To-End Event Geolocation Method Leveraging Hyperbolic Space
and Toponym Hierarchies | [
"cs.CL"
] | Timely detection and geolocation of events based on social data can provide critical information for applications such as crisis response and resource allocation. However, most existing methods are greatly affected by event detection errors, leading to insufficient geolocation accuracy. To this end, this paper proposes... | {
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2412.10871 | Fully Test-time Adaptation for Tabular Data | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Tabular data plays a vital role in various real-world scenarios and finds extensive applications. Although recent deep tabular models have shown remarkable success, they still struggle to handle data distribution shifts, leading to performance degradation when testing distributions change. To remedy this, a robust tabu... | {
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2412.10873 | Supervised cooperation on interdependent public goods games | [
"physics.soc-ph",
"cs.SI"
] | It is a challenging task to reach global cooperation among self-interested agents, which often requires sophisticated design or usage of incentives. For example, we may apply supervisors or referees who are able to detect and punish selfishness. As a response, defectors may offer bribes for corrupt referees to remain h... | {
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2412.10877 | Catch Me If You Can: Finding the Source of Infections in Temporal
Networks | [
"cs.DS",
"cs.SI"
] | Source detection (SD) is the task of finding the origin of a spreading process in a network. Algorithms for SD help us combat diseases, misinformation, pollution, and more, and have been studied by physicians, physicists, sociologists, and computer scientists. The field has received considerable attention and been anal... | {
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2412.10878 | Adaptive Quantization Resolution and Power Control for Federated
Learning over Cell-free Networks | [
"cs.LG",
"cs.NI",
"eess.SP"
] | Federated learning (FL) is a distributed learning framework where users train a global model by exchanging local model updates with a server instead of raw datasets, preserving data privacy and reducing communication overhead. However, the latency grows with the number of users and the model size, impeding the successf... | {
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2412.10882 | Integrating Generative and Physics-Based Models for Ptychographic
Imaging with Uncertainty Quantification | [
"eess.IV",
"cs.CV",
"cs.LG",
"stat.ML"
] | Ptychography is a scanning coherent diffractive imaging technique that enables imaging nanometer-scale features in extended samples. One main challenge is that widely used iterative image reconstruction methods often require significant amount of overlap between adjacent scan locations, leading to large data volumes an... | {
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2412.10891 | Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via
Self-Reflection | [
"cs.CV",
"cs.LG"
] | Diffusion models, the most popular generative paradigm so far, can inject conditional information into the generation path to guide the latent towards desired directions. However, existing text-to-image diffusion models often fail to maintain high image quality and high prompt-image alignment for those challenging prom... | {
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2412.10892 | Know Unreported Roadway Incidents in Real-time: A Deep Learning
Framework for Early Traffic Anomaly Detection | [
"cs.LG",
"cs.AI"
] | Conventional automatic incident detection (AID) has relied heavily on all incident reports exclusively for training and evaluation. However, these reports suffer from a number of issues, such as delayed reports, inaccurate descriptions, false alarms, missing reports, and incidents that do not necessarily influence traf... | {
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2412.10893 | BgGPT 1.0: Extending English-centric LLMs to other languages | [
"cs.CL",
"cs.AI",
"cs.LG"
] | We present BgGPT-Gemma-2-27B-Instruct and BgGPT-Gemma-2-9B-Instruct: continually pretrained and fine-tuned versions of Google's Gemma-2 models, specifically optimized for Bulgarian language understanding and generation. Leveraging Gemma-2's multilingual capabilities and over 100 billion tokens of Bulgarian and English ... | {
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2412.10895 | Multi-Class and Multi-Task Strategies for Neural Directed Link
Prediction | [
"cs.LG",
"stat.ML"
] | Link Prediction is a foundational task in Graph Representation Learning, supporting applications like link recommendation, knowledge graph completion and graph generation. Graph Neural Networks have shown the most promising results in this domain and are currently the de facto standard approach to learning from graph d... | {
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2412.10896 | Physics-based battery model parametrisation from impedance data | [
"eess.SY",
"cond-mat.mtrl-sci",
"cs.SY"
] | Non-invasive parametrisation of physics-based battery models can be performed by fitting the model to electrochemical impedance spectroscopy (EIS) data containing features related to the different physical processes. However, this requires an impedance model to be derived, which may be complex to obtain analytically. W... | {
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2412.10897 | Task Diversity in Bayesian Federated Learning: Simultaneous Processing
of Classification and Regression | [
"cs.LG",
"stat.ML"
] | This work addresses a key limitation in current federated learning approaches, which predominantly focus on homogeneous tasks, neglecting the task diversity on local devices. We propose a principled integration of multi-task learning using multi-output Gaussian processes (MOGP) at the local level and federated learning... | {
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2412.10898 | Exploring Grokking: Experimental and Mechanistic Investigations | [
"cs.LG",
"stat.ML"
] | The phenomenon of grokking in over-parameterized neural networks has garnered significant interest. It involves the neural network initially memorizing the training set with zero training error and near-random test error. Subsequent prolonged training leads to a sharp transition from no generalization to perfect genera... | {
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2412.10899 | Interharmonic Power: A New Concept for Power System Oscillation Source
Location | [
"eess.SY",
"cs.SY"
] | Power system oscillations are a significant concern for system operators, a problem that has grown due to the interconnection of inverter-based resources. To address this issue, various methods have been proposed to locate the sources of oscillations, which is essential for effective mitigation actions. A common charac... | {
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2412.10900 | PEARL: Input-Agnostic Prompt Enhancement with Negative Feedback
Regulation for Class-Incremental Learning | [
"cs.LG",
"cs.CV"
] | Class-incremental learning (CIL) aims to continuously introduce novel categories into a classification system without forgetting previously learned ones, thus adapting to evolving data distributions. Researchers are currently focusing on leveraging the rich semantic information of pre-trained models (PTMs) in CIL tasks... | {
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2412.10902 | Enhancing Road Crack Detection Accuracy with BsS-YOLO: Optimizing
Feature Fusion and Attention Mechanisms | [
"cs.CV"
] | Effective road crack detection is crucial for road safety, infrastructure preservation, and extending road lifespan, offering significant economic benefits. However, existing methods struggle with varied target scales, complex backgrounds, and low adaptability to different environments. This paper presents the BsS-YOLO... | {
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2412.10904 | CEKER: A Generalizable LLM Framework for Literature Analysis with a Case
Study in Unikernel Security | [
"cs.CR",
"cs.AI"
] | Literature reviews are a critical component of formulating and justifying new research, but are a manual and often time-consuming process. This research introduces a novel, generalizable approach to literature analysis called CEKER which uses a three-step process to streamline the collection of literature, the extracti... | {
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2412.10906 | SusGen-GPT: A Data-Centric LLM for Financial NLP and Sustainability
Report Generation | [
"cs.CL",
"cs.CE",
"cs.LG",
"q-fin.CP"
] | The rapid growth of the financial sector and the rising focus on Environmental, Social, and Governance (ESG) considerations highlight the need for advanced NLP tools. However, open-source LLMs proficient in both finance and ESG domains remain scarce. To address this gap, we introduce SusGen-30K, a category-balanced dat... | {
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2412.10908 | Do large language vision models understand 3D shapes? | [
"cs.CV"
] | Large vision language models (LVLM) are the leading A.I approach for achieving a general visual understanding of the world. Models such as GPT, Claude, Gemini, and LLama can use images to understand and analyze complex visual scenes. 3D objects and shapes are the basic building blocks of the world, recognizing them is ... | {
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2412.10911 | Improving Numerical Stability and Accuracy in Partitioned Methods with
Algebraic Prediction | [
"math.NA",
"cs.NA",
"cs.SY",
"eess.SY"
] | The partitioned approach for the numerical integration of power system differential algebraic equations faces inherent numerical stability challenges due to delays between the computation of state and algebraic variables. Such delays can compromise solution accuracy and computational efficiency, particularly in large-s... | {
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2412.10912 | ST-FiT: Inductive Spatial-Temporal Forecasting with Limited Training
Data | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Spatial-temporal graphs are widely used in a variety of real-world applications. Spatial-Temporal Graph Neural Networks (STGNNs) have emerged as a powerful tool to extract meaningful insights from this data. However, in real-world applications, most nodes may not possess any available temporal data during training. For... | {
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2412.10913 | Quantifying Extreme Opinions on Reddit Amidst the 2023
Israeli-Palestinian Conflict | [
"cs.SI",
"cs.CL"
] | This study investigates the dynamics of extreme opinions on social media during the 2023 Israeli-Palestinian conflict, utilising a comprehensive dataset of over 450,000 posts from four Reddit subreddits (r/Palestine, r/Judaism, r/IsraelPalestine, and r/worldnews). A lexicon-based, unsupervised methodology was developed... | {
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2412.10915 | C3: Learning Congestion Controllers with Formal Certificates | [
"cs.LG",
"cs.NI"
] | Learning-based congestion controllers offer better adaptability compared to traditional heuristic algorithms. However, the inherent unreliability of learning techniques can cause learning-based controllers to behave poorly, creating a need for formal guarantees. While methods for formally verifying learned congestion c... | {
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2412.10916 | Distributed Shape Learning of Complex Objects Using Gaussian Kernel | [
"cs.RO",
"cs.SY",
"eess.SY",
"math.OC"
] | This paper addresses distributed learning of a complex object for multiple networked robots based on distributed optimization and kernel-based support vector machine. In order to overcome a fundamental limitation of polynomial kernels assumed in our antecessor, we employ Gaussian kernel as a kernel function for classif... | {
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2412.10917 | Adaptive Reward Design for Reinforcement Learning in Complex Robotic
Tasks | [
"cs.RO",
"cs.AI",
"cs.LG"
] | There is a surge of interest in using formal languages such as Linear Temporal Logic (LTL) and finite automata to precisely and succinctly specify complex tasks and derive reward functions for reinforcement learning (RL) in robotic applications. However, existing methods often assign sparse rewards (e.g., giving a rewa... | {
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2412.10918 | LLMs-in-the-Loop Part 2: Expert Small AI Models for Anonymization and
De-identification of PHI Across Multiple Languages | [
"cs.CL",
"cs.AI"
] | The rise of chronic diseases and pandemics like COVID-19 has emphasized the need for effective patient data processing while ensuring privacy through anonymization and de-identification of protected health information (PHI). Anonymized data facilitates research without compromising patient confidentiality. This paper i... | {
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2412.10919 | Predicting Survival of Hemodialysis Patients using Federated Learning | [
"cs.LG",
"cs.AI"
] | Hemodialysis patients who are on donor lists for kidney transplant may get misidentified, delaying their wait time. Thus, predicting their survival time is crucial for optimizing waiting lists and personalizing treatment plans. Predicting survival times for patients often requires large quantities of high quality but s... | {
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2412.10923 | Linear Programming based Approximation to Individually Fair k-Clustering
with Outliers | [
"cs.LG",
"cs.DS",
"stat.ML"
] | Individual fairness guarantees are often desirable properties to have, but they become hard to formalize when the dataset contains outliers. Here, we investigate the problem of developing an individually fair $k$-means clustering algorithm for datasets that contain outliers. That is, given $n$ points and $k$ centers, w... | {
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2412.10924 | Tokens, the oft-overlooked appetizer: Large language models, the
distributional hypothesis, and meaning | [
"cs.CL",
"cs.AI"
] | Tokenization is a necessary component within the current architecture of many language models, including the transformer-based large language models (LLMs) of Generative AI, yet its impact on the model's cognition is often overlooked. We argue that LLMs demonstrate that the Distributional Hypothesis (DH) is sufficient ... | {
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2412.10925 | Video Representation Learning with Joint-Embedding Predictive
Architectures | [
"cs.CV",
"cs.AI"
] | Video representation learning is an increasingly important topic in machine learning research. We present Video JEPA with Variance-Covariance Regularization (VJ-VCR): a joint-embedding predictive architecture for self-supervised video representation learning that employs variance and covariance regularization to avoid ... | {
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2412.10933 | Enhancing Discoverability in Enterprise Conversational Systems with
Proactive Question Suggestions | [
"cs.CL"
] | Enterprise conversational AI systems are becoming increasingly popular to assist users in completing daily tasks such as those in marketing and customer management. However, new users often struggle to ask effective questions, especially in emerging systems with unfamiliar or evolving capabilities. This paper proposes ... | {
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2412.10935 | Progressive Compression with Universally Quantized Diffusion Models | [
"cs.LG",
"cs.CV"
] | Diffusion probabilistic models have achieved mainstream success in many generative modeling tasks, from image generation to inverse problem solving. A distinct feature of these models is that they correspond to deep hierarchical latent variable models optimizing a variational evidence lower bound (ELBO) on the data lik... | {
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2412.10939 | Human-Centric NLP or AI-Centric Illusion?: A Critical Investigation | [
"cs.CY",
"cs.AI",
"cs.CL",
"cs.HC"
] | Human-Centric NLP often claims to prioritise human needs and values, yet many implementations reveal an underlying AI-centric focus. Through an analysis of case studies in language modelling, behavioural testing, and multi-modal alignment, this study identifies a significant gap between the ideas of human-centricity an... | {
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2412.10941 | APAR: Modeling Irregular Target Functions in Tabular Regression via
Arithmetic-Aware Pre-Training and Adaptive-Regularized Fine-Tuning | [
"cs.LG",
"cs.AI"
] | Tabular data are fundamental in common machine learning applications, ranging from finance to genomics and healthcare. This paper focuses on tabular regression tasks, a field where deep learning (DL) methods are not consistently superior to machine learning (ML) models due to the challenges posed by irregular target fu... | {
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2412.10942 | Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity | [
"cs.CV"
] | The use of eXplainable Artificial Intelligence (XAI) systems has introduced a set of challenges that need resolution. The XAI robustness, or stability, has been one of the goals of the community from its beginning. Multiple authors have proposed evaluating this feature using objective evaluation measures. Nonetheless, ... | {
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2412.10943 | Unconstrained Salient and Camouflaged Object Detection | [
"cs.CV"
] | Visual Salient Object Detection (SOD) and Camouflaged Object Detection (COD) are two interrelated yet distinct tasks. Both tasks model the human visual system's ability to perceive the presence of objects. The traditional SOD datasets and methods are designed for scenes where only salient objects are present, similarly... | {
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2412.10945 | A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal
Atmospheric Transport | [
"cs.LG",
"physics.ao-ph"
] | High-resolution spatiotemporal simulations effectively capture the complexities of atmospheric plume dispersion in complex terrain. However, their high computational cost makes them impractical for applications requiring rapid responses or iterative processes, such as optimization, uncertainty quantification, or invers... | {
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2412.10946 | SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis
Lesions with Anatomical Constraints and Lesion-aware Augmentation | [
"cs.CV",
"cs.LG"
] | Assessing lesions and tracking their progression over time in brain magnetic resonance (MR) images is essential for diagnosing and monitoring multiple sclerosis (MS). Machine learning models have shown promise in automating the segmentation of MS lesions. However, training these models typically requires large, well-an... | {
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2412.10948 | Generative Modeling with Diffusion | [
"stat.ML",
"cs.LG",
"math.PR"
] | We introduce the diffusion model as a method to generate new samples. Generative models have been recently adopted for tasks such as art generation (Stable Diffusion, Dall-E) and text generation (ChatGPT). Diffusion models in particular apply noise to sample data and then "reverse" this noising process to generate new ... | {
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2412.10950 | ALPACA -- Adaptive Learning Pipeline for Comprehensive AI | [
"cs.DC",
"cs.AI",
"cs.LG",
"cs.SE"
] | The advancement of AI technologies has greatly increased the complexity of AI pipelines as they include many stages such as data collection, pre-processing, training, evaluation and visualisation. To provide effective and accessible AI solutions, it is important to design pipelines for different user groups such as exp... | {
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2412.10953 | Optimizing AI-Assisted Code Generation | [
"cs.SE",
"cs.AI",
"cs.LG"
] | In recent years, the rise of AI-assisted code-generation tools has significantly transformed software development. While code generators have mainly been used to support conventional software development, their use will be extended to powerful and secure AI systems. Systems capable of generating code, such as ChatGPT, ... | {
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2412.10955 | Deep Learning-Based Noninvasive Screening of Type 2 Diabetes with Chest
X-ray Images and Electronic Health Records | [
"cs.LG",
"cs.CV"
] | The imperative for early detection of type 2 diabetes mellitus (T2DM) is challenged by its asymptomatic onset and dependence on suboptimal clinical diagnostic tests, contributing to its widespread global prevalence. While research into noninvasive T2DM screening tools has advanced, conventional machine learning approac... | {
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2412.10956 | Iterative Detection and Decoding for Clustered Cell-Free Massive MIMO
Networks | [
"cs.IT",
"eess.SP",
"math.IT"
] | In this letter, we propose an iterative soft interference cancellation scheme for intra-cluster (ICL) and out-of-cluster (OCL) interference mitigation in user-centric clustered cell-free massive multiple-antenna networks. We propose a minimum mean-square error receive filter with a novel modified parallel interference ... | {
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2412.10958 | SoftVQ-VAE: Efficient 1-Dimensional Continuous Tokenizer | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Efficient image tokenization with high compression ratios remains a critical challenge for training generative models. We present SoftVQ-VAE, a continuous image tokenizer that leverages soft categorical posteriors to aggregate multiple codewords into each latent token, substantially increasing the representation capaci... | {
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2412.10960 | Can LLMs Help Create Grammar?: Automating Grammar Creation for
Endangered Languages with In-Context Learning | [
"cs.CL"
] | Yes! In the present-day documenting and preserving endangered languages, the application of Large Language Models (LLMs) presents a promising approach. This paper explores how LLMs, particularly through in-context learning, can assist in generating grammatical information for low-resource languages with limited amount ... | {
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2412.10961 | PSMGD: Periodic Stochastic Multi-Gradient Descent for Fast
Multi-Objective Optimization | [
"cs.LG",
"cs.AI"
] | Multi-objective optimization (MOO) lies at the core of many machine learning (ML) applications that involve multiple, potentially conflicting objectives (e.g., multi-task learning, multi-objective reinforcement learning, among many others). Despite the long history of MOO, recent years have witnessed a surge in interes... | {
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} |
2412.10964 | A Stability Condition for Online Feedback Optimization without Timescale
Separation | [
"math.OC",
"cs.SY",
"eess.SY",
"math.DS"
] | Online Feedback Optimization (OFO) is a control approach to drive a dynamical plant to an optimal steady state. By interconnecting optimization algorithms with real-time plant measurements, OFO provides all the benefits of feedback control, yet without requiring exact knowledge of plant dynamics for computing a setpoin... | {
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"cs.SY": 1
} |
2412.10966 | FlowDock: Geometric Flow Matching for Generative Protein-Ligand Docking
and Affinity Prediction | [
"cs.LG",
"cs.AI",
"q-bio.BM",
"q-bio.QM"
] | Powerful generative AI models of protein-ligand structure have recently been proposed, but few of these methods support both flexible protein-ligand docking and affinity estimation. Of those that do, none can directly model multiple binding ligands concurrently or have been rigorously benchmarked on pharmacologically r... | {
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"cs.SY": 0
} |
2412.10967 | Biological and Radiological Dictionary of Radiomics Features: Addressing
Understandable AI Issues in Personalized Prostate Cancer; Dictionary Version
PM1.0 | [
"physics.med-ph",
"cs.CV"
] | We investigate the connection between visual semantic features defined in PI-RADS and associated risk factors, moving beyond abnormal imaging findings, establishing a shared framework between medical and AI professionals by creating a standardized dictionary of biological/radiological RFs. Subsequently, 6 interpretable... | {
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} |
2412.10968 | Composers' Evaluations of an AI Music Tool: Insights for Human-Centred
Design | [
"cs.SD",
"cs.AI",
"cs.HC",
"cs.LG",
"eess.AS"
] | We present a study that explores the role of user-centred design in developing Generative AI (GenAI) tools for music composition. Through semi-structured interviews with professional composers, we gathered insights on a novel generative model for creating variations, highlighting concerns around trust, transparency, an... | {
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"cs.SD": 1,
"cs.SI": 0,
"cs.SY": 0
} |
2412.10972 | DCSEG: Decoupled 3D Open-Set Segmentation using Gaussian Splatting | [
"cs.CV"
] | Open-set 3D segmentation represents a major point of interest for multiple downstream robotics and augmented/virtual reality applications. Recent advances introduce 3D Gaussian Splatting as a computationally efficient representation of the underlying scene. They enable the rendering of novel views while achieving real-... | {
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} |
2412.10973 | Semi-autonomous Teleoperation using Differential Flatness of a Crane
Robot for Aircraft In-Wing Inspection | [
"cs.RO"
] | Visual inspection of confined spaces such as aircraft wings is ergonomically challenging for human mechanics. This work presents a novel crane robot that can travel the entire span of the aircraft wing, enabling mechanics to perform inspection from outside of the confined space. However, teleoperation of the crane robo... | {
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"cs.RO": 1,
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"cs.SY": 0
} |
2412.10975 | Recursive Aggregates as Intensional Functions in Answer Set Programming:
Semantics and Strong Equivalence | [
"cs.AI",
"cs.LO"
] | This paper shows that the semantics of programs with aggregates implemented by the solvers clingo and dlv can be characterized as extended First-Order formulas with intensional functions in the logic of Here-and-There. Furthermore, this characterization can be used to study the strong equivalence of programs with aggre... | {
"Other": 1,
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"cs.SY": 0
} |
2412.10977 | Point Cloud to Mesh Reconstruction: A Focus on Key Learning-Based
Paradigms | [
"cs.CV",
"cs.GR"
] | Reconstructing meshes from point clouds is an important task in fields such as robotics, autonomous systems, and medical imaging. This survey examines state-of-the-art learning-based approaches to mesh reconstruction, categorizing them into five paradigms: PointNet family, autoencoder architectures, deformation-based m... | {
"Other": 1,
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"cs.SD": 0,
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"cs.SY": 0
} |
2412.10979 | Distributed Estimation with Quantized Measurements and Communication
over Markovian Switching Topologies | [
"eess.SY",
"cs.SY"
] | This paper addresses distributed parameter estimation in stochastic dynamic systems with quantized measurements, constrained by quantized communication and Markovian switching directed topologies. To enable accurate recovery of the original signal from quantized communication signal, a persistent excitation-compliant l... | {
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} |
2412.10981 | Hybrid Forecasting of Geopolitical Events | [
"cs.CY",
"cs.AI",
"cs.HC",
"cs.LG"
] | Sound decision-making relies on accurate prediction for tangible outcomes ranging from military conflict to disease outbreaks. To improve crowdsourced forecasting accuracy, we developed SAGE, a hybrid forecasting system that combines human and machine generated forecasts. The system provides a platform where users can ... | {
"Other": 0,
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"cs.SY": 0
} |
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