id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.18624 | How to explain grokking | [
"cond-mat.stat-mech",
"cs.LG"
] | Explanation of grokking (delayed generalization) in learning is given by modeling grokking by the stochastic gradient Langevin dynamics (Brownian motion) and applying the ideas of thermodynamics. | {
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2412.18626 | Why Do Large Language Models (LLMs) Struggle to Count Letters? | [
"cs.CL"
] | Large Language Models (LLMs) have achieved unprecedented performance on many complex tasks, being able, for example, to answer questions on almost any topic. However, they struggle with other simple tasks, such as counting the occurrences of letters in a word, as illustrated by the inability of many LLMs to count the n... | {
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2412.18627 | KRAIL: A Knowledge-Driven Framework for Base Human Reliability Analysis
Integrating IDHEAS and Large Language Models | [
"cs.CL"
] | Human reliability analysis (HRA) is crucial for evaluating and improving the safety of complex systems. Recent efforts have focused on estimating human error probability (HEP), but existing methods often rely heavily on expert knowledge,which can be subjective and time-consuming. Inspired by the success of large langua... | {
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2412.18633 | BoostMD: Accelerating molecular sampling by leveraging ML force field
features from previous time-steps | [
"physics.chem-ph",
"cond-mat.stat-mech",
"cs.LG",
"physics.comp-ph",
"stat.ML"
] | Simulating atomic-scale processes, such as protein dynamics and catalytic reactions, is crucial for advancements in biology, chemistry, and materials science. Machine learning force fields (MLFFs) have emerged as powerful tools that achieve near quantum mechanical accuracy, with promising generalization capabilities. H... | {
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2412.18635 | Edge-AI for Agriculture: Lightweight Vision Models for Disease Detection
in Resource-Limited Settings | [
"cs.CV",
"cs.AI",
"cs.CY"
] | This research paper presents the development of a lightweight and efficient computer vision pipeline aimed at assisting farmers in detecting orange diseases using minimal resources. The proposed system integrates advanced object detection, classification, and segmentation models, optimized for deployment on edge device... | {
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2412.18639 | A Grounded Observer Framework for Establishing Guardrails for Foundation
Models in Socially Sensitive Domains | [
"cs.RO",
"cs.AI"
] | As foundation models increasingly permeate sensitive domains such as healthcare, finance, and mental health, ensuring their behavior meets desired outcomes and social expectations becomes critical. Given the complexities of these high-dimensional models, traditional techniques for constraining agent behavior, which typ... | {
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2412.18641 | ZenSVI: An Open-Source Software for the Integrated Acquisition,
Processing and Analysis of Street View Imagery Towards Scalable Urban Science | [
"cs.CV"
] | Street view imagery (SVI) has been instrumental in many studies in the past decade to understand and characterize street features and the built environment. Researchers across a variety of domains, such as transportation, health, architecture, human perception, and infrastructure have employed different methods to anal... | {
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2412.18644 | DynaGRAG | Exploring the Topology of Information for Advancing Language
Understanding and Generation in Graph Retrieval-Augmented Generation | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Graph Retrieval-Augmented Generation (GRAG or Graph RAG) architectures aim to enhance language understanding and generation by leveraging external knowledge. However, effectively capturing and integrating the rich semantic information present in textual and structured data remains a challenge. To address this, a novel ... | {
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2412.18645 | Dissecting CLIP: Decomposition with a Schur Complement-based Approach | [
"cs.CV"
] | The use of CLIP embeddings to assess the alignment of samples produced by text-to-image generative models has been extensively explored in the literature. While the widely adopted CLIPScore, derived from the cosine similarity of text and image embeddings, effectively measures the relevance of a generated image, it does... | {
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2412.18646 | Von Neumann Entropy and Quantum Algorithmic Randomness | [
"quant-ph",
"cs.IT",
"math.IT",
"math.LO"
] | A state $\rho=(\rho_n)_{n=1}^{\infty}$ is a sequence such that $\rho_n$ is a density matrix on $n$ qubits. It formalizes the notion of an infinite sequence of qubits. The von Neumann entropy $H(d)$ of a density matrix $d$ is the Shannon entropy of its eigenvalue distribution. We show: (1) If $\rho$ is a computable quan... | {
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2412.18647 | Nationality, Race, and Ethnicity Biases in and Consequences of Detecting
AI-Generated Self-Presentations | [
"cs.AI",
"cs.HC"
] | This study builds on person perception and human AI interaction (HAII) theories to investigate how content and source cues, specifically race, ethnicity, and nationality, affect judgments of AI-generated content in a high-stakes self-presentation context: college applications. Results of a pre-registered experiment wit... | {
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2412.18649 | FITS: Ensuring Safe and Effective Touchscreen Use in Moving Vehicles | [
"eess.SY",
"cs.HC",
"cs.SY"
] | Touch interfaces are replacing physical buttons, dials, and switches in the new generation of cars, aircraft, and vessels. However, vehicle vibrations and accelerations perturb finger movements and cause erroneous touchscreen inputs by users. Furthermore, unlike physical buttons, touchscreens cannot be operated by touc... | {
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2412.18653 | 1.58-bit FLUX | [
"cs.CV",
"cs.AI",
"cs.LG"
] | We present 1.58-bit FLUX, the first successful approach to quantizing the state-of-the-art text-to-image generation model, FLUX.1-dev, using 1.58-bit weights (i.e., values in {-1, 0, +1}) while maintaining comparable performance for generating 1024 x 1024 images. Notably, our quantization method operates without access... | {
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2412.18655 | Simple is not Enough: Document-level Text Simplification using
Readability and Coherence | [
"cs.CL"
] | In this paper, we present the SimDoc system, a simplification model considering simplicity, readability, and discourse aspects, such as coherence. In the past decade, the progress of the Text Simplification (TS) field has been mostly shown at a sentence level, rather than considering paragraphs or documents, a setting ... | {
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2412.18663 | Comparing analytic and data-driven approaches to parameter
identifiability: A power systems case study | [
"cs.LG"
] | Parameter identifiability refers to the capability of accurately inferring the parameter values of a model from its observations (data). Traditional analysis methods exploit analytical properties of the closed form model, in particular sensitivity analysis, to quantify the response of the model predictions to variation... | {
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2412.18667 | State-of-the-Art Underwater Vehicles and Technologies Enabling Smart
Ocean: Survey and Classifications | [
"eess.SY",
"cs.SY"
] | The exploration and sustainable use of marine environments have become increasingly critical as oceans cover over 70% of surface of Earth. This paper provides a comprehensive survey and classification of state-of-the-art underwater vehicles (UVs) and supporting technologies essential for enabling a smart ocean. We cate... | {
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2412.18668 | Pruning Unrolled Networks (PUN) at Initialization for MRI Reconstruction
Improves Generalization | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Deep learning methods are highly effective for many image reconstruction tasks. However, the performance of supervised learned models can degrade when applied to distinct experimental settings at test time or in the presence of distribution shifts. In this study, we demonstrate that pruning deep image reconstruction ne... | {
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2412.18669 | Advancing Explainability in Neural Machine Translation: Analytical
Metrics for Attention and Alignment Consistency | [
"cs.AI",
"cs.CL"
] | Neural Machine Translation (NMT) models have shown remarkable performance but remain largely opaque in their decision making processes. The interpretability of these models, especially their internal attention mechanisms, is critical for building trust and verifying that these systems behave as intended. In this work, ... | {
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2412.18670 | Interplay of ISMS and AIMS in context of the EU AI Act | [
"cs.AI",
"cs.CR",
"cs.CY"
] | The EU AI Act (AIA) mandates the implementation of a risk management system (RMS) and a quality management system (QMS) for high-risk AI systems. The ISO/IEC 42001 standard provides a foundation for fulfilling these requirements but does not cover all EU-specific regulatory stipulations. To enhance the implementation o... | {
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2412.18672 | From Hallucinations to Facts: Enhancing Language Models with Curated
Knowledge Graphs | [
"cs.CL",
"cs.AI"
] | Hallucination, a persistent challenge plaguing language models, undermines their efficacy and trustworthiness in various natural language processing endeavors by generating responses that deviate from factual accuracy or coherence. This paper addresses language model hallucination by integrating curated knowledge graph... | {
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2412.18673 | Map2Text: New Content Generation from Low-Dimensional Visualizations | [
"cs.AI",
"cs.HC"
] | Low-dimensional visualizations, or "projection maps" of datasets, are widely used across scientific research and creative industries as effective tools for interpreting large-scale and complex information. These visualizations not only support understanding existing knowledge spaces but are often used implicitly to gui... | {
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2412.18675 | TAB: Transformer Attention Bottlenecks enable User Intervention and
Debugging in Vision-Language Models | [
"cs.CV"
] | Multi-head self-attention (MHSA) is a key component of Transformers, a widely popular architecture in both language and vision. Multiple heads intuitively enable different parallel processes over the same input. Yet, they also obscure the attribution of each input patch to the output of a model. We propose a novel 1-he... | {
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2412.18682 | Dissipation Dilution-Driven Topology Optimization for Maximizing the $Q$
Factor of Nanomechanical Resonators | [
"cond-mat.mes-hall",
"cs.CE",
"physics.comp-ph"
] | The quality factor ($Q$ factor) of nanomechanical resonators is influenced by geometry and stress, a phenomenon called dissipation dilution. Studies have explored maximizing this effect, leading to softly-clamped resonator designs. This paper proposes a topology optimization methodology to design two-dimensional nanome... | {
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2412.18688 | Video Is Worth a Thousand Images: Exploring the Latest Trends in Long
Video Generation | [
"cs.CV",
"cs.AI"
] | An image may convey a thousand words, but a video composed of hundreds or thousands of image frames tells a more intricate story. Despite significant progress in multimodal large language models (MLLMs), generating extended videos remains a formidable challenge. As of this writing, OpenAI's Sora, the current state-of-t... | {
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2412.18690 | AgreeMate: Teaching LLMs to Haggle | [
"cs.CL",
"cs.LG"
] | We introduce AgreeMate, a framework for training Large Language Models (LLMs) to perform strategic price negotiations through natural language. We apply recent advances to a negotiation setting where two agents (i.e. buyer or seller) use natural language to bargain on goods using coarse actions. Specifically, we presen... | {
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2412.18693 | Diverse and Effective Red Teaming with Auto-generated Rewards and
Multi-step Reinforcement Learning | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Automated red teaming can discover rare model failures and generate challenging examples that can be used for training or evaluation. However, a core challenge in automated red teaming is ensuring that the attacks are both diverse and effective. Prior methods typically succeed in optimizing either for diversity or for ... | {
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2412.18695 | TimelyLLM: Segmented LLM Serving System for Time-sensitive Robotic
Applications | [
"cs.RO",
"cs.DC",
"cs.LG"
] | Large Language Models (LLMs) such as GPT-4 and Llama3 can already comprehend complex commands and process diverse tasks. This advancement facilitates their application in controlling drones and robots for various tasks. However, existing LLM serving systems typically employ a first-come, first-served (FCFS) batching me... | {
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2412.18696 | STITCH: Surface reconstrucTion using Implicit neural representations
with Topology Constraints and persistent Homology | [
"cs.CV",
"cs.GR",
"cs.LG"
] | We present STITCH, a novel approach for neural implicit surface reconstruction of a sparse and irregularly spaced point cloud while enforcing topological constraints (such as having a single connected component). We develop a new differentiable framework based on persistent homology to formulate topological loss terms ... | {
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2412.18697 | Agents on the Bench: Large Language Model Based Multi Agent Framework
for Trustworthy Digital Justice | [
"cs.AI",
"cs.MA"
] | The justice system has increasingly employed AI techniques to enhance efficiency, yet limitations remain in improving the quality of decision-making, particularly regarding transparency and explainability needed to uphold public trust in legal AI. To address these challenges, we propose a large language model based mul... | {
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2412.18699 | Market Basket Analysis Using Rule-Based Algorithms and Data Mining
Techniques | [
"cs.DB",
"econ.GN",
"q-fin.EC"
] | The research identifies association rules that can inform marketing strategies and enhance operational efficiency. A structured methodology is applied to extract and interpret meaningful relationships within transactional data, emphasizing their implications for managerial decision-making. By demonstrating the potentia... | {
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2412.18702 | CypherBench: Towards Precise Retrieval over Full-scale Modern Knowledge
Graphs in the LLM Era | [
"cs.CL",
"cs.AI",
"cs.DB"
] | Retrieval from graph data is crucial for augmenting large language models (LLM) with both open-domain knowledge and private enterprise data, and it is also a key component in the recent GraphRAG system (edge et al., 2024). Despite decades of research on knowledge graphs and knowledge base question answering, leading LL... | {
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2412.18703 | Uncertainty Quantification in Stereo Matching | [
"cs.CV"
] | Stereo matching plays a crucial role in various applications, where understanding uncertainty can enhance both safety and reliability. Despite this, the estimation and analysis of uncertainty in stereo matching have been largely overlooked. Previous works often provide limited interpretations of uncertainty and struggl... | {
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2412.18706 | SurvAttack: Black-Box Attack On Survival Models through
Ontology-Informed EHR Perturbation | [
"cs.LG",
"cs.AI",
"cs.CR"
] | Survival analysis (SA) models have been widely studied in mining electronic health records (EHRs), particularly in forecasting the risk of critical conditions for prioritizing high-risk patients. However, their vulnerability to adversarial attacks is much less explored in the literature. Developing black-box perturbati... | {
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2412.18707 | Multiple References with Meaningful Variations Improve Literary Machine
Translation | [
"cs.CL"
] | While a source sentence can be translated in many ways, most machine translation (MT) models are trained with only a single reference. Previous work has shown that using synthetic paraphrases can improve MT. This paper investigates best practices for employing multiple references by analyzing the semantic similarity am... | {
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2412.18708 | CAG: Chunked Augmented Generation for Google Chrome's Built-in Gemini
Nano | [
"cs.AI"
] | We present Chunked Augmented Generation (CAG), an architecture specifically designed to overcome the context window limitations of Google Chrome's built-in Gemini Nano model. While Chrome's integration of Gemini Nano represents a significant advancement in bringing AI capabilities directly to the browser, its restricte... | {
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2412.18713 | Enhanced Recommendation Combining Collaborative Filtering and Large
Language Models | [
"cs.AI",
"cs.IR"
] | With the advent of the information explosion era, the importance of recommendation systems in various applications is increasingly significant. Traditional collaborative filtering algorithms are widely used due to their effectiveness in capturing user behavior patterns, but they encounter limitations when dealing with ... | {
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2412.18715 | Optimization and Scalability of Collaborative Filtering Algorithms in
Large Language Models | [
"cs.AI",
"cs.IR"
] | With the rapid development of large language models (LLMs) and the growing demand for personalized content, recommendation systems have become critical in enhancing user experience and driving engagement. Collaborative filtering algorithms, being core to many recommendation systems, have garnered significant attention ... | {
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2412.18717 | Variational Bayesian Inference for Tensor Robust Principal Component
Analysis | [
"math.NA",
"cs.LG",
"cs.NA"
] | Tensor Robust Principal Component Analysis (TRPCA) holds a crucial position in machine learning and computer vision. It aims to recover underlying low-rank structures and characterizing the sparse structures of noise. Current approaches often encounter difficulties in accurately capturing the low-rank properties of ten... | {
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2412.18718 | Evaluating the Adversarial Robustness of Detection Transformers | [
"cs.CV",
"cs.LG"
] | Robust object detection is critical for autonomous driving and mobile robotics, where accurate detection of vehicles, pedestrians, and obstacles is essential for ensuring safety. Despite the advancements in object detection transformers (DETRs), their robustness against adversarial attacks remains underexplored. This p... | {
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2412.18719 | Using Large Language Models for Automated Grading of Student Writing
about Science | [
"cs.CL"
] | Assessing writing in large classes for formal or informal learners presents a significant challenge. Consequently, most large classes, particularly in science, rely on objective assessment tools such as multiple-choice quizzes, which have a single correct answer. The rapid development of AI has introduced the possibili... | {
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2412.18720 | Effective and Lightweight Representation Learning for Link Sign
Prediction in Signed Bipartite Graphs | [
"cs.LG"
] | How can we effectively and efficiently learn node representations in signed bipartite graphs? A signed bipartite graph is a graph consisting of two nodes sets where nodes of different types are positively or negative connected, and it has been extensively used to model various real-world relationships such as e-commerc... | {
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2412.18723 | MRI Reconstruction with Regularized 3D Diffusion Model (R3DM) | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Magnetic Resonance Imaging (MRI) is a powerful imaging technique widely used for visualizing structures within the human body and in other fields such as plant sciences. However, there is a demand to develop fast 3D-MRI reconstruction algorithms to show the fine structure of objects from under-sampled acquisition data,... | {
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2412.18725 | On the Performance of Short Binary BCH Codes for Ultra-Low Latency
Wireless Communications | [
"cs.IT",
"math.IT"
] | In recent years, polar codes have been considered for communication systems that require high re-liability and ultra-low latency, such as sixth generation (6G) wireless communications. This paper presents simulation results showing that short binary extended BCH (eBCH) codes with low-complexity decoding outperform pola... | {
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2412.18727 | SAFLITE: Fuzzing Autonomous Systems via Large Language Models | [
"cs.SE",
"cs.AI",
"cs.SY",
"eess.SY"
] | Fuzz testing effectively uncovers software vulnerabilities; however, it faces challenges with Autonomous Systems (AS) due to their vast search spaces and complex state spaces, which reflect the unpredictability and complexity of real-world environments. This paper presents a universal framework aimed at improving the e... | {
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2412.18729 | Optimizing Large Language Models with an Enhanced LoRA Fine-Tuning
Algorithm for Efficiency and Robustness in NLP Tasks | [
"cs.CL",
"cs.LG"
] | This study proposes a large language model optimization method based on the improved LoRA fine-tuning algorithm, aiming to improve the accuracy and computational efficiency of the model in natural language processing tasks. We fine-tune the large language model through a low-rank adaptation strategy, which significantl... | {
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2412.18730 | Elucidating Flow Matching ODE Dynamics with Respect to Data Geometries | [
"cs.LG"
] | Diffusion-based generative models have become the standard for image generation. ODE-based samplers and flow matching models improve efficiency, in comparison to diffusion models, by reducing sampling steps through learned vector fields. However, the theoretical foundations of flow matching models remain limited, parti... | {
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2412.18731 | Position-aware Graph Transformer for Recommendation | [
"cs.IR"
] | Collaborative recommendation fundamentally involves learning high-quality user and item representations from interaction data. Recently, graph convolution networks (GCNs) have advanced the field by utilizing high-order connectivity patterns in interaction graphs, as evidenced by state-of-the-art methods like PinSage an... | {
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2412.18733 | Intra- and Inter-modal Context Interaction Modeling for Conversational
Speech Synthesis | [
"cs.CL",
"cs.SD",
"eess.AS"
] | Conversational Speech Synthesis (CSS) aims to effectively take the multimodal dialogue history (MDH) to generate speech with appropriate conversational prosody for target utterance. The key challenge of CSS is to model the interaction between the MDH and the target utterance. Note that text and speech modalities in MDH... | {
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2412.18734 | Predicting Time Series of Networked Dynamical Systems without Knowing
Topology | [
"cs.LG",
"cs.AI"
] | Many real-world complex systems, such as epidemic spreading networks and ecosystems, can be modeled as networked dynamical systems that produce multivariate time series. Learning the intrinsic dynamics from observational data is pivotal for forecasting system behaviors and making informed decisions. However, existing m... | {
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2412.18735 | Adaptive Self-supervised Learning for Social Recommendations | [
"cs.IR",
"cs.LG"
] | In recent years, researchers have attempted to exploit social relations to improve the performance in recommendation systems. Generally, most existing social recommendation methods heavily depends on substantial domain knowledge and expertise in primary recommendation tasks for designing useful auxiliary tasks. Meanwhi... | {
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2412.18738 | HELPNet: Hierarchical Perturbations Consistency and Entropy-guided
Ensemble for Scribble Supervised Medical Image Segmentation | [
"cs.CV"
] | Creating fully annotated labels for medical image segmentation is prohibitively time-intensive and costly, emphasizing the necessity for innovative approaches that minimize reliance on detailed annotations. Scribble annotations offer a more cost-effective alternative, significantly reducing the expenses associated with... | {
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2412.18743 | Successes and Limitations of Object-centric Models at Compositional
Generalisation | [
"cs.CV",
"cs.AI",
"cs.LG"
] | In recent years, it has been shown empirically that standard disentangled latent variable models do not support robust compositional learning in the visual domain. Indeed, in spite of being designed with the goal of factorising datasets into their constituent factors of variations, disentangled models show extremely li... | {
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2412.18748 | Towards Expressive Video Dubbing with Multiscale Multimodal Context
Interaction | [
"cs.MM",
"cs.CL",
"cs.SD",
"eess.AS"
] | Automatic Video Dubbing (AVD) generates speech aligned with lip motion and facial emotion from scripts. Recent research focuses on modeling multimodal context to enhance prosody expressiveness but overlooks two key issues: 1) Multiscale prosody expression attributes in the context influence the current sentence's proso... | {
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2412.18749 | RIS-Assisted Simultaneous Legitimate Monitoring and Jamming for
Industrial Wireless Networks | [
"eess.SY",
"cs.SY"
] | In this paper, we study reconfigurable intelligent surface (RIS)-assisted simultaneous legitimate monitoring and jamming techniques for industrial environments, so that egitimate monitor (LM) and legitimate jammers (LJs) can sustainably monitor and interfere with suspicious communications with minimum transmission powe... | {
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2412.18750 | The Impact of Input Order Bias on Large Language Models for Software
Fault Localization | [
"cs.SE",
"cs.AI",
"cs.LG"
] | Large Language Models (LLMs) show great promise in software engineering tasks like Fault Localization (FL) and Automatic Program Repair (APR). This study examines how input order and context size affect LLM performance in FL, a key step for many downstream software engineering tasks. We test different orders for method... | {
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2412.18756 | Towards a Statistical Understanding of Neural Networks: Beyond the
Neural Tangent Kernel Theories | [
"cs.LG",
"math.ST",
"stat.TH"
] | A primary advantage of neural networks lies in their feature learning characteristics, which is challenging to theoretically analyze due to the complexity of their training dynamics. We propose a new paradigm for studying feature learning and the resulting benefits in generalizability. After reviewing the neural tangen... | {
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2412.18757 | Evaluating authorship disambiguation quality through anomaly analysis on
researchers' career transition | [
"cs.DL",
"cs.CE",
"cs.SI",
"physics.data-an"
] | Authorship disambiguation is crucial for advancing studies in science of science. However, assessing the quality of authorship disambiguation in large-scale databases remains challenging since it is difficult to manually curate a gold-standard dataset that contains disambiguated authors. Through estimating the timing o... | {
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2412.18760 | Data clustering: an essential technique in data science | [
"cs.AI"
] | This paper explores the critical role of data clustering in data science, emphasizing its methodologies, tools, and diverse applications. Traditional techniques, such as partitional and hierarchical clustering, are analyzed alongside advanced approaches such as data stream, density-based, graph-based, and model-based c... | {
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2412.18762 | Experimental Study of RCS Diversity with Novel No-divergent OAM Beams | [
"eess.SP",
"cs.SY",
"eess.SY"
] | This research proposes a novel approach utilizing Orbital Angular Momentum (OAM) beams to enhance Radar Cross Section (RCS) diversity for target detection in future transportation systems. Unlike conventional OAM beams with hollow-shaped divergence patterns, the new proposed OAM beams provide uniform illumination acros... | {
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2412.18766 | Hierarchical Multi-Graphs Learning for Robust Group Re-Identification | [
"cs.CV",
"cs.LG"
] | Group Re-identification (G-ReID) faces greater complexity than individual Re-identification (ReID) due to challenges like mutual occlusion, dynamic member interactions, and evolving group structures. Prior graph-based approaches have aimed to capture these dynamics by modeling the group as a single topological structur... | {
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2412.18768 | On the Robustness of Generative Information Retrieval Models | [
"cs.IR"
] | Generative information retrieval methods retrieve documents by directly generating their identifiers. Much effort has been devoted to developing effective generative IR models. Less attention has been paid to the robustness of these models. It is critical to assess the out-of-distribution (OOD) generalization of genera... | {
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2412.18770 | Attack-in-the-Chain: Bootstrapping Large Language Models for Attacks
Against Black-box Neural Ranking Models | [
"cs.IR"
] | Neural ranking models (NRMs) have been shown to be highly effective in terms of retrieval performance. Unfortunately, they have also displayed a higher degree of sensitivity to attacks than previous generation models. To help expose and address this lack of robustness, we introduce a novel ranking attack framework name... | {
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2412.18771 | RIS-Assisted MIMO CV-QKD at THz Frequencies: Channel Estimation and SKR
Analysis | [
"cs.IT",
"eess.SP",
"math.IT"
] | In this paper, a multiple-input multiple-output (MIMO) wireless system incorporating a reconfigurable intelligent surface (RIS) to efficiently operate at terahertz (THz) frequencies is considered. The transmitter, Alice, employs continuous-variable quantum key distribution (CV-QKD) to communicate secret keys to the rec... | {
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2412.18773 | Learning Broken Symmetries with Approximate Invariance | [
"hep-ph",
"cs.LG",
"hep-ex"
] | Recognizing symmetries in data allows for significant boosts in neural network training, which is especially important where training data are limited. In many cases, however, the exact underlying symmetry is present only in an idealized dataset, and is broken in actual data, due to asymmetries in the detector, or vary... | {
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2412.18774 | Embodied Image Quality Assessment for Robotic Intelligence | [
"cs.CV"
] | Image quality assessment (IQA) of user-generated content (UGC) is a critical technique for human quality of experience (QoE). However, for robot-generated content (RGC), will its image quality be consistent with the Moravec paradox and counter to human common sense? Human subjective scoring is more based on the attract... | {
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2412.18775 | ObitoNet: Multimodal High-Resolution Point Cloud Reconstruction | [
"cs.CV",
"cs.AI",
"cs.LG"
] | ObitoNet employs a Cross Attention mechanism to integrate multimodal inputs, where Vision Transformers (ViT) extract semantic features from images and a point cloud tokenizer processes geometric information using Farthest Point Sampling (FPS) and K Nearest Neighbors (KNN) for spatial structure capture. The learned mult... | {
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2412.18778 | Unified Local and Global Attention Interaction Modeling for Vision
Transformers | [
"cs.CV",
"cs.AI",
"cs.LG"
] | We present a novel method that extends the self-attention mechanism of a vision transformer (ViT) for more accurate object detection across diverse datasets. ViTs show strong capability for image understanding tasks such as object detection, segmentation, and classification. This is due in part to their ability to leve... | {
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2412.18779 | Integrating Zero-Shot Classification to Advance Long COVID Literature: A
Systematic Social Media-Centered Review | [
"cs.SI"
] | Long COVID continues to challenge public health by affecting a significant segment of individuals who have recovered from acute SARS-CoV-2 infection yet endure prolonged and often debilitating symptoms. Social media has emerged as a vital resource for those seeking real-time information, peer support, and validating th... | {
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2412.18780 | Skeleton-based Action Recognition with Non-linear Dependency Modeling
and Hilbert-Schmidt Independence Criterion | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Human skeleton-based action recognition has long been an indispensable aspect of artificial intelligence. Current state-of-the-art methods tend to consider only the dependencies between connected skeletal joints, limiting their ability to capture non-linear dependencies between physically distant joints. Moreover, most... | {
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2412.18781 | Robustness Evaluation of Offline Reinforcement Learning for Robot
Control Against Action Perturbations | [
"cs.RO",
"cs.LG"
] | Offline reinforcement learning, which learns solely from datasets without environmental interaction, has gained attention. This approach, similar to traditional online deep reinforcement learning, is particularly promising for robot control applications. Nevertheless, its robustness against real-world challenges, such ... | {
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2412.18783 | ArtNVG: Content-Style Separated Artistic Neighboring-View Gaussian
Stylization | [
"cs.CV"
] | As demand from the film and gaming industries for 3D scenes with target styles grows, the importance of advanced 3D stylization techniques increases. However, recent methods often struggle to maintain local consistency in color and texture throughout stylized scenes, which is essential for maintaining aesthetic coheren... | {
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2412.18784 | Zema Dataset: A Comprehensive Study of Yaredawi Zema with a Focus on
Horologium Chants | [
"eess.AS",
"cs.IR",
"eess.SP"
] | Computational music research plays a critical role in advancing music production, distribution, and understanding across various musical styles worldwide. Despite the immense cultural and religious significance, the Ethiopian Orthodox Tewahedo Church (EOTC) chants are relatively underrepresented in computational music ... | {
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2412.18785 | Simultaneously Recovering Multi-Person Meshes and Multi-View Cameras
with Human Semantics | [
"cs.CV"
] | Dynamic multi-person mesh recovery has broad applications in sports broadcasting, virtual reality, and video games. However, current multi-view frameworks rely on a time-consuming camera calibration procedure. In this work, we focus on multi-person motion capture with uncalibrated cameras, which mainly faces two challe... | {
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2412.18786 | Thermal-Mechanical Physics Informed Deep Learning For Fast Prediction of
Thermal Stress Evolution in Laser Metal Deposition | [
"cs.LG"
] | Understanding thermal stress evolution in metal additive manufacturing (AM) is crucial for producing high-quality components. Recent advancements in machine learning (ML) have shown great potential for modeling complex multiphysics problems in metal AM. While physics-based simulations face the challenge of high computa... | {
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2412.18789 | On Improved Regret Bounds In Bayesian Optimization with Gaussian Noise | [
"cs.LG",
"stat.ML"
] | Bayesian optimization (BO) with Gaussian process (GP) surrogate models is a powerful black-box optimization method. Acquisition functions are a critical part of a BO algorithm as they determine how the new samples are selected. Some of the most widely used acquisition functions include upper confidence bound (UCB) and ... | {
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2412.18790 | Torque-Aware Momentum | [
"cs.LG",
"cs.AI"
] | Efficiently exploring complex loss landscapes is key to the performance of deep neural networks. While momentum-based optimizers are widely used in state-of-the-art setups, classical momentum can still struggle with large, misaligned gradients, leading to oscillations. To address this, we propose Torque-Aware Momentum ... | {
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2412.18791 | Protective Perturbations against Unauthorized Data Usage in
Diffusion-based Image Generation | [
"cs.CV"
] | Diffusion-based text-to-image models have shown immense potential for various image-related tasks. However, despite their prominence and popularity, customizing these models using unauthorized data also brings serious privacy and intellectual property issues. Existing methods introduce protective perturbations based on... | {
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2412.18797 | DRDM: A Disentangled Representations Diffusion Model for Synthesizing
Realistic Person Images | [
"cs.CV"
] | Person image synthesis with controllable body poses and appearances is an essential task owing to the practical needs in the context of virtual try-on, image editing and video production. However, existing methods face significant challenges with details missing, limbs distortion and the garment style deviation. To add... | {
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2412.18798 | Ister: Inverted Seasonal-Trend Decomposition Transformer for Explainable
Multivariate Time Series Forecasting | [
"cs.LG",
"cs.AI"
] | In long-term time series forecasting, Transformer-based models have achieved great success, due to its ability to capture long-range dependencies. However, existing models face challenges in identifying critical components for prediction, leading to limited interpretability and suboptimal performance. To address these ... | {
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2412.18800 | Improving Generated and Retrieved Knowledge Combination Through
Zero-shot Generation | [
"cs.CL"
] | Open-domain Question Answering (QA) has garnered substantial interest by combining the advantages of faithfully retrieved passages and relevant passages generated through Large Language Models (LLMs). However, there is a lack of definitive labels available to pair these sources of knowledge. In order to address this is... | {
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2412.18806 | FOR: Finetuning for Object Level Open Vocabulary Image Retrieval | [
"cs.CV",
"cs.IR",
"cs.LG"
] | As working with large datasets becomes standard, the task of accurately retrieving images containing objects of interest by an open set textual query gains practical importance. The current leading approach utilizes a pre-trained CLIP model without any adaptation to the target domain, balancing accuracy and efficiency ... | {
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2412.18808 | Provable Uncertainty Decomposition via Higher-Order Calibration | [
"cs.LG",
"cs.CV",
"stat.ML"
] | We give a principled method for decomposing the predictive uncertainty of a model into aleatoric and epistemic components with explicit semantics relating them to the real-world data distribution. While many works in the literature have proposed such decompositions, they lack the type of formal guarantees we provide. O... | {
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2412.18810 | DebiasDiff: Debiasing Text-to-image Diffusion Models with
Self-discovering Latent Attribute Directions | [
"cs.CV"
] | While Diffusion Models (DM) exhibit remarkable performance across various image generative tasks, they nonetheless reflect the inherent bias presented in the training set. As DMs are now widely used in real-world applications, these biases could perpetuate a distorted worldview and hinder opportunities for minority gro... | {
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2412.18811 | DCIS: Efficient Length Extrapolation of LLMs via Divide-and-Conquer
Scaling Factor Search | [
"cs.CL"
] | Large language models (LLMs) based on the Transformer architecture usually have their context length limited due to the high training cost. Recent advancements extend the context window by adjusting the scaling factors of RoPE and fine-tuning. However, suboptimal initialization of these factors results in increased fin... | {
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2412.18812 | A Tractable Approach for Queueing Analysis on Buffer-Aware Scheduling | [
"cs.IT",
"eess.SP",
"math.IT"
] | Low-latency communication has recently attracted considerable attention owing to its potential of enabling delay-sensitive services in next-generation industrial cyber-physical systems. To achieve target average or maximum delay given random arrivals and time-varying channels, buffer-aware scheduling is expected to pla... | {
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2412.18815 | Distortion-Aware Adversarial Attacks on Bounding Boxes of Object
Detectors | [
"cs.CV"
] | Deep learning-based object detection has become ubiquitous in the last decade due to its high accuracy in many real-world applications. With this growing trend, these models are interested in being attacked by adversaries, with most of the results being on classifiers, which do not match the context of practical object... | {
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2412.18816 | GSAVS: Gaussian Splatting-based Autonomous Vehicle Simulator | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Modern autonomous vehicle simulators feature an ever-growing library of assets, including vehicles, buildings, roads, pedestrians, and more. While this level of customization proves beneficial when creating virtual urban environments, this process becomes cumbersome when intending to train within a digital twin or a du... | {
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2412.18817 | Wireless Communication with Flexible Reflector: Joint Placement and
Rotation Optimization for Coverage Enhancement | [
"cs.IT",
"eess.SP",
"math.IT"
] | Passive metal reflectors for communication enhancement have appealing advantages such as ultra low cost, zero energy expenditure, maintenance-free operation, long life span, and full compatibility with legacy wireless systems. To unleash the full potential of passive reflectors for wireless communications, this paper p... | {
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2412.18819 | LLM-assisted Vector Similarity Search | [
"cs.AI",
"cs.IR",
"cs.LG"
] | As data retrieval demands become increasingly complex, traditional search methods often fall short in addressing nuanced and conceptual queries. Vector similarity search has emerged as a promising technique for finding semantically similar information efficiently. However, its effectiveness diminishes when handling int... | {
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2412.18820 | CausalTAD: Causal Implicit Generative Model for Debiased Online
Trajectory Anomaly Detection | [
"cs.LG"
] | Trajectory anomaly detection, aiming to estimate the anomaly risk of trajectories given the Source-Destination (SD) pairs, has become a critical problem for many real-world applications. Existing solutions directly train a generative model for observed trajectories and calculate the conditional generative probability $... | {
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} |
2412.18826 | RapGuard: Safeguarding Multimodal Large Language Models via
Rationale-aware Defensive Prompting | [
"cs.CL"
] | While Multimodal Large Language Models (MLLMs) have made remarkable progress in vision-language reasoning, they are also more susceptible to producing harmful content compared to models that focus solely on text. Existing defensive prompting techniques rely on a static, unified safety guideline that fails to account fo... | {
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} |
2412.18827 | PhyloGen: Language Model-Enhanced Phylogenetic Inference via Graph
Structure Generation | [
"q-bio.PE",
"cs.AI"
] | Phylogenetic trees elucidate evolutionary relationships among species, but phylogenetic inference remains challenging due to the complexity of combining continuous (branch lengths) and discrete parameters (tree topology). Traditional Markov Chain Monte Carlo methods face slow convergence and computational burdens. Exis... | {
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} |
2412.18831 | Data-driven $H_{\infty}$ predictive control for constrained systems: a
Lagrange duality approcah | [
"math.OC",
"cs.SY",
"eess.SY"
] | This article proposes a data-driven $H_{\infty}$ control scheme for time-domain constrained systems based on model predictive control formulation. The scheme combines $H_{\infty}$ control and minimax model predictive control, enabling more effective handling of external disturbances and time-domain constraints. First, ... | {
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} |
2412.18833 | Federated Learning with Partially Labeled Data: A Conditional
Distillation Approach | [
"cs.CV"
] | In medical imaging, developing generalized segmentation models that can handle multiple organs and lesions is crucial. However, the scarcity of fully annotated datasets and strict privacy regulations present significant barriers to data sharing. Federated Learning (FL) allows decentralized model training, but existing ... | {
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} |
2412.18834 | Adaptive Rate Control for Deep Video Compression with Rate-Distortion
Prediction | [
"cs.MM",
"cs.CV",
"cs.IT",
"math.IT"
] | Deep video compression has made significant progress in recent years, achieving rate-distortion performance that surpasses that of traditional video compression methods. However, rate control schemes tailored for deep video compression have not been well studied. In this paper, we propose a neural network-based $\lambd... | {
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} |
2412.18836 | MRI2Speech: Speech Synthesis from Articulatory Movements Recorded by
Real-time MRI | [
"cs.SD",
"cs.AI",
"eess.AS"
] | Previous real-time MRI (rtMRI)-based speech synthesis models depend heavily on noisy ground-truth speech. Applying loss directly over ground truth mel-spectrograms entangles speech content with MRI noise, resulting in poor intelligibility. We introduce a novel approach that adapts the multi-modal self-supervised AV-HuB... | {
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} |
2412.18838 | DiFiC: Your Diffusion Model Holds the Secret to Fine-Grained Clustering | [
"cs.CV",
"cs.LG"
] | Fine-grained clustering is a practical yet challenging task, whose essence lies in capturing the subtle differences between instances of different classes. Such subtle differences can be easily disrupted by data augmentation or be overwhelmed by redundant information in data, leading to significant performance degradat... | {
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} |
2412.18839 | Advancing NAM-to-Speech Conversion with Novel Methods and the MultiNAM
Dataset | [
"cs.SD",
"cs.AI",
"eess.AS"
] | Current Non-Audible Murmur (NAM)-to-speech techniques rely on voice cloning to simulate ground-truth speech from paired whispers. However, the simulated speech often lacks intelligibility and fails to generalize well across different speakers. To address this issue, we focus on learning phoneme-level alignments from pa... | {
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} |
2412.18840 | Implicit factorized transformer approach to fast prediction of turbulent
channel flows | [
"physics.flu-dyn",
"cs.AI"
] | Transformer neural operators have recently become an effective approach for surrogate modeling of nonlinear systems governed by partial differential equations (PDEs). In this paper, we introduce a modified implicit factorized transformer (IFactFormer-m) model which replaces the original chained factorized attention wit... | {
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} |
2412.18842 | Context-Based Semantic-Aware Alignment for Semi-Supervised Multi-Label
Learning | [
"cs.CV",
"cs.LG"
] | Due to the lack of extensive precisely-annotated multi-label data in real word, semi-supervised multi-label learning (SSMLL) has gradually gained attention. Abundant knowledge embedded in vision-language models (VLMs) pre-trained on large-scale image-text pairs could alleviate the challenge of limited labeled data unde... | {
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} |
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