id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.17987 | Democratic Ramp Secret Sharing | [
"cs.IT",
"math.IT"
] | In this work we revisit the fundamental findings by Chen et al. in [5] on general information transfer in linear ramp secret sharing schemes to conclude that their method not only gives a way to establish worst case leakage [5, 25] and best case recovery [5, 19], but can also lead to additional insight on non-qualifyin... | {
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2412.17988 | Network Models of Expertise in the Complex Task of Operating Particle
Accelerators | [
"cs.SI",
"cs.SY",
"eess.SY",
"stat.AP"
] | We implement a network-based approach to study expertise in a complex real-world task: operating particle accelerators. Most real-world tasks we learn and perform (e.g., driving cars, operating complex machines, solving mathematical problems) are difficult to learn because they are complex, and the best strategies are ... | {
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2412.17991 | Online Adaptation for Myographic Control of Natural Dexterous Hand and
Finger Movements | [
"cs.RO",
"cs.CV"
] | One of the most elusive goals in myographic prosthesis control is the ability to reliably decode continuous positions simultaneously across multiple degrees-of-freedom. Goal: To demonstrate dexterous, natural, biomimetic finger and wrist control of the highly advanced robotic Modular Prosthetic Limb. Methods: We combin... | {
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2412.17992 | Falsification of Autonomous Systems in Rich Environments | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Validating the behavior of autonomous Cyber-Physical Systems (CPS) and Artificial Intelligence (AI) agents, which rely on automated controllers, is an objective of great importance. In recent years, Neural-Network (NN) controllers have been demonstrating great promise. Unfortunately, such learned controllers are often ... | {
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2412.17993 | Multi-Agent Path Finding in Continuous Spaces with Projected Diffusion
Models | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics, requiring the computation of collision-free paths for multiple agents moving from their respective start to goal positions. Coordinating multiple agents in a shared environment poses significant challenges, especially in continuous spaces where tradi... | {
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2412.17997 | Shifted Composition III: Local Error Framework for KL Divergence | [
"math.ST",
"cs.DS",
"cs.LG",
"cs.NA",
"math.NA",
"stat.ML",
"stat.TH"
] | Coupling arguments are a central tool for bounding the deviation between two stochastic processes, but traditionally have been limited to Wasserstein metrics. In this paper, we apply the shifted composition rule--an information-theoretic principle introduced in our earlier work--in order to adapt coupling arguments to ... | {
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2412.17998 | WavePulse: Real-time Content Analytics of Radio Livestreams | [
"cs.IR",
"cs.AI"
] | Radio remains a pervasive medium for mass information dissemination, with AM/FM stations reaching more Americans than either smartphone-based social networking or live television. Increasingly, radio broadcasts are also streamed online and accessed over the Internet. We present WavePulse, a framework that records, docu... | {
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2412.18003 | Integrated Learning and Optimization for Congestion Management and
Profit Maximization in Real-Time Electricity Market | [
"eess.SY",
"cs.AI",
"cs.SY"
] | We develop novel integrated learning and optimization (ILO) methodologies to solve economic dispatch (ED) and DC optimal power flow (DCOPF) problems for better economic operation. The optimization problem for ED is formulated with load being an unknown parameter while DCOPF consists of load and power transfer distribut... | {
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2412.18004 | Correctness is not Faithfulness in RAG Attributions | [
"cs.CL"
] | Retrieving relevant context is a common approach to reduce hallucinations and enhance answer reliability. Explicitly citing source documents allows users to verify generated responses and increases trust. Prior work largely evaluates citation correctness - whether cited documents support the corresponding statements. B... | {
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2412.18005 | Combinatorial Regularity for Relatively Perfect Discrete Morse Gradient
Vector Fields of ReLU Neural Networks | [
"math.AT",
"cs.CG",
"cs.LG"
] | One common function class in machine learning is the class of ReLU neural networks. ReLU neural networks induce a piecewise linear decomposition of their input space called the canonical polyhedral complex. It has previously been established that it is decidable whether a ReLU neural network is piecewise linear Morse. ... | {
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2412.18011 | StructTest: Benchmarking LLMs' Reasoning through Compositional
Structured Outputs | [
"cs.CL"
] | The rapid development of large language models (LLMs) necessitates robust, unbiased, and scalable methods for evaluating their capabilities. However, human annotations are expensive to scale, model-based evaluations are prone to biases in answer style, while target-answer-based benchmarks are vulnerable to data contami... | {
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2412.18012 | Extended Event Log: Towards a Unified Standard for Process Mining | [
"cs.DB"
] | Process mining has grown popular today given their ability to provide managers with insights into the actual business process as executed by employees. Process mining depends on event logs found in process aware information systems to model business processes. This has raised the need to develop event log standards giv... | {
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2412.18017 | Dynamic Power Management in Modular Reconfigurable Battery Systems with
Energy and Power Modules | [
"eess.SY",
"cs.SY"
] | Integrating power electronics with batteries can offer many advantages, including load sharing and balancing with parallel connectivity. However, parallel batteries with differing voltages and power profiles can cause large circulating currents and uncontrolled energy transfers, risking system instability. To overcome ... | {
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2412.18022 | Trustworthy and Efficient LLMs Meet Databases | [
"cs.DB",
"cs.AI"
] | In the rapidly evolving AI era with large language models (LLMs) at the core, making LLMs more trustworthy and efficient, especially in output generation (inference), has gained significant attention. This is to reduce plausible but faulty LLM outputs (a.k.a hallucinations) and meet the highly increased inference deman... | {
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2412.18023 | More than Chit-Chat: Developing Robots for Small-Talk Interactions | [
"cs.RO",
"cs.AI",
"cs.HC"
] | Beyond mere formality, small talk plays a pivotal role in social dynamics, serving as a verbal handshake for building rapport and understanding. For conversational AI and social robots, the ability to engage in small talk enhances their perceived sociability, leading to more comfortable and natural user interactions. I... | {
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2412.18024 | Multimodal Learning with Uncertainty Quantification based on Discounted
Belief Fusion | [
"cs.LG"
] | Multimodal AI models are increasingly used in fields like healthcare, finance, and autonomous driving, where information is drawn from multiple sources or modalities such as images, texts, audios, videos. However, effectively managing uncertainty - arising from noise, insufficient evidence, or conflicts between modalit... | {
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2412.18027 | LayerDropBack: A Universally Applicable Approach for Accelerating
Training of Deep Networks | [
"cs.CV"
] | Training very deep convolutional networks is challenging, requiring significant computational resources and time. Existing acceleration methods often depend on specific architectures or require network modifications. We introduce LayerDropBack (LDB), a simple yet effective method to accelerate training across a wide ra... | {
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2412.18029 | Same Company, Same Signal: The Role of Identity in Earnings Call
Transcripts | [
"cs.CL"
] | Post-earnings volatility prediction is critical for investors, with previous works often leveraging earnings call transcripts under the assumption that their rich semantics contribute significantly. To further investigate how transcripts impact volatility, we introduce DEC, a dataset featuring accurate volatility calcu... | {
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2412.18030 | Moving boundaries: An appreciation of John Hopfield | [
"physics.hist-ph",
"cond-mat.dis-nn",
"cs.LG",
"q-bio.NC",
"q-bio.OT"
] | The 2024 Nobel Prize in Physics was awarded to John Hopfield and Geoffrey Hinton, "for foundational discoveries and inventions that enable machine learning with artificial neural networks." As noted by the Nobel committee, their work moved the boundaries of physics. This is a brief reflection on Hopfield's work, its im... | {
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2412.18031 | Faces speak louder than words: Emotions versus textual sentiment in the
2024 USA Presidential Election | [
"cs.SI"
] | Sentiment analysis of textual content has become a well-established solution for analyzing social media data. However, with the rise of images and videos as primary modes of expression, more information on social media is conveyed visually. Among these, facial expressions serve as one of the most direct indicators of e... | {
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2412.18032 | A physics-engineering-economic model coupling approach for estimating
the socio-economic impacts of space weather scenarios | [
"physics.geo-ph",
"cs.SY",
"econ.GN",
"eess.SY",
"q-fin.EC"
] | There is growing concern about our vulnerability to space weather hazards and the disruption critical infrastructure failures could cause to society and the economy. However, the socio-economic impacts of space weather hazards, such as from geomagnetic storms, remain under-researched. This study introduces a novel fram... | {
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2412.18036 | Explainability in Neural Networks for Natural Language Processing Tasks | [
"cs.CL",
"cs.AI"
] | Neural networks are widely regarded as black-box models, creating significant challenges in understanding their inner workings, especially in natural language processing (NLP) applications. To address this opacity, model explanation techniques like Local Interpretable Model-Agnostic Explanations (LIME) have emerged as ... | {
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2412.18038 | AA-SGAN: Adversarially Augmented Social GAN with Synthetic Data | [
"cs.CV",
"cs.AI"
] | Accurately predicting pedestrian trajectories is crucial in applications such as autonomous driving or service robotics, to name a few. Deep generative models achieve top performance in this task, assuming enough labelled trajectories are available for training. To this end, large amounts of synthetically generated, la... | {
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2412.18040 | Theoretical Constraints on the Expressive Power of $\mathsf{RoPE}$-based
Tensor Attention Transformers | [
"cs.LG",
"cs.AI",
"cs.CC",
"cs.CL"
] | Tensor Attention extends traditional attention mechanisms by capturing high-order correlations across multiple modalities, addressing the limitations of classical matrix-based attention. Meanwhile, Rotary Position Embedding ($\mathsf{RoPE}$) has shown superior performance in encoding positional information in long-cont... | {
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2412.18041 | An information theoretic limit to data amplification | [
"stat.ML",
"cs.LG",
"hep-ex",
"physics.data-an"
] | In recent years generative artificial intelligence has been used to create data to support science analysis. For example, Generative Adversarial Networks (GANs) have been trained using Monte Carlo simulated input and then used to generate data for the same problem. This has the advantage that a GAN creates data in a si... | {
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2412.18042 | Time-Probability Dependent Knowledge Extraction in IoT-enabled Smart
Building | [
"cs.IR",
"cs.CE"
] | Smart buildings incorporate various emerging Internet of Things (IoT) applications for comprehensive management of energy efficiency, human comfort, automation, and security. However, the development of a knowledge extraction framework is fundamental. Currently, there is a lack of a unified and practical framework for ... | {
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2412.18043 | Aligning AI Research with the Needs of Clinical Coding Workflows: Eight
Recommendations Based on US Data Analysis and Critical Review | [
"cs.CL",
"cs.AI"
] | Clinical coding is crucial for healthcare billing and data analysis. Manual clinical coding is labour-intensive and error-prone, which has motivated research towards full automation of the process. However, our analysis, based on US English electronic health records and automated coding research using these records, sh... | {
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2412.18046 | Emoji Retrieval from Gibberish or Garbled Social Media Text: A Novel
Methodology and A Case Study | [
"cs.SI",
"cs.AI",
"cs.CL",
"cs.CY",
"cs.LG"
] | Emojis are widely used across social media platforms but are often lost in noisy or garbled text, posing challenges for data analysis and machine learning. Conventional preprocessing approaches recommend removing such text, risking the loss of emojis and their contextual meaning. This paper proposes a three-step revers... | {
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2412.18047 | Uncertainty-Aware Critic Augmentation for Hierarchical Multi-Agent EV
Charging Control | [
"eess.SY",
"cs.AI",
"cs.SY"
] | The advanced bidirectional EV charging and discharging technology, aimed at supporting grid stability and emergency operations, has driven a growing interest in workplace applications. It not only reduces electricity expenses but also enhances the resilience in handling practical matters, such as peak power limitation,... | {
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2412.18048 | Fair Knowledge Tracing in Second Language Acquisition | [
"cs.HC",
"cs.AI",
"cs.CY",
"cs.LG"
] | In second-language acquisition, predictive modeling aids educators in implementing diverse teaching strategies, attracting significant research attention. However, while model accuracy is widely explored, model fairness remains under-examined. Model fairness ensures equitable treatment of groups, preventing unintention... | {
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2412.18051 | Factuality or Fiction? Benchmarking Modern LLMs on Ambiguous QA with
Citations | [
"cs.CL"
] | Benchmarking modern large language models (LLMs) on complex and realistic tasks is critical to advancing their development. In this work, we evaluate the factual accuracy and citation performance of state-of-the-art LLMs on the task of Question Answering (QA) in ambiguous settings with source citations. Using three rec... | {
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2412.18052 | Beyond Gradient Averaging in Parallel Optimization: Improved Robustness
through Gradient Agreement Filtering | [
"cs.LG",
"cs.AI"
] | We introduce Gradient Agreement Filtering (GAF) to improve on gradient averaging in distributed deep learning optimization. Traditional distributed data-parallel stochastic gradient descent involves averaging gradients of microbatches to calculate a macrobatch gradient that is then used to update model parameters. We f... | {
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2412.18053 | Neuron Empirical Gradient: Discovering and Quantifying Neurons Global
Linear Controllability | [
"cs.CL",
"cs.AI"
] | Although feed-forward neurons in pre-trained language models (PLMs) can store knowledge and their importance in influencing model outputs has been studied, existing work focuses on finding a limited set of neurons and analyzing their relative importance. However, the global quantitative role of activation values in sha... | {
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2412.18059 | Diverse Concept Proposals for Concept Bottleneck Models | [
"cs.LG"
] | Concept bottleneck models are interpretable predictive models that are often used in domains where model trust is a key priority, such as healthcare. They identify a small number of human-interpretable concepts in the data, which they then use to make predictions. Learning relevant concepts from data proves to be a cha... | {
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2412.18060 | An Ensemble Approach to Short-form Video Quality Assessment Using
Multimodal LLM | [
"cs.CV"
] | The rise of short-form videos, characterized by diverse content, editing styles, and artifacts, poses substantial challenges for learning-based blind video quality assessment (BVQA) models. Multimodal large language models (MLLMs), renowned for their superior generalization capabilities, present a promising solution. T... | {
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2412.18061 | Lla-VAP: LSTM Ensemble of Llama and VAP for Turn-Taking Prediction | [
"cs.SD",
"cs.CL",
"cs.HC",
"eess.AS"
] | Turn-taking prediction is the task of anticipating when the speaker in a conversation will yield their turn to another speaker to begin speaking. This project expands on existing strategies for turn-taking prediction by employing a multi-modal ensemble approach that integrates large language models (LLMs) and voice act... | {
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2412.18063 | LMRPA: Large Language Model-Driven Efficient Robotic Process Automation
for OCR | [
"cs.RO",
"cs.DL",
"cs.HC",
"cs.SE"
] | This paper introduces LMRPA, a novel Large Model-Driven Robotic Process Automation (RPA) model designed to greatly improve the efficiency and speed of Optical Character Recognition (OCR) tasks. Traditional RPA platforms often suffer from performance bottlenecks when handling high-volume repetitive processes like OCR, l... | {
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2412.18065 | BIG-MoE: Bypass Isolated Gating MoE for Generalized Multimodal Face
Anti-Spoofing | [
"cs.CV"
] | In the domain of facial recognition security, multimodal Face Anti-Spoofing (FAS) is essential for countering presentation attacks. However, existing technologies encounter challenges due to modality biases and imbalances, as well as domain shifts. Our research introduces a Mixture of Experts (MoE) model to address the... | {
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2412.18067 | Automated Materials Discovery Platform Realized: Scanning Probe
Microscopy of Combinatorial Libraries | [
"cond-mat.mtrl-sci",
"cond-mat.mes-hall",
"cs.AI"
] | Combinatorial libraries are a powerful approach for exploring the evolution of physical properties across binary and ternary cross-sections in multicomponent phase diagrams. Although the synthesis of these libraries has been developed since the 1960s and expedited with advanced laboratory automation, the broader applic... | {
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2412.18069 | Improving Factuality with Explicit Working Memory | [
"cs.CL"
] | Large language models can generate factually inaccurate content, a problem known as hallucination. Recent works have built upon retrieved-augmented generation to improve factuality through iterative prompting but these methods are limited by the traditional RAG design. To address these challenges, we introduce EWE (Exp... | {
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2412.18072 | MMFactory: A Universal Solution Search Engine for Vision-Language Tasks | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] | With advances in foundational and vision-language models, and effective fine-tuning techniques, a large number of both general and special-purpose models have been developed for a variety of visual tasks. Despite the flexibility and accessibility of these models, no single model is able to handle all tasks and/or appli... | {
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2412.18073 | Understanding Artificial Neural Network's Behavior from Neuron
Activation Perspective | [
"cs.AI"
] | This paper explores the intricate behavior of deep neural networks (DNNs) through the lens of neuron activation dynamics. We propose a probabilistic framework that can analyze models' neuron activation patterns as a stochastic process, uncovering theoretical insights into neural scaling laws, such as over-parameterizat... | {
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2412.18076 | COMO: Cross-Mamba Interaction and Offset-Guided Fusion for Multimodal
Object Detection | [
"cs.CV",
"cs.AI"
] | Single-modal object detection tasks often experience performance degradation when encountering diverse scenarios. In contrast, multimodal object detection tasks can offer more comprehensive information about object features by integrating data from various modalities. Current multimodal object detection methods general... | {
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2412.18078 | Future Pathways for EVTOLs: A Design Optimization Perspective | [
"eess.SY",
"cs.SY"
] | The rapid development of advanced urban air mobility, particularly electric vertical take-off and landing (eVTOL) aircraft, requires interdisciplinary approaches involving the future urban air mobility ecosystem. Operational cost efficiency, regulatory aspects, sustainability, and environmental compatibility must be in... | {
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2412.18081 | Heterogeneous transfer learning for high dimensional regression with
feature mismatch | [
"stat.ML",
"cs.LG"
] | We consider the problem of transferring knowledge from a source, or proxy, domain to a new target domain for learning a high-dimensional regression model with possibly different features. Recently, the statistical properties of homogeneous transfer learning have been investigated. However, most homogeneous transfer and... | {
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2412.18082 | Prompt Tuning for Item Cold-start Recommendation | [
"cs.IR",
"cs.AI"
] | The item cold-start problem is crucial for online recommender systems, as the success of the cold-start phase determines whether items can transition into popular ones. Prompt learning, a powerful technique used in natural language processing (NLP) to address zero- or few-shot problems, has been adapted for recommender... | {
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2412.18084 | Property Enhanced Instruction Tuning for Multi-task Molecule Generation
with Large Language Models | [
"cs.AI"
] | Large language models (LLMs) are widely applied in various natural language processing tasks such as question answering and machine translation. However, due to the lack of labeled data and the difficulty of manual annotation for biochemical properties, the performance for molecule generation tasks is still limited, es... | {
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2412.18086 | Generating Traffic Scenarios via In-Context Learning to Learn Better
Motion Planner | [
"cs.RO",
"cs.AI",
"cs.CL",
"cs.GR",
"cs.LG"
] | Motion planning is a crucial component in autonomous driving. State-of-the-art motion planners are trained on meticulously curated datasets, which are not only expensive to annotate but also insufficient in capturing rarely seen critical scenarios. Failing to account for such scenarios poses a significant risk to motio... | {
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2412.18089 | Convolutional Prompting for Broad-Domain Retinal Vessel Segmentation | [
"cs.CV"
] | Previous research on retinal vessel segmentation is targeted at a specific image domain, mostly color fundus photography (CFP). In this paper we make a brave attempt to attack a more challenging task of broad-domain retinal vessel segmentation (BD-RVS), which is to develop a unified model applicable to varied domains i... | {
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2412.18090 | Multi-Point Positional Insertion Tuning for Small Object Detection | [
"cs.CV",
"cs.AI"
] | Small object detection aims to localize and classify small objects within images. With recent advances in large-scale vision-language pretraining, finetuning pretrained object detection models has emerged as a promising approach. However, finetuning large models is computationally and memory expensive. To address this ... | {
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2412.18091 | AutoSculpt: A Pattern-based Model Auto-pruning Framework Using
Reinforcement Learning and Graph Learning | [
"cs.AI"
] | As deep neural networks (DNNs) are increasingly deployed on edge devices, optimizing models for constrained computational resources is critical. Existing auto-pruning methods face challenges due to the diversity of DNN models, various operators (e.g., filters), and the difficulty in balancing pruning granularity with m... | {
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2412.18092 | BRIDGE: Bundle Recommendation via Instruction-Driven Generation | [
"cs.IR",
"cs.AI",
"cs.LG"
] | Bundle recommendation aims to suggest a set of interconnected items to users. However, diverse interaction types and sparse interaction matrices often pose challenges for previous approaches in accurately predicting user-bundle adoptions. Inspired by the distant supervision strategy and generative paradigm, we propose ... | {
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2412.18093 | Molly: Making Large Language Model Agents Solve Python Problem More
Logically | [
"cs.CL"
] | Applying large language models (LLMs) as teaching assists has attracted much attention as an integral part of intelligent education, particularly in computing courses. To reduce the gap between the LLMs and the computer programming education expert, fine-tuning and retrieval augmented generation (RAG) are the two mains... | {
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2412.18096 | Real-world Deployment and Evaluation of PErioperative AI CHatbot (PEACH)
-- a Large Language Model Chatbot for Perioperative Medicine | [
"cs.AI"
] | Large Language Models (LLMs) are emerging as powerful tools in healthcare, particularly for complex, domain-specific tasks. This study describes the development and evaluation of the PErioperative AI CHatbot (PEACH), a secure LLM-based system integrated with local perioperative guidelines to support preoperative clinic... | {
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2412.18097 | LangYa: Revolutionizing Cross-Spatiotemporal Ocean Forecasting | [
"physics.ao-ph",
"cs.AI"
] | Ocean forecasting is crucial for both scientific research and societal benefits. Currently, the most accurate forecasting systems are global ocean forecasting systems (GOFSs), which represent the ocean state variables (OSVs) as discrete grids and solve partial differential equations (PDEs) governing the transitions of ... | {
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2412.18099 | An Attention-based Framework with Multistation Information for
Earthquake Early Warnings | [
"cs.LG",
"cs.AI",
"physics.geo-ph"
] | Earthquake early warning systems play crucial roles in reducing the risk of seismic disasters. Previously, the dominant modeling system was the single-station models. Such models digest signal data received at a given station and predict earth-quake parameters, such as the p-phase arrival time, intensity, and magnitude... | {
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} |
2412.18100 | EvoPat: A Multi-LLM-based Patents Summarization and Analysis Agent | [
"cs.DL",
"cs.AI"
] | The rapid growth of scientific techniques and knowledge is reflected in the exponential increase in new patents filed annually. While these patents drive innovation, they also present significant burden for researchers and engineers, especially newcomers. To avoid the tedious work of navigating a vast and complex lands... | {
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2412.18105 | Beyond the Known: Enhancing Open Set Domain Adaptation with Unknown
Exploration | [
"cs.CV"
] | Convolutional neural networks (CNNs) can learn directly from raw data, resulting in exceptional performance across various research areas. However, factors present in non-controllable environments such as unlabeled datasets with varying levels of domain and category shift can reduce model accuracy. The Open Set Domain ... | {
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2412.18106 | Tackling the Dynamicity in a Production LLM Serving System with SOTA
Optimizations via Hybrid Prefill/Decode/Verify Scheduling on Efficient
Meta-kernels | [
"cs.AI",
"cs.DC",
"cs.LG"
] | Meeting growing demands for low latency and cost efficiency in production-grade large language model (LLM) serving systems requires integrating advanced optimization techniques. However, dynamic and unpredictable input-output lengths of LLM, compounded by these optimizations, exacerbate the issues of workload variabili... | {
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2412.18107 | SongGLM: Lyric-to-Melody Generation with 2D Alignment Encoding and
Multi-Task Pre-Training | [
"eess.AS",
"cs.AI",
"cs.SD"
] | Lyric-to-melody generation aims to automatically create melodies based on given lyrics, requiring the capture of complex and subtle correlations between them. However, previous works usually suffer from two main challenges: 1) lyric-melody alignment modeling, which is often simplified to one-syllable/word-to-one-note a... | {
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2412.18108 | Unveiling Visual Perception in Language Models: An Attention Head
Analysis Approach | [
"cs.CV"
] | Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated remarkable progress in visual understanding. This impressive leap raises a compelling question: how can language models, initially trained solely on linguistic data, effectively interpret and process visual content? This paper aims to add... | {
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2412.18110 | SlimGPT: Layer-wise Structured Pruning for Large Language Models | [
"cs.AI"
] | Large language models (LLMs) have garnered significant attention for their remarkable capabilities across various domains, whose vast parameter scales present challenges for practical deployment. Structured pruning is an effective method to balance model performance with efficiency, but performance restoration under co... | {
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2412.18111 | AIGT: AI Generative Table Based on Prompt | [
"cs.AI"
] | Tabular data, which accounts for over 80% of enterprise data assets, is vital in various fields. With growing concerns about privacy protection and data-sharing restrictions, generating high-quality synthetic tabular data has become essential. Recent advancements show that large language models (LLMs) can effectively g... | {
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2412.18112 | Spectrum-oriented Point-supervised Saliency Detector for Hyperspectral
Images | [
"cs.CV"
] | Hyperspectral salient object detection (HSOD) aims to extract targets or regions with significantly different spectra from hyperspectral images. While existing deep learning-based methods can achieve good detection results, they generally necessitate pixel-level annotations, which are notably challenging to acquire for... | {
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2412.18116 | AutoDroid-V2: Boosting SLM-based GUI Agents via Code Generation | [
"cs.AI"
] | Large language models (LLMs) have brought exciting new advances to mobile UI agents, a long-standing research field that aims to complete arbitrary natural language tasks through mobile UI interactions. However, existing UI agents usually demand high reasoning capabilities of powerful large models that are difficult to... | {
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2412.18119 | Age Optimal Sampling for Unreliable Channels under Unknown Channel
Statistics | [
"cs.IT",
"cs.LG",
"math.IT"
] | In this paper, we study a system in which a sensor forwards status updates to a receiver through an error-prone channel, while the receiver sends the transmission results back to the sensor via a reliable channel. Both channels are subject to random delays. To evaluate the timeliness of the status information at the re... | {
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2412.18120 | Do Language Models Understand the Cognitive Tasks Given to Them?
Investigations with the N-Back Paradigm | [
"cs.CL",
"cs.AI"
] | Cognitive tasks originally developed for humans are now increasingly used to study language models. While applying these tasks is often straightforward, interpreting their results can be challenging. In particular, when a model underperforms, it is often unclear whether this results from a limitation in the cognitive a... | {
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2412.18121 | SAR Despeckling via Log-Yeo-Johnson Transformation and Sparse
Representation | [
"cs.IT",
"math.IT"
] | Synthetic Aperture Radar (SAR) images are widely used in remote sensing due to their all-weather, all-day imaging capabilities. However, SAR images are highly susceptible to noise, particularly speckle noise, caused by the coherent imaging process, which severely degrades image quality. This has driven increasing resea... | {
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2412.18123 | AEIOU: A Unified Defense Framework against NSFW Prompts in Text-to-Image
Models | [
"cs.CR",
"cs.CL"
] | As text-to-image (T2I) models continue to advance and gain widespread adoption, their associated safety issues are becoming increasingly prominent. Malicious users often exploit these models to generate Not-Safe-for-Work (NSFW) images using harmful or adversarial prompts, highlighting the critical need for robust safeg... | {
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2412.18124 | VisionLLM-based Multimodal Fusion Network for Glottic Carcinoma Early
Detection | [
"cs.CV"
] | The early detection of glottic carcinoma is critical for improving patient outcomes, as it enables timely intervention, preserves vocal function, and significantly reduces the risk of tumor progression and metastasis. However, the similarity in morphology between glottic carcinoma and vocal cord dysplasia results in su... | {
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2412.18125 | Exact Acceleration of Subgraph Graph Neural Networks by Eliminating
Computation Redundancy | [
"cs.AI"
] | Graph neural networks (GNNs) have become a prevalent framework for graph tasks. Many recent studies have proposed the use of graph convolution methods over the numerous subgraphs of each graph, a concept known as subgraph graph neural networks (subgraph GNNs), to enhance GNNs' ability to distinguish non-isomorphic grap... | {
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2412.18131 | UniPLV: Towards Label-Efficient Open-World 3D Scene Understanding by
Regional Visual Language Supervision | [
"cs.CV"
] | We present UniPLV, a powerful framework that unifies point clouds, images and text in a single learning paradigm for open-world 3D scene understanding. UniPLV employs the image modal as a bridge to co-embed 3D points with pre-aligned images and text in a shared feature space without requiring carefully crafted point cl... | {
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2412.18134 | Learning Randomized Reductions and Program Properties | [
"cs.LG",
"cs.CC",
"cs.PL",
"cs.SE"
] | The correctness of computations remains a significant challenge in computer science, with traditional approaches relying on automated testing or formal verification. Self-testing/correcting programs introduce an alternative paradigm, allowing a program to verify and correct its own outputs via randomized reductions, a ... | {
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2412.18135 | LSAQ: Layer-Specific Adaptive Quantization for Large Language Model
Deployment | [
"cs.CL"
] | As large language models (LLMs) demonstrate exceptional performance across various domains, the deployment of these models on edge devices has emerged as a new trend. Quantization techniques, which reduce the size and memory footprint of LLMs, are effective for enabling deployment on resource-constrained edge devices. ... | {
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2412.18136 | ERVD: An Efficient and Robust ViT-Based Distillation Framework for
Remote Sensing Image Retrieval | [
"cs.CV"
] | ERVD: An Efficient and Robust ViT-Based Distillation Framework for Remote Sensing Image Retrieval | {
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2412.18138 | Fundamental Limits in the Search for Less Discriminatory Algorithms --
and How to Avoid Them | [
"cs.CY",
"cs.LG",
"stat.ML"
] | Disparate impact doctrine offers an important legal apparatus for targeting unfair data-driven algorithmic decisions. A recent body of work has focused on conceptualizing and operationalizing one particular construct from this doctrine -- the less discriminatory alternative, an alternative policy that reduces dispariti... | {
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2412.18139 | Ensuring Consistency for In-Image Translation | [
"cs.CL"
] | The in-image machine translation task involves translating text embedded within images, with the translated results presented in image format. While this task has numerous applications in various scenarios such as film poster translation and everyday scene image translation, existing methods frequently neglect the aspe... | {
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2412.18140 | An Instrumental Value for Data Production and its Application to Data
Pricing | [
"cs.GT",
"cs.LG"
] | How much value does a dataset or a data production process have to an agent who wishes to use the data to assist decision-making? This is a fundamental question towards understanding the value of data as well as further pricing of data. This paper develops an approach for capturing the instrumental value of data produc... | {
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2412.18142 | Text-Aware Adapter for Few-Shot Keyword Spotting | [
"eess.AS",
"cs.AI",
"eess.SP"
] | Recent advances in flexible keyword spotting (KWS) with text enrollment allow users to personalize keywords without uttering them during enrollment. However, there is still room for improvement in target keyword performance. In this work, we propose a novel few-shot transfer learning method, called text-aware adapter (... | {
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2412.18143 | NoSQL Graph Databases: an overview | [
"cs.DB",
"cs.SC"
] | Graphs are the most suitable structures for modeling objects and interactions in applications where component inter-connectivity is a key feature. There has been increased interest in graphs to represent domains such as social networks, web site link structures, and biology. Graph stores recently rose to prominence alo... | {
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2412.18144 | Neural Conformal Control for Time Series Forecasting | [
"cs.LG"
] | We introduce a neural network conformal prediction method for time series that enhances adaptivity in non-stationary environments. Our approach acts as a neural controller designed to achieve desired target coverage, leveraging auxiliary multi-view data with neural network encoders in an end-to-end manner to further en... | {
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2412.18145 | Supervised centrality via sparse network influence regression: an
application to the 2021 Henan floods' social network | [
"stat.ME",
"cs.SI",
"physics.soc-ph"
] | The social characteristics of players in a social network are closely associated with their network positions and relational importance. Identifying those influential players in a network is of great importance as it helps to understand how ties are formed, how information is propagated, and, in turn, can guide the dis... | {
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2412.18147 | Accelerating Post-Tornado Disaster Assessment Using Advanced Deep
Learning Models | [
"cs.CV"
] | Post-disaster assessments of buildings and infrastructure are crucial for both immediate recovery efforts and long-term resilience planning. This research introduces an innovative approach to automating post-disaster assessments through advanced deep learning models. Our proposed system employs state-of-the-art compute... | {
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2412.18148 | Are We in the AI-Generated Text World Already? Quantifying and
Monitoring AIGT on Social Media | [
"cs.AI",
"cs.CL",
"cs.CR",
"cs.SI"
] | Social media platforms are experiencing a growing presence of AI-Generated Texts (AIGTs). However, the misuse of AIGTs could have profound implications for public opinion, such as spreading misinformation and manipulating narratives. Despite its importance, a systematic study to assess the prevalence of AIGTs on social... | {
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2412.18149 | Dense-Face: Personalized Face Generation Model via Dense Annotation
Prediction | [
"cs.CV"
] | The text-to-image (T2I) personalization diffusion model can generate images of the novel concept based on the user input text caption. However, existing T2I personalized methods either require test-time fine-tuning or fail to generate images that align well with the given text caption. In this work, we propose a new T2... | {
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2412.18150 | EvalMuse-40K: A Reliable and Fine-Grained Benchmark with Comprehensive
Human Annotations for Text-to-Image Generation Model Evaluation | [
"cs.CV",
"cs.AI"
] | Recently, Text-to-Image (T2I) generation models have achieved significant advancements. Correspondingly, many automated metrics have emerged to evaluate the image-text alignment capabilities of generative models. However, the performance comparison among these automated metrics is limited by existing small datasets. Ad... | {
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2412.18151 | CoAM: Corpus of All-Type Multiword Expressions | [
"cs.CL"
] | Multiword expressions (MWEs) refer to idiomatic sequences of multiple words. MWE identification, i.e., detecting MWEs in text, can play a key role in downstream tasks such as machine translation. Existing datasets for MWE identification are inconsistently annotated, limited to a single type of MWE, or limited in size. ... | {
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2412.18153 | DepthLab: From Partial to Complete | [
"cs.CV"
] | Missing values remain a common challenge for depth data across its wide range of applications, stemming from various causes like incomplete data acquisition and perspective alteration. This work bridges this gap with DepthLab, a foundation depth inpainting model powered by image diffusion priors. Our model features two... | {
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2412.18154 | GeneSUM: Large Language Model-based Gene Summary Extraction | [
"q-bio.GN",
"cs.AI",
"cs.CL"
] | Emerging topics in biomedical research are continuously expanding, providing a wealth of information about genes and their function. This rapid proliferation of knowledge presents unprecedented opportunities for scientific discovery and formidable challenges for researchers striving to keep abreast of the latest advanc... | {
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2412.18156 | scReader: Prompting Large Language Models to Interpret scRNA-seq Data | [
"q-bio.GN",
"cs.AI",
"cs.CL"
] | Large language models (LLMs) have demonstrated remarkable advancements, primarily due to their capabilities in modeling the hidden relationships within text sequences. This innovation presents a unique opportunity in the field of life sciences, where vast collections of single-cell omics data from multiple species prov... | {
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2412.18157 | Smooth-Foley: Creating Continuous Sound for Video-to-Audio Generation
Under Semantic Guidance | [
"cs.SD",
"cs.AI",
"eess.AS"
] | The video-to-audio (V2A) generation task has drawn attention in the field of multimedia due to the practicality in producing Foley sound. Semantic and temporal conditions are fed to the generation model to indicate sound events and temporal occurrence. Recent studies on synthesizing immersive and synchronized audio are... | {
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2412.18158 | Semantics Disentanglement and Composition for Versatile Codec toward
both Human-eye Perception and Machine Vision Task | [
"cs.CV",
"eess.IV"
] | While learned image compression methods have achieved impressive results in either human visual perception or machine vision tasks, they are often specialized only for one domain. This drawback limits their versatility and generalizability across scenarios and also requires retraining to adapt to new applications-a pro... | {
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2412.18160 | Image Quality Assessment: Exploring Regional Heterogeneity via Response
of Adaptive Multiple Quality Factors in Dictionary Space | [
"eess.IV",
"cs.CV"
] | Given that the factors influencing image quality vary significantly with scene, content, and distortion type, particularly in the context of regional heterogeneity, we propose an adaptive multi-quality factor (AMqF) framework to represent image quality in a dictionary space, enabling the precise capture of quality feat... | {
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2412.18161 | VISION: A Modular AI Assistant for Natural Human-Instrument Interaction
at Scientific User Facilities | [
"cs.AI"
] | Scientific user facilities, such as synchrotron beamlines, are equipped with a wide array of hardware and software tools that require a codebase for human-computer-interaction. This often necessitates developers to be involved to establish connection between users/researchers and the complex instrumentation. The advent... | {
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} |
2412.18163 | Survey of Pseudonymization, Abstractive Summarization & Spell Checker
for Hindi and Marathi | [
"cs.CL",
"cs.AI"
] | India's vast linguistic diversity presents unique challenges and opportunities for technological advancement, especially in the realm of Natural Language Processing (NLP). While there has been significant progress in NLP applications for widely spoken languages, the regional languages of India, such as Marathi and Hind... | {
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} |
2412.18164 | Stochastic Control for Fine-tuning Diffusion Models: Optimality,
Regularity, and Convergence | [
"cs.LG",
"math.OC"
] | Diffusion models have emerged as powerful tools for generative modeling, demonstrating exceptional capability in capturing target data distributions from large datasets. However, fine-tuning these massive models for specific downstream tasks, constraints, and human preferences remains a critical challenge. While recent... | {
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2412.18165 | Parallel Neural Computing for Scene Understanding from LiDAR Perception
in Autonomous Racing | [
"cs.CV"
] | Autonomous driving in high-speed racing, as opposed to urban environments, presents significant challenges in scene understanding due to rapid changes in the track environment. Traditional sequential network approaches may struggle to meet the real-time knowledge and decision-making demands of an autonomous agent cover... | {
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} |
2412.18168 | From Pairwise to Ranking: Climbing the Ladder to Ideal Collaborative
Filtering with Pseudo-Ranking | [
"cs.IR"
] | Intuitively, an ideal collaborative filtering (CF) model should learn from users' full rankings over all items to make optimal top-K recommendations. Due to the absence of such full rankings in practice, most CF models rely on pairwise loss functions to approximate full rankings, resulting in an immense performance gap... | {
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} |
2412.18169 | KunServe: Elastic and Efficient Large Language Model Serving with
Parameter-centric Memory Management | [
"cs.DC",
"cs.AI"
] | The stateful nature of large language model (LLM) servingcan easily throttle precious GPU memory under load burstor long-generation requests like chain-of-thought reasoning,causing latency spikes due to queuing incoming requests. However, state-of-the-art KVCache centric approaches handleload spikes by dropping, migrat... | {
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} |
2412.18170 | Unlocking the Hidden Treasures: Enhancing Recommendations with Unlabeled
Data | [
"cs.IR"
] | Collaborative filtering (CF) stands as a cornerstone in recommender systems, yet effectively leveraging the massive unlabeled data presents a significant challenge. Current research focuses on addressing the challenge of unlabeled data by extracting a subset that closely approximates negative samples. Regrettably, the ... | {
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} |
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