id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.16050 | Skeleton-Guided-Translation: A Benchmarking Framework for Code
Repository Translation with Fine-Grained Quality Evaluation | [
"cs.SE",
"cs.AI"
] | The advancement of large language models has intensified the need to modernize enterprise applications and migrate legacy systems to secure, versatile languages. However, existing code translation benchmarks primarily focus on individual functions, overlooking the complexities involved in translating entire repositorie... | {
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2501.16061 | The Unbearable Lightness of Prompting: A Critical Reflection on the
Environmental Impact of genAI use in Design Education | [
"cs.HC",
"cs.AI"
] | Design educators are finding ways to support students in skillfully using GenAI tools in their practices while encouraging the critical scrutiny of the ethical and social issues around these technologies. However, the issue of environmental sustainability remains unaddressed. There is a lack of both resources to grasp ... | {
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2501.16065 | CILP-FGDI: Exploiting Vision-Language Model for Generalizable Person
Re-Identification | [
"cs.CV"
] | The Visual Language Model, known for its robust cross-modal capabilities, has been extensively applied in various computer vision tasks. In this paper, we explore the use of CLIP (Contrastive Language-Image Pretraining), a vision-language model pretrained on large-scale image-text pairs to align visual and textual feat... | {
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2501.16070 | Generalizing Egocentric Temporal Neighborhoods to probe for spatial
correlations in temporal networks and infer their topology | [
"physics.soc-ph",
"cs.SI"
] | Motifs are thought to be some fundamental components of social face-to-face interaction temporal networks. However, the motifs previously considered are either limited to a handful of nodes and edges, or do not include triangles, which are thought to be of critical relevance to understand the dynamics of social systems... | {
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2501.16073 | Challenging Assumptions in Learning Generic Text Style Embeddings | [
"cs.LG",
"cs.CL"
] | Recent advancements in language representation learning primarily emphasize language modeling for deriving meaningful representations, often neglecting style-specific considerations. This study addresses this gap by creating generic, sentence-level style embeddings crucial for style-centric tasks. Our approach is groun... | {
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2501.16075 | PISCO: Pretty Simple Compression for Retrieval-Augmented Generation | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Retrieval-Augmented Generation (RAG) pipelines enhance Large Language Models (LLMs) by retrieving relevant documents, but they face scalability issues due to high inference costs and limited context size. Document compression is a practical solution, but current soft compression methods suffer from accuracy losses and ... | {
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2501.16076 | Minimizing Polarization and Disagreement in the Friedkin-Johnsen Model
with Unknown Innate Opinions | [
"cs.SI"
] | The bulk of the literature on opinion optimization in social networks adopts the Friedkin-Johnsen (FJ) opinion dynamics model, in which the innate opinions of all nodes are known: this is an unrealistic assumption. In this paper, we study opinion optimization under the FJ model without the full knowledge of innate opin... | {
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2501.16077 | RelCAT: Advancing Extraction of Clinical Inter-Entity Relationships from
Unstructured Electronic Health Records | [
"cs.CL"
] | This study introduces RelCAT (Relation Concept Annotation Toolkit), an interactive tool, library, and workflow designed to classify relations between entities extracted from clinical narratives. Building upon the CogStack MedCAT framework, RelCAT addresses the challenge of capturing complete clinical relations disperse... | {
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2501.16078 | Integration of LLM Quality Assurance into an NLG System | [
"cs.CL"
] | In this paper, we present a system that uses a Large Language Model (LLM) to perform grammar and spelling correction as a component of Quality Assurance (QA) for texts generated by NLG systems, which is important for text production in real-world scenarios. Evaluating the results of the system on work-in-progress sport... | {
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2501.16080 | Generating Spatial Synthetic Populations Using Wasserstein Generative
Adversarial Network: A Case Study with EU-SILC Data for Helsinki and
Thessaloniki | [
"cs.LG",
"cs.MA"
] | Using agent-based social simulations can enhance our understanding of urban planning, public health, and economic forecasting. Realistic synthetic populations with numerous attributes strengthen these simulations. The Wasserstein Generative Adversarial Network, trained on census data like EU-SILC, can create robust syn... | {
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2501.16081 | Combating Interference for Over-the-Air Federated Learning: A
Statistical Approach via RIS | [
"cs.IT",
"eess.SP",
"math.IT"
] | Over-the-air computation (AirComp) integrates analog communication with task-oriented computation, serving as a key enabling technique for communication-efficient federated learning (FL) over wireless networks. However, owing to its analog characteristics, AirComp-enabled FL (AirFL) is vulnerable to both unintentional ... | {
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2501.16085 | ARFlow: Autogressive Flow with Hybrid Linear Attention | [
"cs.CV"
] | Flow models are effective at progressively generating realistic images, but they generally struggle to capture long-range dependencies during the generation process as they compress all the information from previous time steps into a single corrupted image. To address this limitation, we propose integrating autoregress... | {
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2501.16086 | Value-oriented forecast reconciliation for renewables in electricity
markets | [
"stat.ML",
"cs.LG"
] | Forecast reconciliation is considered an effective method for achieving coherence and improving forecast accuracy. However, the value of reconciled forecasts in downstream decision-making tasks has been mostly overlooked. In a multi-agent setup with heterogeneous loss functions, this oversight may lead to unfair outcom... | {
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2501.16093 | STAR: Stepwise Task Augmentation and Relation Learning for Aspect
Sentiment Quad Prediction | [
"cs.CL",
"cs.AI"
] | Aspect-based sentiment analysis (ABSA) aims to identify four sentiment elements, including aspect term, aspect category, opinion term, and sentiment polarity. These elements construct the complete picture of sentiments. The most challenging task, aspect sentiment quad prediction (ASQP), predicts these elements simultan... | {
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2501.16098 | Multi-Agent Meta-Offline Reinforcement Learning for Timely UAV Path
Planning and Data Collection | [
"cs.MA"
] | Multi-agent reinforcement learning (MARL) has been widely adopted in high-performance computing and complex data-driven decision-making in the wireless domain. However, conventional MARL schemes face many obstacles in real-world scenarios. First, most MARL algorithms are online, which might be unsafe and impractical. S... | {
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2501.16099 | An Air-Gap Element for the Isogeometric Space-Time-Simulation of
Electric Machines | [
"math.NA",
"cs.CE",
"cs.NA"
] | Space-time methods promise more efficient time-domain simulations, in particular of electrical machines. However, most approaches require the motion to be known in advance so that it can be included in the space-time mesh. To overcome this problem, this paper proposes to use the well-known air-gap element for the rotor... | {
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2501.16100 | Automated Detection of Sport Highlights from Audio and Video Sources | [
"cs.CV",
"cs.AI",
"cs.LG"
] | This study presents a novel Deep Learning-based and lightweight approach for the automated detection of sports highlights (HLs) from audio and video sources. HL detection is a key task in sports video analysis, traditionally requiring significant human effort. Our solution leverages Deep Learning (DL) models trained on... | {
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2501.16101 | 3D Reconstruction of non-visible surfaces of objects from a Single Depth
View -- Comparative Study | [
"cs.RO",
"cs.CV"
] | Scene and object reconstruction is an important problem in robotics, in particular in planning collision-free trajectories or in object manipulation. This paper compares two strategies for the reconstruction of nonvisible parts of the object surface from a single RGB-D camera view. The first method, named DeepSDF predi... | {
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2501.16103 | Static Batching of Irregular Workloads on GPUs: Framework and
Application to Efficient MoE Model Inference | [
"cs.DC",
"cs.LG"
] | It has long been a problem to arrange and execute irregular workloads on massively parallel devices. We propose a general framework for statically batching irregular workloads into a single kernel with a runtime task mapping mechanism on GPUs. We further apply this framework to Mixture-of-Experts (MoE) model inference ... | {
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2501.16106 | Towards Explainable Multimodal Depression Recognition for Clinical
Interviews | [
"cs.CL"
] | Recently, multimodal depression recognition for clinical interviews (MDRC) has recently attracted considerable attention. Existing MDRC studies mainly focus on improving task performance and have achieved significant development. However, for clinical applications, model transparency is critical, and previous works ign... | {
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2501.16110 | Using Generative Models to Produce Realistic Populations of UK
Windstorms | [
"physics.ao-ph",
"cs.LG"
] | This study evaluates the potential of generative models, trained on historical ERA5 reanalysis data, for simulating windstorms over the UK. Four generative models, including a standard GAN, a WGAN-GP, a U-net diffusion model, and a diffusion-GAN were assessed based on their ability to replicate spatial and statistical ... | {
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2501.16111 | Options-Aware Dense Retrieval for Multiple-Choice query Answering | [
"cs.IR"
] | Long-context multiple-choice question answering tasks require robust reasoning over extensive text sources. Since most of the pre-trained transformer models are restricted to processing only a few hundred words at a time, successful completion of such tasks often relies on the identification of evidence spans, such as ... | {
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2501.16112 | Survey: Understand the challenges of MachineLearning Experts using Named
EntityRecognition Tools | [
"cs.IR",
"cs.CL"
] | This paper presents a survey based on Kasunic's survey research methodology to identify the criteria used by Machine Learning (ML) experts to evaluate Named Entity Recognition (NER) tools and frameworks. Comparison and selection of NER tools and frameworks is a critical step in leveraging NER for Information Retrieval ... | {
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2501.16113 | Fixed-sized clusters $k$-Means | [
"cs.LG"
] | We present a $k$-means-based clustering algorithm, which optimizes the mean square error, for given cluster sizes. A straightforward application is balanced clustering, where the sizes of each cluster are equal. In the $k$-means assignment phase, the algorithm solves an assignment problem using the Hungarian algorithm.... | {
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2501.16117 | A Unified Analysis of Stochastic Gradient Descent with Arbitrary Data
Permutations and Beyond | [
"cs.LG"
] | We aim to provide a unified convergence analysis for permutation-based Stochastic Gradient Descent (SGD), where data examples are permuted before each epoch. By examining the relations among permutations, we categorize existing permutation-based SGD algorithms into four categories: Arbitrary Permutations, Independent P... | {
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2501.16120 | Copyright and Competition: Estimating Supply and Demand with
Unstructured Data | [
"econ.EM",
"cs.LG",
"stat.AP",
"stat.ML"
] | Copyright policies play a pivotal role in protecting the intellectual property of creators and companies in creative industries. The advent of cost-reducing technologies, such as generative AI, in these industries calls for renewed attention to the role of these policies. This paper studies product positioning and comp... | {
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2501.16123 | From #Dr00gtiktok to #harmreduction: Exploring Substance Use Hashtags on
TikTok | [
"cs.CL"
] | The rise of TikTok as a primary source of information for youth, combined with its unique short-form video format, creates urgent questions about how substance use content manifests and spreads on the platform. This paper provides the first in-depth exploration of substance use-related content on TikTok, covering all m... | {
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2501.16125 | SampleLLM: Optimizing Tabular Data Synthesis in Recommendations | [
"cs.IR"
] | Tabular data synthesis is crucial in machine learning, yet existing general methods-primarily based on statistical or deep learning models-are highly data-dependent and often fall short in recommender systems. This limitation arises from their difficulty in capturing complex distributions and understanding feature rela... | {
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2501.16128 | Graphene-Assisted Chemical Stabilization of Liquid Metal Nano Droplets
for Liquid Metal Based Energy Storage | [
"eess.SY",
"cond-mat.mtrl-sci",
"cs.SY"
] | Energy storage devices with liquid_metal electrodes have attracted interest in recent years due to their potential for mechanical resilience, self_healing, dendrite_free operation, and fast reaction kinetics. Gallium alloys like Eutectic Gallium Indium (EGaIn) are appealing due to their low melting point and high theor... | {
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2501.16130 | ReFill: Reinforcement Learning for Fill-In Minimization | [
"cs.LG"
] | Efficiently solving sparse linear systems $Ax=b$, where $A$ is a large, sparse, symmetric positive semi-definite matrix, is a core challenge in scientific computing, machine learning, and optimization. A major bottleneck in Gaussian elimination for these systems is fill-in, the creation of non-zero entries that increas... | {
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2501.16135 | Evaluation of NMT-Assisted Grammar Transfer for a Multi-Language
Configurable Data-to-Text System | [
"cs.CL"
] | One approach for multilingual data-to-text generation is to translate grammatical configurations upfront from the source language into each target language. These configurations are then used by a surface realizer and in document planning stages to generate output. In this paper, we describe a rule-based NLG implementa... | {
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2501.16138 | Quantifying the Self-Interest Level of Markov Social Dilemmas | [
"cs.GT",
"cs.MA"
] | This paper introduces a novel method for estimating the self-interest level of computationally intractable Markov social dilemmas. We extend the concept of self-interest level from normal-form games to Markov games, providing a quantitative measure of the minimum reward exchange required to incentivize cooperation by a... | {
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2501.16142 | Towards General-Purpose Model-Free Reinforcement Learning | [
"cs.LG",
"cs.AI"
] | Reinforcement learning (RL) promises a framework for near-universal problem-solving. In practice however, RL algorithms are often tailored to specific benchmarks, relying on carefully tuned hyperparameters and algorithmic choices. Recently, powerful model-based RL methods have shown impressive general results across be... | {
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2501.16145 | Capacity-Achieving Input Distribution of the Additive Uniform Noise
Channel With Peak Amplitude and Cost Constraint | [
"cs.IT",
"math.IT"
] | Under which condition is quantization optimal? We address this question in the context of the additive uniform noise channel under peak amplitude and power constraints. We compute analytically the capacity-achieving input distribution as a function of the noise level, the average power constraint and the exponent of th... | {
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2501.16146 | Toward Efficient Generalization in 3D Human Pose Estimation via a
Canonical Domain Approach | [
"cs.CV",
"cs.AI"
] | Recent advancements in deep learning methods have significantly improved the performance of 3D Human Pose Estimation (HPE). However, performance degradation caused by domain gaps between source and target domains remains a major challenge to generalization, necessitating extensive data augmentation and/or fine-tuning f... | {
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2501.16147 | Efficient Portrait Matte Creation With Layer Diffusion and Connectivity
Priors | [
"cs.CV"
] | Learning effective deep portrait matting models requires training data of both high quality and large quantity. Neither quality nor quantity can be easily met for portrait matting, however. Since the most accurate ground-truth portrait mattes are acquired in front of the green screen, it is almost impossible to harvest... | {
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2501.16150 | AI Agents for Computer Use: A Review of Instruction-based Computer
Control, GUI Automation, and Operator Assistants | [
"cs.AI",
"cs.HC",
"cs.SY",
"eess.SY"
] | Instruction-based computer control agents (CCAs) execute complex action sequences on personal computers or mobile devices to fulfill tasks using the same graphical user interfaces as a human user would, provided instructions in natural language. This review offers a comprehensive overview of the emerging field of instr... | {
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2501.16153 | MILP initialization for solving parabolic PDEs with PINNs | [
"cs.LG"
] | Physics-Informed Neural Networks (PINNs) are a powerful deep learning method capable of providing solutions and parameter estimations of physical systems. Given the complexity of their neural network structure, the convergence speed is still limited compared to numerical methods, mainly when used in applications that m... | {
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2501.16154 | AdaCoT: Rethinking Cross-Lingual Factual Reasoning through Adaptive
Chain-of-Thought | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs) have shown impressive multilingual capabilities through pretraining on diverse corpora. While these models show strong reasoning abilities, their performance varies significantly across languages due to uneven training data distribution. Existing approaches using machine translation, and ex... | {
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2501.16159 | Comprehensive Benchmarking Environment for Worker Flexibility in
Flexible Job Shop Scheduling Problems | [
"cs.NE"
] | In Production Scheduling, the Flexible Job Shop Scheduling Problem (FJSSP) aims to optimize a sequence of operations and assign each to an eligible machine with varying processing times. For integration of the workforce, each machine also requires a worker to be present to process an operation which additionally affect... | {
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2501.16164 | MetaDecorator: Generating Immersive Virtual Tours through Multimodality | [
"cs.HC",
"cs.AI",
"cs.ET",
"cs.MM"
] | MetaDecorator, is a framework that empowers users to personalize virtual spaces. By leveraging text-driven prompts and image synthesis techniques, MetaDecorator adorns static panoramas captured by 360{\deg} imaging devices, transforming them into uniquely styled and visually appealing environments. This significantly e... | {
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2501.16167 | A Dynamic Similarity Index for Assessing Voltage Source Behaviour in
Power Systems | [
"eess.SY",
"cs.SY"
] | Due to the fundamental transition to a power electronic dominated power system, the increasing diversity of dynamic elements underscores the need to assess their similarity to mature electrical engineering models. This article addresses the concept of the Dynamic Similarity Index (DSI) for its use in, power electronics... | {
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2501.16168 | Ringmaster ASGD: The First Asynchronous SGD with Optimal Time Complexity | [
"cs.LG",
"cs.DC",
"math.OC",
"stat.ML"
] | Asynchronous Stochastic Gradient Descent (Asynchronous SGD) is a cornerstone method for parallelizing learning in distributed machine learning. However, its performance suffers under arbitrarily heterogeneous computation times across workers, leading to suboptimal time complexity and inefficiency as the number of worke... | {
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2501.16171 | Separate This, and All of these Things Around It: Music Source
Separation via Hyperellipsoidal Queries | [
"eess.AS",
"cs.IR",
"cs.LG",
"cs.SD"
] | Music source separation is an audio-to-audio retrieval task of extracting one or more constituent components, or composites thereof, from a musical audio mixture. Each of these constituent components is often referred to as a "stem" in literature. Historically, music source separation has been dominated by a stem-based... | {
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2501.16173 | Will Systems of LLM Agents Cooperate: An Investigation into a Social
Dilemma | [
"cs.MA",
"cs.GT"
] | As autonomous agents become more prevalent, understanding their collective behaviour in strategic interactions is crucial. This study investigates the emergent cooperative tendencies of systems of Large Language Model (LLM) agents in a social dilemma. Unlike previous research where LLMs output individual actions, we pr... | {
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2501.16174 | Measuring Heterogeneity in Machine Learning with Distributed Energy
Distance | [
"stat.ML",
"cs.AI",
"cs.DC",
"cs.LG"
] | In distributed and federated learning, heterogeneity across data sources remains a major obstacle to effective model aggregation and convergence. We focus on feature heterogeneity and introduce energy distance as a sensitive measure for quantifying distributional discrepancies. While we show that energy distance is rob... | {
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2501.16177 | BAG: Body-Aligned 3D Wearable Asset Generation | [
"cs.CV",
"cs.AI",
"cs.GR"
] | While recent advancements have shown remarkable progress in general 3D shape generation models, the challenge of leveraging these approaches to automatically generate wearable 3D assets remains unexplored. To this end, we present BAG, a Body-aligned Asset Generation method to output 3D wearable asset that can be automa... | {
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2501.16178 | SWIFT: Mapping Sub-series with Wavelet Decomposition Improves Time
Series Forecasting | [
"cs.LG",
"stat.ML"
] | In recent work on time-series prediction, Transformers and even large language models have garnered significant attention due to their strong capabilities in sequence modeling. However, in practical deployments, time-series prediction often requires operation in resource-constrained environments, such as edge devices, ... | {
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2501.16181 | Can summarization approximate simplification? A gold standard comparison | [
"cs.CL"
] | This study explores the overlap between text summarization and simplification outputs. While summarization evaluation methods are streamlined, simplification lacks cohesion, prompting the question: how closely can abstractive summarization resemble gold-standard simplification? We address this by applying two BART-base... | {
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2501.16182 | The Linear Attention Resurrection in Vision Transformer | [
"cs.CV",
"cs.AI"
] | Vision Transformers (ViTs) have recently taken computer vision by storm. However, the softmax attention underlying ViTs comes with a quadratic complexity in time and memory, hindering the application of ViTs to high-resolution images. We revisit the attention design and propose a linear attention method to address the ... | {
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2501.16184 | Cryptographic Compression | [
"cs.CR",
"cs.IT",
"math.IT"
] | We introduce a protocol called ENCORE which simultaneously compresses and encrypts data in a one-pass process that can be implemented efficiently and possesses a number of desirable features as a streaming encoder/decoder. Motivated by the observation that both lossless compression and encryption consist of performing ... | {
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2501.16186 | Learn to Optimize Resource Allocation under QoS Constraint of AR | [
"cs.LG"
] | This paper studies the uplink and downlink power allocation for interactive augmented reality (AR) services, where live video captured by an AR device is uploaded to the network edge and then the augmented video is subsequently downloaded. By modeling the AR transmission process as a tandem queuing system, we derive an... | {
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2501.16191 | Raiders of the Lost Dependency: Fixing Dependency Conflicts in Python
using LLMs | [
"cs.SE",
"cs.AI"
] | Fixing Python dependency issues is a tedious and error-prone task for developers, who must manually identify and resolve environment dependencies and version constraints of third-party modules and Python interpreters. Researchers have attempted to automate this process by relying on large knowledge graphs and database ... | {
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2501.16193 | Posting Patterns of Members of Parental Subreddits | [
"cs.SI"
] | Online forums (e.g., Reddit) are used by many parents to discuss their challenges, needs, and receive support. While studies have investigated the contents of posts made to popular parental subreddits revealing the family health concerns being expressed, little is known about parents' posting patterns or other issues t... | {
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2501.16201 | Enhancing and Exploring Mild Cognitive Impairment Detection with
W2V-BERT-2.0 | [
"eess.AS",
"cs.CL",
"cs.SD"
] | This study explores a multi-lingual audio self-supervised learning model for detecting mild cognitive impairment (MCI) using the TAUKADIAL cross-lingual dataset. While speech transcription-based detection with BERT models is effective, limitations exist due to a lack of transcriptions and temporal information. To addre... | {
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2501.16207 | From Informal to Formal -- Incorporating and Evaluating LLMs on Natural
Language Requirements to Verifiable Formal Proofs | [
"cs.AI",
"cs.CL",
"cs.PL"
] | The research in AI-based formal mathematical reasoning has shown an unstop- pable growth trend. These studies have excelled in mathematical competitions like IMO and have made significant progress. This paper focuses on formal verification, an immediate application scenario of formal reasoning, and breaks it down into ... | {
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2501.16209 | Solving Turbulent Rayleigh-B\'enard Convection using Fourier Neural
Operators | [
"physics.flu-dyn",
"cs.LG"
] | We train Fourier Neural Operator (FNO) surrogate models for Rayleigh-B\'enard Convection (RBC), a model for convection processes that occur in nature and industrial settings. We compare the prediction accuracy and model properties of FNO surrogates to two popular surrogates used in fluid dynamics: the Dynamic Mode Deco... | {
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2501.16210 | New Frontiers in Fighting Misinformation | [
"cs.SI"
] | Despite extensive research and development of tools and technologies for misinformation tracking and detection, we often find ourselves largely on the losing side of the battle against misinformation. In an era where misinformation poses a substantial threat to public discourse, trust in information sources, and societ... | {
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2501.16211 | UDBE: Unsupervised Diffusion-based Brightness Enhancement in Underwater
Images | [
"cs.CV",
"cs.AI",
"eess.IV"
] | Activities in underwater environments are paramount in several scenarios, which drives the continuous development of underwater image enhancement techniques. A major challenge in this domain is the depth at which images are captured, with increasing depth resulting in a darker environment. Most existing methods for und... | {
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2501.16212 | An FPGA-Based Neuro-Fuzzy Sensor for Personalized Driving Assistance | [
"cs.RO",
"cs.LG"
] | Advanced driving-assistance systems (ADAS) are intended to automatize driver tasks, as well as improve driving and vehicle safety. This work proposes an intelligent neuro-fuzzy sensor for driving style (DS) recognition, suitable for ADAS enhancement. The development of the driving style intelligent sensor uses naturali... | {
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2501.16214 | Provence: efficient and robust context pruning for retrieval-augmented
generation | [
"cs.CL",
"cs.IR"
] | Retrieval-augmented generation improves various aspects of large language models (LLMs) generation, but suffers from computational overhead caused by long contexts as well as the propagation of irrelevant retrieved information into generated responses. Context pruning deals with both aspects, by removing irrelevant par... | {
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2501.16215 | Enhancing Visual Inspection Capability of Multi-Modal Large Language
Models on Medical Time Series with Supportive Conformalized and Interpretable
Small Specialized Models | [
"cs.AI",
"cs.LG",
"eess.SP"
] | Large language models (LLMs) exhibit remarkable capabilities in visual inspection of medical time-series data, achieving proficiency comparable to human clinicians. However, their broad scope limits domain-specific precision, and proprietary weights hinder fine-tuning for specialized datasets. In contrast, small specia... | {
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2501.16218 | Active Hypothesis Testing for Quantum Detection of Phase-Shift Keying
Coherent States | [
"quant-ph",
"cs.IT",
"cs.SY",
"eess.SY",
"math.IT"
] | This paper explores the quantum detection of Phase-Shift Keying (PSK)-coded coherent states through the lens of active hypothesis testing, focusing on a Dolinar-like receiver with constraints on displacement amplitude and energy. With coherent state slicing, we formulate the problem as a controlled sensing task in whic... | {
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2501.16220 | DBRouting: Routing End User Queries to Databases for Answerability | [
"cs.CL"
] | Enterprise level data is often distributed across multiple sources and identifying the correct set-of data-sources with relevant information for a knowledge request is a fundamental challenge. In this work, we define the novel task of routing an end-user query to the appropriate data-source, where the data-sources are ... | {
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2501.16221 | Automatic Calibration of a Multi-Camera System with Limited Overlapping
Fields of View for 3D Surgical Scene Reconstruction | [
"cs.CV"
] | The purpose of this study is to develop an automated and accurate external camera calibration method for multi-camera systems used in 3D surgical scene reconstruction (3D-SSR), eliminating the need for operator intervention or specialized expertise. The method specifically addresses the problem of limited overlapping f... | {
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2501.16222 | SPECIAL: Zero-shot Hyperspectral Image Classification With CLIP | [
"cs.CV"
] | Hyperspectral image (HSI) classification aims at categorizing each pixel in an HSI into a specific land cover class, which is crucial for applications like remote sensing, environmental monitoring, and agriculture. Although deep learning-based HSI classification methods have achieved significant advancements, existing ... | {
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2501.16224 | Language-Based Bayesian Optimization Research Assistant (BORA) | [
"cs.LG",
"cs.AI"
] | Many important scientific problems involve multivariate optimization coupled with slow and laborious experimental measurements. These complex, high-dimensional searches can be defined by non-convex optimization landscapes that resemble needle-in-a-haystack surfaces, leading to entrapment in local minima. Contextualizin... | {
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2501.16226 | The Effect of Optimal Self-Distillation in Noisy Gaussian Mixture Model | [
"stat.ML",
"cond-mat.dis-nn",
"cs.LG"
] | Self-distillation (SD), a technique where a model refines itself from its own predictions, has garnered attention as a simple yet powerful approach in machine learning. Despite its widespread use, the mechanisms underlying its effectiveness remain unclear. In this study, we investigate the efficacy of hyperparameter-tu... | {
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2501.16227 | PDC-ViT : Source Camera Identification using Pixel Difference
Convolution and Vision Transformer | [
"cs.CV"
] | Source camera identification has emerged as a vital solution to unlock incidents involving critical cases like terrorism, violence, and other criminal activities. The ability to trace the origin of an image/video can aid law enforcement agencies in gathering evidence and constructing the timeline of events. Moreover, i... | {
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2501.16235 | Echoes of Discord: Forecasting Hater Reactions to Counterspeech | [
"cs.CL"
] | Hate speech (HS) erodes the inclusiveness of online users and propagates negativity and division. Counterspeech has been recognized as a way to mitigate the harmful consequences. While some research has investigated the impact of user-generated counterspeech on social media platforms, few have examined and modeled hate... | {
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2501.16237 | Application of Structured State Space Models to High energy physics with
locality-sensitive hashing | [
"cs.LG",
"physics.ins-det"
] | Modern high-energy physics (HEP) experiments are increasingly challenged by the vast size and complexity of their datasets, particularly regarding large-scale point cloud processing and long sequences. In this study, to address these challenges, we explore the application of structured state space models (SSMs), propos... | {
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2501.16239 | Distilling foundation models for robust and efficient models in digital
pathology | [
"cs.CV"
] | In recent years, the advent of foundation models (FM) for digital pathology has relied heavily on scaling the pre-training datasets and the model size, yielding large and powerful models. While it resulted in improving the performance on diverse downstream tasks, it also introduced increased computational cost and infe... | {
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2501.16241 | Phase Transitions in Large Language Models and the $O(N)$ Model | [
"cs.LG",
"cs.CL",
"hep-th",
"physics.data-an"
] | Large language models (LLMs) exhibit unprecedentedly rich scaling behaviors. In physics, scaling behavior is closely related to phase transitions, critical phenomena, and field theory. To investigate the phase transition phenomena in LLMs, we reformulated the Transformer architecture as an $O(N)$ model. Our study revea... | {
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2501.16243 | Accelerating Quantum Reinforcement Learning with a Quantum Natural
Policy Gradient Based Approach | [
"quant-ph",
"cs.AI",
"stat.ML"
] | We address the problem of quantum reinforcement learning (QRL) under model-free settings with quantum oracle access to the Markov Decision Process (MDP). This paper introduces a Quantum Natural Policy Gradient (QNPG) algorithm, which replaces the random sampling used in classical Natural Policy Gradient (NPG) estimator... | {
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2501.16245 | SP-IMPact: A Framework for Static Partitioning Interference Mitigation
and Performance Analysis | [
"cs.DC",
"cs.PF",
"cs.SY",
"eess.SY"
] | Modern embedded systems are evolving toward complex, heterogeneous architectures to accommodate increasingly demanding applications. Driven by SWAP-C constraints, this shift has led to consolidating multiple systems onto single hardware platforms. Static Partitioning Hypervisors offer a promising solution to partition ... | {
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2501.16246 | CLISC: Bridging clip and sam by enhanced cam for unsupervised brain
tumor segmentation | [
"cs.CV"
] | Brain tumor segmentation is important for diagnosis of the tumor, and current deep-learning methods rely on a large set of annotated images for training, with high annotation costs. Unsupervised segmentation is promising to avoid human annotations while the performance is often limited. In this study, we present a nove... | {
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2501.16247 | Zero-Shot Decision Tree Construction via Large Language Models | [
"cs.LG",
"cs.CL"
] | This paper introduces a novel algorithm for constructing decision trees using large language models (LLMs) in a zero-shot manner based on Classification and Regression Trees (CART) principles. Traditional decision tree induction methods rely heavily on labeled data to recursively partition data using criteria such as i... | {
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2501.16249 | Lightweight Weighted Average Ensemble Model for Pneumonia Detection in
Chest X-Ray Images | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Pneumonia is a leading cause of illness and death in children, underscoring the need for early and accurate detection. In this study, we propose a novel lightweight ensemble model for detecting pneumonia in children using chest X-ray images. This ensemble model integrates two pre-trained convolutional neural networks (... | {
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2501.16250 | Runtime Analysis of the Compact Genetic Algorithm on the LeadingOnes
Benchmark | [
"cs.NE"
] | The compact genetic algorithm (cGA) is one of the simplest estimation-of-distribution algorithms (EDAs). Next to the univariate marginal distribution algorithm (UMDA) -- another simple EDA -- , the cGA has been subject to extensive mathematical runtime analyses, often showcasing a similar or even superior performance t... | {
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2501.16254 | Multi-Agent Geospatial Copilots for Remote Sensing Workflows | [
"cs.LG"
] | We present GeoLLM-Squad, a geospatial Copilot that introduces the novel multi-agent paradigm to remote sensing (RS) workflows. Unlike existing single-agent approaches that rely on monolithic large language models (LLM), GeoLLM-Squad separates agentic orchestration from geospatial task-solving, by delegating RS tasks to... | {
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2501.16255 | A foundation model for human-AI collaboration in medical literature
mining | [
"cs.CL"
] | Systematic literature review is essential for evidence-based medicine, requiring comprehensive analysis of clinical trial publications. However, the application of artificial intelligence (AI) models for medical literature mining has been limited by insufficient training and evaluation across broad therapeutic areas an... | {
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2501.16256 | Improving DBMS Scheduling Decisions with Fine-grained Performance
Prediction on Concurrent Queries -- Extended | [
"cs.DB",
"cs.LG"
] | Query scheduling is a critical task that directly impacts query performance in database management systems (DBMS). Deeply integrated schedulers, which require changes to DBMS internals, are usually customized for a specific engine and can take months to implement. In contrast, non-intrusive schedulers make coarse-grain... | {
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2501.16265 | Training Dynamics of In-Context Learning in Linear Attention | [
"cs.LG"
] | While attention-based models have demonstrated the remarkable ability of in-context learning, the theoretical understanding of how these models acquired this ability through gradient descent training is still preliminary. Towards answering this question, we study the gradient descent dynamics of multi-head linear self-... | {
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2501.16271 | From Molecules to Mixtures: Learning Representations of Olfactory
Mixture Similarity using Inductive Biases | [
"cs.LG",
"cs.AI"
] | Olfaction -- how molecules are perceived as odors to humans -- remains poorly understood. Recently, the principal odor map (POM) was introduced to digitize the olfactory properties of single compounds. However, smells in real life are not pure single molecules, but complex mixtures of molecules, whose representations r... | {
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2501.16273 | Return of the Encoder: Maximizing Parameter Efficiency for SLMs | [
"cs.CL",
"cs.AI",
"cs.CV"
] | The dominance of large decoder-only language models has overshadowed encoder-decoder architectures, despite their fundamental efficiency advantages in sequence processing. For small language models (SLMs) - those with 1 billion parameters or fewer - our systematic analysis across GPU, CPU, and NPU platforms reveals tha... | {
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2501.16274 | What is Formal Verification without Specifications? A Survey on mining
LTL Specifications | [
"cs.FL",
"cs.AI",
"cs.LO"
] | Virtually all verification techniques using formal methods rely on the availability of a formal specification, which describes the design requirements precisely. However, formulating specifications remains a manual task that is notoriously challenging and error-prone. To address this bottleneck in formal verification, ... | {
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2501.16276 | URAG: Implementing a Unified Hybrid RAG for Precise Answers in
University Admission Chatbots -- A Case Study at HCMUT | [
"cs.CL",
"cs.IR"
] | With the rapid advancement of Artificial Intelligence, particularly in Natural Language Processing, Large Language Models (LLMs) have become pivotal in educational question-answering systems, especially university admission chatbots. Concepts such as Retrieval-Augmented Generation (RAG) and other advanced techniques ha... | {
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2501.16282 | Brain-Adapter: Enhancing Neurological Disorder Analysis with
Adapter-Tuning Multimodal Large Language Models | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Understanding brain disorders is crucial for accurate clinical diagnosis and treatment. Recent advances in Multimodal Large Language Models (MLLMs) offer a promising approach to interpreting medical images with the support of text descriptions. However, previous research has primarily focused on 2D medical images, leav... | {
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2501.16287 | A Unified Representation of Density-Power-Based Divergences Reducible to
M-Estimation | [
"cs.IT",
"math.IT",
"math.ST",
"stat.ML",
"stat.TH"
] | Density-power-based divergences are known to provide robust inference procedures against outliers, and their extensions have been widely studied. A characteristic of successful divergences is that the estimation problem can be reduced to M-estimation. In this paper, we define a norm-based Bregman density power divergen... | {
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2501.16288 | Upside Down Reinforcement Learning with Policy Generators | [
"cs.LG",
"cs.AI"
] | Upside Down Reinforcement Learning (UDRL) is a promising framework for solving reinforcement learning problems which focuses on learning command-conditioned policies. In this work, we extend UDRL to the task of learning a command-conditioned generator of deep neural network policies. We accomplish this using Hypernetwo... | {
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} |
2501.16289 | Multi-view Structural Convolution Network for Domain-Invariant Point
Cloud Recognition of Autonomous Vehicles | [
"cs.CV"
] | Point cloud representation has recently become a research hotspot in the field of computer vision and has been utilized for autonomous vehicles. However, adapting deep learning networks for point cloud data recognition is challenging due to the variability in datasets and sensor technologies. This variability underscor... | {
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} |
2501.16295 | Mixture-of-Mamba: Enhancing Multi-Modal State-Space Models with
Modality-Aware Sparsity | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV"
] | State Space Models (SSMs) have emerged as efficient alternatives to Transformers for sequential modeling, but their inability to leverage modality-specific features limits their performance in multi-modal pretraining. Here, we propose Mixture-of-Mamba, a novel SSM architecture that introduces modality-aware sparsity th... | {
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} |
2501.16296 | Entanglement-Assisted Coding for Arbitrary Linear Computations Over a
Quantum MAC | [
"cs.IT",
"cs.NI",
"eess.SP",
"math.IT",
"quant-ph"
] | We study a linear computation problem over a quantum multiple access channel (LC-QMAC), where $S$ servers share an entangled state and separately store classical data streams $W_1,\cdots, W_S$ over a finite field $\mathbb{F}_d$. A user aims to compute $K$ linear combinations of these data streams, represented as $Y = \... | {
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"cs.NE": 0,
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"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.16297 | FALCON: Resolving Visual Redundancy and Fragmentation in High-resolution
Multimodal Large Language Models via Visual Registers | [
"cs.CV"
] | The incorporation of high-resolution visual input equips multimodal large language models (MLLMs) with enhanced visual perception capabilities for real-world tasks. However, most existing high-resolution MLLMs rely on a cropping-based approach to process images, which leads to fragmented visual encoding and a sharp inc... | {
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"cs.SY": 0
} |
2501.16298 | Uncoded Download in Lagrange-Coded Elastic Computing with Straggler
Tolerance | [
"cs.IT",
"cs.DC",
"math.IT"
] | Coded elastic computing, introduced by Yang et al. in 2018, is a technique designed to mitigate the impact of elasticity in cloud computing systems, where machines can be preempted or be added during computing rounds. This approach utilizes maximum distance separable (MDS) coding for both storage and download in matrix... | {
"Other": 1,
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} |
2501.16300 | Large Models in Dialogue for Active Perception and Anomaly Detection | [
"cs.CV",
"cs.AI"
] | Autonomous aerial monitoring is an important task aimed at gathering information from areas that may not be easily accessible by humans. At the same time, this task often requires recognizing anomalies from a significant distance or not previously encountered in the past. In this paper, we propose a novel framework tha... | {
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} |
2501.16302 | Matryoshka Re-Ranker: A Flexible Re-Ranking Architecture With
Configurable Depth and Width | [
"cs.CL"
] | Large language models (LLMs) provide powerful foundations to perform fine-grained text re-ranking. However, they are often prohibitive in reality due to constraints on computation bandwidth. In this work, we propose a \textbf{flexible} architecture called \textbf{Matroyshka Re-Ranker}, which is designed to facilitate \... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
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"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.16303 | RAPID: Retrieval-Augmented Parallel Inference Drafting for Text-Based
Video Event Retrieval | [
"cs.CL",
"cs.IR"
] | Retrieving events from videos using text queries has become increasingly challenging due to the rapid growth of multimedia content. Existing methods for text-based video event retrieval often focus heavily on object-level descriptions, overlooking the crucial role of contextual information. This limitation is especiall... | {
"Other": 0,
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} |
2501.16306 | Graph Neural Network Based Hybrid Beamforming Design in Wideband
Terahertz MIMO-OFDM Systems | [
"eess.SP",
"cs.LG",
"cs.NI"
] | 6G wireless technology is projected to adopt higher and wider frequency bands, enabled by highly directional beamforming. However, the vast bandwidths available also make the impact of beam squint in massive multiple input and multiple output (MIMO) systems non-negligible. Traditional approaches such as adding a true-t... | {
"Other": 1,
"cs.AI": 0,
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"cs.CR": 0,
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"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.16309 | Evaluating The Performance of Using Large Language Models to Automate
Summarization of CT Simulation Orders in Radiation Oncology | [
"physics.med-ph",
"cs.AI"
] | Purpose: This study aims to use a large language model (LLM) to automate the generation of summaries from the CT simulation orders and evaluate its performance. Materials and Methods: A total of 607 CT simulation orders for patients were collected from the Aria database at our institution. A locally hosted Llama 3.1 ... | {
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"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
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