id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.01271 | Energy-Efficiency and Spectral-Efficiency Trade-off in Distributed
Massive-MIMO Networks | [
"cs.NI",
"cs.IT",
"math.IT"
] | This paper investigates the inherent trade-off between energy efficiency (EE) and spectral efficiency (SE) in distributed massive-MIMO (D-mMIMO) systems. Optimizing the EE and SE together is crucial as increasing spectral efficiency often leads to higher energy consumption. Joint power allocation and AP-UE association ... | {
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2501.01273 | Does a Large Language Model Really Speak in Human-Like Language? | [
"cs.CL",
"stat.AP"
] | Large Language Models (LLMs) have recently emerged, attracting considerable attention due to their ability to generate highly natural, human-like text. This study compares the latent community structures of LLM-generated text and human-written text within a hypothesis testing procedure. Specifically, we analyze three t... | {
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2501.01275 | HybridTrack: A Hybrid Approach for Robust Multi-Object Tracking | [
"cs.CV",
"cs.RO"
] | The evolution of Advanced Driver Assistance Systems (ADAS) has increased the need for robust and generalizable algorithms for multi-object tracking. Traditional statistical model-based tracking methods rely on predefined motion models and assumptions about system noise distributions. Although computationally efficient,... | {
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2501.01276 | Marketing Mix Modeling in Lemonade | [
"stat.AP",
"cs.LG"
] | Marketing mix modeling (MMM) is a widely used method to assess the effectiveness of marketing campaigns and optimize marketing strategies. Bayesian MMM is an advanced approach that allows for the incorporation of prior information, uncertainty quantification, and probabilistic predictions (1). In this paper, we describ... | {
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2501.01282 | CultureVLM: Characterizing and Improving Cultural Understanding of
Vision-Language Models for over 100 Countries | [
"cs.AI",
"cs.CL",
"cs.CV"
] | Vision-language models (VLMs) have advanced human-AI interaction but struggle with cultural understanding, often misinterpreting symbols, gestures, and artifacts due to biases in predominantly Western-centric training data. In this paper, we construct CultureVerse, a large-scale multimodal benchmark covering 19, 682 cu... | {
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2501.01284 | NeutraSum: A Language Model can help a Balanced Media Diet by
Neutralizing News Summaries | [
"cs.CL",
"cs.AI"
] | Media bias in news articles arises from the political polarisation of media outlets, which can reinforce societal stereotypes and beliefs. Reporting on the same event often varies significantly between outlets, reflecting their political leanings through polarised language and focus. Although previous studies have atte... | {
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2501.01287 | Optimized Relay Lens Design For High-Resolution Image Transmission In
Military Target Detection Systems | [
"cs.LG",
"physics.optics"
] | The design and performance analysis of relay lenses that provide high-performance image transmission for target acquisition and tracking in military optical systems. Relay lenses are critical components for clear and lossless image transmission over long distances. In this study, the optical performance of a relay lens... | {
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2501.01290 | ToolComp: A Multi-Tool Reasoning & Process Supervision Benchmark | [
"cs.CL"
] | Despite recent advances in AI, the development of systems capable of executing complex, multi-step reasoning tasks involving multiple tools remains a significant challenge. Current benchmarks fall short in capturing the real-world complexity of tool-use reasoning, where verifying the correctness of not only the final a... | {
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2501.01291 | Change Detection-Based Procedures for Piecewise Stationary MABs: A
Modular Approach | [
"cs.AI",
"cs.LG",
"cs.SY",
"eess.SY",
"stat.ML"
] | Conventional Multi-Armed Bandit (MAB) algorithms are designed for stationary environments, where the reward distributions associated with the arms do not change with time. In many applications, however, the environment is more accurately modeled as being nonstationary. In this work, piecewise stationary MAB (PS-MAB) en... | {
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2501.01293 | LEO-Split: A Semi-Supervised Split Learning Framework over LEO Satellite
Networks | [
"cs.LG",
"cs.AI",
"cs.DC",
"cs.NI"
] | Recently, the increasing deployment of LEO satellite systems has enabled various space analytics (e.g., crop and climate monitoring), which heavily relies on the advancements in deep learning (DL). However, the intermittent connectivity between LEO satellites and ground station (GS) significantly hinders the timely tra... | {
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2501.01303 | Citations and Trust in LLM Generated Responses | [
"cs.CL",
"cs.AI"
] | Question answering systems are rapidly advancing, but their opaque nature may impact user trust. We explored trust through an anti-monitoring framework, where trust is predicted to be correlated with presence of citations and inversely related to checking citations. We tested this hypothesis with a live question-answer... | {
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2501.01305 | Large Language Models for Mental Health Diagnostic Assessments:
Exploring The Potential of Large Language Models for Assisting with Mental
Health Diagnostic Assessments -- The Depression and Anxiety Case | [
"cs.CL"
] | Large language models (LLMs) are increasingly attracting the attention of healthcare professionals for their potential to assist in diagnostic assessments, which could alleviate the strain on the healthcare system caused by a high patient load and a shortage of providers. For LLMs to be effective in supporting diagnost... | {
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2501.01306 | Think More, Hallucinate Less: Mitigating Hallucinations via Dual Process
of Fast and Slow Thinking | [
"cs.CL"
] | Large language models (LLMs) demonstrate exceptional capabilities, yet still face the hallucination issue. Typical text generation approaches adopt an auto-regressive generation without deliberate reasoning, which often results in untrustworthy and factually inaccurate responses. In this paper, we propose HaluSearch, a... | {
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2501.01311 | Multi-Head Explainer: A General Framework to Improve Explainability in
CNNs and Transformers | [
"cs.CV",
"cs.AI"
] | In this study, we introduce the Multi-Head Explainer (MHEX), a versatile and modular framework that enhances both the explainability and accuracy of Convolutional Neural Networks (CNNs) and Transformer-based models. MHEX consists of three core components: an Attention Gate that dynamically highlights task-relevant feat... | {
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2501.01312 | Learning Spectral Methods by Transformers | [
"stat.ML",
"cs.LG",
"math.ST",
"stat.TH"
] | Transformers demonstrate significant advantages as the building block of modern LLMs. In this work, we study the capacities of Transformers in performing unsupervised learning. We show that multi-layered Transformers, given a sufficiently large set of pre-training instances, are able to learn the algorithms themselves ... | {
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2501.01317 | Understanding Difficult-to-learn Examples in Contrastive Learning: A
Theoretical Framework for Spectral Contrastive Learning | [
"cs.LG",
"cs.AI"
] | Unsupervised contrastive learning has shown significant performance improvements in recent years, often approaching or even rivaling supervised learning in various tasks. However, its learning mechanism is fundamentally different from that of supervised learning. Previous works have shown that difficult-to-learn exampl... | {
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2501.01320 | SeedVR: Seeding Infinity in Diffusion Transformer Towards Generic Video
Restoration | [
"cs.CV"
] | Video restoration poses non-trivial challenges in maintaining fidelity while recovering temporally consistent details from unknown degradations in the wild. Despite recent advances in diffusion-based restoration, these methods often face limitations in generation capability and sampling efficiency. In this work, we pre... | {
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2501.01323 | Kiri-Spoon: A Kirigami Utensil for Robot-Assisted Feeding | [
"cs.RO"
] | For millions of adults with mobility limitations, eating meals is a daily challenge. A variety of robotic systems have been developed to address this societal need. Unfortunately, end-user adoption of robot-assisted feeding is limited, in part because existing devices are unable to seamlessly grasp, manipulate, and fee... | {
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2501.01326 | Domain-invariant feature learning in brain MR imaging for content-based
image retrieval | [
"cs.LG",
"cs.CV",
"cs.IR"
] | When conducting large-scale studies that collect brain MR images from multiple facilities, the impact of differences in imaging equipment and protocols at each site cannot be ignored, and this domain gap has become a significant issue in recent years. In this study, we propose a new low-dimensional representation (LDR)... | {
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2501.01327 | Enhancement of Neural Inertial Regression Networks: A Data-Driven
Perspective | [
"cs.RO",
"eess.SP"
] | Inertial sensors are integral components in numerous applications, powering crucial features in robotics and our daily lives. In recent years, deep learning has significantly advanced inertial sensing performance and robustness. Deep-learning techniques are used in different domains and platforms to enhance network per... | {
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2501.01329 | The Prompt Alchemist: Automated LLM-Tailored Prompt Optimization for
Test Case Generation | [
"cs.SE",
"cs.AI",
"cs.CL"
] | Test cases are essential for validating the reliability and quality of software applications. Recent studies have demonstrated the capability of Large Language Models (LLMs) to generate useful test cases for given source code. However, the existing work primarily relies on human-written plain prompts, which often leads... | {
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2501.01332 | Decoding Knowledge in Large Language Models: A Framework for
Categorization and Comprehension | [
"cs.CL"
] | Understanding how large language models (LLMs) acquire, retain, and apply knowledge remains an open challenge. This paper introduces a novel framework, K-(CSA)^2, which categorizes LLM knowledge along two dimensions: correctness and confidence. The framework defines six categories of knowledge, ranging from highly conf... | {
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2501.01333 | On the Robustness of Cover Version Identification Models: A Study Using
Cover Versions from YouTube | [
"cs.MM",
"cs.IR",
"cs.SI"
] | Recent advances in cover song identification have shown great success. However, models are usually tested on a fixed set of datasets which are relying on the online cover song database SecondHandSongs. It is unclear how well models perform on cover songs on online video platforms, which might exhibit alterations that a... | {
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2501.01335 | CySecBench: Generative AI-based CyberSecurity-focused Prompt Dataset for
Benchmarking Large Language Models | [
"cs.CR",
"cs.AI",
"cs.LG"
] | Numerous studies have investigated methods for jailbreaking Large Language Models (LLMs) to generate harmful content. Typically, these methods are evaluated using datasets of malicious prompts designed to bypass security policies established by LLM providers. However, the generally broad scope and open-ended nature of ... | {
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2501.01336 | Aligning Large Language Models for Faithful Integrity Against Opposing
Argument | [
"cs.CL"
] | Large Language Models (LLMs) have demonstrated impressive capabilities in complex reasoning tasks. However, they can be easily misled by unfaithful arguments during conversations, even when their original statements are correct. To this end, we investigate the problem of maintaining faithful integrity in LLMs. This inv... | {
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2501.01339 | Simultaneous Latent State Estimation and Latent Linear Dynamics
Discovery from Image Observations | [
"cs.LG"
] | The problem of state estimation has a long history with many successful algorithms that allow analytical derivation or approximation of posterior filtering distribution given the noisy observations. This report tries to conclude previous works to resolve the problem of latent state estimation given image-based observat... | {
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2501.01342 | DeepFilter: An Instrumental Baseline for Accurate and Efficient Process
Monitoring | [
"cs.AI",
"cs.LG"
] | Effective process monitoring is increasingly vital in industrial automation for ensuring operational safety, necessitating both high accuracy and efficiency. Although Transformers have demonstrated success in various fields, their canonical form based on the self-attention mechanism is inadequate for process monitoring... | {
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2501.01344 | Machine Learning for Modeling Wireless Radio Metrics with Crowdsourced
Data and Local Environment Features | [
"cs.LG"
] | This paper presents a suite of machine learning models, CRC-ML-Radio Metrics, designed for modeling RSRP, RSRQ, and RSSI wireless radio metrics in 4G environments. These models utilize crowdsourced data with local environmental features to enhance prediction accuracy across both indoor at elevation and outdoor urban se... | {
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2501.01346 | Large Vision-Language Model Alignment and Misalignment: A Survey Through
the Lens of Explainability | [
"cs.CV",
"cs.CL"
] | Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in processing both visual and textual information. However, the critical challenge of alignment between visual and textual representations is not fully understood. This survey presents a comprehensive examination of alignment and misalignmen... | {
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2501.01347 | AdaptVC: High Quality Voice Conversion with Adaptive Learning | [
"cs.SD",
"cs.CL",
"eess.AS"
] | The goal of voice conversion is to transform the speech of a source speaker to sound like that of a reference speaker while preserving the original content. A key challenge is to extract disentangled linguistic content from the source and voice style from the reference. While existing approaches leverage various method... | {
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2501.01349 | Rethinking Relation Extraction: Beyond Shortcuts to Generalization with
a Debiased Benchmark | [
"cs.AI"
] | Benchmarks are crucial for evaluating machine learning algorithm performance, facilitating comparison and identifying superior solutions. However, biases within datasets can lead models to learn shortcut patterns, resulting in inaccurate assessments and hindering real-world applicability. This paper addresses the issue... | {
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2501.01353 | Privacy Preservation in MIMO-OFDM Localization Systems: A Beamforming
Approach | [
"eess.SP",
"cs.IT",
"math.IT"
] | We investigate an uplink MIMO-OFDM localization scenario where a legitimate base station (BS) aims to localize a user equipment (UE) using pilot signals transmitted by the UE, while an unauthorized BS attempts to localize the UE by eavesdropping on these pilots, posing a risk to the UE's location privacy. To enhance le... | {
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2501.01359 | Smoothing traffic flow through automated vehicle control with optimal
parameter selection | [
"eess.SY",
"cs.SY"
] | Stop-and-go traffic waves are known for reducing the efficiency of transportation systems by increasing traffic oscillations and energy consumption. In this study, we develop an approach to synthesize a class of additive feedback controllers for automated vehicles (AVs) to smooth nonlinear mixed traffic flow, including... | {
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2501.01366 | ViGiL3D: A Linguistically Diverse Dataset for 3D Visual Grounding | [
"cs.CV",
"cs.AI",
"cs.CL"
] | 3D visual grounding (3DVG) involves localizing entities in a 3D scene referred to by natural language text. Such models are useful for embodied AI and scene retrieval applications, which involve searching for objects or patterns using natural language descriptions. While recent works have focused on LLM-based scaling o... | {
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2501.01367 | Contrastive Learning from Exploratory Actions: Leveraging Natural
Interactions for Preference Elicitation | [
"cs.RO",
"cs.AI",
"cs.HC",
"cs.LG"
] | People have a variety of preferences for how robots behave. To understand and reason about these preferences, robots aim to learn a reward function that describes how aligned robot behaviors are with a user's preferences. Good representations of a robot's behavior can significantly reduce the time and effort required f... | {
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2501.01368 | Test-time Controllable Image Generation by Explicit Spatial Constraint
Enforcement | [
"cs.CV"
] | Recent text-to-image generation favors various forms of spatial conditions, e.g., masks, bounding boxes, and key points. However, the majority of the prior art requires form-specific annotations to fine-tune the original model, leading to poor test-time generalizability. Meanwhile, existing training-free methods work w... | {
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2501.01370 | Embedding-based Approaches to Hyperpartisan News Detection | [
"cs.LG",
"cs.CL"
] | In this paper, we describe our systems in which the objective is to determine whether a given news article could be considered as hyperpartisan. Hyperpartisan news is news that takes an extremely polarized political standpoint with an intention of creating political divide among the public. We attempted several approac... | {
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2501.01371 | CLIP-UP: CLIP-Based Unanswerable Problem Detection for Visual Question
Answering | [
"cs.CV"
] | Recent Vision-Language Models (VLMs) have demonstrated remarkable capabilities in visual understanding and reasoning, and in particular on multiple-choice Visual Question Answering (VQA). Still, these models can make distinctly unnatural errors, for example, providing (wrong) answers to unanswerable VQA questions, such... | {
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2501.01372 | ScarNet: A Novel Foundation Model for Automated Myocardial Scar
Quantification from LGE in Cardiac MRI | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Background: Late Gadolinium Enhancement (LGE) imaging is the gold standard for assessing myocardial fibrosis and scarring, with left ventricular (LV) LGE extent predicting major adverse cardiac events (MACE). Despite its importance, routine LGE-based LV scar quantification is hindered by labor-intensive manual segmenta... | {
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2501.01375 | Iris Recognition for Infants | [
"cs.CV"
] | Non-invasive, efficient, physical token-less, accurate and stable identification methods for newborns may prevent baby swapping at birth, limit baby abductions and improve post-natal health monitoring across geographies, within the context of both the formal (i.e., hospitals) and informal (i.e., humanitarian and fragil... | {
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2501.01377 | Training Medical Large Vision-Language Models with Abnormal-Aware
Feedback | [
"cs.CL",
"cs.AI",
"cs.CV",
"cs.LG"
] | Existing Medical Large Vision-Language Models (Med-LVLMs), which encapsulate extensive medical knowledge, demonstrate excellent capabilities in understanding medical images and responding to human queries based on these images. However, there remain challenges in visual localization in medical images, which is crucial ... | {
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2501.01383 | Electrical networks and data analysis in phylogenetics | [
"math.CO",
"cs.IT",
"math-ph",
"math.IT",
"math.MP",
"q-bio.PE"
] | A classic problem in data analysis is studying the systems of subsets defined by either a similarity or a dissimilarity function on $X$ which is either observed directly or derived from a data set. For an electrical network there are two functions on the set of the nodes defined by the resistance matrix and the respons... | {
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2501.01384 | OmniChat: Enhancing Spoken Dialogue Systems with Scalable Synthetic Data
for Diverse Scenarios | [
"cs.CL",
"cs.HC",
"cs.SD",
"eess.AS"
] | With the rapid development of large language models, researchers have created increasingly advanced spoken dialogue systems that can naturally converse with humans. However, these systems still struggle to handle the full complexity of real-world conversations, including audio events, musical contexts, and emotional ex... | {
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2501.01389 | Optimal Strategy Revision in Population Games: A Mean Field Game Theory
Perspective | [
"cs.MA",
"cs.GT"
] | This paper investigates the design of optimal strategy revision in Population Games (PG) by establishing its connection to finite-state Mean Field Games (MFG). Specifically, by linking Evolutionary Dynamics (ED) -- which models agent decision-making in PG -- to the MFG framework, we demonstrate that optimal strategy re... | {
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2501.01392 | ProjectedEx: Enhancing Generation in Explainable AI for Prostate Cancer | [
"eess.IV",
"cs.CV"
] | Prostate cancer, a growing global health concern, necessitates precise diagnostic tools, with Magnetic Resonance Imaging (MRI) offering high-resolution soft tissue imaging that significantly enhances diagnostic accuracy. Recent advancements in explainable AI and representation learning have significantly improved prost... | {
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2501.01393 | Learning 3D Garment Animation from Trajectories of A Piece of Cloth | [
"cs.CV",
"cs.GR"
] | Garment animation is ubiquitous in various applications, such as virtual reality, gaming, and film producing. Recently, learning-based approaches obtain compelling performance in animating diverse garments under versatile scenarios. Nevertheless, to mimic the deformations of the observed garments, data-driven methods r... | {
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2501.01394 | A Unified Hyperparameter Optimization Pipeline for Transformer-Based
Time Series Forecasting Models | [
"cs.LG",
"cs.AI"
] | Transformer-based models for time series forecasting (TSF) have attracted significant attention in recent years due to their effectiveness and versatility. However, these models often require extensive hyperparameter optimization (HPO) to achieve the best possible performance, and a unified pipeline for HPO in transfor... | {
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2501.01402 | Best Transition Matrix Esitimation or Best Label Noise Robustness
Classifier? Two Possible Methods to Enhance the Performance of T-revision | [
"cs.LG"
] | Label noise refers to incorrect labels in a dataset caused by human errors or collection defects, which is common in real-world applications and can significantly reduce the accuracy of models. This report explores how to estimate noise transition matrices and construct deep learning classifiers that are robust against... | {
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2501.01406 | nnY-Net: Swin-NeXt with Cross-Attention for 3D Medical Images
Segmentation | [
"cs.CV"
] | This paper provides a novel 3D medical image segmentation model structure called nnY-Net. This name comes from the fact that our model adds a cross-attention module at the bottom of the U-net structure to form a Y structure. We integrate the advantages of the two latest SOTA models, MedNeXt and SwinUNETR, and use Swin ... | {
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2501.01407 | Nested Attention: Semantic-aware Attention Values for Concept
Personalization | [
"cs.CV",
"cs.GR",
"cs.LG"
] | Personalizing text-to-image models to generate images of specific subjects across diverse scenes and styles is a rapidly advancing field. Current approaches often face challenges in maintaining a balance between identity preservation and alignment with the input text prompt. Some methods rely on a single textual token ... | {
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2501.01409 | On Unifying Video Generation and Camera Pose Estimation | [
"cs.CV",
"cs.AI"
] | Inspired by the emergent 3D capabilities in image generators, we explore whether video generators similarly exhibit 3D awareness. Using structure-from-motion (SfM) as a benchmark for 3D tasks, we investigate if intermediate features from OpenSora, a video generation model, can support camera pose estimation. We first e... | {
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2501.01411 | Maximally Extendable Product Codes are Good Coboundary Expanders | [
"cs.IT",
"math.IT",
"quant-ph"
] | We investigate the coboundary expansion property of product codes called product expansion, which plays an important role in the recent constructions of good quantum LDPC codes and classical locally testable codes. Prior research revealed that this property is equivalent to agreement testability and robust testability ... | {
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2501.01414 | Deep Discrete Encoders: Identifiable Deep Generative Models for Rich
Data with Discrete Latent Layers | [
"stat.ML",
"cs.LG",
"stat.ME"
] | In the era of generative AI, deep generative models (DGMs) with latent representations have gained tremendous popularity. Despite their impressive empirical performance, the statistical properties of these models remain underexplored. DGMs are often overparametrized, non-identifiable, and uninterpretable black boxes, r... | {
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2501.01416 | Hierarchical Alignment-enhanced Adaptive Grounding Network for
Generalized Referring Expression Comprehension | [
"cs.CV"
] | In this work, we address the challenging task of Generalized Referring Expression Comprehension (GREC). Compared to the classic Referring Expression Comprehension (REC) that focuses on single-target expressions, GREC extends the scope to a more practical setting by further encompassing no-target and multi-target expres... | {
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2501.01420 | A Multi-task Supervised Compression Model for Split Computing | [
"cs.CV",
"cs.LG",
"eess.IV"
] | Split computing ($\neq$ split learning) is a promising approach to deep learning models for resource-constrained edge computing systems, where weak sensor (mobile) devices are wirelessly connected to stronger edge servers through channels with limited communication capacity. State-of-theart work on split computing pres... | {
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2501.01421 | R-SCoRe: Revisiting Scene Coordinate Regression for Robust Large-Scale
Visual Localization | [
"cs.CV"
] | Learning-based visual localization methods that use scene coordinate regression (SCR) offer the advantage of smaller map sizes. However, on datasets with complex illumination changes or image-level ambiguities, it remains a less robust alternative to feature matching methods. This work aims to close the gap. We introdu... | {
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2501.01422 | Multi-Modal Video Feature Extraction for Popularity Prediction | [
"cs.CV",
"cs.AI",
"cs.LG"
] | This work aims to predict the popularity of short videos using the videos themselves and their related features. Popularity is measured by four key engagement metrics: view count, like count, comment count, and share count. This study employs video classification models with different architectures and training methods... | {
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2501.01423 | Reconstruction vs. Generation: Taming Optimization Dilemma in Latent
Diffusion Models | [
"cs.CV",
"cs.LG"
] | Latent diffusion models with Transformer architectures excel at generating high-fidelity images. However, recent studies reveal an optimization dilemma in this two-stage design: while increasing the per-token feature dimension in visual tokenizers improves reconstruction quality, it requires substantially larger diffus... | {
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2501.01424 | Object-level Visual Prompts for Compositional Image Generation | [
"cs.CV",
"cs.AI",
"cs.GR"
] | We introduce a method for composing object-level visual prompts within a text-to-image diffusion model. Our approach addresses the task of generating semantically coherent compositions across diverse scenes and styles, similar to the versatility and expressiveness offered by text prompts. A key challenge in this task i... | {
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2501.01425 | Free-Form Motion Control: A Synthetic Video Generation Dataset with
Controllable Camera and Object Motions | [
"cs.CV"
] | Controlling the movements of dynamic objects and the camera within generated videos is a meaningful yet challenging task. Due to the lack of datasets with comprehensive motion annotations, existing algorithms can not simultaneously control the motions of both camera and objects, resulting in limited controllability ove... | {
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2501.01426 | Unifying Specialized Visual Encoders for Video Language Models | [
"cs.CV",
"cs.CL",
"cs.LG"
] | The recent advent of Large Language Models (LLMs) has ushered sophisticated reasoning capabilities into the realm of video through Video Large Language Models (VideoLLMs). However, VideoLLMs currently rely on a single vision encoder for all of their visual processing, which limits the amount and type of visual informat... | {
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2501.01427 | VideoAnydoor: High-fidelity Video Object Insertion with Precise Motion
Control | [
"cs.CV"
] | Despite significant advancements in video generation, inserting a given object into videos remains a challenging task. The difficulty lies in preserving the appearance details of the reference object and accurately modeling coherent motions at the same time. In this paper, we propose VideoAnydoor, a zero-shot video obj... | {
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2501.01428 | GPT4Scene: Understand 3D Scenes from Videos with Vision-Language Models | [
"cs.CV"
] | In recent years, 2D Vision-Language Models (VLMs) have made significant strides in image-text understanding tasks. However, their performance in 3D spatial comprehension, which is critical for embodied intelligence, remains limited. Recent advances have leveraged 3D point clouds and multi-view images as inputs, yieldin... | {
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2501.01429 | Item Association Factorization Mixed Markov Chains for Sequential
Recommendation | [
"cs.IR"
] | Sequential recommendation refers to recommending the next item of interest for a specific user based on his/her historical behavior sequence up to a certain time. While previous research has extensively examined Markov chain-based sequential recommendation models, the majority of these studies has focused on the user's... | {
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2501.01430 | TERA: A Simulation Environment for Terrain Excavation Robot Autonomy | [
"cs.RO"
] | Developing excavation autonomy is challenging given the environments where excavators operate, the complexity of physical interaction and the degrees of freedom of operation of the excavator itself. Simulation is a useful tool to build parts of the autonomy without the complexity of experimentation. Traditional excavat... | {
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2501.01431 | CSI Compression using Channel Charting | [
"cs.IT",
"cs.LG",
"eess.SP",
"math.IT"
] | Reaping the benefits of multi-antenna communication systems in frequency division duplex (FDD) requires channel state information (CSI) reporting from mobile users to the base station (BS). Over the last decades, the amount of CSI to be collected has become very challenging owing to the dramatic increase of the number ... | {
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2501.01432 | Survey on safe robot control via learning | [
"cs.RO",
"cs.AI"
] | Control systems are critical to modern technological infrastructure, spanning industries from aerospace to healthcare. This survey explores the landscape of safe robot learning, investigating methods that balance high-performance control with rigorous safety constraints. By examining classical control techniques, learn... | {
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2501.01433 | Mathematical Definition and Systematization of Puzzle Rules | [
"cs.AI",
"math.HO"
] | While logic puzzles have engaged individuals through problem-solving and critical thinking, the creation of new puzzle rules has largely relied on ad-hoc processes. Pencil puzzles, such as Slitherlink and Sudoku, represent a prominent subset of these games, celebrated for their intellectual challenges rooted in combina... | {
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2501.01435 | Fundamental Risks in the Current Deployment of General-Purpose AI
Models: What Have We (Not) Learnt From Cybersecurity? | [
"cs.CR",
"cs.AI"
] | General Purpose AI - such as Large Language Models (LLMs) - have seen rapid deployment in a wide range of use cases. Most surprisingly, they have have made their way from plain language models, to chat-bots, all the way to an almost ``operating system''-like status that can control decisions and logic of an application... | {
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2501.01437 | On the reconstruction limits of complex networks | [
"stat.AP",
"cs.IT",
"math.IT",
"physics.data-an"
] | Network reconstruction consists in retrieving the hidden interaction structure of a system from observations. Many reconstruction algorithms have been proposed, although less research has been devoted to describe their theoretical limitations. In this work, we adopt an information-theoretic perspective and define the r... | {
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2501.01438 | Toi uu hieu suat toc do dong co Servo DC su dung bo dieu khien PID ket
hop mang no-ron | [
"cs.RO"
] | DC motors have been widely used in many industrial applications, from small jointed robots with multiple degrees of freedom to household appliances and transportation vehicles such as electric cars and trains. The main function of these motors is to ensure stable positioning performance and speed for mechanical systems... | {
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2501.01439 | Probabilistic Mission Design in Neuro-Symbolic Systems | [
"cs.AI",
"cs.RO"
] | Advanced Air Mobility (AAM) is a growing field that demands accurate modeling of legal concepts and restrictions in navigating intelligent vehicles. In addition, any implementation of AAM needs to face the challenges posed by inherently dynamic and uncertain human-inhabited spaces robustly. Nevertheless, the employment... | {
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2501.01441 | Explanatory Debiasing: Involving Domain Experts in the Data Generation
Process to Mitigate Representation Bias in AI Systems | [
"cs.HC",
"cs.AI"
] | Representation bias is one of the most common types of biases in artificial intelligence (AI) systems, causing AI models to perform poorly on underrepresented data segments. Although AI practitioners use various methods to reduce representation bias, their effectiveness is often constrained by insufficient domain knowl... | {
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2501.01443 | Feedback Design and Implementation for Integrated Posture Manipulation
and Thrust Vectoring | [
"cs.RO"
] | This MS thesis outlines my contributions to the closed loop control and system integration of two robotic platforms: 1) Aerobat, a flapping wing robot stabilized by air jets, and 2) Harpy, a bipedal robot equipped with dual thrusters. Both systems share a common theme of the integration of posture manipulation and thru... | {
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2501.01447 | Analyzing Country-Level Vaccination Rates and Determinants of Practical
Capacity to Administer COVID-19 Vaccines | [
"econ.GN",
"cs.LG",
"econ.EM",
"q-fin.EC",
"stat.AP"
] | The COVID-19 vaccine development, manufacturing, transportation, and administration proved an extreme logistics operation of global magnitude. Global vaccination levels, however, remain a key concern in preventing the emergence of new strains and minimizing the impact of the pandemic's disruption of daily life. In this... | {
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2501.01449 | LS-GAN: Human Motion Synthesis with Latent-space GANs | [
"cs.CV",
"cs.AI"
] | Human motion synthesis conditioned on textual input has gained significant attention in recent years due to its potential applications in various domains such as gaming, film production, and virtual reality. Conditioned Motion synthesis takes a text input and outputs a 3D motion corresponding to the text. While previou... | {
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2501.01450 | Real-Time Computational Visual Aberration Correcting Display Through
High-Contrast Inverse Blurring | [
"eess.IV",
"cs.CV"
] | This paper presents a framework for developing a live vision-correcting display (VCD) to address refractive visual aberrations without the need for traditional vision correction devices like glasses or contact lenses, particularly in scenarios where wearing them may be inconvenient. We achieve this correction through d... | {
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2501.01451 | Human-AI Teaming Using Large Language Models: Boosting Brain-Computer
Interfacing (BCI) and Brain Research | [
"cs.HC",
"cs.AI"
] | Recently, there is an increasing interest in using artificial intelligence (AI) to automate aspects of the research process, or even autonomously conduct the full research cycle from idea generation, over data analysis, to composing and evaluation of scientific manuscripts. Examples of working AI scientist systems have... | {
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2501.01453 | Geometry Matters: Benchmarking Scientific ML Approaches for Flow
Prediction around Complex Geometries | [
"cs.LG",
"physics.flu-dyn"
] | Rapid yet accurate simulations of fluid dynamics around complex geometries is critical in a variety of engineering and scientific applications, including aerodynamics and biomedical flows. However, while scientific machine learning (SciML) has shown promise, most studies are constrained to simple geometries, leaving co... | {
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2501.01454 | A Fourfold Pathogen Reference Ontology Suite | [
"q-bio.OT",
"cs.AI",
"cs.LO"
] | Infectious diseases remain a critical global health challenge, and the integration of standardized ontologies plays a vital role in managing related data. The Infectious Disease Ontology (IDO) and its extensions, such as the Coronavirus Infectious Disease Ontology (CIDO), are essential for organizing and disseminating ... | {
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2501.01456 | SS-CTML: Self-Supervised Cross-Task Mutual Learning for CT Image
Reconstruction | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Supervised deep-learning (SDL) techniques with paired training datasets have been widely studied for X-ray computed tomography (CT) image reconstruction. However, due to the difficulties of obtaining paired training datasets in clinical routine, the SDL methods are still away from common uses in clinical practices. In ... | {
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2501.01457 | Reinforcing Thinking through Reasoning-Enhanced Reward Models | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Large Language Models (LLMs) exhibit great potential in complex multi-step reasoning through inference-time thinking but still struggle with deciding when to stop thinking due to limited self-awareness about their knowledge boundaries. While human preference alignment has shown extraordinary opportunities, expensive la... | {
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2501.01458 | GAN-TAT: A Novel Framework Using Protein Interaction Networks in
Druggable Gene Identification | [
"cs.LG",
"cs.AI",
"q-bio.QM"
] | Identifying druggable genes is essential for developing effective pharmaceuticals. With the availability of extensive, high-quality data, computational methods have become a significant asset. Protein Interaction Network (PIN) is valuable but challenging to implement due to its high dimensionality and sparsity. Previou... | {
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2501.01460 | GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet
Losses for Remote Sensing Image Super-Resolution | [
"eess.IV",
"cs.CV",
"cs.LG"
] | In recent years, deep neural networks, including Convolutional Neural Networks, Transformers, and State Space Models, have achieved significant progress in Remote Sensing Image (RSI) Super-Resolution (SR). However, existing SR methods typically overlook the complementary relationship between global and local dependenci... | {
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2501.01462 | Pan-infection Foundation Framework Enables Multiple Pathogen Prediction | [
"cs.LG",
"cs.AI",
"q-bio.GN"
] | Host-response-based diagnostics can improve the accuracy of diagnosing bacterial and viral infections, thereby reducing inappropriate antibiotic prescriptions. However, the existing cohorts with limited sample size and coarse infections types are unable to support the exploration of an accurate and generalizable diagno... | {
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2501.01463 | Goal Recognition using Actor-Critic Optimization | [
"cs.LG",
"cs.AI",
"cs.MA"
] | Goal Recognition aims to infer an agent's goal from a sequence of observations. Existing approaches often rely on manually engineered domains and discrete representations. Deep Recognition using Actor-Critic Optimization (DRACO) is a novel approach based on deep reinforcement learning that overcomes these limitations b... | {
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2501.01464 | Estimation of 3T MR images from 1.5T images regularized with Physics
based Constraint | [
"eess.IV",
"cs.CV",
"cs.LG",
"physics.med-ph"
] | Limited accessibility to high field MRI scanners (such as 7T, 11T) has motivated the development of post-processing methods to improve low field images. Several existing post-processing methods have shown the feasibility to improve 3T images to produce 7T-like images [3,18]. It has been observed that improving lower fi... | {
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2501.01465 | Tech Report: Divide and Conquer 3D Real-Time Reconstruction for Improved
IGS | [
"eess.IV",
"cs.CV"
] | Tracking surgical modifications based on endoscopic videos is technically feasible and of great clinical advantages; however, it still remains challenging. This report presents a modular pipeline to divide and conquer the clinical challenges in the process. The pipeline integrates frame selection, depth estimation, and... | {
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2501.01470 | Balance-aware Sequence Sampling Makes Multi-modal Learning Better | [
"cs.LG",
"cs.AI"
] | To address the modality imbalance caused by data heterogeneity, existing multi-modal learning (MML) approaches primarily focus on balancing this difference from the perspective of optimization objectives. However, almost all existing methods ignore the impact of sample sequences, i.e., an inappropriate training order t... | {
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2501.01472 | Augmented Contrastive Clustering with Uncertainty-Aware Prototyping for
Time Series Test Time Adaptation | [
"cs.LG",
"cs.AI"
] | Test-time adaptation aims to adapt pre-trained deep neural networks using solely online unlabelled test data during inference. Although TTA has shown promise in visual applications, its potential in time series contexts remains largely unexplored. Existing TTA methods, originally designed for visual tasks, may not effe... | {
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} |
2501.01473 | Unraveling Indirect In-Context Learning Using Influence Functions | [
"cs.LG",
"cs.AI"
] | This work introduces a novel paradigm for generalized In-Context Learning (ICL), termed Indirect In-Context Learning. In Indirect ICL, we explore demonstration selection strategies tailored for two distinct real-world scenarios: Mixture of Tasks and Noisy Demonstrations. We systematically evaluate the effectiveness of ... | {
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} |
2501.01477 | A Survey of Deep Learning Methods in Protein Bioinformatics and its
Impact on Protein Design | [
"q-bio.BM",
"cs.AI"
] | Proteins are sequences of amino acids that serve as the basic building blocks of living organisms. Despite rapidly growing databases documenting structural and functional information for various protein sequences, our understanding of proteins remains limited because of the large possible sequence space and the complex... | {
"Other": 0,
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} |
2501.01478 | Enhancing Reasoning through Process Supervision with Monte Carlo Tree
Search | [
"cs.AI",
"cs.CL",
"cs.LG"
] | Large language models (LLMs) have demonstrated their remarkable capacity across a variety of tasks. However, reasoning remains a challenge for LLMs. To improve LLMs' reasoning ability, process supervision has proven to be better than outcome supervision. In this work, we study using Monte Carlo Tree Search (MCTS) to ge... | {
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} |
2501.01480 | CORAL: Concept Drift Representation Learning for Co-evolving Time-series | [
"cs.LG",
"cs.AI"
] | In the realm of time series analysis, tackling the phenomenon of concept drift poses a significant challenge. Concept drift -- characterized by the evolving statistical properties of time series data, affects the reliability and accuracy of conventional analysis models. This is particularly evident in co-evolving scena... | {
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} |
2501.01481 | Unleashing Correlation and Continuity for Hyperspectral Reconstruction
from RGB Images | [
"eess.IV",
"cs.CV"
] | Reconstructing Hyperspectral Images (HSI) from RGB images can yield high spatial resolution HSI at a lower cost, demonstrating significant application potential. This paper reveals that local correlation and global continuity of the spectral characteristics are crucial for HSI reconstruction tasks. Therefore, we fully ... | {
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} |
2501.01482 | An unsupervised method for MRI recovery: Deep image prior with
structured sparsity | [
"eess.IV",
"cs.CV",
"cs.LG",
"eess.SP"
] | Objective: To propose and validate an unsupervised MRI reconstruction method that does not require fully sampled k-space data. Materials and Methods: The proposed method, deep image prior with structured sparsity (DISCUS), extends the deep image prior (DIP) by introducing group sparsity to frame-specific code vectors, ... | {
"Other": 0,
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} |
2501.01483 | Embedding Similarity Guided License Plate Super Resolution | [
"eess.IV",
"cs.CV"
] | Super-resolution (SR) techniques play a pivotal role in enhancing the quality of low-resolution images, particularly for applications such as security and surveillance, where accurate license plate recognition is crucial. This study proposes a novel framework that combines pixel-based loss with embedding similarity lea... | {
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} |
2501.01484 | Sequencing Silicates in the IRS Debris Disk Catalog I: Methodology for
Unsupervised Clustering | [
"astro-ph.EP",
"astro-ph.IM",
"cs.LG"
] | Debris disks, which consist of dust, planetesimals, planets, and gas, offer a unique window into the mineralogical composition of their parent bodies, especially during the critical phase of terrestrial planet formation spanning 10 to a few hundred million years. Observations from the $\textit{Spitzer}$ Space Telescope... | {
"Other": 0,
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} |
2501.01496 | ORACLE: A Real-Time, Hierarchical, Deep-Learning Photometric Classifier
for the LSST | [
"astro-ph.IM",
"astro-ph.HE",
"cs.AI",
"cs.LG"
] | We present ORACLE, the first hierarchical deep-learning model for real-time, context-aware classification of transient and variable astrophysical phenomena. ORACLE is a recurrent neural network with Gated Recurrent Units (GRUs), and has been trained using a custom hierarchical cross-entropy loss function to provide hig... | {
"Other": 0,
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} |
2501.01502 | Block components of generalized quaternion group codes | [
"cs.IT",
"math.CO",
"math.IT"
] | Codes in the generalized quaternion group algebra $\mathbb{F}_q[Q_{4n}]$ are considered. Restricting to char$\mathbb{F}_q \nmid 4n$ the structure of an arbitrary code $C \subseteq \mathbb{F}_q[Q_{4n}]$ is described via the Wedderburn decomposition. Moreover it is known that in this case every code $C \subseteq \mathbb{... | {
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"cs.SY": 0
} |
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