id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.04373 | FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal
3D Object Detection | [
"cs.CV"
] | Multimodal 3D object detection has garnered considerable interest in autonomous driving. However, multimodal detectors suffer from dimension mismatches that derive from fusing 3D points with 2D pixels coarsely, which leads to sub-optimal fusion performance. In this paper, we propose a multimodal framework FGU3R to tack... | {
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2501.04374 | Instructive3D: Editing Large Reconstruction Models with Text
Instructions | [
"cs.CV"
] | Transformer based methods have enabled users to create, modify, and comprehend text and image data. Recently proposed Large Reconstruction Models (LRMs) further extend this by providing the ability to generate high-quality 3D models with the help of a single object image. These models, however, lack the ability to mani... | {
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2501.04376 | Exploring Unbiased Deepfake Detection via Token-Level Shuffling and
Mixing | [
"cs.CV"
] | The generalization problem is broadly recognized as a critical challenge in detecting deepfakes. Most previous work believes that the generalization gap is caused by the differences among various forgery methods. However, our investigation reveals that the generalization issue can still occur when forgery-irrelevant fa... | {
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2501.04377 | On Computational Limits and Provably Efficient Criteria of Visual
Autoregressive Models: A Fine-Grained Complexity Analysis | [
"cs.LG",
"cs.AI",
"cs.CC",
"cs.CV"
] | Recently, Visual Autoregressive ($\mathsf{VAR}$) Models introduced a groundbreaking advancement in the field of image generation, offering a scalable approach through a coarse-to-fine ``next-scale prediction'' paradigm. Suppose that $n$ represents the height and width of the last VQ code map generated by $\mathsf{VAR}$... | {
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2501.04387 | The unbearable lightness of Restricted Boltzmann Machines: Theoretical
Insights and Biological Applications | [
"cond-mat.dis-nn",
"cs.LG",
"physics.data-an"
] | Restricted Boltzmann Machines are simple yet powerful neural networks. They can be used for learning structure in data, and are used as a building block of more complex neural architectures. At the same time, their simplicity makes them easy to use, amenable to theoretical analysis, yielding interpretable models in app... | {
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2501.04389 | Evidence-based multimodal fusion on structured EHRs and free-text notes
for ICU outcome prediction | [
"cs.IT",
"math.IT"
] | Objective: Accurate Intensive Care Unit (ICU) outcome prediction is critical for improving patient treatment quality and ICU resource allocation. Existing research mainly focuses on structured data and lacks effective frameworks to integrate clinical notes from heterogeneous electronic health records (EHRs). This study... | {
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2501.04390 | iFADIT: Invertible Face Anonymization via Disentangled Identity
Transform | [
"cs.CV"
] | Face anonymization aims to conceal the visual identity of a face to safeguard the individual's privacy. Traditional methods like blurring and pixelation can largely remove identifying features, but these techniques significantly degrade image quality and are vulnerable to deep reconstruction attacks. Generative models ... | {
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2501.04393 | SEO: Stochastic Experience Optimization for Large Language Models | [
"cs.CL"
] | Large Language Models (LLMs) can benefit from useful experiences to improve their performance on specific tasks. However, finding helpful experiences for different LLMs is not obvious, since it is unclear what experiences suit specific LLMs. Previous studies intended to automatically find useful experiences using LLMs,... | {
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2501.04398 | Implementation Of Wildlife Observation System | [
"cs.RO"
] | By entering the habitats of wild animals, wildlife watchers can engage closely with them. There are some wild animals that are not always safe to approach. Therefore, we suggest this system for observing wildlife. Android phones can be used by users to see live events. Wildlife observers can thus get a close-up view of... | {
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2501.04401 | Tracking UWB Devices Through Radio Frequency Fingerprinting Is Possible | [
"cs.LG",
"cs.IT",
"cs.NI",
"math.IT"
] | Ultra-wideband (UWB) is a state-of-the-art technology designed for applications requiring centimeter-level localization. Its widespread adoption by smartphone manufacturer naturally raises security and privacy concerns. Successfully implementing Radio Frequency Fingerprinting (RFF) to UWB could enable physical layer se... | {
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2501.04403 | Rising Rested MAB with Linear Drift | [
"cs.LG"
] | We consider non-stationary multi-arm bandit (MAB) where the expected reward of each action follows a linear function of the number of times we executed the action. Our main result is a tight regret bound of $\tilde{\Theta}(T^{4/5}K^{3/5})$, by providing both upper and lower bounds. We extend our results to derive insta... | {
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2501.04408 | Resource Allocation for the Training of Image Semantic Communication
Networks | [
"cs.SI"
] | Semantic communication is a new paradigm that aims at providing more efficient communication for the next-generation wireless network. It focuses on transmitting extracted, meaningful information instead of the raw data. However, deep learning-enabled image semantic communication models often require a significant amou... | {
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2501.04409 | Lossless Privacy-Preserving Aggregation for Decentralized Federated
Learning | [
"cs.LG"
] | Privacy concerns arise as sensitive data proliferate. Despite decentralized federated learning (DFL) aggregating gradients from neighbors to avoid direct data transmission, it still poses indirect data leaks from the transmitted gradients. Existing privacy-preserving methods for DFL add noise to gradients. They either ... | {
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2501.04410 | User Simulation in the Era of Generative AI: User Modeling, Synthetic
Data Generation, and System Evaluation | [
"cs.AI",
"cs.HC",
"cs.IR",
"cs.LG"
] | User simulation is an emerging interdisciplinary topic with multiple critical applications in the era of Generative AI. It involves creating an intelligent agent that mimics the actions of a human user interacting with an AI system, enabling researchers to model and analyze user behaviour, generate synthetic data for t... | {
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2501.04413 | Machine Learning and statistical classification of CRISPR-Cas12a
diagnostic assays | [
"q-bio.QM",
"cs.LG"
] | CRISPR-based diagnostics have gained increasing attention as biosensing tools able to address limitations in contemporary molecular diagnostic tests. To maximise the performance of CRISPR-based assays, much effort has focused on optimizing the chemistry and biology of the biosensing reaction. However, less attention ha... | {
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2501.04418 | Not All Bonds Are Created Equal: Dyadic Latent Class Models for
Relational Event Data | [
"cs.SI"
] | Dynamic social networks can be conceptualized as sequences of dyadic interactions between individuals over time. The relational event model has been the workhorse to analyze such interaction sequences in empirical social network research. When addressing possible unobserved heterogeneity in the interaction mechanisms, ... | {
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2501.04420 | A Closer Look on Gender Stereotypes in Movie Recommender Systems and
Their Implications with Privacy | [
"cs.IR"
] | The movie recommender system typically leverages user feedback to provide personalized recommendations that align with user preferences and increase business revenue. This study investigates the impact of gender stereotypes on such systems through a specific attack scenario. In this scenario, an attacker determines use... | {
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2501.04421 | Risk-averse policies for natural gas futures trading using
distributional reinforcement learning | [
"cs.LG"
] | Financial markets have experienced significant instabilities in recent years, creating unique challenges for trading and increasing interest in risk-averse strategies. Distributional Reinforcement Learning (RL) algorithms, which model the full distribution of returns rather than just expected values, offer a promising ... | {
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2501.04422 | A new methodology for the optimization of bolt tightening sequences for
ring type joints | [
"eess.SY",
"cs.SY"
] | Achieving uniform bolt load distribution is critical to obtain leak-free service in pressure vessel gasketed joints used in offshore pipelines. This is a difficult task due to bolt load variations during the assembly process. In this sense, the Elastic Interaction Coefficients Method has been developed in previous work... | {
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2501.04424 | NSA: Neuro-symbolic ARC Challenge | [
"cs.AI",
"cs.CL"
] | The Abstraction and Reasoning Corpus (ARC) evaluates general reasoning capabilities that are difficult for both machine learning models and combinatorial search methods. We propose a neuro-symbolic approach that combines a transformer for proposal generation with combinatorial search using a domain-specific language. T... | {
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2501.04425 | End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark:
Leveraging Large Language Model Using Integrated Approach | [
"cs.CL"
] | This work introduces systematic approach for enhancing large language models (LLMs) to address Bangla AI mathematical challenges. Through the assessment of diverse LLM configurations, fine-tuning with specific datasets, and the implementation of Retrieval-Augmented Generation (RAG), we enhanced the model's reasoning pr... | {
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2501.04426 | Dual-Force: Enhanced Offline Diversity Maximization under Imitation
Constraints | [
"cs.LG",
"cs.AI",
"cs.RO"
] | While many algorithms for diversity maximization under imitation constraints are online in nature, many applications require offline algorithms without environment interactions. Tackling this problem in the offline setting, however, presents significant challenges that require non-trivial, multi-stage optimization proc... | {
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2501.04435 | A Digital Shadow for Modeling, Studying and Preventing Urban Crime | [
"cs.AI",
"cs.MA",
"cs.SI"
] | Crime is one of the greatest threats to urban security. Around 80 percent of the world's population lives in countries with high levels of criminality. Most of the crimes committed in the cities take place in their urban environments. This paper presents the development and validation of a digital shadow platform for m... | {
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2501.04436 | Federated Fine-Tuning of LLMs: Framework Comparison and Research
Directions | [
"cs.LG",
"cs.AI"
] | Federated learning (FL) provides a privacy-preserving solution for fine-tuning pre-trained large language models (LLMs) using distributed private datasets, enabling task-specific adaptation while preserving data privacy. However, fine-tuning the extensive parameters in LLMs is particularly challenging in resource-const... | {
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2501.04437 | Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and
Future Directions | [
"eess.SY",
"cs.AI",
"cs.ET",
"cs.SY"
] | Intelligent Transportation Systems (ITS) are crucial for the development and operation of smart cities, addressing key challenges in efficiency, productivity, and environmental sustainability. This paper comprehensively reviews the transformative potential of Large Language Models (LLMs) in optimizing ITS. Initially, w... | {
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2501.04438 | Effect of Information Technology on Job Creation to Support Economic:
Case Studies of Graduates in Universities (2023-2024) of the KRG of Iraq | [
"cs.CY",
"cs.AI"
] | The aim of this study is to assess the impact of information technology (IT) on university graduates in terms of employment development, which will aid in economic issues. This study uses a descriptive research methodology and a quantitative approach to understand variables. The focus of this study is to ascertain how ... | {
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2501.04440 | RSAR: Restricted State Angle Resolver and Rotated SAR Benchmark | [
"cs.CV"
] | Rotated object detection has made significant progress in the optical remote sensing. However, advancements in the Synthetic Aperture Radar (SAR) field are laggard behind, primarily due to the absence of a large-scale dataset. Annotating such a dataset is inefficient and costly. A promising solution is to employ a weak... | {
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2501.04441 | Motif Discovery Framework for Psychiatric EEG Data Classification | [
"cs.LG"
] | In current medical practice, patients undergoing depression treatment must wait four to six weeks before a clinician can assess medication response due to the delayed noticeable effects of antidepressants. Identification of a treatment response at any earlier stage is of great importance, since it can reduce the emotio... | {
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2501.04442 | A Survey on Path Planning Problem of Rolling Contacts: Approaches,
Applications and Future Challenges | [
"cs.RO"
] | This paper explores an eclectic range of path-planning methodologies engineered for rolling surfaces. Our focus is on the kinematic intricacies of rolling contact systems, which are investigated through a motion planning lens. Beyond summarizing the approaches to single-contact rotational surfaces, we explore the chall... | {
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2501.04443 | Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis | [
"math.OC",
"cs.DC",
"cs.LG"
] | LocalSGD and SCAFFOLD are widely used methods in distributed stochastic optimization, with numerous applications in machine learning, large-scale data processing, and federated learning. However, rigorously establishing their theoretical advantages over simpler methods, such as minibatch SGD (MbSGD), has proven challen... | {
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2501.04444 | A novel Facial Recognition technique with Focusing on Masked Faces | [
"cs.CV",
"cs.AI"
] | Recognizing the same faces with and without masks is important for ensuring consistent identification in security, access control, and public safety. This capability is crucial in scenarios like law enforcement, healthcare, and surveillance, where accurate recognition must be maintained despite facial occlusion. This r... | {
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2501.04453 | Gradient Purification: Defense Against Poisoning Attack in Decentralized
Federated Learning | [
"cs.LG"
] | Decentralized federated learning (DFL) is inherently vulnerable to poisoning attacks, as malicious clients can transmit manipulated model gradients to neighboring clients. Existing defense methods either reject suspicious gradients per iteration or restart DFL aggregation after detecting all malicious clients. They ove... | {
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2501.04455 | Hidden Entity Detection from GitHub Leveraging Large Language Models | [
"cs.CL",
"cs.DL"
] | Named entity recognition is an important task when constructing knowledge bases from unstructured data sources. Whereas entity detection methods mostly rely on extensive training data, Large Language Models (LLMs) have paved the way towards approaches that rely on zero-shot learning (ZSL) or few-shot learning (FSL) by ... | {
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2501.04459 | Rapid Automated Mapping of Clouds on Titan With Instance Segmentation | [
"astro-ph.IM",
"astro-ph.EP",
"cs.CV",
"eess.IV"
] | Despite widespread adoption of deep learning models to address a variety of computer vision tasks, planetary science has yet to see extensive utilization of such tools to address its unique problems. On Titan, the largest moon of Saturn, tracking seasonal trends and weather patterns of clouds provides crucial insights ... | {
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2501.04467 | A Histologic Dataset of Normal and Atypical Mitotic Figures on Human
Breast Cancer (AMi-Br) | [
"cs.CV",
"cs.DB"
] | Assessment of the density of mitotic figures (MFs) in histologic tumor sections is an important prognostic marker for many tumor types, including breast cancer. Recently, it has been reported in multiple works that the quantity of MFs with an atypical morphology (atypical MFs, AMFs) might be an independent prognostic c... | {
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2501.04470 | Regularising NARX models with multi-task learning | [
"cs.LG"
] | A Nonlinear Auto-Regressive with eXogenous inputs (NARX) model can be used to describe time-varying processes; where the output depends on both previous outputs and current/previous external input variables. One limitation of NARX models is their propensity to overfit and result in poor generalisation for future predic... | {
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2501.04472 | Hybrid Artificial Intelligence Strategies for Drone Navigation | [
"cs.AI",
"cs.RO"
] | Objective: This paper describes the development of hybrid artificial intelligence strategies for drone navigation. Methods: The navigation module combines a deep learning model with a rule-based engine depending on the agent state. The deep learning model has been trained using reinforcement learning. The rule-based en... | {
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2501.04473 | When LLMs Struggle: Reference-less Translation Evaluation for
Low-resource Languages | [
"cs.CL"
] | This paper investigates the reference-less evaluation of machine translation for low-resource language pairs, known as quality estimation (QE). Segment-level QE is a challenging cross-lingual language understanding task that provides a quality score (0-100) to the translated output. We comprehensively evaluate large la... | {
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2501.04477 | Rethinking High-speed Image Reconstruction Framework with Spike Camera | [
"cs.CV"
] | Spike cameras, as innovative neuromorphic devices, generate continuous spike streams to capture high-speed scenes with lower bandwidth and higher dynamic range than traditional RGB cameras. However, reconstructing high-quality images from the spike input under low-light conditions remains challenging. Conventional lear... | {
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2501.04480 | Research on environment perception and behavior prediction of
intelligent UAV based on semantic communication | [
"cs.AI",
"cs.RO"
] | The convergence of drone delivery systems, virtual worlds, and blockchain has transformed logistics and supply chain management, providing a fast, and environmentally friendly alternative to traditional ground transportation methods;Provide users with a real-world experience, virtual service providers need to collect u... | {
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2501.04481 | Safe Reinforcement Learning with Minimal Supervision | [
"cs.LG",
"cs.RO",
"cs.SY",
"eess.SY"
] | Reinforcement learning (RL) in the real world necessitates the development of procedures that enable agents to explore without causing harm to themselves or others. The most successful solutions to the problem of safe RL leverage offline data to learn a safe-set, enabling safe online exploration. However, this approach... | {
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2501.04483 | Demystification and Near-perfect Estimation of Minimum Gas Limit and Gas
Used for Ethereum Smart Contracts | [
"cs.SE",
"cs.CE",
"cs.DC",
"cs.ET",
"cs.NI"
] | The Ethereum blockchain has a \emph{gas system} that associates operations with a cost in gas units. Two central concepts of this system are the \emph{gas limit} assigned by the issuer of a transaction and the \emph{gas used} by a transaction. The former is a budget that must not be exhausted before the completion of t... | {
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2501.04484 | PolInterviews -- A Dataset of German Politician Public Broadcast
Interviews | [
"cs.CL"
] | This paper presents a novel dataset of public broadcast interviews featuring high-ranking German politicians. The interviews were sourced from YouTube, transcribed, processed for speaker identification, and stored in a tidy and open format. The dataset comprises 99 interviews with 33 different German politicians across... | {
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2501.04486 | MB-TaylorFormer V2: Improved Multi-branch Linear Transformer Expanded by
Taylor Formula for Image Restoration | [
"cs.CV"
] | Recently, Transformer networks have demonstrated outstanding performance in the field of image restoration due to the global receptive field and adaptability to input. However, the quadratic computational complexity of Softmax-attention poses a significant limitation on its extensive application in image restoration ta... | {
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2501.04487 | Integrating remote sensing data assimilation, deep learning and large
language model for interactive wheat breeding yield prediction | [
"cs.LG",
"cs.AI"
] | Yield is one of the core goals of crop breeding. By predicting the potential yield of different breeding materials, breeders can screen these materials at various growth stages to select the best performing. Based on unmanned aerial vehicle remote sensing technology, high-throughput crop phenotyping data in breeding ar... | {
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2501.04493 | The Role of Machine Learning in Congenital Heart Disease Diagnosis:
Datasets, Algorithms, and Insights | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Congenital heart disease is among the most common fetal abnormalities and birth defects. Despite identifying numerous risk factors influencing its onset, a comprehensive understanding of its genesis and management across diverse populations remains limited. Recent advancements in machine learning have demonstrated the ... | {
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2501.04503 | Developing a Modular Compiler for a Subset of a C-like Language | [
"cs.PL",
"cs.CL",
"cs.DC",
"cs.PF"
] | The paper introduces the development of a modular compiler for a subset of a C-like language, which addresses the challenges in constructing a compiler for high-level languages. This modular approach will allow developers to modify a language by adding or removing subsets as required, resulting in a minimal and memory-... | {
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2501.04508 | Linear Model of Aggregated Homogeneous Energy Storage Elements with
Realizable Dispatch Guarantees | [
"eess.SY",
"cs.SY"
] | To optimize battery dispatch, a model is required that can predict the state of charge (SOC) trajectory and ensure dispatch is admissible (i.e., does not lead to unexpected SOC saturation). However, battery dispatch optimization is inherently challenging since batteries cannot simultaneously charge and discharge, which... | {
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2501.04510 | CGP-Tuning: Structure-Aware Soft Prompt Tuning for Code Vulnerability
Detection | [
"cs.SE",
"cs.AI"
] | Large language models (LLMs) have been proposed as powerful tools for detecting software vulnerabilities, where task-specific fine-tuning is typically employed to provide vulnerability-specific knowledge to the LLMs for this purpose. However, traditional full-parameter fine-tuning is inefficient for modern, complex LLM... | {
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2501.04513 | Improving Image Captioning by Mimicking Human Reformulation Feedback at
Inference-time | [
"cs.CV",
"cs.CL"
] | Incorporating automatically predicted human feedback into the process of training generative models has attracted substantial recent interest, while feedback at inference time has received less attention. The typical feedback at training time, i.e., preferences of choice given two samples, does not naturally transfer t... | {
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2501.04515 | SplineFormer: An Explainable Transformer-Based Approach for Autonomous
Endovascular Navigation | [
"eess.IV",
"cs.CV",
"cs.RO"
] | Endovascular navigation is a crucial aspect of minimally invasive procedures, where precise control of curvilinear instruments like guidewires is critical for successful interventions. A key challenge in this task is accurately predicting the evolving shape of the guidewire as it navigates through the vasculature, whic... | {
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2501.04517 | Histogram-Equalized Quantization for logic-gated Residual Neural
Networks | [
"cs.LG",
"cs.AR"
] | Adjusting the quantization according to the data or to the model loss seems mandatory to enable a high accuracy in the context of quantized neural networks. This work presents Histogram-Equalized Quantization (HEQ), an adaptive framework for linear symmetric quantization. HEQ automatically adapts the quantization thres... | {
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2501.04519 | rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep
Thinking | [
"cs.CL"
] | We present rStar-Math to demonstrate that small language models (SLMs) can rival or even surpass the math reasoning capability of OpenAI o1, without distillation from superior models. rStar-Math achieves this by exercising "deep thinking" through Monte Carlo Tree Search (MCTS), where a math policy SLM performs test-tim... | {
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2501.04527 | Towards Fair Class-wise Robustness: Class Optimal Distribution
Adversarial Training | [
"cs.LG",
"cs.CV"
] | Adversarial training has proven to be a highly effective method for improving the robustness of deep neural networks against adversarial attacks. Nonetheless, it has been observed to exhibit a limitation in terms of robust fairness, characterized by a significant disparity in robustness across different classes. Recent... | {
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2501.04528 | Towards a Problem-Oriented Domain Adaptation Framework for Machine
Learning | [
"cs.LG",
"cs.AI"
] | Domain adaptation is a sub-field of machine learning that involves transferring knowledge from a source domain to perform the same task in the target domain. It is a typical challenge in machine learning that arises, e.g., when data is obtained from various sources or when using a data basis that changes over time. Rec... | {
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2501.04529 | A Plug-and-Play Bregman ADMM Module for Inferring Event Branches in
Temporal Point Processes | [
"cs.LG"
] | An event sequence generated by a temporal point process is often associated with a hidden and structured event branching process that captures the triggering relations between its historical and current events. In this study, we design a new plug-and-play module based on the Bregman ADMM (BADMM) algorithm, which infers... | {
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2501.04534 | Combining YOLO and Visual Rhythm for Vehicle Counting | [
"cs.CV",
"cs.LG"
] | Video-based vehicle detection and counting play a critical role in managing transport infrastructure. Traditional image-based counting methods usually involve two main steps: initial detection and subsequent tracking, which are applied to all video frames, leading to a significant increase in computational complexity. ... | {
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2501.04538 | HypeRL: Parameter-Informed Reinforcement Learning for Parametric PDEs | [
"cs.LG"
] | In this work, we devise a new, general-purpose reinforcement learning strategy for the optimal control of parametric partial differential equations (PDEs). Such problems frequently arise in applied sciences and engineering and entail a significant complexity when control and/or state variables are distributed in high-d... | {
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2501.04541 | Cyber-Physical Steganography in Robotic Motion Control | [
"cs.RO",
"cs.AI",
"cs.CR"
] | Steganography, the art of information hiding, has continually evolved across visual, auditory and linguistic domains, adapting to the ceaseless interplay between steganographic concealment and steganalytic revelation. This study seeks to extend the horizons of what constitutes a viable steganographic medium by introduc... | {
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2501.04547 | Medical artificial intelligence toolbox (MAIT): an explainable machine
learning framework for binary classification, survival modelling, and
regression analyses | [
"cs.LG"
] | While machine learning offers diverse techniques suitable for exploring various medical research questions, a cohesive synergistic framework can facilitate the integration and understanding of new approaches within unified model development and interpretation. We therefore introduce the Medical Artificial Intelligence ... | {
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2501.04561 | OpenOmni: Large Language Models Pivot Zero-shot Omnimodal Alignment
across Language with Real-time Self-Aware Emotional Speech Synthesis | [
"cs.CL",
"cs.CV"
] | Recent advancements in omnimodal learning have been achieved in understanding and generation across images, text, and speech, though mainly within proprietary models. Limited omnimodal datasets and the inherent challenges associated with real-time emotional speech generation have hindered open-source progress. To addre... | {
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2501.04565 | Learnable Scaled Gradient Descent for Guaranteed Robust Tensor PCA | [
"cs.CV"
] | Robust tensor principal component analysis (RTPCA) aims to separate the low-rank and sparse components from multi-dimensional data, making it an essential technique in the signal processing and computer vision fields. Recently emerging tensor singular value decomposition (t-SVD) has gained considerable attention for it... | {
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2501.04566 | Recursive Least Squares with Fading Regularization for Finite-Time
Convergence without Persistent Excitation | [
"eess.SP",
"cs.SY",
"eess.SY"
] | This paper extends recursive least squares (RLS) to include time-varying regularization. This extension provides flexibility for updating the least squares regularization term in real time. Existing results with constant regularization imply that the parameter-estimation error dynamics of RLS are globally attractive to... | {
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2501.04568 | Supervision-free Vision-Language Alignment | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] | Vision-language models (VLMs) have demonstrated remarkable potential in integrating visual and linguistic information, but their performance is often constrained by the need for extensive, high-quality image-text training data. Curation of these image-text pairs is both time-consuming and computationally expensive. To ... | {
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2501.04570 | Large-Scale Spectral Graph Neural Networks via Laplacian Sparsification:
Technical Report | [
"cs.LG"
] | Graph Neural Networks (GNNs) play a pivotal role in graph-based tasks for their proficiency in representation learning. Among the various GNN methods, spectral GNNs employing polynomial filters have shown promising performance on tasks involving both homophilous and heterophilous graph structures. However, The scalabil... | {
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2501.04572 | Regret Analysis: a control perspective | [
"eess.SY",
"cs.LG",
"cs.SY",
"math.OC"
] | Online learning and model reference adaptive control have many interesting intersections. One area where they differ however is in how the algorithms are analyzed and what objective or metric is used to discriminate "good" algorithms from "bad" algorithms. In adaptive control there are usually two objectives: 1) prove ... | {
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2501.04575 | InfiGUIAgent: A Multimodal Generalist GUI Agent with Native Reasoning
and Reflection | [
"cs.AI",
"cs.CL",
"cs.HC"
] | Graphical User Interface (GUI) Agents, powered by multimodal large language models (MLLMs), have shown great potential for task automation on computing devices such as computers and mobile phones. However, existing agents face challenges in multi-step reasoning and reliance on textual annotations, limiting their effect... | {
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2501.04577 | A 65 nm Bayesian Neural Network Accelerator with 360 fJ/Sample In-Word
GRNG for AI Uncertainty Estimation | [
"cs.AR",
"cs.AI",
"cs.LG",
"cs.RO"
] | Uncertainty estimation is an indispensable capability for AI-enabled, safety-critical applications, e.g. autonomous vehicles or medical diagnosis. Bayesian neural networks (BNNs) use Bayesian statistics to provide both classification predictions and uncertainty estimation, but they suffer from high computational overhe... | {
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2501.04578 | Analysis of Climatic Trends and Variability in Indian Topography | [
"cs.SI"
] | The climatic change is one of the serious concerns nowadays. The impacts of climate change are global in scope and unprecedented in scale. Moreover, a small perturbation in climatic changes affects not only the pristine ecosystem but also the socioeconomic sectors. Specifically, the affect of climatic changes is relate... | {
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2501.04579 | Unified Coding for Both Human Perception and Generalized Machine
Analytics with CLIP Supervision | [
"cs.CV",
"cs.MM"
] | The image compression model has long struggled with adaptability and generalization, as the decoded bitstream typically serves only human or machine needs and fails to preserve information for unseen visual tasks. Therefore, this paper innovatively introduces supervision obtained from multimodal pre-training models and... | {
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2501.04582 | Boosting Salient Object Detection with Knowledge Distillated from Large
Foundation Models | [
"cs.CV"
] | Salient Object Detection (SOD) aims to identify and segment prominent regions within a scene. Traditional models rely on manually annotated pseudo labels with precise pixel-level accuracy, which is time-consuming. We developed a low-cost, high-precision annotation method by leveraging large foundation models to address... | {
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2501.04584 | A Direct-adjoint Approach for Material Point Model Calibration with
Application to Plasticity | [
"cs.CE"
] | This paper proposes a new approach for the calibration of material parameters in elastoplastic constitutive models. The calibration is posed as a constrained optimization problem, where the constitutive evolution equations serve as constraints. The objective function quantifies the mismatch between the stress predicted... | {
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2501.04586 | Identity-Preserving Video Dubbing Using Motion Warping | [
"cs.CV"
] | Video dubbing aims to synthesize realistic, lip-synced videos from a reference video and a driving audio signal. Although existing methods can accurately generate mouth shapes driven by audio, they often fail to preserve identity-specific features, largely because they do not effectively capture the nuanced interplay b... | {
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2501.04588 | Federated-Continual Dynamic Segmentation of Histopathology guided by
Barlow Continuity | [
"cs.LG",
"cs.AI"
] | Federated- and Continual Learning have been established as approaches to enable privacy-aware learning on continuously changing data, as required for deploying AI systems in histopathology images. However, data shifts can occur in a dynamic world, spatially between institutions and temporally, due to changing data over... | {
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2501.04591 | Quantum-inspired Embeddings Projection and Similarity Metrics for
Representation Learning | [
"cs.CL",
"cond-mat.dis-nn",
"quant-ph"
] | Over the last decade, representation learning, which embeds complex information extracted from large amounts of data into dense vector spaces, has emerged as a key technique in machine learning. Among other applications, it has been a key building block for large language models and advanced computer vision systems bas... | {
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2501.04594 | Understanding Expectations for a Robotic Guide Dog for Visually Impaired
People | [
"cs.RO"
] | Robotic guide dogs hold significant potential to enhance the autonomy and mobility of blind or visually impaired (BVI) individuals by offering universal assistance over unstructured terrains at affordable costs. However, the design of robotic guide dogs remains underexplored, particularly in systematic aspects such as ... | {
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2501.04595 | MobileH2R: Learning Generalizable Human to Mobile Robot Handover
Exclusively from Scalable and Diverse Synthetic Data | [
"cs.RO"
] | This paper introduces MobileH2R, a framework for learning generalizable vision-based human-to-mobile-robot (H2MR) handover skills. Unlike traditional fixed-base handovers, this task requires a mobile robot to reliably receive objects in a large workspace enabled by its mobility. Our key insight is that generalizable ha... | {
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2501.04597 | FrontierNet: Learning Visual Cues to Explore | [
"cs.RO",
"cs.CV"
] | Exploration of unknown environments is crucial for autonomous robots; it allows them to actively reason and decide on what new data to acquire for tasks such as mapping, object discovery, and environmental assessment. Existing methods, such as frontier-based methods, rely heavily on 3D map operations, which are limited... | {
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2501.04606 | Enhancing Low-Cost Video Editing with Lightweight Adaptors and
Temporal-Aware Inversion | [
"cs.CV"
] | Recent advancements in text-to-image (T2I) generation using diffusion models have enabled cost-effective video-editing applications by leveraging pre-trained models, eliminating the need for resource-intensive training. However, the frame-independence of T2I generation often results in poor temporal consistency. Existi... | {
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2501.04608 | Comprehensive Examination of Unrolled Networks for Solving Linear
Inverse Problems | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Unrolled networks have become prevalent in various computer vision and imaging tasks. Although they have demonstrated remarkable efficacy in solving specific computer vision and computational imaging tasks, their adaptation to other applications presents considerable challenges. This is primarily due to the multitude o... | {
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2501.04610 | Resilient Peer-to-peer Learning based on Adaptive Aggregation | [
"cs.LG"
] | Collaborative learning in peer-to-peer networks offers the benefits of distributed learning while mitigating the risks associated with single points of failure inherent in centralized servers. However, adversarial workers pose potential threats by attempting to inject malicious information into the network. Thus, ensur... | {
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2501.04613 | A Semantic Partitioning Method for Large-Scale Training of Knowledge
Graph Embeddings | [
"cs.LG",
"cs.DC"
] | In recent years, knowledge graph embeddings have achieved great success. Many methods have been proposed and achieved state-of-the-art results in various tasks. However, most of the current methods present one or more of the following problems: (i) They only consider fact triplets, while ignoring the ontology informati... | {
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2501.04614 | MedCoDi-M: A Multi-Prompt Foundation Model for Multimodal Medical Data
Generation | [
"cs.AI",
"cs.LG"
] | Artificial Intelligence is revolutionizing medical practice, enhancing diagnostic accuracy and healthcare delivery. However, its adaptation in medical settings still faces significant challenges, related to data availability and privacy constraints. Synthetic data has emerged as a promising solution to mitigate these i... | {
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2501.04623 | Large-scale Grid Optimization: The Workhorse of Future Grid Computations | [
"eess.SY",
"cs.SY"
] | Purpose: The computation methods for modeling, controlling and optimizing the transforming grid are evolving rapidly. We review and systemize knowledge for a special class of computation methods that solve large-scale power grid optimization problems. Summary: Large-scale grid optimizations are pertinent for, amongst o... | {
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2501.04628 | FatesGS: Fast and Accurate Sparse-View Surface Reconstruction using
Gaussian Splatting with Depth-Feature Consistency | [
"cs.CV"
] | Recently, Gaussian Splatting has sparked a new trend in the field of computer vision. Apart from novel view synthesis, it has also been extended to the area of multi-view reconstruction. The latest methods facilitate complete, detailed surface reconstruction while ensuring fast training speed. However, these methods st... | {
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2501.04630 | Evaluating Interval-based Tokenization for Pitch Representation in
Symbolic Music Analysis | [
"cs.IR",
"cs.SD",
"eess.AS"
] | Symbolic music analysis tasks are often performed by models originally developed for Natural Language Processing, such as Transformers. Such models require the input data to be represented as sequences, which is achieved through a process of tokenization. Tokenization strategies for symbolic music often rely on absolut... | {
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2501.04631 | Disentangled Clothed Avatar Generation with Layered Representation | [
"cs.CV"
] | Clothed avatar generation has wide applications in virtual and augmented reality, filmmaking, and more. Previous methods have achieved success in generating diverse digital avatars, however, generating avatars with disentangled components (\eg, body, hair, and clothes) has long been a challenge. In this paper, we propo... | {
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2501.04633 | "Can you be my mum?": Manipulating Social Robots in the Large Language
Models Era | [
"cs.HC",
"cs.CY",
"cs.RO"
] | Recent advancements in robots powered by large language models have enhanced their conversational abilities, enabling interactions closely resembling human dialogue. However, these models introduce safety and security concerns in HRI, as they are vulnerable to manipulation that can bypass built-in safety measures. Imag... | {
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2501.04635 | Knowledge Retrieval Based on Generative AI | [
"cs.IR",
"cs.AI"
] | This study develops a question-answering system based on Retrieval-Augmented Generation (RAG) using Chinese Wikipedia and Lawbank as retrieval sources. Using TTQA and TMMLU+ as evaluation datasets, the system employs BGE-M3 for dense vector retrieval to obtain highly relevant search results and BGE-reranker to reorder ... | {
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2501.04641 | A Statistical Theory of Contrastive Pre-training and Multimodal
Generative AI | [
"cs.LG",
"math.ST",
"stat.ML",
"stat.TH"
] | Multi-modal generative AI systems, such as those combining vision and language, rely on contrastive pre-training to learn representations across different modalities. While their practical benefits are widely acknowledged, a rigorous theoretical understanding of the contrastive pre-training framework remains limited. T... | {
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2501.04643 | Discrete Wavelet Transform-Based Capsule Network for Hyperspectral Image
Classification | [
"cs.CV"
] | Hyperspectral image (HSI) classification is a crucial technique for remote sensing to build large-scale earth monitoring systems. HSI contains much more information than traditional visual images for identifying the categories of land covers. One recent feasible solution for HSI is to leverage CapsNets for capturing sp... | {
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} |
2501.04648 | FlairGPT: Repurposing LLMs for Interior Designs | [
"cs.GR",
"cs.CL",
"cs.CV"
] | Interior design involves the careful selection and arrangement of objects to create an aesthetically pleasing, functional, and harmonized space that aligns with the client's design brief. This task is particularly challenging, as a successful design must not only incorporate all the necessary objects in a cohesive styl... | {
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} |
2501.04652 | Multi-task retriever fine-tuning for domain-specific and efficient RAG | [
"cs.CL",
"cs.IR",
"cs.LG"
] | Retrieval-Augmented Generation (RAG) has become ubiquitous when deploying Large Language Models (LLMs), as it can address typical limitations such as generating hallucinated or outdated information. However, when building real-world RAG applications, practical issues arise. First, the retrieved information is generally... | {
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} |
2501.04661 | Assessing Language Comprehension in Large Language Models Using
Construction Grammar | [
"cs.CL",
"cs.AI"
] | Large Language Models, despite their significant capabilities, are known to fail in surprising and unpredictable ways. Evaluating their true `understanding' of language is particularly challenging due to the extensive web-scale data they are trained on. Therefore, we construct an evaluation to systematically assess nat... | {
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} |
2501.04662 | On The Origin of Cultural Biases in Language Models: From Pre-training
Data to Linguistic Phenomena | [
"cs.CL"
] | Language Models (LMs) have been shown to exhibit a strong preference towards entities associated with Western culture when operating in non-Western languages. In this paper, we aim to uncover the origins of entity-related cultural biases in LMs by analyzing several contributing factors, including the representation of ... | {
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} |
2501.04665 | HyFusion: Enhanced Reception Field Transformer for Hyperspectral Image
Fusion | [
"eess.IV",
"cs.CV"
] | Hyperspectral image (HSI) fusion addresses the challenge of reconstructing High-Resolution HSIs (HR-HSIs) from High-Resolution Multispectral images (HR-MSIs) and Low-Resolution HSIs (LR-HSIs), a critical task given the high costs and hardware limitations associated with acquiring high-quality HSIs. While existing metho... | {
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} |
2501.04666 | Enhancing Virtual Try-On with Synthetic Pairs and Error-Aware Noise
Scheduling | [
"cs.CV"
] | Given an isolated garment image in a canonical product view and a separate image of a person, the virtual try-on task aims to generate a new image of the person wearing the target garment. Prior virtual try-on works face two major challenges in achieving this goal: a) the paired (human, garment) training data has limit... | {
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} |
2501.04667 | Natural Variational Annealing for Multimodal Optimization | [
"stat.ML",
"cs.LG",
"stat.CO"
] | We introduce a new multimodal optimization approach called Natural Variational Annealing (NVA) that combines the strengths of three foundational concepts to simultaneously search for multiple global and local modes of black-box nonconvex objectives. First, it implements a simultaneous search by using variational poster... | {
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} |
2501.04670 | Are They the Same? Exploring Visual Correspondence Shortcomings of
Multimodal LLMs | [
"cs.CV"
] | Recent advancements in multimodal models have shown a strong ability in visual perception, reasoning abilities, and vision-language understanding. However, studies on visual matching ability are missing, where finding the visual correspondence of objects is essential in vision research. Our research reveals that the ma... | {
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} |
2501.04671 | DRIVINGVQA: Analyzing Visual Chain-of-Thought Reasoning of Vision
Language Models in Real-World Scenarios with Driving Theory Tests | [
"cs.CV",
"cs.AI"
] | Large vision-language models (LVLMs) augment language models with visual understanding, enabling multimodal reasoning. However, due to the modality gap between textual and visual data, they often face significant challenges, such as over-reliance on text priors, hallucinations, and limited capacity for complex visual r... | {
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} |
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