id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.03508 | A Sequential Optimal Learning Approach to Automated Prompt Engineering
in Large Language Models | [
"cs.CL"
] | Designing effective prompts is essential to guiding large language models (LLMs) toward desired responses. Automated prompt engineering aims to reduce reliance on manual effort by streamlining the design, refinement, and optimization of natural language prompts. This paper proposes an optimal learning framework for aut... | {
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2501.03510 | Salient Region Matching for Fully Automated MR-TRUS Registration | [
"eess.IV",
"cs.CV"
] | Prostate cancer is a leading cause of cancer-related mortality in men. The registration of magnetic resonance (MR) and transrectal ultrasound (TRUS) can provide guidance for the targeted biopsy of prostate cancer. In this study, we propose a salient region matching framework for fully automated MR-TRUS registration. Th... | {
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2501.03515 | Effects of Robot Competency and Motion Legibility on Human Correction
Feedback | [
"cs.RO"
] | As robot deployments become more commonplace, people are likely to take on the role of supervising robots (i.e., correcting their mistakes) rather than directly teaching them. Prior works on Learning from Corrections (LfC) have relied on three key assumptions to interpret human feedback: (1) people correct the robot on... | {
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2501.03516 | The Multiple Equal-Difference Structure of Cyclotomic Cosets | [
"math.NT",
"cs.IT",
"math.IT"
] | In this paper we introduce the definition of equal-difference cyclotomic coset, and prove that in general any cyclotomic coset can be decomposed into a disjoint union of equal-difference subsets. Among the equal-difference decompositions of a cyclotomic coset, an important class consists of those in the form of cycloto... | {
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2501.03518 | Transfer Learning for Deep-Unfolded Combinatorial Optimization Solver
with Quantum Annealer | [
"quant-ph",
"cs.LG"
] | Quantum annealing (QA) has attracted research interest as a sampler and combinatorial optimization problem (COP) solver. A recently proposed sampling-based solver for QA significantly reduces the required number of qubits, being capable of large COPs. In relation to this, a trainable sampling-based COP solver has been ... | {
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2501.03523 | Vocal Tract Length Warped Features for Spoken Keyword Spotting | [
"cs.SD",
"cs.AI",
"cs.LG",
"eess.AS"
] | In this paper, we propose several methods that incorporate vocal tract length (VTL) warped features for spoken keyword spotting (KWS). The first method, VTL-independent KWS, involves training a single deep neural network (DNN) that utilizes VTL features with various warping factors. During training, a specific VTL feat... | {
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2501.03525 | TexHOI: Reconstructing Textures of 3D Unknown Objects in Monocular
Hand-Object Interaction Scenes | [
"cs.CV"
] | Reconstructing 3D models of dynamic, real-world objects with high-fidelity textures from monocular frame sequences has been a challenging problem in recent years. This difficulty stems from factors such as shadows, indirect illumination, and inaccurate object-pose estimations due to occluding hand-object interactions. ... | {
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2501.03526 | FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model
for Multi-modality Missing MRI Synthesis | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Multi-modality magnetic resonance imaging (MRI) is essential for the diagnosis and treatment of brain tumors. However, missing modalities are commonly observed due to limitations in scan time, scan corruption, artifacts, motion, and contrast agent intolerance. Synthesis of missing MRI has been a means to address the li... | {
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2501.03533 | Anomaly Triplet-Net: Progress Recognition Model Using Deep Metric
Learning Considering Occlusion for Manual Assembly Work | [
"cs.CV"
] | In this paper, a progress recognition method consider occlusion using deep metric learning is proposed to visualize the product assembly process in a factory. First, the target assembly product is detected from images acquired from a fixed-point camera installed in the factory using a deep learning-based object detecti... | {
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2501.03535 | SenseRAG: Constructing Environmental Knowledge Bases with Proactive
Querying for LLM-Based Autonomous Driving | [
"cs.AI",
"cs.RO"
] | This study addresses the critical need for enhanced situational awareness in autonomous driving (AD) by leveraging the contextual reasoning capabilities of large language models (LLMs). Unlike traditional perception systems that rely on rigid, label-based annotations, it integrates real-time, multimodal sensor data int... | {
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2501.03538 | Efficient and Accurate Tuberculosis Diagnosis: Attention Residual U-Net
and Vision Transformer Based Detection Framework | [
"eess.IV",
"cs.CV"
] | Tuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis, continues to be a major global health threat despite being preventable and curable. This burden is particularly high in low and middle income countries. Microscopy remains essential for diagnosing TB by enabling direct visualization of Mycob... | {
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2501.03539 | Enhanced Tuberculosis Bacilli Detection using Attention-Residual U-Net
and Ensemble Classification | [
"eess.IV",
"cs.CV"
] | Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a critical global health issue, necessitating timely diagnosis and treatment. Current methods for detecting tuberculosis bacilli from bright field microscopic sputum smear images suffer from low automation, inadequate segmentation performance, and limited... | {
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2501.03540 | Deep Learning within Tabular Data: Foundations, Challenges, Advances and
Future Directions | [
"cs.LG",
"cs.AI"
] | Tabular data remains one of the most prevalent data types across a wide range of real-world applications, yet effective representation learning for this domain poses unique challenges due to its irregular patterns, heterogeneous feature distributions, and complex inter-column dependencies. This survey provides a compre... | {
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2501.03543 | Distributionally Robust Joint Chance-Constrained Optimal Power Flow
using Relative Entropy | [
"math.OC",
"cs.SY",
"eess.SY"
] | Designing robust algorithms for the optimal power flow (OPF) problem is critical for the control of large-scale power systems under uncertainty. The chance-constrained OPF (CCOPF) problem provides a natural formulation of the trade-off between the operating cost and the constraint satisfaction rate. In this work, we pr... | {
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2501.03544 | PromptGuard: Soft Prompt-Guided Unsafe Content Moderation for
Text-to-Image Models | [
"cs.CV",
"cs.AI",
"cs.CR"
] | Text-to-image (T2I) models have been shown to be vulnerable to misuse, particularly in generating not-safe-for-work (NSFW) content, raising serious ethical concerns. In this work, we present PromptGuard, a novel content moderation technique that draws inspiration from the system prompt mechanism in large language model... | {
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2501.03545 | Beyond Factual Accuracy: Evaluating Coverage of Diverse Factual
Information in Long-form Text Generation | [
"cs.CL"
] | This paper presents ICAT, an evaluation framework for measuring coverage of diverse factual information in long-form text generation. ICAT breaks down a long output text into a list of atomic claims and not only verifies each claim through retrieval from a (reliable) knowledge source, but also computes the alignment be... | {
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2501.03552 | Proxy Control Barrier Functions: Integrating Barrier-Based and
Lyapunov-Based Safety-Critical Control Design | [
"eess.SY",
"cs.SY",
"math.OC"
] | This work introduces a novel Proxy Control Barrier Function (PCBF) scheme that integrates barrier-based and Lyapunov-based safety-critical control strategies for strict-feedback systems with potentially unknown dynamics. The proposed method employs a modular design procedure, decomposing the original system into a prox... | {
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2501.03560 | KG-TRICK: Unifying Textual and Relational Information Completion of
Knowledge for Multilingual Knowledge Graphs | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Multilingual knowledge graphs (KGs) provide high-quality relational and textual information for various NLP applications, but they are often incomplete, especially in non-English languages. Previous research has shown that combining information from KGs in different languages aids either Knowledge Graph Completion (KGC... | {
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2501.03562 | Rethinking Adversarial Attacks in Reinforcement Learning from Policy
Distribution Perspective | [
"cs.LG",
"cs.AI"
] | Deep Reinforcement Learning (DRL) suffers from uncertainties and inaccuracies in the observation signal in realworld applications. Adversarial attack is an effective method for evaluating the robustness of DRL agents. However, existing attack methods targeting individual sampled actions have limited impacts on the over... | {
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2501.03565 | Bridged Semantic Alignment for Zero-shot 3D Medical Image Diagnosis | [
"cs.CV"
] | 3D medical images such as Computed tomography (CT) are widely used in clinical practice, offering a great potential for automatic diagnosis. Supervised learning-based approaches have achieved significant progress but rely heavily on extensive manual annotations, limited by the availability of training data and the dive... | {
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2501.03566 | Applying Large Language Models in Knowledge Graph-based Enterprise
Modeling: Challenges and Opportunities | [
"cs.MA",
"cs.AI",
"cs.SE"
] | The role of large language models (LLMs) in enterprise modeling has recently started to shift from academic research to that of industrial applications. Thereby, LLMs represent a further building block for the machine-supported generation of enterprise models. In this paper we employ a knowledge graph-based approach fo... | {
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2501.03567 | Evaluating Image Caption via Cycle-consistent Text-to-Image Generation | [
"cs.CV"
] | Evaluating image captions typically relies on reference captions, which are costly to obtain and exhibit significant diversity and subjectivity. While reference-free evaluation metrics have been proposed, most focus on cross-modal evaluation between captions and images. Recent research has revealed that the modality ga... | {
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2501.03568 | Advanced Tutorial: Label-Efficient Two-Sample Tests | [
"cs.LG",
"stat.ME"
] | Hypothesis testing is a statistical inference approach used to determine whether data supports a specific hypothesis. An important type is the two-sample test, which evaluates whether two sets of data points are from identical distributions. This test is widely used, such as by clinical researchers comparing treatment ... | {
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2501.03571 | AADNet: Exploring EEG Spatiotemporal Information for Fast and Accurate
Orientation and Timbre Detection of Auditory Attention Based on A Cue-Masked
Paradigm | [
"cs.LG",
"cs.SD",
"eess.AS",
"q-bio.NC"
] | Auditory attention decoding from electroencephalogram (EEG) could infer to which source the user is attending in noisy environments. Decoding algorithms and experimental paradigm designs are crucial for the development of technology in practical applications. To simulate real-world scenarios, this study proposed a cue-... | {
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2501.03572 | From Code to Compliance: Assessing ChatGPT's Utility in Designing an
Accessible Webpage -- A Case Study | [
"cs.HC",
"cs.AI",
"cs.CL"
] | Web accessibility ensures that individuals with disabilities can access and interact with digital content without barriers, yet a significant majority of most used websites fail to meet accessibility standards. This study evaluates ChatGPT's (GPT-4o) ability to generate and improve web pages in line with Web Content Ac... | {
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2501.03573 | Neural Cellular Automata and Deep Equilibrium Models | [
"cs.NE",
"cs.FL"
] | This essay discusses the connections and differences between two emerging paradigms in deep learning, namely Neural Cellular Automata and Deep Equilibrium Models, and train a simple Deep Equilibrium Convolutional model to demonstrate the inherent similarity of NCA and DEQ based methods. Finally, this essay speculates a... | {
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2501.03575 | Cosmos World Foundation Model Platform for Physical AI | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.RO"
] | Physical AI needs to be trained digitally first. It needs a digital twin of itself, the policy model, and a digital twin of the world, the world model. In this paper, we present the Cosmos World Foundation Model Platform to help developers build customized world models for their Physical AI setups. We position a world ... | {
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2501.03577 | Wireless Channel Measurements and Characterization in Industrial IoT
Scenarios | [
"eess.SY",
"cs.SY"
] | Wireless Fidelity (Wi-Fi) communication technologies hold significant potential for realizing the Industrial Internet of Things (IIoT). In this paper, both Single-Input Single-Output (SISO) and polarized Multiple-Input Multiple-Output (MIMO) channel measurements are conducted in an IIoT scenario at the less congested W... | {
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2501.03580 | BASIC: Semi-supervised Multi-organ Segmentation with Balanced Subclass
Regularization and Semantic-conflict Penalty | [
"cs.CV"
] | Semi-supervised learning (SSL) has shown notable potential in relieving the heavy demand of dense prediction tasks on large-scale well-annotated datasets, especially for the challenging multi-organ segmentation (MoS). However, the prevailing class-imbalance problem in MoS caused by the substantial variations in organ s... | {
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2501.03583 | STContext: A Multifaceted Dataset for Developing Context-aware
Spatio-temporal Crowd Mobility Prediction Models | [
"cs.AI",
"cs.LG"
] | In smart cities, context-aware spatio-temporal crowd flow prediction (STCFP) models leverage contextual features (e.g., weather) to identify unusual crowd mobility patterns and enhance prediction accuracy. However, the best practice for incorporating contextual features remains unclear due to inconsistent usage of cont... | {
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2501.03584 | Discriminative Representation learning via Attention-Enhanced
Contrastive Learning for Short Text Clustering | [
"cs.LG",
"cs.CL"
] | Contrastive learning has gained significant attention in short text clustering, yet it has an inherent drawback of mistakenly identifying samples from the same category as negatives and then separating them in the feature space (false negative separation), which hinders the generation of superior representations. To ge... | {
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2501.03585 | Collision Risk Quantification and Conflict Resolution in Trajectory
Tracking for Acceleration-Actuated Multi-Robot Systems | [
"cs.RO"
] | One of the pivotal challenges in a multi-robot system is how to give attention to accuracy and efficiency while ensuring safety. Prior arts cannot strictly guarantee collision-free for an arbitrarily large number of robots or the results are considerably conservative. Smoothness of the avoidance trajectory also needs t... | {
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2501.03592 | A Value Mapping Virtual Staining Framework for Large-scale Histological
Imaging | [
"eess.IV",
"cs.CV",
"physics.optics"
] | The emergence of virtual staining technology provides a rapid and efficient alternative for researchers in tissue pathology. It enables the utilization of unlabeled microscopic samples to generate virtual replicas of chemically stained histological slices, or facilitate the transformation of one staining type into anot... | {
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2501.03598 | RecKG: Knowledge Graph for Recommender Systems | [
"cs.IR",
"cs.AI"
] | Knowledge graphs have proven successful in integrating heterogeneous data across various domains. However, there remains a noticeable dearth of research on their seamless integration among heterogeneous recommender systems, despite knowledge graph-based recommender systems garnering extensive research attention. This s... | {
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2501.03605 | ConcealGS: Concealing Invisible Copyright Information in 3D Gaussian
Splatting | [
"cs.CV",
"cs.MM",
"eess.IV"
] | With the rapid development of 3D reconstruction technology, the widespread distribution of 3D data has become a future trend. While traditional visual data (such as images and videos) and NeRF-based formats already have mature techniques for copyright protection, steganographic techniques for the emerging 3D Gaussian S... | {
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2501.03606 | VTAO-BiManip: Masked Visual-Tactile-Action Pre-training with Object
Understanding for Bimanual Dexterous Manipulation | [
"cs.RO",
"cs.CV"
] | Bimanual dexterous manipulation remains significant challenges in robotics due to the high DoFs of each hand and their coordination. Existing single-hand manipulation techniques often leverage human demonstrations to guide RL methods but fail to generalize to complex bimanual tasks involving multiple sub-skills. In thi... | {
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2501.03608 | A 3D Continuous-Space Electromagnetic Channel Model for 6G Tri-Polarized
Multi-user Communications | [
"eess.SY",
"cs.SY"
] | It is envisioned that the sixth generation (6G) and beyond 6G (B6G) wireless communication networks will enable global coverage in space, air, ground, and sea. In this case, both base stations and users can be mobile and will tend to move continuously in three-dimensional (3D) space. Therefore, obtaining channel state ... | {
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2501.03611 | Is social media hindering or helping Academic Performance? A case study
of Walter Sisulu University Buffalo City Campus | [
"cs.CY",
"cs.SI"
] | Social media platforms are popular among higher education students and have seen increased usage for academic purposes, especially during the COVID-19 pandemic. However, excessive use of social media can negatively impact students' academic performance. This preliminary study examines social media's impact on students'... | {
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2501.03616 | BTMTrack: Robust RGB-T Tracking via Dual-template Bridging and
Temporal-Modal Candidate Elimination | [
"cs.CV"
] | RGB-T tracking leverages the complementary strengths of RGB and thermal infrared (TIR) modalities to address challenging scenarios such as low illumination and adverse weather. However, existing methods often fail to effectively integrate temporal information and perform efficient cross-modal interactions, which constr... | {
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2501.03619 | Deep Learning-based Compression Detection for explainable Face Image
Quality Assessment | [
"cs.CV"
] | The assessment of face image quality is crucial to ensure reliable face recognition. In order to provide data subjects and operators with explainable and actionable feedback regarding captured face images, relevant quality components have to be measured. Quality components that are known to negatively impact the utilit... | {
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2501.03624 | LlaMADRS: Prompting Large Language Models for Interview-Based Depression
Assessment | [
"cs.HC",
"cs.CL"
] | This study introduces LlaMADRS, a novel framework leveraging open-source Large Language Models (LLMs) to automate depression severity assessment using the Montgomery-Asberg Depression Rating Scale (MADRS). We employ a zero-shot prompting strategy with carefully designed cues to guide the model in interpreting and scori... | {
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2501.03627 | Coupled Hierarchical Structure Learning using Tree-Wasserstein Distance | [
"cs.LG",
"stat.ML"
] | In many applications, both data samples and features have underlying hierarchical structures. However, existing methods for learning these latent structures typically focus on either samples or features, ignoring possible coupling between them. In this paper, we introduce a coupled hierarchical structure learning metho... | {
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2501.03628 | A Novel Approach to Real-Time Short-Term Traffic Prediction based on
Distributed Fiber-Optic Sensing and Data Assimilation with a Stochastic
Cell-Automata Model | [
"cond-mat.stat-mech",
"cs.SY",
"eess.SY",
"nlin.CG",
"physics.soc-ph"
] | This paper demonstrates real-time short-term traffic flow prediction through distributed fiber-optic sensing (DFOS) and data assimilation with a stochastic cell-automata-based traffic model. Traffic congestion on expressways is a severe issue. To alleviate its negative impacts, it is necessary to optimize traffic flow ... | {
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2501.03629 | CFFormer: Cross CNN-Transformer Channel Attention and Spatial Feature
Fusion for Improved Segmentation of Low Quality Medical Images | [
"cs.CV"
] | Hybrid CNN-Transformer models are designed to combine the advantages of Convolutional Neural Networks (CNNs) and Transformers to efficiently model both local information and long-range dependencies. However, most research tends to focus on integrating the spatial features of CNNs and Transformers, while overlooking the... | {
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2501.03630 | MC-VTON: Minimal Control Virtual Try-On Diffusion Transformer | [
"cs.CV"
] | Virtual try-on methods based on diffusion models achieve realistic try-on effects. They use an extra reference network or an additional image encoder to process multiple conditional image inputs, which adds complexity pre-processing and additional computational costs. Besides, they require more than 25 inference steps,... | {
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2501.03631 | Exploring Iterative Manifold Constraint for Zero-shot Image Editing | [
"cs.CV"
] | Editability and fidelity are two essential demands for text-driven image editing, which expects that the editing area should align with the target prompt and the rest remain unchanged separately. The current cutting-edge editing methods usually obey an "inversion-then-editing" pipeline, where the input image is inverte... | {
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2501.03635 | MHGNet: Multi-Heterogeneous Graph Neural Network for Traffic Prediction | [
"cs.LG",
"cs.AI"
] | In recent years, traffic flow prediction has played a crucial role in the management of intelligent transportation systems. However, traditional forecasting methods often model non-Euclidean low-dimensional traffic data as a simple graph with single-type nodes and edges, failing to capture similar trends among nodes of... | {
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2501.03637 | Advancing the Understanding of Fine-Grained 3D Forest Structures using
Digital Cousins and Simulation-to-Reality: Methods and Datasets | [
"cs.CV"
] | Understanding and analyzing the spatial semantics and structure of forests is essential for accurate forest resource monitoring and ecosystem research. However, the lack of large-scale and annotated datasets has limited the widespread use of advanced intelligent techniques in this field. To address this challenge, a fu... | {
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2501.03639 | A case study on the transformative potential of AI in software
engineering on LeetCode and ChatGPT | [
"cs.DB",
"cs.SE"
] | The recent surge in the field of generative artificial intelligence (GenAI) has the potential to bring about transformative changes across a range of sectors, including software engineering and education. As GenAI tools, such as OpenAI's ChatGPT, are increasingly utilised in software engineering, it becomes imperative ... | {
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2501.03643 | Effective and Efficient Mixed Precision Quantization of Speech
Foundation Models | [
"cs.SD",
"cs.AI",
"eess.AS"
] | This paper presents a novel mixed-precision quantization approach for speech foundation models that tightly integrates mixed-precision learning and quantized model parameter estimation into one single model compression stage. Experiments conducted on LibriSpeech dataset with fine-tuned wav2vec2.0-base and HuBERT-large ... | {
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2501.03647 | Hierarchical Datacubes | [
"cs.DB"
] | Many approaches have been proposed to pre-compute data cubes in order to efficiently respond to OLAP queries in data warehouses. However, few have proposed solutions integrating all of the possible outcomes, and it is this idea that leads the integration of hierarchical dimensions into these responses. To meet this nee... | {
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2501.03653 | Study of Frictional and Impact Transients in Active-Passive Mechanical
Pair | [
"eess.SY",
"cs.SY"
] | We consider an active-passive mechanical pair in which the relative motion of the latter is constrained by the mechanical impact. The system dynamics is described by the previously introduced modeling frameworks of force transition and dissipation through the nonlinear Coulomb friction and structural damping, the later... | {
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2501.03654 | Data Augmentation for Deep Learning Regression Tasks by Machine Learning
Models | [
"cs.LG"
] | Deep learning (DL) models have gained prominence in domains such as computer vision and natural language processing but remain underutilized for regression tasks involving tabular data. In these cases, traditional machine learning (ML) models often outperform DL models. In this study, we propose and evaluate various da... | {
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2501.03659 | DehazeGS: Seeing Through Fog with 3D Gaussian Splatting | [
"cs.CV"
] | Current novel view synthesis tasks primarily rely on high-quality and clear images. However, in foggy scenes, scattering and attenuation can significantly degrade the reconstruction and rendering quality. Although NeRF-based dehazing reconstruction algorithms have been developed, their use of deep fully connected neura... | {
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2501.03664 | Local Compositional Complexity: How to Detect a Human-readable Messsage | [
"cs.CV"
] | Data complexity is an important concept in the natural sciences and related areas, but lacks a rigorous and computable definition. In this paper, we focus on a particular sense of complexity that is high if the data is structured in a way that could serve to communicate a message. In this sense, human speech, written l... | {
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2501.03666 | Hybrid Machine Learning Model with a Constrained Action Space for
Trajectory Prediction | [
"cs.RO",
"cs.LG"
] | Trajectory prediction is crucial to advance autonomous driving, improving safety, and efficiency. Although end-to-end models based on deep learning have great potential, they often do not consider vehicle dynamic limitations, leading to unrealistic predictions. To address this problem, this work introduces a novel hybr... | {
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2501.03670 | A Diversity-Enhanced Knowledge Distillation Model for Practical Math
Word Problem Solving | [
"cs.CL",
"cs.AI"
] | Math Word Problem (MWP) solving is a critical task in natural language processing, has garnered significant research interest in recent years. Various recent studies heavily rely on Seq2Seq models and their extensions (e.g., Seq2Tree and Graph2Tree) to generate mathematical equations. While effective, these models stru... | {
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2501.03671 | Imitation Learning of MPC with Neural Networks: Error Guarantees and
Sparsification | [
"eess.SY",
"cs.LG",
"cs.SY"
] | This paper presents a framework for bounding the approximation error in imitation model predictive controllers utilizing neural networks. Leveraging the Lipschitz properties of these neural networks, we derive a bound that guides dataset design to ensure the approximation error remains at chosen limits. We discuss how ... | {
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2501.03674 | Action Quality Assessment via Hierarchical Pose-guided Multi-stage
Contrastive Regression | [
"cs.CV",
"cs.AI"
] | Action Quality Assessment (AQA), which aims at automatic and fair evaluation of athletic performance, has gained increasing attention in recent years. However, athletes are often in rapid movement and the corresponding visual appearance variances are subtle, making it challenging to capture fine-grained pose difference... | {
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2501.03675 | SMIR: Efficient Synthetic Data Pipeline To Improve Multi-Image Reasoning | [
"cs.CV"
] | Vision-Language Models (VLMs) excel at understanding single images, aided by high-quality instruction datasets. However, multi-image reasoning remains underexplored in the open-source community due to two key challenges: (1) scaling datasets with correlated images and complex reasoning instructions is resource-intensiv... | {
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2501.03676 | SALE-Based Offline Reinforcement Learning with Ensemble Q-Networks | [
"cs.LG",
"cs.AI"
] | In this work, we build upon the offline reinforcement learning algorithm TD7, which incorporates State-Action Learned Embeddings (SALE) and a prioritized experience replay buffer (LAP). We propose a model-free actor-critic algorithm that integrates ensemble Q-networks and a gradient diversity penalty from EDAC. The ens... | {
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2501.03681 | SLAM: Towards Efficient Multilingual Reasoning via Selective Language
Alignment | [
"cs.CL",
"cs.AI"
] | Despite the significant improvements achieved by large language models (LLMs) in English reasoning tasks, these models continue to struggle with multilingual reasoning. Recent studies leverage a full-parameter and two-stage training paradigm to teach models to first understand non-English questions and then reason. How... | {
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2501.03687 | Run-and-tumble chemotaxis using reinforcement learning | [
"q-bio.CB",
"cs.LG",
"physics.bio-ph"
] | Bacterial cells use run-and-tumble motion to climb up attractant concentration gradient in their environment. By extending the uphill runs and shortening the downhill runs the cells migrate towards the higher attractant zones. Motivated by this, we formulate a reinforcement learning (RL) algorithm where an agent moves ... | {
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2501.03689 | MAJL: A Model-Agnostic Joint Learning Framework for Music Source
Separation and Pitch Estimation | [
"cs.SD",
"cs.AI",
"eess.AS"
] | Music source separation and pitch estimation are two vital tasks in music information retrieval. Typically, the input of pitch estimation is obtained from the output of music source separation. Therefore, existing methods have tried to perform these two tasks simultaneously, so as to leverage the mutually beneficial re... | {
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2501.03691 | Stabilization of Strictly Pre-Dissipative Receding Horizon Linear
Quadratic Control by Terminal Costs | [
"math.OC",
"cs.SY",
"eess.SY"
] | Asymptotic stability in receding horizon control is obtained under a strict pre-dissipativity assumption, in the presence of suitable state constraints. In this paper we analyze how terminal constraints can be replaced by suitable terminal costs. We restrict to the linear-quadratic setting as that allows us to obtain s... | {
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2501.03696 | Exploring Molecule Generation Using Latent Space Graph Diffusion | [
"cs.LG",
"cs.AI"
] | Generating molecular graphs is a challenging task due to their discrete nature and the competitive objectives involved. Diffusion models have emerged as SOTA approaches in data generation across various modalities. For molecular graphs, graph neural networks (GNNs) as a diffusion backbone have achieved impressive resul... | {
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2501.03697 | Deep Networks are Reproducing Kernel Chains | [
"cs.LG",
"math.FA",
"stat.ML"
] | Identifying an appropriate function space for deep neural networks remains a key open question. While shallow neural networks are naturally associated with Reproducing Kernel Banach Spaces (RKBS), deep networks present unique challenges. In this work, we extend RKBS to chain RKBS (cRKBS), a new framework that composes ... | {
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2501.03699 | Motion-Aware Generative Frame Interpolation | [
"cs.CV"
] | Generative frame interpolation, empowered by large-scale pre-trained video generation models, has demonstrated remarkable advantages in complex scenes. However, existing methods heavily rely on the generative model to independently infer the correspondences between input frames, an ability that is inadequately develope... | {
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2501.03700 | AuxDepthNet: Real-Time Monocular 3D Object Detection with
Depth-Sensitive Features | [
"cs.CV",
"cs.AI"
] | Monocular 3D object detection is a challenging task in autonomous systems due to the lack of explicit depth information in single-view images. Existing methods often depend on external depth estimators or expensive sensors, which increase computational complexity and hinder real-time performance. To overcome these limi... | {
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2501.03707 | A Poincar\'e Lower Bound Approach for Performance Trade-offs in MIMO
ISAC Systems with Blockage | [
"cs.IT",
"math.IT"
] | Characterizing the performance trade-offs between sensing and communication subsystems is essential for enabling integrated sensing and communication systems. Various metrics exist for each subsystem; however, this study focuses on the ergodic capacity of the communication subsystem. Due to the complexity of deriving t... | {
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2501.03711 | Unsupervised Speech Segmentation: A General Approach Using Speech
Language Models | [
"cs.CL",
"cs.AI",
"cs.LG",
"cs.SD",
"eess.AS"
] | In this paper, we introduce an unsupervised approach for Speech Segmentation, which builds on previously researched approaches, e.g., Speaker Diarization, while being applicable to an inclusive set of acoustic-semantic distinctions, paving a path towards a general Unsupervised Speech Segmentation approach. Unlike tradi... | {
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2501.03714 | MoDec-GS: Global-to-Local Motion Decomposition and Temporal Interval
Adjustment for Compact Dynamic 3D Gaussian Splatting | [
"cs.CV"
] | 3D Gaussian Splatting (3DGS) has made significant strides in scene representation and neural rendering, with intense efforts focused on adapting it for dynamic scenes. Despite delivering remarkable rendering quality and speed, existing methods struggle with storage demands and representing complex real-world motions. T... | {
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2501.03715 | Neural Deconstruction Search for Vehicle Routing Problems | [
"cs.AI",
"cs.LG"
] | Autoregressive construction approaches generate solutions to vehicle routing problems in a step-by-step fashion, leading to high-quality solutions that are nearing the performance achieved by handcrafted, operations research techniques. In this work, we challenge the conventional paradigm of sequential solution constru... | {
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2501.03717 | Materialist: Physically Based Editing Using Single-Image Inverse
Rendering | [
"cs.CV",
"cs.AI",
"cs.GR"
] | To perform image editing based on single-view, inverse physically based rendering, we present a method combining a learning-based approach with progressive differentiable rendering. Given an image, our method leverages neural networks to predict initial material properties. Progressive differentiable rendering is then ... | {
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2501.03722 | Self-adaptive vision-language model for 3D segmentation of pulmonary
artery and vein | [
"cs.CV",
"cs.AI"
] | Accurate segmentation of pulmonary structures iscrucial in clinical diagnosis, disease study, and treatment planning. Significant progress has been made in deep learning-based segmentation techniques, but most require much labeled data for training. Consequently, developing precise segmentation methods that demand fewe... | {
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2501.03727 | Detecting Neurocognitive Disorders through Analyses of Topic Evolution
and Cross-modal Consistency in Visual-Stimulated Narratives | [
"eess.AS",
"cs.LG"
] | Early detection of neurocognitive disorders (NCDs) is crucial for timely intervention and disease management. Speech analysis offers a non-intrusive and scalable screening method, particularly through narrative tasks in neuropsychological assessment tools. Traditional narrative analysis often focuses on local indicator... | {
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2501.03729 | Realistic Test-Time Adaptation of Vision-Language Models | [
"cs.CV"
] | The zero-shot capabilities of Vision-Language Models (VLMs) have been widely leveraged to improve predictive performance. However, previous works on transductive or test-time adaptation (TTA) often make strong assumptions about the data distribution, such as the presence of all classes. Our work challenges these favora... | {
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2501.03737 | Re-Visible Dual-Domain Self-Supervised Deep Unfolding Network for MRI
Reconstruction | [
"eess.IV",
"cs.CV"
] | Magnetic Resonance Imaging (MRI) is widely used in clinical practice, but suffered from prolonged acquisition time. Although deep learning methods have been proposed to accelerate acquisition and demonstrate promising performance, they rely on high-quality fully-sampled datasets for training in a supervised manner. How... | {
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2501.03746 | A Multimodal Lightweight Approach to Fault Diagnosis of Induction Motors
in High-Dimensional Dataset | [
"cs.LG",
"cs.SY",
"eess.SP",
"eess.SY"
] | An accurate AI-based diagnostic system for induction motors (IMs) holds the potential to enhance proactive maintenance, mitigating unplanned downtime and curbing overall maintenance costs within an industrial environment. Notably, among the prevalent faults in IMs, a Broken Rotor Bar (BRB) fault is frequently encounter... | {
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2501.03747 | Context-Alignment: Activating and Enhancing LLM Capabilities in Time
Series | [
"cs.LG",
"cs.CL",
"stat.AP"
] | Recently, leveraging pre-trained Large Language Models (LLMs) for time series (TS) tasks has gained increasing attention, which involves activating and enhancing LLMs' capabilities. Many methods aim to activate LLMs' capabilities based on token-level alignment but overlook LLMs' inherent strength on natural language pr... | {
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2501.03763 | 3D Printable Gradient Lattice Design for Multi-Stiffness Robotic Fingers | [
"cs.RO"
] | Human fingers achieve exceptional dexterity and adaptability by combining structures with varying stiffness levels, from soft tissues (low) to tendons and cartilage (medium) to bones (high). This paper explores developing a robotic finger with similar multi-stiffness characteristics. Specifically, we propose using a la... | {
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2501.03764 | SelectiveFinetuning: Enhancing Transfer Learning in Sleep Staging
through Selective Domain Alignment | [
"eess.SP",
"cs.AI"
] | In practical sleep stage classification, a key challenge is the variability of EEG data across different subjects and environments. Differences in physiology, age, health status, and recording conditions can lead to domain shifts between data. These domain shifts often result in decreased model accuracy and reliability... | {
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2501.03765 | Image Segmentation: Inducing graph-based learning | [
"cs.CV",
"eess.IV"
] | This study explores the potential of graph neural networks (GNNs) to enhance semantic segmentation across diverse image modalities. We evaluate the effectiveness of a novel GNN-based U-Net architecture on three distinct datasets: PascalVOC, a standard benchmark for natural image segmentation, WoodScape, a challenging d... | {
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2501.03767 | AutoFish: Dataset and Benchmark for Fine-grained Analysis of Fish | [
"cs.CV"
] | Automated fish documentation processes are in the near future expected to play an essential role in sustainable fisheries management and for addressing challenges of overfishing. In this paper, we present a novel and publicly available dataset named AutoFish designed for fine-grained fish analysis. The dataset comprise... | {
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2501.03769 | Multi-label Cross-lingual automatic music genre classification from
lyrics with Sentence BERT | [
"cs.IR",
"cs.LG",
"cs.SD",
"eess.AS"
] | Music genres are shaped by both the stylistic features of songs and the cultural preferences of artists' audiences. Automatic classification of music genres using lyrics can be useful in several applications such as recommendation systems, playlist creation, and library organization. We present a multi-label, cross-lin... | {
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2501.03775 | Strip R-CNN: Large Strip Convolution for Remote Sensing Object Detection | [
"cs.CV"
] | While witnessed with rapid development, remote sensing object detection remains challenging for detecting high aspect ratio objects. This paper shows that large strip convolutions are good feature representation learners for remote sensing object detection and can detect objects of various aspect ratios well. Based on ... | {
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2501.03782 | Vision Transformer Neural Architecture Search for Out-of-Distribution
Generalization: Benchmark and Insights | [
"cs.LG"
] | While ViTs have achieved across machine learning tasks, deploying them in real-world scenarios faces a critical challenge: generalizing under OoD shifts. A crucial research gap exists in understanding how to design ViT architectures, both manually and automatically, for better OoD generalization. To this end, we introd... | {
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2501.03783 | How to Select Pre-Trained Code Models for Reuse? A Learning Perspective | [
"cs.SE",
"cs.CL"
] | Pre-training a language model and then fine-tuning it has shown to be an efficient and effective technique for a wide range of code intelligence tasks, such as code generation, code summarization, and vulnerability detection. However, pretraining language models on a large-scale code corpus is computationally expensive... | {
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2501.03786 | KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt
Learning and Enhanced Cross-Modal Integration | [
"cs.CV"
] | Zero-shot anomaly detection (ZSAD) identifies anomalies without needing training samples from the target dataset, essential for scenarios with privacy concerns or limited data. Vision-language models like CLIP show potential in ZSAD but have limitations: relying on manually crafted fixed textual descriptions or anomaly... | {
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} |
2501.03795 | Self-Adaptive ERP: Embedding NLP into Petri-Net creation and Model
Matching | [
"cs.SE",
"cs.AI"
] | Enterprise Resource Planning (ERP) consultants play a vital role in customizing systems to meet specific business needs by processing large amounts of data and adapting functionalities. However, the process is resource-intensive, time-consuming, and requires continuous adjustments as business demands evolve. This resea... | {
"Other": 1,
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} |
2501.03799 | Some properties and applications of the new quantum $f$-divergences | [
"quant-ph",
"cs.IT",
"math.IT"
] | Recently, a new definition for quantum $f$-divergences was introduced based on an integral representation. These divergences have shown remarkable properties, for example when investigating contraction coefficients under noisy channels. At the same time, many properties well known for other definitions have remained el... | {
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} |
2501.03800 | MADation: Face Morphing Attack Detection with Foundation Models | [
"cs.CV",
"cs.CR"
] | Despite the considerable performance improvements of face recognition algorithms in recent years, the same scientific advances responsible for this progress can also be used to create efficient ways to attack them, posing a threat to their secure deployment. Morphing attack detection (MAD) systems aim to detect a speci... | {
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} |
2501.03802 | Quasi-optimal cyclic orbit codes | [
"cs.IT",
"math.CO",
"math.IT"
] | We focus on two aspects of cyclic orbit codes: invariants under equivalence and quasi-optimality. Regarding the first aspect, we establish a connection between the codewords of a cyclic orbit code and a certain linear set on the projective line. This allows us to derive new bounds on the parameters of the code. In the ... | {
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} |
2501.03805 | Detecting the Undetectable: Assessing the Efficacy of Current Spoof
Detection Methods Against Seamless Speech Edits | [
"cs.SD",
"cs.CL",
"eess.AS"
] | Neural speech editing advancements have raised concerns about their misuse in spoofing attacks. Traditional partially edited speech corpora primarily focus on cut-and-paste edits, which, while maintaining speaker consistency, often introduce detectable discontinuities. Recent methods, like A\textsuperscript{3}T and Voi... | {
"Other": 0,
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"cs.SD": 1,
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} |
2501.03811 | Extending ChatGPT with a Browserless System for Web Product Price
Extraction | [
"cs.IR"
] | With the advenement of ChatGPT, we can find very clean, precise answers to a varied amount of questions. However, for questions such as 'find the price of the lemon cake at zingerman's', the answer looks like 'I can't browse the web right now'. In this paper, we propose a system, called Wextractor, which extends ChatGP... | {
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} |
2501.03819 | An innovative mixed reality approach for Robotics Surgery | [
"cs.RO"
] | Robotic-assisted procedures offer numerous advantages over traditional approaches, including improved dexterity, reduced fatigue, minimized trauma, and superior outcomes. However, the main challenge of these systems remains the poor visualization and perception of the surgical field. The goal of this paper is to provid... | {
"Other": 0,
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"cs.RO": 1,
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} |
2501.03821 | The Choice of Normalization Influences Shrinkage in Regularized
Regression | [
"stat.ML",
"cs.LG",
"stat.ME"
] | Regularized models are often sensitive to the scales of the features in the data and it has therefore become standard practice to normalize (center and scale) the features before fitting the model. But there are many different ways to normalize the features and the choice may have dramatic effects on the resulting mode... | {
"Other": 0,
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} |
2501.03824 | Online Reinforcement Learning-Based Dynamic Adaptive Evaluation Function
for Real-Time Strategy Tasks | [
"cs.AI"
] | Effective evaluation of real-time strategy tasks requires adaptive mechanisms to cope with dynamic and unpredictable environments. This study proposes a method to improve evaluation functions for real-time responsiveness to battle-field situation changes, utilizing an online reinforcement learning-based dynam-ic weight... | {
"Other": 0,
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} |
2501.03825 | Deep Sylvester Posterior Inference for Adaptive Compressed Sensing in
Ultrasound Imaging | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Ultrasound images are commonly formed by sequential acquisition of beam-steered scan-lines. Minimizing the number of required scan-lines can significantly enhance frame rate, field of view, energy efficiency, and data transfer speeds. Existing approaches typically use static subsampling schemes in combination with spar... | {
"Other": 0,
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} |
2501.03826 | Investigating the Impact of Data Selection Strategies on Language Model
Performance | [
"cs.CL",
"cs.LG"
] | Data selection is critical for enhancing the performance of language models, particularly when aligning training datasets with a desired target distribution. This study explores the effects of different data selection methods and feature types on model performance. We evaluate whether selecting data subsets can influen... | {
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} |
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