id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.09327 | On Learning Informative Trajectory Embeddings for Imitation,
Classification and Regression | [
"cs.LG",
"cs.AI"
] | In real-world sequential decision making tasks like autonomous driving, robotics, and healthcare, learning from observed state-action trajectories is critical for tasks like imitation, classification, and clustering. For example, self-driving cars must replicate human driving behaviors, while robots and healthcare syst... | {
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2501.09328 | Neural Honeytrace: A Robust Plug-and-Play Watermarking Framework against
Model Extraction Attacks | [
"cs.CR",
"cs.AI"
] | Developing high-performance deep learning models is resource-intensive, leading model owners to utilize Machine Learning as a Service (MLaaS) platforms instead of publicly releasing their models. However, malicious users may exploit query interfaces to execute model extraction attacks, reconstructing the target model's... | {
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2501.09331 | Identifying Information from Observations with Uncertainty and Novelty | [
"cs.LG",
"stat.ML"
] | A machine learning tasks from observations must encounter and process uncertainty and novelty, especially when it is expected to maintain performance when observing new information and to choose the best fitting hypothesis to the currently observed information. In this context, some key questions arise: what is informa... | {
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2501.09333 | Prompt-CAM: A Simpler Interpretable Transformer for Fine-Grained
Analysis | [
"cs.CV",
"cs.AI"
] | We present a simple usage of pre-trained Vision Transformers (ViTs) for fine-grained analysis, aiming to identify and localize the traits that distinguish visually similar categories, such as different bird species or dog breeds. Pre-trained ViTs such as DINO have shown remarkable capabilities to extract localized, inf... | {
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2501.09334 | Jodes: Efficient Oblivious Join in the Distributed Setting | [
"cs.CR",
"cs.DB",
"cs.DC"
] | Trusted execution environment (TEE) has provided an isolated and secure environment for building cloud-based analytic systems, but it still suffers from access pattern leakages caused by side-channel attacks. To better secure the data, computation inside TEE enclave should be made oblivious, which introduces significan... | {
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2501.09336 | Estimating shared subspace with AJIVE: the power and limitation of
multiple data matrices | [
"stat.ML",
"cs.LG",
"math.ST",
"stat.TH"
] | Integrative data analysis often requires disentangling joint and individual variations across multiple datasets, a challenge commonly addressed by the Joint and Individual Variation Explained (JIVE) model. While numerous methods have been developed to estimate the shared subspace under JIVE, the theoretical understandi... | {
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2501.09338 | Robust UAV Path Planning with Obstacle Avoidance for Emergency Rescue | [
"cs.RO",
"cs.SY",
"eess.SY"
] | The unmanned aerial vehicles (UAVs) are efficient tools for diverse tasks such as electronic reconnaissance, agricultural operations and disaster relief. In the complex three-dimensional (3D) environments, the path planning with obstacle avoidance for UAVs is a significant issue for security assurance. In this paper, w... | {
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2501.09341 | SE-BSFV: Online Subspace Learning based Shadow Enhancement and
Background Suppression for ViSAR under Complex Background | [
"cs.CV"
] | Video synthetic aperture radar (ViSAR) has attracted substantial attention in the moving target detection (MTD) field due to its ability to continuously monitor changes in the target area. In ViSAR, the moving targets' shadows will not offset and defocus, which is widely used as a feature for MTD. However, the shadows ... | {
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2501.09345 | Rational Tuning of LLM Cascades via Probabilistic Modeling | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Understanding the reliability of large language models (LLMs) has recently garnered significant attention. Given LLMs' propensity to hallucinate, as well as their high sensitivity to prompt design, it is already challenging to predict the performance of an individual LLM. However, the problem becomes more complex for c... | {
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2501.09347 | UVRM: A Scalable 3D Reconstruction Model from Unposed Videos | [
"cs.CV"
] | Large Reconstruction Models (LRMs) have recently become a popular method for creating 3D foundational models. Training 3D reconstruction models with 2D visual data traditionally requires prior knowledge of camera poses for the training samples, a process that is both time-consuming and prone to errors. Consequently, 3D... | {
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2501.09349 | ChartInsighter: An Approach for Mitigating Hallucination in Time-series
Chart Summary Generation with A Benchmark Dataset | [
"cs.CL",
"cs.HC"
] | Effective chart summary can significantly reduce the time and effort decision makers spend interpreting charts, enabling precise and efficient communication of data insights. Previous studies have faced challenges in generating accurate and semantically rich summaries of time-series data charts. In this paper, we ident... | {
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2501.09350 | Making Your Dreams A Reality: Decoding the Dreams into a Coherent Video
Story from fMRI Signals | [
"cs.CV"
] | This paper studies the brave new idea for Multimedia community, and proposes a novel framework to convert dreams into coherent video narratives using fMRI data. Essentially, dreams have intrigued humanity for centuries, offering glimpses into our subconscious minds. Recent advancements in brain imaging, particularly fu... | {
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2501.09351 | RIS-Aided Fluid Antenna Array-Mounted UAV Networks | [
"cs.IT",
"eess.SP",
"math.IT"
] | This paper investigates reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV) downlink networks with fluid antennas (FA), where RIS enables non-line-of-sight (NLoS) transmissions. Moreover, the FA is equipped on the UAV offering dynamic antenna position adjustment, enhancing spatial diversity ... | {
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2501.09352 | PAL: Prompting Analytic Learning with Missing Modality for Multi-Modal
Class-Incremental Learning | [
"cs.LG",
"cs.MM",
"eess.IV"
] | Multi-modal class-incremental learning (MMCIL) seeks to leverage multi-modal data, such as audio-visual and image-text pairs, thereby enabling models to learn continuously across a sequence of tasks while mitigating forgetting. While existing studies primarily focus on the integration and utilization of multi-modal inf... | {
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2501.09354 | Style4Rec: Enhancing Transformer-based E-commerce Recommendation Systems
with Style and Shopping Cart Information | [
"cs.IR",
"cs.AI"
] | Understanding users' product preferences is essential to the efficacy of a recommendation system. Precision marketing leverages users' historical data to discern these preferences and recommends products that align with them. However, recent browsing and purchase records might better reflect current purchasing inclinat... | {
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2501.09355 | YETI (YET to Intervene) Proactive Interventions by Multimodal AI Agents
in Augmented Reality Tasks | [
"cs.AI",
"cs.CV",
"cs.ET",
"cs.MA"
] | Multimodal AI Agents are AI models that have the capability of interactively and cooperatively assisting human users to solve day-to-day tasks. Augmented Reality (AR) head worn devices can uniquely improve the user experience of solving procedural day-to-day tasks by providing egocentric multimodal (audio and video) ob... | {
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2501.09357 | Path Planning for a UAV Swarm Using Formation Teaching-Learning-Based
Optimization | [
"cs.RO",
"cs.SY",
"eess.SY"
] | This work addresses the path planning problem for a group of unmanned aerial vehicles (UAVs) to maintain a desired formation during operation. Our approach formulates the problem as an optimization task by defining a set of fitness functions that not only ensure the formation but also include constraints for optimal an... | {
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2501.09359 | A Multi-tiered Solution for Personalized Baggage Item Recommendations
using FastText and Association Rule Mining | [
"cs.IR"
] | This paper introduces an intelligent baggage item recommendation system to optimize packing for air travelers by providing tailored suggestions based on specific travel needs and destinations. Using FastText word embeddings and Association Rule Mining (ARM), the system ensures efficient luggage space utilization, compl... | {
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2501.09361 | Strategic Base Representation Learning via Feature Augmentations for
Few-Shot Class Incremental Learning | [
"cs.CV"
] | Few-shot class incremental learning implies the model to learn new classes while retaining knowledge of previously learned classes with a small number of training instances. Existing frameworks typically freeze the parameters of the previously learned classes during the incorporation of new classes. However, this appro... | {
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2501.09362 | A Revisit to Rate-distortion Theory via Optimal Weak Transport | [
"cs.IT",
"math.IT"
] | This paper revisits the rate-distortion theory from the perspective of optimal weak transport, as recently introduced by Gozlan et al. While the conditions for optimality and the existence of solutions are well-understood in the case of discrete alphabets, the extension to abstract alphabets requires more intricate ana... | {
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2501.09363 | Identification of Traditional Medicinal Plant Leaves Using an effective
Deep Learning model and Self-Curated Dataset | [
"cs.CV"
] | Medicinal plants have been a key component in producing traditional and modern medicines, especially in the field of Ayurveda, an ancient Indian medical system. Producing these medicines and collecting and extracting the right plant is a crucial step due to the visually similar nature of some plants. The extraction of ... | {
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2501.09368 | Aligning Instruction Tuning with Pre-training | [
"cs.AI"
] | Instruction tuning enhances large language models (LLMs) to follow human instructions across diverse tasks, relying on high-quality datasets to guide behavior. However, these datasets, whether manually curated or synthetically generated, are often narrowly focused and misaligned with the broad distributions captured du... | {
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2501.09372 | Image Segmentation with transformers: An Overview, Challenges and Future | [
"cs.CV"
] | Image segmentation, a key task in computer vision, has traditionally relied on convolutional neural networks (CNNs), yet these models struggle with capturing complex spatial dependencies, objects with varying scales, need for manually crafted architecture components and contextual information. This paper explores the s... | {
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2501.09384 | Evaluating LLM Abilities to Understand Tabular Electronic Health
Records: A Comprehensive Study of Patient Data Extraction and Retrieval | [
"cs.CL",
"cs.IR"
] | Electronic Health Record (EHR) tables pose unique challenges among which is the presence of hidden contextual dependencies between medical features with a high level of data dimensionality and sparsity. This study presents the first investigation into the abilities of LLMs to comprehend EHRs for patient data extraction... | {
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2501.09393 | SVIA: A Street View Image Anonymization Framework for Self-Driving
Applications | [
"cs.CV"
] | In recent years, there has been an increasing interest in image anonymization, particularly focusing on the de-identification of faces and individuals. However, for self-driving applications, merely de-identifying faces and individuals might not provide sufficient privacy protection since street views like vehicles and... | {
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2501.09394 | Quantum-Enhanced Transformers for Robust Acoustic Scene Classification
in IoT Environments | [
"eess.AS",
"cs.AI",
"cs.LG",
"cs.PF",
"cs.SD"
] | The proliferation of Internet of Things (IoT) devices equipped with acoustic sensors necessitates robust acoustic scene classification (ASC) capabilities, even in noisy and data-limited environments. Traditional machine learning methods often struggle to generalize effectively under such conditions. To address this, we... | {
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2501.09395 | ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks
via Extreme Learning Machines | [
"cs.LG",
"cs.AI",
"cs.NA",
"math.NA"
] | Deep Operator Networks (DeepONets) are among the most prominent frameworks for operator learning, grounded in the universal approximation theorem for operators. However, training DeepONets typically requires significant computational resources. To address this limitation, we propose ELM-DeepONets, an Extreme Learning M... | {
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2501.09396 | Joint Transmission and Deblurring: A Semantic Communication Approach
Using Events | [
"eess.IV",
"cs.CV"
] | Deep learning-based joint source-channel coding (JSCC) is emerging as a promising technology for effective image transmission. However, most existing approaches focus on transmitting clear images, overlooking real-world challenges such as motion blur caused by camera shaking or fast-moving objects. Motion blur often de... | {
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2501.09399 | Fast Searching of Extreme Operating Conditions for Relay Protection
Setting Calculation Based on Graph Neural Network and Reinforcement Learning | [
"cs.LG"
] | Searching for the Extreme Operating Conditions (EOCs) is one of the core problems of power system relay protection setting calculation. The current methods based on brute-force search, heuristic algorithms, and mathematical programming can hardly meet the requirements of today's power systems in terms of computation sp... | {
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2501.09400 | Joint Antenna Selection and Beamforming Design for Active RIS-aided ISAC
Systems | [
"cs.IT",
"eess.SP",
"math.IT"
] | Active reconfigurable intelligent surface (A-RIS) aided integrated sensing and communications (ISAC) system has been considered as a promising paradigm to improve spectrum efficiency. However, massive energy-hungry radio frequency (RF) chains hinder its large-scale deployment. To address this issue, an A-RIS-aided ISAC... | {
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2501.09403 | PISCO: Self-Supervised k-Space Regularization for Improved Neural
Implicit k-Space Representations of Dynamic MRI | [
"eess.IV",
"cs.CV",
"cs.LG",
"eess.SP",
"physics.med-ph"
] | Neural implicit k-space representations (NIK) have shown promising results for dynamic magnetic resonance imaging (MRI) at high temporal resolutions. Yet, reducing acquisition time, and thereby available training data, results in severe performance drops due to overfitting. To address this, we introduce a novel self-su... | {
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2501.09408 | On the distribution of the statistical sum related to BSC | [
"cs.IT",
"math.IT",
"math.PR"
] | The distribution function of the sum of i.i.d. random variables of the special form is considered. Such sum describes messages posterior probabilities for random coding in binary symmetric channel. Close non-asymptotic lower and upper bounds for that function are derived. | {
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2501.09409 | mGeNTE: A Multilingual Resource for Gender-Neutral Language and
Translation | [
"cs.CL"
] | Gender-neutral language reflects societal and linguistic shifts towards greater inclusivity by avoiding the implication that one gender is the norm over others. This is particularly relevant for grammatical gender languages, which heavily encode the gender of terms for human referents and over-relies on masculine forms... | {
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2501.09410 | MoE$^2$: Optimizing Collaborative Inference for Edge Large Language
Models | [
"cs.NI",
"cs.AI",
"cs.LG"
] | Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of natural language processing tasks. Exploiting the heterogeneous capabilities of edge LLMs is crucial for diverse emerging applications, as it enables greater cost-effectiveness and reduced latency. In this work, we introduce \... | {
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2501.09411 | Towards Robust and Realistic Human Pose Estimation via WiFi Signals | [
"cs.CV"
] | Robust WiFi-based human pose estimation is a challenging task that bridges discrete and subtle WiFi signals to human skeletons. This paper revisits this problem and reveals two critical yet overlooked issues: 1) cross-domain gap, i.e., due to significant variations between source-target domain pose distributions; and 2... | {
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2501.09412 | FASP: Fast and Accurate Structured Pruning of Large Language Models | [
"cs.LG"
] | The rapid increase in the size of large language models (LLMs) has significantly escalated their computational and memory demands, posing challenges for efficient deployment, especially on resource-constrained devices. Structured pruning has emerged as an effective model compression method that can reduce these demands... | {
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2501.09420 | Dynamic Neural Style Transfer for Artistic Image Generation using VGG19 | [
"cs.CV",
"cs.AI",
"cs.LG",
"eess.IV"
] | Throughout history, humans have created remarkable works of art, but artificial intelligence has only recently started to make strides in generating visually compelling art. Breakthroughs in the past few years have focused on using convolutional neural networks (CNNs) to separate and manipulate the content and style of... | {
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2501.09425 | Vision-Language Models Do Not Understand Negation | [
"cs.CV",
"cs.CL"
] | Many practical vision-language applications require models that understand negation, e.g., when using natural language to retrieve images which contain certain objects but not others. Despite advancements in vision-language models (VLMs) through large-scale training, their ability to comprehend negation remains underex... | {
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2501.09426 | AutoCBT: An Autonomous Multi-agent Framework for Cognitive Behavioral
Therapy in Psychological Counseling | [
"cs.CL"
] | Traditional in-person psychological counseling remains primarily niche, often chosen by individuals with psychological issues, while online automated counseling offers a potential solution for those hesitant to seek help due to feelings of shame. Cognitive Behavioral Therapy (CBT) is an essential and widely used approa... | {
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2501.09428 | AugRefer: Advancing 3D Visual Grounding via Cross-Modal Augmentation and
Spatial Relation-based Referring | [
"cs.CV"
] | 3D visual grounding (3DVG), which aims to correlate a natural language description with the target object within a 3D scene, is a significant yet challenging task. Despite recent advancements in this domain, existing approaches commonly encounter a shortage: a limited amount and diversity of text3D pairs available for ... | {
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2501.09429 | ADAGE: A generic two-layer framework for adaptive agent based modelling | [
"cs.MA",
"cs.AI",
"cs.LG",
"econ.GN",
"q-fin.CP",
"q-fin.EC"
] | Agent-based models (ABMs) are valuable for modelling complex, potentially out-of-equilibria scenarios. However, ABMs have long suffered from the Lucas critique, stating that agent behaviour should adapt to environmental changes. Furthermore, the environment itself often adapts to these behavioural changes, creating a c... | {
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2501.09430 | HpC: A Calculus for Hybrid and Mobile Systems -- Full Version | [
"cs.PL",
"cs.LO",
"cs.NI",
"cs.SY",
"eess.SY"
] | Networked cybernetic and physical systems of the Internet of Things (IoT) immerse civilian and industrial infrastructures into an interconnected and dynamic web of hybrid and mobile devices. The key feature of such systems is the hybrid and tight coupling of mobile and pervasive discrete communications in a continuousl... | {
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2501.09431 | A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and
Mitigation Strategy | [
"cs.AI",
"cs.CL",
"cs.CR",
"cs.CY"
] | While large language models (LLMs) present significant potential for supporting numerous real-world applications and delivering positive social impacts, they still face significant challenges in terms of the inherent risk of privacy leakage, hallucinated outputs, and value misalignment, and can be maliciously used for ... | {
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2501.09433 | CaPa: Carve-n-Paint Synthesis for Efficient 4K Textured Mesh Generation | [
"cs.CV",
"cs.GR"
] | The synthesis of high-quality 3D assets from textual or visual inputs has become a central objective in modern generative modeling. Despite the proliferation of 3D generation algorithms, they frequently grapple with challenges such as multi-view inconsistency, slow generation times, low fidelity, and surface reconstruc... | {
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2501.09436 | Scaling up self-supervised learning for improved surgical foundation
models | [
"cs.CV"
] | Foundation models have revolutionized computer vision by achieving vastly superior performance across diverse tasks through large-scale pretraining on extensive datasets. However, their application in surgical computer vision has been limited. This study addresses this gap by introducing SurgeNetXL, a novel surgical fo... | {
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2501.09444 | Solving the Unsolvable: Translating Case Law in Hong Kong | [
"cs.CL",
"cs.AI",
"cs.LG",
"cs.MA"
] | This paper addresses the challenges translating case law under Hong Kong's bilingual legal system. It highlights the initial success of translating all written statutes into Chinese before the 1997 handover, a task mandated by the Basic Law. The effort involved significant collaboration among legal, linguistic, and tra... | {
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2501.09446 | Double Visual Defense: Adversarial Pre-training and Instruction Tuning
for Improving Vision-Language Model Robustness | [
"cs.CV"
] | This paper investigates the robustness of vision-language models against adversarial visual perturbations and introduces a novel ``double visual defense" to enhance this robustness. Unlike previous approaches that resort to lightweight adversarial fine-tuning of a pre-trained CLIP model, we perform large-scale adversar... | {
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2501.09450 | Real-Time Generation of Near-Minimum-Energy Trajectories via
Constraint-Informed Residual Learning | [
"cs.RO"
] | Industrial robotics demands significant energy to operate, making energy-reduction methodologies increasingly important. Strategies for planning minimum-energy trajectories typically involve solving nonlinear optimal control problems (OCPs), which rarely cope with real-time requirements. In this paper, we propose a par... | {
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2501.09451 | Scaling Graph-Based Dependency Parsing with Arc Vectorization and
Attention-Based Refinement | [
"cs.CL"
] | We propose a novel architecture for graph-based dependency parsing that explicitly constructs vectors, from which both arcs and labels are scored. Our method addresses key limitations of the standard two-pipeline approach by unifying arc scoring and labeling into a single network, reducing scalability issues caused by ... | {
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2501.09456 | On the Relation between Optical Aperture and Automotive Object Detection | [
"cs.CV"
] | We explore the impact of aperture size and shape on automotive camera systems for deep-learning-based tasks like traffic sign recognition and light state detection. A method is proposed to simulate optical effects using the point spread function (PSF), enhancing realism and reducing the domain gap between synthetic and... | {
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2501.09459 | Teaching Wav2Vec2 the Language of the Brain | [
"cs.LG"
] | The decoding of continuously spoken speech from neuronal activity has the potential to become an important clinical solution for paralyzed patients. Deep Learning Brain Computer Interfaces (BCIs) have recently successfully mapped neuronal activity to text contents in subjects who attempted to formulate speech. However,... | {
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2501.09460 | Normal-NeRF: Ambiguity-Robust Normal Estimation for Highly Reflective
Scenes | [
"cs.CV"
] | Neural Radiance Fields (NeRF) often struggle with reconstructing and rendering highly reflective scenes. Recent advancements have developed various reflection-aware appearance models to enhance NeRF's capability to render specular reflections. However, the robust reconstruction of highly reflective scenes is still hind... | {
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2501.09464 | Pruning for Sparse Diffusion Models based on Gradient Flow | [
"cs.LG"
] | Diffusion Models (DMs) have impressive capabilities among generation models, but are limited to slower inference speeds and higher computational costs. Previous works utilize one-shot structure pruning to derive lightweight DMs from pre-trained ones, but this approach often leads to a significant drop in generation qua... | {
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2501.09465 | RE-POSE: Synergizing Reinforcement Learning-Based Partitioning and
Offloading for Edge Object Detection | [
"cs.CV",
"cs.AI",
"cs.DC"
] | Object detection plays a crucial role in smart video analysis, with applications ranging from autonomous driving and security to smart cities. However, achieving real-time object detection on edge devices presents significant challenges due to their limited computational resources and the high demands of deep neural ne... | {
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2501.09466 | DEFOM-Stereo: Depth Foundation Model Based Stereo Matching | [
"cs.CV"
] | Stereo matching is a key technique for metric depth estimation in computer vision and robotics. Real-world challenges like occlusion and non-texture hinder accurate disparity estimation from binocular matching cues. Recently, monocular relative depth estimation has shown remarkable generalization using vision foundatio... | {
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2501.09468 | Sensorimotor Control Strategies for Tactile Robotics | [
"cs.RO"
] | How are robots becoming smarter at interacting with their surroundings? Recent advances have reshaped how robots use tactile sensing to perceive and engage with the world. Tactile sensing is a game-changer, allowing robots to embed sensorimotor control strategies to interact with complex environments and skillfully han... | {
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2501.09469 | Predicting Air Temperature from Volumetric Urban Morphology with Machine
Learning | [
"cs.LG",
"cs.AI"
] | In this study, we firstly introduce a method that converts CityGML data into voxels which works efficiently and fast in high resolution for large scale datasets such as cities but by sacrificing some building details to overcome the limitations of previous voxelization methodologies that have been computationally inten... | {
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2501.09480 | Utilizing AI Language Models to Identify Prognostic Factors for Coronary
Artery Disease: A Study in Mashhad Residents | [
"cs.LG"
] | Abstract: Background: Understanding cardiovascular artery disease risk factors, the leading global cause of mortality, is crucial for influencing its etiology, prevalence, and treatment. This study aims to evaluate prognostic markers for coronary artery disease in Mashhad using Naive Bayes, REP Tree, J48, CART, and CHA... | {
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2501.09481 | MonoSOWA: Scalable monocular 3D Object detector Without human
Annotations | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Detecting the three-dimensional position and orientation of objects using a single RGB camera is a foundational task in computer vision with many important applications. Traditionally, 3D object detection methods are trained in a fully-supervised setup, requiring vast amounts of human annotations, which are laborious, ... | {
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2501.09484 | Exploring the Inquiry-Diagnosis Relationship with Advanced Patient
Simulators | [
"cs.CL"
] | Online medical consultation (OMC) restricts doctors to gathering patient information solely through inquiries, making the already complex sequential decision-making process of diagnosis even more challenging. Recently, the rapid advancement of large language models has demonstrated a significant potential to transform ... | {
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2501.09485 | The Devil is in the Details: Simple Remedies for Image-to-LiDAR
Representation Learning | [
"cs.CV"
] | LiDAR is a crucial sensor in autonomous driving, commonly used alongside cameras. By exploiting this camera-LiDAR setup and recent advances in image representation learning, prior studies have shown the promising potential of image-to-LiDAR distillation. These prior arts focus on the designs of their own losses to effe... | {
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2501.09490 | Comparison of Various SLAM Systems for Mobile Robot in an Indoor
Environment | [
"cs.RO",
"cs.CV"
] | This article presents a comparative analysis of a mobile robot trajectories computed by various ROS-based SLAM systems. For this reason we developed a prototype of a mobile robot with common sensors: 2D lidar, a monocular and ZED stereo cameras. Then we conducted experiments in a typical office environment and collecte... | {
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2501.09493 | Large Language Models as Evaluators for Conversational Recommender
Systems: Benchmarking System Performance from a User-Centric Perspective | [
"cs.IR"
] | Conversational recommender systems (CRS) involve both recommendation and dialogue tasks, which makes their evaluation a unique challenge. Although past research has analyzed various factors that may affect user satisfaction with CRS interactions from the perspective of user studies, few evaluation metrics for CRS have ... | {
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2501.09499 | VanGogh: A Unified Multimodal Diffusion-based Framework for Video
Colorization | [
"cs.CV"
] | Video colorization aims to transform grayscale videos into vivid color representations while maintaining temporal consistency and structural integrity. Existing video colorization methods often suffer from color bleeding and lack comprehensive control, particularly under complex motion or diverse semantic cues. To this... | {
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2501.09502 | Omni-Emotion: Extending Video MLLM with Detailed Face and Audio Modeling
for Multimodal Emotion Analysis | [
"cs.CV"
] | Understanding emotions accurately is essential for fields like human-computer interaction. Due to the complexity of emotions and their multi-modal nature (e.g., emotions are influenced by facial expressions and audio), researchers have turned to using multi-modal models to understand human emotions rather than single-m... | {
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2501.09503 | AnyStory: Towards Unified Single and Multiple Subject Personalization in
Text-to-Image Generation | [
"cs.CV"
] | Recently, large-scale generative models have demonstrated outstanding text-to-image generation capabilities. However, generating high-fidelity personalized images with specific subjects still presents challenges, especially in cases involving multiple subjects. In this paper, we propose AnyStory, a unified approach for... | {
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2501.09504 | HydraMix: Multi-Image Feature Mixing for Small Data Image Classification | [
"cs.CV"
] | Training deep neural networks requires datasets with a large number of annotated examples. The collection and annotation of these datasets is not only extremely expensive but also faces legal and privacy problems. These factors are a significant limitation for many real-world applications. To address this, we introduce... | {
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2501.09506 | Multimodal Marvels of Deep Learning in Medical Diagnosis: A
Comprehensive Review of COVID-19 Detection | [
"cs.LG",
"cs.SD",
"eess.AS",
"eess.IV"
] | This study presents a comprehensive review of the potential of multimodal deep learning (DL) in medical diagnosis, using COVID-19 as a case example. Motivated by the success of artificial intelligence applications during the COVID-19 pandemic, this research aims to uncover the capabilities of DL in disease screening, p... | {
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2501.09509 | Power-Efficient RAN Intelligent Controllers Through Optimized KPI
Monitoring | [
"eess.SY",
"cs.SY"
] | The Open Radio Access Network (RAN) paradigm envisions a more flexible, interoperable, and intelligent RAN ecosystem via new open interfaces and elements like the RAN Intelligent Controller (RIC). However, the impact of these elements on Open RAN's power consumption remains heavily unexplored. This work for the first t... | {
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2501.09512 | PIER: A Novel Metric for Evaluating What Matters in Code-Switching | [
"cs.CL",
"cs.LG"
] | Code-switching, the alternation of languages within a single discourse, presents a significant challenge for Automatic Speech Recognition. Despite the unique nature of the task, performance is commonly measured with established metrics such as Word-Error-Rate (WER). However, in this paper, we question whether these gen... | {
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2501.09513 | A Dataset Generation Toolbox for Dynamic Security Assessment: On the
Role of the Security Boundary | [
"eess.SY",
"cs.SY"
] | Dynamic security assessment (DSA) is crucial for ensuring the reliable operation of power systems. However, conventional DSA approaches are becoming intractable for future power systems, driving interest in more computationally efficient data-driven methods. Efficient dataset generation is a cornerstone of these method... | {
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2501.09514 | A Runtime Analysis of the Multi-Valued Compact Genetic Algorithm on
Generalized LeadingOnes | [
"cs.NE"
] | In the literature on runtime analyses of estimation of distribution algorithms (EDAs), researchers have recently explored univariate EDAs for multi-valued decision variables. Particularly, Jedidia et al. gave the first runtime analysis of the multi-valued UMDA on the r-valued LeadingOnes (r-LeadingOnes) functions and A... | {
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2501.09519 | Multi-task deep-learning for sleep event detection and stage
classification | [
"eess.SP",
"cs.LG"
] | Polysomnographic sleep analysis is the standard clinical method to accurately diagnose and treat sleep disorders. It is an intricate process which involves the manual identification, classification, and location of multiple sleep event patterns. This is complex, for which identification of different types of events inv... | {
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2501.09520 | RWZC: A Model-Driven Approach for Learning-based Robust Wyner-Ziv Coding | [
"cs.IT",
"math.IT"
] | In this paper, a novel learning-based Wyner-Ziv coding framework is considered under a distributed image transmission scenario, where the correlated source is only available at the receiver. Unlike other learnable frameworks, our approach demonstrates robustness to non-stationary source correlation, where the overlappi... | {
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2501.09521 | Augmenting a Large Language Model with a Combination of Text and Visual
Data for Conversational Visualization of Global Geospatial Data | [
"cs.HC",
"cs.CL"
] | We present a method for augmenting a Large Language Model (LLM) with a combination of text and visual data to enable accurate question answering in visualization of scientific data, making conversational visualization possible. LLMs struggle with tasks like visual data interaction, as they lack contextual visual inform... | {
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2501.09522 | Merging Models on the Fly Without Retraining: A Sequential Approach to
Scalable Continual Model Merging | [
"cs.LG"
] | Deep model merging represents an emerging research direction that combines multiple fine-tuned models to harness their specialized capabilities across different tasks and domains. Current model merging techniques focus on merging all available models simultaneously, with weight interpolation-based methods being the pre... | {
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2501.09525 | Class Incremental Fault Diagnosis under Limited Fault Data via
Supervised Contrastive Knowledge Distillation | [
"cs.LG",
"cs.AI"
] | Class-incremental fault diagnosis requires a model to adapt to new fault classes while retaining previous knowledge. However, limited research exists for imbalanced and long-tailed data. Extracting discriminative features from few-shot fault data is challenging, and adding new fault classes often demands costly model r... | {
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2501.09527 | Confidence Estimation for Error Detection in Text-to-SQL Systems | [
"cs.LG",
"cs.CL"
] | Text-to-SQL enables users to interact with databases through natural language, simplifying the retrieval and synthesis of information. Despite the success of large language models (LLMs) in converting natural language questions into SQL queries, their broader adoption is limited by two main challenges: achieving robust... | {
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2501.09528 | Comprehensive Survey of QML: From Data Analysis to Algorithmic
Advancements | [
"quant-ph",
"cs.IT",
"math.IT"
] | Quantum Machine Learning represents a paradigm shift at the intersection of Quantum Computing and Machine Learning, leveraging quantum phenomena such as superposition, entanglement, and quantum parallelism to address the limitations of classical approaches in processing high-dimensional and large-scale datasets. This s... | {
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2501.09531 | MOGNET: A Mux-residual quantized Network leveraging Online-Generated
weights | [
"cs.LG",
"cs.AR"
] | This paper presents a compact model architecture called MOGNET, compatible with a resource-limited hardware. MOGNET uses a streamlined Convolutional factorization block based on a combination of 2 point-wise (1x1) convolutions with a group-wise convolution in-between. To further limit the overall model size and reduce ... | {
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2501.09532 | AdaFV: Rethinking of Visual-Language alignment for VLM acceleration | [
"cs.CV"
] | The success of VLMs often relies on the dynamic high-resolution schema that adaptively augments the input images to multiple crops, so that the details of the images can be retained. However, such approaches result in a large number of redundant visual tokens, thus significantly reducing the efficiency of the VLMs. To ... | {
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2501.09534 | AI in Support of Diversity and Inclusion | [
"cs.AI"
] | In this paper, we elaborate on how AI can support diversity and inclusion and exemplify research projects conducted in that direction. We start by looking at the challenges and progress in making large language models (LLMs) more transparent, inclusive, and aware of social biases. Even though LLMs like ChatGPT have imp... | {
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2501.09538 | Analyzing Continuous Semantic Shifts with Diachronic Word Similarity
Matrices | [
"cs.CL"
] | The meanings and relationships of words shift over time. This phenomenon is referred to as semantic shift. Research focused on understanding how semantic shifts occur over multiple time periods is essential for gaining a detailed understanding of semantic shifts. However, detecting change points only between adjacent t... | {
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2501.09551 | Intra-day Solar and Power Forecast for Optimization of Intraday Market
Participation | [
"cs.LG",
"cs.SY",
"eess.SP",
"eess.SY"
] | The prediction of solar irradiance enhances reliability in photovoltaic (PV) solar plant generation and grid integration. In Colombia, PV plants face penalties if energy production deviates beyond governmental thresholds from intraday market offers. This research employs Long Short-Term Memory (LSTM) and Bidirectional-... | {
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2501.09552 | Exploring AI-based System Design for Pixel-level Protected Health
Information Detection in Medical Images | [
"cs.CV"
] | Purpose: This study aims to evaluate different setups of an AI-based solution to detect Protected Health Information (PHI) in medical images. Materials and Methods: Text from eight PHI and eight non-PHI categories are simulated and incorporated into a curated dataset comprising 1,000 medical images across four modali... | {
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2501.09555 | Text-driven Adaptation of Foundation Models for Few-shot Surgical
Workflow Analysis | [
"cs.CV",
"cs.AI"
] | Purpose: Surgical workflow analysis is crucial for improving surgical efficiency and safety. However, previous studies rely heavily on large-scale annotated datasets, posing challenges in cost, scalability, and reliance on expert annotations. To address this, we propose Surg-FTDA (Few-shot Text-driven Adaptation), desi... | {
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2501.09556 | Overshoot: Taking advantage of future gradients in momentum-based
stochastic optimization | [
"cs.LG"
] | Overshoot is a novel, momentum-based stochastic gradient descent optimization method designed to enhance performance beyond standard and Nesterov's momentum. In conventional momentum methods, gradients from previous steps are aggregated with the gradient at current model weights before taking a step and updating the mo... | {
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2501.09561 | Stylomech: Unveiling Authorship via Computational Stylometry in English
and Romanized Sinhala | [
"cs.CL"
] | With the advent of Web 2.0, the development in social technology coupled with global communication systematically brought positive and negative impacts to society. Copyright claims and Author identification are deemed crucial as there has been a considerable amount of increase in content violation owing to the lack of ... | {
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2501.09563 | A Multi-agent System for Hybrid Optimization | [
"math.OC",
"cs.MA"
] | Optimization problems in process engineering, including design and operation, can often pose challenges to many solvers: multi-modal, non-smooth, and discontinuous models often with large computational requirements. In such cases, the optimization problem is often treated as a black box in which only the value of the o... | {
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2501.09565 | A New Teacher-Reviewer-Student Framework for Semi-supervised 2D Human
Pose Estimation | [
"cs.CV"
] | Conventional 2D human pose estimation methods typically require extensive labeled annotations, which are both labor-intensive and expensive. In contrast, semi-supervised 2D human pose estimation can alleviate the above problems by leveraging a large amount of unlabeled data along with a small portion of labeled data. E... | {
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2501.09571 | MatrixNet: Learning over symmetry groups using learned group
representations | [
"cs.LG",
"cs.AI",
"math.RT"
] | Group theory has been used in machine learning to provide a theoretically grounded approach for incorporating known symmetry transformations in tasks from robotics to protein modeling. In these applications, equivariant neural networks use known symmetry groups with predefined representations to learn over geometric in... | {
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} |
2501.09572 | Towards Spectral Convergence of Locally Linear Embedding on Manifolds
with Boundary | [
"math.AP",
"cs.LG"
] | We study the eigenvalues and eigenfunctions of a differential operator that governs the asymptotic behavior of the unsupervised learning algorithm known as Locally Linear Embedding when a large data set is sampled from an interval or disc. In particular, the differential operator is of second order, mixed-type, and deg... | {
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} |
2501.09579 | Sequential PatchCore: Anomaly Detection for Surface Inspection using
Synthetic Impurities | [
"cs.CV",
"cs.GR",
"cs.LG"
] | The appearance of surface impurities (e.g., water stains, fingerprints, stickers) is an often-mentioned issue that causes degradation of automated visual inspection systems. At the same time, synthetic data generation techniques for visual surface inspection have focused primarily on generating perfect examples and def... | {
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} |
2501.09588 | Atleus: Accelerating Transformers on the Edge Enabled by 3D
Heterogeneous Manycore Architectures | [
"cs.AR",
"cs.LG"
] | Transformer architectures have become the standard neural network model for various machine learning applications including natural language processing and computer vision. However, the compute and memory requirements introduced by transformer models make them challenging to adopt for edge applications. Furthermore, fi... | {
"Other": 1,
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} |
2501.09591 | Metrics for Inter-Dataset Similarity with Example Applications in
Synthetic Data and Feature Selection Evaluation -- Extended Version | [
"cs.LG"
] | Measuring inter-dataset similarity is an important task in machine learning and data mining with various use cases and applications. Existing methods for measuring inter-dataset similarity are computationally expensive, limited, or sensitive to different entities and non-trivial choices for parameters. They also lack a... | {
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} |
2501.09595 | IFRA: a machine learning-based Instrumented Fall Risk Assessment Scale
derived from Instrumented Timed Up and Go test in stroke patients | [
"cs.LG",
"cs.AI"
] | Effective fall risk assessment is critical for post-stroke patients. The present study proposes a novel, data-informed fall risk assessment method based on the instrumented Timed Up and Go (ITUG) test data, bringing in many mobility measures that traditional clinical scales fail to capture. IFRA, which stands for Instr... | {
"Other": 0,
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} |
2501.09597 | Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology
via Pretraining | [
"cs.LG",
"cs.AI"
] | Meshes are used to represent complex objects in high fidelity physics simulators across a variety of domains, such as radar sensing and aerodynamics. There is growing interest in using neural networks to accelerate physics simulations, and also a growing body of work on applying neural networks directly to irregular me... | {
"Other": 0,
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} |
2501.09600 | Mesh2SLAM in VR: A Fast Geometry-Based SLAM Framework for Rapid
Prototyping in Virtual Reality Applications | [
"cs.RO",
"cs.CV"
] | SLAM is a foundational technique with broad applications in robotics and AR/VR. SLAM simulations evaluate new concepts, but testing on resource-constrained devices, such as VR HMDs, faces challenges: high computational cost and restricted sensor data access. This work proposes a sparse framework using mesh geometry pro... | {
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} |
2501.09604 | From Scarcity to Capability: Empowering Fake News Detection in
Low-Resource Languages with LLMs | [
"cs.CL"
] | The rapid spread of fake news presents a significant global challenge, particularly in low-resource languages like Bangla, which lack adequate datasets and detection tools. Although manual fact-checking is accurate, it is expensive and slow to prevent the dissemination of fake news. Addressing this gap, we introduce Ba... | {
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} |
2501.09605 | Managed-Retention Memory: A New Class of Memory for the AI Era | [
"cs.AR",
"cs.AI",
"cs.DC",
"cs.ET"
] | AI clusters today are one of the major uses of High Bandwidth Memory (HBM). However, HBM is suboptimal for AI workloads for several reasons. Analysis shows HBM is overprovisioned on write performance, but underprovisioned on density and read bandwidth, and also has significant energy per bit overheads. It is also expen... | {
"Other": 1,
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"cs.SY": 0
} |
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