id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.19080 | Mask Factory: Towards High-quality Synthetic Data Generation for
Dichotomous Image Segmentation | [
"cs.CV"
] | Dichotomous Image Segmentation (DIS) tasks require highly precise annotations, and traditional dataset creation methods are labor intensive, costly, and require extensive domain expertise. Although using synthetic data for DIS is a promising solution to these challenges, current generative models and techniques struggl... | {
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2412.19085 | Assessing Pre-trained Models for Transfer Learning through Distribution
of Spectral Components | [
"cs.LG"
] | Pre-trained model assessment for transfer learning aims to identify the optimal candidate for the downstream tasks from a model hub, without the need of time-consuming fine-tuning. Existing advanced works mainly focus on analyzing the intrinsic characteristics of the entire features extracted by each pre-trained model ... | {
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2412.19086 | Investigating the Temporal Dynamics of Cyber Threat Intelligence | [
"cs.CR",
"cs.IT",
"cs.SI",
"math.IT"
] | Indicators of Compromise (IoCs) play a crucial role in the rapid detection and mitigation of cyber threats. However, the existing body of literature lacks in-depth analytical studies on the temporal aspects of IoC publication, especially when considering up-to-date datasets related to Common Vulnerabilities and Exposur... | {
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2412.19087 | MoPD: Mixture-of-Prompts Distillation for Vision-Language Models | [
"cs.CV",
"cs.CL",
"cs.LG"
] | Soft prompt learning methods are effective for adapting vision-language models (VLMs) to downstream tasks. Nevertheless, empirical evidence reveals a tendency of existing methods that they overfit seen classes and exhibit degraded performance on unseen classes. This limitation is due to the inherent bias in the trainin... | {
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2412.19088 | Integrating Artificial Open Generative Artificial Intelligence into
Software Supply Chain Security | [
"cs.CR",
"cs.AI",
"cs.ET"
] | While new technologies emerge, human errors always looming. Software supply chain is increasingly complex and intertwined, the security of a service has become paramount to ensuring the integrity of products, safeguarding data privacy, and maintaining operational continuity. In this work, we conducted experiments on th... | {
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2412.19089 | Humans as a Calibration Pattern: Dynamic 3D Scene Reconstruction from
Unsynchronized and Uncalibrated Videos | [
"cs.CV"
] | Recent works on dynamic neural field reconstruction assume input from synchronized multi-view videos with known poses. These input constraints are often unmet in real-world setups, making the approach impractical. We demonstrate that unsynchronized videos with unknown poses can generate dynamic neural fields if the vid... | {
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2412.19091 | From Coin to Data: The Impact of Object Detection on Digital Numismatics | [
"cs.CV"
] | In this work we investigate the application of advanced object detection techniques to digital numismatics, focussing on the analysis of historical coins. Leveraging models such as Contrastive Language-Image Pre-training (CLIP), we develop a flexible framework for identifying and classifying specific coin features usin... | {
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2412.19092 | TrajGEOS: Trajectory Graph Enhanced Orientation-based Sequential Network
for Mobility Prediction | [
"cs.AI"
] | Human mobility studies how people move to access their needed resources and plays a significant role in urban planning and location-based services. As a paramount task of human mobility modeling, next location prediction is challenging because of the diversity of users' historical trajectories that gives rise to comple... | {
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2412.19098 | Tint Your Models Task-wise for Improved Multi-task Model Merging | [
"cs.LG"
] | Traditional model merging methods for multi-task learning (MTL) address task conflicts with straightforward strategies such as weight averaging, sign consensus, or minimal test-time adjustments. This presumably counts on the assumption that a merged encoder still retains abundant task knowledge from individual encoders... | {
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2412.19101 | Reconstruction Target Matters in Masked Image Modeling for Cross-Domain
Few-Shot Learning | [
"cs.CV"
] | Cross-Domain Few-Shot Learning (CDFSL) requires the model to transfer knowledge from the data-abundant source domain to data-scarce target domains for fast adaptation, where the large domain gap makes CDFSL a challenging problem. Masked Autoencoder (MAE) excels in effectively using unlabeled data and learning image's g... | {
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2412.19102 | "I've Heard of You!": Generate Spoken Named Entity Recognition Data for
Unseen Entities | [
"cs.CL"
] | Spoken named entity recognition (NER) aims to identify named entities from speech, playing an important role in speech processing. New named entities appear every day, however, annotating their Spoken NER data is costly. In this paper, we demonstrate that existing Spoken NER systems perform poorly when dealing with pre... | {
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2412.19104 | Improving Generative Pre-Training: An In-depth Study of Masked Image
Modeling and Denoising Models | [
"cs.CV",
"cs.LG"
] | In this work, we dive deep into the impact of additive noise in pre-training deep networks. While various methods have attempted to use additive noise inspired by the success of latent denoising diffusion models, when used in combination with masked image modeling, their gains have been marginal when it comes to recogn... | {
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2412.19106 | ERGNN: Spectral Graph Neural Network With Explicitly-Optimized Rational
Graph Filters | [
"cs.LG",
"cs.NA",
"eess.SP",
"math.NA"
] | Approximation-based spectral graph neural networks, which construct graph filters with function approximation, have shown substantial performance in graph learning tasks. Despite their great success, existing works primarily employ polynomial approximation to construct the filters, whereas another superior option, name... | {
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2412.19108 | Graph Mixture of Experts and Memory-augmented Routers for Multivariate
Time Series Anomaly Detection | [
"cs.LG",
"cs.AI"
] | Multivariate time series (MTS) anomaly detection is a critical task that involves identifying abnormal patterns or events in data that consist of multiple interrelated time series. In order to better model the complex interdependence between entities and the various inherent characteristics of each entity, the GNN base... | {
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2412.19109 | Stochastic normalizing flows for Effective String Theory | [
"hep-lat",
"cs.LG",
"hep-th"
] | Effective String Theory (EST) is a powerful tool used to study confinement in pure gauge theories by modeling the confining flux tube connecting a static quark-anti-quark pair as a thin vibrating string. Recently, flow-based samplers have been applied as an efficient numerical method to study EST regularized on the lat... | {
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2412.19110 | A Selective Secure Precoding Framework for MU-MIMO Rate-Splitting
Multiple Access Networks Under Limited CSIT | [
"cs.IT",
"eess.SP",
"math.IT"
] | In this paper, we propose a robust and adaptable secure precoding framework designed to encapsulate a intricate scenario where legitimate users have different information security: secure private or normal public information. Leveraging rate-splitting multiple access (RSMA), we formulate the sum secrecy spectral effici... | {
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2412.19111 | Spectral Enhancement and Pseudo-Anchor Guidance for Infrared-Visible
Person Re-Identification | [
"cs.CV",
"cs.LG",
"eess.IV"
] | The development of deep learning has facilitated the application of person re-identification (ReID) technology in intelligent security. Visible-infrared person re-identification (VI-ReID) aims to match pedestrians across infrared and visible modality images enabling 24-hour surveillance. Current studies relying on unsu... | {
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2412.19112 | Future Success Prediction in Open-Vocabulary Object Manipulation Tasks
Based on End-Effector Trajectories | [
"cs.RO",
"cs.CV"
] | This study addresses a task designed to predict the future success or failure of open-vocabulary object manipulation. In this task, the model is required to make predictions based on natural language instructions, egocentric view images before manipulation, and the given end-effector trajectories. Conventional methods ... | {
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2412.19113 | SketchFill: Sketch-Guided Code Generation for Imputing Derived Missing
Values | [
"cs.CL",
"cs.DB",
"cs.LG"
] | Missing value is a critical issue in data science, significantly impacting the reliability of analyses and predictions. Missing value imputation (MVI) is a longstanding problem because it highly relies on domain knowledge. Large language models (LLMs) have emerged as a promising tool for data cleaning, including MVI fo... | {
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2412.19114 | Discrete vs. Continuous Trade-offs for Generative Models | [
"cs.LG",
"cs.AI",
"cs.IT",
"cs.NA",
"math.IT",
"math.NA"
] | This work explores the theoretical and practical foundations of denoising diffusion probabilistic models (DDPMs) and score-based generative models, which leverage stochastic processes and Brownian motion to model complex data distributions. These models employ forward and reverse diffusion processes defined through sto... | {
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2412.19124 | Evaluating Self-Supervised Learning in Medical Imaging: A Benchmark for
Robustness, Generalizability, and Multi-Domain Impact | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Self-supervised learning (SSL) has emerged as a promising paradigm in medical imaging, addressing the chronic challenge of limited labeled data in healthcare settings. While SSL has shown impressive results, existing studies in the medical domain are often limited in scope, focusing on specific datasets or modalities, ... | {
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2412.19125 | Advanced Knowledge Transfer: Refined Feature Distillation for Zero-Shot
Quantization in Edge Computing | [
"cs.CV",
"cs.LG"
] | We introduce AKT (Advanced Knowledge Transfer), a novel method to enhance the training ability of low-bit quantized (Q) models in the field of zero-shot quantization (ZSQ). Existing research in ZSQ has focused on generating high-quality data from full-precision (FP) models. However, these approaches struggle with reduc... | {
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2412.19126 | Polycyclic Codes over the Product Ring $\mathbb{F}_q^l$ and their
Annihilator Dual | [
"cs.IT",
"math.IT"
] | In this article, for the finite field $\mathbb{F}_q$, we show that the $\mathbb{F}_q$-algebra $\mathbb{F}_q[x]/\langle f(x) \rangle$ is isomorphic to the product ring $\mathbb{F}_q^{\deg f(x)}$ if and only if $f(x)$ splits over $\mathbb{F}_q$ into distinct factors. We generalize this result to the quotient of the polyn... | {
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2412.19127 | SDRS: Shape-Differentiable Robot Simulator | [
"cs.RO"
] | Robot simulators are indispensable tools across many fields, and recent research has significantly improved their functionality by incorporating additional gradient information. However, existing differentiable robot simulators suffer from non-differentiable singularities, when robots undergo substantial shape changes.... | {
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2412.19128 | Semantic Residual for Multimodal Unified Discrete Representation | [
"cs.CV",
"cs.LG"
] | Recent research in the domain of multimodal unified representations predominantly employs codebook as representation forms, utilizing Vector Quantization(VQ) for quantization, yet there has been insufficient exploration of other quantization representation forms. Our work explores more precise quantization methods and ... | {
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2412.19130 | MVS-GS: High-Quality 3D Gaussian Splatting Mapping via Online Multi-View
Stereo | [
"cs.CV"
] | This study addresses the challenge of online 3D model generation for neural rendering using an RGB image stream. Previous research has tackled this issue by incorporating Neural Radiance Fields (NeRF) or 3D Gaussian Splatting (3DGS) as scene representations within dense SLAM methods. However, most studies focus primari... | {
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2412.19131 | Synthetic Discrete Inertia | [
"eess.SY",
"cs.SY"
] | This letter demonstrates how synthetic inertia can be obtained with the control of flexible discrete devices to keep the power balance of power systems, even if the system does not include any synchronous generator or conventional grid-forming converter. The letter also discusses solutions to cycling issues, which can ... | {
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2412.19133 | A Rhetorical Relations-Based Framework for Tailored Multimedia Document
Summarization | [
"cs.MM",
"cs.AI",
"cs.HC"
] | In the rapidly evolving landscape of digital content, the task of summarizing multimedia documents, which encompass textual, visual, and auditory elements, presents intricate challenges. These challenges include extracting pertinent information from diverse formats, maintaining the structural integrity and semantic coh... | {
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2412.19134 | Extended Cross-Modality United Learning for Unsupervised
Visible-Infrared Person Re-identification | [
"cs.CV",
"cs.LG",
"eess.IV"
] | Unsupervised learning visible-infrared person re-identification (USL-VI-ReID) aims to learn modality-invariant features from unlabeled cross-modality datasets and reduce the inter-modality gap. However, the existing methods lack cross-modality clustering or excessively pursue cluster-level association, which makes it d... | {
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2412.19138 | SUTrack: Towards Simple and Unified Single Object Tracking | [
"cs.CV",
"cs.LG"
] | In this paper, we propose a simple yet unified single object tracking (SOT) framework, dubbed SUTrack. It consolidates five SOT tasks (RGB-based, RGB-Depth, RGB-Thermal, RGB-Event, RGB-Language Tracking) into a unified model trained in a single session. Due to the distinct nature of the data, current methods typically ... | {
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2412.19139 | PlanLLM: Video Procedure Planning with Refinable Large Language Models | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.MM"
] | Video procedure planning, i.e., planning a sequence of action steps given the video frames of start and goal states, is an essential ability for embodied AI. Recent works utilize Large Language Models (LLMs) to generate enriched action step description texts to guide action step decoding. Although LLMs are introduced, ... | {
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2412.19140 | SILC-EFSA: Self-aware In-context Learning Correction for Entity-level
Financial Sentiment Analysis | [
"cs.CL",
"cs.AI",
"cs.CE"
] | In recent years, fine-grained sentiment analysis in finance has gained significant attention, but the scarcity of entity-level datasets remains a key challenge. To address this, we have constructed the largest English and Chinese financial entity-level sentiment analysis datasets to date. Building on this foundation, w... | {
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2412.19141 | How Panel Layouts Define Manga: Insights from Visual Ablation
Experiments | [
"cs.CV"
] | Today, manga has gained worldwide popularity. However, the question of how various elements of manga, such as characters, text, and panel layouts, reflect the uniqueness of a particular work, or even define it, remains an unexplored area. In this paper, we aim to quantitatively and qualitatively analyze the visual char... | {
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2412.19142 | CLIP-GS: Unifying Vision-Language Representation with 3D Gaussian
Splatting | [
"cs.CV"
] | Recent works in 3D multimodal learning have made remarkable progress. However, typically 3D multimodal models are only capable of handling point clouds. Compared to the emerging 3D representation technique, 3D Gaussian Splatting (3DGS), the spatially sparse point cloud cannot depict the texture information of 3D object... | {
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2412.19145 | Impact of color and mixing proportion of synthetic point clouds on
semantic segmentation | [
"cs.CV"
] | Deep learning (DL)-based point cloud segmentation is essential for understanding built environment. Despite synthetic point clouds (SPC) having the potential to compensate for data shortage, how synthetic color and mixing proportion impact DL-based segmentation remains a long-standing question. Therefore, this paper ad... | {
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2412.19146 | AskChart: Universal Chart Understanding through Textual Enhancement | [
"cs.CV",
"cs.AI"
] | Chart understanding tasks such as ChartQA and Chart-to-Text involve automatically extracting and interpreting key information from charts, enabling users to query or convert visual data into structured formats. State-of-the-art approaches primarily focus on visual cues from chart images, failing to explicitly incorpora... | {
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2412.19149 | Generating Editable Head Avatars with 3D Gaussian GANs | [
"cs.CV"
] | Generating animatable and editable 3D head avatars is essential for various applications in computer vision and graphics. Traditional 3D-aware generative adversarial networks (GANs), often using implicit fields like Neural Radiance Fields (NeRF), achieve photorealistic and view-consistent 3D head synthesis. However, th... | {
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2412.19152 | To Predict or Not To Predict? Proportionally Masked Autoencoders for
Tabular Data Imputation | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Masked autoencoders (MAEs) have recently demonstrated effectiveness in tabular data imputation. However, due to the inherent heterogeneity of tabular data, the uniform random masking strategy commonly used in MAEs can disrupt the distribution of missingness, leading to suboptimal performance. To address this, we propos... | {
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2412.19153 | Sketch-MoMa: Teleoperation for Mobile Manipulator via Interpretation of
Hand-Drawn Sketches | [
"cs.RO"
] | To use assistive robots in everyday life, a remote control system with common devices, such as 2D devices, is helpful to control the robots anytime and anywhere as intended. Hand-drawn sketches are one of the intuitive ways to control robots with 2D devices. However, since similar sketches have different intentions fro... | {
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2412.19155 | Referencing Where to Focus: Improving VisualGrounding with Referential
Query | [
"cs.CV",
"cs.CL"
] | Visual Grounding aims to localize the referring object in an image given a natural language expression. Recent advancements in DETR-based visual grounding methods have attracted considerable attention, as they directly predict the coordinates of the target object without relying on additional efforts, such as pre-gener... | {
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2412.19159 | Mobile Robots through Task-Based Human Instructions using Incremental
Curriculum Learning | [
"cs.RO",
"cs.AI"
] | This paper explores the integration of incremental curriculum learning (ICL) with deep reinforcement learning (DRL) techniques to facilitate mobile robot navigation through task-based human instruction. By adopting a curriculum that mirrors the progressive complexity encountered in human learning, our approach systemat... | {
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2412.19160 | Dual Channel Multi-Attention in ViT for Biometric Authentication using
Forehead Subcutaneous Vein Pattern and Periocular Pattern | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Traditional biometric systems, like face and fingerprint recognition, have encountered significant setbacks due to wearing face masks and hygiene concerns. To meet the challenges of the partially covered face due to face masks and hygiene concerns of fingerprint recognition, this paper proposes a novel dual-channel mul... | {
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2412.19161 | Robust $H_{\infty}$ Position Controller for Steering Systems | [
"eess.SY",
"cs.SY",
"eess.SP"
] | This paper presents a robust position controller for electric power assisted steering and steer-by-wire force-feedback systems. A position controller is required in steering systems for haptic feedback control, advanced driver assistance systems and automated driving. However, the driver's \textit{physical} arm impedan... | {
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2412.19163 | Master Stability Functions in Complex Networks | [
"nlin.AO",
"cs.AI",
"nlin.CD"
] | Synchronization is an emergent phenomenon in coupled dynamical networks. The Master Stability Function (MSF) is a highly elegant and powerful tool for characterizing the stability of synchronization states. However, a significant challenge lies in determining the MSF for complex dynamical networks driven by nonlinear i... | {
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2412.19165 | Revisiting Monocular 3D Object Detection from Scene-Level Depth
Retargeting to Instance-Level Spatial Refinement | [
"cs.CV"
] | Monocular 3D object detection is challenging due to the lack of accurate depth. However, existing depth-assisted solutions still exhibit inferior performance, whose reason is universally acknowledged as the unsatisfactory accuracy of monocular depth estimation models. In this paper, we revisit monocular 3D object detec... | {
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2412.19168 | GFG -- Gender-Fair Generation: A CALAMITA Challenge | [
"cs.CL"
] | Gender-fair language aims at promoting gender equality by using terms and expressions that include all identities and avoid reinforcing gender stereotypes. Implementing gender-fair strategies is particularly challenging in heavily gender-marked languages, such as Italian. To address this, the Gender-Fair Generation cha... | {
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2412.19172 | Towards Popularity-Aware Recommendation: A Multi-Behavior Enhanced
Framework with Orthogonality Constraint | [
"cs.IR"
] | Top-$K$ recommendation involves inferring latent user preferences and generating personalized recommendations accordingly, which is now ubiquitous in various decision systems. Nonetheless, recommender systems usually suffer from severe \textit{popularity bias}, leading to the over-recommendation of popular items. Such ... | {
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2412.19178 | Reversed in Time: A Novel Temporal-Emphasized Benchmark for Cross-Modal
Video-Text Retrieval | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.IR",
"cs.LG"
] | Cross-modal (e.g. image-text, video-text) retrieval is an important task in information retrieval and multimodal vision-language understanding field. Temporal understanding makes video-text retrieval more challenging than image-text retrieval. However, we find that the widely used video-text benchmarks have shortcoming... | {
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2412.19179 | Mask Approximation Net: A Novel Diffusion Model Approach for Remote
Sensing Change Captioning | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Remote sensing image change description represents an innovative multimodal task within the realm of remote sensing processing. This task not only facilitates the detection of alterations in surface conditions, but also provides comprehensive descriptions of these changes, thereby improving human interpretability and i... | {
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2412.19184 | Multi-Head Attention Driven Dynamic Visual-Semantic Embedding for
Enhanced Image-Text Matching | [
"cs.CV",
"cs.CL"
] | With the rapid development of multimodal learning, the image-text matching task, as a bridge connecting vision and language, has become increasingly important. Based on existing research, this study proposes an innovative visual semantic embedding model, Multi-Headed Consensus-Aware Visual-Semantic Embedding (MH-CVSE).... | {
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2412.19189 | An End-to-End Depth-Based Pipeline for Selfie Image Rectification | [
"cs.CV",
"cs.LG"
] | Portraits or selfie images taken from a close distance typically suffer from perspective distortion. In this paper, we propose an end-to-end deep learning-based rectification pipeline to mitigate the effects of perspective distortion. We learn to predict the facial depth by training a deep CNN. The estimated depth is u... | {
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2412.19191 | Biology Instructions: A Dataset and Benchmark for Multi-Omics Sequence
Understanding Capability of Large Language Models | [
"q-bio.BM",
"cs.AI",
"cs.LG"
] | Large language models have already demonstrated their formidable capabilities in general domains, ushering in a revolutionary transformation. However, exploring and exploiting the extensive knowledge of these models to comprehend multi-omics biology remains underexplored. To fill this research gap, we first introduce B... | {
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2412.19194 | Provably Efficient Exploration in Reward Machines with Low Regret | [
"cs.LG",
"cs.AI"
] | We study reinforcement learning (RL) for decision processes with non-Markovian reward, in which high-level knowledge of the task in the form of reward machines is available to the learner. We consider probabilistic reward machines with initially unknown dynamics, and investigate RL under the average-reward criterion, w... | {
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2412.19198 | Multi-Attribute Constraint Satisfaction via Language Model Rewriting | [
"cs.AI"
] | Obeying precise constraints on top of multiple external attributes is a common computational problem underlying seemingly different domains, from controlled text generation to protein engineering. Existing language model (LM) controllability methods for multi-attribute constraint satisfaction often rely on specialized ... | {
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2412.19200 | Personalized Dynamic Music Emotion Recognition with Dual-Scale
Attention-Based Meta-Learning | [
"cs.SD",
"cs.IR",
"eess.AS"
] | Dynamic Music Emotion Recognition (DMER) aims to predict the emotion of different moments in music, playing a crucial role in music information retrieval. The existing DMER methods struggle to capture long-term dependencies when dealing with sequence data, which limits their performance. Furthermore, these methods ofte... | {
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2412.19201 | GAIS: A Novel Approach to Instance Selection with Graph Attention
Networks | [
"cs.LG"
] | Instance selection (IS) is a crucial technique in machine learning that aims to reduce dataset size while maintaining model performance. This paper introduces a novel method called Graph Attention-based Instance Selection (GAIS), which leverages Graph Attention Networks (GATs) to identify the most informative instances... | {
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2412.19206 | NADER: Neural Architecture Design via Multi-Agent Collaboration | [
"cs.CV"
] | Designing effective neural architectures poses a significant challenge in deep learning. While Neural Architecture Search (NAS) automates the search for optimal architectures, existing methods are often constrained by predetermined search spaces and may miss critical neural architectures. In this paper, we introduce NA... | {
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2412.19208 | Developing Explainable Machine Learning Model using Augmented Concept
Activation Vector | [
"cs.LG"
] | Machine learning models use high dimensional feature spaces to map their inputs to the corresponding class labels. However, these features often do not have a one-to-one correspondence with physical concepts understandable by humans, which hinders the ability to provide a meaningful explanation for the decisions made b... | {
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2412.19209 | Context-Aware Deep Learning for Multi Modal Depression Detection | [
"cs.LG"
] | In this study, we focus on automated approaches to detect depression from clinical interviews using multi-modal machine learning (ML). Our approach differentiates from other successful ML methods such as context-aware analysis through feature engineering and end-to-end deep neural networks for depression detection util... | {
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2412.19211 | Large Language Models Meet Graph Neural Networks: A Perspective of Graph
Mining | [
"cs.LG"
] | Graph mining is an important area in data mining and machine learning that involves extracting valuable information from graph-structured data. In recent years, significant progress has been made in this field through the development of graph neural networks (GNNs). However, GNNs are still deficient in generalizing to ... | {
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2412.19212 | Towards Better Spherical Sliced-Wasserstein Distance Learning with
Data-Adaptive Discriminative Projection Direction | [
"cs.LG"
] | Spherical Sliced-Wasserstein (SSW) has recently been proposed to measure the discrepancy between spherical data distributions in various fields, such as geology, medical domains, computer vision, and deep representation learning. However, in the original SSW, all projection directions are treated equally, which is too ... | {
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2412.19215 | Optimizing Fantasy Sports Team Selection with Deep Reinforcement
Learning | [
"cs.AI",
"cs.LG"
] | Fantasy sports, particularly fantasy cricket, have garnered immense popularity in India in recent years, offering enthusiasts the opportunity to engage in strategic team-building and compete based on the real-world performance of professional athletes. In this paper, we address the challenge of optimizing fantasy crick... | {
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2412.19217 | Applying the maximum entropy principle to neural networks enhances
multi-species distribution models | [
"cs.LG",
"stat.ML"
] | The rapid expansion of citizen science initiatives has led to a significant growth of biodiversity databases, and particularly presence-only (PO) observations. PO data are invaluable for understanding species distributions and their dynamics, but their use in a Species Distribution Model (SDM) is curtailed by sampling ... | {
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2412.19218 | Transformer-Based Wireless Capsule Endoscopy Bleeding Tissue Detection
and Classification | [
"cs.CV"
] | Informed by the success of the transformer model in various computer vision tasks, we design an end-to-end trainable model for the automatic detection and classification of bleeding and non-bleeding frames extracted from Wireless Capsule Endoscopy (WCE) videos. Based on the DETR model, our model uses the Resnet50 for f... | {
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2412.19225 | Completion as Enhancement: A Degradation-Aware Selective Image Guided
Network for Depth Completion | [
"cs.CV",
"eess.IV"
] | In this paper, we introduce the Selective Image Guided Network (SigNet), a novel degradation-aware framework that transforms depth completion into depth enhancement for the first time. Moving beyond direct completion using convolutional neural networks (CNNs), SigNet initially densifies sparse depth data through non-CN... | {
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2412.19226 | VINEVI: A Virtualized Network Vision Architecture for Smart Monitoring
of Heterogeneous Applications and Infrastructures | [
"cs.NI",
"cs.AI",
"cs.CV",
"cs.LG"
] | Monitoring heterogeneous infrastructures and applications is essential to cope with user requirements properly, but it still lacks enhancements. The well-known state-of-the-art methods and tools do not support seamless monitoring of bare-metal, low-cost infrastructures, neither hosted nor virtualized services with fine... | {
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2412.19227 | Multi-view Fake News Detection Model Based on Dynamic Hypergraph | [
"cs.LG"
] | With the rapid development of online social networks and the inadequacies in content moderation mechanisms, the detection of fake news has emerged as a pressing concern for the public. Various methods have been proposed for fake news detection, including text-based approaches as well as a series of graph-based approach... | {
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2412.19228 | Learning Cross-Domain Representations for Transferable Drug
Perturbations on Single-Cell Transcriptional Responses | [
"cs.LG",
"cs.AI"
] | Phenotypic drug discovery has attracted widespread attention because of its potential to identify bioactive molecules. Transcriptomic profiling provides a comprehensive reflection of phenotypic changes in cellular responses to external perturbations. In this paper, we propose XTransferCDR, a novel generative framework ... | {
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2412.19229 | Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph
Learning | [
"cs.LG",
"cs.DC"
] | Federated Graph Learning (FGL) enables multiple clients to jointly train powerful graph learning models, e.g., Graph Neural Networks (GNNs), without sharing their local graph data for graph-related downstream tasks, such as graph property prediction. In the real world, however, the graph data can suffer from significan... | {
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2412.19235 | Are Two Hidden Layers Still Enough for the Physics-Informed Neural
Networks? | [
"math.NA",
"cs.AI",
"cs.LG",
"cs.NA",
"physics.comp-ph"
] | The article discusses the development of various methods and techniques for initializing and training neural networks with a single hidden layer, as well as training a separable physics-informed neural network consisting of neural networks with a single hidden layer to solve physical problems described by ordinary diff... | {
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2412.19237 | SeaMo: A Multi-Seasonal and Multimodal Remote Sensing Foundation Model | [
"cs.CV",
"cs.LG"
] | Remote Sensing (RS) data contains a wealth of multi-dimensional information crucial for Earth observation. Owing to its vast volume, diverse sources, and temporal properties, RS data is highly suitable for the development of large Visual Foundation Models (VFMs). VFMs act as robust feature extractors, learning from ext... | {
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2412.19238 | FineVQ: Fine-Grained User Generated Content Video Quality Assessment | [
"cs.CV",
"cs.LG",
"cs.MM",
"eess.IV"
] | The rapid growth of user-generated content (UGC) videos has produced an urgent need for effective video quality assessment (VQA) algorithms to monitor video quality and guide optimization and recommendation procedures. However, current VQA models generally only give an overall rating for a UGC video, which lacks fine-g... | {
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2412.19241 | Latenrgy: Model Agnostic Latency and Energy Consumption Prediction for
Binary Classifiers | [
"cs.LG",
"cs.AI"
] | Machine learning systems increasingly drive innovation across scientific fields and industry, yet challenges in compute overhead, specifically during inference, limit their scalability and sustainability. Responsible AI guardrails, essential for ensuring fairness, transparency, and privacy, further exacerbate these com... | {
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2412.19245 | Sentiment trading with large language models | [
"q-fin.CP",
"cs.LG",
"econ.EM",
"q-fin.PM",
"q-fin.TR"
] | We investigate the efficacy of large language models (LLMs) in sentiment analysis of U.S. financial news and their potential in predicting stock market returns. We analyze a dataset comprising 965,375 news articles that span from January 1, 2010, to June 30, 2023; we focus on the performance of various LLMs, including ... | {
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2412.19252 | Localized exploration in contextual dynamic pricing achieves
dimension-free regret | [
"stat.ML",
"cs.LG",
"math.OC"
] | We study the problem of contextual dynamic pricing with a linear demand model. We propose a novel localized exploration-then-commit (LetC) algorithm which starts with a pure exploration stage, followed by a refinement stage that explores near the learned optimal pricing policy, and finally enters a pure exploitation st... | {
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2412.19254 | Leveraging Self-Training and Variational Autoencoder for Agitation
Detection in People with Dementia Using Wearable Sensors | [
"cs.AI"
] | Dementia is a neurodegenerative disorder that has been growing among elder people over the past decades. This growth profoundly impacts the quality of life for patients and caregivers due to the symptoms arising from it. Agitation and aggression (AA) are some of the symptoms of people with severe dementia (PwD) in long... | {
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2412.19255 | Multi-matrix Factorization Attention | [
"cs.LG",
"cs.CL"
] | We propose novel attention architectures, Multi-matrix Factorization Attention (MFA) and MFA-Key-Reuse (MFA-KR). Existing variants for standard Multi-Head Attention (MHA), including SOTA methods like MLA, fail to maintain as strong performance under stringent Key-Value cache (KV cache) constraints. MFA enhances model c... | {
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2412.19260 | MEDEC: A Benchmark for Medical Error Detection and Correction in
Clinical Notes | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Several studies showed that Large Language Models (LLMs) can answer medical questions correctly, even outperforming the average human score in some medical exams. However, to our knowledge, no study has been conducted to assess the ability of language models to validate existing or generated medical text for correctnes... | {
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2412.19265 | Optimizing Multi-Stage Language Models for Effective Text Retrieval | [
"cs.IR",
"cs.CL",
"cs.LG"
] | Efficient text retrieval is critical for applications such as legal document analysis, particularly in specialized contexts like Japanese legal systems. Existing retrieval methods often underperform in such domain-specific scenarios, necessitating tailored approaches. In this paper, we introduce a novel two-phase text ... | {
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2412.19272 | Implementing a Robot Intrusion Prevention System (RIPS) for ROS 2 | [
"cs.RO",
"cs.CR"
] | It is imperative to develop an intrusion prevention system (IPS), specifically designed for autonomous robotic systems. This is due to the unique nature of these cyber-physical systems (CPS), which are not merely typical distributed systems. These systems employ their own systems software (i.e. robotic middleware and f... | {
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2412.19279 | Improving Generalization for AI-Synthesized Voice Detection | [
"cs.SD",
"cs.LG",
"eess.AS"
] | AI-synthesized voice technology has the potential to create realistic human voices for beneficial applications, but it can also be misused for malicious purposes. While existing AI-synthesized voice detection models excel in intra-domain evaluation, they face challenges in generalizing across different domains, potenti... | {
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2412.19280 | Parametrizations of All Stable Closed-loop Responses: From Theory to
Neural Network Control Design | [
"eess.SY",
"cs.SY"
] | The complexity of modern control systems necessitates architectures that achieve high performance while ensuring robust stability, particularly for nonlinear systems. In this work, we tackle the challenge of designing optimal output-feedback controllers to boost the performance of $\ell_p$-stable discrete-time nonlinea... | {
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2412.19282 | Reflective Gaussian Splatting | [
"cs.CV"
] | Novel view synthesis has experienced significant advancements owing to increasingly capable NeRF- and 3DGS-based methods. However, reflective object reconstruction remains challenging, lacking a proper solution to achieve real-time, high-quality rendering while accommodating inter-reflection. To fill this gap, we intro... | {
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2412.19284 | PearSAN: A Machine Learning Method for Inverse Design using Pearson
Correlated Surrogate Annealing | [
"cs.LG",
"cs.AI"
] | PearSAN is a machine learning-assisted optimization algorithm applicable to inverse design problems with large design spaces, where traditional optimizers struggle. The algorithm leverages the latent space of a generative model for rapid sampling and employs a Pearson correlated surrogate model to predict the figure of... | {
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2412.19286 | Time Series Foundational Models: Their Role in Anomaly Detection and
Prediction | [
"cs.LG",
"cs.AI"
] | Time series foundational models (TSFM) have gained prominence in time series forecasting, promising state-of-the-art performance across various applications. However, their application in anomaly detection and prediction remains underexplored, with growing concerns regarding their black-box nature, lack of interpretabi... | {
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2412.19289 | ViPCap: Retrieval Text-Based Visual Prompts for Lightweight Image
Captioning | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] | Recent lightweight image captioning models using retrieved data mainly focus on text prompts. However, previous works only utilize the retrieved text as text prompts, and the visual information relies only on the CLIP visual embedding. Because of this issue, there is a limitation that the image descriptions inherent in... | {
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2412.19291 | RAG with Differential Privacy | [
"cs.LG",
"cs.AI",
"cs.CR"
] | Retrieval-Augmented Generation (RAG) has emerged as the dominant technique to provide \emph{Large Language Models} (LLM) with fresh and relevant context, mitigating the risk of hallucinations and improving the overall quality of responses in environments with large and fast moving knowledge bases. However, the integrat... | {
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2412.19293 | When SAM2 Meets Video Shadow and Mirror Detection | [
"cs.CV"
] | As the successor to the Segment Anything Model (SAM), the Segment Anything Model 2 (SAM2) not only improves performance in image segmentation but also extends its capabilities to video segmentation. However, its effectiveness in segmenting rare objects that seldom appear in videos remains underexplored. In this study, ... | {
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2412.19302 | RecLM: Recommendation Instruction Tuning | [
"cs.IR"
] | Modern recommender systems aim to deeply understand users' complex preferences through their past interactions. While deep collaborative filtering approaches using Graph Neural Networks (GNNs) excel at capturing user-item relationships, their effectiveness is limited when handling sparse data or zero-shot scenarios, pr... | {
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2412.19303 | Manga Generation via Layout-controllable Diffusion | [
"cs.CV"
] | Generating comics through text is widely studied. However, there are few studies on generating multi-panel Manga (Japanese comics) solely based on plain text. Japanese manga contains multiple panels on a single page, with characteristics such as coherence in storytelling, reasonable and diverse page layouts, consistenc... | {
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} |
2412.19304 | Perceive, Query & Reason: Enhancing Video QA with Question-Guided
Temporal Queries | [
"cs.CV"
] | Video Question Answering (Video QA) is a challenging video understanding task that requires models to comprehend entire videos, identify the most relevant information based on contextual cues from a given question, and reason accurately to provide answers. Recent advancements in Multimodal Large Language Models (MLLMs)... | {
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} |
2412.19311 | xSRL: Safety-Aware Explainable Reinforcement Learning -- Safety as a
Product of Explainability | [
"cs.AI",
"cs.HC",
"cs.LG",
"cs.MA"
] | Reinforcement learning (RL) has shown great promise in simulated environments, such as games, where failures have minimal consequences. However, the deployment of RL agents in real-world systems such as autonomous vehicles, robotics, UAVs, and medical devices demands a higher level of safety and transparency, particula... | {
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} |
2412.19312 | From Interests to Insights: An LLM Approach to Course Recommendations
Using Natural Language Queries | [
"cs.IR",
"cs.AI"
] | Most universities in the United States encourage their students to explore academic areas before declaring a major and to acquire academic breadth by satisfying a variety of requirements. Each term, students must choose among many thousands of offerings, spanning dozens of subject areas, a handful of courses to take. T... | {
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} |
2412.19318 | Adaptive Conformal Inference by Betting | [
"stat.ML",
"cs.LG"
] | Conformal prediction is a valuable tool for quantifying predictive uncertainty of machine learning models. However, its applicability relies on the assumption of data exchangeability, a condition which is often not met in real-world scenarios. In this paper, we consider the problem of adaptive conformal inference witho... | {
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} |
2412.19321 | A novel framework for MCDM based on Z numbers and soft likelihood
function | [
"cs.AI"
] | The optimization on the structure of process of information management under uncertain environment has attracted lots of attention from researchers around the world. Nevertheless, how to obtain accurate and rational evaluation from assessments produced by experts is still an open problem. Specially, intuitionistic fuzz... | {
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} |
2412.19322 | Mixed-precision numerics in scientific applications: survey and
perspectives | [
"cs.CE",
"cs.NA",
"math.NA"
] | The explosive demand for artificial intelligence (AI) workloads has led to a significant increase in silicon area dedicated to lower-precision computations on recent high-performance computing hardware designs. However, mixed-precision capabilities, which can achieve performance improvements of 8x compared to double-pr... | {
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} |
2412.19325 | Performance Control in Early Exiting to Deploy Large Models at the Same
Cost of Smaller Ones | [
"cs.LG",
"cs.AI"
] | Early Exiting (EE) is a promising technique for speeding up inference by adaptively allocating compute resources to data points based on their difficulty. The approach enables predictions to exit at earlier layers for simpler samples while reserving more computation for challenging ones. In this study, we first present... | {
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} |
2412.19326 | Task Preference Optimization: Improving Multimodal Large Language Models
with Vision Task Alignment | [
"cs.CV"
] | Current multimodal large language models (MLLMs) struggle with fine-grained or precise understanding of visuals though they give comprehensive perception and reasoning in a spectrum of vision applications. Recent studies either develop tool-using or unify specific visual tasks into the autoregressive framework, often a... | {
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} |
2412.19328 | Resolving the Ambiguity of Complete-to-Partial Point Cloud Registration
for Image-Guided Liver Surgery with Patches-to-Partial Matching | [
"cs.CV"
] | In image-guided liver surgery, the initial rigid alignment between preoperative and intraoperative data, often represented as point clouds, is crucial for providing sub-surface information from preoperative CT/MRI images to the surgeon during the procedure. Currently, this alignment is typically performed using semi-au... | {
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} |
2412.19329 | Deep learning and whole-brain networks for biomarker discovery: modeling
the dynamics of brain fluctuations in resting-state and cognitive tasks | [
"q-bio.NC",
"cs.LG"
] | Background: Brain network models offer insights into brain dynamics, but the utility of model-derived bifurcation parameters as biomarkers remains underexplored. Objective: This study evaluates bifurcation parameters from a whole-brain network model as biomarkers for distinguishing brain states associated with resting-... | {
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} |
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