id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.14496 | Multi-agent reinforcement learning strategy to maximize the lifetime of
Wireless Rechargeable | [
"cs.LG",
"cs.CV",
"cs.GT",
"cs.MA"
] | The thesis proposes a generalized charging framework for multiple mobile chargers to maximize the network lifetime and ensure target coverage and connectivity in large scale WRSNs. Moreover, a multi-point charging model is leveraged to enhance charging efficiency, where the MC can charge multiple sensors simultaneously... | {
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2411.14497 | Star-Agents: Automatic Data Optimization with LLM Agents for Instruction
Tuning | [
"cs.CL",
"cs.AI"
] | The efficacy of large language models (LLMs) on downstream tasks usually hinges on instruction tuning, which relies critically on the quality of training data. Unfortunately, collecting high-quality and diverse data is both expensive and time-consuming. To mitigate this issue, we propose a novel Star-Agents framework, ... | {
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2411.14498 | Delta-NAS: Difference of Architecture Encoding for Predictor-based
Evolutionary Neural Architecture Search | [
"cs.CV"
] | Neural Architecture Search (NAS) continues to serve a key roll in the design and development of neural networks for task specific deployment. Modern NAS techniques struggle to deal with ever increasing search space complexity and compute cost constraints. Existing approaches can be categorized into two buckets: fine-gr... | {
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2411.14499 | Understanding World or Predicting Future? A Comprehensive Survey of
World Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | The concept of world models has garnered significant attention due to advancements in multimodal large language models such as GPT-4 and video generation models such as Sora, which are central to the pursuit of artificial general intelligence. This survey offers a comprehensive review of the literature on world models.... | {
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2411.14500 | Exploring Accuracy-Fairness Trade-off in Large Language Models | [
"cs.CL",
"cs.AI",
"cs.CY",
"cs.LG"
] | Large Language Models (LLMs) have made significant strides in the field of artificial intelligence, showcasing their ability to interact with humans and influence human cognition through information dissemination. However, recent studies have brought to light instances of bias inherent within these LLMs, presenting a c... | {
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2411.14501 | U-Motion: Learned Point Cloud Video Compression with U-Structured Motion
Estimation | [
"cs.CV",
"eess.IV"
] | Point cloud video (PCV) is a versatile 3D representation of dynamic scenes with many emerging applications. This paper introduces U-Motion, a learning-based compression scheme for both PCV geometry and attributes. We propose a U-Structured multiscale inter-frame prediction framework, U-Inter, which performs layer-wise ... | {
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2411.14502 | Global Challenge for Safe and Secure LLMs Track 1 | [
"cs.CR",
"cs.AI",
"cs.CY"
] | This paper introduces the Global Challenge for Safe and Secure Large Language Models (LLMs), a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO) to foster the development of advanced defense mechanisms against automated jailbreaking attacks. With the increasing integrati... | {
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2411.14503 | Planning-Driven Programming: A Large Language Model Programming Workflow | [
"cs.SE",
"cs.AI"
] | The strong performance of large language models (LLMs) raises extensive discussion on their application to code generation. Recent research suggests continuous program refinements through visible tests to improve code generation accuracy in LLMs. However, these methods suffer from LLMs' inefficiency and limited reasoni... | {
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2411.14504 | Night-to-Day Translation via Illumination Degradation Disentanglement | [
"cs.CV"
] | Night-to-Day translation (Night2Day) aims to achieve day-like vision for nighttime scenes. However, processing night images with complex degradations remains a significant challenge under unpaired conditions. Previous methods that uniformly mitigate these degradations have proven inadequate in simultaneously restoring ... | {
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2411.14505 | LLaVA-MR: Large Language-and-Vision Assistant for Video Moment Retrieval | [
"cs.CV"
] | Multimodal Large Language Models (MLLMs) are widely used for visual perception, understanding, and reasoning. However, long video processing and precise moment retrieval remain challenging due to LLMs' limited context size and coarse frame extraction. We propose the Large Language-and-Vision Assistant for Moment Retrie... | {
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2411.14507 | FuseGPT: Learnable Layers Fusion of Generative Pre-trained Transformers | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Generative Pre-trained Transformers (GPTs) have demonstrated remarkable performance across diverse domains through the extensive scaling of model parameters. Recent works observe the redundancy across the transformer blocks and develop compression methods by structured pruning of the unimportant blocks. However, such s... | {
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2411.14508 | Multi-objective Bayesian Optimisation of Spinodoid Cellular Structures
for Crush Energy Absorption | [
"cond-mat.mtrl-sci",
"cs.CE"
] | In the pursuit of designing safer and more efficient energy-absorbing structures, engineers must tackle the challenge of improving crush performance while balancing multiple conflicting objectives, such as maximising energy absorption and minimising peak impact forces. Accurately simulating real-world conditions necess... | {
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2411.14509 | End-to-End Convolutional Activation Anomaly Analysis for Anomaly
Detection | [
"cs.LG"
] | We propose an End-to-end Convolutional Activation Anomaly Analysis (E2E-CA$^3$), which is a significant extension of A$^3$ anomaly detection approach proposed by Sperl, Schulze and B\"ottinger, both in terms of architecture and scope of application. In contrast to the original idea, we utilize a convolutional autoencod... | {
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2411.14511 | Variational Autoencoders for Efficient Simulation-Based Inference | [
"cs.LG",
"cs.AI"
] | We present a generative modeling approach based on the variational inference framework for likelihood-free simulation-based inference. The method leverages latent variables within variational autoencoders to efficiently estimate complex posterior distributions arising from stochastic simulations. We explore two variati... | {
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2411.14512 | Detecting Distributed Denial of Service Attacks Using Logistic
Regression and SVM Methods | [
"cs.CR",
"cs.LG"
] | A distributed denial-of-service (DDoS) attack is an attempt to produce humongous traffic within a network by overwhelming a targeted server or its neighboring infrastructure with a flood of service requests ceaselessly coming from multiple remotely controlled malware-infected computers or network-connected devices. Thu... | {
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2411.14513 | Towards a Middleware for Large Language Models | [
"cs.SE",
"cs.CL"
] | Large language models have gained widespread popularity for their ability to process natural language inputs and generate insights derived from their training data, nearing the qualities of true artificial intelligence. This advancement has prompted enterprises worldwide to integrate LLMs into their services. So far, t... | {
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2411.14514 | NexusSplats: Efficient 3D Gaussian Splatting in the Wild | [
"cs.CV"
] | While 3D Gaussian Splatting (3DGS) has recently demonstrated remarkable rendering quality and efficiency in 3D scene reconstruction, it struggles with varying lighting conditions and incidental occlusions in real-world scenarios. To accommodate varying lighting conditions, existing 3DGS extensions apply color mapping t... | {
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2411.14515 | Are Anomaly Scores Telling the Whole Story? A Benchmark for Multilevel
Anomaly Detection | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Anomaly detection (AD) is a machine learning task that identifies anomalies by learning patterns from normal training data. In many real-world scenarios, anomalies vary in severity, from minor anomalies with little risk to severe abnormalities requiring immediate attention. However, existing models primarily operate in... | {
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2411.14516 | Memory Backdoor Attacks on Neural Networks | [
"cs.CR",
"cs.CV",
"cs.LG"
] | Neural networks, such as image classifiers, are frequently trained on proprietary and confidential datasets. It is generally assumed that once deployed, the training data remains secure, as adversaries are limited to query response interactions with the model, where at best, fragments of arbitrary data can be inferred ... | {
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2411.14517 | The Double-Ellipsoid Geometry of CLIP | [
"cs.CV"
] | Contrastive Language-Image Pre-Training (CLIP) is highly instrumental in machine learning applications within a large variety of domains. We investigate the geometry of this embedding, which is still not well understood. We examine the raw unnormalized embedding and show that text and image reside on linearly separable... | {
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2411.14519 | Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for
Adaptive Policy Conditioning | [
"cs.RO"
] | Learning from multiple domains is a primary factor that influences the generalization of a single unified robot system. In this paper, we aim to learn the trajectory prediction model by using broad out-of-domain data to improve its performance and generalization ability. Trajectory model is designed to predict any-poin... | {
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2411.14520 | Open Challenges in the Formal Verification of Autonomous Driving | [
"cs.SE",
"cs.AI",
"cs.DC",
"cs.RO"
] | In the realm of autonomous driving, the development and integration of highly complex and heterogeneous systems are standard practice. Modern vehicles are not monolithic systems; instead, they are composed of diverse hardware components, each running its own software systems. An autonomous vehicle comprises numerous in... | {
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2411.14521 | MyTimeMachine: Personalized Facial Age Transformation | [
"cs.CV"
] | Facial aging is a complex process, highly dependent on multiple factors like gender, ethnicity, lifestyle, etc., making it extremely challenging to learn a global aging prior to predict aging for any individual accurately. Existing techniques often produce realistic and plausible aging results, but the re-aged images o... | {
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2411.14522 | GMAI-VL & GMAI-VL-5.5M: A Large Vision-Language Model and A
Comprehensive Multimodal Dataset Towards General Medical AI | [
"cs.CV"
] | Despite significant advancements in general artificial intelligence, such as GPT-4, their effectiveness in the medical domain (general medical AI, GMAI) remains constrained due to the absence of specialized medical knowledge. To address this challenge, we present GMAI-VL-5.5M, a comprehensive multimodal medical dataset... | {
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2411.14525 | SegBook: A Simple Baseline and Cookbook for Volumetric Medical Image
Segmentation | [
"eess.IV",
"cs.CV"
] | Computed Tomography (CT) is one of the most popular modalities for medical imaging. By far, CT images have contributed to the largest publicly available datasets for volumetric medical segmentation tasks, covering full-body anatomical structures. Large amounts of full-body CT images provide the opportunity to pre-train... | {
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2411.14550 | The importance of the clustering model to detect new types of intrusion
in data traffic | [
"cs.CR",
"cs.AI"
] | In the current digital age, the volume of data generated by various cyber activities has become enormous and is constantly increasing. The data may contain valuable insights that can be harnessed to improve cyber security measures. However, much of this data is unclassified and qualitative, which poses significant chal... | {
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2411.14551 | An Experimental Study on Data Augmentation Techniques for Named Entity
Recognition on Low-Resource Domains | [
"cs.CL",
"cs.IR",
"cs.LG"
] | Named Entity Recognition (NER) is a machine learning task that traditionally relies on supervised learning and annotated data. Acquiring such data is often a challenge, particularly in specialized fields like medical, legal, and financial sectors. Those are commonly referred to as low-resource domains, which comprise l... | {
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2411.14553 | Reducibility among NP-Hard graph problems and boundary classes | [
"cs.CC",
"cs.CL",
"cs.DM"
] | Many NP-hard graph problems become easy for some classes of graphs, such as coloring is easy for bipartite graphs, but NP-hard in general. So we can ask question like when does a hard problem become easy? What is the minimum substructure for which the problem remains hard? We use the notion of boundary classes to study... | {
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2411.14555 | Deep operator network models for predicting post-burn contraction | [
"math.NA",
"cs.LG",
"cs.NA",
"physics.bio-ph",
"q-bio.TO"
] | Burn injuries present a significant global health challenge. Among the most severe long-term consequences are contractures, which can lead to functional impairments and disfigurement. Understanding and predicting the evolution of post-burn wounds is essential for developing effective treatment strategies. Traditional m... | {
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2411.14557 | Privacy-Preserving Power Flow Analysis via Secure Multi-Party
Computation | [
"cs.CR",
"cs.SY",
"eess.SY"
] | Smart grids feature a bidirectional flow of electricity and data, enhancing flexibility, efficiency, and reliability in increasingly volatile energy grids. However, data from smart meters can reveal sensitive private information. Consequently, the adoption of smart meters is often restricted via legal means and hampere... | {
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2411.14560 | Enhancing GeoAI and location encoding with spatial point pattern
statistics: A Case Study of Terrain Feature Classification | [
"cs.CV",
"cs.LG"
] | This study introduces a novel approach to terrain feature classification by incorporating spatial point pattern statistics into deep learning models. Inspired by the concept of location encoding, which aims to capture location characteristics to enhance GeoAI decision-making capabilities, we improve the GeoAI model by ... | {
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2411.14565 | Privacy-Preserving Video Anomaly Detection: A Survey | [
"cs.CV",
"cs.CR",
"cs.LG"
] | Video Anomaly Detection (VAD) aims to automatically analyze spatiotemporal patterns in surveillance videos collected from open spaces to detect anomalous events that may cause harm without physical contact. However, vision-based surveillance systems such as closed-circuit television often capture personally identifiabl... | {
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2411.14567 | Energy Efficient Automated Driving as a GNEP: Vehicle-in-the-loop
Experiments | [
"eess.SY",
"cs.SY"
] | In this paper, a multi-agent motion planning problem is studied aiming to minimize energy consumption of connected automated vehicles (CAVs) in lane change scenarios. We model this interactive motion planning as a generalized Nash equilibrium problem and formalize how vehicle-to-vehicle intention sharing enables soluti... | {
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2411.14568 | Maximum Solar Energy Tracking Leverage High-DoF Robotics System with
Deep Reinforcement Learning | [
"cs.RO"
] | Solar trajectory monitoring is a pivotal challenge in solar energy systems, underpinning applications such as autonomous energy harvesting and environmental sensing. A prevalent failure mode in sustained solar tracking arises when the predictive algorithm erroneously diverges from the solar locus, erroneously anchoring... | {
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2411.14569 | Variable Extraction for Model Recovery in Scientific Literature | [
"cs.IR",
"cs.LG"
] | The global output of academic publications exceeds 5 million articles per year, making it difficult for humans to keep up with even a tiny fraction of scientific output. We need methods to navigate and interpret the artifacts -- texts, graphs, charts, code, models, and datasets -- that make up the literature. This pape... | {
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2411.14571 | Assessment of LLM Responses to End-user Security Questions | [
"cs.CR",
"cs.AI",
"cs.CL",
"cs.HC"
] | Answering end user security questions is challenging. While large language models (LLMs) like GPT, LLAMA, and Gemini are far from error-free, they have shown promise in answering a variety of questions outside of security. We studied LLM performance in the area of end user security by qualitatively evaluating 3 popular... | {
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2411.14572 | Towards Knowledge Checking in Retrieval-augmented Generation: A
Representation Perspective | [
"cs.LG",
"cs.CL"
] | Retrieval-Augmented Generation (RAG) systems have shown promise in enhancing the performance of Large Language Models (LLMs). However, these systems face challenges in effectively integrating external knowledge with the LLM's internal knowledge, often leading to issues with misleading or unhelpful information. This wor... | {
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2411.14574 | SRSA: A Cost-Efficient Strategy-Router Search Agent for Real-world
Human-Machine Interactions | [
"cs.AI"
] | Recently, as Large Language Models (LLMs) have shown impressive emerging capabilities and gained widespread popularity, research on LLM-based search agents has proliferated. In real-world situations, users often input contextual and highly personalized queries to chatbots, challenging LLMs to capture context and genera... | {
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2411.14576 | EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots | [
"cs.RO"
] | Optical flow estimation is a critical task for tiny mobile robotics to enable safe and accurate navigation, obstacle avoidance, and other functionalities. However, optical flow estimation on tiny robots is challenging due to limited onboard sensing and computation capabilities. In this paper, we propose EdgeFlowNet , a... | {
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2411.14585 | Efficient Spatio-Temporal Signal Recognition on Edge Devices Using
PointLCA-Net | [
"cs.LG",
"cs.ET"
] | Recent advancements in machine learning, particularly through deep learning architectures like PointNet, have transformed the processing of three-dimensional (3D) point clouds, significantly improving 3D object classification and segmentation tasks. While 3D point clouds provide detailed spatial information, spatio-tem... | {
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2411.14592 | G-RAG: Knowledge Expansion in Material Science | [
"cs.IR",
"cs.AI"
] | In the field of Material Science, effective information retrieval systems are essential for facilitating research. Traditional Retrieval-Augmented Generation (RAG) approaches in Large Language Models (LLMs) often encounter challenges such as outdated information, hallucinations, limited interpretability due to context ... | {
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2411.14593 | A Systematic Study of Multi-Agent Deep Reinforcement Learning for Safe
and Robust Autonomous Highway Ramp Entry | [
"cs.RO",
"cs.AI",
"cs.LG",
"cs.MA",
"cs.SY",
"eess.SY"
] | Vehicles today can drive themselves on highways and driverless robotaxis operate in major cities, with more sophisticated levels of autonomous driving expected to be available and become more common in the future. Yet, technically speaking, so-called "Level 5" (L5) operation, corresponding to full autonomy, has not bee... | {
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2411.14594 | Solving Zero-Shot 3D Visual Grounding as Constraint Satisfaction
Problems | [
"cs.CV"
] | 3D visual grounding (3DVG) aims to locate objects in a 3D scene with natural language descriptions. Supervised methods have achieved decent accuracy, but have a closed vocabulary and limited language understanding ability. Zero-shot methods mostly utilize large language models (LLMs) to handle natural language descript... | {
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2411.14596 | Conjugate momentum based thruster force estimate in dynamic multimodal
robot | [
"cs.RO"
] | In a multi-modal system which combines thruster and legged locomotion such our state-of-the-art Harpy platform to perform dynamic locomotion. Therefore, it is very important to have a proper estimate of Thruster force. Harpy is a bipedal robot capable of legged-aerial locomotion using its legs and thrusters attached to... | {
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2411.14601 | On Linear Convergence in Smooth Convex-Concave Bilinearly-Coupled
Saddle-Point Optimization: Lower Bounds and Optimal Algorithms | [
"math.OC",
"cs.LG"
] | We revisit the smooth convex-concave bilinearly-coupled saddle-point problem of the form $\min_x\max_y f(x) + \langle y,\mathbf{B} x\rangle - g(y)$. In the highly specific case where each of the functions $f(x)$ and $g(y)$ is either affine or strongly convex, there exist lower bounds on the number of gradient evaluatio... | {
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2411.14611 | CodeSAM: Source Code Representation Learning by Infusing Self-Attention
with Multi-Code-View Graphs | [
"cs.SE",
"cs.LG"
] | Machine Learning (ML) for software engineering (SE) has gained prominence due to its ability to significantly enhance the performance of various SE applications. This progress is largely attributed to the development of generalizable source code representations that effectively capture the syntactic and semantic charac... | {
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2411.14612 | Exploiting Boosting in Hyperdimensional Computing for Enhanced
Reliability in Healthcare | [
"cs.LG",
"cs.AI"
] | Hyperdimensional computing (HDC) enables efficient data encoding and processing in high-dimensional space, benefiting machine learning and data analysis. However, underutilization of these spaces can lead to overfitting and reduced model reliability, especially in data-limited systems a critical issue in sectors like h... | {
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2411.14618 | Active Learning-Based Optimization of Hydroelectric Turbine Startup to
Minimize Fatigue Damage | [
"cs.LG",
"cs.SY",
"eess.SY"
] | Hydro-generating units (HGUs) play a crucial role in integrating intermittent renewable energy sources into the power grid due to their flexible operational capabilities. This evolving role has led to an increase in transient events, such as startups, which impose significant stresses on turbines, leading to increased ... | {
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2411.14619 | Path Planning and Task Assignment for Data Retrieval from Wireless
Sensor Nodes Relying on Game-Theoretic Learning | [
"eess.SY",
"cs.SY"
] | The energy-efficient trip allocation of mobile robots employing differential drives for data retrieval from stationary sensor locations is the scope of this article. Given a team of robots and a set of targets (wireless sensor nodes), the planner computes all possible tours that each robot can make if it needs to visit... | {
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2411.14622 | Learning Autonomous Surgical Irrigation and Suction with the da Vinci
Research Kit Using Reinforcement Learning | [
"cs.RO"
] | The irrigation-suction process is a common procedure to rinse and clean up the surgical field in minimally invasive surgery (MIS). In this process, surgeons first irrigate liquid, typically saline, into the surgical scene for rinsing and diluting the contaminant, and then suction the liquid out of the surgical field. W... | {
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2411.14625 | Predictive Analytics of Air Alerts in the Russian-Ukrainian War | [
"cs.LG",
"cs.AI",
"cs.CY"
] | The paper considers exploratory data analysis and approaches in predictive analytics for air alerts during the Russian-Ukrainian war which broke out on Feb 24, 2022. The results illustrate that alerts in regions correlate with one another and have geospatial patterns which make it feasible to build a predictive model w... | {
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2411.14626 | Evaluating the Impact of Underwater Image Enhancement on Object
Detection Performance: A Comprehensive Study | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Underwater imagery often suffers from severe degradation that results in low visual quality and object detection performance. This work aims to evaluate state-of-the-art image enhancement models, investigate their impact on underwater object detection, and explore their potential to improve detection performance. To th... | {
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2411.14627 | Generative AI for Music and Audio | [
"cs.SD",
"cs.AI",
"cs.LG",
"cs.MM",
"eess.AS"
] | Generative AI has been transforming the way we interact with technology and consume content. In the next decade, AI technology will reshape how we create audio content in various media, including music, theater, films, games, podcasts, and short videos. In this dissertation, I introduce the three main directions of my ... | {
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2411.14628 | HotSpot: Screened Poisson Equation for Signed Distance Function
Optimization | [
"cs.CV",
"cs.LG"
] | We propose a method, HotSpot, for optimizing neural signed distance functions, based on a relation between the solution of a screened Poisson equation and the distance function. Existing losses such as the eikonal loss cannot guarantee the recovered implicit function to be a distance function, even when the implicit fu... | {
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2411.14630 | ACE-Net: AutofoCus-Enhanced Convolutional Network for Field Imperfection
Estimation with application to high b-value spiral Diffusion MRI | [
"physics.med-ph",
"cs.LG",
"eess.IV"
] | Spatiotemporal magnetic field variations from B0-inhomogeneity and diffusion-encoding-induced eddy-currents can be detrimental to rapid image-encoding schemes such as spiral, EPI and 3D-cones, resulting in undesirable image artifacts. In this work, a data driven approach for automatic estimation of these field imperfec... | {
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2411.14633 | Evaluating Representational Similarity Measures from the Lens of
Functional Correspondence | [
"q-bio.NC",
"cs.AI",
"cs.CV"
] | Neuroscience and artificial intelligence (AI) both face the challenge of interpreting high-dimensional neural data, where the comparative analysis of such data is crucial for revealing shared mechanisms and differences between these complex systems. Despite the widespread use of representational comparisons and the abu... | {
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2411.14637 | Enhancing Clinical Trial Patient Matching through Knowledge Augmentation
and Reasoning with Multi-Agents | [
"cs.MA"
] | Matching patients effectively and efficiently for clinical trials is a significant challenge due to the complexity and variability of patient profiles and trial criteria. This paper introduces Multi-Agents for Knowledge Augmentation and Reasoning (MAKAR), a novel framework that enhances patient-trial matching by integr... | {
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2411.14639 | Differentially Private Adaptation of Diffusion Models via Noisy
Aggregated Embeddings | [
"cs.CV",
"cs.CR",
"cs.LG"
] | We introduce novel methods for adapting diffusion models under differential privacy (DP) constraints, enabling privacy-preserving style and content transfer without fine-tuning. Traditional approaches to private adaptation, such as DP-SGD, incur significant computational overhead and degrade model performance when appl... | {
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2411.14642 | VQalAttent: a Transparent Speech Generation Pipeline based on
Transformer-learned VQ-VAE Latent Space | [
"cs.LG",
"eess.AS"
] | Generating high-quality speech efficiently remains a key challenge for generative models in speech synthesis. This paper introduces VQalAttent, a lightweight model designed to generate fake speech with tunable performance and interpretability. Leveraging the AudioMNIST dataset, consisting of human utterances of decimal... | {
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2411.14647 | Benchmarking Multimodal Models for Ukrainian Language Understanding
Across Academic and Cultural Domains | [
"cs.CL"
] | While the evaluation of multimodal English-centric models is an active area of research with numerous benchmarks, there is a profound lack of benchmarks or evaluation suites for low- and mid-resource languages. We introduce ZNO-Vision, a comprehensive multimodal Ukrainian-centric benchmark derived from standardized uni... | {
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2411.14652 | Social Media Algorithms Can Shape Affective Polarization via Exposure to
Antidemocratic Attitudes and Partisan Animosity | [
"cs.CY",
"cs.AI",
"cs.HC",
"cs.SI"
] | There is widespread concern about the negative impacts of social media feed ranking algorithms on political polarization. Leveraging advancements in large language models (LLMs), we develop an approach to re-rank feeds in real-time to test the effects of content that is likely to polarize: expressions of antidemocratic... | {
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2411.14654 | Comparative Analysis of Pooling Mechanisms in LLMs: A Sentiment Analysis
Perspective | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) have revolutionized natural language processing (NLP) by delivering state-of-the-art performance across a variety of tasks. Among these, Transformer-based models like BERT and GPT rely on pooling layers to aggregate token-level embeddings into sentence-level representations. Common pooling ... | {
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2411.14662 | Multiset Transformer: Advancing Representation Learning in Persistence
Diagrams | [
"cs.LG"
] | To improve persistence diagram representation learning, we propose Multiset Transformer. This is the first neural network that utilizes attention mechanisms specifically designed for multisets as inputs and offers rigorous theoretical guarantees of permutation invariance. The architecture integrates multiset-enhanced a... | {
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2411.14663 | BrightVAE: Luminosity Enhancement in Underexposed Endoscopic Images | [
"eess.IV",
"cs.CV"
] | The enhancement of image luminosity is especially critical in endoscopic images. Underexposed endoscopic images often suffer from reduced contrast and uneven brightness, significantly impacting diagnostic accuracy and treatment planning. Internal body imaging is challenging due to uneven lighting and shadowy regions. E... | {
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2411.14664 | Sparsifying Suprema of Gaussian Processes | [
"stat.ML",
"cs.CC",
"cs.DS",
"cs.LG",
"math.PR"
] | We give a dimension-independent sparsification result for suprema of centered Gaussian processes: Let $T$ be any (possibly infinite) bounded set of vectors in $\mathbb{R}^n$, and let $\{{\boldsymbol{X}}_t\}_{t\in T}$ be the canonical Gaussian process on $T$. We show that there is an $O_\varepsilon(1)$-size subset $S \s... | {
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2411.14665 | Double Machine Learning for Adaptive Causal Representation in
High-Dimensional Data | [
"stat.ML",
"cs.LG",
"stat.AP",
"stat.CO"
] | Adaptive causal representation learning from observational data is presented, integrated with an efficient sample splitting technique within the semiparametric estimating equation framework. The support points sample splitting (SPSS), a subsampling method based on energy distance, is employed for efficient double machi... | {
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2411.14666 | Brain-Computer Interfaces for Emotional Regulation in Patients with
Various Disorders | [
"cs.HC",
"cs.LG"
] | Neurological and Physiological Disorders that impact emotional regulation each have their own unique characteristics which are important to understand in order to create a generalized solution to all of them. The purpose of this experiment is to explore the potential applications of EEG-based Brain-Computer Interfaces ... | {
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2411.14672 | Multiverse of Greatness: Generating Story Branches with LLMs | [
"cs.CL",
"cs.AI"
] | This paper presents Dynamic Context Prompting/Programming (DCP/P), a novel framework for interacting with LLMs to generate graph-based content with a dynamic context window history. While there is an existing study utilizing LLMs to generate a visual novel game, the previous study involved a manual process of output ex... | {
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2411.14677 | Exploring the Use of Machine Learning Weather Models in Data
Assimilation | [
"physics.ao-ph",
"cs.LG"
] | The use of machine learning (ML) models in meteorology has attracted significant attention for their potential to improve weather forecasting efficiency and accuracy. GraphCast and NeuralGCM, two promising ML-based weather models, are at the forefront of this innovation. However, their suitability for data assimilation... | {
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2411.14678 | Reinterpreting PID Controller From the Perspective of State Feedback and
Lumped Disturbance Compensation | [
"eess.SY",
"cs.SY",
"math.OC"
] | This paper analyzes the motion of solutions to non-homogeneous linear differential equations. It further clarifies that a proportional-integral-derivative (PID) controller essentially comprises two parts: a homogeneous controller and a disturbance observer, which are responsible for stabilizing the homogeneous system a... | {
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2411.14679 | Recursive Gaussian Process State Space Model | [
"cs.LG",
"cs.SY",
"eess.SY",
"stat.ML"
] | Learning dynamical models from data is not only fundamental but also holds great promise for advancing principle discovery, time-series prediction, and controller design. Among various approaches, Gaussian Process State-Space Models (GPSSMs) have recently gained significant attention due to their combination of flexibi... | {
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2411.14680 | Self-Supervised Learning for Ordered Three-Dimensional Structures | [
"cs.LG"
] | Recent work has proven that training large language models with self-supervised tasks and fine-tuning these models to complete new tasks in a transfer learning setting is a powerful idea, enabling the creation of models with many parameters, even with little labeled data; however, the number of domains that have harnes... | {
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2411.14684 | Cross Group Attention and Group-wise Rolling for Multimodal Medical
Image Synthesis | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Multimodal MR image synthesis aims to generate missing modality image by fusing and mapping a few available MRI data. Most existing approaches typically adopt an image-to-image translation scheme. However, these methods often suffer from sub-optimal performance due to the spatial misalignment between different modaliti... | {
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2411.14688 | Whats in a Video: Factorized Autoregressive Decoding for Online Dense
Video Captioning | [
"cs.CV",
"cs.CL",
"cs.LG"
] | Generating automatic dense captions for videos that accurately describe their contents remains a challenging area of research. Most current models require processing the entire video at once. Instead, we propose an efficient, online approach which outputs frequent, detailed and temporally aligned captions, without acce... | {
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2411.14691 | EV-PINN: A Physics-Informed Neural Network for Predicting Electric
Vehicle Dynamics | [
"cs.LG"
] | An onboard prediction of dynamic parameters (e.g. Aerodynamic drag, rolling resistance) enables accurate path planning for EVs. This paper presents EV-PINN, a Physics-Informed Neural Network approach in predicting instantaneous battery power and cumulative energy consumption during cruising while generalizing to the no... | {
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2411.14694 | A Data-Driven Pool Strategy for Price-Makers Under Imperfect Information | [
"eess.SY",
"cs.LG",
"cs.SY"
] | This paper studies the pool strategy for price-makers under imperfect information. In this occasion, market participants cannot obtain essential transmission parameters of the power system. Thus, price-makers should estimate the market results with respect to their offer curves using available historical information. T... | {
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2411.14695 | Anti-Forgetting Adaptation for Unsupervised Person Re-identification | [
"cs.CV",
"cs.LG"
] | Regular unsupervised domain adaptive person re-identification (ReID) focuses on adapting a model from a source domain to a fixed target domain. However, an adapted ReID model can hardly retain previously-acquired knowledge and generalize to unseen data. In this paper, we propose a Dual-level Joint Adaptation and Anti-f... | {
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2411.14696 | Quantum Hamiltonian Descent for Graph Partition | [
"quant-ph",
"cs.AI",
"cs.LG"
] | We introduce Quantum Hamiltonian Descent as a novel approach to solve the graph partition problem. By reformulating graph partition as a Quadratic Unconstrained Binary Optimization (QUBO) problem, we leverage QHD's quantum-inspired dynamics to identify optimal community structures. Our method implements a multi-level r... | {
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2411.14698 | Improving Mathematical Reasoning Capabilities of Small Language Models
via Feedback-Driven Distillation | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) demonstrate exceptional reasoning capabilities, often achieving state-of-the-art performance in various tasks. However, their substantial computational and memory demands, due to billions of parameters, hinder deployment in resource-constrained environments. A promising solution is knowledg... | {
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2411.14699 | DNN based Two-stage Compensation Algorithm for THz Hybrid Beamforming
with imperfect Hardware | [
"cs.IT",
"eess.SP",
"math.IT"
] | Terahertz (THz) communication is envisioned as a key technology for 6G and beyond wireless systems owing to its multi-GHz bandwidth. To maintain the same aperture area and the same link budget as the lower frequencies, ultra-massive multi-input and multi-output (UM-MIMO) with hybrid beamforming is promising. Neverthele... | {
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2411.14700 | Optimal Energy Dispatch of Grid-Connected Electric Vehicle Considering
Lithium Battery Electrochemical Model | [
"eess.SY",
"cs.SY"
] | The grid-connected electric vehicles (EVs) serve as a promising regulating resource in the distribution grid with Vehicle-to-Grid (V2G) facilities. In the day-ahead stage, electric vehicle batteries (EVBs) need to be precisely dispatched and controlled to ensure high efficiency and prevent degradation. This article foc... | {
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2411.14701 | Personalised 3D Human Digital Twin with Soft-Body Feet for Walking
Simulation | [
"cs.RO"
] | With the increasing use of assistive robots in rehabilitation and assisted mobility of human patients, there has been a need for a deeper understanding of human-robot interactions particularly through simulations, allowing an understanding of these interactions in a digital environment. There is an emphasis on accurate... | {
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2411.14704 | Cross-Modal Pre-Aligned Method with Global and Local Information for
Remote-Sensing Image and Text Retrieval | [
"cs.CV",
"cs.IR"
] | Remote sensing cross-modal text-image retrieval (RSCTIR) has gained attention for its utility in information mining. However, challenges remain in effectively integrating global and local information due to variations in remote sensing imagery and ensuring proper feature pre-alignment before modal fusion, which affects... | {
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2411.14707 | High-Bandwidth, Low-Computational Approach: Estimator-Based Control for
Hybrid Flying Capacitor Multilevel Converters Using Multi-Cost Gradient
Descent and State Feedforward | [
"eess.SY",
"cs.SY"
] | This paper presents an estimator-based control framework for hybrid flying capacitor multilevel (FCML) converters, achieving high-bandwidth control and reduced computational complexity. Utilizing a hybrid estimation method that combines closed-loop and open-loop dynamics, the proposed approach enables accurate and fast... | {
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2411.14708 | Understanding LLM Embeddings for Regression | [
"cs.LG",
"cs.AI",
"cs.CL"
] | With the rise of large language models (LLMs) for flexibly processing information as strings, a natural application is regression, specifically by preprocessing string representations into LLM embeddings as downstream features for metric prediction. In this paper, we provide one of the first comprehensive investigation... | {
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2411.14711 | Can GNNs Learn Link Heuristics? A Concise Review and Evaluation of Link
Prediction Methods | [
"cs.SI",
"cs.LG"
] | This paper explores the ability of Graph Neural Networks (GNNs) in learning various forms of information for link prediction, alongside a brief review of existing link prediction methods. Our analysis reveals that GNNs cannot effectively learn structural information related to the number of common neighbors between two... | {
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2411.14713 | LIBER: Lifelong User Behavior Modeling Based on Large Language Models | [
"cs.IR",
"cs.AI"
] | CTR prediction plays a vital role in recommender systems. Recently, large language models (LLMs) have been applied in recommender systems due to their emergence abilities. While leveraging semantic information from LLMs has shown some improvements in the performance of recommender systems, two notable limitations persi... | {
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2411.14715 | Any-to-3D Generation via Hybrid Diffusion Supervision | [
"cs.CV"
] | Recent progress in 3D object generation has been fueled by the strong priors offered by diffusion models. However, existing models are tailored to specific tasks, accommodating only one modality at a time and necessitating retraining to change modalities. Given an image-to-3D model and a text prompt, a naive approach i... | {
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2411.14716 | VisionPAD: A Vision-Centric Pre-training Paradigm for Autonomous Driving | [
"cs.CV",
"cs.LG",
"cs.RO"
] | This paper introduces VisionPAD, a novel self-supervised pre-training paradigm designed for vision-centric algorithms in autonomous driving. In contrast to previous approaches that employ neural rendering with explicit depth supervision, VisionPAD utilizes more efficient 3D Gaussian Splatting to reconstruct multi-view ... | {
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2411.14717 | FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data | [
"cs.LG",
"cs.CL",
"cs.CV"
] | Multimodal Large Language Models (MLLMs) have made significant advancements, demonstrating powerful capabilities in processing and understanding multimodal data. Fine-tuning MLLMs with Federated Learning (FL) allows for expanding the training data scope by including private data sources, thereby enhancing their practic... | {
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} |
2411.14720 | Optimizing Social Media Annotation of HPV Vaccine Skepticism and
Misinformation Using Large Language Models: An Experimental Evaluation of
In-Context Learning and Fine-Tuning Stance Detection Across Multiple Models | [
"cs.CL"
] | This paper leverages large-language models (LLMs) to experimentally determine optimal strategies for scaling up social media content annotation for stance detection on HPV vaccine-related tweets. We examine both conventional fine-tuning and emergent in-context learning methods, systematically varying strategies of prom... | {
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} |
2411.14721 | MolReFlect: Towards In-Context Fine-grained Alignments between Molecules
and Texts | [
"cs.CL",
"cs.LG",
"q-bio.QM"
] | Molecule discovery is a pivotal research field, impacting everything from the medicines we take to the materials we use. Recently, Large Language Models (LLMs) have been widely adopted in molecule understanding and generation, yet the alignments between molecules and their corresponding captions remain a significant ch... | {
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} |
2411.14723 | Effective SAM Combination for Open-Vocabulary Semantic Segmentation | [
"cs.CV"
] | Open-vocabulary semantic segmentation aims to assign pixel-level labels to images across an unlimited range of classes. Traditional methods address this by sequentially connecting a powerful mask proposal generator, such as the Segment Anything Model (SAM), with a pre-trained vision-language model like CLIP. But these ... | {
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} |
2411.14725 | Evaluating and Advancing Multimodal Large Language Models in Ability
Lens | [
"cs.CV",
"cs.CL",
"cs.LG"
] | As multimodal large language models (MLLMs) advance rapidly, rigorous evaluation has become essential, providing further guidance for their development. In this work, we focus on a unified and robust evaluation of \textbf{vision perception} abilities, the foundational skill of MLLMs. We find that existing perception be... | {
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} |
2411.14726 | Enhancing Molecular Design through Graph-based Topological Reinforcement
Learning | [
"cs.LG",
"q-bio.BM"
] | The generation of drug-like molecules is crucial for drug design. Existing reinforcement learning (RL) methods often overlook structural information. However, feature engineering-based methods usually merely focus on binding affinity prediction without substantial molecular modification. To address this, we present Gra... | {
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} |
2411.14727 | HyReaL: Clustering Attributed Graph via Hyper-Complex Space
Representation Learning | [
"cs.LG"
] | Clustering complex data in the form of attributed graphs has attracted increasing attention, where powerful graph representation is a critical prerequisite. However, the well-known Over-Smoothing (OS) effect makes Graph Convolutional Networks tend to homogenize the representation of graph nodes, while the existing OS s... | {
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} |
2411.14728 | K-GBS3FCM -- KNN Graph-Based Safe Semi-Supervised Fuzzy C-Means | [
"cs.LG"
] | Clustering data using prior domain knowledge, starting from a partially labeled set, has recently been widely investigated. Often referred to as semi-supervised clustering, this approach leverages labeled data to enhance clustering accuracy. To maximize algorithm performance, it is crucial to ensure the safety of this ... | {
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} |
2411.14733 | FLARE: FP-Less PTQ and Low-ENOB ADC Based AMS-PiM for Error-Resilient,
Fast, and Efficient Transformer Acceleration | [
"cs.LG",
"cs.SY",
"eess.IV",
"eess.SY"
] | Encoder-based transformers, powered by self-attention layers, have revolutionized machine learning with their context-aware representations. However, their quadratic growth in computational and memory demands presents significant bottlenecks. Analog-Mixed-Signal Process-in-Memory (AMS-PiM) architectures address these c... | {
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"cs.SY": 1
} |
2411.14737 | AI Tailoring: Evaluating Influence of Image Features on Fashion Product
Popularity | [
"cs.CV",
"cs.LG"
] | Identifying key product features that influence consumer preferences is essential in the fashion industry. In this study, we introduce a robust methodology to ascertain the most impactful features in fashion product images, utilizing past market sales data. First, we propose the metric called "influence score" to quant... | {
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} |
2411.14738 | Universal and Context-Independent Triggers for Precise Control of LLM
Outputs | [
"cs.CL",
"cs.AI",
"cs.CR"
] | Large language models (LLMs) have been widely adopted in applications such as automated content generation and even critical decision-making systems. However, the risk of prompt injection allows for potential manipulation of LLM outputs. While numerous attack methods have been documented, achieving full control over th... | {
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} |
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