id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.11560 | Topology-aware Preemptive Scheduling for Co-located LLM Workloads | [
"cs.DC",
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] | Hosting diverse large language model workloads in a unified resource pool through co-location is cost-effective. For example, long-running chat services generally follow diurnal traffic patterns, which inspire co-location of batch jobs to fulfill resource valleys between successive peaks, and thus to saturate resource ... | {
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2411.11562 | MSSIDD: A Benchmark for Multi-Sensor Denoising | [
"cs.CV",
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] | The cameras equipped on mobile terminals employ different sensors in different photograph modes, and the transferability of raw domain denoising models between these sensors is significant but remains sufficient exploration. Industrial solutions either develop distinct training strategies and models for different senso... | {
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2411.11567 | GNN-Based Code Annotation Logic for Establishing Security Boundaries in
C Code | [
"cs.CR",
"cs.LG"
] | Securing sensitive operations in today's interconnected software landscape is crucial yet challenging. Modern platforms rely on Trusted Execution Environments (TEEs), such as Intel SGX and ARM TrustZone, to isolate security sensitive code from the main system, reducing the Trusted Computing Base (TCB) and providing str... | {
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2411.11574 | Reduced Network Cumulative Constraint Violation for Distributed Bandit
Convex Optimization under Slater Condition | [
"eess.SY",
"cs.SY"
] | This paper studies the distributed bandit convex optimization problem with time-varying inequality constraints, where the goal is to minimize network regret and cumulative constraint violation. To calculate network cumulative constraint violation, existing distributed bandit online algorithms solving this problem direc... | {
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2411.11575 | Analysis of Generalized Hebbian Learning Algorithm for Neuromorphic
Hardware Using Spinnaker | [
"cs.NE"
] | Neuromorphic computing, inspired by biological neural networks, has emerged as a promising approach for solving complex machine learning tasks with greater efficiency and lower power consumption. The integration of biologically plausible learning algorithms, such as the Generalized Hebbian Algorithm (GHA), is key to en... | {
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2411.11576 | Hybrid Data-Driven SSM for Interpretable and Label-Free mmWave Channel
Prediction | [
"eess.SP",
"cs.AI",
"cs.LG"
] | Accurate prediction of mmWave time-varying channels is essential for mitigating the issue of channel aging in complex scenarios owing to high user mobility. Existing channel prediction methods have limitations: classical model-based methods often struggle to track highly nonlinear channel dynamics due to limited expert... | {
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2411.11581 | OASIS: Open Agent Social Interaction Simulations with One Million Agents | [
"cs.CL"
] | There has been a growing interest in enhancing rule-based agent-based models (ABMs) for social media platforms (i.e., X, Reddit) with more realistic large language model (LLM) agents, thereby allowing for a more nuanced study of complex systems. As a result, several LLM-based ABMs have been proposed in the past year. W... | {
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2411.11582 | Exploring LLMs for Verifying Technical System Specifications Against
Requirements | [
"cs.SE",
"cs.SY",
"eess.SY"
] | Requirements engineering is a knowledge intensive process and crucial for the success of engineering projects. The field of knowledge-based requirements engineering (KBRE) aims to support engineers by providing knowledge to assist in the elicitation, validation, and management of system requirements. The advent of larg... | {
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2411.11587 | A Linear Differential Inclusion for Contraction Analysis to Known
Trajectories | [
"eess.SY",
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] | Infinitesimal contraction analysis provides exponential convergence rates between arbitrary pairs of trajectories of a system by studying the system's linearization. An essentially equivalent viewpoint arises through stability analysis of a linear differential inclusion (LDI) encompassing the incremental behavior of th... | {
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2411.11590 | Robust Causal Analysis of Linear Cyclic Systems With Hidden Confounders | [
"stat.ML",
"cs.LG"
] | We live in a world full of complex systems which we need to improve our understanding of. To accomplish this, purely probabilistic investigations are often not enough. They are only the first step and must be followed by learning the system's underlying mechanisms. This is what the discipline of causality is concerned ... | {
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2411.11592 | Generative Spatio-temporal GraphNet for Transonic Wing Pressure
Distribution Forecasting | [
"cs.LG",
"cs.CE"
] | This study presents a framework for predicting unsteady transonic wing pressure distributions, integrating an autoencoder architecture with graph convolutional networks and graph-based temporal layers to model time dependencies. The framework compresses high-dimensional pressure distribution data into a lower-dimension... | {
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2411.11596 | Integrating and Comparing Radiality Constraints for Optimized
Distribution System Reconfiguration | [
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] | The reconfiguration of electrical power distribution systems is a crucial optimization problem aimed at minimizing power losses by altering the system topology through the operation of interconnection switches. This problem, typically modelled as a mixed integer nonlinear program demands high computational resources fo... | {
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2411.11598 | Carleman-Fourier Linearization of Complex Dynamical Systems: Convergence
and Explicit Error Bounds | [
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"eess.SY"
] | This paper presents a Carleman-Fourier linearization method for nonlinear dynamical systems with periodic vector fields involving multiple fundamental frequencies. By employing Fourier basis functions, the nonlinear dynamical system is transformed into a linear model on an infinite-dimensional space. The proposed appro... | {
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2411.11603 | Feature Selection for Network Intrusion Detection | [
"cs.LG",
"cs.CR"
] | Network Intrusion Detection (NID) remains a key area of research within the information security community, while also being relevant to Machine Learning (ML) practitioners. The latter generally aim to detect attacks using network features, which have been extracted from raw network data typically using dimensionality ... | {
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2411.11607 | Performance evaluation of a ROS2 based Automated Driving System | [
"cs.RO"
] | Automated driving is currently a prominent area of scientific work. In the future, highly automated driving and new Advanced Driver Assistance Systems will become reality. While Advanced Driver Assistance Systems and automated driving functions for certain domains are already commercially available, ubiquitous automate... | {
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2411.11609 | VLN-Game: Vision-Language Equilibrium Search for Zero-Shot Semantic
Navigation | [
"cs.RO"
] | Following human instructions to explore and search for a specified target in an unfamiliar environment is a crucial skill for mobile service robots. Most of the previous works on object goal navigation have typically focused on a single input modality as the target, which may lead to limited consideration of language d... | {
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2411.11610 | Approximate predictive control barrier function for discrete-time
systems | [
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"math.OC"
] | We propose integrating an explicit approximation of a predictive control barrier function (PCBF) in a safety filter framework. The approximated PCBF is implicitly defined through an optimal control problem and allows guaranteeing invariance of an implicitly defined safe set as well as stability of this safe set within ... | {
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2411.11611 | Improved PIR Schemes using Matching Vectors and Derivatives | [
"cs.CC",
"cs.IT",
"math.IT"
] | In this paper, we construct new t-server Private Information Retrieval (PIR) schemes with communication complexity subpolynomial in the previously best known, for all but finitely many t. Our results are based on combining derivatives (in the spirit of Woodruff-Yekhanin) with the Matching Vector based PIRs of Yekhanin ... | {
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2411.11613 | Leveraging Computational Pathology AI for Noninvasive Optical Imaging
Analysis Without Retraining | [
"cs.CV"
] | Noninvasive optical imaging modalities can probe patient's tissue in 3D and over time generate gigabytes of clinically relevant data per sample. There is a need for AI models to analyze this data and assist clinical workflow. The lack of expert labelers and the large dataset required (>100,000 images) for model trainin... | {
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2411.11614 | On the physics of nested Markov models: a generalized probabilistic
theory perspective | [
"quant-ph",
"cs.LG",
"math.ST",
"stat.TH"
] | Determining potential probability distributions with a given causal graph is vital for causality studies. To bypass the difficulty in characterizing latent variables in a Bayesian network, the nested Markov model provides an elegant algebraic approach by listing exactly all the equality constraints on the observed vari... | {
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2411.11616 | Signaling and Social Learning in Swarms of Robots | [
"cs.RO",
"cs.AI",
"cs.LG",
"cs.MA"
] | This paper investigates the role of communication in improving coordination within robot swarms, focusing on a paradigm where learning and execution occur simultaneously in a decentralized manner. We highlight the role communication can play in addressing the credit assignment problem (individual contribution to the ov... | {
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2411.11619 | FERT: Real-Time Facial Expression Recognition with Short-Range FMCW
Radar | [
"cs.CV",
"cs.LG",
"eess.SP"
] | This study proposes a novel approach for real-time facial expression recognition utilizing short-range Frequency-Modulated Continuous-Wave (FMCW) radar equipped with one transmit (Tx), and three receive (Rx) antennas. The system leverages four distinct modalities simultaneously: Range-Doppler images (RDIs), micro range... | {
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2411.11620 | ST-Tree with Interpretability for Multivariate Time Series
Classification | [
"cs.LG",
"cs.AI"
] | Multivariate time series classification is of great importance in practical applications and is a challenging task. However, deep neural network models such as Transformers exhibit high accuracy in multivariate time series classification but lack interpretability and fail to provide insights into the decision-making pr... | {
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2411.11623 | Federated Incremental Named Entity Recognition | [
"cs.CL"
] | Federated Named Entity Recognition (FNER) boosts model training within each local client by aggregating the model updates of decentralized local clients, without sharing their private data. However, existing FNER methods assume fixed entity types and local clients in advance, leading to their ineffectiveness in practic... | {
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2411.11635 | Chapter 7 Review of Data-Driven Generative AI Models for Knowledge
Extraction from Scientific Literature in Healthcare | [
"cs.CL",
"cs.AI"
] | This review examines the development of abstractive NLP-based text summarization approaches and compares them to existing techniques for extractive summarization. A brief history of text summarization from the 1950s to the introduction of pre-trained language models such as Bidirectional Encoder Representations from Tr... | {
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2411.11636 | SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly
semi-supervised medical image segmentation | [
"cs.CV",
"cs.AI"
] | Deep learning-based medical image segmentation helps assist diagnosis and accelerate the treatment process while the model training usually requires large-scale dense annotation datasets. Weakly semi-supervised medical image segmentation is an essential application because it only requires a small amount of scribbles a... | {
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2411.11637 | The ethical landscape of robot-assisted surgery. A systematic review | [
"cs.CY",
"cs.RO"
] | Background: Robot-assisted surgery has been widely adopted in recent years. However, compared to other health technologies operating in close proximity to patients in a vulnerable state, ethical issues of robot-assisted surgery have received less attention. Against the background of increasing automation that are expec... | {
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2411.11641 | TSINR: Capturing Temporal Continuity via Implicit Neural Representations
for Time Series Anomaly Detection | [
"cs.LG",
"cs.AI"
] | Time series anomaly detection aims to identify unusual patterns in data or deviations from systems' expected behavior. The reconstruction-based methods are the mainstream in this task, which learn point-wise representation via unsupervised learning. However, the unlabeled anomaly points in training data may cause these... | {
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2411.11647 | No-regret Exploration in Shuffle Private Reinforcement Learning | [
"cs.LG",
"cs.AI",
"cs.CR"
] | Differential privacy (DP) has recently been introduced into episodic reinforcement learning (RL) to formally address user privacy concerns in personalized services. Previous work mainly focuses on two trust models of DP: the central model, where a central agent is responsible for protecting users' sensitive data, and t... | {
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2411.11652 | On the Incorporation of Stability Constraints into Sequential
Operational Scheduling | [
"eess.SY",
"cs.SY"
] | With the increasing penetration of Inverter-Based Resources (IBRs), power system stability constraints must be incorporated into the operational framework, transforming it into stability-constrained optimization. Currently, there exist parallel research efforts on developing the stability constraints within DC power fl... | {
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2411.11667 | Dissecting Representation Misalignment in Contrastive Learning via
Influence Function | [
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"cs.AI",
"cs.CV"
] | Contrastive learning, commonly applied in large-scale multimodal models, often relies on data from diverse and often unreliable sources, which can include misaligned or mislabeled text-image pairs. This frequently leads to robustness issues and hallucinations, ultimately causing performance degradation. Data valuation ... | {
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2411.11668 | Efficient and Robust Continual Graph Learning for Graph Classification
in Biology | [
"cs.LG",
"q-bio.QM"
] | Graph classification is essential for understanding complex biological systems, where molecular structures and interactions are naturally represented as graphs. Traditional graph neural networks (GNNs) perform well on static tasks but struggle in dynamic settings due to catastrophic forgetting. We present Perturbed and... | {
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2411.11672 | Artificial Scientific Discovery | [
"cs.AI",
"cs.LG"
] | Rooted in the explosion of deep learning over the past decade, this thesis spans from AlphaGo to ChatGPT to empirically examine the fundamental concepts needed to realize the vision of an artificial scientist: a machine with the capacity to autonomously generate original research and contribute to the expansion of huma... | {
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2411.11677 | Few-shot Model Extraction Attacks against Sequential Recommender Systems | [
"cs.LG",
"cs.CR",
"cs.IR"
] | Among adversarial attacks against sequential recommender systems, model extraction attacks represent a method to attack sequential recommendation models without prior knowledge. Existing research has primarily concentrated on the adversary's execution of black-box attacks through data-free model extraction. However, a ... | {
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2411.11678 | Analysis of Hardware Synthesis Strategies for Machine Learning in
Collider Trigger and Data Acquisition | [
"physics.ins-det",
"cs.AR",
"cs.LG",
"hep-ex"
] | To fully exploit the physics potential of current and future high energy particle colliders, machine learning (ML) can be implemented in detector electronics for intelligent data processing and acquisition. The implementation of ML in real-time at colliders requires very low latencies that are unachievable with a softw... | {
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2411.11681 | PSPO*: An Effective Process-supervised Policy Optimization for Reasoning
Alignment | [
"cs.AI",
"cs.LG"
] | Process supervision enhances the performance of large language models in reasoning tasks by providing feedback at each step of chain-of-thought reasoning. However, due to the lack of effective process supervision methods, even advanced large language models are prone to logical errors and redundant reasoning. We claim ... | {
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2411.11682 | Learning Differentiable Surrogate Losses for Structured Prediction | [
"stat.ML",
"cs.LG"
] | Structured prediction involves learning to predict complex structures rather than simple scalar values. The main challenge arises from the non-Euclidean nature of the output space, which generally requires relaxing the problem formulation. Surrogate methods build on kernel-induced losses or more generally, loss functio... | {
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2411.11683 | TrojanRobot: Physical-World Backdoor Attacks Against VLM-based Robotic
Manipulation | [
"cs.RO",
"cs.AI"
] | Robotic manipulation in the physical world is increasingly empowered by \textit{large language models} (LLMs) and \textit{vision-language models} (VLMs), leveraging their understanding and perception capabilities. Recently, various attacks against such robotic policies have been proposed, with backdoor attacks drawing ... | {
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2411.11687 | Coevolution of Opinion Dynamics and Recommendation System: Modeling
Analysis and Reinforcement Learning Based Manipulation | [
"eess.SY",
"cs.SY"
] | In this work, we develop an analytical framework that integrates opinion dynamics with a recommendation system. By incorporating elements such as collaborative filtering, we provide a precise characterization of how recommendation systems shape interpersonal interactions and influence opinion formation. Moreover, the p... | {
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2411.11688 | Conceptwm: A Diffusion Model Watermark for Concept Protection | [
"cs.CR",
"cs.AI",
"cs.MM"
] | The personalization techniques of diffusion models succeed in generating specific concepts but also pose threats to copyright protection and illegal use. Model Watermarking is an effective method to prevent the unauthorized use of subject-driven or style-driven image generation, safeguarding concept copyrights. However... | {
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2411.11691 | Towards Degradation-Robust Reconstruction in Generalizable NeRF | [
"cs.CV"
] | Generalizable Neural Radiance Field (GNeRF) across scenes has been proven to be an effective way to avoid per-scene optimization by representing a scene with deep image features of source images. However, despite its potential for real-world applications, there has been limited research on the robustness of GNeRFs to d... | {
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2411.11692 | Do Captioning Metrics Reflect Music Semantic Alignment? | [
"cs.SD",
"cs.IR",
"eess.AS"
] | Music captioning has emerged as a promising task, fueled by the advent of advanced language generation models. However, the evaluation of music captioning relies heavily on traditional metrics such as BLEU, METEOR, and ROUGE which were developed for other domains, without proper justification for their use in this new ... | {
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2411.11693 | From Spectra to Geography: Intelligent Mapping of RRUFF Mineral Data | [
"cs.CV",
"eess.IV",
"physics.comp-ph"
] | Accurately determining the geographic origin of mineral samples is pivotal for applications in geology, mineralogy, and material science. Leveraging the comprehensive Raman spectral data from the RRUFF database, this study introduces a novel machine learning framework aimed at geolocating mineral specimens at the count... | {
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2411.11694 | Enhancing LLM Reasoning with Reward-guided Tree Search | [
"cs.CL",
"cs.AI"
] | Recently, test-time scaling has garnered significant attention from the research community, largely due to the substantial advancements of the o1 model released by OpenAI. By allocating more computational resources during the inference phase, large language models~(LLMs) can extensively explore the solution space by ge... | {
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2411.11697 | Robust Reinforcement Learning under Diffusion Models for Data with Jumps | [
"cs.LG",
"stat.ML"
] | Reinforcement Learning (RL) has proven effective in solving complex decision-making tasks across various domains, but challenges remain in continuous-time settings, particularly when state dynamics are governed by stochastic differential equations (SDEs) with jump components. In this paper, we address this challenge by... | {
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2411.11698 | A New Finite-Horizon Dynamic Programming Analysis of Nonanticipative
Rate-Distortion Function for Markov Sources | [
"cs.IT",
"cs.SY",
"eess.SY",
"math.IT"
] | This paper deals with the computation of a non-asymptotic lower bound by means of the nonanticipative rate-distortion function (NRDF) on the discrete-time zero-delay variable-rate lossy compression problem for discrete Markov sources with per-stage, single-letter distortion. First, we derive a new information structure... | {
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2411.11706 | MC-LLaVA: Multi-Concept Personalized Vision-Language Model | [
"cs.CV",
"cs.AI"
] | Current vision-language models (VLMs) show exceptional abilities across diverse tasks including visual question answering. To enhance user experience in practical applications, recent studies investigate VLM personalization to understand user-provided concepts. However, existing studies mainly focus on single-concept p... | {
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2411.11707 | FedCoLLM: A Parameter-Efficient Federated Co-tuning Framework for Large
and Small Language Models | [
"cs.CL",
"cs.AI"
] | By adapting Large Language Models (LLMs) to domain-specific tasks or enriching them with domain-specific knowledge, we can fully harness the capabilities of LLMs. Nonetheless, a gap persists in achieving simultaneous mutual enhancement between the server's LLM and the downstream clients' Small Language Models (SLMs). T... | {
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2411.11713 | FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for
Federated Learning | [
"cs.LG",
"cs.DC"
] | Federated Learning (FL), as a mainstream privacy-preserving machine learning paradigm, offers promising solutions for privacy-critical domains such as healthcare and finance. Although extensive efforts have been dedicated from both academia and industry to improve the vanilla FL, little work focuses on the data pricing... | {
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2411.11714 | Semantic-Geometric-Physical-Driven Robot Manipulation Skill Transfer via
Skill Library and Tactile Representation | [
"cs.RO",
"cs.AI"
] | Deploying robots in open-world environments involves complex tasks characterized by long sequences and rich interactions, necessitating efficient transfer of robotic skills across diverse and complex scenarios. To address this challenge, we propose a skill library framework based on knowledge graphs, which endows robot... | {
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2411.11717 | RAWMamba: Unified sRGB-to-RAW De-rendering With State Space Model | [
"cs.CV"
] | Recent advancements in sRGB-to-RAW de-rendering have increasingly emphasized metadata-driven approaches to reconstruct RAW data from sRGB images, supplemented by partial RAW information. In image-based de-rendering, metadata is commonly obtained through sampling, whereas in video tasks, it is typically derived from the... | {
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2411.11726 | Wideband Ultrasonic Acoustic Underwater Channels: Measurements and
Characterization | [
"cs.IT",
"eess.SP",
"math.IT"
] | In this work we present the results of a measurement campaign carried out in the Mediterranean sea aimed at characterizing the underwater acoustic channel in a wideband at ultrasonic frequencies centered at 80 kHz with a width of 96 kHz, covering two octaves from 32 to 128 kHz. So far, these type of wideband measuremen... | {
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2411.11727 | Aligning Few-Step Diffusion Models with Dense Reward Difference Learning | [
"cs.LG",
"cs.CV"
] | Aligning diffusion models with downstream objectives is essential for their practical applications. However, standard alignment methods often struggle with step generalization when directly applied to few-step diffusion models, leading to inconsistent performance across different denoising step scenarios. To address th... | {
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2411.11730 | Lifted Model Construction without Normalisation: A Vectorised Approach
to Exploit Symmetries in Factor Graphs | [
"cs.AI",
"cs.LG"
] | Lifted probabilistic inference exploits symmetries in a probabilistic model to allow for tractable probabilistic inference with respect to domain sizes of logical variables. We found that the current state-of-the-art algorithm to construct a lifted representation in form of a parametric factor graph misses symmetries b... | {
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2411.11731 | Moral Persuasion in Large Language Models: Evaluating Susceptibility and
Ethical Alignment | [
"cs.CL",
"cs.AI"
] | We explore how large language models (LLMs) can be influenced by prompting them to alter their initial decisions and align them with established ethical frameworks. Our study is based on two experiments designed to assess the susceptibility of LLMs to moral persuasion. In the first experiment, we examine the susceptibi... | {
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2411.11733 | Integrating Active Sensing and Rearrangement Planning for Efficient
Object Retrieval from Unknown, Confined, Cluttered Environments | [
"cs.RO"
] | Retrieving target objects from unknown, confined spaces remains a challenging task that requires integrated, task-driven active sensing and rearrangement planning. Previous approaches have independently addressed active sensing and rearrangement planning, limiting their practicality in real-world scenarios. This paper ... | {
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2411.11734 | Joint-Space Control of a Structurally Elastic Humanoid Robot | [
"cs.RO"
] | In this work, the joint-control strategy is presented for the humanoid robot, PANDORA, whose structural components are designed to be compliant. As opposed to contemporary approaches which design the elasticity internal to the actuator housing, PANDORA's structural components are designed to be compliant under load or,... | {
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2411.11736 | Advacheck at GenAI Detection Task 1: AI Detection Powered by
Domain-Aware Multi-Tasking | [
"cs.CL"
] | The paper describes a system designed by Advacheck team to recognise machine-generated and human-written texts in the monolingual subtask of GenAI Detection Task 1 competition. Our developed system is a multi-task architecture with shared Transformer Encoder between several classification heads. One head is responsible... | {
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2411.11738 | WoodYOLO: A Novel Object Detector for Wood Species Detection in
Microscopic Images | [
"cs.CV",
"cs.AI"
] | Wood species identification plays a crucial role in various industries, from ensuring the legality of timber products to advancing ecological conservation efforts. This paper introduces WoodYOLO, a novel object detection algorithm specifically designed for microscopic wood fiber analysis. Our approach adapts the YOLO a... | {
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2411.11739 | QARM: Quantitative Alignment Multi-Modal Recommendation at Kuaishou | [
"cs.IR",
"cs.AI"
] | In recent years, with the significant evolution of multi-modal large models, many recommender researchers realized the potential of multi-modal information for user interest modeling. In industry, a wide-used modeling architecture is a cascading paradigm: (1) first pre-training a multi-modal model to provide omnipotent... | {
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2411.11740 | Revitalizing Electoral Trust: Enhancing Transparency and Efficiency
through Automated Voter Counting with Machine Learning | [
"cs.LG",
"cs.CV"
] | In order to address issues with manual vote counting during election procedures, this study intends to examine the viability of using advanced image processing techniques for automated voter counting. The study aims to shed light on how automated systems that utilize cutting-edge technologies like OpenCV, CVZone, and t... | {
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2411.11745 | BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration | [
"cs.LG",
"cs.AR"
] | Large language models (LLMs) have demonstrated remarkable performance across various machine learning tasks. Yet the substantial memory footprint of LLMs significantly hinders their deployment. In this paper, we improve the accessibility of LLMs through BitMoD, an algorithm-hardware co-design solution that enables effi... | {
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2411.11748 | Debiased Regression for Root-N-Consistent Conditional Mean Estimation | [
"stat.ML",
"cs.LG",
"econ.EM",
"math.ST",
"stat.ME",
"stat.TH"
] | This study introduces a debiasing method for regression estimators, including high-dimensional and nonparametric regression estimators. For example, nonparametric regression methods allow for the estimation of regression functions in a data-driven manner with minimal assumptions; however, these methods typically fail t... | {
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2411.11758 | The Power of Many: Multi-Agent Multimodal Models for Cultural Image
Captioning | [
"cs.CV",
"cs.AI",
"cs.CL"
] | Large Multimodal Models (LMMs) exhibit impressive performance across various multimodal tasks. However, their effectiveness in cross-cultural contexts remains limited due to the predominantly Western-centric nature of most data and models. Conversely, multi-agent models have shown significant capability in solving comp... | {
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2411.11761 | Mapping out the Space of Human Feedback for Reinforcement Learning: A
Conceptual Framework | [
"cs.LG",
"cs.HC"
] | Reinforcement Learning from Human feedback (RLHF) has become a powerful tool to fine-tune or train agentic machine learning models. Similar to how humans interact in social contexts, we can use many types of feedback to communicate our preferences, intentions, and knowledge to an RL agent. However, applications of huma... | {
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2411.11762 | High-Speed Cornering Control and Real-Vehicle Deployment for Autonomous
Electric Vehicles | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Executing drift maneuvers during high-speed cornering presents significant challenges for autonomous vehicles, yet offers the potential to minimize turning time and enhance driving dynamics. While reinforcement learning (RL) has shown promising results in simulated environments, discrepancies between simulations and re... | {
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2411.11764 | Freezing of Gait Detection Using Gramian Angular Fields and Federated
Learning from Wearable Sensors | [
"cs.LG",
"eess.SP"
] | Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease (PD) that impairs mobility and safety. Traditional detection methods face challenges due to intra and inter-patient variability, and most systems are tested in controlled settings, limiting their real-world applicability. Addressing these gaps, we ... | {
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2411.11767 | Drowning in Documents: Consequences of Scaling Reranker Inference | [
"cs.IR",
"cs.CL",
"cs.LG"
] | Rerankers, typically cross-encoders, are often used to re-score the documents retrieved by cheaper initial IR systems. This is because, though expensive, rerankers are assumed to be more effective. We challenge this assumption by measuring reranker performance for full retrieval, not just re-scoring first-stage retriev... | {
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2411.11768 | AdaptLIL: A Gaze-Adaptive Visualization for Ontology Mapping | [
"cs.HC",
"cs.AI"
] | This paper showcases AdaptLIL, a real-time adaptive link-indented list ontology mapping visualization that uses eye gaze as the primary input source. Through a multimodal combination of real-time systems, deep learning, and web development applications, this system uniquely curtails graphical overlays (adaptations) to ... | {
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2411.11770 | CNMBERT: A Model for Converting Hanyu Pinyin Abbreviations to Chinese
Characters | [
"cs.CL",
"cs.AI"
] | The task of converting Hanyu Pinyin abbreviations to Chinese characters is a significant branch within the domain of Chinese Spelling Correction (CSC). It plays an important role in many downstream applications such as named entity recognition and sentiment analysis. This task typically involves text-length alignment a... | {
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2411.11774 | Exploring the Requirements of Clinicians for Explainable AI Decision
Support Systems in Intensive Care | [
"cs.HC",
"cs.AI"
] | There is a growing need to understand how digital systems can support clinical decision-making, particularly as artificial intelligence (AI) models become increasingly complex and less human-interpretable. This complexity raises concerns about trustworthiness, impacting safe and effective adoption of such technologies.... | {
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2411.11777 | Assistive Control of Knee Exoskeletons for Human Walking on Granular
Terrains | [
"cs.RO"
] | Human walkers traverse diverse environments and demonstrate different gait locomotion and energy cost on granular terrains compared to solid ground. We present a stiffness-based model predictive control approach of knee exoskeleton assistance on sand. The gait and locomotion comparison is first discussed for human walk... | {
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2411.11778 | Design And Optimization Of Multi-rendezvous Manoeuvres Based On
Reinforcement Learning And Convex Optimization | [
"eess.SY",
"cs.SY"
] | Optimizing space vehicle routing is crucial for critical applications such as on-orbit servicing, constellation deployment, and space debris de-orbiting. Multi-target Rendezvous presents a significant challenge in this domain. This problem involves determining the optimal sequence in which to visit a set of targets, an... | {
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2411.11779 | LLM-IE: A Python Package for Generative Information Extraction with
Large Language Models | [
"cs.LG"
] | Objectives: Despite the recent adoption of large language models (LLMs) for biomedical information extraction, challenges in prompt engineering and algorithms persist, with no dedicated software available. To address this, we developed LLM-IE: a Python package for building complete information extraction pipelines. Our... | {
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2411.11781 | Towards Scalable and Practical Batch-Dynamic Connectivity | [
"cs.DS",
"cs.DB",
"cs.DC"
] | We study the problem of dynamically maintaining the connected components of an undirected graph subject to edge insertions and deletions. We give the first parallel algorithm for the problem which is work-efficient, supports batches of updates, runs in polylogarithmic depth, and uses only linear total space. The existi... | {
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2411.11786 | Parallelly Tempered Generative Adversarial Networks | [
"stat.ML",
"cs.LG"
] | A generative adversarial network (GAN) has been a representative backbone model in generative artificial intelligence (AI) because of its powerful performance in capturing intricate data-generating processes. However, the GAN training is well-known for its notorious training instability, usually characterized by the oc... | {
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2411.11788 | Enabling steep slope walking on Husky using reduced order modeling and
quadratic programming | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Wing-assisted inclined running (WAIR) observed in some young birds, is an attractive maneuver that can be extended to legged aerial systems. This study proposes a control method using a modified Variable Length Inverted Pendulum (VLIP) by assuming a fixed zero moment point and thruster forces collocated at the center o... | {
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2411.11791 | Machine Learning-Assisted Distribution System Network Reconfiguration
Problem | [
"eess.SY",
"cs.SY"
] | High penetration from volatile renewable energy resources in the grid and the varying nature of loads raise the need for frequent line switching to ensure the efficient operation of electrical distribution networks. Operators must ensure maximum load delivery, reduced losses, and the operation between voltage limits. H... | {
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2411.11793 | A Potential Game Perspective in Federated Learning | [
"cs.LG"
] | Federated learning (FL) is an emerging paradigm for training machine learning models across distributed clients. Traditionally, in FL settings, a central server assigns training efforts (or strategies) to clients. However, from a market-oriented perspective, clients may independently choose their training efforts based... | {
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2411.11794 | Competing Bandits in Decentralized Large Contextual Matching Markets | [
"cs.LG",
"stat.ML"
] | Sequential learning in a multi-agent resource constrained matching market has received significant interest in the past few years. We study decentralized learning in two-sided matching markets where the demand side (aka players or agents) competes for a `large' supply side (aka arms) with potentially time-varying prefe... | {
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2411.11795 | Exploring adversarial robustness of JPEG AI: methodology, comparison and
new methods | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Adversarial robustness of neural networks is an increasingly important area of research, combining studies on computer vision models, large language models (LLMs), and others. With the release of JPEG AI - the first standard for end-to-end neural image compression (NIC) methods - the question of its robustness has beco... | {
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2411.11798 | COST CA20120 INTERACT Framework of Artificial Intelligence Based Channel
Modeling | [
"cs.IT",
"cs.AI",
"eess.SP",
"math.IT"
] | Accurate channel models are the prerequisite for communication-theoretic investigations as well as system design. Channel modeling generally relies on statistical and deterministic approaches. However, there are still significant limits for the traditional modeling methods in terms of accuracy, generalization ability, ... | {
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2411.11799 | Edge-Enhanced Dilated Residual Attention Network for Multimodal Medical
Image Fusion | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Multimodal medical image fusion is a crucial task that combines complementary information from different imaging modalities into a unified representation, thereby enhancing diagnostic accuracy and treatment planning. While deep learning methods, particularly Convolutional Neural Networks (CNNs) and Transformers, have s... | {
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2411.11801 | KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for
ordinary/partial differential equation discovery of nonlinear dynamic systems | [
"physics.comp-ph",
"cs.LG"
] | Machine learning for scientific discovery is increasingly becoming popular because of its ability to extract and recognize the nonlinear characteristics from the data. The black-box nature of deep learning methods poses difficulties in interpreting the identified model. There is a dire need to interpret the machine lea... | {
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2411.11803 | Scalable control synthesis for stochastic systems via structural IMDP
abstractions | [
"eess.SY",
"cs.SY"
] | This paper introduces a novel abstraction-based framework for controller synthesis of nonlinear discrete-time stochastic systems. The focus is on probabilistic reach-avoid specifications. The framework is based on abstracting a stochastic system into a new class of robust Markov models, called orthogonally decoupled In... | {
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2411.11808 | Character-level Tokenizations as Powerful Inductive Biases for RNA
Foundational Models | [
"q-bio.QM",
"cs.AI",
"cs.LG",
"q-bio.BM"
] | RNA is a vital biomolecule with numerous roles and functions within cells, and interest in targeting it for therapeutic purposes has grown significantly in recent years. However, fully understanding and predicting RNA behavior, particularly for applications in drug discovery, remains a challenge due to the complexity o... | {
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2411.11812 | cHyRRT and cHySST: Two Motion Planning Tools for Hybrid Dynamical
Systems | [
"cs.RO"
] | This paper describes two C++/Open Motion Planning Library implementations of the recently developed motion planning algorithms HyRRT arXiv:2210.15082v1 [cs.RO] and HySST arXiv:2305.18649v1 [cs.RO]. Specifically, cHyRRT, an implementation of the HyRRT algorithm, is capable of generating a solution to a motion planning p... | {
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2411.11819 | Equivariant spatio-hemispherical networks for diffusion MRI
deconvolution | [
"eess.IV",
"cs.CV"
] | Each voxel in a diffusion MRI (dMRI) image contains a spherical signal corresponding to the direction and strength of water diffusion in the brain. This paper advances the analysis of such spatio-spherical data by developing convolutional network layers that are equivariant to the $\mathbf{E(3) \times SO(3)}$ group and... | {
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2411.11826 | LightFFDNets: Lightweight Convolutional Neural Networks for Rapid Facial
Forgery Detection | [
"cs.CV",
"cs.AI"
] | Accurate and fast recognition of forgeries is an issue of great importance in the fields of artificial intelligence, image processing and object detection. Recognition of forgeries of facial imagery is the process of classifying and defining the faces in it by analyzing real-world facial images. This process is usually... | {
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} |
2411.11829 | Tackling prediction tasks in relational databases with LLMs | [
"cs.LG",
"cs.CL",
"cs.DB"
] | Though large language models (LLMs) have demonstrated exceptional performance across numerous problems, their application to predictive tasks in relational databases remains largely unexplored. In this work, we address the notion that LLMs cannot yield satisfactory results on relational databases due to their interconn... | {
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} |
2411.11833 | Differentiable GPU-Parallelized Task and Motion Planning | [
"cs.RO"
] | We present a differentiable optimization-based framework for Task and Motion Planning (TAMP) that is massively parallelizable on GPUs, enabling thousands of sampled seeds to be optimized simultaneously. Existing sampling-based approaches inherently disconnect the parameters by generating samples for each independently ... | {
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} |
2411.11838 | Pairwise Markov Chains for Volatility Forecasting | [
"stat.ML",
"cs.LG"
] | The Pairwise Markov Chain (PMC) is a probabilistic graphical model extending the well-known Hidden Markov Model. This model, although highly effective for many tasks, has been scarcely utilized for continuous value prediction. This is mainly due to the issue of modeling observations inherent in generative probabilistic... | {
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} |
2411.11839 | RoboGSim: A Real2Sim2Real Robotic Gaussian Splatting Simulator | [
"cs.RO",
"cs.CV"
] | Efficient acquisition of real-world embodied data has been increasingly critical. However, large-scale demonstrations captured by remote operation tend to take extremely high costs and fail to scale up the data size in an efficient manner. Sampling the episodes under a simulated environment is a promising way for large... | {
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} |
2411.11843 | Bi-Mamba: Towards Accurate 1-Bit State Space Models | [
"cs.CL",
"cs.AI"
] | The typical selective state-space model (SSM) of Mamba addresses several limitations of Transformers, such as quadratic computational complexity with sequence length and significant inference-time memory requirements due to the key-value cache. However, the growing size of Mamba models continues to pose training and de... | {
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} |
2411.11844 | Generative World Explorer | [
"cs.CV",
"cs.RO"
] | Planning with partial observation is a central challenge in embodied AI. A majority of prior works have tackled this challenge by developing agents that physically explore their environment to update their beliefs about the world state. In contrast, humans can $\textit{imagine}$ unseen parts of the world through a ment... | {
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} |
2411.11845 | UniHands: Unifying Various Wild-Collected Keypoints for Personalized
Hand Reconstruction | [
"cs.CV",
"cs.HC"
] | Accurate hand motion capture and standardized 3D representation are essential for various hand-related tasks. Collecting keypoints-only data, while efficient and cost-effective, results in low-fidelity representations and lacks surface information. Furthermore, data inconsistencies across sources challenge their integr... | {
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} |
2411.11848 | Robust Graph Neural Networks for Stability Analysis in Dynamic Networks | [
"q-fin.ST",
"cs.LG"
] | In the current context of accelerated globalization and digitalization, the complexity and uncertainty of financial markets are increasing, and the identification and prevention of economic risks have become a key link in maintaining the stability of the financial system. Traditional risk identification methods often h... | {
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} |
2411.11852 | LUTMUL: Exceed Conventional FPGA Roofline Limit by LUT-based Efficient
Multiplication for Neural Network Inference | [
"cs.AR",
"cs.AI",
"cs.LG"
] | For FPGA-based neural network accelerators, digital signal processing (DSP) blocks have traditionally been the cornerstone for handling multiplications. This paper introduces LUTMUL, which harnesses the potential of look-up tables (LUTs) for performing multiplications. The availability of LUTs typically outnumbers that... | {
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} |
2411.11853 | Chat Bankman-Fried: an Exploration of LLM Alignment in Finance | [
"cs.CY",
"cs.AI",
"cs.CL",
"q-fin.GN"
] | Advancements in large language models (LLMs) have renewed concerns about AI alignment - the consistency between human and AI goals and values. As various jurisdictions enact legislation on AI safety, the concept of alignment must be defined and measured across different domains. This paper proposes an experimental fram... | {
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} |
2411.11856 | Can EDA Tool Feedback Improve Verilog Generation by LLMs? | [
"cs.AR",
"cs.AI",
"cs.PL"
] | Traditionally, digital hardware designs are written in the Verilog hardware description language (HDL) and debugged manually by engineers. This can be time-consuming and error-prone for complex designs. Large Language Models (LLMs) are emerging as a potential tool to help generate fully functioning HDL code, but most w... | {
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} |
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