id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.11361 | Block Flow: Learning Straight Flow on Data Blocks | [
"cs.LG",
"cs.CV"
] | Flow-matching models provide a powerful framework for various applications, offering efficient sampling and flexible probability path modeling. These models are characterized by flows with low curvature in learned generative trajectories, which results in reduced truncation error at each sampling step. To further reduc... | {
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2501.11371 | Reed-Solomon Codes Against Insertions and Deletions: Full-Length and
Rate-$1/2$ Codes | [
"cs.IT",
"math.IT"
] | The performance of Reed-Solomon codes (RS codes, for short) in the presence of insertion and deletion errors has been studied recently in several papers. In this work, we further study this intriguing mathematical problem, focusing on two regimes. First, we study the question of how well full-length RS codes perform ag... | {
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2501.11374 | Linear ADRC is equivalent to PID with set-point weighting and
measurement filter | [
"eess.SY",
"cs.SY"
] | We show that linear Active Disturbance-Rejection Control (ADRC) tuned using the "bandwidth method" is equivalent to PI(D) control with set-point weighting and a lowpass filter on the measurement signal. We also provide simple expressions that make it possible to implement linear ADRC for first and second-order systems ... | {
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2501.11378 | Investigation of Whisper ASR Hallucinations Induced by Non-Speech Audio | [
"cs.SD",
"cs.AI",
"eess.AS"
] | Hallucinations of deep neural models are amongst key challenges in automatic speech recognition (ASR). In this paper, we investigate hallucinations of the Whisper ASR model induced by non-speech audio segments present during inference. By inducting hallucinations with various types of sounds, we show that there exists ... | {
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2501.11384 | Transductive Conformal Inference for Ranking | [
"cs.LG",
"stat.ME",
"stat.ML"
] | We introduce a method based on Conformal Prediction (CP) to quantify the uncertainty of full ranking algorithms. We focus on a specific scenario where $n + m$ items are to be ranked by some ''black box'' algorithm. It is assumed that the relative (ground truth) ranking of n of them is known. The objective is then to qu... | {
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2501.11388 | UniTrans: A Unified Vertical Federated Knowledge Transfer Framework for
Enhancing Cross-Hospital Collaboration | [
"cs.LG",
"cs.DC"
] | Cross-hospital collaboration has the potential to address disparities in medical resources across different regions. However, strict privacy regulations prohibit the direct sharing of sensitive patient information between hospitals. Vertical federated learning (VFL) offers a novel privacy-preserving machine learning pa... | {
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2501.11391 | Revisiting Language Models in Neural News Recommender Systems | [
"cs.IR"
] | Neural news recommender systems (RSs) have integrated language models (LMs) to encode news articles with rich textual information into representations, thereby improving the recommendation process. Most studies suggest that (i) news RSs achieve better performance with larger pre-trained language models (PLMs) than shal... | {
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2501.11393 | Trace Reconstruction of First-Order Reed-Muller Codewords Using Run
Statistics | [
"cs.IT",
"math.IT",
"math.PR"
] | In this paper, we derive an expression for the expected number of runs in a trace of a binary sequence $x \in \{0,1\}^n$ obtained by passing $x$ through a deletion channel that independently deletes each bit with probability $q$. We use this expression to show that if $x$ is a codeword of a first-order Reed-Muller code... | {
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2501.11395 | To BEE or not to BEE: Estimating more than Entropy with Biased Entropy
Estimators | [
"cs.IT",
"cs.SE",
"math.IT"
] | Entropy estimation plays a significant role in biology, economics, physics, communication engineering and other disciplines. It is increasingly used in software engineering, e.g. in software confidentiality, software testing, predictive analysis, machine learning, and software improvement. However accurate estimation i... | {
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2501.11403 | Verifying Cross-modal Entity Consistency in News using Vision-language
Models | [
"cs.CL",
"cs.IR",
"cs.MM"
] | The web has become a crucial source of information, but it is also used to spread disinformation, often conveyed through multiple modalities like images and text. The identification of inconsistent cross-modal information, in particular entities such as persons, locations, and events, is critical to detect disinformati... | {
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2501.11406 | Efficient Reduction of Interconnected Subsystem Models using Abstracted
Environments | [
"eess.SY",
"cs.SY"
] | We present two frameworks for structure-preserving model order reduction of interconnected subsystems, improving tractability of the reduction methods while ensuring stability and accuracy bounds of the reduced interconnected model. Instead of reducing each subsystem independently, we take a low-order abstraction of it... | {
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2501.11407 | A Truly Sparse and General Implementation of Gradient-Based Synaptic
Plasticity | [
"cs.NE",
"cs.AI",
"cs.LG"
] | Online synaptic plasticity rules derived from gradient descent achieve high accuracy on a wide range of practical tasks. However, their software implementation often requires tediously hand-derived gradients or using gradient backpropagation which sacrifices the online capability of the rules. In this work, we present ... | {
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2501.11409 | Unsupervised Learning in Echo State Networks for Input Reconstruction | [
"cs.LG",
"cs.AI",
"eess.SP",
"nlin.CD",
"q-bio.NC"
] | Conventional echo state networks (ESNs) require supervised learning to train the readout layer, using the desired outputs as training data. In this study, we focus on input reconstruction (IR), which refers to training the readout layer to reproduce the input time series in its output. We reformulate the learning algor... | {
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2501.11411 | Beyond the Hype: Benchmarking LLM-Evolved Heuristics for Bin Packing | [
"cs.NE"
] | Coupling Large Language Models (LLMs) with Evolutionary Algorithms has recently shown significant promise as a technique to design new heuristics that outperform existing methods, particularly in the field of combinatorial optimisation. An escalating arms race is both rapidly producing new heuristics and improving the ... | {
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2501.11413 | Generalization and Informativeness of Weighted Conformal Risk Control
Under Covariate Shift | [
"cs.LG",
"cs.AI",
"cs.IT",
"math.IT"
] | Predictive models are often required to produce reliable predictions under statistical conditions that are not matched to the training data. A common type of training-testing mismatch is covariate shift, where the conditional distribution of the target variable given the input features remains fixed, while the marginal... | {
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2501.11414 | Algorithm Selection with Probing Trajectories: Benchmarking the Choice
of Classifier Model | [
"cs.LG",
"cs.NE"
] | Recent approaches to training algorithm selectors in the black-box optimisation domain have advocated for the use of training data that is algorithm-centric in order to encapsulate information about how an algorithm performs on an instance, rather than relying on information derived from features of the instance itself... | {
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2501.11416 | Mapping network structures and dynamics of decentralised
cryptocurrencies: The evolution of Bitcoin (2009-2023) | [
"cs.CE"
] | Cryptocurrencies have recently been in the spotlight of public debate due to their embrace by the new US President, with crypto fans expecting a 'bull run'. The global cryptocurrency market capitalisation is more than \$3.50 trillion, with 1 Bitcoin exchanging for more than \$97,000 at the end of November 2024. Monitor... | {
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2501.11417 | Neural Contextual Reinforcement Framework for Logical Structure Language
Generation | [
"cs.CL",
"cs.AI"
] | The Neural Contextual Reinforcement Framework introduces an innovative approach to enhancing the logical coherence and structural consistency of text generated by large language models. Leveraging reinforcement learning principles, the framework integrates custom reward functions and dynamic context alignment mechanism... | {
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2501.11419 | An Analysis of the Correctness and Computational Complexity of Path
Planning in Payment Channel Networks | [
"cs.DM",
"cs.CE"
] | Payment Channel Networks (PCNs) are a method for improving the scaling and latency of cryptocurrency transactions. For a payment to be made between two peers in a PCN, a feasible low-fee path in the network must be planned. Many PCN path planning algorithms use a search algorithm that is a variant of Dijkstra's algorit... | {
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2501.11421 | Online Clustering with Bandit Information | [
"cs.LG",
"cs.IT",
"math.IT",
"math.ST",
"stat.TH"
] | We study the problem of online clustering within the multi-armed bandit framework under the fixed confidence setting. In this multi-armed bandit problem, we have $M$ arms, each providing i.i.d. samples that follow a multivariate Gaussian distribution with an {\em unknown} mean and a known unit covariance. The arms are ... | {
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2501.11422 | Multi-View Spectral Clustering for Graphs with Multiple View Structures | [
"cs.LG",
"cs.AI"
] | Despite the fundamental importance of clustering, to this day, much of the relevant research is still based on ambiguous foundations, leading to an unclear understanding of whether or how the various clustering methods are connected with each other. In this work, we provide an additional stepping stone towards resolvin... | {
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2501.11425 | Agent-R: Training Language Model Agents to Reflect via Iterative
Self-Training | [
"cs.AI"
] | Large Language Models (LLMs) agents are increasingly pivotal for addressing complex tasks in interactive environments. Existing work mainly focuses on enhancing performance through behavior cloning from stronger experts, yet such approaches often falter in real-world applications, mainly due to the inability to recover... | {
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2501.11428 | Enhancing Coronary Artery Calcium Scoring via Multi-Organ Segmentation
on Non-Contrast Cardiac Computed Tomography | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Despite coronary artery calcium scoring being considered a largely solved problem within the realm of medical artificial intelligence, this paper argues that significant improvements can still be made. By shifting the focus from pathology detection to a deeper understanding of anatomy, the novel algorithm proposed in t... | {
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2501.11429 | The Explanation Game -- Rekindled (Extended Version) | [
"cs.AI"
] | Recent work demonstrated the existence of critical flaws in the current use of Shapley values in explainable AI (XAI), i.e. the so-called SHAP scores. These flaws are significant in that the scores provided to a human decision-maker can be misleading. Although these negative results might appear to indicate that Shaple... | {
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2501.11430 | A Survey on Diffusion Models for Anomaly Detection | [
"cs.LG",
"cs.AI"
] | Diffusion models (DMs) have emerged as a powerful class of generative AI models, showing remarkable potential in anomaly detection (AD) tasks across various domains, such as cybersecurity, fraud detection, healthcare, and manufacturing. The intersection of these two fields, termed diffusion models for anomaly detection... | {
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2501.11434 | An Incremental Sampling and Segmentation-Based Approach for Motion
Planning Infeasibility | [
"cs.RO"
] | We present a simple and easy-to-implement algorithm to detect plan infeasibility in kinematic motion planning. Our method involves approximating the robot's configuration space to a discrete space, where each degree of freedom has a finite set of values. The obstacle region separates the free configuration space into d... | {
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2501.11440 | RACCOON: A Retrieval-Augmented Generation Approach for Location
Coordinate Capture from News Articles | [
"cs.CL"
] | Geocoding involves automatic extraction of location coordinates of incidents reported in news articles, and can be used for epidemic intelligence or disaster management. This paper introduces Retrieval-Augmented Coordinate Capture Of Online News articles (RACCOON), an open-source geocoding approach that extracts geoloc... | {
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2501.11441 | Ontology Matching with Large Language Models and Prioritized Depth-First
Search | [
"cs.IR",
"cs.CL"
] | Ontology matching (OM) plays a key role in enabling data interoperability and knowledge sharing, but it remains challenging due to the need for large training datasets and limited vocabulary processing in machine learning approaches. Recently, methods based on Large Language Model (LLMs) have shown great promise in OM,... | {
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2501.11447 | Decomposing Interventional Causality into Synergistic, Redundant, and
Unique Components | [
"cs.AI",
"cs.IT",
"math.IT",
"physics.data-an"
] | We introduce a novel framework for decomposing interventional causal effects into synergistic, redundant, and unique components, building on the intuition of Partial Information Decomposition (PID) and the principle of M\"obius inversion. While recent work has explored a similar decomposition of an observational measur... | {
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2501.11453 | Integrate-and-Fire from a Mathematical and Signal Processing Perspective | [
"eess.SP",
"cs.NE"
] | Integrate-and-Fire (IF) is an idealized model of the spike-triggering mechanism of a biological neuron. It is used to realize the bio-inspired event-based principle of information processing in neuromorphic computing. We show that IF is closely related to the concept of Send-on-Delta (SOD) as used in threshold-based sa... | {
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2501.11454 | Improving thermal state preparation of Sachdev-Ye-Kitaev model with
reinforcement learning on quantum hardware | [
"quant-ph",
"cs.AI",
"cs.LG",
"hep-lat",
"hep-th"
] | The Sachdev-Ye-Kitaev (SYK) model, known for its strong quantum correlations and chaotic behavior, serves as a key platform for quantum gravity studies. However, variationally preparing thermal states on near-term quantum processors for large systems (N>12, where N is the number of Majorana fermions) presents a signifi... | {
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2501.11459 | Multi-Stage Active Sequential Hypothesis Testing with Clustered
Hypotheses | [
"cs.IT",
"math.IT"
] | We consider the problem where an active Decision-Maker (DM) is tasked to identify the true hypothesis using as few as possible observations while maintaining accuracy. The DM collects observations according to its determined actions and knows the distributions under each hypothesis. We propose a deterministic and adapt... | {
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2501.11462 | On the Adversarial Vulnerabilities of Transfer Learning in Remote
Sensing | [
"cs.CV",
"eess.IV"
] | The use of pretrained models from general computer vision tasks is widespread in remote sensing, significantly reducing training costs and improving performance. However, this practice also introduces vulnerabilities to downstream tasks, where publicly available pretrained models can be used as a proxy to compromise do... | {
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2501.11463 | Curiosity-Driven Reinforcement Learning from Human Feedback | [
"cs.CL"
] | Reinforcement learning from human feedback (RLHF) has proven effective in aligning large language models (LLMs) with human preferences, but often at the cost of reduced output diversity. This trade-off between diversity and alignment quality remains a significant challenge. Drawing inspiration from curiosity-driven exp... | {
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2501.11467 | Fixed Point Certificates for Reachability and Expected Rewards in MDPs | [
"cs.LO",
"cs.DM",
"cs.SY",
"eess.SY"
] | The possibility of errors in human-engineered formal verification software, such as model checkers, poses a serious threat to the purpose of these tools. An established approach to mitigate this problem are certificates -- lightweight, easy-to-check proofs of the verification results. In this paper, we develop novel ce... | {
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2501.11469 | MASS: Overcoming Language Bias in Image-Text Matching | [
"cs.CV",
"cs.LG"
] | Pretrained visual-language models have made significant advancements in multimodal tasks, including image-text retrieval. However, a major challenge in image-text matching lies in language bias, where models predominantly rely on language priors and neglect to adequately consider the visual content. We thus present Mul... | {
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2501.11473 | Strong Data Processing Properties of R\'enyi-divergences via
Pinsker-type Inequalities | [
"cs.IT",
"math.IT"
] | We investigate strong data processing inequalities (SDPIs) of the R\'enyi-divergence between two discrete distributions when both distributions are passed through a fixed channel. We provide a condition on the channel for which the DPI holds with equality given two arbitrary distributions in the probability simplex. Mo... | {
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2501.11477 | QGAIC: Quantum Inspired Genetic Algorithm for Image Classification | [
"cs.NE"
] | This study uses two meta-heuristics methodologies to introduce two novel quantum-inspired meta heuristic approaches: quantum-inspired genetic algorithm (QIGA1) and quantum-inspired genetic algorithm with dynamic approach (QIGA2). The two suggested methods combine a classical and quantum genetic algorithm approach. Both... | {
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2501.11478 | Each Graph is a New Language: Graph Learning with LLMs | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Recent efforts leverage Large Language Models (LLMs) for modeling text-attributed graph structures in node classification tasks. These approaches describe graph structures for LLMs to understand or aggregate LLM-generated textual attribute embeddings through graph structure. However, these approaches face two main limi... | {
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2501.11485 | SimLabel: Consistency-Guided OOD Detection with Pretrained
Vision-Language Models | [
"cs.CV"
] | Detecting out-of-distribution (OOD) data is crucial in real-world machine learning applications, particularly in safety-critical domains. Existing methods often leverage language information from vision-language models (VLMs) to enhance OOD detection by improving confidence estimation through rich class-wise text infor... | {
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2501.11487 | Detecting Convolutional Codes: A Markovian Approach with LRT and DNN | [
"cs.IT",
"math.IT"
] | Identifying the unknown convolutional code corresponding to the given intercepted data is an important problem in military surveillance and in wireless communication. While a variety of code identification algorithms are available in the literature, the key contribution of our work lies in the novel solution and the co... | {
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2501.11493 | Communication-Efficient Federated Learning Based on Explanation-Guided
Pruning for Remote Sensing Image Classification | [
"cs.CV",
"cs.AI"
] | Federated learning (FL) is a decentralized machine learning paradigm, where multiple clients collaboratively train a global model by exchanging only model updates with the central server without sharing the local data of clients. Due to the large volume of model updates required to be transmitted between clients and th... | {
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2501.11495 | Discrete-Time Passivity-Based Control using Hermite-Obreschkoff Methods | [
"eess.SY",
"cs.SY"
] | The motivation for this paper is the implementation of nonlinear state feedback control, designed based on the continuous-time plant model, in a sampled control loop under relatively slow sampling. In previous work we have shown that using one-step predictions of the target dynamics with higher order integration scheme... | {
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2501.11496 | Generative AI and Large Language Models in Language Preservation:
Opportunities and Challenges | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Generative AI and large-scale language models (LLM) have emerged as powerful tools in language preservation, particularly for near-native and endangered languages. With the increasing reliance on technology for communication, education, and cultural documentation, new opportunities have emerged to mitigate the dramatic... | {
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2501.11498 | Dialect2SQL: A Novel Text-to-SQL Dataset for Arabic Dialects with a
Focus on Moroccan Darija | [
"cs.SE",
"cs.AI",
"cs.CL",
"cs.DB"
] | The task of converting natural language questions (NLQs) into executable SQL queries, known as text-to-SQL, has gained significant interest in recent years, as it enables non-technical users to interact with relational databases. Many benchmarks, such as SPIDER and WikiSQL, have contributed to the development of new mo... | {
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2501.11499 | KEIR @ ECIR 2025: The Second Workshop on Knowledge-Enhanced Information
Retrieval | [
"cs.IR"
] | Pretrained language models (PLMs) like BERT and GPT-4 have become the foundation for modern information retrieval (IR) systems. However, existing PLM-based IR models primarily rely on the knowledge learned during training for prediction, limiting their ability to access and incorporate external, up-to-date, or domain-s... | {
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2501.11502 | Hierarchical Coded Caching in High Memory Regime with Coded Placement | [
"cs.IT",
"math.IT"
] | We consider a two-layer hierarchical coded caching network where a server with a library of $N$ files is connected to $K_1$ mirrors, each having a cache memory of size $M_1$. Each mirror is further connected to $K_2$ users, each equipped with a dedicated cache of size $M_2$. In this paper, we propose two distinct coded... | {
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2501.11505 | Sun-Jafar-Type Schemes for Weak Private Information Retrieval | [
"cs.IT",
"math.IT"
] | In information-theoretic private information retrieval (PIR), a client wants to retrieve one desired file out of $M$ files, stored across $N$ servers, while keeping the index of the desired file private from each $T$-sized subset of servers. A PIR protocol must ideally maximize the rate, which is the ratio of the file ... | {
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2501.11508 | See In Detail: Enhancing Sparse-view 3D Gaussian Splatting with Local
Depth and Semantic Regularization | [
"cs.CV"
] | 3D Gaussian Splatting (3DGS) has shown remarkable performance in novel view synthesis. However, its rendering quality deteriorates with sparse inphut views, leading to distorted content and reduced details. This limitation hinders its practical application. To address this issue, we propose a sparse-view 3DGS method. G... | {
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2501.11511 | Subjective and Objective Quality Assessment of Non-Uniformly Distorted
Omnidirectional Images | [
"eess.IV",
"cs.CV"
] | Omnidirectional image quality assessment (OIQA) has been one of the hot topics in IQA with the continuous development of VR techniques, and achieved much success in the past few years. However, most studies devote themselves to the uniform distortion issue, i.e., all regions of an omnidirectional image are perturbed by... | {
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2501.11512 | Multitask Auxiliary Network for Perceptual Quality Assessment of
Non-Uniformly Distorted Omnidirectional Images | [
"eess.IV",
"cs.CV"
] | Omnidirectional image quality assessment (OIQA) has been widely investigated in the past few years and achieved much success. However, most of existing studies are dedicated to solve the uniform distortion problem in OIQA, which has a natural gap with the non-uniform distortion problem, and their ability in capturing n... | {
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2501.11513 | Transferability of labels between multilens cameras | [
"cs.CV"
] | In this work, a new method for automatically extending Bounding Box (BB) and mask labels across different channels on multilens cameras is presented. For that purpose, the proposed method combines the well known phase correlation method with a refinement process. During the first step, images are aligned by localizing ... | {
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2501.11515 | UltraFusion: Ultra High Dynamic Imaging using Exposure Fusion | [
"cs.CV"
] | Capturing high dynamic range (HDR) scenes is one of the most important issues in camera design. Majority of cameras use exposure fusion technique, which fuses images captured by different exposure levels, to increase dynamic range. However, this approach can only handle images with limited exposure difference, normally... | {
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2501.11520 | Fundus Image Quality Assessment and Enhancement: a Systematic Review | [
"eess.IV",
"cs.CV"
] | As an affordable and convenient eye scan, fundus photography holds the potential for preventing vision impairment, especially in resource-limited regions. However, fundus image degradation is common under intricate imaging environments, impacting following diagnosis and treatment. Consequently, image quality assessment... | {
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2501.11522 | Optimal Trajectory Control of Geometrically Exact Strings with
Space-Time Finite Elements | [
"eess.SY",
"cs.SY"
] | In this contribution, we present a variational space-time formulation which generates an optimal feed-forward controller for geometrically exact strings. More concretely, the optimization problem is solved with an indirect approach, and the space-time finite element method translates the problem to a set of algebraic e... | {
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2501.11525 | Technical Report for the Forgotten-by-Design Project: Targeted
Obfuscation for Machine Learning | [
"cs.LG",
"cs.AI",
"cs.CR"
] | The right to privacy, enshrined in various human rights declarations, faces new challenges in the age of artificial intelligence (AI). This paper explores the concept of the Right to be Forgotten (RTBF) within AI systems, contrasting it with traditional data erasure methods. We introduce Forgotten by Design, a proactiv... | {
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2501.11526 | Meta-Instance Selection. Instance Selection as a Classification Problem
with Meta-Features | [
"cs.LG",
"cs.AI"
] | Data pruning, or instance selection, is an important problem in machine learning especially in terms of nearest neighbour classifier. However, in data pruning which speeds up the prediction phase, there is an issue related to the speed and efficiency of the process itself. In response, the study proposes an approach in... | {
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2501.11532 | Early Stopping Bayesian Optimization for Controller Tuning | [
"eess.SY",
"cs.SY"
] | Manual tuning of performance-critical controller parameters can be tedious and sub-optimal. Bayesian Optimization (BO) is an increasingly popular practical alternative to automatically optimize controller parameters from few experiments. Standard BO practice is to evaluate the closed-loop performance of parameters prop... | {
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2501.11533 | The impact of intrinsic rewards on exploration in Reinforcement Learning | [
"cs.AI",
"cs.LG"
] | One of the open challenges in Reinforcement Learning is the hard exploration problem in sparse reward environments. Various types of intrinsic rewards have been proposed to address this challenge by pushing towards diversity. This diversity might be imposed at different levels, favouring the agent to explore different ... | {
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2501.11535 | A baseline for machine-learning-based hepatocellular carcinoma diagnosis
using multi-modal clinical data | [
"cs.CV"
] | The objective of this paper is to provide a baseline for performing multi-modal data classification on a novel open multimodal dataset of hepatocellular carcinoma (HCC), which includes both image data (contrast-enhanced CT and MRI images) and tabular data (the clinical laboratory test data as well as case report forms)... | {
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2501.11538 | DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals | [
"cs.LG"
] | We propose Denoising Masked Autoencoder (Deno-MAE), a novel multimodal autoencoder framework for denoising modulation signals during pretraining. DenoMAE extends the concept of masked autoencoders by incorporating multiple input modalities, including noise as an explicit modality, to enhance cross-modal learning and im... | {
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2501.11540 | A Hands-free Spatial Selection and Interaction Technique using Gaze and
Blink Input with Blink Prediction for Extended Reality | [
"cs.HC",
"cs.LG"
] | Gaze-based interaction techniques have created significant interest in the field of spatial interaction. Many of these methods require additional input modalities, such as hand gestures (e.g., gaze coupled with pinch). Those can be uncomfortable and difficult to perform in public or limited spaces, and pose challenges ... | {
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2501.11542 | DLinear-based Prediction of Remaining Useful Life of Lithium-Ion
Batteries: Feature Engineering through Explainable Artificial Intelligence | [
"eess.SY",
"cs.LG",
"cs.SY"
] | Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is essential for ensuring safety, reducing maintenance costs, and optimizing usage. However, predicting RUL is challenging due to the nonlinear characteristics of the degradation caused by complex chemical reactions. Machine learning allows... | {
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2501.11549 | Whose Boat Does it Float? Improving Personalization in Preference Tuning
via Inferred User Personas | [
"cs.CL"
] | LLMs are tuned to follow instructions (aligned) by learning which of two outputs users prefer for a prompt. However, this preference data format does not convey why users prefer responses that are chosen or rejected, so LLMs trained on these datasets cannot tailor responses to varied user needs. To surface these parame... | {
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2501.11551 | PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented Generation | [
"cs.CL"
] | Despite notable advancements in Retrieval-Augmented Generation (RAG) systems that expand large language model (LLM) capabilities through external retrieval, these systems often struggle to meet the complex and diverse needs of real-world industrial applications. The reliance on retrieval alone proves insufficient for e... | {
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2501.11553 | Clinically Ready Magnetic Microrobots for Targeted Therapies | [
"cs.RO",
"cond-mat.mtrl-sci",
"cs.SY",
"eess.SY",
"physics.app-ph",
"physics.bio-ph",
"physics.med-ph"
] | Systemic drug administration often causes off-target effects limiting the efficacy of advanced therapies. Targeted drug delivery approaches increase local drug concentrations at the diseased site while minimizing systemic drug exposure. We present a magnetically guided microrobotic drug delivery system capable of preci... | {
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2501.11554 | Event-based vision for egomotion estimation using precise event timing | [
"cs.CV",
"cs.AR",
"cs.RO"
] | Egomotion estimation is crucial for applications such as autonomous navigation and robotics, where accurate and real-time motion tracking is required. However, traditional methods relying on inertial sensors are highly sensitive to external conditions, and suffer from drifts leading to large inaccuracies over long dist... | {
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2501.11555 | Beyond R-barycenters: an effective averaging method on Stiefel and
Grassmann manifolds | [
"stat.ML",
"cs.LG"
] | In this paper, the issue of averaging data on a manifold is addressed. While the Fr\'echet mean resulting from Riemannian geometry appears ideal, it is unfortunately not always available and often computationally very expensive. To overcome this, R-barycenters have been proposed and successfully applied to Stiefel and ... | {
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2501.11557 | Secure Resource Allocation via Constrained Deep Reinforcement Learning | [
"cs.LG"
] | The proliferation of Internet of Things (IoT) devices and the advent of 6G technologies have introduced computationally intensive tasks that often surpass the processing capabilities of user devices. Efficient and secure resource allocation in serverless multi-cloud edge computing environments is essential for supporti... | {
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2501.11560 | Explainable Lane Change Prediction for Near-Crash Scenarios Using
Knowledge Graph Embeddings and Retrieval Augmented Generation | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.IR"
] | Lane-changing maneuvers, particularly those executed abruptly or in risky situations, are a significant cause of road traffic accidents. However, current research mainly focuses on predicting safe lane changes. Furthermore, existing accident datasets are often based on images only and lack comprehensive sensory data. I... | {
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2501.11561 | Teaching Large Language Models to Regress Accurate Image Quality Scores
using Score Distribution | [
"cs.CV"
] | With the rapid advancement of Multi-modal Large Language Models (MLLMs), MLLM-based Image Quality Assessment (IQA) methods have shown promising performance in linguistic quality description. However, current methods still fall short in accurately scoring image quality. In this work, we aim to leverage MLLMs to regress ... | {
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2501.11568 | Graph Defense Diffusion Model | [
"cs.LG"
] | Graph Neural Networks (GNNs) demonstrate significant potential in various applications but remain highly vulnerable to adversarial attacks, which can greatly degrade their performance. Existing graph purification methods attempt to address this issue by filtering attacked graphs; however, they struggle to effectively d... | {
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2501.11570 | Uncertainty Estimation in the Real World: A Study on Music Emotion
Recognition | [
"cs.SD",
"cs.IR",
"cs.LG",
"eess.AS"
] | Any data annotation for subjective tasks shows potential variations between individuals. This is particularly true for annotations of emotional responses to musical stimuli. While older approaches to music emotion recognition systems frequently addressed this uncertainty problem through probabilistic modeling, modern s... | {
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2501.11577 | Rethinking Membership Inference Attacks Against Transfer Learning | [
"cs.CR",
"cs.LG"
] | Transfer learning, successful in knowledge translation across related tasks, faces a substantial privacy threat from membership inference attacks (MIAs). These attacks, despite posing significant risk to ML model's training data, remain limited-explored in transfer learning. The interaction between teacher and student ... | {
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2501.11584 | GCSAM: Gradient Centralized Sharpness Aware Minimization | [
"cs.LG"
] | The generalization performance of deep neural networks (DNNs) is a critical factor in achieving robust model behavior on unseen data. Recent studies have highlighted the importance of sharpness-based measures in promoting generalization by encouraging convergence to flatter minima. Among these approaches, Sharpness-Awa... | {
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2501.11586 | Compressibility Analysis for the differentiable shift-variant Filtered
Backprojection Model | [
"cs.CV",
"eess.IV"
] | The differentiable shift-variant filtered backprojection (FBP) model enables the reconstruction of cone-beam computed tomography (CBCT) data for any non-circular trajectories. This method employs deep learning technique to estimate the redundancy weights required for reconstruction, given knowledge of the specific traj... | {
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2501.11587 | Recurrent Diffusion for Large-Scale Parameter Generation | [
"cs.LG",
"cs.AI"
] | Parameter generation has long struggled to match the scale of today large vision and language models, curbing its broader utility. In this paper, we introduce Recurrent Diffusion for Large Scale Parameter Generation (RPG), a novel framework that generates full neural network parameters up to hundreds of millions on a s... | {
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2501.11592 | Training-free Ultra Small Model for Universal Sparse Reconstruction in
Compressed Sensing | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Pre-trained large models attract widespread attention in recent years, but they face challenges in applications that require high interpretability or have limited resources, such as physical sensing, medical imaging, and bioinformatics. Compressed Sensing (CS) is a well-proved theory that drives many recent breakthroug... | {
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2501.11593 | Optimal User and Target Scheduling, User-Target Pairing, and
Low-Resolution Phase-Only Beamforming for ISAC Systems | [
"eess.SP",
"cs.IT",
"cs.NI",
"math.IT"
] | We investigate the joint user and target scheduling, user-target pairing, and low-resolution phase-only beamforming design for integrated sensing and communications (ISAC). Scheduling determines which users and targets are served, while pairing specifies which users and targets are grouped into pairs. Additionally, the... | {
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2501.11597 | Fairness Testing through Extreme Value Theory | [
"cs.SE",
"cs.AI",
"cs.CY",
"cs.LG"
] | Data-driven software is increasingly being used as a critical component of automated decision-support systems. Since this class of software learns its logic from historical data, it can encode or amplify discriminatory practices. Previous research on algorithmic fairness has focused on improving average-case fairness. ... | {
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2501.11599 | SR-FoT: A Syllogistic-Reasoning Framework of Thought for Large Language
Models Tackling Knowledge-based Reasoning Tasks | [
"cs.AI",
"cs.CL"
] | Deductive reasoning is a crucial logical capability that assists us in solving complex problems based on existing knowledge. Although augmented by Chain-of-Thought prompts, Large Language Models (LLMs) might not follow the correct reasoning paths. Enhancing the deductive reasoning abilities of LLMs, and leveraging thei... | {
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} |
2501.11605 | Bootstrapping Social Networks: Lessons from Bluesky Starter Packs | [
"cs.SI",
"cs.NI"
] | Microblogging is a crucial mode of online communication. However, launching a new microblogging platform remains challenging, largely due to network effects. This has resulted in entrenched (and undesirable) dominance by established players, such as X/Twitter. To overcome these network effects, Bluesky, an emerging mic... | {
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2501.11613 | Conversation Routines: A Prompt Engineering Framework for Task-Oriented
Dialog Systems | [
"cs.CL",
"cs.AI",
"cs.ET",
"cs.HC",
"cs.PL"
] | This study introduces Conversation Routines (CR), a structured prompt engineering framework for developing task-oriented dialog systems using Large Language Models (LLMs). While LLMs demonstrate remarkable natural language understanding capabilities, engineering them to reliably execute complex business workflows remai... | {
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2501.11621 | Trojan Detection Through Pattern Recognition for Large Language Models | [
"cs.CL",
"cs.LG"
] | Trojan backdoors can be injected into large language models at various stages, including pretraining, fine-tuning, and in-context learning, posing a significant threat to the model's alignment. Due to the nature of causal language modeling, detecting these triggers is challenging given the vast search space. In this st... | {
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2501.11622 | Causal Learning for Heterogeneous Subgroups Based on Nonlinear Causal
Kernel Clustering | [
"cs.LG",
"stat.ML"
] | Due to the challenge posed by multi-source and heterogeneous data collected from diverse environments, causal relationships among features can exhibit variations influenced by different time spans, regions, or strategies. This diversity makes a single causal model inadequate for accurately representing complex causal r... | {
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2501.11623 | Early evidence of how LLMs outperform traditional systems on OCR/HTR
tasks for historical records | [
"cs.CV",
"cs.AI",
"cs.LG"
] | We explore the ability of two LLMs -- GPT-4o and Claude Sonnet 3.5 -- to transcribe historical handwritten documents in a tabular format and compare their performance to traditional OCR/HTR systems: EasyOCR, Keras, Pytesseract, and TrOCR. Considering the tabular form of the data, two types of experiments are executed: ... | {
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2501.11626 | DRL-Based Maximization of the Sum Cross-Layer Achievable Rate for
Networks Under Jamming | [
"eess.SY",
"cs.SY"
] | In quasi-static wireless networks characterized by infrequent changes in the transmission schedules of user equipment (UE), malicious jammers can easily deteriorate network performance. Accordingly, a key challenge in these networks is managing channel access amidst jammers and under dynamic channel conditions. In this... | {
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2501.11628 | Investigating the Scalability of Approximate Sparse Retrieval Algorithms
to Massive Datasets | [
"cs.IR"
] | Learned sparse text embeddings have gained popularity due to their effectiveness in top-k retrieval and inherent interpretability. Their distributional idiosyncrasies, however, have long hindered their use in real-world retrieval systems. That changed with the recent development of approximate algorithms that leverage ... | {
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2501.11631 | Noise-Agnostic Multitask Whisper Training for Reducing False Alarm
Errors in Call-for-Help Detection | [
"cs.SD",
"cs.AI",
"eess.AS"
] | Keyword spotting is often implemented by keyword classifier to the encoder in acoustic models, enabling the classification of predefined or open vocabulary keywords. Although keyword spotting is a crucial task in various applications and can be extended to call-for-help detection in emergencies, however, the previous m... | {
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} |
2501.11632 | Biomedical Knowledge Graph: A Survey of Domains, Tasks, and Real-World
Applications | [
"cs.CL",
"cs.AI",
"cs.CE",
"cs.IR"
] | Biomedical knowledge graphs (BKGs) have emerged as powerful tools for organizing and leveraging the vast and complex data found across the biomedical field. Yet, current reviews of BKGs often limit their scope to specific domains or methods, overlooking the broader landscape and the rapid technological progress reshapi... | {
"Other": 0,
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} |
2501.11633 | PSO-based Sliding Mode Current Control of Grid-Forming Inverter in
Rotating Frame | [
"eess.SY",
"cs.SY"
] | The Grid-Forming Inverter (GFMI) is an emerging topic that is attracting significant attention from both academic and industrial communities, particularly in the area of control design. The Decoupled Average Model-based Sliding Mode Current Controller (DAM-SMC) has been used to address the need such as fast response, f... | {
"Other": 0,
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} |
2501.11636 | Characterization of the Arithmetic Complexity of the Secrecy Capacity of
Fast-Fading Gaussian Channels | [
"cs.IT",
"math.IT"
] | This paper studies the computability of the secrecy capacity of fast-fading wiretap channels from an algorithmic perspective, examining whether it can be computed algorithmically or not. To address this question, the concept of Turing machines is used, which establishes fundamental performance limits of digital compute... | {
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} |
2501.11638 | Class Imbalance in Anomaly Detection: Learning from an Exactly Solvable
Model | [
"cs.LG",
"cond-mat.dis-nn",
"stat.ML"
] | Class imbalance (CI) is a longstanding problem in machine learning, slowing down training and reducing performances. Although empirical remedies exist, it is often unclear which ones work best and when, due to the lack of an overarching theory. We address a common case of imbalance, that of anomaly (or outlier) detecti... | {
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} |
2501.11639 | StAyaL | Multilingual Style Transfer | [
"cs.CL",
"cs.AI"
] | Stylistic text generation plays a vital role in enhancing communication by reflecting the nuances of individual expression. This paper presents a novel approach for generating text in a specific speaker's style across different languages. We show that by leveraging only 100 lines of text, an individuals unique style ca... | {
"Other": 0,
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} |
2501.11641 | A Common Ancestor of PDL, Conjunctive Queries, and Unary Negation
First-order | [
"cs.LO",
"cs.DB"
] | We introduce and study UCPDL+, a family of expressive logics rooted in Propositional Dynamic Logic (PDL) with converse (CPDL) and universal modality (UCPDL). In terms of expressive power, UCPDL+ strictly contains PDL extended with intersection and converse (a.k.a. ICPDL), as well as Conjunctive Queries (CQ), Conjunctiv... | {
"Other": 1,
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"cs.SY": 0
} |
2501.11651 | Advancing Language Model Reasoning through Reinforcement Learning and
Inference Scaling | [
"cs.LG",
"cs.CL"
] | Large language models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks. However, existing approaches mainly rely on imitation learning and struggle to achieve effective test-time scaling. While reinforcement learning (RL) holds promise for enabling self-exploration and learning from feedback,... | {
"Other": 0,
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"cs.CE": 0,
"cs.CL": 1,
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"cs.MA": 0,
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"cs.SD": 0,
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"cs.SY": 0
} |
2501.11653 | Dynamic Scene Understanding from Vision-Language Representations | [
"cs.CV",
"cs.LG"
] | Images depicting complex, dynamic scenes are challenging to parse automatically, requiring both high-level comprehension of the overall situation and fine-grained identification of participating entities and their interactions. Current approaches use distinct methods tailored to sub-tasks such as Situation Recognition ... | {
"Other": 0,
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"cs.SY": 0
} |
2501.11655 | KKL Observer Synthesis for Nonlinear Systems via Physics-Informed
Learning | [
"eess.SY",
"cs.LG",
"cs.SY"
] | This paper proposes a novel learning approach for designing Kazantzis-Kravaris/Luenberger (KKL) observers for autonomous nonlinear systems. The design of a KKL observer involves finding an injective map that transforms the system state into a higher-dimensional observer state, whose dynamics is linear and stable. The o... | {
"Other": 0,
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"cs.MA": 0,
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"cs.RO": 0,
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"cs.SY": 1
} |
2501.11657 | Classification of HI Galaxy Profiles Using Unsupervised Learning and
Convolutional Neural Networks: A Comparative Analysis and Methodological
Cases of Studies | [
"astro-ph.GA",
"cs.LG"
] | Hydrogen, the most abundant element in the universe, is crucial for understanding galaxy formation and evolution. The 21 cm neutral atomic hydrogen - HI spectral line maps the gas kinematics within galaxies, providing key insights into interactions, galactic structure, and star formation processes. With new radio instr... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
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"cs.MA": 0,
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"cs.SD": 0,
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"cs.SY": 0
} |
2501.11671 | Exploring Preference-Guided Diffusion Model for Cross-Domain
Recommendation | [
"cs.IR"
] | Cross-domain recommendation (CDR) has been proven as a promising way to alleviate the cold-start issue, in which the most critical problem is how to draw an informative user representation in the target domain via the transfer of user preference existing in the source domain. Prior efforts mostly follow the embedding-a... | {
"Other": 0,
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"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
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