id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.03891 | Counterfactual Query Rewriting to Use Historical Relevance Feedback | [
"cs.IR"
] | When a retrieval system receives a query it has encountered before, previous relevance feedback, such as clicks or explicit judgments can help to improve retrieval results. However, the content of a previously relevant document may have changed, or the document might not be available anymore. Despite this evolved corpu... | {
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2502.03895 | Rule-Based Modeling of Low-Dimensional Data with PCA and Binary Particle
Swarm Optimization (BPSO) in ANFIS | [
"cs.CV"
] | Fuzzy rule-based systems interpret data in low-dimensional domains, providing transparency and interpretability. In contrast, deep learning excels in complex tasks like image and speech recognition but is prone to overfitting in sparse, unstructured, or low-dimensional data. This interpretability is crucial in fields l... | {
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2502.03897 | UniForm: A Unified Diffusion Transformer for Audio-Video Generation | [
"cs.MM",
"cs.AI",
"cs.CV",
"cs.SD",
"eess.AS"
] | As a natural multimodal content, audible video delivers an immersive sensory experience. Consequently, audio-video generation systems have substantial potential. However, existing diffusion-based studies mainly employ relatively independent modules for generating each modality, which lack exploration of shared-weight g... | {
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2502.03900 | How to introduce an initial crack in phase field simulations to
accurately predict the linear elastic fracture propagation threshold? | [
"cs.CE"
] | Variational phase field fracture models are now widely used to simulate crack propagation in structures. A critical aspect of these simulations is the correct determination of the propagation threshold of pre-existing cracks, as it highly relies on how the initial cracks are implemented. While prior studies briefly dis... | {
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2502.03901 | LeAP: Consistent multi-domain 3D labeling using Foundation Models | [
"cs.CV",
"cs.RO"
] | Availability of datasets is a strong driver for research on 3D semantic understanding, and whilst obtaining unlabeled 3D point cloud data is straightforward, manually annotating this data with semantic labels is time-consuming and costly. Recently, Vision Foundation Models (VFMs) enable open-set semantic segmentation o... | {
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2502.03902 | Geometric Stabilization of Virtual Nonlinear Nonholonomic Constraints | [
"eess.SY",
"cs.SY",
"math.DS",
"math.OC"
] | In this paper, we address the problem of stabilizing a system around a desired manifold determined by virtual nonlinear nonholonomic constraints. Virtual constraints are relationships imposed on a control system that are rendered invariant through feedback control. Virtual nonholonomic constraints represent a specific ... | {
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2502.03904 | A Gaussian-Sinc Pulse Shaping Filter for Zak-OTFS | [
"cs.IT",
"eess.SP",
"math.IT"
] | The choice of delay-Doppler domain (DD) pulse shaping filter plays an important role in determining the performance of Zak-OTFS. Sinc filter has good main lobe characteristics (with nulls at information grid points) which is good for equalization/detection, but has high side lobes which are detrimental for input-output... | {
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2502.03907 | No Free Lunch in Annotation either: An objective evaluation of
foundation models for streamlining annotation in animal tracking | [
"cs.CV"
] | We analyze the capabilities of foundation models addressing the tedious task of generating annotations for animal tracking. Annotating a large amount of data is vital and can be a make-or-break factor for the robustness of a tracking model. Robustness is particularly crucial in animal tracking, as accurate tracking ove... | {
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2502.03909 | Technical Report: Generating the WEB-IDS23 Dataset | [
"cs.NI",
"cs.CR",
"cs.LG"
] | Anomaly-based Network Intrusion Detection Systems (NIDS) require correctly labelled, representative and diverse datasets for an accurate evaluation and development. However, several widely used datasets do not include labels which are fine-grained enough and, together with small sample sizes, can lead to overfitting is... | {
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2502.03914 | A Flexible FBG-Based Contact Force Sensor for Robotic Gripping Systems | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Soft robotic grippers demonstrate great potential for gently and safely handling objects; however, their full potential for executing precise and secure grasping has been limited by the lack of integrated sensors, leading to problems such as slippage and excessive force exertion. To address this challenge, we present a... | {
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2502.03916 | Experiments with Large Language Models on Retrieval-Augmented Generation
for Closed-Source Simulation Software | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) are increasingly helpful in text generation, even writing code in programming languages based on user prompts written in natural language. They are even applied to generate simulation models for multibody systems from natural language. Research results suggest that LLMs surpass the mere rep... | {
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2502.03917 | On the existence of strong functional observer | [
"eess.SY",
"cs.SY"
] | For arbitrary linear time-invariant systems, the existence of a strong functional observer is investigated. Such observer determines, from the available measurement on the plant, an estimate of a function of the state and the input. This estimate converges irrespective to initial state and input. This formulation encom... | {
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2502.03918 | Adaptation of Task Goal States from Prior Knowledge | [
"cs.RO",
"cs.AI"
] | This paper presents a framework to define a task with freedom and variability in its goal state. A robot could use this to observe the execution of a task and target a different goal from the observed one; a goal that is still compatible with the task description but would be easier for the robot to execute. We define ... | {
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2502.03919 | Blackwell's Approachability with Approximation Algorithms | [
"math.OC",
"cs.LG"
] | We revisit Blackwell's celebrated approachability problem which considers a repeated vector-valued game between a player and an adversary. Motivated by settings in which the action set of the player or adversary (or both) is difficult to optimize over, for instance when it corresponds to the set of all possible solutio... | {
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2502.03930 | DiTAR: Diffusion Transformer Autoregressive Modeling for Speech
Generation | [
"eess.AS",
"cs.AI",
"cs.CL",
"cs.LG",
"cs.SD"
] | Several recent studies have attempted to autoregressively generate continuous speech representations without discrete speech tokens by combining diffusion and autoregressive models, yet they often face challenges with excessive computational loads or suboptimal outcomes. In this work, we propose Diffusion Transformer A... | {
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2502.03933 | HEP-JEPA: A foundation model for collider physics using joint embedding
predictive architecture | [
"cs.LG",
"hep-ex",
"hep-ph"
] | We present a transformer architecture-based foundation model for tasks at high-energy particle colliders such as the Large Hadron Collider. We train the model to classify jets using a self-supervised strategy inspired by the Joint Embedding Predictive Architecture. We use the JetClass dataset containing 100M jets of va... | {
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2502.03935 | Thermal Model Calibration of a Squirrel-Cage Induction Machine | [
"cs.CE"
] | Accurate and efficient thermal simulations of induction machines are indispensable for detecting thermal hot spots and hence avoiding potential material failure in an early design stage. A goal is the better utilization of the machines with reduced safety margins due to a better knowledge of the critical conditions. In... | {
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2502.03937 | Quantifying Correlations of Machine Learning Models | [
"cs.LG",
"cs.NA",
"math.NA"
] | Machine Learning models are being extensively used in safety critical applications where errors from these models could cause harm to the user. Such risks are amplified when multiple machine learning models, which are deployed concurrently, interact and make errors simultaneously. This paper explores three scenarios wh... | {
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2502.03938 | Unravelling Causal Genetic Biomarkers of Alzheimer's Disease via Neuron
to Gene-token Backtracking in Neural Architecture: A Groundbreaking
Reverse-Gene-Finder Approach | [
"cs.LG"
] | Alzheimer's Disease (AD) affects over 55 million people globally, yet the key genetic contributors remain poorly understood. Leveraging recent advancements in genomic foundation models, we present the innovative Reverse-Gene-Finder technology, a ground-breaking neuron-to-gene-token backtracking approach in a neural net... | {
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2502.03943 | Multimodal Data-Driven Classification of Mental Disorders: A
Comprehensive Approach to Diagnosing Depression, Anxiety, and Schizophrenia | [
"cs.LG"
] | This study investigates the potential of multimodal data integration, which combines electroencephalogram (EEG) data with sociodemographic characteristics like age, sex, education, and intelligence quotient (IQ), to diagnose mental diseases like schizophrenia, depression, and anxiety. Using Apache Spark and convolution... | {
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2502.03944 | Exact Covariance Characterization for Controlled Linear Systems subject
to Stochastic Parametric and Additive Uncertainties | [
"eess.SY",
"cs.SY"
] | This work addresses the exact characterization of the covariance dynamics related to linear discrete-time systems subject to both additive and parametric stochastic uncertainties that are potentially unbounded. The derived exact representation allows to understand how the covariance of the multiplicative parametric unc... | {
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2502.03945 | Afrispeech-Dialog: A Benchmark Dataset for Spontaneous English
Conversations in Healthcare and Beyond | [
"cs.CL"
] | Speech technologies are transforming interactions across various sectors, from healthcare to call centers and robots, yet their performance on African-accented conversations remains underexplored. We introduce Afrispeech-Dialog, a benchmark dataset of 50 simulated medical and non-medical African-accented English conver... | {
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2502.03946 | CleanSurvival: Automated data preprocessing for time-to-event models
using reinforcement learning | [
"cs.LG"
] | Data preprocessing is a critical yet frequently neglected aspect of machine learning, often paid little attention despite its potentially significant impact on model performance. While automated machine learning pipelines are starting to recognize and integrate data preprocessing into their solutions for classification... | {
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2502.03948 | Enhancing Online Learning Efficiency Through Heterogeneous Resource
Integration with a Multi-Agent RAG System | [
"cs.AI",
"cs.CL",
"cs.MA"
] | Efficient online learning requires seamless access to diverse resources such as videos, code repositories, documentation, and general web content. This poster paper introduces early-stage work on a Multi-Agent Retrieval-Augmented Generation (RAG) System designed to enhance learning efficiency by integrating these heter... | {
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2502.03950 | LR0.FM: Low-Resolution Zero-shot Classification Benchmark For Foundation
Models | [
"cs.CV"
] | Visual-language foundation Models (FMs) exhibit remarkable zero-shot generalization across diverse tasks, largely attributed to extensive pre-training on largescale datasets. However, their robustness on low-resolution/pixelated (LR) images, a common challenge in real-world scenarios, remains underexplored. We introduc... | {
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2502.03952 | Bridging the inference gap in Mutimodal Variational Autoencoders | [
"cs.LG",
"stat.ML"
] | From medical diagnosis to autonomous vehicles, critical applications rely on the integration of multiple heterogeneous data modalities. Multimodal Variational Autoencoders offer versatile and scalable methods for generating unobserved modalities from observed ones. Recent models using mixturesof-experts aggregation suf... | {
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2502.03953 | Fairness Aware Reinforcement Learning via Proximal Policy Optimization | [
"cs.MA",
"cs.LG"
] | Fairness in multi-agent systems (MAS) focuses on equitable reward distribution among agents in scenarios involving sensitive attributes such as race, gender, or socioeconomic status. This paper introduces fairness in Proximal Policy Optimization (PPO) with a penalty term derived from demographic parity, counterfactual ... | {
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2502.03954 | MAQInstruct: Instruction-based Unified Event Relation Extraction | [
"cs.CL",
"cs.AI"
] | Extracting event relations that deviate from known schemas has proven challenging for previous methods based on multi-class classification, MASK prediction, or prototype matching. Recent advancements in large language models have shown impressive performance through instruction tuning. Nevertheless, in the task of even... | {
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2502.03957 | Improving the Perturbation-Based Explanation of Deepfake Detectors
Through the Use of Adversarially-Generated Samples | [
"cs.CV",
"cs.AI",
"cs.CR"
] | In this paper, we introduce the idea of using adversarially-generated samples of the input images that were classified as deepfakes by a detector, to form perturbation masks for inferring the importance of different input features and produce visual explanations. We generate these samples based on Natural Evolution Str... | {
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2502.03958 | Non-convex composite federated learning with heterogeneous data | [
"cs.LG",
"cs.DC"
] | We propose an innovative algorithm for non-convex composite federated learning that decouples the proximal operator evaluation and the communication between server and clients. Moreover, each client uses local updates to communicate less frequently with the server, sends only a single d-dimensional vector per communica... | {
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2502.03960 | Bilevel Multi-Armed Bandit-Based Hierarchical Reinforcement Learning for
Interaction-Aware Self-Driving at Unsignalized Intersections | [
"cs.RO"
] | In this work, we present BiM-ACPPO, a bilevel multi-armed bandit-based hierarchical reinforcement learning framework for interaction-aware decision-making and planning at unsignalized intersections. Essentially, it proactively takes the uncertainties associated with surrounding vehicles (SVs) into consideration, which ... | {
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2502.03962 | Quantum Circuit Design using a Progressive Widening Monte Carlo Tree
Search | [
"quant-ph",
"cs.AI",
"cs.ET"
] | The performance of Variational Quantum Algorithms (VQAs) strongly depends on the choice of the parameterized quantum circuit to optimize. One of the biggest challenges in VQAs is designing quantum circuits tailored to the particular problem and to the quantum hardware. This article proposes a gradient-free Monte Carlo ... | {
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2502.03963 | AL-PINN: Active Learning-Driven Physics-Informed Neural Networks for
Efficient Sample Selection in Solving Partial Differential Equations | [
"cs.LG"
] | Physics-Informed Neural Networks (PINNs) have emerged as a promising approach for solving Partial Differential Equations (PDEs) by incorporating physical constraints into deep learning models. However, standard PINNs often require a large number of training samples to achieve high accuracy, leading to increased computa... | {
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2502.03965 | Innovative Framework for Early Estimation of Mental Disorder Scores to
Enable Timely Interventions | [
"cs.LG"
] | Individual's general well-being is greatly impacted by mental health conditions including depression and Post-Traumatic Stress Disorder (PTSD), underscoring the importance of early detection and precise diagnosis in order to facilitate prompt clinical intervention. An advanced multimodal deep learning system for the au... | {
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2502.03966 | MultiFloodSynth: Multi-Annotated Flood Synthetic Dataset Generation | [
"cs.CV",
"cs.AI",
"cs.LG"
] | In this paper, we present synthetic data generation framework for flood hazard detection system. For high fidelity and quality, we characterize several real-world properties into virtual world and simulate the flood situation by controlling them. For the sake of efficiency, recent generative models in image-to-3D and u... | {
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2502.03971 | RWKV-UI: UI Understanding with Enhanced Perception and Reasoning | [
"cs.CV",
"cs.HC"
] | Existing Visual Language Modelsoften struggle with information loss and limited reasoning abilities when handling high-resolution web interfaces that combine complex visual, textual, and interactive elements. These challenges are particularly evident in tasks requiring webpage layout comprehension and multi-step intera... | {
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2502.03974 | Spatiotemporal Trajectory Tracking Method for Vehicles Incorporating
Lead-Lag Judgement | [
"eess.SY",
"cs.SY"
] | In the domain of intelligent transportation systems, especially within the context of autonomous vehicle control, the preemptive holistic collaborative system has been presented as a promising solution to bring a remarkable enhancement in traffic efficiency and a substantial reduction in the accident rate, demonstratin... | {
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2502.03976 | Small Signal Stability Analysis of Kurdistan Regional Power System | [
"eess.SY",
"cs.SY"
] | This paper presents for the first time a mathematical model for evaluating the Planned Kurdistan Regional Power System (KRPS) for its ability to maintain stability under small disturbances and fluctuations during normal operating conditions. To achieve this objective, practical field data, manufacture's datasheets, rel... | {
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2502.03979 | Towards Unified Music Emotion Recognition across Dimensional and
Categorical Models | [
"cs.SD",
"cs.AI",
"eess.AS"
] | One of the most significant challenges in Music Emotion Recognition (MER) comes from the fact that emotion labels can be heterogeneous across datasets with regard to the emotion representation, including categorical (e.g., happy, sad) versus dimensional labels (e.g., valence-arousal). In this paper, we present a unifie... | {
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2502.03982 | Temporal Distribution Shift in Real-World Pharmaceutical Data:
Implications for Uncertainty Quantification in QSAR Models | [
"cs.LG"
] | The estimation of uncertainties associated with predictions from quantitative structure-activity relationship (QSAR) models can accelerate the drug discovery process by identifying promising experiments and allowing an efficient allocation of resources. Several computational tools exist that estimate the predictive unc... | {
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2502.03984 | PGB: One-Shot Pruning for BERT via Weight Grouping and Permutation | [
"cs.CL",
"cs.AI"
] | Large pretrained language models such as BERT suffer from slow inference and high memory usage, due to their huge size. Recent approaches to compressing BERT rely on iterative pruning and knowledge distillation, which, however, are often too complicated and computationally intensive. This paper proposes a novel semi-st... | {
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2502.03988 | Tight Bounds on Jensen's Gap: Novel Approach with Applications in
Generative Modeling | [
"cs.LG"
] | Among various mathematical tools of particular interest are those that provide a common basis for researchers in different scientific fields. One of them is Jensen's inequality, which states that the expectation of a convex function is greater than or equal to the function evaluated at the expectation. The resulting di... | {
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2502.03990 | Frequency Control and Power Sharing in Combined Heat and Power Networks | [
"eess.SY",
"cs.SY"
] | We consider the problem of using district heating systems as ancillary services for primary frequency control in power networks. We propose a novel power sharing scheme for heating systems based on the average temperature, which enables an optimal power allocation among the diverse heat sources without having a prior k... | {
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2502.03992 | Ontology-Guided, Hybrid Prompt Learning for Generalization in Knowledge
Graph Question Answering | [
"cs.CL",
"cs.AI"
] | Most existing Knowledge Graph Question Answering (KGQA) approaches are designed for a specific KG, such as Wikidata, DBpedia or Freebase. Due to the heterogeneity of the underlying graph schema, topology and assertions, most KGQA systems cannot be transferred to unseen Knowledge Graphs (KGs) without resource-intensive ... | {
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2502.03997 | CAD-Editor: A Locate-then-Infill Framework with Automated Training Data
Synthesis for Text-Based CAD Editing | [
"cs.CV"
] | Computer Aided Design (CAD) is indispensable across various industries. \emph{Text-based CAD editing}, which automates the modification of CAD models based on textual instructions, holds great potential but remains underexplored. Existing methods primarily focus on design variation generation or text-based CAD generati... | {
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2502.03998 | Online Learning of Counter Categories and Ratings in PvP Games | [
"cs.LG",
"cs.AI",
"cs.GT",
"cs.MA"
] | In competitive games, strength ratings like Elo are widely used to quantify player skill and support matchmaking by accounting for skill disparities better than simple win rate statistics. However, scalar ratings cannot handle complex intransitive relationships, such as counter strategies seen in Rock-Paper-Scissors. T... | {
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2502.03999 | A Self-supervised Multimodal Deep Learning Approach to Differentiate
Post-radiotherapy Progression from Pseudoprogression in Glioblastoma | [
"eess.IV",
"cs.CV"
] | Accurate differentiation of pseudoprogression (PsP) from True Progression (TP) following radiotherapy (RT) in glioblastoma (GBM) patients is crucial for optimal treatment planning. However, this task remains challenging due to the overlapping imaging characteristics of PsP and TP. This study therefore proposes a multim... | {
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2502.04004 | Near-optimal Regret Using Policy Optimization in Online MDPs with
Aggregate Bandit Feedback | [
"cs.LG"
] | We study online finite-horizon Markov Decision Processes with adversarially changing loss and aggregate bandit feedback (a.k.a full-bandit). Under this type of feedback, the agent observes only the total loss incurred over the entire trajectory, rather than the individual losses at each intermediate step within the tra... | {
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2502.04008 | Automating a Complete Software Test Process Using LLMs: An Automotive
Case Study | [
"cs.SE",
"cs.AI"
] | Vehicle API testing verifies whether the interactions between a vehicle's internal systems and external applications meet expectations, ensuring that users can access and control various vehicle functions and data. However, this task is inherently complex, requiring the alignment and coordination of API systems, commun... | {
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2502.04012 | Malleable Robots | [
"cs.RO"
] | This chapter is about the fundamentals of fabrication, control, and human-robot interaction of a new type of collaborative robotic manipulators, called malleable robots, which are based on adjustable architectures of varying stiffness for achieving high dexterity with lower mobility arms. Collaborative robots, or cobot... | {
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2502.04014 | Enhancing people localisation in drone imagery for better crowd
management by utilising every pixel in high-resolution images | [
"cs.CV",
"cs.RO"
] | Accurate people localisation using drones is crucial for effective crowd management, not only during massive events and public gatherings but also for monitoring daily urban crowd flow. Traditional methods for tiny object localisation using high-resolution drone imagery often face limitations in precision and efficienc... | {
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2502.04018 | PINT: Physics-Informed Neural Time Series Models with Applications to
Long-term Inference on WeatherBench 2m-Temperature Data | [
"cs.LG"
] | This paper introduces PINT (Physics-Informed Neural Time Series Models), a framework that integrates physical constraints into neural time series models to improve their ability to capture complex dynamics. We apply PINT to the ERA5 WeatherBench dataset, focusing on long-term forecasting of 2m-temperature data. PINT in... | {
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2502.04021 | Variational Quantum Optimization with Continuous Bandits | [
"cs.LG",
"quant-ph"
] | We introduce a novel approach to variational Quantum algorithms (VQA) via continuous bandits. VQA are a class of hybrid Quantum-classical algorithms where the parameters of Quantum circuits are optimized by classical algorithms. Previous work has used zero and first order gradient based methods, however such algorithms... | {
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2502.04022 | Quantification of Biodiversity from Historical Survey Text with
LLM-based Best-Worst Scaling | [
"cs.CL"
] | In this study, we evaluate methods to determine the frequency of species via quantity estimation from historical survey text. To that end, we formulate classification tasks and finally show that this problem can be adequately framed as a regression task using Best-Worst Scaling (BWS) with Large Language Models (LLMs). ... | {
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2502.04024 | A Robust Optimization Model for Cost-Efficient and Fast Electric Vehicle
Charging with L2-norm Uncertainty | [
"eess.SY",
"cs.SY"
] | In this paper, we propose a robust optimization model that addresses both the cost-efficiency and fast charging requirements for electric vehicles (EVs) at charging stations. By combining elements from traditional cost-minimization models and a fast charging objective, we construct an optimization model that balances u... | {
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2502.04028 | Deep Meta Coordination Graphs for Multi-agent Reinforcement Learning | [
"cs.LG"
] | This paper presents deep meta coordination graphs (DMCG) for learning cooperative policies in multi-agent reinforcement learning (MARL). Coordination graph formulations encode local interactions and accordingly factorize the joint value function of all agents to improve efficiency in MARL. However, existing approaches ... | {
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2502.04030 | Fine, I'll Merge It Myself: A Multi-Fidelity Framework for Automated
Model Merging | [
"cs.AI",
"cs.LG"
] | Reasoning capabilities represent a critical frontier for large language models (LLMs), but developing them requires extensive proprietary datasets and computational resources. One way to efficiently supplement capabilities with is by model merging, which offers a promising alternative by combining multiple models witho... | {
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2502.04034 | Generalize Drug Response Prediction by Latent Independent Projection for
Asymmetric Constrained Domain Generalization | [
"cs.LG",
"cs.AI"
] | The accurate prediction of drug responses remains a formidable challenge, particularly at the single-cell level and in clinical treatment contexts. Some studies employ transfer learning techniques to predict drug responses in individual cells and patients, but they require access to target-domain data during training, ... | {
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2502.04037 | Exploring Imbalanced Annotations for Effective In-Context Learning | [
"cs.CL",
"cs.LG"
] | Large language models (LLMs) have shown impressive performance on downstream tasks through in-context learning (ICL), which heavily relies on the demonstrations selected from annotated datasets. Existing selection methods may hinge on the distribution of annotated datasets, which can often be long-tailed in real-world ... | {
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2502.04038 | Simulating the Emergence of Differential Case Marking with Communicating
Neural-Network Agents | [
"cs.CL"
] | Differential Case Marking (DCM) refers to the phenomenon where grammatical case marking is applied selectively based on semantic, pragmatic, or other factors. The emergence of DCM has been studied in artificial language learning experiments with human participants, which were specifically aimed at disentangling the eff... | {
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2502.04039 | A Cloud-native Agile approach to cyber platform prototyping and
integration for astronomy: the ENGAGE SKA case | [
"astro-ph.IM",
"cs.SY",
"eess.SY",
"physics.med-ph"
] | The Square Kilometre Array (SKA) Observatory is gearing up the formal construction of its two radio interferometers in Australia and South Africa after the end of design and pre-construction phases. Agile methodologies, the Cloud native Computing technologies and the DevOps software ideas are influencing the design of ... | {
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2502.04040 | Leveraging Reasoning with Guidelines to Elicit and Utilize Knowledge for
Enhancing Safety Alignment | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Training safe LLMs is one of the most critical research challenge. However, the commonly used method, Refusal Training (RT), struggles to generalize against various OOD jailbreaking attacks. Many safety training methods have been proposed to address this issue. While they offer valuable insights, we aim to complement t... | {
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2502.04043 | Probe-Free Low-Rank Activation Intervention | [
"cs.LG",
"cs.AI"
] | Language models (LMs) can produce texts that appear accurate and coherent but contain untruthful or toxic content. Inference-time interventions that edit the hidden activations have shown promising results in steering the LMs towards desirable generations. Existing activation intervention methods often comprise an acti... | {
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2502.04045 | Comparing privacy notions for protection against reconstruction attacks
in machine learning | [
"cs.LG",
"cs.CR",
"cs.IT",
"math.IT"
] | Within the machine learning community, reconstruction attacks are a principal concern and have been identified even in federated learning (FL), which was designed with privacy preservation in mind. In response to these threats, the privacy community recommends the use of differential privacy (DP) in the stochastic grad... | {
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2502.04048 | Adaptive Output Feedback MPC with Guaranteed Stability and Robustness | [
"eess.SY",
"cs.SY",
"math.OC"
] | This work proposes an adaptive output feedback model predictive control (MPC) framework for uncertain systems subject to external disturbances. In the absence of exact knowledge about the plant parameters and complete state measurements, the MPC optimization problem is reformulated in terms of their estimates derived f... | {
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2502.04050 | PartEdit: Fine-Grained Image Editing using Pre-Trained Diffusion Models | [
"cs.CV"
] | We present the first text-based image editing approach for object parts based on pre-trained diffusion models. Diffusion-based image editing approaches capitalized on the deep understanding of diffusion models of image semantics to perform a variety of edits. However, existing diffusion models lack sufficient understan... | {
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2502.04052 | Decision Trees That Remember: Gradient-Based Learning of Recurrent
Decision Trees with Memory | [
"cs.LG"
] | Neural architectures such as Recurrent Neural Networks (RNNs), Transformers, and State-Space Models have shown great success in handling sequential data by learning temporal dependencies. Decision Trees (DTs), on the other hand, remain a widely used class of models for structured tabular data but are typically not desi... | {
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2502.04054 | Precision Agriculture Revolution: Integrating Digital Twins and Advanced
Crop Recommendation for Optimal Yield | [
"cs.LG"
] | With the help of a digital twin structure, Agriculture 4.0 technologies like weather APIs (Application programming interface), GPS (Global Positioning System) modules, and NPK (Nitrogen, Phosphorus and Potassium) soil sensors and machine learning recommendation models, we seek to revolutionize agricultural production t... | {
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2502.04055 | Evaluating Inter-Column Logical Relationships in Synthetic Tabular Data
Generation | [
"cs.LG"
] | Current evaluations of synthetic tabular data mainly focus on how well joint distributions are modeled, often overlooking the assessment of their effectiveness in preserving realistic event sequences and coherent entity relationships across columns.This paper proposes three evaluation metrics designed to assess the pre... | {
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2502.04056 | TQ-DiT: Efficient Time-Aware Quantization for Diffusion Transformers | [
"cs.LG",
"eess.SP"
] | Diffusion transformers (DiTs) combine transformer architectures with diffusion models. However, their computational complexity imposes significant limitations on real-time applications and sustainability of AI systems. In this study, we aim to enhance the computational efficiency through model quantization, which repre... | {
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2502.04057 | Smart IoT Security: Lightweight Machine Learning Techniques for
Multi-Class Attack Detection in IoT Networks | [
"cs.LG"
] | In the growing terrain of the Internet of Things (IoT), it is vital that networks are secure to protect against a range of cyber threats. Based on the strong machine learning framework, this study proposes novel lightweight ensemble approaches for improving multi-class attack detection of IoT devices. Using the large C... | {
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2502.04058 | Strategic Learning with Local Explanations as Feedback | [
"cs.AI"
] | We investigate algorithmic decision problems where agents can respond strategically to the decision maker's (DM) models. The demand for clear and actionable explanations from DMs to (potentially strategic) agents continues to rise. While prior work often treats explanations as full model disclosures, explanations in pr... | {
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2502.04062 | On Sufficient Richness for Linear Time-Invariant Systems | [
"eess.SY",
"cs.SY"
] | Persistent excitation (PE) is a necessary and sufficient condition for uniform exponential parameter convergence in several adaptive, identification, and learning schemes. In this article, we consider, in the context of multi-input linear time-invariant (LTI) systems, the problem of guaranteeing PE of commonly-used reg... | {
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2502.04064 | Inteligencia artificial para la multi-clasificaci\'on de fauna en
fotograf\'ias autom\'aticas utilizadas en investigaci\'on cient\'ifica | [
"cs.CV"
] | The management of natural environments, whether for conservation or production, requires a deep understanding of wildlife. The number, location, and behavior of wild animals are among the main subjects of study in ecology and wildlife research. The use of camera traps offers the opportunity to quickly collect large qua... | {
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2502.04066 | Predicting Large Language Model Capabilities on Closed-Book QA Tasks
Using Only Information Available Prior to Training | [
"cs.CL",
"cs.AI"
] | The GPT-4 technical report from OpenAI suggests that model performance on specific tasks can be predicted prior to training, though methodologies remain unspecified. This approach is crucial for optimizing resource allocation and ensuring data alignment with target tasks. To achieve this vision, we focus on predicting ... | {
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2502.04074 | 3D Prior is All You Need: Cross-Task Few-shot 2D Gaze Estimation | [
"cs.CV"
] | 3D and 2D gaze estimation share the fundamental objective of capturing eye movements but are traditionally treated as two distinct research domains. In this paper, we introduce a novel cross-task few-shot 2D gaze estimation approach, aiming to adapt a pre-trained 3D gaze estimation network for 2D gaze prediction on uns... | {
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2502.04075 | Controllable Emotion Generation with Emotion Vectors | [
"cs.CL"
] | In recent years, technologies based on large-scale language models (LLMs) have made remarkable progress in many fields, especially in customer service, content creation, and embodied intelligence, showing broad application potential. However, The LLM's ability to express emotions with proper tone, timing, and in both d... | {
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2502.04076 | Content-Rich AIGC Video Quality Assessment via Intricate Text Alignment
and Motion-Aware Consistency | [
"cs.CV"
] | The advent of next-generation video generation models like \textit{Sora} poses challenges for AI-generated content (AIGC) video quality assessment (VQA). These models substantially mitigate flickering artifacts prevalent in prior models, enable longer and complex text prompts and generate longer videos with intricate, ... | {
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2502.04077 | AttentionPredictor: Temporal Pattern Matters for Efficient LLM Inference | [
"cs.CL",
"cs.LG"
] | With the development of large language models (LLMs), efficient inference through Key-Value (KV) cache compression has attracted considerable attention, especially for long-context generation. To compress the KV cache, recent methods identify critical KV tokens through heuristic ranking with attention scores. However, ... | {
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2502.04079 | DEALing with Image Reconstruction: Deep Attentive Least Squares | [
"eess.IV",
"cs.CV",
"cs.LG"
] | State-of-the-art image reconstruction often relies on complex, highly parameterized deep architectures. We propose an alternative: a data-driven reconstruction method inspired by the classic Tikhonov regularization. Our approach iteratively refines intermediate reconstructions by solving a sequence of quadratic problem... | {
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2502.04083 | Automatic quantification of breast cancer biomarkers from multiple
18F-FDG PET image segmentation | [
"cs.CV",
"cs.AI"
] | Neoadjuvant chemotherapy (NAC) has become a standard clinical practice for tumor downsizing in breast cancer with 18F-FDG Positron Emission Tomography (PET). Our work aims to leverage PET imaging for the segmentation of breast lesions. The focus is on developing an automated system that accurately segments primary tumo... | {
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2502.04088 | Quantifying imperfect cognition via achieved information gain | [
"cs.IT",
"math.IT"
] | Cognition, the process of information processing in form of inference, communication, and memorization, is the central activity of any intelligence. Its physical realization in a brain, computer, or in any other intelligent system requires resources like time, energy, memory, bandwidth, money, and others. Due to limite... | {
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2502.04094 | Soft and Highly-Integrated Optical Fiber Bending Sensors for
Proprioception in Multi-Material 3D Printed Fingers | [
"cs.RO"
] | Accurate shape sensing, only achievable through distributed proprioception, is a key requirement for closed-loop control of soft robots. Low-cost power efficient optoelectronic sensors manufactured from flexible materials represent a natural choice as they can cope with the large deformations of soft robots without los... | {
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2502.04095 | LLMs to Support a Domain Specific Knowledge Assistant | [
"cs.CL",
"cs.AI"
] | This work presents a custom approach to developing a domain specific knowledge assistant for sustainability reporting using the International Financial Reporting Standards (IFRS). In this domain, there is no publicly available question-answer dataset, which has impeded the development of a high-quality chatbot to suppo... | {
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2502.04098 | Efficient Few-Shot Continual Learning in Vision-Language Models | [
"cs.CV",
"cs.AI"
] | Vision-language models (VLMs) excel in tasks such as visual question answering and image captioning. However, VLMs are often limited by their use of pretrained image encoders, like CLIP, leading to image understanding errors that hinder overall performance. On top of that, real-world applications often require the mode... | {
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2502.04101 | Safe Quadrotor Navigation using Composite Control Barrier Functions | [
"cs.RO"
] | This paper introduces a safety filter to ensure collision avoidance for multirotor aerial robots. The proposed formalism leverages a single Composite Control Barrier Function from all position constraints acting on a third-order nonlinear representation of the robot's dynamics. We analyze the recursive feasibility of t... | {
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2502.04103 | VTutor: An Open-Source SDK for Generative AI-Powered Animated
Pedagogical Agents with Multi-Media Output | [
"cs.HC",
"cs.AI",
"cs.SE"
] | The rapid evolution of large language models (LLMs) has transformed human-computer interaction (HCI), but the interaction with LLMs is currently mainly focused on text-based interactions, while other multi-model approaches remain under-explored. This paper introduces VTutor, an open-source Software Development Kit (SDK... | {
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2502.04110 | Ancient Greek Technology: An Immersive Learning Use Case Described Using
a Co-Intelligent Custom ChatGPT Assistant | [
"cs.HC",
"cs.AI"
] | Achieving consistency in immersive learning case descriptions is essential but challenging due to variations in research focus, methodology, and researchers' background. We address these challenges by leveraging the Immersive Learning Case Sheet (ILCS), a methodological instrument to standardize case descriptions, that... | {
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2502.04111 | Adaptive Margin Contrastive Learning for Ambiguity-aware 3D Semantic
Segmentation | [
"cs.CV"
] | In this paper, we propose an adaptive margin contrastive learning method for 3D point cloud semantic segmentation, namely AMContrast3D. Most existing methods use equally penalized objectives, which ignore per-point ambiguities and less discriminated features stemming from transition regions. However, as highly ambiguou... | {
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2502.04115 | A Neural Network-based Multi-timestep Command Governor for Nonlinear
Systems with Constraints | [
"eess.SY",
"cs.SY"
] | The multi-timestep command governor (MCG) is an add-on algorithm that enforces constraints by modifying, at each timestep, the reference command to a pre-stabilized control system. The MCG can be interpreted as a Model-Predictive Control scheme operating on the reference command. The implementation of MCG on nonlinear ... | {
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2502.04116 | Generative Adversarial Networks Bridging Art and Machine Intelligence | [
"cs.LG",
"cs.CV"
] | Generative Adversarial Networks (GAN) have greatly influenced the development of computer vision and artificial intelligence in the past decade and also connected art and machine intelligence together. This book begins with a detailed introduction to the fundamental principles and historical development of GANs, contra... | {
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2502.04121 | Optimizing Perturbations for Improved Training of Machine Learning
Models | [
"cs.LG",
"cond-mat.dis-nn",
"physics.chem-ph"
] | Machine learning models have become indispensable tools in applications across the physical sciences. Their training is often time-consuming, vastly exceeding the inference timescales. Several protocols have been developed to perturb the learning process and improve the training, such as shrink and perturb, warm restar... | {
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} |
2502.04126 | RC Measurement Uncertainty Estimation Method for Directive Antennas and
Turntable Stirring | [
"eess.SY",
"cs.SY"
] | This paper investigates measurement uncertainty in a Reverberation Chamber (RC) within the lower FR2 bands (24.25-29.5 GHz). The study focuses on the impact of several factors contributing to RC measurement uncertainty, including finite sample size, polarization imbalance, and spatial non-uniformity. A series of 24 mea... | {
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} |
2502.04128 | Llasa: Scaling Train-Time and Inference-Time Compute for Llama-based
Speech Synthesis | [
"eess.AS",
"cs.AI",
"cs.CL",
"cs.MM",
"cs.SD"
] | Recent advances in text-based large language models (LLMs), particularly in the GPT series and the o1 model, have demonstrated the effectiveness of scaling both training-time and inference-time compute. However, current state-of-the-art TTS systems leveraging LLMs are often multi-stage, requiring separate models (e.g.,... | {
"Other": 1,
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} |
2502.04131 | On the importance of structural identifiability for machine learning
with partially observed dynamical systems | [
"cs.LG"
] | The successful application of modern machine learning for time series classification is often hampered by limitations in quality and quantity of available training data. To overcome these limitations, available domain expert knowledge in the form of parametrised mechanistic dynamical models can be used whenever it is a... | {
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} |
2502.04132 | Transfer Learning for Covert Speech Classification Using EEG Hilbert
Envelope and Temporal Fine Structure | [
"cs.LG"
] | Brain-Computer Interfaces (BCIs) can decode imagined speech from neural activity. However, these systems typically require extensive training sessions where participants imaginedly repeat words, leading to mental fatigue and difficulties identifying the onset of words, especially when imagining sequences of words. This... | {
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} |
2502.04134 | The Order Effect: Investigating Prompt Sensitivity in Closed-Source LLMs | [
"cs.CL"
] | As large language models (LLMs) become integral to diverse applications, ensuring their reliability under varying input conditions is crucial. One key issue affecting this reliability is order sensitivity, wherein slight variations in input arrangement can lead to inconsistent or biased outputs. Although recent advance... | {
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} |
2502.04139 | Beyond the Final Layer: Hierarchical Query Fusion Transformer with
Agent-Interpolation Initialization for 3D Instance Segmentation | [
"cs.CV"
] | 3D instance segmentation aims to predict a set of object instances in a scene and represent them as binary foreground masks with corresponding semantic labels. Currently, transformer-based methods are gaining increasing attention due to their elegant pipelines, reduced manual selection of geometric properties, and supe... | {
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} |
2502.04140 | Synthetic Datasets for Machine Learning on Spatio-Temporal Graphs using
PDEs | [
"cs.LG",
"cs.AI"
] | Many physical processes can be expressed through partial differential equations (PDEs). Real-world measurements of such processes are often collected at irregularly distributed points in space, which can be effectively represented as graphs; however, there are currently only a few existing datasets. Our work aims to ma... | {
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} |
2502.04141 | Behavioral Entropy-Guided Dataset Generation for Offline Reinforcement
Learning | [
"cs.LG"
] | Entropy-based objectives are widely used to perform state space exploration in reinforcement learning (RL) and dataset generation for offline RL. Behavioral entropy (BE), a rigorous generalization of classical entropies that incorporates cognitive and perceptual biases of agents, was recently proposed for discrete sett... | {
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} |
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