id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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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 cor... |
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... |
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... |
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 d... |
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... |
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 specifi... |
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-outp... |
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 o... |
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 ... |
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... |
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 r... |
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 enc... |
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 defin... |
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 solut... |
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... |
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 ... |
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. ... |
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 ... |
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 n... |
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
convoluti... |
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 u... |
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 conv... |
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
classificati... |
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 het... |
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 introd... |
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 s... |
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, counterfactua... |
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 ev... |
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 S... |
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 communi... |
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, whic... |
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 Carl... |
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
compu... |
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 ... |
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... |
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 inte... |
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, demonstrat... |
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, r... |
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 unif... |
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 u... |
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-... |
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 ... |
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... |
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-intensiv... |
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 genera... |
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.... |
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 mult... |
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 t... |
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, comm... |
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 cob... |
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 efficie... |
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 ... |
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 algorith... |
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)... |
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... |
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 approache... |
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 wit... |
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... |
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-worl... |
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 e... |
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 o... |
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... |
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 ac... |
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 gr... |
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... |
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
underst... |
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
de... |
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... |
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 p... |
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 rep... |
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... |
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 ... |
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 r... |
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 q... |
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
predictin... |
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 u... |
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... |
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... |
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... |
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 probl... |
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 tu... |
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 limi... |
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 l... |
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 sup... |
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 mo... |
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... |
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 (S... |
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, th... |
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 ambigu... |
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
nonlinea... |
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, cont... |
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 rest... |
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 m... |
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... |
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... |
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. Th... |
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 advan... |
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 su... |
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 ... |
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 se... |
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