Defensa y seguridad

Mapping the Phase Diagram of the Vicsek Model with Machine Learning

- In this study, we use machine learning to classify and interpolate the phase structure of the Vicsek flocking model across the three-dimensional parameter space $(\eta,\rho,v_0)$. We construct a...

Defending Quantum Classifiers against Adversarial Perturbations through Quantum Autoencoders

- Machine learning models can learn from data samples to carry out various tasks efficiently. When data samples are adversarially manipulated, such as by insertion of carefully crafted noise, it can...

Sequential Inference for Gaussian Processes: A Signal Processing Perspective

- The proliferation of capable and efficient machine learning (ML) models marks one of the strongest methodological shifts in signal processing (SP) in its nearly 100-year history. ML models support...

Assessing the Role of Intersection Proximity in Pedestrian Crashes: Insights from Data Mining Approach

- Although intersections are the most complex parts of the roadway network, pedestrian crashes at non-intersection locations are disproportionately frequent, highlighting a serious traffic safety...

Prediction-powered Inference by Mixture of Experts

- The rapidly expanding artificial intelligence (AI) industry has produced diverse yet powerful prediction tools, each with its own network architecture, training strategy, data-processing pipeline,...

Heisenberg-limited Hamiltonian learning without short-time control

- Characterizing quantum systems by learning their underlying Hamiltonians is a central task in quantum information science. While recent algorithmic advances have achieved near-optimal efficiency in...

Data-Efficient Indentation Size Effect Correction in Steels Using Machine Learning and Physics-Guided Augmentation

- Shallow nanoindentation enables mechanical characterization of thin films, individual phases and other volume-constrained materials, but measured hardness is often inflated by the indentation size...

Sampling two-dimensional spin systems with transformers

- Autoregressive Neural Networks based on dense or convolutional layers have recently been shown to be a viable strategy for generating classical spin systems. Unlike these methods, sampling with...

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials

- While machine-learned interatomic potentials (MLIPs) accelerate phonon dispersion calculations, merely identifying dynamical instabilities in computationally predicted materials is insufficient;...

Simulating Infant First-Person Sensorimotor Experience via Motion Retargeting from Babies to Humanoids

- Motion retargeting from humans to human-like artificial agents is becoming increasingly important as humanoid robots grow more capable. However, most existing approaches focus only on reproducing...

Spectral Dynamic Attention Network for Hyperspectral Image Super-Resolution

- Hyperspectral image super-resolution is essential for enhancing the spatial fidelity of HSI data, yet existing deep learning methods often struggle with substantial spectral redundancy and the...

A Novel Computational Framework for Causal Inference: Tree-Based Discretization with ILP-Based Matching

- Causal inference is essential for data-driven decision-making, as it aims to uncover causal relationships from observational data. However, identifying causality remains challenging due to the...

Linear Models, Variable Selection, Artificial Intelligence

- Variable selection in linear regression models has been a problem since hypothesis testing began. Which variables to include or exclude from a model is not an easy task. Techniques such as Forward,...

Man, Machine, and Mathematics

- Nonlinear models and optimization methods have successfully tackled a rapidly growing set of problems in recent years. Indeed, a relatively small toolbox of such models and methods can provide...

Validating the Clinical Utility of CineECG 3D Reconstructions through Cross-Modal Feature Attribution

- Deep learning models for 12-lead electrocardiogram (ECG) analysis achieve high diagnostic performance but lack the intuitive interpretability required for clinical integration. Standard feature...

An adaptive wavelet-based PINN for problems with localized high-magnitude source

- In recent years, physics-informed neural networks (PINNs) have gained significant attention for solving differential equations, although they suffer from two fundamental limitations, namely, spectral...

Strait: Perceiving Priority and Interference in ML Inference Serving

- Machine learning (ML) inference serving systems host deep neural network (DNN) models and schedule incoming inference requests across deployed GPUs. However, limited support for task prioritization...

RopeDreamer: A Kinematic Recurrent State Space Model for Dynamics of Flexible Deformable Linear Objects

- The robotic manipulation of Deformable Linear Objects (DLOs) is a fundamental challenge due to the high-dimensional, non-linear dynamics of flexible structures and the complexity of maintaining...

Continuous-tone Simple Points: An $ell_0$-Norm of Cyclic Gradient for Topology-Preserving Data-Driven Image Segmentation

- Topological features play an essential role in ensuring geometric plausibility and structural consistency in image analysis tasks such as segmentation and skeletonization. However, integrating...

Neural Aided Kalman Filtering for UAV State Estimation in Degraded Sensing Environments

- Accurate state estimation of nonlinear dynamical systems is fundamental to modern aerospace operations across air, sea, and space domains. Online tracking of adversarial unmanned aerial vehicles...

DEFault++: Automated Fault Detection, Categorization, and Diagnosis for Transformer Architectures

- Transformer models are widely deployed in critical AI applications, yet faults in their attention mechanisms, projections, and other internal components often degrade behavior silently without...