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...

Diffusion-OAMP for Joint Image Compression and Wireless Transmission

- Joint image compression and wireless transmission remain relatively underexplored compared to generic image restoration, despite its importance in practical communication systems. We formulate this...

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,...

Unentangled stoquastic Merlin-Arthur proof systems: the power of unentanglement without destructive interference

- Stoquasticity, originating in sign-problem-free physical systems, gives rise to $\sf StoqMA$, introduced by Bravyi, Bessen, and Terhal (2006), a quantum-inspired intermediate class between $\sf MA$...

Decoupled Descent: Exact Test Error Tracking Via Approximate Message Passing

- In modern parametric model training, full-batch gradient descent (and its variants) suffers due to progressively stronger biasing towards the exact realization of training data; this drives the...

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...

LRS-VoxMM: A benchmark for in-the-wild audio-visual speech recognition

- We introduce LRS-VoxMM, an in-the-wild benchmark for audio-visual speech recognition (AVSR). The benchmark is derived from VoxMM, a dataset of diverse real-world spoken conversations with...

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;...

Sensing-Assisted Channel Estimation for Flexible-Antenna Systems: A Unified Framework

- Flexible-antenna systems, which use a small number of radio frequency (RF) chains to dynamically access a large set of candidate antenna locations, have emerged as a hardware-efficient architecture...

Computation of frequency- and time-domain Jacobians in optical tomography with Monte Carlo simulations

- Significance: Jacobians, or spatially resolved sensitivity profiles, are central to image reconstruction in model-based optical tomography of biological tissue. Although Monte Carlo (MC) simulations...

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...

A Real-time Scale-robust Network for Glottis Segmentation in Nasal Transnasal Intubation

- Nasotracheal intubation (NTI) is a critical clinical procedure for establishing and maintaining patient airway patency. Machine-assisted NTI has emerged as a pivotal approach for optimizing...

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...

Representative Spectral Correlation Network for Multi-source Remote Sensing Image Classification

- Hyperspectral image (HSI) and SAR/LiDAR data offer complementary spectral and structural information for land-cover classification. However, their effective fusion remains challenging due to two...

Quantum Anonymous Secret Sharing with Permutation Invariant Codes

- Quantum secret sharing schemes are a family of quantum cryptographic protocols which provide secure quantum encodings, mapping one secret to multiple shares of information such that the original...