Deep Boltzmann Machines: Rigorous Results at Arbitrary Depth

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Genetic diseases can be classified according to their modes of inheritance and their underlying molecular mechanisms. Autosomal dominant disorders often result from DNA variants that cause loss-of-function, gain-of-function, or dominant-negative effects, w ...

This thesis explores the intersection of physiological modelling and computational techniques in advancing Automatic Speech Recognition (ASR) systems. Contemporary ASR, often driven by attention models and self-supervised learning, has achieved remarkable ...

This paper addresses the significant challenge of recognizing behaviors in non-human primates, specifically focusing on chimpanzees. Automated behavior recognition is crucial for both conservation efforts and the advancement of behavioral research. However ...

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

The magnet feeders are part of the critical systems of a fusion reactor since they represent the interface between the in-cryostat components at 4.5 K and the room-temperature cryogenic plant and power supply. The European DEMO tokamak foresees 16 toroidal ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

The increasing adoption of measurement units in electrical power distribution grids has enabled the deployment of data-driven and measurement-based control schemes. Such schemes rely on measurement-based estimated models, where the models are first estimat ...

This dataset contains a collection of ultrafast ultrasound acquisitions from nine volunteers and the CIRS 054G phantom. For a comprehensive understanding of the dataset, please refer to the paper: Viñals, R.; Thiran, J.-P. A KL Divergence-Based Loss for In ...

We study the hitting probabilities of the solution to a system of d stochastic heat equations with additive noise subject to Dirichlet boundary conditions. We show that for any bounded Borel set with positive (d-6)\documentclass[12pt]{minimal} \usepackage{ ...

Designing novel materials is greatly dependent on understanding the design principles, physical mechanisms, and modeling methods of material microstructures, requiring experienced designers with expertise and several rounds of trial and error. Although rec ...

A central principle in neuroscience is that neurons within the brain act in concert to produce perception, cognition, and adaptive behavior. Neurons are organized into specialized brain areas, dedicated to different functions to varying extents, and their ...

We study the statistical mechanics and the equilibrium dynamics of a system of classical Heisenberg spins with frustrated interactions on a d -dimensional simple hypercubic lattice, in the limit of infinite dimensionality d -> infinity . In the analysis we ...

Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison. Managing their complexity and facilitating their design drove part of the co-evolution of mode ...

Information theory has allowed us to determine the fundamental limit of various communication and algorithmic problems, e.g., the channel coding problem, the compression problem, and the hypothesis testing problem. In this work, we revisit the assumptions ...

We consider nonlinear parabolic stochastic PDEs on a bounded Lipschitz domain driven by a Gaussian noise that is white in time and colored in space, with Dirichlet or Neumann boundary condition. We establish existence, uniqueness and moment bounds of the r ...

Current transformer-based skeletal action recognition models tend to focus on a limited set of joints and low-level motion patterns to predict action classes. This results in significant performance degradation under small skeleton perturbations or changin ...

High-temperature superconductors (HTSs) enable exclusive operating conditions for fusion magnets, boosting their performance up to 20 T generated magnetic fields in the temperature range from 4 K to 20 K. One of the main technological issues of HTS conduct ...

Plantar pressure plays a crucial role in the pathogenesis of foot ulcers among patients with diabetes and peripheral polyneuropathy. Pressure relief is a key requirement for both the prevention and treatment of plantar ulcers. Conventional medical practice ...

We prove global in time well-posedness for perturbations of the 2D stochastic Navier-Stokes equations partial derivative( t)u + u center dot del u = Delta u - del p + sigma + xi, u(0, center dot ) = u(0),div (u) = 0, driven by additive space-time white noi ...

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

Deep Boltzmann Machines: Rigorous Results at Arbitrary Depth

Chattez avec Graph Search

Proteome-wide prediction of the mode of inheritance and molecular mechanisms underlying genetic diseases using structural interactomics

Modelling cochlea and its interaction with the auditory path for speech processing

From Forest to Zoo: Great Ape Behavior Recognition with Chimpbehave

Spectral Estimators for High-Dimensional Matrix Inference

The magnet feeders for the European DEMO fusion reactor: Conceptual design and recent advances

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Measurement - Based/Model-Less Estimation of Voltage Sensitivity Coefficients by Feedforward and LSTM Neural Networks in Power Distribution Grids

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (4/6)

Hitting with Probability One for Stochastic Heat Equations with Additive Noise

Text-to-Microstructure Generation Using Generative Deep Learning

Decoding the brain: From neural representations to mechanistic models

Frustrated magnets in the limit of infinite dimensions: Dynamics and disorder-free glass transition

Graph generative deep learning models with an application to circuit topologies

On Speed and Advantage : Results in Information Velocity and Monitoring Problems

Parabolic stochastic PDEs on bounded domains with rough initial conditions: moment and correlation bounds

MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning

Experimental study of stability, quench propagation and detection methods on 15 kA sub-scale HTS fusion conductors in SULTAN

A Tunable Self-Offloading Module for Plantar Pressure Regulation in Diabetic Patients

Global existence for perturbations of the 2D stochastic Navier-Stokes equations with space-time white noise

Augmented Lagrangian Methods for Provable and Scalable Machine Learning