Fast and robust parameter estimation with uncertainty quantification for the cardiac function

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This special issue explores the burgeoning field of deep learning for remote sensing time series analysis. The 20 contributed papers showcase diverse applications, including land cover mapping, change detection, atmospheric and biophysical/biochemical para ...

Research in artificial intelligence (AI) and neural network (NN) technologies has been boosted by recent hardware advances, while the emergence of self-synthesized materials such as nanocarbons has attracted much attention to overcome the limitations of Si ...

Belief propagation (BP) decoding has been recognized for its capacity-approaching performance and high throughput when decoding long low-density parity-check (LDPC) codes. However, the application of BP decoding for short codes is hindered by dense parity- ...

The crowdedness of the RF spectrum constitutes problems in communication, however it also accommodates large amount of useful information. This information can be used to determine existing signals and operating contexts. However, spectrum condition varies ...

The rapid progress in machine learning in recent years has been based on a highly productive connection to gradient-based optimization. Further progress hinges in part on a shift in focus from pattern recognition to decision-making and multi-agent problems ...

Understanding how convolutional neural networks (CNNs) can efficiently learn high-dimensional functions remains a fundamental challenge. A popular belief is that these models harness the local and hierarchical structure of natural data such as images. Yet, ...

The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...

The need for high-quality automated seizure detection algorithms based on electroencephalography (EEG) becomes ever more pressing with the increasing use of ambulatory and long-term EEG monitoring. Heterogeneity in validation methods of these algorithms in ...

Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...

Interacting systems are ubiquitous in nature and engineering, ranging from particle dynamics in physics to functionally connected brain regions. Revealing interaction laws is of fundamental importance but also particularly challenging due to underlying con ...

Differentiable rendering (DR) is an emerging field that facilitates the calculation and propagation of gradients of 3D objects through images. In contrast to deep neural networks (DNNs), which generally lack the understanding of 3D objects that form the im ...

We introduce contextual stochastic bilevel optimization (CSBO) -- a stochastic bilevel optimization framework with the lower-level problem minimizing an expectation conditioned on some contextual information and the upper-level decision variable. This fram ...

Biomass resources can play an important role in the energy transition, being a dynamic feedstock that can be transformed either into solid, liquid or gaseous fuels. The biomass gasification is a versatile way to convert wastes into energy. In this work, th ...

In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to address the time it ...

This paper establishes the close relation that exists between the Fano noise suppression factor F and the Gm/ID FoM showing that F is proportional to the product of the thermal noise excess factor γn and the normalized Gm/ID function. Taking advantage of t ...

We investigate the problem of fluid injection at constant pressure in a 2D Discrete Fracture Network (DFN) with randomly oriented and uniformly distributed frictionally-stable fractures. We show that this problem shares similarities with the simpler scenar ...

It is well established that surface topography can affect cell functions. However, finding a reproducible and reliable method for regulating stem cell behavior is still under investigation. It has been shown that cell imprinted substrates contain micro- an ...

Control design for robotic systems is complex and often requires solving an optimization to follow a trajectory accurately. Online optimization approaches like Model Predictive Control (MPC) have been shown to achieve great tracking performance, but requir ...

We consider the computational task of sampling a bit string x from a distribution pi(x) = |(x|0)|2, where 0 is the unique ground state of a local Hamiltonian H. Our main result describes a direct link between the inverse spectral gap of H and the mixing ti ...

The goal of this paper is to characterize function distributions that general neural networks trained by descent algorithms (GD/SGD), can or cannot learn in polytime. The results are: (1) The paradigm of general neural networks trained by SGD is poly-time ...

Fast and robust parameter estimation with uncertainty quantification for the cardiac function

Chattez avec Graph Search

Preface: Advancing deep learning for remote sensing time series data analysis

Split weight distribution learning for binary neural networks exploiting nano-memristive graphene/sumanene/graphene devices

Toward Universal Belief Propagation Decoding for Short Binary Block Codes

Intelligent RF System for Ultra Low Power Spectrum Sensing with Machine Learning

A gentle introduction to gradient-based optimization and variational inequalities for machine learning

What can be learnt with wide convolutional neural networks?

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

SzCORE: Seizure Community Open-Source Research Evaluation framework for the validation of electroencephalography-based automated seizure detection algorithms

Scalable constrained optimization

Collective relational inference for learning heterogeneous interactions

Inverse Rendering for PET Image Reconstruction

Contextual Stochastic Bilevel Optimization

Comparison of regression techniques for generating surrogate models to predict the thermodynamic behavior of biomass gasification systems

Accelerated chemical science with AI

The Fano Noise Suppression Factor and the Gm/ID FoM

Injection-Induced Aseismic Slip in Tight Fractured Rocks

Engineered substrates incapable of induction of chondrogenic differentiation compared to the chondrocyte imprinted substrates

Training Efficient Controllers via Analytic Policy Gradient

A rapidly mixing Markov chain from any gapped quantum many-body system

Polynomial-time universality and limitations of deep learning