Gaussian Process Regression for Materials and Molecules

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In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

We present a framework for performing regression when both covariate and response are probability distributions on a compact and convex subset of

\R^d

. Our regression model is based on the theory of optimal transport and links the conditional Fr'echet m ...

Nodal properties are of particular importance when investigating patterns in brain networks. Nodal information is usually studied by comparing a few nodal measurements (up to three), resulting in analyses in three-dimensional spaces, at maximum. We offer a ...

Time has always been a central factor in understanding the challenges of daily mobility. For a long time, and still today, methods of economic evaluation of transport projects have monetized time savings so that they can be included in the cost–benefit ana ...

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

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 ...

In this Editorial, our Chief Editor and members of our Advisory Editorial Board discuss recent breakthroughs, current challenges, and emerging opportunities in single-cell biology and share their vision of "where the field is headed." ...

Background and Objective: Computational models of the cardiovascular system allow for a detailed and quantitative investigation of both physiological and pathological conditions, thanks to their ability to combine clinical-possibly patient-specific-data wi ...

This contribution presents an isotropic field probe to assess human exposition to broadband magnetic fields in the 400 MHz to 6 GHz band according to the ICNIRP 2020 guidelines. This device complements the current practice in this band, based on the measur ...

Materials that efficiently promote the thermodynamically uphill water-splitting reaction under solar illumination are essential for generating carbon-free ("green") hydrogen. Mapping out the combinatorial space of potential photocatalysts for this reaction ...

The design and discovery of potential novel materials is critical for the advancement of climate change mitigation technologies. In this respect, metal-organic frameworks (MOFs) have received considerable attention over the last two decades. The combinatio ...

Two related methods for inverting line-integrated measurements are presented in this research paper in the context of the recent deuterium-tritium experiments in the JET tokamak. Unlike traditional methods of tomography, these methods rely on making use of ...

We propose a framework for the construction of 4D scientific models for cities of the past. It leverages both the use of computational methods for bootstrapping, and a collaborative interface for visualisation and edition. This permits to continuously enri ...

Machine learned interatomic interaction potentials have enabled efficient and accurate molecular simulations of closed systems. However, external fields, which can greatly change the chemical structure and/or reactivity, have been seldom included in curren ...

Let k be a field, and let L be an etale k-algebra of finite rank. If a is an element of k(x), let X-a be the affine variety defined by N-L/k(x) = a. Assuming that L has at least one factor that is a cyclic field extension of k, we give a combinatorial desc ...

Gaussian Process Regression for Materials and Molecules

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Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Distributional Regression and Autoregression via Optimal Transport

Connectome embedding in multidimensional graph spaces

Appropriations and Uses of Travel Time

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Graph generative deep learning models with an application to circuit topologies

Single-cell biology: what does the future hold?

The role of computational methods in cardiovascular medicine: a narrative review

Tri-axial H-Field Sensor with Frequency-shaped Response in the band 400 MHz - 6 GHz

Data-Intensive Exploration of the Photoelectrochemical Responses of Main-Group Metal Sulfides

A diversity-driven discovery of potential nanoporous materials for energy-related applications

Regressive Tomography and Direct Regressive Reconstruction: A Research Note

From Automated Bootstrapping to Collaborative Editing: A Framework for 4D City Reconstruction

Universal machine learning for the response of atomistic systems to external fields

NORM TORI OF ETALE ALGEBRAS AND UNRAMIFIED BRAUER GROUPS