Surface segregation in high-entropy alloys from alchemical machine learning

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Estimating ecosystem-atmosphere fluxes such as evapotranspiration (ET) in a robust manner and at a global scale remains a challenge. Methods based on machine learning (ML) have shown promising results in achieving such upscaling, providing a complementary ...

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 ...

Monitoring of cardiac output (CO) is a mainstay of hemodynamic management in the acutely or critically ill patient. Invasive determination of CO using thermodilution, albeit regarded as the gold standard, is cumbersome and bears risks inherent to catheteri ...

A hybrid photothermal catalytic system, which combines both the photochemical (light) and thermal (heat) activation pathways over a bifunctional catalyst, has demonstrated remarkable levels of reaction activity and selectivity when compared with individual ...

This study presents an augmented hybrid approach for improving the diagnosis of malignant skin lesions by combining convolutional neural network (CNN) predictions with selective human interventions based on prediction confidence. The algorithm retains high ...

Two design studios were successively taught in two schools of architecture: one at the Harvard Graduate School of Design (GSD) and the other at the Swiss Federal Institute of Technology in Lausanne (EPFL) in Lausanne, Switzerland, based on "A Global Morato ...

This paper introduces a novel architecture for Quantum Graph Neural Networks, which is significantly different from previous approaches found in the literature. The proposed approach produces similar outcomes with respect to previous models but with fewer ...

To retrieve Surface Solar Radiation (SSR) from satellite images, a baseline reflectance of the observed ground in clear-sky conditions, also called background reflectance, is necessary to distinguish atmospheric absorption and scattering effects from surfa ...

This book has been designed for instructors in higher education or adult training who wish to develop a new course or revise an existing one. It adopts a practical, visual, and modular approach based on the principle of constructive alignment. At its heart ...

The digitization of 3D deformable objects remains a significant challenge in computer graphics and vision, particularly in the accurate modeling of garments. Garments exhibit complex shape variability, non-rigid deformations, and frequent self-occlusion, m ...

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 ...

This study integrates deep learning and geospatial analysis to enhance soil loss estimation in the Moulouya Watershed, a region prone to erosion due to diverse topography and climatic conditions. Traditional models like the Universal Soil Loss Equation (US ...

This article reports on the current state of the OBI DICT project, a bilingual e-dictionary of oracle-bone inscriptions (OBI), incorporating artificial intelligence (AI) image recognition technology. It first provides a brief overview of the development of ...

Diazo compounds of the type R1R2CN2 are widely used in organometallic catalysis and synthetic chemistry. Stable diazoolefins, R1R2C=CN2, have only been isolated recently. Diazoolefins undergo cycloaddition reactions and tend to be carbon donors when used a ...

Scientific Machine Learning (SciML) is a recently emerged research field which combines physics-based and data-driven models for the numerical approximation of differential problems. Physics-based models rely on the physical understanding of the problem at ...

The spatially resolved identification of active sites on the heterogeneous catalyst surface is an essential step toward directly visualizing a catalytic reaction with atomic scale. To date, ferrous centers on platinum group metals have shown promising pote ...

Integer linear programming (ILP) is an elegant approach to solve linear optimization problems, naturally described using integer decision variables. Within the context of physics-inspired machine learning (ML) applied to chemistry, we demonstrate the relev ...

The official Spherical Tokamak for Energy Production mission aims to demonstrate the ability to generate net electricity from fusion with the STEP Prototype Power plant. One of the key technological and engineering challenges in fusion power plants is mana ...

Faithful human performance capture and free-view rendering from sparse RGB observations is a long-standing problem in Vision and Graphics. The main challenges are the lack of observations and the inherent ambiguities of the setting, e.g. occlusions and dep ...

Superconducting electronics provide us with cryogenic digital circuits that can rival established technologies in performance and energy consumption. Today, the lack of tools for the design of large-scale integrated superconducting circuits is a major obst ...

Surface segregation in high-entropy alloys from alchemical machine learning

Chattez avec Graph Search

On the added value of sequential deep learning for the upscaling of evapotranspiration

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

Machine learning-enabled estimation of cardiac output from peripheral waveforms is independent of blood pressure measurement location in an in silico population

Heterogeneous catalysis via light-heat dual activation: A path to the breakthrough in C1 chemistry

Enhancing skin lesion classification: a CNN approach with human baseline comparison

Teaching Not To

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Retrieval of Surface Solar Radiation through Implicit Albedo Recovery from Temporal Context

Course Design - A Visual and Modular Approach

Neural Garment Modeling

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

Application of Deep Learning and Geospatial Analysis in Soil Loss Risk in the Moulouya Watershed, Morocco

The Current State of the OBI DICT Project: A Bilingual e-Dictionary of Oracle-Bone Inscriptions with AI Image Recognition

Exploring the reactivity of N-heterocyclic diazoolefins

Combining physics-based and data-driven models: advancing the frontiers of research with scientific machine learning

Probing Catalytic Sites and Adsorbate Spillover on Ultrathin FeO2-x Film on Ir(111) during CO Oxidation

Integer Linear Programming for Unsupervised Training Set Selection in Molecular Machine Learning

Models implemented in the methodological approach to design the initial STEP first wall contour

MetaCap: Meta-learning Priors from Multi-view Imagery for Sparse-View Human Performance Capture and Rendering

Technology-Aware Logic Synthesis for Superconducting Electronics