Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements

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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 ...

Quantum optics studies how photons interact with other forms of matter, the understanding of which was crucial for the development of quantum mechanics as a whole. Starting from the photoelectric effect, the quantum property of light has led to the develop ...

Legged robots are becoming increasingly agile in exhibiting dynamic behaviors such as running and jumping. Usually, such behaviors are either optimized and engineered offline (i.e. the behavior is designed for before it is needed), either through model-bas ...

A device (1) and method for processing data comprises at least one input layer (2), at least one modulation layer (3), and at least one output layer (4). The input layer (2) is configured to receive at least one carrier signal (Sc). The modulation layer (3 ...

Primordial features, in particular oscillatory signals, imprinted in the primordial power spectrum of density perturbations represent a clear window of opportunity for detecting new physics at high-energy scales. Future spectroscopic and photometric measur ...

We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the ...

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

Computing servers have played a key role in developing and processing emerging compute-intensive applications in recent years. Consolidating multiple virtual machines (VMs) inside one server to run various applications introduces severe competence for limi ...

Context. The Euclid mission of the European Space Agency will perform a survey of weak lensing cosmic shear and galaxy clustering in order to constrain cosmological models and fundamental physics. Aims. We expand and adjust the mock Euclid likelihoods of t ...

Existing machine learning potentials for predicting phonon properties of crystals are typically limited on a material-to-material basis, primarily due to the exponential scaling of model complexity with the number of atomic species. We address this bottlen ...

A range of behavioral and contextual factors, including eating and drinking behavior, mood, social context, and other daily activities, can significantly impact an individual's quality of life and overall well-being. Therefore, inferring everyday life aspe ...

We present a comprehensive study shedding light on how thermal fluctuations affect correlations in a Bose gas with contact repulsive interactions in one spatial dimension. The pair correlation function, the static structure factor, and the one-body density ...

Secondary electron emission is an important process that plays a significant role in several plasma-related applications. As measuring the secondary electron yield experimentally is very challenging, quantitative modelling of this process to obtain reliabl ...

Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

Point clouds are effective data structures for the rep- resentation of three-dimensional media and hence adopted in a wide range of practical applications. In many cases, the portrayed data is expected to be visualized by humans. After acquisition, point c ...

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 ...

Earth scientists study a variety of problems with remote sensing data, but they most often consider them in isolation from each other, which limits information flows across disciplines. In this work, we present METEOR, a meta-learning methodology for Earth ...

Saliency models are image-based prediction models that estimate human visual attention. Such models, when applied to architectural spaces, could pave the way for design decisions where visual attention is taken into account. In this study, we tested the pe ...

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 ...

Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven si ...

Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements

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Graph generative deep learning models with an application to circuit topologies

Room-Temperature Quantum Optomechanics and Free-Electron Quantum Optics

Quadruped-Frog: Rapid Online Optimization of Continuous Quadruped Jumping

Optical data processing using structural non-linearity

Euclid: The search for primordial features

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds

Euclid: Validation of the MontePython forecasting tools

Million-scale data integrated deep neural network for phonon properties of heuslers spanning the periodic table

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

Correlation properties of a one-dimensional repulsive Bose gas at finite temperature

Plasmonic effects in the neutralization of slow ions at a metallic surface

Machine learning-aided generative molecular design

Towards a multiscale point cloud structural similarity metric

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

Meta-learning to address diverse Earth observation problems across resolutions

Saliency prediction in 360° architectural scenes: Performance and impact of daylight variations

Decoding the brain: From neural representations to mechanistic models

Robustness of Local Predictions in Atomistic Machine Learning Models