Machine learning for metallurgy I. A neural-network potential for Al-Cu

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In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

Neural networks have shown to be a powerful tool to represent the ground state of quantum many-body systems, including fermionic systems. However, efficiently integrating lattice symmetries into neural representations remains a significant challenge. In th ...

Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict t ...

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 ...

Driven by the need for more efficient and seamless integration of physical models and data, physics -informed neural networks (PINNs) have seen a surge of interest in recent years. However, ensuring the reliability of their convergence and accuracy remains ...

The evolution of airborne mapping witnesses the introduction of hybrid lidar-camera systems to enhance data collection, i.e. to obtain simultaneously high-density point-cloud and texture. Yet, the common adjustment of both optical data streams is a nontriv ...

Data augmentation is vital in deep learning for enhancing model robustness by artificially expanding training datasets. However, advanced methods like CutMix blend images and assign labels based on pixel ratios, often introducing label noise by neglecting ...

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

Electrical stimulation of the nervous system has emerged as a promising assistive technology in case of many injuries and illnesses across various parts of the nervous system. In particular, the invasive neuromodulation of the peripheral nervous system see ...

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 ...

Visual affordance segmentation identifies image regions of an object an agent can interact with. Existing methods re-use and adapt learning-based architectures for semantic segmentation to the affordance segmentation task and evaluate on small-size dataset ...

A search is presented for the pair production of new heavy resonances, each decaying into a top quark (t) or antiquark and a gluon (g). The analysis uses data recorded with the CMS detector from proton-proton collisions at a center-of-mass energy of 13 TeV ...

Throughout history, the pace of knowledge and information sharing has evolved into an unthinkable speed and media. At the end of the XVII century, in Europe, the ideas that would shape the "Age of Enlightenment" were slowly being developed in coffeehouses, ...

Federated learning (FL) is a machine learning approach where nodes collaboratively train a global model. As more nodes participate in a round of FL, the effectiveness of individual model updates by nodes also diminishes. In this study, we increase the effe ...

Machine learning and artificial intelligence methods have achieved remarkable success, matching and even surpassing human capabilities in various complex tasks. However, many demonstrations have generally neglected a critical part of the intelligence that ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

Recent surging interest in strengthening of High Entropy Alloys (HEAs) with possible chemical ordering motivates the development of new theory. Here, an existing theory for random alloys that accounts for solute-dislocation and solute–solute interactions i ...

The CO2/CO electroreduction reaction (CO2RR/CORR) to liquid products presents an enticing pathway to store intermittent renewable electricity. However, the selectivity for desirable high-value C3 products, such as n-propanol, remains unsatisfactory in the ...

Web search engines process billions of queries daily, making the balance between computational efficiency and ranking quality crucial. While neural ranking models have shown impressive performance, their computational costs, particularly in feature extract ...

In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...

Machine learning for metallurgy I. A neural-network potential for Al-Cu

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Random matrix methods for high-dimensional machine learning models

Spectroscopy of two-dimensional interacting lattice electrons using symmetry-aware neural backflow transformations

Driving and suppressing the human language network using large language models

Machine learning-aided generative molecular design

Residual-based attention in physics-informed neural networks

On the Perspectives of Image-to-Lidar Constraints in Dynamic Network Optimisation

SaliencyMix plus : Noise-Minimized Image Mixing Method With Saliency Map in Data Augmentation

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Biophysically accurate and machine learning-based surrogate models to optimize neuroprosthesis design and operation

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

Segmenting Object Affordances: Reproducibility and Sensitivity to Scale

Search for Pair Production of Heavy Particles Decaying to a Top Quark and a Gluon in the Lepton Plus Jets Final State in Proton-proton Collisions at √s=13 Tev

Machine Learning for Modeling Stock Returns

Harnessing Increased Client Participation with Cohort-Parallel Federated Learning

Sequential Reasoning with Socially Caused Beliefs

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Solute-strengthening in metal alloys with short-range order

Cooperative promotion of electroreduction of CO to n-propanol by *CO enrichment and proton regulation

Cost-Efficiency Trade-offs for Neural Cascade Rankers in Web Search

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange