∇<sup>2</sup>DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials

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PSI and North Carolina State University are developing a high-resolution multi-physics core solver for Pressurized Water Reactor (PWR) analysis in Cartesian geometry, using the neutron transport code nTRACER and two Machine Learning (ML) models providing t ...

Visual grouping is the ability to combine parts of objects to form whole objects, and it is crucial for general and robust vision. Despite this, a comprehensive model of how the process works in humans remains elusive. In my thesis, I investigate how well ...

This study introduces a novel neuromechanical model employing a detailed spiking neural network to explore the role of axial proprioceptive sensory feedback, namely stretch feedback, in salamander locomotion. Unlike previous studies that often oversimplifi ...

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We study the impact of the batch

n_b

on the iteration time

T

of training two-layer neural networks with one-pass stochastic gradient descent (SGD) on multi-index target functions of isotropic covariates. We characterize the optimal batch size minimizin ...

Graph neural network (GNN) models have presented astonishing achievements in various application fields. However, they are shown to be vulnerable to adversarial attacks on graph structure and unnoticeable perturbations on the graph structure can cause sign ...

Discovering principles underlying the control of animal behavior requires a tight dialogue between experiments and neuromechanical models. Such models have primarily been used to investigate motor control with less emphasis on how the brain and motor syste ...

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The process of quantifying and analyzing animal behavior involves translating the naturally occurring descriptive language of their actions into machine-readable code. Yet, codifying behavior analysis is often challenging without deep understanding of anim ...

The imaginary-time evolution of quantum states is integral to various fields, ranging from natural sciences to classical optimization or machine learning. Since simulating quantum imaginary-time evolution generally requires storing an exponentially large w ...

Multi-pod systolic arrays are emerging as the architecture of choice in DNN inference accelerators. Despite their potential, designing multi-pod systolic arrays to maximize effective throughput/Watt-i.e., throughput/Watt adjusted when accounting for array ...

The identification of nanomaterials with the properties required for energy-efficient electronic systems is usually a tedious human task. A workflow to rapidly localize and characterize nanomaterials at the various stages of their integration into large-sc ...

The effects of the finite size of the simulation box in equilibrium molecular dynamics simulations are investigated for prototypical superionic conductors of different types, namely, the fluorite-structure materials PbF2, CaF2, and UO2 (type II), and the a ...

∇<sup>2</sup>DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials

Chattez avec Graph Search

High-resolution predictions of the coolant properties for the 3D PWR core with artificial neural networks based on CTF

Differences in Visual Perception in Humans and Deep Neural Networks

Balancing central control and sensory feedback produces adaptable and robust locomotor patterns in a spiking, neuromechanical model of the salamander spinal cord

Drone-Based Urban Traffic Monitoring and User Behavior Prediction

Hierarchical Training of Deep Neural Networks Using Early Exiting

Programming nonlinear propagation for efficient optical learning machines

Online Learning and Information Exponents: On The Importance of Batch size, and Time/Complexity Tradeoffs

A Joint Graph Signal and Laplacian Denoising Network

NeuroMechFly v2: simulating embodied sensorimotor control in adult Drosophila

Closing the Gap to Quadratic Invariance: A Regret Minimization Approach to Optimal Distributed Control

Euclid preparation XLIII. Measuring detailed galaxy morphologies for Euclid with machine learning

AmadeusGPT: a natural language interface for interactive animal behavioral analysis

Stochastic Approximation of Variational Quantum Imaginary Time Evolution

Scale-out Systolic Arrays

High-speed identification of suspended carbon nanotubes using Raman spectroscopy and deep learning

NEST: The Neural Simulation Tool

Investigating finite-size effects in molecular dynamics simulations of ion diffusion, heat transport, and thermal motion in superionic materials