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Diffuse correlation spectroscopy (DCS) is a powerful tool for assessing microvascular hemodynamic in deep tissues. Recent advances in sensors, lasers, and deep learning have further boosted the development of new DCS methods. However,…

Multiscale modeling, which integrates material properties from ab initio calculations into continuum-scale simulations, is a promising strategy for optimizing semiconductor devices. However, a key challenge remains: while ab initio methods…

Materials Science · Physics 2026-01-12 Taeyoung Jeong , Kun Hee Ye , Seungjae Yoon , Dohyun Kim , Yunjae Kim , Jung-Hae Choi

We reconstructed the 3D Fourier intensity distribution of mono-disperse prolate nano-particles using single-shot 2D coherent diffraction patterns collected at DESY's FLASH facility when a bright, coherent, ultrafast X-ray pulse intercepted…

The design of the project named CYGNO is presented. CYGNO is a new proposal supported by INFN, the Italian National Institute for Nuclear Physics, within CYGNUs proto-collaboration (CYGNUS-TPC) that aims to realize a distributed observatory…

We have previously introduced Spectral Diffusion Posterior Sampling (Spectral DPS) as a framework for accurate one-step material decomposition by integrating analytic spectral system models with priors learned from large datasets. This work…

Medical Physics · Physics 2025-03-31 Xiao Jiang , Grace J. Gang , J. Webster Stayman

Recently, the advancements in Virtual/Augmented Reality (VR/AR) have driven the demand for Dynamic Point Clouds (DPC). Unlike static point clouds, DPCs are capable of capturing temporal changes within objects or scenes, offering a more…

Computer Vision and Pattern Recognition · Computer Science 2025-05-20 Yating Liu , Yujie Zhang , Qi Yang , Yiling Xu , Zhu Li , Ye-Kui Wang

The direct detection and characterization of planetary and substellar companions at small angular separations is a rapidly advancing field. Dedicated high-contrast imaging instruments deliver unprecedented sensitivity, enabling detailed…

Earth and Planetary Astrophysics · Physics 2019-01-25 Tomas Stolker , Markus J. Bonse , Sascha P. Quanz , Adam Amara , Gabriele Cugno , Alexander J. Bohn , Anna Boehle

Discrete diffusion models (DDMs) are a powerful class of generative models for categorical data, but they typically require many function evaluations for a single sample, making inference expensive. Existing acceleration methods either rely…

Machine Learning · Computer Science 2025-12-16 Yansong Gao , Yu Sun

A sampling calorimeter equipped with gaseous sensor layers with digital readout is near perfect for "Particle Flow Algorithm" approach, since it is homogeneous over large surfaces, robust, cost efficient, easily segmentable to any readout…

Instrumentation and Detectors · Physics 2015-06-19 Y. Haddad , M. Ruan , V. Boudry

The udkm1Dsim toolbox is a collection of Python classes and routines to simulate the thermal, structural, and magnetic dynamics after laser excitation as well as the according X-ray scattering response in one-dimensional sample structures.…

Computational Physics · Physics 2021-05-31 Daniel Schick

A series of test experiments on three different Si-based neutron detectors, namely a 1-d 128 channel, 0.5 mm space resolution Si microstrip sensor coupled to natural Gd converter, a medium size ? 1 cm2 PIN diode coupled to nGd2O3 or…

Instrumentation and Detectors · Physics 2018-05-04 G. Mauri , M. Mariotti , F. Casinini , F. Sacchetti , C. Petrillo

We provide a mathematical study of the modified Diffusion Monte Carlo (DMC) algorithm introduced in the companion article \cite{DMC}. DMC is a simulation technique that uses branching particle systems to represent expectations associated…

Probability · Mathematics 2014-04-11 Martin Hairer , Jonathan Weare

The nitrogen vacancy (NV) center in diamond is an increasingly popular quantum sensor for microscopy of electrical current, magnetization, and spins. However, efficient NV-sample integration with a robust, high-quality interface remains an…

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely…

Despite the recent advances in various Structural Genomics Projects, a large gap remains between the number of sequenced and structurally characterized proteins. Some reasons for this discrepancy include technical difficulties, labor, and…

Computer Vision and Pattern Recognition · Computer Science 2023-04-07 Arjang Fahim , Stephanie Irausquin , Homayoun Valafar

Silicon photomultipliers (SiPMs) are currently the most prevalent photon detection technology in modern experiments in high-energy physics, astroparticle physics, neutrino physics, and dark matter searches. The high detection efficiency for…

Instrumentation and Detectors · Physics 2026-03-31 Tanay Dey , Suraj Shaw , Ritabrata Banerjee , Pratik Majumdar , Satyaki Bhattacharya

High-performance computing (HPC) requires resilience techniques such as checkpointing in order to tolerate failures in supercomputers. As the number of nodes and memory in supercomputers keeps on increasing, the size of checkpoint data also…

Distributed, Parallel, and Cluster Computing · Computer Science 2019-06-13 Kai Keller , Leonardo Bautista Gomez

Differential dynamic microscopy (DDM) is a form of video image analysis that combines the sensitivity of scattering and the direct visualization benefits of microscopy. DDM is broadly useful in determining dynamical properties including the…

Soft Condensed Matter · Physics 2022-04-12 Mengyang Gu , Yimin Luo , Yue He , Matthew E. Helgeson , Megan T. Valentine

Recently, coherent digital subcarrier multiplexing (DSCM) technology has become an attractive solution for next-generation ultra-high-speed datacenter interconnects (DCIs). To meet the requirements of low-cost and low-power consumption in…

Signal Processing · Electrical Eng. & Systems 2024-07-16 Wei Wang , Dongdong Zou , Weihao Ni , Fan Li

DADApy is a python software package for analysing and characterising high-dimensional data manifolds. It provides methods for estimating the intrinsic dimension and the probability density, for performing density-based clustering and for…