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Ultraweak electron-phonon coupling strength in cubic boron arsenide unveiled by ultrafast dynamics

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Physical Review B 2022年第17期105卷 174306-174306页

作者： Z. Y. Tian Q. Y. Zhang Y. W. Xiao G. A. Gamage F. Tian S. Yue V. G. Hadjiev Jiming Bao Zhifeng Ren Erjun Liang Jimin Zhao School of Physics & Microelectronics and Key Laboratory of Materials Physics Zhengzhou University Zhengzhou 450052 China Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing 100190 China School of Physical Science and Technology ShanghaiTech University Shanghai 201210 China Department of Physics University of Houston Houston Texas 77204 USA Texas Center for Superconductivity at the University of Houston (TcSUH) University of Houston Houston Texas 77204 USA Department of Electrical and Computer Engineering University of Houston Houston Texas 77204 USA Materials Science & Engineering University of Houston Houston Texas 77204 USA School of Physical Sciences University of Chinese Academy of Sciences Beijing 100049 China Songshan Lake Materials Laboratory Dongguan Guangdong 523808 China

We report a time-resolved ultrafast quasiparticle dynamics investigation of cubic boron arsenide (c-BAs), which is a recently discovered highly thermally conducting material. The excited-state ultrafast relaxation channels dictated by the electron-phonon coupling (EPC), phonon-phonon scattering, and radiative electron-hole recombination have been unambiguously identified, along with their typical interaction times. Significantly, the EPC strength is obtained from the dynamics, with a value of λT2=0.008 (corresponding to λ〈Ω2〉=1.18±0.08ps–2), demonstrating an unusually weak coupling between the electrons and phonons. As a comparison, an ultraweak EPC strength for graphene is also expected. We propose that preserving an ultrasmall EPC strength may be a prerequisite for exhibiting an ultrahigh thermal conductivity. Our investigation provides insight for searching and designing ultrahigh thermal conductivity materials. Notably, during our analysis we have generalized the fluence-dependence method for obtaining the EPC strength to room temperature, which can be applied to many other types of quantum materials in the future.

关键词： Carrier dynamics Carrier generation & recombination Heat transfer Lifetimes & widths Phonons Ordered compounds Semiconductor compounds Single crystal materials Coherent control Light scattering Liquid helium cooling

Spatial atlas of the mouse central nervous system at molecular resolution (vol 622, pg 552, 2023)

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NATURE 2024年第7993期625卷 E6-E6页

作者： Shi, Hailing He, Yichun Zhou, Yiming Huang, Jiahao Maher, Kamal Wang, Brandon Tang, Zefang Luo, Shuchen Tan, Peng Wu, Morgan Lin, Zuwan Ren, Jingyi Thapa, Yaman Tang, Xin Chan, Ken Y. Deverman, Benjamin E. Shen, Hao Liu, Albert Liu, Jia Wang, Xiao Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry Massachusetts Institute of Technology Cambridge MA USA John A. Paulson School of Engineering and Applied Sciences Harvard University Boston MA USA Computational and Systems Biology PhD Program Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Biology Massachusetts Institute of Technology Cambridge MA USA Klarman Cell Observatory Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry and Chemical Biology Harvard University Cambridge MA USA Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA

Deep equilibrium architectures for inverse problems in imaging

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arXiv 2021年

作者： Gilton, Davis Ongie, Gregory Willett, Rebecca Department of Electrical and Computer Engineering University of Wisconsin-Madison 1415 Engineering Dr MadisonWI53706 United States Department of Mathematical and Statistical Sciences Marquette University 1250 W Wisconsin Ave MilwaukeeWI53233 United States Departments of Computer Science and Statistics University of Chicago 5747 S Ellis Ave ChicagoIL60637 United States

Recent efforts on solving inverse problems in imaging via deep neural networks use architectures inspired by a fixed number of iterations of an optimization method. The number of iterations is typically quite small due to difficulties in training networks corresponding to more iterations;the resulting solvers cannot be run for more iterations at test time without incurring significant errors. This paper describes an alternative approach corresponding to an infinite number of iterations, yielding a consistent improvement in reconstruction accuracy above state-of-the-art alternatives and where the computational budget can be selected at test time to optimize context-dependent trade-offs between accuracy and computation. The proposed approach leverages ideas from Deep Equilibrium Models, where the fixed-point iteration is constructed to incorporate a known forward model and insights from classical optimization-based reconstruction methods. © 2021, CC BY.

关键词： Deep neural networks

Non-Destructive, High-Resolution, Chemically Specific, 3D Nanostructure Characterization using Phase-Sensitive EUV Imaging Reflectometry

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arXiv 2024年

作者： Tanksalvala, Michael Porter, Christina L. Esashi, Yuka Wang, Bin Jenkins, Nicholas W. Zhang, Zhe Miley, Galen P. Knobloch, Joshua L. McBennett, Brendan Horiguchi, Naoto Yazdi, Sadegh Zhou, Jihan Jacobs, Matthew N. Bevis, Charles S. Karl, Robert M. Johnsen, Peter Ren, David Waller, Laura Adams, Daniel E. Cousin, Seth L. Liao, Chen-Ting Miao, Jianwei Gerrity, Michael Kapteyn, Henry C. Murnane, Margaret M. STROBE Science and Technology Center JILA Department of Physics University of Colorado BoulderCO80309 United States Department of Chemistry Northwestern University 2145 Sheridan Road EvanstonIL60208 United States Imec Kapeldreef 75 Leuven3001 Belgium University of Colorado BoulderCO80309 United States Department of Physics & Astronomy California NanoSystem Institute University of California Los AngelesCA90095 United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States KMLabs Inc. 4775 Walnut St #102 BoulderCO80301 United States

Next-generation nano and quantum devices have increasingly complex 3D structure. As the dimensions of these devices shrink to the nanoscale, their performance is often governed by interface quality or precise chemical or dopant composition. Here we present the first phase-sensitive extreme ultraviolet imaging reflectometer. It combines the excellent phase stability of coherent high-harmonic sources, the unique chemical- and phase-sensitivity of extreme ultraviolet reflectometry, and state-of-the-art ptychography imaging algorithms. This tabletop microscope can non-destructively probe surface topography, layer thicknesses, and interface quality, as well as dopant concentrations and profiles. High-fidelity imaging was achieved by implementing variable-angle ptychographic imaging, by using total variation regularization to mitigate noise and artifacts in the reconstructed image, and by using a high-brightness, high-harmonic source with excellent intensity and wavefront stability. We validate our measurements through multiscale, multimodal imaging to show that this technique has unique advantages compared with other techniques based on electron and scanning-probe microscopies. © 2024, CC BY-NC-ND.

关键词： Reflectometers

Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images

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arXiv 2021年

作者： Sharifrazi, Danial Alizadehsani, Roohallah Roshanzamir, Mohamad Joloudari, Javad Hassannataj Shoeibi, Afshin Jafari, Mahboobeh Hussain, Sadiq Sani, Zahra Alizadeh Hasanzadeh, Fereshteh Khozeimeh, Fahime Khosravi, Abbas Nahavandi, Saeid Panahiazar, Maryam Zare, Assef Shariful Islam, Sheikh Mohammed Rajendra, U. Acharya Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Deakin University Geelong Australia Department of Engineering Fasa Branch Islamic Azad University Post Box No 364 Fasa Fars*** Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran System Administrator Dibrugarh University Assam786004 India Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Omid hospital Iran University of Medical Sciences Tehran Iran Institute for Computational Health Sciences University of California San Francisco United States Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Institute for Physical Activity and Nutrition Deakin University Melbourne Australia Cardiovascular Division The George Institute for Global Health Australia Sydney Medical School University of Sydney Australia Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to another. Therefore, it is essential to use an automatic diagnosis system along with clinical procedures for the rapid diagnosis of COVID-19 to prevent its spread. Artificial intelligence techniques using computed tomography (CT) images of the lungs and chest radiography have the potential to obtain high diagnostic performance for Covid-19 diagnosis. In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images. A new X-ray image dataset was collected and subjected to high pass filter using a Sobel filter to obtain the edges of the images. Then these images are fed to CNN deep learning model followed by SVM classifier with ten-fold cross validation strategy. This method is designed so that it can learn with not many data. Our results show that the proposed CNN-SVM with Sobel filtering (CNN-SVM+Sobel) achieved the highest classification accuracy of 99.02% in accurate detection of COVID-19. It showed that using Sobel filter can improve the performance of CNN. Unlike most of the other researches, this method does not use a pre-trained network. We have also validated our developed model using six public databases and obtained the highest performance. Hence, our developed model is ready for clinical application. Copyright © 2021, The Authors. All rights reserved.

关键词： Data mining

PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation

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PolarNet: An Improved Grid Representation for Online LiDAR P...

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yang Zhang Zixiang Zhou Philip David Xiangyu Yue Zerong Xi Boqing Gong Hassan Foroosh Department of Computer Science University of Central Florida Computational and Information Sciences Directorate U.S. Army Research Laboratory Department of Electrical Engineering and Computer Sciences University of California Berkeley

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

The requirement of fine-grained perception by autonomous driving systems has resulted in recently increased research in the online semantic segmentation of single-scan LiDAR. Emerging datasets and technological advancements have enabled researchers to benchmark this problem and improve the applicable semantic segmentation algorithms. Still, online semantic segmentation of LiDAR scans in autonomous driving applications remains challenging due to three reasons: (1) the need for near-real-time latency with limited hardware, (2) points are distributed unevenly across space, and (3) an increasing number of more fine-grained semantic classes. The combination of the aforementioned challenges motivates us to propose a new LiDAR-specific, KNN-free segmentation algorithm - PolarNet. Instead of using common spherical or bird's-eye-view projection, our polar bird's-eye-view representation balances the points per grid and thus indirectly redistributes the network's attention over the long-tailed points distribution over the radial axis in polar coordination. We find that our encoding scheme greatly increases the mIoU in three drastically different real urban LiDAR single-scan segmentation datasets while retaining ultra low latency and near real-time throughput.

关键词： Three-dimensional displays Laser radar Semantics Image segmentation Two dimensional displays Neural networks Task analysis

Investigation of Conditions for Non-degeneracy and Normality in Control Problems with Equality and Inequality State Constraints

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IFAC-PapersOnLine 2020年第2期53卷 6869-6874页

作者： Aram Arutyunov Dmitry Karamzin Fernando L. Pereira Institute of Control Sciences of the Russian Academy of Science Moscow Russia Federal Research Center “Computer Science and Control” of the Russian Academy of Science Moscow Russia Research Center for Systems and Technologies (SYSTEC) Electrical and Computer Engineering Department Faculty of Engineering University of Porto 4200-465 Portugal

This work aims to investigate conditions for normality and non-degeneracy of the maximum principle for general state-constrained optimal control problems in which the state constraints are given by equalities and inequalities. Non-degeneracy is proved under a regularity condition formulated in terms of the limiting normal cone to the feasible control set. The same regularity condition implies normality of the maximum principle if one of the end-points is free.

关键词： Optimal control maximum principle state constraints

CLEP: Contrastive Learning for Epileptic Seizure Prediction Using a Spatio-Temporal-Spectral Network

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TechRxiv

TechRxiv 2022年

作者： Guo, Lianghui Yu, Tao Zhao, Shijie Li, Xiaoli Liao, Xiaofeng Li, Yang Department of Automation Sciences and Electrical Engineering Beihang University Beijing100083 China The Beijing Institute of Functional Neurosurgery Xuanwu Hospital Capital Medical University Beijing100053 China The Northwestern Polytechnical University Xi’an710072 China The Beijing Normal University Beijing100875 China The College of Computer Science Chongqing University Chongqing400715 China

Seizure prediction of epileptic preictal period through electroencephalogram (EEG) signals is important for clinical epilepsy diagnosis. However, recent deep learning-based methods commonly employ intra-subject training strategy and need sufficient data, which are laborious and time-consuming for a practical system and pose a great challenge for seizure predicting. Besides, multi-domain characterizations, including spatio-temporal-spectral dependencies in an epileptic brain are generally neglected or not considered simultaneously in current approaches, and this insufficiency commonly leads to suboptimal seizure prediction performance. To tackle the above issues, in this paper, we propose Contrastive Learning for Epileptic seizure Prediction (CLEP) using a Spatio-Temporal-Spectral Network (STS-Net). Specifically, the CLEP learns intrinsic epileptic EEG patterns across subjects by contrastive learning. The STS-Net extracts multi-scale temporal and spectral representations under different rhythms from raw EEG signals. Then, a novel triple attention layer (TAL) is employed to construct inter-dimensional interaction among multi-domain features. Moreover, a spatio dynamic graph convolution network (sdGCN) is proposed to dynamically model the spatial relationships between electrodes and aggregate spatial information. The proposed CLEP-STS-Net achieved a sensitivity of 96.7% and a false prediction rate of 0.072/h on the CHB-MIT scalp EEG database. We also validate the proposed method on clinical intracranial EEG (iEEG) database from the Xuanwu Hospital of Capital Medical University, and the predicting system yielded a sensitivity of 95%, a false prediction rate of 0.087/h. The experimental results outperform the state-of-the-art studies which validate the efficacy of our method. Our code is available at https://***/LianghuiGuo/CLEP-STS-Net. © 2022, CC BY.

关键词： Convolution

Randomized multilevel Monte Carlo for embarrassingly parallel inference

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arXiv 2021年

作者： Jasra, Ajay Law, Kody J.H. Tarakanov, Alexander Yu, Fangyuan Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal 23955 Saudi Arabia Department of Mathematics University of Manchester ManchesterM13 9PL United Kingdom

This position paper summarizes a recently developed research program focused on inference in the context of data centric science and engineering applications, and forecasts its trajectory forward over the next decade. Often one endeavours in this context to learn complex systems in order to make more informed predictions and high stakes decisions under uncertainty. Some key challenges which must be met in this context are robustness, generalizability, and interpretability. The Bayesian framework addresses these three challenges, while bringing with it a fourth, undesirable feature: it is typically far more expensive than its deterministic counterparts. In the 21st century, and increasingly over the past decade, a growing number of methods have emerged which allow one to leverage cheap low-fidelity models in order to precondition algorithms for performing inference with more expensive models and make Bayesian inference tractable in the context of high-dimensional and expensive models. Notable examples are multilevel Monte Carlo (MLMC), multi-index Monte Carlo (MIMC), and their randomized counterparts (rMLMC), which are able to provably achieve a dimension-independent (including ∞−dimension) canonical complexity rate with respect to mean squared error (MSE) of 1/MSE. Some parallelizability is typically lost in an inference context, but recently this has been largely recovered via novel double randomization approaches. Such an approach delivers independent and identically distributed samples of quantities of interest which are unbiased with respect to the infinite resolution target distribution. Over the coming decade, this family of algorithms has the potential to transform data centric science and engineering, as well as classical machine learning applications such as deep learning, by scaling up and scaling out fully Bayesian inference. Copyright © 2021, The Authors. All rights reserved.

关键词： Monte Carlo methods