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arXiv 2023年

作者： Lyu, Guitao Minami, Yuki Kim, Na Young Byrnes, Tim Watanabe, Gentaro School of Physics Zhejiang Institute of Modern Physics Zhejiang University Zhejiang Hangzhou310027 China Division of Natural and Applied Sciences Duke Kunshan University Jiangsu Kunshan215300 China Faculty of Engineering Gifu University 1-1 Yanagido Gifu501-1193 Japan Institute for Quantum Computing University of Waterloo 200 University Ave. West WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering University of Waterloo 200 University Ave. West WaterlooONN2L 3G1 Canada Waterloo Institute for Nanotechnology University of Waterloo 200 University Ave. West WaterlooONN2L 3G1 Canada New York University Shanghai NYU-ECNU Institute of Physics at NYU Shanghai Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning 567 West Yangsi Road Shanghai200126 China State Key Laboratory of Precision Spectroscopy School of Physical and Material Sciences East China Normal University Shanghai200062 China NYUAD Research Institute New York University Abu Dhabi United Arab Emirates Department of Physics New York University New YorkNY10003 United States Zhejiang Province Key Laboratory of Quantum Technology and Device Zhejiang University Zhejiang Hangzhou310027 China

The phase distribution in a Bose-Einstein condensate can realize various topological states classified by distinct winding numbers. While states with different winding numbers are topologically protected in the linear Schrödinger equation, when nonlinearities are introduced, violations of the topological protection can occur, leading to unwinding. Exciton-polariton condensates constitute a nonlinear open-dissipative system that is well suited to studying such physics. Here we show that a one-dimensional array of exciton-polariton condensates displays a spontaneous phase unwinding from a πto zero-state. We clarify that this collective mode transition is caused by the combined effect of nonlinearity and topological defects in the condensates. While the mode-switching phenomenon observed in our previous experiment was interpreted as the single-particle mode competition, we offer an alternative explanation in terms the collective phase unwinding and find its evidence by reanalyzing the experimental data. Our results open a route towards active control of the mode switching by manipulating the topological defects in prospective quantum polaritonic devices. Copyright © 2023, The Authors. All rights reserved.

关键词： Winding

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Frontispiece: Peptoid-Directed Formation of Five-Fold Twinned Au Nanostars through Particle Attachment and Facet Stabilization

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Angewandte Chemie International Edition 2022年第14期61卷

作者： Dr. Biao Jin Dr. Feng Yan Dr. Xin Qi Dr. Bin Cai Dr. Jinhui Tao Dr. Xiaofeng Fu Dr. Susheng Tan Dr. Peijun Zhang Dr. Jim Pfaendtner Nada Y. Naser Dr. François Baneyx Dr. Xin Zhang Dr. James J. DeYoreo Dr. Chun-Long Chen Physical Sciences Division Pacific Northwest National Laboratory 902 Battelle Boulevard Richland WA 99352 USA School of Chemistry & Chemical Engineering Linyi University The Middle Part of Shuangling Road Linyi Shandong Province 276005 China Department of Chemical Engineering University of Washington 1410 NE Campus Parkway Seattle WA 98195 USA Present address: School of Chemistry and Chemical Engineering Shandong University Shanda Nan Road 27 Jinan (China) Department of Biological Science Florida State University 600 W College Ave Tallahassee FL 32306 USA Department of Electrical and Computer Engineering & Petersen Institute of Nanoscience and Engineering (PINSE) University of Pittsburgh 4200 Fifth Ave Pittsburgh PA 15260 USA Division of Structural Biology Wellcome Trust Centre for Human Genetics University of Oxford Roosevelt Drive Wellington Square Oxford OX3 7BN UK Diamond Light Source Harwell Science and Innovation Campus Didcot OX11 0DE UK Department of Materials Science and Engineering University of Washington 1410 NE Campus Parkway Seattle WA 98195 USA

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Peptoid-Directed Formation of Five-Fold Twinned Au Nanostars through Particle Attachment and Facet Stabilization

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Angewandte Chemie 2022年第14期134卷

While bio-inspired synthesis offers great potential for controlling nucleation and growth of inorganic particles, precisely tuning biomolecule–particle interactions is a long-standing challenge. Herein, we used variations in peptoid sequence to manipulate peptoid–Au interactions, leading to the synthesis of concave five-fold twinned, five-pointed Au nanostars via a process of repeated particle attachment and facet stabilization. Ex situ and liquid-phase TEM observations show that a balance between particle attachment biased to occur near the star points, preferential growth along the [100] direction, and stabilization of (111) facets is critical to forming star-shaped particles. Molecular simulations predict that interaction strengths between peptoids and distinct Au facets differ significantly and thus can alter attachment kinetics and surface energies to form the stars. This work provides new insights into how sequence-defined ligands affect particle growth to regulate crystal morphology.

关键词： Facet Stabilization Five-Fold Twinned Au Star Particle Attachment Sequence-Defined Peptoids

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Frontispiz: Peptoid-Directed Formation of Five-Fold Twinned Au Nanostars through Particle Attachment and Facet Stabilization

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Angewandte Chemie 2022年第14期134卷

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Closed-loop sparse channel estimation for wideband millimeter-wave full-dimensional MIMO systems

arXiv

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arXiv 2019年

作者： Liao, Anwen Gao, Zhen Wang, Hua Chen, Sheng Alouini, Mohamed-Slim Yin, Hao School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom King Abdulaziz University Jeddah21589 Saudi Arabia Electrical Engineering Program Division of Physical Sciences and Engineering King Abdullah University of Science and Technology Thuwal Makkah Province Saudi Arabia Institute of China Electronic System Engineering Corporation Beijing100141 China

This paper proposes a closed-loop sparse channel estimation (CE) scheme for wideband millimeter-wave hybrid full-dimensional multiple-input multiple-output and time division duplexing based systems, which exploits the channel sparsity in both angle and delay domains. At the downlink CE stage, random transmit precoding matrix is designed at base station (BS) for channel sounding, and receive combining matrices at user devices (UDs) are designed whereby the hybrid array is visualized as a low-dimensional digital array for facilitating the multi-dimensional unitary ESPRIT (MDU-ESPRIT) algorithm to estimate respective angle-of-arrivals (AoAs). At the uplink CE stage, the estimated downlink AoAs, namely, uplink angle-of-departures (AoDs), are exploited to design multi-beam transmit precoding matrices at UDs to enable BS to estimate the uplink AoAs, i.e., the downlink AoDs, and delays of different UDs, whereby the MDU-ESPRIT algorithm is used based on the designed receive combining matrix at BS. Furthermore, a maximum likelihood approach is proposed to pair the channel parameters acquired at the two stages, and the path gains are then obtained using least squares estimator. According to spectrum estimation theory, our solution can acquire the super-resolution estimations of the AoAs/AoDs and delays of sparse multipath components with low training overhead. Simulation results verify the better CE performance and lower computational complexity of our solution over state-of-the-art approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： Millimeter waves

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Modulating motor cortical oscillation with coordinated reset multifocal transcranial magnetic stimulation

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Journal of neurophysiology 2023年第5期129卷 1061-1071页

作者： Kai-Hsiang Stanley Chen Tzu-Kang Hung Rupesh Kumar Chikara Yu-Ting Kuo Yi-Ping Liu Yan-Siou Dong Yih-Ru Wu Li-Wei Ko Ying-Zu Huang Robert Chen Department of Neurology National Taiwan University Hospital Hsinchu Branch Hsinchu Taiwan. Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu Taiwan. Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) National Yang Ming Chiao Tung University Hsinchu Taiwan. Jane and John Justin Neurosciences Center Cook Children's Health Care System Fort Worth Texas United States. Department of Bioengineering University of Texas at Arlington Arlington Texas United States. Department of Neurology Chang Gung Memorial Hospital and Chang Gung University College of Medicine Taoyuan Taiwan. Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan. Neuroscience Research Center and Department of Neurology Chang Gung Memorial Hospital and Chang Gung University College of Medicine Taoyuan Taiwan. Institute of Cognitive Neuroscience College of Health Science and Technology Taoyuan Taiwan. Krembil Research Institute University Health Network Toronto Ontario Canada. Division of Neurology Department of Medicine University of Toronto Toronto Ontario Canada. Edmond J. Safra Program in Parkinson's Disease University Health Network Toronto Ontario Canada.

According to the theory of coordinated reset (CR) stimulation, multifocal bursts of stimuli delivered in a random order with a specific interval may reduce the resonance power of the oscillatory generator in the epicenter. We develop a noninvasive coordinated multifocal burst stimulation (COMBS) with three repetitive transcranial stimulation machines based on CR theory to modulate the target frequency in the primary motor cortex and to assess its effect on motor cortical excitability in separate experiments. Electroencephalography and electromyography were recorded in 16 healthy participants during a finger-tapping task, both before and after the intervention. The resting oscillatory power at the targeted frequency was not changed by COMBS. α-Band power was increased in both preparation and movement stages and the low β-band power was increased in the movement stage of the finger tapping task. The extent of low β-band event-related desynchronization was reduced by COMBS. There were no changes in reaction time, but there was a trend for a reduced error rate after COMBS. In another 14 healthy participants, there were no significant changes in cortical excitability before and after COMBS measured by rest motor threshold, short interval intracortical inhibition, short interval intracortical facilitation, and cortical silent period. The result indicates that COMBS may modify the cortical oscillatory power and its perturbation within specific movement stage. This is the first study, to our knowledge, to apply coordinated reset (CR) neuromodulation to the motor cortex with three repetitive transcranial magnetic stimulation (rTMS) stimulators to assess its effect on cortical oscillation. The results revealed enhancement of α-band power specifically in preparation and movement stages and low β-band power in the movement stage of a motor task. It postulated that CR stimulation may modify the motor cortical oscillation in the specific movement stages.

关键词： coordinated reset event-related desynchronization low beta oscillation motor cortical excitability repetitive transcranial magnetic stimulation (rTMS)

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Benchmarking Emergency Department Triage Prediction Models with Machine Learning and Large Public Electronic Health Records

arXiv

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arXiv 2021年

作者： Xie, Feng Zhou, Jun Lee, Jin Wee Tan, Mingrui Li, Siqi Rajnthern, Logasan S.O. Chee, Marcel Lucas Chakraborty, Bibhas Wong, An-Kwok Ian Dagan, Alon Ong, Marcus Eng Hock Gao, Fei Liu, Nan Centre for Quantitative Medicine and Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Singapore Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Faculty of Medicine Nursing and Health Sciences Monash University VIC Australia Department of Statistics and Data Science National University of Singapore Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Division of Pulmonary Allergy and Critical Care Medicine Duke University DurhamNC United States Department of Emergency Medicine Beth Israel Deaconess Medical Center Harvard Medical School BostonMA United States MIT Critical Data Laboratory for Computational Physiology Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA United States Department of Emergency Medicine Singapore General Hospital Singapore Singapore SingHealth AI Health Program Singapore Health Services Singapore Singapore Institute of Data Science National University of Singapore Singapore Singapore

The demand for emergency department (ED) services is increasing across the globe, particularly during the current COVID-19 pandemic. Clinical triage and risk assessment have become increasingly challenging due to the shortage of medical resources and the strain on hospital infrastructure caused by the pandemic. As a result of the widespread use of electronic health records (EHRs), we now have access to a vast amount of clinical data, which allows us to develop predictive models and decision support systems to address these challenges. To date, however, there are no widely accepted benchmark ED triage prediction models based on large-scale public EHR data. An open-source benchmarking platform would streamline research workflows by eliminating cumbersome data preprocessing, and facilitate comparisons among different studies and methodologies. In this paper, based on the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database, we developed a publicly available benchmark suite for ED triage predictive models and created a benchmark dataset that contains over 400,000 ED visits from 2011 to 2019. We introduced three ED-based outcomes (hospitalization, critical outcomes, and 72-hour ED reattendance) and implemented a variety of popular methodologies, ranging from machine learning methods to clinical scoring systems. We evaluated and compared the performance of these methods against benchmark tasks. Our codes are open-source, allowing anyone with MIMIC-IV-ED data access to perform the same steps in data processing, benchmark model building, and experiments. This study provides future researchers with insights, suggestions, and protocols for managing raw data and developing risk triaging tools for emergency care. © 2021, CC BY.

关键词： Learning systems

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Quantum Nanophotonics with Group IV defects in Diamond

arXiv

引用

arXiv 2019年

作者： Bradac, Carlo Gao, Weibo Forneris, Jacopo Trusheim, Matt Aharonovich, Igor School of Mathematical and Physical Sciences Faculty of Science University of Technology Sydney 2007 Australia Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore Torino10125 Italy Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

Diamond photonics is an ever-growing field of research driven by the prospects of harnessing diamond and its colour centres as suitable hardware for solid-state quantum applications. The last two decades have seen the field been shaped by the nitrogen-vacancy (NV) centre both with breakthrough fundamental physics demonstrations and practical realizations. Recently however, an entire suite of other diamond defects has emerged. They are M-V colour centres, where M indicates one of the elements in the IV column of the periodic table—Si, Ge, Sn and Pb, and V indicates lattice vacancies, i.e. missing next-neighbour carbon atoms. These centres exhibit a much stronger emission into the zero-phonon line then the NV centre, they display inversion symmetry, and can be engineered using ion implantation—all attractive features for scalable quantum photonic architectures based on solid-state, single-photon sources. In this perspective, we highlight the leading techniques for engineering and characterizing these diamond defects, discuss the current state-of-the-art of group IV-based devices and provide an outlook of the future directions the field is taking towards the realisation of solid-state quantum photonics with diamond. Copyright © 2019, The Authors. All rights reserved.

关键词： Diamonds

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Solution Lasing: Optical Microfluidic Waveguides and Solution Lasers of Colloidal Semiconductor Quantum Wells (Adv. Mater. 10/2021)

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Advanced Materials 2021年第10期33卷

作者： Joudi Maskoun Negar Gheshlaghi Furkan Isik Savas Delikanli Onur Erdem Emine Yegan Erdem Hilmi Volkan Demir UNAM Institute of Materials Science and Nanotechnology Bilkent University Ankara 06800 Turkey LUMINOUS! Center of Excellence for Semiconductor Lighting and Displays School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore 639798 Singapore Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link Singapore 639798 Singapore Department of Electrical and Electronics Engineering Department of Physics Bilkent University Ankara 06800 Turkey Department of Mechanical Engineering Bilkent University Ankara 06800 Turkey

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