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arXiv 2018年

作者： Cotrufo, Michele Alù, Andrea Department of Electrical and Computer Engineering University of Texas at Austin AustinTX78712 United States Photonics Initiative Advanced Science Research Center City University of New York NY10031 United States Physics Program Graduate Center City University of New York NY10016 United States Department of Electrical Engineering City College of The City University of New York NY10031 United States

Confining light in open structures is a long-sought goal in nanophotonics and cavity quantum electrodynamics. Embedded eigenstates provide infinite lifetime despite the presence of available leakage channels, but in linear time-invariant systems they cannot be excited from the outside, due to reciprocity. Here, we investigate how atomic nonlinearities may support single-photon embedded eigenstates, which can be populated by a multi-photon excitation followed by internal relaxation. We calculate the system dynamics and show that photon trapping, as well as the reverse release process, can be achieved with arbitrarily high efficiencies. We also discuss the impact of loss, and a path towards the experimental verification of these concepts. Copyright © 2018, The Authors. All rights reserved.

关键词： Particle beams

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Automatic ICD-10 Coding and Training System: Deep Neural Network Based on Supervised Learning

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JMIR medical informatics 2021年第8期9卷 e23230页

作者： Pei-Fu Chen Ssu-Ming Wang Wei-Chih Liao Lu-Cheng Kuo Kuan-Chih Chen Yu-Cheng Lin Chi-Yu Yang Chi-Hao Chiu Shu-Chih Chang Feipei Lai Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan. Department of Anesthesiology Far Eastern Memorial Hospital New Taipei City Taiwan. Department of Internal Medicine National Taiwan University Hospital National Taiwan University College of Medicine Taipei Taiwan. Department of Internal Medicine Far Eastern Memorial Hospital New Taipei City Taiwan. Department of Medical Affairs Far Eastern Memorial Hospital New Taipei City Taiwan. Department of Healthcare Administration Oriental Institute of Technology New Taipei City Taiwan. Department of Information Technology Far Eastern Memorial Hospital New Taipei City Taiwan. Section of Cardiovascular Medicine Cardiovascular Center Far Eastern Memorial Hospital New Taipei City Taiwan. Section of Health Insurance Department of Medical Affairs Far Eastern Memorial Hospital New Taipei City Taiwan. Medical Records Department Far Eastern Memorial Hospital New Taipei City Taiwan. Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan. Department of Electrical Engineering National Taiwan University Taipei Taiwan.

BACKGROUND:The International Classification of Diseases (ICD) code is widely used as the reference in medical system and billing purposes. However, classifying diseases into ICD codes still mainly relies on humans reading a large amount of written material as the basis for coding. Coding is both laborious and time-consuming. Since the conversion of ICD-9 to ICD-10, the coding task became much more complicated, and deep learning- and natural language processing-related approaches have been studied to assist disease coders. OBJECTIVE:This paper aims at constructing a deep learning model for ICD-10 coding, where the model is meant to automatically determine the corresponding diagnosis and procedure codes based solely on free-text medical notes to improve accuracy and reduce human effort. METHODS:We used diagnosis records of the National Taiwan University Hospital as resources and apply natural language processing techniques, including global vectors, word to vectors, embeddings from language models, bidirectional encoder representations from transformers, and single head attention recurrent neural network, on the deep neural network architecture to implement ICD-10 auto-coding. Besides, we introduced the attention mechanism into the classification model to extract the keywords from diagnoses and visualize the coding reference for training freshmen in ICD-10. Sixty discharge notes were randomly selected to examine the change in the F-score and the coding time by coders before and after using our model. RESULTS:In experiments on the medical data set of National Taiwan University Hospital, our prediction results revealed F-scores of 0.715 and 0.618 for the ICD-10 Clinical Modification code and Procedure Coding System code, respectively, with a bidirectional encoder representations from transformers embedding approach in the Gated Recurrent Unit classification model. The well-trained models were applied on the ICD-10 web service for coding and training to ICD-10 users. Wit

关键词： International Classification of Diseases Recurrent Neural Network deep learning natural language processing text classification

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Correction to: Comparison of 12 surrogates to characterize CT radiation risk across a clinical population

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European radiology 2021年第10期31卷 8068页

作者： Francesco Ria Wanyi Fu Jocelyn Hoye W Paul Segars Anuj J Kapadia Ehsan Samei Carl E. Ravin Advanced Imaging Labs Duke University Health System 2424 Erwin Road Suite 302 Durham NC 27710 USA. francesco.ria@duke.edu. Clinical Imaging Physics Group Duke University Health System 2424 Erwin Road Suite 302 Durham NC 27710 USA. francesco.ria@duke.edu. Carl E. Ravin Advanced Imaging Labs Duke University Health System 2424 Erwin Road Suite 302 Durham NC 27710 USA. Clinical Imaging Physics Group Duke University Health System 2424 Erwin Road Suite 302 Durham NC 27710 USA. Medical Physics Graduate Program Departments of Radiology Physics Biomedical Engineering and Electrical and Computer Engineering Duke University 2424 Erwin Road Suite 302 Durham NC 27710 USA.

A Correction to this paper has been published: https://***/10.1007/s00330-021-07903-z

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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Nonreciprocal cavities and the time-bandwidth limit

arXiv

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arXiv 2018年

作者： Mann, Sander A. Sounas, Dimitrios L. Alù, Andrea Department of Electrical and Computer Engineering University of Texas at Austin AustinTX United States Photonics Initiative Advanced Science Research Center City University of New York New York10031 United States Physics Program Graduate Center City University of New York New York10016 United States Department of Electrical Engineering City College of the City University of New York New York10031 United States

The time-bandwidth limit inherently relates the lifetime of a resonance and its spectral bandwidth, with direct implications on the maximum storage time of a pulse versus its frequency content. It has been recently argued that nonreciprocal cavities may overcome this constraint, by breaking the strict equality of their incoupling and outcoupling coefficients. Here, we generally study the implications of nonreciprocity on resonant cavities and derive general relations, stemming from microscopic reversibility, that govern their dynamics. We show that nonreciprocal cavities do not provide specific advantages in terms of the time-bandwidth limit, but they may have other attractive properties for nanophotonic systems. Copyright © 2018, The Authors. All rights reserved.

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Resonant photonic structures in porous silicon for biosensing 9

Resonant photonic structures in porous silicon for biosensin...

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Frontiers in Biological Detection: From Nanosensors to Systems IX

作者： Zhao, Yiliang Rodriguez, Gilberto A. Graham, Yasmin M. Cao, Tengfei Gaur, Girija Weiss, Sharon M. Interdisciplinary Graduate Program in Materials Science Vanderbilt University NashvilleTN37235 United States Department of Electrical Engineering and Computer Science Vanderbilt University NashvilleTN37235 United States Department of Mechanical Engineering University of Maryland Baltimore County BaltimoreMD21250 United States

ISBN: (数字)9781510606043

ISBN: (纸本)9781510606036

The formation of resonant photonic structures in porous silicon leverages the benefit of high surface area for improved molecular capture that is characteristic of porous materials with the advantage of high detection sensitivity that is a feature of resonant optical devices. This review provides an overview of the biosensing capabilities of a variety of resonant porous silicon photonic structures including microcavities, Bloch surface waves, ring resonators, and annular Bragg resonators. Detection sensitivities > 1000 nm/RIU are achieved for small molecule detection. The challenge of detecting molecules that approach and exceed the pore diameter is also addressed. © 2017 SPIE.

关键词： Porous silicon

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Stretchable Electronics: Nylon Fabric Enabled Tough and Flaw Insensitive Stretchable Electronics (Adv. Mater. Technol. 4/2019)

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Advanced Materials Technologies 2019年第4期4卷

作者： Kyoseung Sim Yang Gao Zhou Chen Jizhou Song Cunjiang Yu Materials Science and Engineering Program University of Houston Houston TX 77204 USA Department of Mechanical Engineering University of Houston Houston TX 77204 USA School of Mechanical and Power Engineering East China University of Science and Technology Shanghai 200237 China Department of Engineering Mechanics Soft Matter Research Center and Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Zhejiang University Hangzhou Zhejiang 310027 China Department of Electrical and Computer Engineering Department of Biomedical Engineering University of Houston Houston TX 77204 USA

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Using a Recurrent Neural Network Model for Classification of Tweets Conveyed Influenza-related Information 1

Using a Recurrent Neural Network Model for Classification of...

引用

1st International Workshop on Digital Disease Detection using Social Media, DDDSM 2017, co-located with the 8th International Joint Conference on Natural Language Processing, IJCNLP 2017

作者： Wang, Chen-Kai Singh, Onkar Tang, Zhao-Li Dai, Hong-Jie Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan Bioinformatics Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biomedical Informatics National Yang-Ming University Taipei Taiwan Department of Computer Science and Information Engineering National Taitung University Taitung Taiwan Interdisciplinary Program of Green and Information Technology National Taitung University Taitung Taiwan

ISBN: (纸本)9781948087070

Traditional disease surveillance systems depend on outpatient reporting and virological test results released by hospitals. These data have valid and accurate information about emerging outbreaks but it's often not timely. In recent years the exponential growth of users getting connected to social media provides immense knowledge about epidemics by sharing related information. Social media can now flag more immediate concerns related to outbreaks in real time. In this paper we apply the long short-term memory recurrent neural network (RNN) architecture to classify tweets conveyed influenza-related information and compare its performance with baseline algorithms including support vector machine (SVM), decision tree, naive Bayes, simple logistics, and naive Bayes multinomial. The developed RNN model achieved an F-score of 0.845 on the MedWeb task test set, which outperforms the F-score of SVM without applying the synthetic minority oversampling technique by 0.08. The F-score of the RNN model is within 1% of the highest score achieved by SVM with oversampling technique. © 2017 AFNLP

关键词： Support vector machines

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Erratum to 'Relation Between Mitral Valve Prolapse and Erectile Dysfunction (from a Nationwide Case-Control Study)': The American Journal of Cardiology 2019 Nov 15;124(10):1590-1593./ doi: 10.1016/***.2019.08.010

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The American journal of cardiology 2021年 142卷 161页

作者： Shiu-Dong Chung Ju-Chi Liu Tsai-Ning Lou Ben-Chang Shia Herng-Ching Lin Li-Ting Kao Division of Urology Department of Surgery Far Eastern Memorial Hospital Taipei Taiwan Department of Computer Science and Engineering and Graduate Program in Biomedical Informatics College of Informatics Yuan-Ze University Chung-Li Taiwan Sleep Research Center Taipei Medical University Hospital Taipei Taiwan. Department of Internal Medicine School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Division of Cardiovascular Medicine Department of Medicine Medical University Hospital-Shuang Ho Hospital TaipeiTaiwan. School of Health Care Administration Taipei Medical University Taipei Taiwan. Big Data Research Center Taipei Medical University Taipei Taiwan. Sleep Research Center Taipei Medical University Hospital Taipei Taiwan School of Health Care Administration Taipei Medical University Taipei Taiwan Graduate Institute of Life Sciences National Defense Medical Center Taipei Taiwan Department of Pharmacy Practice Tri-Service General Hospital Taipei Taiwan School of Pharmacy National Defense Medical Center Taipei Taiwan.. Electronic address: kaoliting@***.

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On the role of theory and modeling in neuroscience

arXiv

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arXiv 2020年

作者： Levenstein, Daniel Alvarez, Veronica A. Amarasingham, Asohan Azab, Habiba Chen, Zhe S. Gerkin, Richard C. Hasenstaub, Andrea Iyer, Ramakrishnan Jolivet, Renaud B. Marzen, Sarah Monaco, Joseph D. Prinz, Astrid A. Quraishi, Salma Santamaria, Fidel Shivkumar, Sabyasachi Singh, Matthew F. Traub, Roger Rotstein, Horacio G. Nadim, Farzan Redish, A. David Montreal Neurological Institute McGill University MontrealQC Canada Laboratory on Neurobiology of Compulsive Behaviors National Institute on Alcohol Abuse and Alcoholism NIH BethesdaMD United States Department of Neuroscience Center for Magnetic Resonance Research University of Minnesota MinneapolisMN United States School of Life Sciences Arizona State University TempeAZ United States Department of Otolaryngology - Head and Neck Surgery University of California San Francisco San FranciscoCA United States Allen Institute for Brain Science SeattleWA United States Maastricht University Maastricht Netherlands Department of Biomedical Engineering Johns Hopkins University School of Medicine BaltimoreMD United States Department of Biology University of Texas at San Antonio San AntonioTX United States Department of Psychological & Brain Sciences Department of Electrical & Systems Engineering Washington University in St. Louis St. LouisMO United States IBM T.J. Watson Research Center AI Foundations Yorktown HeightsNY United States W. M. Keck Science Department Pitzer Scripps and Claremont McKenna Colleges ClaremontCA United States Department of Biology Emory University AtlantaGA United States Department of Mathematics City College of New York United States Departments of Biology Computer Science and Psychology The Graduate Center City University of New York United States Brain and Cognitive Sciences University of Rochester RochesterNY United States Federated Department of Biological Sciences New Jersey Institute of Technology Rutgers University Institute for Brain and Neuroscience Research New Jersey Institute of Technology NewarkNJ United States Department of Neuroscience University of Minnesota MinneapolisMN United States Department of Psychiatry Neuroscience & Physiology New York University School of Medicine United States Mila - Quebec AI Institute MontrealQC Canada

In recent years, the field of neuroscience has gone through rapid experimental advances and a significant increase in the use of quantitative and computational methods. This growth has created a need for clearer analyses of the theory and modeling approaches used in the field. This issue is particularly complex in neuroscience because the field studies phenomena that cross a wide range of scales and often require consideration at varying degrees of abstraction, from precise biophysical interactions to the computations they implement. We argue that a pragmatic perspective of science, in which descriptive, mechanistic, and normative models and theories each play a distinct role in defining and bridging levels of abstraction will facilitate neuroscientific practice. This analysis leads to methodological suggestions, including selecting a level of abstraction that is appropriate for a given problem, identifying transfer functions to connect models and data, and the use of models themselves as a form of experiment. © 2020, CC BY.

关键词： Computation theory

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