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眼科学报 2023年第10期38卷 665-675页

作者： WU Xiaohang HUANG Yelin LIU Zhenzhen LAI Weiyi LONG Erping ZHANG Kai JIANG Jiewei LIN Duoru CHEN Kexin YU Tongyong WU Dongxuan LI Cong CHEN Yanyi ZOU Minjie CHEN Chuan ZHU Yi GUO Chong ZHANG Xiayin WANG Ruixin YANG Yahan XIANG Yifan CHEN Lijian LIU Congxin XIONG Jianhao GE Zongyuan WANG Dingding XU Guihua DU Shaolin XIAO Chi WU Jianghao ZHU Ke NIE Danyao XU Fan LV Jian CHEN Weirong LIU Yizhi 林浩添无王厚硕(审校) 罗明杰(审校) State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangzhouChina Beijing Tulip Partners Technology Co. LtdBeijingChina School of Computer Science and Technology Xidian UniversityXi’anChina Zhongshan School of Medicine Sun Yat-sen UniversityGuangzhouChina Department of Molecular and Cellular Pharmacology University of Miami Miller School of MedicineMiamiFloridaUSA Department of Electrical and Computer Systems Engineering Faculty of EngineeringMonash UniversityMelbourneVictoriaAustralia Huizhou Municipal Central Hospital HuizhouChina Tung Wah Hospital Sun Yat-sen UniversityDongguanChina Dongguan Guangming Ophthalmic Hospital DongguanChina Kaifeng Eye Hospital KaifengChina Shenzhen Eye Hospital Shenzhen Key Laboratory of OphthalmologyShenzhen University School of MedicineShenzhenChina Department of Ophthalmology People’s Hospital of Guangxi Zhuang Autonomous RegionNanningChina 《眼科学报》出版团队西安交通大学第一附属医院中山大学中山眼科中心眼病防治全国重点实验室广东省眼科视觉科学重点实验室

目的:建立和验证一个涉及多级临床场景的白内障协作通用的人工智能(artificial intelligence,AI)管理平台,探索基于AI的医疗转诊模式,以提高协作效率和资源覆盖率。方法:训练和验证的数据集来自中国AI医学联盟,涵盖多级医疗机构和采集... 详细信息

目的:建立和验证一个涉及多级临床场景的白内障协作通用的人工智能(artificial intelligence,AI)管理平台,探索基于AI的医疗转诊模式,以提高协作效率和资源覆盖率。方法:训练和验证的数据集来自中国AI医学联盟,涵盖多级医疗机构和采集模式。使用三步策略对数据集进行标记:1)识别采集模式;2)白内障诊断包括正常晶体眼、白内障眼或白内障术后眼;3)从病因和严重程度检测需转诊的白内障患者。此外,将白内障AI系统与真实世界中的居家自我监测、初级医疗保健机构和专科医院等多级转诊模式相结合。结果:通用AI平台和多级协作模式在三步任务中表现出可靠的诊断性能:1)识别采集模式的受试者操作特征(receiver operating characteristic curve,ROC)曲线下面积(area under the curve,AUC)为99.28%~99.71%);2)白内障诊断对正常晶体眼、白内障或术后眼,在散瞳-裂隙灯模式下的AUC分别为99.82%、99.96%和99.93%,其他采集模式的AUC均>99%;3)需转诊白内障的检测(在所有测试中AUC>91%)。在真实世界的三级转诊模式中,该系统建议30.3%的人转诊,与传统模式相比,眼科医生与人群服务比率大幅提高了10.2倍。结论:通用AI平台和多级协作模式显示了准确的白内障诊断性能和有效的白内障转诊服务。建议AI的医疗转诊模式扩展应用到其他常见疾病和资源密集型情景当中。

关键词：人工智能通用平台资源密集型协同管理采集模式专科医院眼科医生自我监测

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Broadband mid-infrared second harmonic generation using epitaxial polydomain barium titanate thin films

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Photonics Research 2019年第10期7卷 1193-1199页

作者： JUNCHAO ZHOU WENRUI ZHANG MINGZHAO LIU PAO TAI LIN Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTexas 77843USA Department of Materials Science and Engineering Texas A&M UniversityCollege StationTexas 77843USA Center for Remote Health Technologies and Systems Texas A&M UniversityCollege StationTexas 77843USA The Center for Functional Nanomaterials Brookhaven National LaboratoryUptonNew York 11973USA

The mid-infrared(mid-IR) second-order optical nonlinearity of the barium titanate(BTO) thin films was characterized by second harmonic generation(SHG). The epitaxial BTO thin films were grown on strontium titanate substrates by pulsed-laser deposition. From the azimuthal-dependent polarized SHG measurements, the tensorial optical nonlinear coefficients, dij, and ferroelectric domain fraction ratio, δAY∕δAz, were resolved. Strong SHG signals were obtained at the pumping laser wavelength λ between 3.0 and 3.6 μm. The SHG intensity was linearly dependent upon the square of the pumping laser power. The broadband mid-IR optical nonlinearity enables BTO thin films for applications in chip-scale quantum optics and nonlinear integrated photonic circuits.

关键词： pumping harmonic barium

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A unified analysis of extra-gradient and optimistic gradient methods for saddle point problems: Proximal point approach

arXiv

引用

arXiv 2019年

作者： Mokhtari, Aryan Ozdaglar, Asuman Pattathil, Sarath Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States

In this paper we consider solving saddle point problems using two variants of Gradient Descent- Ascent algorithms, Extra-gradient (EG) and Optimistic Gradient Descent Ascent (OGDA) methods. We show that both of these algorithms admit a unified analysis as approximations of the classical proximal point method for solving saddle point problems. This viewpoint enables us to develop a new framework for analyzing EG and OGDA for bilinear and strongly convex- strongly concave settings. Moreover, we use the proximal point approximation interpretation to generalize the results for OGDA for a wide range of parameters. Copyright © 2019, The Authors. All rights reserved.

关键词： Gradient methods

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Convergence rate of O(1/k) for optimistic gradient and extra-gradient methods in smooth convex-concave saddle point problems

arXiv

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arXiv 2019年

We study the iteration complexity of the optimistic gradient descent-ascent (OGDA) method and the extra-gradient (EG) method for finding a saddle point of a convex-convex uncon- strained min-max problem. To do so, we first show that both OGDA and EG can be interpreted as approximate variants of the proximal point method. We then exploit this interpretation to show that both algorithms produce iterates that remain within a bounded set. We further show that the function value of the averaged iterates generated by both of these algorithms converge with a rate of O(1/k) to the function value at the saddle point. Our theoretical analysis is of interest as it provides a simple convergence analysis for the EG algorithm in terms of function value without using compactness assumption. Moreover, it provides the first convergence rate estimate for OGDA in the general convex-concave setting. Copyright © 2019, The Authors. All rights reserved.

关键词： Gradient methods

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A holomorphic embedding based continuation method for identifying multiple power flow solutions

arXiv

引用

arXiv 2019年

作者： Wu, Dan Wang, Bin Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA United States Department of Electrical and Computer Engineering Taxes A&M University College StationTX Singapore

—In this paper, we propose an efficient continuation method for locating multiple power flow solutions. We adopt the holomorphic embedding technique to represent solution curves as holomorphic functions in the complex plane. The holomorphicity, which provides global information of the curve at any regular point, enables large step sizes in the path-following procedure such that non-singular curve segments can be traversed with very few steps. When approaching singular points, we switch to the traditional predictor-corrector routine to pass through them and switch back afterward to the holomorphic embedding routine. We also propose a warm starter when switching to the predictor-corrector routine, i.e. a large initial step size based on the poles of the Padé approximation of the derived holomorphic function, since these poles reveal the locations of singularities on the curve. Numerical analysis and experiments on many standard IEEE test cases are presented, along with the comparison to the full predictor-corrector routine, confirming the efficiency of the method. Copyright © 2019, The Authors. All rights reserved.

关键词： Poles

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Distributed constrained policy learning for power management of networked microgrids

arXiv

引用

arXiv 2019年

作者： Zhang, Qianzhi Dehghanpour, Kaveh Wang, Zhaoyu Qiu, Feng Zhao, Dongbo Department of Electrical and Computer Engineering Iowa State University AmesIA50011 United States Energy Systems Division Argonne National Laboratory LemontIL60439 United States

This paper presents a multi-agent constrained reinforcement learning (RL) policy gradient method for optimal power management of networked microgrids (MGs) in distribution systems. While conventional RL algorithms are black box decision models that could fail to satisfy grid operational constraints, our proposed RL technique is constrained by AC power flow equations and other operational limits. Accordingly, the training process employs the gradient information of the power management problem constraints to ensure that the optimal control policy functions generate feasible decisions. Furthermore, we have proposed a distributed primal-dual consensus-based training approach for the RL solver to maintain the privacy of MGs' control policies. After training, the learned optimal policy functions can be safely used by the MGs to dispatch their local resources, without the need to solve a complex optimization problem from scratch. Numerical experiments have been devised to verify the performance of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Power management

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No switching policy is optimal for a positive linear system with a bottleneck entrance

arXiv

引用

arXiv 2019年

作者： Sadeghi, Mahdiar Al-Radhawi, M. Ali Margaliot, Michael Sontag, Eduardo D. Departments of Electrical and Computer Engineering and of Bioengineering Northeastern University BostonMA02115 United States Department of Electrical Engineering-Systems Tel Aviv University Tel Aviv-Yafo Israel Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States

We consider a nonlinear SISO system that is a cascade of a scalar "bottleneck entrance" and an arbitrary Hurwitz positive linear system. This system entrains i.e. in response to a T-periodic inflow every solution converges to a unique T-periodic solution of the system. We study the problem of maximizing the averaged throughput via controlled switching. The objective is to choose a periodic inflow rate with a given mean value that maximizes the averaged outflow rate of the system. We compare two strategies: 1) switching between a high and low value, and 2) using a constant inflow equal to the prescribed mean value. We show that no switching policy can outperform a constant inflow rate, though it can approach it asymptotically. We describe several potential applications of this problem in traffic systems, ribosome flow models, and scheduling at security checks. Copyright © 2019, The Authors. All rights reserved.

关键词： Switching

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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 2023年第6期20卷 824-835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

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Strong Consistency of Variable Selection for Stationary Linear Stochastic systems

Strong Consistency of Variable Selection for Stationary Line...

引用

第三十八届中国控制会议

作者： ZHAO Wenxiao YIN G.George BAI Er-Wei Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences Department of Mathematics Wayne State University Department of Electrical and Computer Engineering University of Iowa

In this paper, we consider the variable selection for linear stochastic systems. A modified LASSO-type estimator is introduced. Then based on the classical persistent excitation(PE) for systems identification, the strong consistency of the estimates is established, i.e., the zero elements in the unknown parameter vector being correctly identified and estimates for the nonzero elements in the unknown parameter vector converging to the true values with probability one. Compared with the existing results on similar topics, the strong consistency of estimates is established while in existing literature only the convergence in probability was obtained. For this, new theoretical analysis method is adopted in this paper.

关键词： Variable Selection Linear Stochastic System Strong Consistency

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An extreme test case for planet formation: a close-in Neptune orbiting an ultracool star

arXiv

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arXiv 2023年

作者： Stefánsson, Guðmundur Mahadevan, Suvrath Miguel, Yamila Robertson, Paul Delamer, Megan Kanodia, Shubham Cañas, Caleb Winn, Joshua Ninan, Joe R., Terrien R., Holcomb E., Ford B., Zawadzki B.P., Bowler C., Bender W., Cochran S., Diddams M., Endl C., Fredrick S., Halverson F., Hearty G.J., Hill A., Lin A., Metcalf A., Monson L., Ramsey A., Roy C., Schwab J., Wright G., Zeimann Princeton University Department of Astrophysical Sciences United States Department of Astronomy & Astrophysics The Pennsylvania State University United States Center for Exoplanets and Habitable Worlds The Pennsylvania State University United States ETH Zurich Institute for Particle Physics & Astrophysics Switzerland Leiden Observatory University of Leiden Netherlands SRON Netherlands Institute for Space Research Netherlands Department of Physics & Astronomy University of California Irvine United States Earth and Planets Laboratory Carnegie Institution for Science United States NASA Goddard Space Flight Center United States Department of Astronomy & Astrophysics Tata Institute of Fundamental Research India Carleton College United States Center for Astrostatistics The Pennsylvania State University United States Institute for Computational & Data Sciences The Pennsylvania State University United States Department of Astronomy The University of Texas at Austin AustinTX78712 United States Steward Observatory The University of Arizona United States Center for Planetary Systems Habitability and McDonald Observatory UT Austin United States Electrical Computer and Energy Engineering University of Colorado United States National Institute of Standards & Technology United States Department of Physics University of Colorado United States Jet Propulsion Laboratory California Institute of Technology United States McDonald Observatory UT Austin United States Space Vehicles Directorate Air Force Research Laboratory United States Space Telescope Science Institute United States Department of Physics and Astronomy Johns Hopkins University United States School of Mathematical and Physical Sciences Macquarie University Australia Penn State Extraterrestrial Intelligence Center The Pennsylvania State University United States Hobby-Eberly Telescope University of Texas Austin AustinTX78712 United States

In current theories of planet formation, close-orbiting planets as massive as Neptune are expected to be very rare around low-mass stars. We report the discovery of a Neptune-mass planet orbiting the 'ultracool' star LHS 3154, which is nine times less massive than the Sun. The planet's orbital period is 3.7 days and its minimum mass is 13.2 Earth masses, giving it the largest known planet-to-star mass ratio among short-period planets ( Copyright © 2023, The Authors. All rights reserved.

关键词： Planets

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