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Journal of computers (Taiwan) 2019年第5期30卷 75-87S页

作者： Qu, Zhao-Yang Xie, Shu-Ya Feng, Rong-qiang Qu, Nan Lv, Hong-bo Chi, Zuo-Wei Qu, Chong Department of computer science and technology of the Institute of Information Engineering Northeast Electric Power University Jilin Jilin132012 China Intelligent Electric Power Big Data Processing Jilin Engineering Technology Research Center Jilin Jilin132012 China Maintenance Company of Jiangsu Power Company NanJing210000 China State Grid Jilin Electric Power Supply Company Jilin130000 China Fushun Power Supply Company of State Grid Liaoning Electric Power Supply Company LiaoNing113001 China Guizhou Power Grid Co. Ltd. Guiyang Baiyun Power Supply Bureau GuiYang550014 China

The model to estimate electric sales in electric market of the optimal weighting nonlinear regression combination is brought forth to improve the accuracy of electricity sales forecast. Firstly, the least square method is used to estimate the nonlinear regression equation in order to obtain the result sets of the prediction sequence. Secondly, the design method of minimizing the calculation of fitting variance and weighting com-bination to calculate the residual sequence and weighting of the Radial Basis Function (RBF) neural network and the least squares vector machine, finally the result sets of residual sequence concluded. At the end, a nonlinear decreasing strategy of inertia weight is introduced on the basis of cuckoo algorithm to optimize the extended search space of model parameters and the model solution. The simulation results show that the model can accurately and comprehensively reflect the law of electric sales and improve the accuracy of sales prediction. © 2019 computer Society of the Republic of China. All rights reserved.

关键词： Sales

Infinite factorial finite state machine for blind multiuser channel estimation

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arXiv 2018年

作者： Ruiz, Francisco J.R. Valera, Isabel Svensson, Lennart Perez-Cruz, Fernando Department of Computer Science Columbia University in the City of New York United States Engineering Department University of Cambridge United Kingdom Department of Empirical Inference Max Planck Institute for Intelligent Systems in Tübingen Germany Department of Signal Processing and Communications University Carlos III Madrid Spain Chalmers University of Technology Gothenburg Sweden Swiss Data Science Center Zurich Switzerland

New communication standards need to deal with machine-to-machine communications, in which users may start or stop transmitting at any time in an asynchronous manner. Thus, the number of users is an unknown and time-varying parameter that needs to be accurately estimated in order to properly recover the symbols transmitted by all users in the system. In this paper, we address the problem of joint channel parameter and data estimation in a multiuser communication channel in which the number of transmitters is not known. For that purpose, we develop the infinite factorial finite state machine model, a Bayesian nonparametric model based on the Markov Indian buffet that allows for an unbounded number of transmitters with arbitrary channel length. We propose an inference algorithm that makes use of slice sampling and particle Gibbs with ancestor sampling. Our approach is fully blind as it does not require a prior channel estimation step, prior knowledge of the number of transmitters, or any signaling information. Our experimental results, loosely based on the LTE random access channel, show that the proposed approach can effectively recover the data-generating process for a wide range of scenarios, with varying number of transmitters, number of receivers, constellation order, channel length, and signal-to-noise ratio. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

A robotic multidimensional directed evolution approach applied to fluorescent voltage reporters (vol 14, pg 352, 2018)

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NATURE CHEMICAL BIOLOGY 2018年第9期14卷 901-901页

作者： Piatkevich, Kiryl D. Jung, Erica E. Straub, Christoph Linghu, Changyang Park, Demian Suk, Ho-Jun Hochbaum, Daniel R. Goodwin, Daniel Pnevmatikakis, Eftychios Pak, Nikita Kawashima, Takashi Yang, Chao-Tsung Rhoades, Jeffrey L. Shemesh, Or Asano, Shoh Yoon, Young-Gyu Freifeld, Limor Saulnier, Jessica L. Riegler, Clemens Engert, Florian Hughes, Thom Drobizhev, Mikhail Szabo, Balint Ahrens, Misha B. Flavell, Steven W. Sabatini, Bernardo L. Boyden, Edward S. Media Lab Massachusetts Institute of Technology (MIT) Cambridge MA USA. Howard Hughes Medical Institute Department of Neurobiology Harvard Medical School Boston MA USA. Department of Electrical Engineering and Computer Science MIT Cambridge MA USA. Harvard-MIT Division of Health Sciences and Technology MIT Cambridge MA USA. Simons Center Data Analysis Simons Foundation New York NY USA. Department of Mechanical Engineering MIT Cambridge MA USA. Janelia Research Campus Howard Hughes Medical Institute Ashburn Virginia USA. Picower Institute for Learning & Memory and Department of Brain & Cognitive Sciences MIT Cambridge MA USA. Department of Molecular and Cellular Biology and Center for Brain Science Harvard University Cambridge MA USA. Department of Neurobiology Faculty of Life Sciences University of Vienna Wien Austria. Department of Cell Biology and Neuroscience Montana State University Bozeman Montana USA. Department of Biological Physics Eotvos University Budapest Hungary. Media Lab Massachusetts Institute of Technology (MIT) Cambridge MA USA. esb@media.mit.edu. Department of Biological Engineering MIT Cambridge MA USA. esb@media.mit.edu. MIT Center for Neurobiological Engineering MIT Cambridge MA USA. esb@media.mit.edu. Department of Brain and Cognitive Sciences MIT Cambridge MA USA. esb@media.mit.edu. MIT McGovern Institute for Brain Research MIT Cambridge MA USA. esb@media.mit.edu.

We developed a new way to engineer complex proteins toward multidimensional specifications using a simple, yet scalable, directed evolution strategy. By robotically picking mammalian cells that were identified, under a microscope, as expressing proteins that simultaneously exhibit several specific properties, we can screen hundreds of thousands of proteins in a library in just a few hours, evaluating each along multiple performance axes. To demonstrate the power of this approach, we created a genetically encoded fluorescent voltage indicator, simultaneously optimizing its brightness and membrane localization using our microscopy-guided cell-picking strategy. We produced the high-performance opsin-based fluorescent voltage reporter Archon1 and demonstrated its utility by imaging spiking and millivolt-scale subthreshold and synaptic activity in acute mouse brain slices and in larval zebrafish in vivo. We also measured postsynaptic responses downstream of optogenetically controlled neurons in C. elegans.

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Alert classification for the alerce broker system: The real-time stamp classifier

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arXiv 2020年

作者： Carrasco-Davis, Rodrigo Reyes, E. Valenzuela, C. Förster, F. Estévez, P.A. Pignata, G. Bauer, F.E. Reyes, I. Sánchez-Sáez, P. Cabrera-Vives, G. Eyheramendy, S. Catelan, M. Arredondo, J. Castillo-Navarrete, E. Rodríguez-Mancini, D. Ruz-Mieres, D. Moya, A. Sabatini-Gacitúa, L. Sepúlveda-Cobo, C. Mahabal, A.A. Silva-Farfán, J. Camacho-Iñiguez, E. Galbany, L. Nuncio Monseñor Sótero Sanz 100 Providencia Santiago Chile Department of Electrical Engineering Universidad de Chile Av. Tupper 2007 Santiago8320000 Chile Center for Mathematical Modeling Universidad de Chile Beauchef 851 North building 7th floor Santiago8320000 Chile Faculty of Engineering and Sciences Universidad Adolfo Ibañez Diagonal Las Torres 2700 Peñalolén Santiago Chile Data Observatory Santiago Chile Departamento de Astronomía Universidad de Chile Casilla 36D Santiago Chile Departamento de Ciencias Físicas Universidad Andres Bello Av. Republica 230 Santiago8370146 Chile Instituto de Astrofísica and Centro de Astroingeniería Facultad de Física Pontificia Universidad Católica de Chile Casilla 306 Santiago 22 Chile Space Science Institute 4750 Walnut Street Suite 205 BoulderCO80301 United States Inria Chile Research Center Av. Apoquindo 2827 Las Condes Chile Department of Computer Science Universidad de Concepción Edmundo Larenas 219 Concepción Chile Institute of Astronomy University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Cahill Center for Astrophysics California Institute of Technology 1200 E. California Boulevard PasadenaCA91125 United States Center for Data Driven Discovery California Institute of Technology PasadenaCA91125 United States Departamento de Física Teórica y del Cosmos Universidad de Granada GranadaE-18071 Spain

We present a real-time stamp classifier of astronomical events for the ALeRCE (Automatic Learning for the Rapid Classification of Events) broker. The classifier is based on a convolutional neural network, trained on alerts ingested from the Zwicky Transient Facility (ZTF). Using only the science, reference and difference images of the first detection as inputs, along with the metadata of the alert as features, the classifier is able to correctly classify alerts from active galactic nuclei, supernovae (SNe), variable stars, asteroids and bogus classes, with high accuracy (∼94%) in a balanced test set. In order to find and analyze SN candidates selected by our classifier from the ZTF alert stream, we designed and deployed a visualization tool called SN Hunter, where relevant information about each possible SN is displayed for the experts to choose among candidates to report to the Transient Name Server database. From June 26th 2019 to February 28th 2021, we have reported 6846 SN candidates to date (11.8 candidates per day on average), of which 971 have been confirmed spectroscopically. Our ability to report objects using only a single detection means that 70% of the reported SNe occurred within one day after the first detection. ALeRCE has only reported candidates not otherwise detected or selected by other groups, therefore adding new early transients to the bulk of objects available for early follow-up. Our work represents an important milestone toward rapid alert classifications with the next generation of large etendue telescopes, such as the Vera C. Rubin Observatory. Copyright © 2020, The Authors. All rights reserved.

关键词： Supernovae

Dense sampling of bird diversity increases power of comparative genomics (vol 587, pg 252, 2020)

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NATURE 2021年第7856期592卷 E24-E24页

作者： Feng, Shaohong Stiller, Josefin Deng, Yuan Armstrong, Joel Fang, Qi Reeve, Andrew Hart Xie, Duo Chen, Guangji Guo, Chunxue Faircloth, Brant C. Petersen, Bent Wang, Zongji Zhou, Qi Diekhans, Mark Chen, Wanjun Andreu-Sanchez, Sergio Margaryan, Ashot Howard, Jason Travis Parent, Carole Pacheco, George Sinding, Mikkel-Holger S. Puetz, Lara Cavill, Emily Ribeiro, Angela M. Eckhart, Leopold Fjeldsa, Jon Hosner, Peter A. Brumfield, Robb T. Christidis, Les Bertelsen, Mads F. Sicheritz-Ponten, Thomas Tietze, Dieter Thomas Robertson, Bruce C. Song, Gang Borgia, Gerald Claramunt, Santiago Lovette, Irby J. Cowen, Saul J. Njoroge, Peter Dumbacher, John Philip Ryder, Oliver A. Fuchs, Jerome Bunce, Michael Burt, David W. Cracraft, Joel Meng, Guanliang Hackett, Shannon J. Ryan, Peter G. Jonsson, Knud Andreas Jamieson, Ian G. da Fonseca, Rute R. Braun, Edward L. Houde, Peter Mirarab, Siavash Suh, Alexander Hansson, Bengt Ponnikas, Suvi Sigeman, Hanna Stervander, Martin Frandsen, Paul B. van der Zwan, Henriette van der Sluis, Rencia Visser, Carina Balakrishnan, Christopher N. Clark, Andrew G. Fitzpatrick, John W. Bowman, Reed Chen, Nancy Cloutier, Alison Sackton, Timothy B. Edwards, Scott V. Foote, Dustin J. Shakya, Subir B. Sheldon, Frederick H. Vignal, Alain Soares, Andre E. R. Shapiro, Beth Gonzalez-Solis, Jacob Ferrer-Obiol, Joan Rozas, Julio Riutort, Marta Tigano, Anna Friesen, Vicki Dalen, Love Urrutia, Araxi O. Szekely, Tamas Liu, Yang Campana, Michael G. Corvelo, Andre Fleischer, Robert C. Rutherford, Kim M. Gemmell, Neil J. Dussex, Nicolas Mouritsen, Henrik Thiele, Nadine Delmore, Kira Liedvogel, Miriam Franke, Andre Hoeppner, Marc P. Krone, Oliver Fudickar, Adam M. Mila, Borja Ketterson, Ellen D. Fidler, Andrew Eric Friis, Guillermo Parody-Merino, Angela M. Battley, Phil F. Cox, Murray P. Lima, Nicholas Costa Barroso Prosdocimi, Francisco Parchman, Thomas Lee Schlinger, Barney A. Loiselle, Bette A. Blake, John G. Lim, Haw Chuan Day, Lainy B. Fuxjager, Matthew J. Baldwin, Maude W. Braun, Michael J. Wirthlin, Morgan Dikow, China National GeneBank BGI-Shenzhen Shenzhen China State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming China BGI-Shenzhen Shenzhen China Villum Centre for Biodiversity Genomics Section for Ecology and Evolution Department of Biology University of Copenhagen Copenhagen Denmark BGI Education Center University of Chinese Academy of Sciences Shenzhen China MOE Laboratory of Biosystems Homeostasis and Protection Life Sciences Institute Zhejiang University Hangzhou China Department of Neuroscience and Developmental Biology University of Vienna Vienna Austria Center for Excellence in Animal Evolution and Genetics Chinese Academy of Sciences Kunming China James D. Watson Institute of Genome Sciences Hangzhou China UC Santa Cruz Genomics Institute UC Santa Cruz Santa Cruz CA USA Natural History Museum of Denmark University of Copenhagen Copenhagen Denmark Center for Macroecology Evolution and Climate GLOBE Institute University of Copenhagen Copenhagen Denmark Department of Biological Sciences Louisiana State University Baton Rouge LA USA Museum of Natural Science Louisiana State University Baton Rouge LA USA Centre of Excellence for Omics-Driven Computational Biodiscovery (COMBio) Faculty of Applied Sciences AIMST University Kedah Malaysia Section for Evolutionary Genomics The GLOBE Institute Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark Institute of Molecular Biology National Academy of Sciences Yerevan Armenia University Museum Norwegian University of Science and Technology Trondheim Norway Center for Reproductive Medicine The 2nd Affiliated Hospital School of Medicine Zhejiang University Hangzhou China Novogene Durham NC USA Duke University Medical Center Durham NC USA The Rockefeller University New York NY USA Howard Hughes Medical Institute Chevy Chase MD USA Department of Dermatology Medical University of Vienna Vienna Austria

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穿山甲可能不是新型冠状病毒的中间宿主

中国预防兽医学报 2020年第12期42卷 1311-1311页

作者： LIU PING JIANG JING-ZHE WANXIU-FENG Guangdong Key Laboratory of Animal Conservation and Resource Utilization Guangdong Public Laboratory of Wild Animal Conservation and Utilization Guangdong Institute of Applied Biological Resources Guangdong Academy of Science Guangzhou Guangdong Province China. Key Laboratory of South China Sea Fishery Resources Exploitation & Utilization Ministry of Agriculture South China Sea Fisheries Research Institute Chinese Academy of Fishery Sciences Guangzhou Guangdong Province China. Department of Molecular Microbiology and Immunology School of Medicine University of Missouri Columbia Missouri United States of America. Department of Electrical Engineering & Computer Science College of Engineering University of Missouri Columbia Missouri United States of America. MU Center for Research on Influenza Systems Biology (CRISB) University of Missouri Columbia Missouri United States of America. Bond Life Sciences Center University of Missouri Columbia Missouri United States of America. MU Institute for Data Science and Informatics University of Missouri Columbia Missouri United States of America. Guangdong Provincial Key Laboratory of Silviculture Protection and Utilization Guangdong Academy of Forestry Guangzhou Guangdong Province China. Institute of Animal Health Guangdong Academy of Agricultural Sciences Guangzhou Guangdong Province China. Guangdong Provincial Wildlife Rescue Center Guangzhou Guangdong Province China.

当前,新型冠状病毒肺炎(COVID-19)疫情仍在全球范围内肆虐,新冠肺炎的防控工作依然是重中之重。研究表明新型冠状病毒与中菊头蝠携带的Bat-CoV-RaTG13冠状病毒全基因组核苷酸一致性高达96%,推测该病毒可能起源于蝙蝠,再由中间野生动物... 详细信息

当前,新型冠状病毒肺炎(COVID-19)疫情仍在全球范围内肆虐,新冠肺炎的防控工作依然是重中之重。研究表明新型冠状病毒与中菊头蝠携带的Bat-CoV-RaTG13冠状病毒全基因组核苷酸一致性高达96%,推测该病毒可能起源于蝙蝠,再由中间野生动物宿主进一步传播,但目前中间宿主仍然未知。

关键词：中间宿主防控工作动物宿主穿山甲冠状病毒全球范围全基因组肺炎

Author Correction: Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations

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Nature medicine 2024年第12期30卷 3779页

作者： Sebastian Moguilner Sandra Baez Hernan Hernandez Joaquín Migeot Agustina Legaz Raul Gonzalez-Gomez Francesca R Farina Pavel Prado Jhosmary Cuadros Enzo Tagliazucchi Florencia Altschuler Marcelo Adrián Maito María E Godoy Josephine Cruzat Pedro A Valdes-Sosa Francisco Lopera John Fredy Ochoa-Gómez Alfredis Gonzalez Hernandez Jasmin Bonilla-Santos Rodrigo A Gonzalez-Montealegre Renato Anghinah Luís E d'Almeida Manfrinati Sol Fittipaldi Vicente Medel Daniela Olivares Görsev G Yener Javier Escudero Claudio Babiloni Robert Whelan Bahar Güntekin Harun Yırıkoğulları Hernando Santamaria-Garcia Alberto Fernández Lucas David Huepe Gaetano Di Caterina Marcio Soto-Añari Agustina Birba Agustin Sainz-Ballesteros Carlos Coronel-Oliveros Amanuel Yigezu Eduar Herrera Daniel Abasolo Kerry Kilborn Nicolás Rubido Ruaridh A Clark Ruben Herzog Deniz Yerlikaya Kun Hu Mario A Parra Pablo Reyes Adolfo M García Diana L Matallana José Alberto Avila-Funes Andrea Slachevsky María I Behrens Nilton Custodio Juan F Cardona Pablo Barttfeld Ignacio L Brusco Martín A Bruno Ana L Sosa Ortiz Stefanie D Pina-Escudero Leonel T Takada Elisa Resende Katherine L Possin Maira Okada de Oliveira Alejandro Lopez-Valdes Brian Lawlor Ian H Robertson Kenneth S Kosik Claudia Duran-Aniotz Victor Valcour Jennifer S Yokoyama Bruce Miller Agustin Ibanez Latin American Brain Health Institute Universidad Adolfo Ibañez Santiago de Chile Chile. Cognitive Neuroscience Center Universidad de San Andrés Buenos Aires Argentina. Department of Neurology Massachusetts General Hospital and Harvard Medical School Boston MA USA. Universidad de los Andes Bogota Colombia. Global Brain Health Institute (GBHI) University of California San Francisco CA USA. Global Brain Health Institute (GBHI) Trinity College Dublin Dublin Ireland. The University of California Santa Barbara (UCSB) Santa Barbara CA USA. Escuela de Fonoaudiología Universidad San Sebastián Santiago de Chile Chile. Grupo de Bioingeniería Decanato de Investigación Universidad Nacional Experimental del Táchira San Cristóbal Venezuela. Advanced Center for Electrical and Electronic Engineering Universidad Técnica Federico Santa María Valparaíso Chile. University of Buenos Aires Buenos Aires Argentina. The Clinical Hospital of Chengdu Brain Sciences Institute University of Electronic Sciences and Technology of China Chengdu China. Technology of China Chengdu China. Cuban Neuroscience Center La Habana Cuba. Grupo de Neurociencias de Antioquia (GNA) University of Antioquia Medellín Colombia. Department of Psychology Master Program of Clinical Neuropsychology Universidad Surcolombiana Neiva Neiva Colombia. Department of Psychology Universidad Cooperativa de Colombia Arauca Colombia. Neurocognition and Psychophysiology Laboratory Universidad Surcolombiana Neiva Colombia. Reference Center of Behavioural Disturbances and Dementia School of Medicine University of Sao Paulo Sao Paulo Brazil. Traumatic Brain Injury Cognitive Rehabilitation Out-Patient Center University of Sao Paulo Sao Paulo Brazil. Center for Social and Cognitive Neuroscience School of Psychology Universidad Adolfo Ibáñez Santiago Chile. Neuropsychology and Clinical Neuroscience Laboratory (LANNEC) Physiopathology Program-Institute of Biomedical Sciences (ICBM) Neuroscience and East Neuroscience Dep

Convolutional phase retrieval via gradient descent

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arXiv 2017年

作者： Qu, Qing Zhang, Yuqian Eldar, Yonina C. Wright, John Center for Data Science New York University Department of Computer Science Cornell University Weizmann Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot Israel Department of Electrical Engineering Department of Applied Physics and Applied Mathematics Data Science Institute Columbia University Department of Electrical Engineering and Data Science Institute Columbia University

We study the convolutional phase retrieval problem, of recovering an unknown signal x ∈ Cn from m measurements consisting of the magnitude of its cyclic convolution with a given kernel a ∈ Cm. This model is motivated by applications such as channel estimation, optics, and underwater acoustic communication, where the signal of interest is acted on by a given channel/filter, and phase information is difficult or impossible to acquire. We show that when a is random and the number of observations m is sufficiently large, with high probability x can be efficiently recovered up to a global phase shift using a combination of spectral initialization and generalized gradient descent. The main challenge is coping with dependencies in the measurement operator. We overcome this challenge by using ideas from decoupling theory, suprema of chaos processes and the restricted isometry property of random circulant matrices, and recent analysis of alternating minimization methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Convolution

Comments on "Design of fractional-order variants of complex LMS and NLMS algorithms for adaptive channel equalization"

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arXiv 2018年

作者： Khan, Shujaat Wahab, Abdul Naseem, Imran Moinuddin, Muhammad Bio-imaging and Signal Processing Laboratory Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology 291 Daehak-ro Daejeon Yuseong-gu34141 Korea Republic of Department of Mathematics University of Education Attock Campus43600 Pakistan School of Electrical Electronic and Computer Engineering University of Western Australia 35 Stirling Highway CrawleyWA6009 Australia College of Engineering Karachi Institute of Economics and Technology Korangi Creek Karachi75190 Pakistan King Abdulaziz University Jeddah Saudi Arabia Electrical and Computer Engineering Department King Abdulaziz University Jeddah Saudi Arabia

The purpose of this note is to highlight some critical flaws in recently proposed fractional-order variants of complex least mean square (CLMS) and normalized least mean square (NLMS) algorithms in "Design of Fractional-order Variants of Complex LMS and Normalized LMS Algorithms for Adaptive Channel Equalization" [Non-linear Dyn. 88(2), 839-858 (2017)]. It is substantiated that these algorithms do not always converge whereas they have no advantage over the CLMS and NLMS algorithms whenever they converge. Our claims are based on analytical reasoning supported by simulations. Copyright © 2018, The Authors. All rights reserved.

关键词： Equalizers

Quality Assessment of DEM Generated from SAR Radargrammetry Based on Cross-Correlation and Spatial Resolution Setting

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Journal of Physics: Conference Series 2019年第1期1201卷

作者： R Arief A Setiyoko R Maulana S Ali D Sudiana A M Arymurthy Remote Sensing Technology and Data Center Indonesian National Institute of Aeronautics and Space (LAPAN) Jakarta Indonesia Faculty of Computer Science Universitas Indonesia Depok West Java Indonesia Department of Electrical Engineering Universitas Indonesia Depok West Java Indonesia

DEM as a model of earth's surface model plays an important role in the environmental analysis. The most common recent technology in producing DEM is SAR. Even though it has advantages compared to optical approach, DEM generated from SAR still has issues regarding process setting regarding cross-correlation and spatial resolution determination. Both aspects will affect the volume of output data and computational effort. This research project is aimed to analyze the effects of cross-correlation and spatial resolution determination in DEM generation process to its accuracy. Several experiment scenarios of cross-correlation and spatial resolution setting had been applied to TerraSAR-X data. Overall output is shown that determination of cross-correlation threshold and spatial resolution in processing stages will have a significant effect on the accuracy of DEM generated. Gaps of data often occur in several sets of both aspects.

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