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Publisher Correction: A robotic multidimensional directed evolution approach applied to fluorescent voltage reporters

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Nature chemical biology 2018年第9期14卷 901-901页

作者： Kiryl D Piatkevich Erica E Jung Christoph Straub Changyang Linghu Demian Park HoJun Suk Daniel R Hochbaum Daniel Goodwin Eftychios Pnevmatikakis Nikita Pak Takashi Kawashima ChaoTsung Yang Jeffrey L Rhoades Or Shemesh Shoh Asano YoungGyu Yoon Limor Freifeld Jessica L Saulnier Clemens Riegler Florian Engert Thom Hughes Mikhail Drobizhev Balint Szabo Misha B Ahrens Steven W Flavell Bernardo L Sabatini Edward S Boyden 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.

In the version of this article originally published, the bottom of Figure 4f,g was partially truncated in the PDF. The error has been corrected in the PDF version of this article.

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Big data: Prospects and Challenges

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Vikalpa 2015年第1期40卷 74 - 96页

作者： Janakiraman Moorthy Rangin Lahiri Neelanjan Biswas Dipyaman Sanyal Jayanthi Ranjan Krishnadas Nanath Pulak Ghosh (Coordinator) Director and Professor of Marketing at the Institute of Management Technology Dubai. Earlier he was Professor of Marketing at the IIM Calcutta and IIM Lucknow. He received his PhD from IIM Ahmedabad. His recent research papers were published in the leading scholarly ournals such as Marketing Science British Food Journal Journal of Information Technology Case and Application Research Journal of Database Marketing & Customer Strategy Management. He has wide experience in the banking and investment industry. He was earlier the Global Research and Project Director of the Institute for Customer Relationship Management Atlanta USA. He was the Convener of the prestigious CAT Exam 2011. e-mail: Practice Director leading Atos India's CRM practice while supporting Strategic Business Development for North American Market. With an experience of more than 15 years Rangin has worked extensively as a Business Consultant in Information Technology (Sales Automation Marketing & Service Management area) Customer Data Management and CRM Analytics. e-mail: Business Consultant at Atos with extensive experience in Business Analysis Risk Management Analytics Business Development Presales Solution Ideation on Enterprise Data Management Enterprise Reference Data and Master Data Management area. e-mail: founder and CEO of dono consulting a boutique quantitative analytics and investment research firm. He has worked for leading financial firms in New York and India including Dow Jones Blackstone Sorin Capital (VP Quantitative Modeling) and Thomson Reuters (Head of Real Estate Analytics). A CFA charter holder and Commonwealth Scholar Deep has an MS (Applied Economics) from University of Texas Dallas and an MA (Economics) from Jadavpur University e-mail: Professor in the Information Systems Group of the Institute of Management Technology Ghaziabad. Her PhD is in the field of data mining from Jamia Millia Islamia Central University India. She has published five edited books. She is serving on the editorial b

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Proceedings of the 3rd Biennial Conference of the Society for Implementation research Collaboration (SIRC) 2015: advancing efficient methodologies through community partnerships and team science : Seattle, WA, USA. 24-26 September 2015

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Implementation science : IS 2016年第1期11 Suppl 1卷 85页

作者： Cara Lewis Doyanne Darnell Suzanne Kerns Maria Monroe-DeVita Sara J. Landes Aaron R. Lyon Cameo Stanick Shannon Dorsey Jill Locke Brigid Marriott Ajeng Puspitasari Caitlin Dorsey Karin Hendricks Andria Pierson Phil Fizur Katherine A. Comtois Lawrence A. Palinkas Patricia Chamberlain Gregory A. Aarons Amy E. Green Mark. G. Ehrhart Elise M. Trott Cathleen E. Willging Maria E. Fernandez Nicholas H. Woolf Shuting Lily Liang Natalia I. Heredia Michelle Kegler Betsy Risendal Andrea Dwyer Vicki Young Dayna Campbell Michelle Carvalho Yvonne Kellar-Guenther Laura J. Damschroder Julie C. Lowery Sarah S. Ono Kathleen F. Carlson Erika K. Cottrell Maya E. O’Neil Travis L. Lovejoy Joanna J. Arch Jill L. Mitchell Cara C. Lewis Brigid R. Marriott Kelli Scott Jennifer Schurer Coldiron Eric J. Bruns Alyssa N. Hook Benjamin C. Graham Katelin Jordan Rochelle F. Hanson Angela Moreland Benjamin E. Saunders Heidi S. Resnick Shannon Wiltsey Stirman Cassidy A. Gutner Jennifer Gamarra Dawne Vogt Michael Suvak Jennifer Schuster Wachen Katherine Dondanville Jeffrey S. Yarvis Jim Mintz Alan L. Peterson Elisa V. Borah Brett T. Litz Alma Molino Stacey Young McCaughan Patricia A. Resick Nancy Pandhi Nora Jacobson Neftali Serrano Armando Hernandez Elizabeth Zeidler- Schreiter Natalie Wietfeldt Zaher Karp Michael D. Pullmann Barbara Lucenko Bridget Pavelle Jacqueline A. Uomoto Andrea Negrete Molly Cevasco Suzanne E. U. Kerns Robert P. Franks Christopher Bory Edward J. Miech Teresa M. Damush Jason Satterfield Derek Satre Maria Wamsley Patrick Yuan Patricia O’Sullivan Helen Best Susan Velasquez Miya Barnett Lauren Brookman-Frazee Jennifer Regan Nicole Stadnick Alison Hamilton Anna Lau Scott Roesch Byron J. Powell Thomas J. Waltz Matthew J. Chinman Laura Damschroder Jeffrey L. Smith Monica M. Matthieu Enola K. Proctor JoAnn E. Kirchner Monica J. Matthieu Craig S. Rosen Sarah C. Walker Asia S. Bishop Mariko Lockhart Allison L. Rodriguez Luisa Manfredi Andrea Nevedal Joel Rosenthal Daniel M. Blonigen Anne M. Mauricio Thomas D. Dishion Jenna Rudo-Stern Department of Psychological and Brain Sciences Indiana University Bloomington USA Department of Psychiatry & Behavioral Sciences University of Washington Seattle USA Psychology Department University of Montana Missoula USA Department of Psychology La Salle University Philadelphia USA Division of Public Behavioral Health and Justice Policy University of Washington Seattle USA National Center for PTSD VA Palo Alto Health Care System Menlo Park USA Department of Psychiatry University of Arkansas for Medical Sciences Little Rock USA Department of Psychology University of Washington Seattle USA Department of Speech and Hearing Sciences University of Washington Seattle USA Department of Psychological Sciences University of Missouri Columbia USA Department of Children Youth and Families School of Social Work University of Southern California Los Angeles USA Oregon Social Learning Center Eugene USA Department of Psychiatry University of California San Diego La Jolla USA Child and Adolescent Services Research Center (CASRC) San Diego USA Department of Psychology San Diego State University San Diego USA Pacific Institute for Research and Evaluation Behavioral Health Research Center of the Southwest Albuquerque USA School of Public Health University of Texas Health Science Center at Houston Houston USA Woolf Consulting Santa Barbara USA Rollins School of Public Health Emory University Atlanta USA University of Colorado Cancer Center at Denver Aurora USA South Carolina Primary Health Care Association Columbia USA Veterans Affairs (VA) Ann Arbor Center for Clinical Management Research Ann Arbor USA Personalizing Options through Veteran Engagement (PROVE) Quality Enhancement Research Initiative (QUERI) Program Ann Arbor USA Center to Improve Veteran Involvement in Care (CIVIC) Veterns Affairs (VA) Portland Health Care System (VAPORHCS) Portland USA Department of Family Medicine Oregon Health & Science University Portland USA Division of General Internal Medi

Introduction to the 3rd Biennial Conference of the Society for Implementation research Collaboration: advancing efficient methodologies through team science and community partnerships Cara Lewis, Doyanne Darnell, Suzanne Kerns, Maria Monroe-DeVita, Sara J. Landes, Aaron R. Lyon, Cameo Stanick, Shannon Dorsey, Jill Locke, Brigid Marriott, Ajeng Puspitasari, Caitlin Dorsey, Karin Hendricks, Andria Pierson, Phil Fizur, Katherine A. Comtois A1: A behavioral economic perspective on adoption, implementation, and sustainment of evidence-based interventions Lawrence A. Palinkas A2: Towards making scale up of evidence-based practices in child welfare systems more efficient and affordable Patricia Chamberlain A3: Mixed method examination of strategic leadership for evidence-based practice implementation Gregory A. Aarons, Amy E. Green, Mark. G. Ehrhart, Elise M. Trott, Cathleen E. Willging A4: Implementing practice change in Federally Qualified Health centers: learning from leaders’ experiences Maria E. Fernandez, Nicholas H. Woolf, Shuting (Lily) Liang, Natalia I. Heredia, Michelle Kegler, Betsy Risendal, Andrea Dwyer, Vicki Young, Dayna Campbell, Michelle Carvalho, Yvonne Kellar-Guenther A3: Mixed method examination of strategic leadership for evidence-based practice implementation Gregory A. Aarons, Amy E. Green, Mark. G. Ehrhart, Elise M. Trott, Cathleen E. Willging A4: Implementing practice change in Federally Qualified Health centers: learning from leaders’ experiences Maria E. Fernandez, Nicholas H. Woolf, Shuting (Lily) Liang, Natalia I. Heredia, Michelle Kegler, Betsy Risendal, Andrea Dwyer, Vicki Young, Dayna Campbell, Michelle Carvalho, Yvonne Kellar-Guenther A5: Efficient synthesis: Using qualitative comparative analysis and the Consolidated Framework for Implementation research across diverse studies Laura J. Damschroder, Julie C. Lowery A6: Establishing a veterans engagement group to empower patients and inform Veterans Affairs (VA) health services research Sara

关键词： Veteran Affair Motivational Interview Veteran Health Administration Dialectical Behavior Therapy Qualitative Comparative analysis

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Pitfalls in EEG-based brain effective connectivity analysis

Pitfalls in EEG-based brain effective connectivity analysis

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International Workshop on machine learning and Interpretation in Neuroimaging, MLINI 2011, Held at Neural Information Processing, NIPS 2011

作者： Haufe, Stefan Nikulin, Vadim V. Nolte, Guido Müller, Klaus-Robert Berlin Institute of Technology Machine Learning Germany Bernstein Focus Neurotechnology Berlin Germany Neurophysics Charité University Medicine Berlin Germany Bernstein Center for Computational Neuroscience Berlin Germany Intelligent Data Analysis Fraunhofer Institute FIRST Berlin Germany

We consider the problem of estimating brain effective connectivity from electroencephalographic (EEG) measurements, which is challenging due to instantaneous correlations in the sensor data caused by volume conduction in the head. We present selected results of a larger realistic simulation study in which we tested the ability of various measures of effective connectivity to recover the information flow between the underlying sources, as well as the ability of linear and nonlinear inverse source reconstruction approaches to improve the estimation. It turns out that factors related to volume conduction dramatically limit the neurophysiological interpretability of sensor-space connectivity maps and may even (depending on the connectivity measure used) lead to conflicting results. The success of connectivity estimation on inverse source estimates crucially depends on the correctness of the source demixing. This in turn depends on the capability of the method to model (multiple) interacting sources, which is in general not achievable by linear inverses. © 2012 Springer-Verlag.

关键词： Inverse problems

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Aid to regional development agencies: Finding and matching research funding opportunities

Aid to regional development agencies: Finding and matching r...

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13th Annual International Digital Government research Conference: Bridging research and Practice, dg.o 2012

作者： Kaschesky, Michael Gschwend, Adrian Bouchard, Guillaume Furrer, Patrick Gamard, Stephane Riedl, Reinhard Bern University of Applied Sciences E-Government Unit Bern Switzerland Xerox Research Center Europe Data Mining and Machine Learning Grenoble France EuresearCH Effingerstrasse 19 Bern Switzerland SalsaDev SA Chemin de Aulx 18 Plan-les-Ouates Switzerland

ISBN: (纸本)9781450314039

Regional development agencies are confronted with a plethora of research funding programs that provide opportunities for regional research groups and SMEs. This paper describes the practical benefits and technological building blocks of an online matching service of research profiles with funding opportunities. It gives an overview of the infrastructure for data sourcing and processing based on a scalable cloud computing platform. It then describes how data is consolidated, analyzed, and interlinked so as to facilitate the finding and matching of funding opportunities. Integrating concepts and themes with those available as Linked Open data (LOD) adds value to finding and matching by interlinking results with publicly available data resources, and to improve search performance for related content on the internet. In order to optimize matching, the paper describes the user-profiling capabilities based on statistical analyses and machine learning. The paper concludes by discussing the lessons learned so far as well as possible extensions into other application areas. © 2012 ACM.

关键词： learning systems

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Support vector novelty detection in hidden space

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Journal of Computational Information Systems 2011年第15期7卷 5581-5590页

作者： Zhang, Li Wang, Bangjun Li, Fanzhang He, Shuping Research Center of Machine Learning and Data Analysis School of Computer Science and Technology Soochow University Suzhou 215006 China

In this paper, a family of support vector novelty detection (or SVND) in hidden space is presented. Firstly a hidden-space SVND (or HSVND) algorithm is proposed. The data in an input space is mapped into a hidden space by using some hidden function. In the hidden space, we can perform the SVND with linear kernel. The advantage of HSVND over SVND is that a hidden function can be an arbitrary real-valued function including Mercer kernels and their combination. In SVND algorithm, only Mercer kernels can be used. Unfortunately, the mapped data may be non-spherical distributed data. In this case, HSVND works badly. Secondly a whitening HSVND (WHSVND) is proposed. We perform whitening method on the mapped data and then the SVND with linear kernel. The whitened data is spherical approximately. We prove that WHSVND is identical to whitening SVND (WSVND) if a single and same Mercer kernel is taken as a mapping function. In fact, above algorithms cannot work well if the data locates a subspace of input sample space or is rank deficiency. In this case, a mapping function only maps data from one subspace to another subspace and cannot make data spanning whole sample space. Thus noised and whitening HSVND (NWHSVND) is present for kernel mapping function. Before the data is mapped, some small randomly noise is put on it. Based on the noised data, the data is mapped into the hidden space. Next we whiten the mapped data, and perform the SVND with linear kernel. Experiments are performed on artificial and real-world data. © 2011 Binary Information Press December, 2011.

关键词： Vectors

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Opinion mining in social media: Modeling, simulating, and visualizing political opinion formation in the web 11

Opinion mining in social media: Modeling, simulating, and vi...

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12th Annual International Digital Government research Conference: Digital Government Innovation in Challenging Times, dg.o 2011

作者： Kaschesky, Michael Sobkowicz, Pawel Bouchard, Guillaume Bern University of Applied Sciences E-Government Unit Bern Switzerland Xerox Research Center Europe Data Mining and Machine Learning Grenoble France

ISBN: (纸本)9781450307628

Affordable and ubiquitous online communications (social media) provide the means for flows of ideas and opinions and play an increasing role for the transformation and cohesion of society - yet little is understood about how online opinions emerge, diffuse, and gain momentum. To address this problem, an opinion formation framework based on content analysis of social media and sociophysical system modeling is proposed. Based on prior research and own projects, three building blocks of online opinion tracking and simulation are described: (1) automated topic and opinion detection in real-time, (2) topic and opinion modeling and agent-based simulation, and (3) visualizations of topic and opinion networks. Finally, two application scenarios are presented to illustrate the framework and motivate further research. © 2011 ACM.

关键词： Standardization

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Cloud-based event-processing architecture for opinion mining

Cloud-based event-processing architecture for opinion mining

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2011 IEEE World Congress on Services, SERVICES 2011

作者： Grivas, Stella Gatziu Schaaf, Marc Kaschesky, Michael Bouchard, Guillaume Information and Knowledge Management Unit University of Applied Sciences Switzerland E-Government Unit Bern University of Applied Sciences Switzerland Data Mining and Machine Learning Group Xerox Research Center Europe Grenoble France

ISBN: (纸本)9780769544618

The viability of cloud computing for information-intensive tasks arising in real-time opinion mining and sentiment analysis of large online text streams is described. We show how a smart distributed architecture enables an efficient and scalable design for opinion mining on internet-based content that answers key challenges, such as integrating heterogeneous data sources and adapting to events through dynamic system configuration. In particular, we present a novel approach of semantic complex event processing in a cloud environment capturing different levels of information, such as event data (e.g. content from various heterogeneous, distributed sources) as well as associations identified during the opinion mining and sentiment analysis process (e.g. dynamic co-reference resolution). © 2011 IEEE.

关键词： Sentiment analysis

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Excel in mathematics: Applications of calculus

Excel in mathematics: Applications of calculus

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2010 ASEE Annual Conference and Exposition

作者： Young, Cynthia Georgiopoulos, Michael Crouse, Tace Geiger, Cherie Islas, Alvaro Hagen, Scott Dagley-Falls, Melissa Ramsey, Patricia Lancey, Patrice Department of Mathematics College of Sciences UCF United States School of Electrical Engineering and Computer Science UCF United States Machine Learning Laboratory UCF United States Faculty Center for Teaching and Learning University of Central Florida United States Department of Chemistry College of Sciences UCF United States Civil Environmental and Construction Department UCF United States Coastal Hydroscience Analysis Modeling and Predictive Simulations Laboratory UCF United States Academic Affairs College of Engineering and Computer Science UCF United States Institutional Research University of Central Florida United States Department of Operational Excellence and Assessment Support University of Central Florida United States

Nationally only 40% of the incoming freshmen STEM majors are successful in earning a STEM degree [1]. The University of Central Florida (UCF) EXCEL program is an NSF funded STEP (Science, Technology, Engineering and Mathematics Talent Expansion Program) whose goal is to increase the number of UCF STEM graduates. One of the activities that EXCEL has identified as essential in retaining students in science and engineering disciplines is the development and teaching of special courses at the freshman level, called Applications of Calculus I and Applications of Calculus II, or Apps I and Apps II, respectively. In Apps I and II, science and engineering professors are asked to lecture twice, as guest lectures, and to demonstrate to students where calculus topics appear in upper level science and engineering classes as well as where faculty use calculus in their research programs. This paper outlines the process used in producing the educational materials for Apps I and II courses (textbook, presentations slides, in-class homework assignments, demos) and it also discusses the assessment results pertaining to this specific EXCEL activity. © American Society for Engineering Education, 2010.

关键词： Calculations

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