检索结果-内蒙古大学图书馆

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

引用

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

ELM vs. WiSARD: A performance comparison 25

ELM vs. WiSARD: A performance comparison

引用

25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2017

作者： Oliveira, Luiz Fernando R. França, Felipe M.G. Systems Engineering and Computer Science Program COPPE Universidade Federal Do Rio de Janeiro Brazil

ISBN: (纸本)9782875870391

The extreme learning machine (ELM) is known for being a fast learning neural model. This work presents a performance comparison between ELM and the WiSARD weightless neural network model, regarding training and testing times, and classification accuracy as well. The two models were implemented in the same programming language and experiments were carried out on the same hardware environment. By using a group of datasets from the public repositories UCI and Statlog, experimental results shows that the WiSARD presented training times approximately one order of magnitude smaller than ELM, while classification accuracy varied according the number of classes involved. However, while WiSARD's architecture setups were not exhaustively searched, architecture setups for ELM were kept the same as the ones found in the literature as the best for each given dataset. © ESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. All rights reserved.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Data quality measures and data cleansing for research information systems

arXiv

引用

arXiv 2019年

作者： Azeroual, Otmane Saake, Gunter Abuosba, Mohammad Berlin10117 Germany Otto-Von-Guericke University Magdeburg Department of Computer Science Institute for Technical and Business Information Systems Database Research Group P.O. Box 4120 Magdeburg39106 Germany University of Applied Sciences Htw Berlin Study Program Computational Science and Engineering Wilhelminenhofstrasse 75a Berlin12459 Germany

The collection, transfer and integration of research information into different research information systems can result in different data errors that can have a variety of negative effects on data quality. In order to detect errors at an early stage and treat them efficiently, it is necessary to determine the clean-up measures and the new techniques of data cleansing for quality improvement in research institutions. Thereby an adequate and reliable basis for decision-making using an RIS is provided, and confidence in a given dataset increased. In this paper, possible measures and the new techniques of data cleansing for improving and increasing the data quality in research information systems will be presented and how these are to be applied to the research information. Copyright © 2019, The Authors. All rights reserved.

关键词： Standardization

来源：评论

学校读者我要写书评

暂无评论

Design and Implementation of OLED Driving and OPD Readout Circuitry for an Optical Vibration Sensor 17

Design and Implementation of OLED Driving and OPD Readout Ci...

引用

17th IEEE SENSORS Conference, SENSORS 2018

作者： Wu, Yi-Cheng Kao, Yung-Hua Chao, Paul C.-P. Wey, Chin-Long Sauter, Thilo Eka, Fitrah P. Pandey, Rajeev Department of Electrical Engineering National Chiao Tung University Hsinchu300 Taiwan Institute of Electrical and Control Engineering National Chiao Tung University Hsinchu City Taiwan EECS International Graduate Program National Chiao Tung University Hsinchu City Taiwan Department for Integrated Sensor Systems Danube University Krems Austria Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu City Taiwan

ISBN: (纸本)9781538647073

This study presents an organic light-emitting diode (OLED) drive circuit and an organic photo detector (OPD) readout circuits for a vibration sensor. Vibration sensor consist of micro-electro-mechanical systems (MEMS), which are OLED and OPD needs to be integrated for many applications in automation or consumer electronics. The light flux is generated by an OLED located on top of the structure and the modulated light flux is measured by an OPD. The OLED drive circuit contains pulse width modulation (PWM) driver to control PWM output for optimization of voltage operation is designed in order to minimize power consumption efficiency of device. The readout circuit includes a trans-impedance amplifier (TIA) to convert incident light of OPD to voltage output, a 14-bit analog-to-digital converter (ADC) to extract amplitude of vibration level, circuit to generate clock for time-to-digital converter (TDC) which detect frequency of vibration sensor. The designed circuit has been successfully fabricated by using TSMC 0.18 μm CMOS process, the chip area is 1.012 mm2. The power consumption of chip is about 6.2 mW. The measured frequency of PWM is about 200 kHz with 50% duty cycle. The frequency range of the vibration can be sensed well as about 10 Hz1.5 kHz, and resulting in high SNR at frequencies under 60 Hz. © 2018 IEEE.

关键词： Electric power utilization

来源：评论

学校读者我要写书评

暂无评论

Stakeholder Act as an IT and General Compliance Auditor

Stakeholder Act as an IT and General Compliance Auditor

引用

International Conference on Sustainable Information engineering and Technology (SIET)

作者： Surjandy Erick Fernando Meyliana Raymond Kosala Suhono Harso Supangkat Diani Dwiningrum Merry Marshela Angelica Renita Amalia Information Systems Audit Program Information Systems Department Bina Nusantara University Jakarta Indonesia Information Systems Departement Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9781538674086;9781538674079

Recently, the Indonesian government requires auditors, the need for auditors around 40,000 auditors in 2016, currently has only about 10,800 auditors, however, the lack of the auditors impacting the risk of financial loss for the state. Referring to the problem, this experimental research was conducted. The experiment of the research was conducted two times and the audit topic is a compliance audit in the year 2017 and year 2018. The goal of this research is to know, can the stakeholder act as an IT and general compliance auditor? The experiment was conducted by 8 university students in 2017 and 19 university students in 2018, and the supporting tools of audit activities the university student use the smartphone to capture the object as evidence. The experiment results with descriptive method show university students can act as an auditor and found several pieces of evidence that might cause future impact to the institution.

关键词： Regulation Stakeholders Companies Libraries Finance

来源：评论

学校读者我要写书评

暂无评论

Computationally designed proteins mimic antibody immune evasion in viral evolution

引用

Immunity 2025年第6期58卷 1411-1421.e6页

作者： Youssef, Noor Gurev, Sarah Ghantous, Fadi Brock, Kelly P. Jaimes, Javier A. Thadani, Nicole N. Dauphin, Ann Sherman, Amy C. Yurkovetskiy, Leonid Soto, Daria Estanboulieh, Ralph Kotzen, Ben Notin, Pascal Kollasch, Aaron W. Cohen, Alexander A. Dross, Sandra E. Erasmus, Jesse Fuller, Deborah H. Bjorkman, Pamela J. Lemieux, Jacob E. Luban, Jeremy Seaman, Michael S. Marks, Debora S. Department of Systems Biology Harvard Medical School Boston 02115 MA United States Broad Institute of Harvard and MIT Cambridge 02139 MA United States Department of Electrical Engineering and Computer Science MIT Cambridge 02139 MA United States Center for Virology and Vaccine Research Beth Israel Deaconess Medical Center Boston 02215 MA United States Program in Molecular Medicine University of Massachusetts Chan Medical School Worcester 01655 MA United States Division of Infectious Diseases Department of Medicine Brigham and Women's Hospital Boston 02115 MA United States Massachusetts General Hospital Boston 02114 MA United States Division of Biology and Biological Engineering California Institute of Technology Pasadena 91125 CA United States Department of Microbiology University of Washington Seattle 98195 WA United States National Primate Research Center Seattle 98109 WA United States HDT Bio Seattle 98109 WA United States Massachusetts Consortium on Pathogen Readiness Boston 02115 MA United States Ragon Institute of MGH MIT and Harvard Cambridge 02139 MA United States

Recurrent waves of viral infection necessitate vaccines and therapeutics that remain effective against emerging viruses. Our ability to evaluate interventions is currently limited to assessments against past or circulating variants, which likely differ in their immune escape potential compared with future variants. To address this, we developed EVE-Vax, a computational method for designing antigens that foreshadow immune escape observed in future viral variants. We designed 83 SARS-CoV-2 spike proteins that transduced ACE2-positive cells and displayed neutralization resistance comparable to variants that emerged up to 12 months later in the COVID-19 pandemic. Designed spikes foretold antibody escape from B.1-BA.4/5 bivalent booster sera seen in later variants. The designed constructs also highlighted the increased neutralization breadth elicited by nanoparticle-based, compared with mRNA-based, boosters in non-human primates. Our approach offers targeted panels of synthetic proteins that map the immune landscape for early vaccine and therapeutic evaluation against future viral strains. © 2025 The Authors

关键词： antibody escape generative protein models immune landscape machine learning protein design vaccine evaluation viral evolution

来源：评论

学校读者我要写书评

暂无评论

Formation flying techniques for the virtual telescope for X-ray observations 69

Formation flying techniques for the virtual telescope for X-...

引用

69th International Astronautical Congress: #InvolvingEveryone, IAC 2018

作者： Kyle, Rankin Neerav, Shah John, Krizmanic Steven, Stochaj Asal, Naseri Department of Mechanical and Aerospace Engineering New Mexico State University United States Goddard Space Flight Center NASA United States CRESST NASA GSFC University of Maryland Baltimore County United States Klipsch School of Electrical and Computer Engineering New Mexico State University United States Program Manage/Systems Engineer Space Dynamics Laboratory United States

The Virtual Telescope for X-Ray Observations (VTXO) is an Astrophysics SmallSat mission being developed to demonstrate 10-milliarcsecond X-ray imaging using a Phase Fresnel Lense (PFL)-based space telescope. PFLs promise to provide several orders of magnitude improvement in angular resolution over current state of the art X-ray optics. However, PFLs for astronomical applications requires a very long focal length, for VTXO the focal length is estimated to be in the range of 0.5 km to 4 km. Since these focal lengths are not feasible on a single spacecraft, the proposed solution is to use two separate spacecraft, one with the lense(s), and the second with an X-ray camera. These two spacecrafts will then fly in a formation approximating a single rigid telescope. In order to achieve this configuration, the two spacecraft must maintain the formation a focal length distance apart, with centimeter level control, and sub-millimeter level knowledge requirements. Additionally, for the telescope to achieve sufficient exposure time, the system must keep the telescope axis pointed at a fixed target on the celestial sphere for durations on the order of a few hours. VTXO's system architecture calls for two CubeSats to operate in a highly eccentric Earth orbit with one of the spacecraft's traveling on a natural keplarian orbit. The second spacecraft will then fly on a â€œpseudoâ€¥ orbit maintaining a fixed offset relative to the celestial sphere during observations. Observations with this system will occur near apogee where differential forces on the spacecrafts are minimal which in turn minimizes fuel consumption. The â€œpseudoâ€¥ orbit where the second spacecraft is located is not quite a stable orbit requiring small continuous propulsion to maintain formation. This paper and presentation will overview VTXO's system architecture, additionally it will look in depth at the formation flying techniques, including fuel consumption, and methods for establishing, and maintaining the form

关键词： Orbits

来源：评论

学校读者我要写书评

暂无评论

The association for the advancement of artificial intelligence 2020 workshop program

The association for the advancement of artificial intelligen...

引用

作者： Bang, Grace Barash, Guy Bea, Ryan Cali, Jacques Castillo-Effen, Mauricio Chen, Xin Cynthia Chhaya, Niyati Dhoopar, Rohan Dumancic, Sebastijan Espinoza, Huáscar Farchi, Eitan Fioretto, Ferdinando Fuentetaja, Raquel Galley, Michel Geib, Christopher Hernández-Orallo, José Huang, Xiaowei Keren, Sarah Kim, Seokhwan Jaidka, Kokil Liu, Xiaomo Lu, Tyler Ma, Zhiqiang Mallah, Richard McDermid, John Michalowski, Martin Mirsky, Reuth Héigeartaigh, Seán Ó. Ramachandran, Deepak Segovia-Aguas, Javier Shaban-Nejad, Arash Shehory, Onn Shwartz, Vered Srivastava, Siddharth Talamadupula, Kartik Tang, Jian Zhang, Dell Zhang, Jian S&P Global. Western Digital University of Southampton United Kingdom Blue Prism Group PLC Lockheed Martin's Advanced Technology Laboratories ArlingtonVA United States University of Hong Kong Hong Kong Adobe Research Google Research Katholieke Universiteit Leuven Belgium Commissariat à ĺÉnergie Atomique France IBM Research Department of Electrical Engineering and Computer Science Syracuse University United States Universidad Carlos III de Madrid Spain Microsoft Research AI SIFT LLC Universitat Politècnica de València Spain Leverhulme Centre for the Future of Intelligence United Kingdom Department of Computer Science University of Liverpool United Kingdom National University of Singapore Singapore Harvard School of Engineering and Applied Sciences in the of Fields of Multiagent Environment Design and Goal Recognition Design Amazon Alexa AI Future of Life Institute United States University of York Assuring Autonomy International Program United Kingdom University of Minnesota School of Nursing MinneapolisMN United States University of Texas at Austin AustinTX United States Institut de Robòtica i Informàtica Industrial Spanish Council for Scientific Research Technical University of Catalonia Spain Oak Ridge National Laboratory Center for Biomedical Informatics Department of Pediatrics University of Tennessee Health Science Center United States Bar Ilan University Israel University of Washington SeattleWA United States Autonomous Agents and Intelligent Robots lab of the School of Computing Informatics Decision Systems Arizona State University United States DiDi AI Labs School of Industrial and System Engineering Georgia Institute of Technology United States University of London United Kingdom Microsoft Azure

The Association for the Advancement of Artificial Intelligence 2020 Workshop program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but not all, of the workshop chairs. Copyright © 2020, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Author Correction: Greengenes2 unifies microbial data in a single reference tree

引用

Nature biotechnology 2024年第5期42卷 813页

作者： Daniel McDonald Yueyu Jiang Metin Balaban Kalen Cantrell Qiyun Zhu Antonio Gonzalez James T Morton Giorgia Nicolaou Donovan H Parks Søren M Karst Mads Albertsen Philip Hugenholtz Todd DeSantis Se Jin Song Andrew Bartko Aki S Havulinna Pekka Jousilahti Susan Cheng Michael Inouye Teemu Niiranen Mohit Jain Veikko Salomaa Leo Lahti Siavash Mirarab Rob Knight Department of Pediatrics University of California San Diego School of Medicine La Jolla CA USA. Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA. Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA USA. Department of Computer Science and Engineering University of California San Diego La Jolla CA USA. School of Life Sciences Arizona State University Tempe AZ USA. Biodesign Center for Fundamental and Applied Microbiomics Arizona State University Tempe AZ USA. Biostatistics & Bioinformatics Branch Eunice Kennedy Shriver National Institute of Child Health and Human Development National Institutes of Health Bethesda MD USA. Halicioglu Data Science Institute University of California San Diego La Jolla CA USA. Australian Centre for Ecogenomics School of Chemistry and Molecular Biosciences The University of Queensland St Lucia Queensland Australia. Department of Obstetrics and Gynecology Columbia University New York NY USA. Department of Chemistry and Bioscience Aalborg University Aalborg Denmark. Department of Informatics Second Genome Brisbane CA USA. Center for Microbiome Innovation Jacobs School of Engineering University of California San Diego La Jolla CA USA. Finnish Institute for Health and Welfare Helsinki Finland. Institute for Molecular Medicine Finland FIMM-HiLIFE Helsinki Finland. Division of Cardiology Brigham and Women's Hospital Boston MA USA. Cedars-Sinai Medical Center Los Angeles CA USA. Cambridge Baker Systems Genomics Initiative Baker Heart and Diabetes Institute Melbourne Victoria Australia. Cambridge Baker Systems Genomics Initiative Department of Public Health and Primary Care University of Cambridge Cambridge UK. Division of Medicine Turku University Hospital and University of Turku Turku Finland. Sapient Bioanalytics LLC San Diego CA USA. Department of Computing University of Turku Turku Finland. Department of Pediatrics Uni

来源：评论

学校读者我要写书评

暂无评论

A Collection of Software engineering Challenges for Big Data System Development

A Collection of Software Engineering Challenges for Big Data...

引用

Euromicro Conference on Software engineering and Advanced Applications (SEAA)

作者： Oliver Hummel Holger Eichelberger Andreas Giloj Dominik Werle Klaus Schmid Faculty of Computer Science Mannheim University of Applied Sciences Software Systems Engineering University of Hildesheim Institute for Program Structures and Data Organization Karlsruhe Institute of Technology

ISBN: (纸本)9781538673843

In recent years, the development of systems for processing and analyzing large amounts of data (so-called Big Data) has become an important sub-discipline of software engineering. However, to date there exits no comprehensive summary of the specific idiosyncrasies and challenges that the development of Big Data systems imposes on software engineers. With this paper, we aim to provide a first step towards filling this gap based on our collective experience from industry and academic projects as well as from consulting and initial literature reviews. The main contribution of our work is a concise summary of 26 challenges in engineering Big Data systems, collected and consolidated by means of a systematic identification process. The aim is to make practitioners more aware of common challenges and to offer researchers a solid baseline for identifying novel software engineering research directions.

关键词： Big Data Software Software engineering computer architecture Data analysis Data privacy Systematics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：