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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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Publisher Correction: Socio-spatial equity analysis of relative wealth index and emergency obstetric care accessibility in urban Nigeria

Communications medicine

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Communications medicine 2024年第1期4卷 82页

作者： Kerry L M Wong Aduragbemi Banke-Thomas Tope Olubodun Peter M Macharia Charlotte Stanton Narayanan Sundararajan Yash Shah Gautam Prasad Mansi Kansal Swapnil Vispute Tomer Shekel Olakunmi Ogunyemi Uchenna Gwacham-Anisiobi Jia Wang Ibukun-Oluwa Omolade Abejirinde Prestige Tatenda Makanga Bosede B Afolabi Lenka Beňová Faculty of Epidemiology and Population Health London School of Hygiene & Tropical Medicine London UK. Faculty of Epidemiology and Population Health London School of Hygiene & Tropical Medicine London UK. Aduragbemi.Banke-Thomas@lshtm.ac.uk. School of Human Sciences University of Greenwich London UK. Aduragbemi.Banke-Thomas@lshtm.ac.uk. Maternal and Reproductive Health Research Collective Lagos Nigeria. Aduragbemi.Banke-Thomas@lshtm.ac.uk. Department of Community Medicine and Primary Care Federal Medical Centre Abeokuta Abeokuta Ogun Nigeria. Department of Public Health Institute of Tropical Medicine Antwerp Belgium. Population & Health Impact Surveillance Group Kenya Medical Research Institute-Wellcome Trust Research Programme Nairobi Kenya. Centre for Health Informatics Computing and Statistics Lancaster Medical School Lancaster University Lancaster UK. Google LLC Mountain View CA USA. chstanton@***. Google LLC Mountain View CA USA. Lagos State Ministry of Health Ikeja Lagos Nigeria. Nuffield Department of Population Health University of Oxford Oxford UK. School of Computing & Mathematical Sciences University of Greenwich London UK. Dalla Lana School of Public Health University of Toronto Toronto Canada. Women's College Hospital Institute for Health System Solutions and Virtual Care Toronto Canada. Surveying and Geomatics Department Midlands State University Faculty of Science and Technology Gweru Midlands Zimbabwe. Climate and Health Division Centre for Sexual Health and HIV/AIDS Research Harare Zimbabwe. Maternal and Reproductive Health Research Collective Lagos Nigeria. Department of Obstetrics and Gynaecology College of Medicine of the University of Lagos Lagos Nigeria.

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Post-Hartree-Fock method in quantum chemistry for quantum computer

arXiv

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arXiv 2020年

作者： Shikano, Yutaka Watanabe, Hiroshi C. Nakanishi, Ken M. Ohnishi, Yu-Ya Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku Yokohama223-8522 Japan Institute for Quantum Studies Chapman University 1 University Dr. OrangeCA92866 United States JST PRESTO 4-1-8 Honcho Kawaguchi Saitama332-0012 Japan Department of Physics Graduate School of Science University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Materials Informatics Initiative RD Technology & Digital Transformation Center JSR Corporation 100 Kawajiri-cho Yokkaichi Mie510-8552 Japan

Quantum computational chemistry is a potential application of quantum computers that is expected to effectively solve several quantum-chemistry problems, particularly the electronic structure problem. Quantum computational chemistry can be compared to the conventional computational devices. This review comprehensively investigates the applications and overview of quantum computational chemistry, including a review of the Hartree-Fock method for quantum information scientists. Quantum algorithms, quantum phase estimation, and variational quantum eigensolver, have been applied to the post-Hartree-Fock method. © 2020, CC BY.

关键词： Quantum chemistry

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A cyber-enabled mission-critical system for post-flood response: Exploiting TV white space as network backhaul links

A cyber-enabled mission-critical system for post-flood respo...

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作者： Rahman, M.D. Arafatur Asyhari, A. Taufiq Azad, Saiful Hasan, M.D. Munirul Munaiseche, Cindy P.C. Krisnanda, Made Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Kuantan26300 Malaysia IBM Center of Excellence and Earth Resources and Sustainability Center Universiti Malaysia Pahang Kuantan26300 Malaysia School of Computing Electronics and Mathematics Coventry University CoventryCV1 5FB United Kingdom Telkom University Jalan Telekomunikasi Terusan Buah Batu Bandung40257 Indonesia Faculty of Engineering Department of Informatics Universitas Negeri Manado Tondano95618 Indonesia Faculty of Engineering Department of Information and Communication Technology Education Universitas Negeri Manado Tondano95618 Indonesia

A crucial problem in post-flood recovery actions is the ability to rapidly establish communication and collaboration among rescuers to conduct timely and effective search and rescue (SAR) mission given disrupted telecommunication infrastructure to support the service. Aimed at providing such proximity service (ProSe) for mission-critical data exchange in the post-flood environment, the majority of existing solutions rely heavily upon ad-hoc networking approaches, which suffer from restricted communication range and the limited scope of interaction. As an effort to broaden the ProSe coverage and expand integrated global-local information exchange in the post-flood SAR activities, this paper proposes a novel network architecture in the form of a cyber-enabled mission-critical system (CEMCS) for acquiring and communicating post-flood emergency data by exploiting TV white space spectrum as network backhaul links. The primary method of developing the proposed system builds upon a layered architecture of wireless local, regional and wide-area communications, and incorporates collaborative network components among these layers. The desirable functionalities of CEMCS are showcased through formulation and the development of an efficient global search strategy exploiting a wide range of collaboration among network agents. The simulation results demonstrate the capability of CEMCS to provide ProSe in the post-flood scenarios as reflected by reliable network performance (e.g., packet delivery ratio nearing 80%–90%) and the optimality of efficient search algorithm. © 2017 Oxford University Press. All rights reserved.

关键词： Cyber Physical System

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Minimizing Communication Overheads in Container-based Clouds for HPC Applications

Minimizing Communication Overheads in Container-based Clouds...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Anderson M. Maliszewski Adriano Vogel Dalvan Griebler Eduardo Roloff Luiz G. Fernandes Navaux Philippe O. A. Informatics Institute Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil School of Technology Pontifical Catholic University of Rio Grande do Sul (PUCRS) Porto Alegre Brazil Laboratory of Advanced Research on Cloud Computing (LARCC) Três de Maio Faculty (SETREM) Três de Maio Brazil

Although the industry has embraced the cloud computing model, there are still significant challenges to be addressed concerning the quality of cloud services. Network-intensive applications may not scale in the cloud due to the sharing of the network infrastructure. In the literature, performance evaluation studies are showing that the network tends to limit the scalability and performance of HPC applications. Therefore, we proposed the aggregation of Network Interface Cards (NICs) in a ready-to-use integration with the OpenNebula cloud manager using Linux containers. We perform a set of experiments using a network microbenchmark to get specific network performance metrics and NAS parallel benchmarks to analyze the performance impact on HPC applications. Our results highlight that the implementation of NIC aggregation improves network performance in terms of throughput and latency. Moreover, HPC applications have different patterns of behavior when using our approach, which depends on communication and the amount of data transferring. While network-intensive applications increased the performance up to 38%, other applications with aggregated NICs maintained the same performance or presented slightly worse performance.

关键词： Cloud computing Throughput Containers Linux Bonding Servers Optimization

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Accuracy and Convergence Analysis of uFA-FastSLAM for Robot and Landmarks Position Estimation

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Journal of Physics: Conference Series 2021年第1期2129卷

作者： R J Musridho H Hasan H Haron D Gusman M A Mohammad Department of Informatics Engineering Faculty of Engineering Universitas Pahlawan Tuanku Tambusai Bangkinang Kota 28412 Indonesia. School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru 81310 Malaysia. Applied Industrial Analytics Research Group Johor Bahru 81310 Malaysia. iKohza Software Engineering for Industrial Revolution Malaysia-Japan International Institute of Technology (MJIIT) Universiti Teknologi Malaysia Kuala Lumpur 54100 Malaysia.

In autonomous mobile robots, Simultaneous Localization and Mapping (SLAM) is a demanding and vital topic. One of two primary solutions of SLAM problem is FastSLAM. In terms of accuracy and convergence, FastSLAM is known to degenerate over time. Previous work has hybridized FastSLAM with a modified Firefly Algorithm (FA), called unranked Firefly Algorithm (uFA), to optimize the accuracy and convergence of the robot and landmarks position estimation. However, it has not shown the performance of the accuracy and convergence. Therefore, this work is done to present both mentioned performances of FastSLAM and uFA-FastSLAM to see which one is better. The result of the experiment shows that uFA-FastSLAM has successfully improved the accuracy (in other words, reduced estimation error) and the convergence consistency of FastSLAM. The proposed uFA-FastSLAM is superior compared to conventional FastSLAM in estimation of landmarks position and robot position with 3.30 percent and 7.83 percent in terms of accuracy model respectively. Furthermore, the proposed uFA-FastSLAM also exhibits better performances compared to FastSLAM in terms of convergence consistency by 93.49 percent and 94.20 percent for estimation of landmarks position and robot position respectively.

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Analysis and Implementation of Ontology Based Text Classification on Criminality Digital News

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IOP Conference Series: Materials Science and Engineering 2019年第2期662卷

作者： F Rahma D D Pangestuti A Herdiani N Selviandro Informatics Engineering School of Computing Telkom University Indonesia Information System Information Technology Institut Teknologi Sepuluh Nopember Indonesia

This research, we search the trend by utillize all information about the criminality type. When the trend has found, the possibility of crime in West Java will decrease. We need to analyse the data to get the information about the criminality trend by using Ontology based Text Classification Method. A news will be grouped into some criminality classes based on the relation around that groups so the process of classification can be done simply and specifically. The criminality trend of West Java is Property Crime with 47,5% of occurrences. We used several testing method such as F1 Score, Precision, Recall, and Accuracy. Based on the methods, the performance of system is running well or not in different point of view. The researcher obtained satisfactory result with F1 score 87,05%, accuracy 86,74%, recall 100% and precision 77,08% with using comparison of composition crime dataset and non-crime dataset is 40 : 40. It happens because testing needs to be done by considering the number of both datasets. The more balance the comparison while do preprocess, the higher accuracy that will get. In order to know whether the system is able to clarify the information accurately and can separate the non-criminal news dataset.

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Ultra-scalable spectral clustering and ensemble clustering

arXiv

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arXiv 2019年

作者： Huang, Dong Wang, Chang-Dong Wu, Jian-Sheng Lai, Jian-Huang Kwoh, Chee-Keong College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China School of Information Engineering Nanchang University Nanchang China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

This paper focuses on scalability and robustness of spectral clustering for extremely large-scale datasets with limited resources. Two novel algorithms are proposed, namely, ultra-scalable spectral clustering (U-SPEC) and ultra-scalable ensemble clustering (U-SENC). In U-SPEC, a hybrid representative selection strategy and a fast approximation method for K-nearest representatives are proposed for the construction of a sparse affinity sub-matrix. By interpreting the sparse sub-matrix as a bipartite graph, the transfer cut is then utilized to efficiently partition the graph and obtain the clustering result. In U-SENC, multiple U-SPEC clusterers are further integrated into an ensemble clustering framework to enhance the robustness of U-SPEC while maintaining high efficiency. Based on the ensemble generation via multiple U-SEPC's, a new bipartite graph is constructed between objects and base clusters and then efficiently partitioned to achieve the consensus clustering result. It is noteworthy that both U-SPEC and U-SENC have nearly linear time and space complexity, and are capable of robustly and efficiently partitioning ten-million-level nonlinearly-separable datasets on a PC with 64GB memory. Experiments on various large-scale datasets have demonstrated the scalability and robustness of our algorithms. The MATLAB code and experimental data are available at https://***/publication/330760669. Copyright © 2019, The Authors. All rights reserved.

关键词： Clustering algorithms

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Integrated presentation attack detection and automatic speaker verification: Common features and Gaussian back-end fusion 19

Integrated presentation attack detection and automatic speak...

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19th Annual Conference of the International Speech Communication, INTERSPEECH 2018

作者： Todisco, Massimiliano Delgado, Héctor Lee, Kong Aik Sahidullah, Md Evans, Nicholas Kinnunen, Tomi Yamagishi, Junichi Department of Digital Security EURECOM France Data Science Research Laboratories NEC Corporation Japan MULTISPEECH Inria France School of Computing University of Eastern Finland Finland Digital Content and Media Sciences Research Division National Institute of Informatics Japan Centre of Speech Technology Research University of Edinburgh United Kingdom

The vulnerability of automatic speaker verification (ASV) systems to spoofing is widely acknowledged. Recent years have seen an intensification in research efforts to develop spoofing countermeasures, also known as presentation attack detection (PAD) systems. Much of this work has involved the exploration of features that discriminate reliably between bona fide and spoofed speech. While there are grounds to use different front-ends for ASV and PAD systems (they are different tasks) the use of a single front-end has obvious benefits, not least convenience and computational efficiency, especially when ASV and PAD are combined. This paper investigates the performance of a variety of different features used previously for both ASV and PAD and assesses their performance when combined for both tasks. The paper also presents a Gaussian back-end fusion approach to system combination. In contrast to cascaded architectures, it relies upon the modelling of the two-dimensional score distribution stemming from the combination of ASV and PAD in parallel. This approach to combination is shown to generalise particularly well across independent ASVspoof 2017 v2.0 development and evaluation datasets. © 2018 International Speech Communication Association. All rights reserved.

关键词： Computational efficiency

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ASVspoof 2017 Version 2.0: meta-data analysis and baseline enhancements

ASVspoof 2017 Version 2.0: meta-data analysis and baseline e...

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2018 Speaker and Language Recognition Workshop, ODYSSEY 2018

作者： Delgado, Héctor Todisco, Massimiliano Sahidullah, Md Evans, Nicholas Kinnunen, Tomi Lee, Kong Aik Yamagishi, Junichi Department of Digital Security EURECOM France MULTISPEECH Inria France School of Computing University of Eastern Finland Finland Data Science Research Laboratories NEC Corporation Japan Digital Content and Media Sciences Research Division National Institute of Informatics Japan Centre of Speech Technology Research University of Edinburgh United Kingdom

The now-acknowledged vulnerabilities of automatic speaker verification (ASV) technology to spoofing attacks have spawned interests to develop so-called spoofing countermeasures. By providing common databases, protocols and metrics for their assessment, the ASVspoof initiative was born to spearhead research in this area. The first competitive ASVspoof challenge held in 2015 focused on the assessment of countermeasures to protect ASV technology from voice conversion and speech synthesis spoofing attacks. The second challenge switched focus to the consideration of replay spoofing attacks and countermeasures. This paper describes Version 2.0 of the ASVspoof 2017 database which was released to correct data anomalies detected post-evaluation. The paper contains as-yet unpublished meta-data which describes recording and playback devices and acoustic environments. These support the analysis of replay detection performance and limits. Also described are new results for the official ASVspoof baseline system which is based upon a constant Q cesptral coefficient frontend and a Gaussian mixture model backend. Reported are enhancements to the baseline system in the form of log-energy coefficients and cepstral mean and variance normalisation in addition to an alternative i-vector backend. The best results correspond to a 48% relative reduction in equal error rate when compared to the original baseline system. © ODYSSEY 2018. All rights reserved.

关键词： Metadata

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