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BENCHIP： Benchmarking Intelligence Processors

Journal of Computer Science & Technology 2018年第1期33卷 1-23页

作者： Jin-Hua Tao Zi-Dong Du Qi Guo Hui-Ying Lan Lei Zhang Sheng-Yuan Zhou Ling-Jie Xu Cong Liu Hai-Feng Liu Shah Tang Allen Rush Willian Chen Shao-Li Liu Yun-Ji Chen Tian-Shi Chen State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100049 China Intelligent Processor Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Cambricon Ltd. Beijing 100190 China A libaba Infrastructure Service A libaba Group Hangzhou 311121 China Iflytek Co. Ltd. Hefei 230088 China Beijing Jingdong Century Trading Co. Ltd. Beijing 100176 China RDA Microdectronics Inc. Shanghai 201203 China Advanced Micro Devices Inc. Sunnyvale CA 94085 U.S.A.

The increasing attention on deep learning has tremendously spurred the design of intelligence processing hardware. The variety of emerging intelligence processors requires standard benchmarks for fair comparison and system optimization （in both software and hardware）. However, existing benchmarks are unsuitable for benchmarking intelligence processors due to their non-diversity and nonrepresentativeness. Also, the lack of a standard benchmarking methodology further exacerbates this problem. In this paper, we propose BENCHIP, a benchmark suite and benchmarking methodology for intelligence processors. The benchmark suite in BENCHIP consists of two sets of benchmarks： microbenchmarks and macrobenchmarks. The microbenchmarks consist of single-layer networks, They are mainly designed for bottleneck analysis and system optimization. The macrobenchmarks contain state-of-the-art industrial networks, so as to offer a realistic comparison of different platforms. We also propose a standard benchmarking methodology built upon an industrial software stack and evaluation metrics that comprehensively reflect various characteristics of the evaluated intelligence processors, BENCHIP is utilized for evaluating various hardware platforms, including CPUs, GPUs, and accelerators. BENCHIP will be open-sourced soon.

关键词： deep learning intelligence processor benchmark

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AGlobal benchmark of algorithms for segmentinglate gadolinium-enhanced cardiac magnetic resonance imaging

arXiv

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arXiv 2020年

作者： Xiong, Zhaohan Xia, Qing Hu, Zhiqiang Huang, Ning Bian, Cheng Zheng, Yefeng Vesal, Sulaiman Ravikumar, Nishant Maier, Andreas Yang, Xin Heng, Pheng-Ann Ni, Dong Li, Caizi Tong, Qianqian Si, Weixin Puybareau, Elodie Khoudli, Younes Géraud, Thierry Chen, Chen Bai, Wenjia Rueckert, Daniel Xu, Lingchao Zhuang, Xiahai Luo, Xinzhe Jia, Shuman Sermesant, Maxime Liu, Yashu Wang, Kuanquan Borra, Davide Masci, Alessandro Corsi, Cristiana De Vente, Coen Veta, Mitko Karim, Rashed Preetha, Chandrakanth Jayachandran Engelhardt, Sandy Qiao, Menyun Wang, Yuanyuan Tao, Qian Nuñez-Garcia, Marta Camara, Oscar Savioli, Nicolo Lamata, Pablo Zhao, Jichao Auckland Bioengineering Institute University of Auckland Auckland New Zealand State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China SenseTime Inc Shenzhen China Tencent Jarvis Laboratory Shenzhen China Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Wuhan University Wuhan China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Epita Research and Development Laboratory Paris France Department of Computing Imperial College London London United Kingdom School of Naval Architecture Shanghai Jiao Tong University Ocean & Civil Engineering Shanghai China School of Data Science Fudan University Shanghai China Epione Research Group Universite Cote d'Azur Inria Sophia Antipolis France Harbin Institute of Technology School of Computer Science and Technology Harbin China Department of Electric University of Bologna Electronic and Information Engineering Cesena Italy Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Heidelberg University Hospital Germany Biomedical Engineering Center Fudan University Shanghai China Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain D

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI)widely used for visualizing diseased cardiacstructures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation ofLGE-MRIsischallenging due to *** most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning *** address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atriumsegmented by three medical experts,ultimately attractingthe participation of27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis,offeringan overall picture ofthe major design choices of convolutional neural networks(CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top methodachieved a dice score of 93.2%and a mean surface to a surface distance of 0.7mm, significantly outperforming prior state-of-the-art. Particularly, our analysisdemonstrated that double, sequentially usedCNNs, in which a first CNN is used for automatic region-of-interestlocalization and a subsequent CNN is used for refinedregional segmentation, achieved far superiorresults than traditional methods and pipelinescontaining *** findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging *** large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serveas animportant benchmark for evaluating and comparing the future works in the field. Copyright © 2020, Th

关键词： Magnetic resonance imaging

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Efficient Verification of Hypergraph states

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Physical Review Applied 2019年第5期12卷 054047-054047页

作者： Huangjun Zhu Masahito Hayashi Department of Physics and Center for Field Theory and Particle Physics Fudan University Shanghai 200433 China State Key Laboratory of Surface Physics Fudan University Shanghai 200433 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai 200433 China Collaborative Innovation Center of Advanced Microstructures Nanjing 210093 China Institute for Theoretical Physics University of Cologne Cologne 50937 Germany Graduate School of Mathematics Nagoya University Nagoya 464-8602 Japan Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen 518000 China Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 117542 Singapore

Graph states and hypergraph states are of wide interest in quantum information processing and foundational studies. Efficient verification of these states is a key to various applications. Here we propose a simple method for verifying hypergraph states, which requires only two distinct Pauli measurements for each party, yet its efficiency is comparable to the best strategy based on entangling measurements. For a given state, the overhead is bounded by the chromatic number and degree of the underlying hypergraph. Our protocol is dramatically more efficient than all previous protocols based on local measurements, including tomography and direct-fidelity estimation. It enables the verification of hypergraph states and genuine multipartite entanglement of thousands of qubits. The protocol can also be generalized to the adversarial scenario, while achieving almost the same efficiency. This merit is particularly appealing to demonstrating blind measurement-based quantum computation and quantum supremacy.

关键词： Entanglement detection Measurement-based quantum computing Quantum benchmarking Quantum information processing Quantum networks Quantum tomography Resource theories Graph theory

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Author Correction: Multidimensional nanoscopic chiroptics

Nature Reviews Physics

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Nature Reviews Physics 2022年 138页

作者： Yang Chen Qing Zhang Cheng-Wei Qiu Wei Du Oscar Ávalos-Ovando Alexander O. Govorov Jing Wu Qing-Hua Xu Na Liu Hiromi Okamoto Qihua Xiong Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore CAS Key Laboratory of Mechanical Behaviour and Design of Materials Department of Precision Machinery and Precision Instrumentation University of Science and Technology of China Hefei China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore Singapore Institute of Functional Nano and Soft Materials (FUNSOM) Soochow University Suzhou China Department of Physics and Astronomy Ohio University Athens OH USA Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu China Institute of Materials Research and Engineering (IMRE) Agency for Science Technology and Research (A*STAR) Singapore Singapore Department of Chemistry National University of Singapore Singapore Singapore 2nd Physics Institute University of Stuttgart Stuttgart Germany Max Planck Institute for Solid State Research Stuttgart Germany Institute for Molecular Science and the Graduate University for Advanced Studies (SOKENDAI) Okazaki Aichi Japan State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing China Beijing Academy of Quantum Information Sciences (BAQIS) Beijing China

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Correction to: A robust image steganography based on the concatenated error correction encoder and discrete cosine transform coefficients

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Journal of Ambient Intelligence and Humanized computing 2019年第5期11卷 1903-1903页

作者： Bao, Zhenkun Guo, Yanqing Li, Xiaolong Zhang, Yi Xu, Meng Luo, Xiangyang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Zhengzhou Science and Technology Institute Zhengzhou China School of Information and Communication Engineering Dalian University of Technology Dalian China Institute of Information Science Beijing Jiaotong University Beijing China

The article “A robust image steganography based on the concatenated error correction encoder and discrete cosine transform coefficients” written by Zhenkun Bao et al. was originally published electronically on the p...

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Accountable multi-authority ciphertext-policy attribute-based encryption without key escrow and key abuse 9th

Accountable multi-authority ciphertext-policy attribute-base...

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9th International Symposium on Cyberspace Safety and Security, CSS 2017

作者： Yu, Gang Ma, Xiaoxiao Cao, Zhenfu Zhu, Weihua Zeng, Junjie State Key Laboratory of Mathematical Engineering and Advanced Computing Information Science and Technology Institute Zhengzhou450001 China Shanghai Key Lab for Trustworthy Computing East China Normal University Shanghai200062 China Zheng Zhou Vocational University of Information and Technology Zhengzhou450046 China

ISBN: (纸本)9783319694702

Ciphertext-policy attribute-based encryption (CP-ABE) is a promising public key encryption primitive enabling fine-grained access control on shared data in public cloud. However, two quite challenging issues, the prevention of key escrow and key abuse, still exist in CP-ABE system. In this paper, we propose a multi-authority CP-ABE scheme without key escrow and key abuse. To prevent key escrow, multiple authorities are employed to perform the same procedure of key generation for an attribute. Thus, no individual authority or colluded authorities that manage no common attribute can decrypt any ciphertext, and it can also resist collusion attack from curious authority with the help of dishonest users. To prevent key abuse of dishonest users, user’s global identifier along with a signature is embedded into the secret key. Thus, any third party can learn the identity from a shared secret key and publicly verify its validity. An advantage of simultaneously preventing key escrow and key abuse is that the proposed scheme can achieve accountability, i.e. an auditor can publicly audit a user or authorities abuse the secret key. At last, the proposed scheme is fully secure in the random oracle model, and due to a key aggregate algorithm its efficiency is comparable to the decentralizing CP-ABE scheme [18] on which it is based. © 2017, Springer International Publishing AG.

关键词： Access control

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2023 Astrophotonics Roadmap: pathways to realizing multi-functional integrated astrophotonic instruments

arXiv

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arXiv 2023年

作者： Jovanovic, Nemanja Gatkine, Pradip Anugu, Narsireddy Amezcua-Correa, Rodrigo Thakur, Ritoban Basu Beichman, Charles Bender, Chad Berger, Jean-Philippe Bigioli, Azzurra Bland-Hawthorn, Joss Bourdarot, Guillaume Bradford, Charles M. Broeke, Ronald Bryant, Julia Bundy, Kevin Cheriton, Ross Cvetojevic, Nick Diab, Momen Diddams, Scott A. Dinkelaker, Aline N. Duis, Jeroen Eikenberry, Stephen Ellis, Simon Endo, Akira Figer, Donald F. Fitzgerald, Michael Gris-Sanchez, Itandehui Gross, Simon Grossard, Ludovic Guyon, Olivier Haffert, Sebastiaan Y. Halverson, Samuel Harris, Robert J. He, Jinping Herr, Tobias Hottinger, Philipp Huby, Elsa Ireland, Michael Jenson-Clem, Rebecca Jewell, Jeffrey Jocou, Laurent Kraus, Stefan Labadie, Lucas Lacour, Sylvestre Laugier, Romain Lawniczuk, Katarzyna Lin, Jonathan Leifer, Stephanie Leon-Saval, Sergio Martin, Guillermo Martinache, Frantz Martinod, Marc-Antoine Mazin, Benjamin A. Minardi, Stefano Monnier, John D. Moreira, Reinan Mourard, Denis Nayak, Abani Shankar Norris, Barnaby Obrzud, Ewelina Perraut, Karine Reynaud, François Sallum, Steph Schiminovich, David Schwab, Christian Serbayn, Eugene Soliman, Sherif Stoll, Andreas Tang, Liang Tuthill, Peter Vahala, Kerry Vasisht, Gautam Veilleux, Sylvain Walter, Alexander B. Wollack, Edward J. Xin, Yinzi Yang, Zongyin Yerolatsitis, Stephanos Zhang, Yang Zou, Chang-Ling Department of Astronomy California Institute of Technology PasadenaCA United States The CHARA Array of Georgia State University Mount Wilson Observatory Mount WilsonCA91203 United States CREOL The College of Optics and Photonics University of Central Florida OrlandoFL United States IPAC NASA Exoplanet Science Institute Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Steward Observatory University of Arizona TucsonAZ United States Institut de Planétologie et d'Astrophysique UGA CNRS France Katholieke Universiteit Leuven Belgium School of Physics The University of Sydney Australia Max Planck Institute for Extraterrestrial Physics Garching Germany Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Bright Photonics BV Eindhoven Netherlands Department of Astronomy and Astrophysics University of California Santa CruzCA United States Advanced Electronics and Photonics Research Centre National Research Council Canada Ottawa Canada Université Côte d’Azur Observatoire de la Côte d’Azur CNRS Laboratoire Lagrange France Dunlap Institute for Astronomy and Astrophysics University of Toronto Toronto Canada Electrical Computer and Energy Engineering Department of Physics University of Colorado BoulderCO United States Potsdam Germany PHIX Photonics Assembly Enschede Netherlands Australian Astronomical Optics Macquarie University NSW Australia Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Center for Detectors Rochester Institute of Technology RochesterNY United States UCLA Physics & Astronomy Department Los AngelesCA United States ITEAM Research Institute Universitat Politècnica de València Valencia Spain MQ Photonics Research Centre Macquarie University NSW Australia XLIM Research Institute University of Limoges France Subaru Telescope National Astronomical Observatory of Japan National Institute of Natural Scien

Photonic technologies offer numerous functionalities that can be used to realize astrophotonic instruments. The most spectacular example to date is the ESO Gravity instrument at the Very Large Telescope in Chile that combines the light-gathering power of four 8 m telescopes through a complex photonic interferometer. Fully integrated astrophotonic devices stand to offer critical advantages for instrument development, including extreme miniaturization when operating at the diffraction-limit, as well as integration, superior thermal and mechanical stabilization owing to the small footprint, and high replicability offering significant cost savings. Numerous astrophotonic technologies have been developed to address shortcomings of conventional instruments to date, including for example the development of photonic lanterns to convert from multimode inputs to single mode outputs, complex aperiodic fiber Bragg gratings to filter OH emission from the atmosphere, complex beam combiners to enable long baseline interferometry with for example, ESO Gravity, and laser frequency combs for high precision spectral calibration of spectrometers. Despite these successes, the facility implementation of photonic solutions in astronomical instrumentation is currently limited because of 1) low throughputs from coupling to fibers, coupling fibers to chips, propagation and bend losses, device losses, etc., 2) difficulties with scaling to large channel count devices needed for large bandwidths and high resolutions, and 3) efficient integration of photonics with detectors, to name a few. In this roadmap, we identify 23 key areas that need further development. We outline the challenges and advances needed across those areas covering design tools, simulation capabilities, fabrication processes, the need for entirely new components, integration and hybridization and the characterization of devices. To realize these advances the astrophotonics community will have to work cooperatively with industr

关键词： Fiber Bragg gratings

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Author Correction: Constrained catecholamines gain β2AR selectivity through allosteric effects on pocket dynamics

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Nature Communications 2023年第1期14卷 1页

作者： Xinyu Xu Xiangyu Liu Jeremy Shonberg Jonas Kaindl Anne Stößel Luis Maul Harald Hübner Peter Gmeiner Mary J. Clark Daniel Mayer Roger K. Sunahara Kunio Hirata A. J. Venkatakrishnan Ron O. Dror Brian K. Kobilka State Key laboratory of Membrane Biology Tsinghua-Peking Center for Life Sciences School of Pharmaceutical Sciences Tsinghua University Beijing 100084 China Beijing Frontier Research Center for Biological Structure Beijing Advanced Innovation Center for Structural Biology Tsinghua University Beijing 100084 China Beijing Key Laboratory of Cardiovascular Receptors Research Peking University Beijing China Department of Chemistry and Pharmacy Medicinal Chemistry Friedrich-Alexander University Erlangen-Nurnberg Nikolaus-Fiebiger-Straße 10 91058 Erlangen Germany Department of Pharmacology University of California San Diego School of Medicine 9500 Gilman Drive La Jolla California 92093 USA Advanced Photon Technology Division Research Infrastructure Group SR Life Science Instrumentation Unit RIKEN/SPring-8 Center 1-1-1 Kouto Sayo-cho Sayo-gun Hyogo 679-5148 Japan Precursory Research for Embryonic Science and Technology (PRESTO) Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi Saitama 332-0012 Japan Department of Computer Science Stanford University Stanford CA 94305 USA Department of Molecular and Cellular Physiology Stanford University School of Medicine Stanford CA 94305 USA Department of Structural Biology Stanford University School of Medicine Stanford CA 94305 USA Institute for Computational and Mathematical Engineering Stanford University Stanford CA 94305 USA

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Two orders of magnitude enhancement in oxygen evolution reactivity on amorphous Ba0.5Sr0.5Co0.8Fe0.2O3-δ nanofilms with tunable oxidation state

Two orders of magnitude enhancement in oxygen evolution reac...

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2018电催化与电合成国际研讨会（ISEE 2018）

作者： Gao Chen Wei Zhou Daqin Guan Jaka Sunarso Yanping Zhu Xuefeng Hu Wei Zhang Zongping Shao Jiangsu National Synergetic Innovation Center for Advanced Materials State Key Laboratory of Materials-Oriented Chemical EngineeringCollege of Chemical EngineeringNanjing Tech University Faculty of Engineering Computing and ScienceSwinburne University of Technology Jiangsu National Synergetic Innovation Center for Advanced Materials State Key Laboratory of Materials-Oriented Chemical EngineeringCollege of EnergyNanj ing Tech University Department of Chemical Engineering Curtin University

Perovskite oxides exhibit potential for use as electrocatalysts in the oxygen evolution reaction(OER). However, their low specific surface area is the main obstacle to realizing a high mass-specific activity that is r... 详细信息

关键词： Two orders of magnitude enhancement in oxygen evolution reactivity on amorphous Ba nanofilms with tunable oxidation state

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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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