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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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Universal mechanical response of metallic glasses during strain-rate-dependent uniaxial compression

arXiv

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arXiv 2022年

作者： Jin, Weiwei Datye, Amit Schwarz, Udo D. Shattuck, Mark D. O'Hern, Corey S. Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Chemical and Environmental Engineering Yale University New HavenCT06520 United States Benjamin Levich Institute Physics Department The City College of New York New YorkNY10031 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States

Experimental data on the compressive strength σmax versus strain rate Ε eng for metallic glasses undergoing uniaxial compression shows significantly different behavior for different alloys. For some metallic glasses, σmax decreases with increasing Ε eng, for others, σmax increases with increasing Ε eng, and for others σmax versus Ε eng is nonmonotonic. Using numerical simulations of metallic glasses undergoing uniaxial compression at nonzero strain rate and temperature, we show that they obey a universal relation for the compressive strength versus temperature, which determines their mechanical response. At low Ε eng, increasing strain rate leads to increases in temperature and decreases in σmax∗, whereas at high Ε eng, increasing strain rate leads to decreases in temperature and increases in σmax∗. This non-monotonic behavior of σmax∗ versus temperature causes the nonmonotonic behavior of σmax∗ versus Ε eng. Variations in the internal dissipation change the characteristic strain rate at which the nonmonotonic behavior occurs. These results are general for a wide range of metallic glasses with different atomic interactions, damping coefficients, and chemical compositions. © 2022, CC BY.

关键词： Strain rate

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Efficient and scalable neural residual waveform coding with collaborative quantization

arXiv

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arXiv 2020年

作者： Zhen, Kai Lee, Mi Suk Sung, Jongmo Beack, Seungkwon Kim, Minje Indiana University Luddy School of Informatics Computing and Engineering BloomingtonIN United States Indiana University Cognitive Science Program BloomingtonIN United States Electronics and Telecommunications Research Institute Daejeon Korea Republic of

Scalability and efficiency are desired in neural speech codecs, which supports a wide range of bitrates for applications on various devices. We propose a collaborative quantization (CQ) scheme to jointly learn the codebook of LPC coefficients and the corresponding residuals. CQ does not simply shoehorn LPC to a neural network, but bridges the computational capacity of advanced neural network models and traditional, yet efficient and domain-specific digital signal processing methods in an integrated manner. We demonstrate that CQ achieves much higher quality than its predecessor at 9 kbps with even lower model complexity. We also show that CQ can scale up to 24 kbps where it outperforms AMR-WB and Opus. As a neural waveform codec, CQ models are with less than 1 million parameters, significantly less than many other generative models. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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An integrated transcriptomic cell atlas of human neural organoids (vol 635, pg 690, 2024)

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NATURE 2025年第8045期637卷 E21-E21页

作者： He, Zhisong Dony, Leander Fleck, Jonas Simon Szalata, Artur Li, Katelyn X. Sliskovic, Irena Lin, Hsiu-Chuan Santel, Malgorzata Atamian, Alexander Quadrato, Giorgia Sun, Jieran Pasca, Sergiu P. Camp, J. Gray Theis, Fabian J. Treutlein, Barbara Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Institute of Computational Biology Computational Health Center Helmholtz Munich Neuherberg Germany Department Genes and Environment Max Planck Institute of Psychiatry Munich Germany International Max Planck Research School for Translational Psychiatry (IMPRS-TP) Munich Germany TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany School of Computation Information and Technology Technical University of Munich Munich Germany Department of Physiology and Pharmacology Karolinska Institutet Stockholm Sweden Institute of Human Biology (IHB) Roche Pharma Research and Early Development Roche Innovation Center Basel Basel Switzerland Biozentrum University of Basel Basel Switzerland Department of Stem Cell Biology and Regenerative Medicine Keck School of Medicine University of Southern California Los Angeles CA USA Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research at USC Keck School of Medicine University of Southern California Los Angeles CA USA Department of Psychiatry and Behavioral Sciences Stanford University Stanford CA USA Stanford Brain Organogenesis Program Wu Tsai Neurosciences Institute and Bio-X Stanford CA USA Neural Stem Cell Institute Albany NY USA UK Dementia Research Institute at the University of Edinburgh Edinburgh Bioquarter Edinburgh UK Centre for Discovery Brain Sciences School of Biomedical Sciences College of Medicine and Veterinary Medicine The University of Edinburgh Edinburgh UK Human Technopole Milan Italy Department of Oncology and Hemato-Oncology University of Milan Milan Italy

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Performance Analysis and Optimization of the Vector-Kronecker Product Multiplication

Performance Analysis and Optimization of the Vector-Kronecke...

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International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

作者： Alexandre Azevedo Cristiana Bentes Maria Clicia Castro Claude Tadonki Computational Sciences Program State University of Rio de Janeiro Department of Systems Engineering State University of Rio de Janeiro Department of Informatics and Computer Science State University of Rio de Janeiro CRI (Centre de Recherche en Informatique) MINES ParisTech

ISBN: (数字)9781728199245

ISBN: (纸本)9781728199252

The Kronecker product, also called tensor product, is a fundamental matrix algebra operation, used to model complex systems using structured descriptions. This operation needs to be computed efficiently, since it is a critical kernel for iterative algorithms. In this work, we focus on the vector-kronecker product operation, where we present an in-depth performance analysis of a sequential and a parallel algorithm previously proposed. Based on this analysis, we proposed three optimizations: changing the memory access pattern, reducing load imbalance and manually vectorizing some portions of the code with Intel SSE4.2 intrinsics. The obtained results show better cache usage and load balance, thus improving the performance, especially for larger matrices.

关键词： Complexity theory Optimization program processors computational modeling Tensors Parallel algorithms Scalability

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Pruning neural networks without any data by iteratively conserving synaptic flow

arXiv

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arXiv 2020年

作者： Tanaka, Hidenori Yamins, Daniel L.K. Kunin, Daniel Ganguli, Surya Physics & Informatics Laboratories NTT Reserach Inc Department of Applied Physics Stanford University Department of Psychology Department of Computer Science Stanford University Institute for Computational and Mathematical Engineering Stanford University Department of Applied Physics Stanford University

Pruning the parameters of deep neural networks has generated intense interest due to potential savings in time, memory and energy both during training and at test time. Recent works have identified, through an expensive sequence of training and pruning cycles, the existence of winning lottery tickets or sparse trainable subnetworks at initialization. This raises a foundational question: can we identify highly sparse trainable subnetworks at initialization, without ever training, or indeed without ever looking at the data? We provide an affirmative answer to this question through theory driven algorithm design. We first mathematically formulate and experimentally verify a conservation law that explains why existing gradient-based pruning algorithms at initialization suffer from layer-collapse, the premature pruning of an entire layer rendering a network untrainable. This theory also elucidates how layer-collapse can be entirely avoided, motivating a novel pruning algorithm Iterative Synaptic Flow Pruning (SynFlow). This algorithm can be interpreted as preserving the total flow of synaptic strengths through the network at initialization subject to a sparsity constraint. Notably, this algorithm makes no reference to the training data and consistently outperforms existing state-of-the-art pruning algorithms at initialization over a range of models (VGG and ResNet), datasets (CIFAR-10/100 and Tiny ImageNet), and sparsity constraints (up to 99.9 percent). Thus our data-agnostic pruning algorithm challenges the existing paradigm that data must be used to quantify which synapses are important. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

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Correction: Interfacial rheology of lanthanide binding peptide surfactants at the air-water interface (Soft Matter (2024) 20 (9161-9173) DOI: 10.1039/D4SM00493K)

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Soft Matter 2025年第18期21卷 3657-3657页

作者： Stephen A. Crane Felipe Jiménez-Ángeles Yiming Wang Luis E. Ortuno Macias Jason G. Marmorstein Jiayi Deng Mehdi Molaei E. James Petersson Ravi Radhakrishnan Cesar de la Fuente-Nunez Monica Olvera de la Cruz Raymond S. Tu Charles Maldarelli Ivan J. Dmochowski Kathleen J. Stebe Department of Chemical and Biomolecular Engineering University of Pennsylvania Philadelphia PA 19104 USA Department of Materials Science and Engineering Northwestern University Evanston IL 60208 USA Department of Chemical Engineering The City College of New York New York NY 10031 USA Levich Institute The City College of New York New York NY 10031 USA Department of Chemistry University of Pennsylvania Philadelphia PA 19104 USA Department of Bioengineering University of Pennsylvania Philadelphia PA 19104 USA Machine Biology Group Departments of Psychiatry and Microbiology Institute for Biomedical Informatics Institute for Translational Medicine and Therapeutics Perelman School of Medicine University of Pennsylvania Philadelphia PA 19104 USA Penn Institute for Computational Science University of Pennsylvania Philadelphia PA 19104 USA

The authors regret the omission of a funding acknowledgement in the original article. This acknowledgement is given below. The authors would like to acknowledge that this work was partially supported by the U.S. National science Foundation under award number 1943384. The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers. © 2025 The Royal Society of Chemistry.

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InP/ZnS core/shell quantum dots: Synthesis, characterization and their application in fullerene derivative based flexible solar cells

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Results in Physics 2025年 69卷

作者： Zhichu, Chen Tahir, Muhammad Muhammad, Fida Aziz, Fakhra Ayub, Ambreen Badshah, Fazal Refat, Moamen S. Cakmak, Sadiye M. Rehan, Shanza Adam, Abdel Majid A. Alsuhaibani, Amnah Mohammed Ayob, Afida Sarker, Mahidur R Khan, Adam School of Electrical and Information Engineering Hubei University of Automotive Technology Shiyan 442002 China Semiconductor Physics & Devices Laboratory Department of Physics Faculty of Physical and Numerical Sciences Abdul Wali Khan University Mardan 23200 Pakistan Department of Electronics University of Peshawar KP Pakistan Department of Physics Women University Mardan KP 23200 Pakistan Department of Chemistry College of Science Taif University P.O. Box 11099 Taif 21944 Saudi Arabia Program of Medical Monitoring Techniques Osmangazi University Eskisehir Eskisehir 26040 Turkey Faculty of Materials and Chemical Engineering GIK Institute of Engineering Sciences and Technology Topi 23640 Pakistan Department of Physical Sport Sciences College of Sport Sciences & Physical Activity Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Centre for Integrated Systems Engineering and Advanced Technologies (INTEGRA) Department of Electrical Electronic and Systems Engineering Universiti Kebangsaan Malaysia Bangi 43600 Malaysia Institute of Visual Informatics Universiti Kebangsaan Selangor Malaysia Bangi 43600 Malaysia Universidad de Diseno Innovacion y Tecnología UDIT Av. Alfonso XIII 97 Madrid 28016 Spain Department of Electronics Engineering University of Engineering & Technology Peshawar (Abbottabad Campus) Abbottabad Pakistan

Herein, we report on the synthesis and characterization of p-type semiconductor indium phosphide/zinc sulphide (InP/ZnS) core/shell quantum dots (QDs) prepared via a simple solvothermal method and their potential application in bulk heterojunction (BHJ) flexible solar cell (SC). The BHJ SC composed of donor–acceptor (D-A) configuration at nanoscale provides larger interfacial junction area throughout the bulk volume of the photo-absorber D-A blend that facilitates excitons to efficiently dissociate within the bulk layer at D-A interface. Hence, InP/ZnS core–shell QDs and a fullerene derivative – phenyl C61 butyric acid methyl ester (PCBM) – are used as hybrid inorganic–organic D-A materials, respectively, to fabricate Ag/LiF/InP/ZnS-PCBM/P3HT/ITO/PET BHJ flexible SC. Indium tin oxide (ITO) coated on polyethylene terephthalate (PET), poly(3-hexylthiophene) (P3HT), lithium fluoride (LiF) and silver (Ag) are employed as a flexible substrate, hole transport layer (HTL), electron transport layer (ETL) and counter electrode, respectively. Photovoltaic (PV) properties of the fabricated BHJ flexible SC are studied under standard testing conditions (STC) i.e., 25 ⁰C, 1.5 AM global and 100 mW/cm² irradiation, which exhibit fill factor (FF) of 59 ± 0.02 % and power conversion efficiency (PCE) of 4.22 ± 0.05 %. Also, the heterojunction characteristics are investigated in dark condition (0 lx) to find series resistance (R_s), ideality factor (n), and reverse saturation current (I₀). X-ray diffraction (XRD), atomic force microscopy (AFM), scanning electron microscopy (SEM) and ultraviolet–visible (UV–vis) spectroscopy are performed to further investigate the properties of InP/ZnS-PCBM thin films. Transmission electron microscopy (TEM) image of InP/ZnS core/shell QDs confirmed the successful synthesis of QDs having an average size of 5 ± 1 nm. The InP/ZnS-PCBM D-A photo-absorber layer reveals its potential as one of good candidates for hi

关键词： Flexible solar cell Hybrid bulk heterojunction (BHJ) InP/ZnS core/shell QDs Power conversion efficiency (PCE) Quantum dots (QDs) synthesis

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Applying mobility business intelligence concept in analyzing oil palm plantation productivity

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AIP Conference Proceedings 2024年第1期3026卷

作者： Andreas Wahyu Krisdiarto Irya Wisnubhadra Anzaludin Samsinga Perbangsa Teddy Suparyanto Bens Pardamean Faculty of Agricultural Technology STIPER Institute of Agriculture Yogyakarta Indonesia Informatics Engineering Department Atma Jaya Yogyakarta University Yogyakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia BDSRC Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

The palm oil business employs almost 20 million people, generates USD 21 billion in revenue, and plays a vital role in Indonesia’s social economy. The Fresh Fruit Bunches (FFB) to Palm Oil Mills (POM) distribution system is one important aspect of fruit quality. Three steps are involved in getting Oil Palm FFB from the plantation to the POM. The first part of the procedure involves cutting FFB from the tree, the second stage involves gathering the fruit at a fruit collection point (FCP), and the third stage involves transporting the fruit to the palm oil mill (POM). As of now, the cost of the FFB transportation is still considerable, accounting for roughly 15% to 20% of the FFB pricing. The use of the Business Intelligence (BI) idea in the oil palm harvesting system is presented in this study as a foundation for creating web-based applications.

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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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