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The white rat of numerical reproducibility

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AIP Conference Proceedings 2021年第1期2365卷

作者： Michael Mascagni Departments of Computer Science Mathematics and Scientific Computing Florida State University Tallahassee FL 32306-4530 USA Applied and Computational Mathematics Division National Institute of Standards and Technology (NIST) Gaithersburg MD 20899-8910 USA

We explore an application from the author’s work in neuroscience. A code used to investigate neural development modeled 100 neurons with all-to-all excitatory connectivity. We used a simple ordinary differential equation system to model each neuron, and this 100-neuron model was used to produce a paper published in the Journal of Neurophysiology. Later a colleague used our code to continue this work, and found he could not reproduce our results. This lead us to thoroughly investigate this code and we discovered that it offered many different ways to thwart reproducibility that could be explained by round-off error arising from floating-point *** reproducibility is considered a task that directly follows from the determinism in computations. However, reproducibility has become an intense concern and and issue for research. In fact, the author developed an series of international workshops on numerical reproducibility and computational reproducibility that is now a regular offering at the annual international supercomputing conferences. We will show how this particular code provides a lack of reproducibility from the following three mechanisms: (i) the introduction of floating-point errors in an inner product; (ii) introduction of floating-point errors at each an increasing number of time steps during temporal refinement (ii); and (iii) differences in the output of library mathematical functions at the level of round-off error. This code’s sensitivity makes it a very powerful tool to explore many different manifestations of numerical reproducibility. However, this code is by no means exceptional, as in neuroscience these types of models are used extensively to gain insights on the functioning of the nervous system. In addition, these types of models are widely used in many other fields of study as they just nonlinear evolution equations.

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Correction to: A sharp interface method using enriched finite elements for elliptic interface problems

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Numerische Mathematik 2021年第4期147卷 783-783页

作者： Susanne Höllbacher Gabriel Wittum Computer Electrical and Mathematical Sciences and Engineering (CEMSE) King Abdullah University of Science and Technology (KAUST) Thuwal Kingdom of Saudi Arabia Goethe-Center for Scientific Computing (G-CSC) Johann Wolfgang Goethe University Frankfurt Germany

A correction to this paper has been published: https://***/10.1007/s00211-021-01191-x

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Long-lived topological time-crystalline order on a quantum processor

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arXiv 2024年

作者： Xiang, Liang Jiang, Wenjie Bao, Zehang Song, Zixuan Xu, Shibo Wang, Ke Chen, Jiachen Jin, Feitong Zhu, Xuhao Zhu, Zitian Shen, Fanhao Wang, Ning Zhang, Chuanyu Wu, Yaozu Zou, Yiren Zhong, Jiarun Cui, Zhengyi Zhang, Aosai Tan, Ziqi Li, Tingting Gao, Yu Deng, Jinfeng Zhang, Xu Dong, Hang Zhang, Pengfei Jiang, Si Li, Weikang Lu, Zhide Sun, Zheng-Zhi Li, Hekang Wang, Zhen Song, Chao Guo, Qiujiang Liu, Fangli Gong, Zhe-Xuan Gorshkov, Alexey V. Yao, Norman Y. Iadecola, Thomas Machado, Francisco Wang, H. Deng, Dong-Ling School of Physics ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang Province Key Laboratory of Quantum Technology and Device Zhejiang University Hangzhou China Center for Quantum Information IIIS Tsinghua University Beijing100084 China Hefei National Laboratory Hefei230088 China Joint Quantum Institute Joint Center for Quantum Information and Computer Science NIST University of Maryland College ParkMD United States QuEra Computing Inc. BostonMA United States Department of Physics Colorado School of Mines GoldenCO United States National Institute of standards and Technology BoulderCO United States Department of Physics Harvard University Cambridge02138 MA United States Department of Physics and Astronomy Iowa State University AmesIA United States Ames Laboratory AmesIA United States ITAMP Harvard-Smithsonian Center for Astrophysics CambridgeMA02138 United States Shanghai Qi Zhi Institute 41th Floor AI Tower No. 701 Yunjin Road Xuhui District Shanghai200232 China

Topologically ordered phases of matter [1–3] elude Landau’s symmetry-breaking theory [4], featuring a variety of intriguing properties such as long-range entanglement and intrinsic robustness against local perturbations. Their extension to periodically driven systems gives rise to exotic new phenomena that are forbidden in thermal equilibrium [5, 6]. Here, we report the observation of signatures of such a phenomenon—a prethermal topologically ordered time crystal—with programmable superconducting qubits arranged on a square lattice. By periodically driving the superconducting qubits with a surface-code Hamiltonian [7, 8], we observe discrete time-translation symmetry breaking dynamics [9–16] that is only manifested in the subharmonic temporal response of nonlocal logical operators. We further connect the observed dynamics to the underlying topological order by measuring a nonzero topological entanglement entropy [17, 18] and studying its subsequent dynamics. Our results demonstrate the potential to explore exotic topologically ordered nonequilibrium phases of matter with noisy intermediate-scale quantum processors [19]. Copyright © 2024, The Authors. All rights reserved.

关键词： Dynamics

Unified attentional generative adversarial network for brain tumor segmentation from multimodal unpaired images

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arXiv 2019年

作者： Yuan, Wenguang Wei, Jia Wang, Jiabing Ma, Qianli Tasdizen, Tolga School of Computer Science and Engineering South China University of Technology Guangzhou China Scientific Computing and Imaging Institute University of Utah Salt Lake City United States

In medical applications, the same anatomical structures may be observed in multiple modalities despite the different image characteristics. Currently, most deep models for multimodal segmentation rely on paired registered images. However, multimodal paired registered images are difficult to obtain in many cases. Therefore, developing a model that can segment the target objects from different modalities with unpaired images is significant for many clinical applications. In this work, we propose a novel two-stream translation and segmentation unified attentional generative adversarial network (UAGAN), which can perform any-to-any image modality translation and segment the target objects simultaneously in the case where two or more modalities are available. The translation stream is used to capture modality-invariant features of the target anatomical structures. In addition, to focus on segmentation-related features, we add attentional blocks to extract valuable features from the translation stream. Experiments on three-modality brain tumor segmentation indicate that UAGAN outperforms the existing methods in most cases. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

Distributional property testing in a quantum world

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arXiv 2019年

作者： Gilyén, András Liy, Tongyang QuSoft CWI University of Amsterdam Netherlands Department of Computer Science Institute for Advanced Computer Studies Joint Center for Quantum Information and Computer Science University of Maryland U.S. Department of Energy Office of Science Office of Advanced Scientific Computing Research Quantum Algorithms Teams Program

A fundamental problem in statistics and learning theory is to test properties of distributions. We show that quantum computers can solve such problems with significant speed-ups. In particular, we give fast quantum algorithms for testing closeness between unknown distributions, testing independence between two distributions, and estimating the Shannon / von Neumann entropy of distributions. The distributions can be either classical or quantum, however our quantum algorithms require coherent quantum access to a process preparing the samples. Our results build on the recent technique of quantum singular value transformation, combined with more standard tricks such as divide-and-conquer. The presented approach is a natural fit for distributional property testing both in the classical and the quantum case, demonstrating the first speed-ups for testing properties of density operators that can be accessed coherently rather than only via sampling;for classical distributions our algorithms significantly improve the precision dependence of some earlier results. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum theory

Democratizing Uncertainty Quantification

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arXiv 2024年

作者： Seelinger, Linus Reinarz, Anne Lykkegaard, Mikkel B. Akers, Robert Alghamdi, Amal M.A. Aristoff, David Bangerth, Wolfgang Bénézech, Jean Diez, Matteo Frey, Kurt Jakeman, John D. Jørgensen, Jakob S. Kim, Ki-Tae Kent, Benjamin M. Martinelli, Massimiliano Parno, Matthew Pellegrini, Riccardo Petra, Noemi Riis, Nicolai A.B. Rosenfeld, Katherine Serani, Andrea Tamellini, Lorenzo Villa, Umberto Dodwell, Tim J. Scheichl, Robert Scientific Computing Center Karlsruhe Institute of Technology Karlsruhe Germany Department of Computer Science Durham University Durham United Kingdom digiLab Exeter United Kingdom UK Atomic Energy Authority Oxford United Kingdom Lyngby Denmark Department of Mathematics Colorado State University Fort CollinsCO United States Department of Mathematics Department of Geosciences Colorado State University Fort CollinsCO United States Centre for Integrated Materials Processes and Structures Department of Mechanical Engineering University of Bath Bath United Kingdom National Research Council Institute of Marine Engineering Rome Italy Institute for Disease Modeling Global Health Division Bill & Melinda Gates Foundation United States Optimization and Uncertainty Quantification Sandia National Laboratories AlbuquerqueNM United States University of California Merced United States National Research Council-Institute for Applied Mathematics and Information Technologies "E. Magenes" Pavia Italy Solea Energy ThetfordVT United States Copenhagen Imaging ApS Herlev Denmark The University of Texas Austin United States Heidelberg University Germany

Uncertainty Quantification (UQ) is vital to safety-critical model-based analyses, but the widespread adoption of sophisticated UQ methods is limited by technical complexity. In this paper, we introduce UM-Bridge (the UQ and Modeling Bridge), a high-level abstraction and software protocol that facilitates universal interoperability of UQ software with simulation codes. It breaks down the technical complexity of advanced UQ applications and enables separation of concerns between experts. UM-Bridge democratizes UQ by allowing effective interdisciplinary collaboration, accelerating the development of advanced UQ methods, and making it easy to perform UQ analyses from prototype to High Performance computing (HPC) scale. In addition, we present a library of ready-to-run UQ benchmark problems, all easily accessible through UM-Bridge. These benchmarks support UQ methodology research, enabling reproducible performance comparisons. We demonstrate UM-Bridge with several scientific applications, harnessing HPC resources even using UQ codes not designed with HPC support. © 2024, CC BY-NC-ND.

关键词： Application programs

A benchmarking study to evaluate apache spark on large-scale supercomputers

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arXiv 2019年

作者： Thiruvathukal, George K. Christensen, Cameron Jin, Xiaoyong Tessier, François Vishwanath, Venkatram Loyola University Chicago Computer Science Department University of Utah Scientific Computing and Imaging Institute Argonne National Laboratory Computational Science Division Argonne National Laboratory Leadership Computing Facility Swiss National Supercomputing Center ETH Zurich Lugano Switzerland

As dataset sizes increase, data analysis tasks in high performance computing (HPC) are increasingly dependent on sophisticated dataflows and out-of-core methods for efficient system utilization. In addition, as HPC systems grow, memory access and data sharing are becoming performance bottlenecks. Cloud computing employs a data processing paradigm typically built on a loosely connected group of low-cost computing nodes without relying upon shared storage and/or memory. Apache Spark is a popular engine for large-scale data analysis in the cloud, which we have successfully deployed via job submission scripts on production clusters. In this paper, we describe common parallel analysis dataflows for both Message Passing Interface (MPI) and cloud based applications. We developed an effective benchmark to measure the performance characteristics of these tasks using both types of systems, specifically comparing MPI/C-based analyses with Spark. The benchmark is a data processing pipeline representative of a typical analytics framework implemented using mapreduce. In the case of Spark, we also consider whether language plays a role by writing tests using both Python and Scala, a language built on the Java Virtual Machine (JVM). We include performance results from two large systems at Argonne National Laboratory including THETA, a Cray XC40 supercomputer on which our experiments run with 65,536 cores (1024 nodes with 64 cores each). The results of our experiments are discussed in the context of their applicability to future HPC architectures. Beyond understanding performance, our work demonstrates that technologies such as Spark, while typically aimed at multi-tenant cloud-based environments, show promise for data analysis needs in a traditional clustering/supercomputing environment. Copyright © 2019, The Authors. All rights reserved.

关键词： Message passing

SimObturation: Virtual Reality Application for Simulation of Dental Obturation Procedure Training with Phantom Omni Device Integration

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SimObturation: Virtual Reality Application for Simulation of...

Symposium on Virtual Reality (SVR)

作者： Tamirys Virgulino Ribeiro Jauvane C. de Oliveira Computer Science Departament Millitary Institute id Engineering (IME) Rio de Janeiro RJ Brazil National Laboratory for Scientific Computing (LNCC) Petrópolis RJ Brazil

The constant evolution of virtual reality applications in areas of dentistry has been gaining proeminence and contributing in several areas of knowledge associated with the training of professionals to perform the dental procedures. In this work, we present the modeling of a virtual simulator prototype for training an obturation process, using the Phantom device a represent of the clinical instrument with interaction with the tooth represent by an OBJ file, allowing interaction of the professional in surgical procedure. In addition, the device simulates the generation of strength, tactile sensation, kinesthetic and proprioceptive sensation through its indicator when collision occurs with the object. The prototype is under development, preliminary testing will be supported by experts in the field, who will validate the visual and tactile feedback of the prototype.

关键词： Dentistry Solid modeling Haptic interfaces Teeth Phantoms Three-dimensional displays Training

Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic