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arXiv 2023年

作者： Goth, Florian Alves, Renato Braun, Matthias Castro, Leyla Jael Chourdakis, Gerasimos Christ, Simon Cohen, Jeremy Druskat, Stephan Erxleben, Fredo Grad, Jean-Noël Hagdorn, Magnus Hodges, Toby Juckeland, Guido Kempf, Dominic Lamprecht, Anna-Lena Linxweiler, Jan Löffler, Frank Martone, Michele Schwarzmeier, Moritz Seibold, Heidi Thiele, Jan Philipp Waldow, Harald von Wittke, Samantha Institute for Theoretical Physics and Astrophysics University of Würzburg Germany European Molecular Biology Laboratory Heidelberg Germany Cluster of Excellence IntCDC University of Stuttgart Germany ZB MED Information Centre for Life Sciences Cologne Germany School of Computation Information and Technology Technical University of Munich Garching Germany Institute for Parallel and Distributed Systems University of Stuttgart Stuttgart Germany Leibniz University Hannover Department of Cell Biology and Biophysics Computational Biology Germany Imperial College London London United Kingdom Institute for Software Technology Berlin Germany Humboldt-Universität zu Berlin Department of Computer Science Berlin Germany Helmholtz-Zentrum Dresden-Rossendorf Germany Institute for Computational Physics University of Stuttgart Germany Geschäftsbereich IT Charité Universitätsmedizin Berlin Germany The Carpentries United States Heidelberg University Scientific Software Center Germany Institute of Computer Science University of Potsdam Germany Technische Universität Braunschweig Germany Michael Stifel Center Jena Friedrich Schiller University Jena Germany Leibniz Supercomputing Centre Garching Germany Mathematical Modeling and Analysis TU Darmstadt Germany IGDORE Munich Germany Weierstrass Institute Berlin Germany Leibniz University Hannover Institute of Applied Mathematics Scientific Computing Hannover Germany Johann Heinrich von Thünen Institute Centre for Information Management Germany

The term Research Software Engineer, or RSE, emerged a little over 10 years ago as a way to represent individuals working in the research community but focusing on software development. The term has been widely adopted and there are a number of high-level definitions of what an RSE is. However, the roles of RSEs vary depending on the institutional context they work in. At one end of the spectrum, RSE roles may look similar to a traditional research role. At the other extreme, they resemble that of a software engineer in industry. Most RSE roles inhabit the space between these two extremes. Therefore, providing a straightforward, comprehensive definition of what an RSE does and what experience, skills and competencies are required to become one is challenging. In this community paper we define the broad notion of what an RSE is, explore the different types of work they undertake, and define a list of foundational competencies as well as values that outline the general profile of an RSE. Further research and training can build upon this foundation of skills and focus on various aspects in greater detail. We expect that graduates and practitioners will have a larger and more diverse set of skills than outlined here. On this basis, we elaborate on the progression of these skills along different dimensions. We look at specific types of RSE roles, propose recommendations for organisations, and give examples of future specialisations. An appendix details how existing curricula fit into this framework. © 2023, CC BY.

关键词： computer aided software engineering

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modeling of Soil Basis of Headed Hydrotechnical Structure’s Deformations Under Action Of Filtration Water Flow

Modeling of Soil Basis of Headed Hydrotechnical Structure’s...

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International Conference on Advanced computer Information Technologies (ACIT)

作者： Anatolyy Vlasyuk Mykola Kuzlo Nataliia Zhukovska Viktor Zhukovskyy Nataliia Tarasyuk The Department of Economics Mathematical Modeling and Information Technologies The National University of Ostroh Academy Ostroh Ukraine The Department of Automobile Roads Bases and Foundations The National University of Water and Environmental Engineering Rivne Ukraine The Department of Computer Science and Applied Mathematics The National University of Water and Environmental Engineering Rivne Ukraine The Department of Foreign Languages The National University of Water and Environmental Engineering Rivne Ukraine

ISBN: (数字)9781728167602

ISBN: (纸本)9781728167619

Mathematical modeling of the pressure hydraulic structure base soil deformations under the influence of a filtration water flow has been carried out. Based on the two-dimensional mathematical model, equations have been transformed, a difference scheme has been constructed, and the solution of boundary-value problems has been found by numerical methods. Due to the results of numerical experiments, the influence of the filtration water flow on the vertical and horizontal displacements of the base soil has been established.

关键词： Soil Filtration Mathematical model Strain Hydraulic systems Hydrodynamics Boundary conditions

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Gradient descent in materia through homodyne gradient extraction

arXiv

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arXiv 2021年

作者： Boon, Marcus N. Cassola, Lorenzo Euler, Hans-Christian Ruiz Chen, Tao van de Ven, Bram Ibarra, Unai Alegre Bobbert, Peter A. van der Wiel, Wilfred G. NanoElectronics Group MESA+ Institute for Nanotechnology BRAINS Center for Brain-Inspired Nano Systems University of Twente PO Box 217 Enschede7500 AE Netherlands Exzellenzcluster Science of Intelligence Technische Universität Berlin Marchstr. 23 Berlin10587 Germany Department for Electrical Engineering and Computer Science Modeling of Cognitive Processes Technische Universität Berlin Berlin Germany Molecular Materials and Nanosystems Eindhoven Institute for Renewable Energy Systems Department of Applied Physics Eindhoven University of Technology PO Box 513 Eindhoven5600 MB Netherlands Institute of Physics University of Münster Münster48149 Germany

Deep learning, a multi-layered neural network approach inspired by the brain, has revolutionized machine learning [1]. One of its key enablers has been backpropagation [2], an algorithm that computes the gradient of a loss function with respect to the weights and biases in the neural network model, in combination with its use in gradient descent [3]. However, the implementation of deep learning in digital computers is intrinsically energy hungry, with energy consumption becoming prohibitively high for many applications [4]. This has stimulated the development of specialized hardware, ranging from neuromorphic CMOS integrated circuits [5] [6, 7] and integrated photonic tensor cores [8] to unconventional, material-based computing systems [9] [10] [11] [12] [13] [14] [15] [16]. The learning process in these material systems, realized, e.g., by artificial evolution [13] [14], equilibrium propagation [17] or surrogate modelling [18] [19], is a complicated and time-consuming process. Here, we demonstrate a simple yet efficient and accurate gradient extraction method, based on the principle of homodyne detection, for performing gradient descent on a loss function directly in a physical system without the need of an analytical description. By perturbing the parameters that need to be optimized using sinusoidal waveforms with distinct frequencies, we effectively obtain the gradient information in a highly robust and scalable manner. We illustrate the method in dopant network processing units [14], but argue that it is applicable in a wide range of physical systems. Homodyne gradient extraction can in principle be fully implemented in materia, facilitating the development of autonomously learning material systems. © 2021, CC BY-NC-SA.

关键词： CMOS integrated circuits

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Contactless IoT Sensor of Liquid Level based on Impedance Method 11

Contactless IoT Sensor of Liquid Level based on Impedance Me...

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11th International Scientific and Practical Conference on Electronics and Information Technologies, ELIT 2019

作者： Blagitko, Bohdan Mochulskyi, Yuriy Zajachuk, Ihor Batiuk, Micle Kravets, Ihor Bihday, Volodymyr Ivan Franko Lviv's National University Department of Radiophysics and Computer Technologies Lviv Ukraine Centre of Mathematical Modeling Pidstryhach Institute for Applied Problems of Mechanics and Mathematics Ukrainian Academy of Science Lviv Ukraine Natural Sciences College Ivan Franko Lviv's National University Lviv Ukraine Cypress Semiconductor Lviv Ukraine

ISBN: (纸本)9781728135618

The field of the study was the development of a microprocessor device for contactless measurement of a liquid level using the impedance method. The result was a noncontact liquid level meter on the PSoC4 microcontroller in the form of a working model. The results of tests of the developed liquid level sensor on tap water, distilled water, AdBlue, and diesel fuel are presented. © 2019 IEEE.

关键词： Liquids

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OP0231 MASS CYTOMETRY DATA RECLASSIFY SYSTEMIC AUTOIMMUNE DISEASE PATIENTS IN PHENOTYPICALLY DISTINCTIVE GROUPS

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Annals of the Rheumatic Diseases 2022年 81卷 152-153页

作者： P. Rybakowska S. van Gassen C. Perez-Sanchez A. Ibañez-Costa N. Varela R. Ortega Castro C. Fernández-Roldán I. Jiménez-Moleón N. Ortego E. Raya R. Aguilar Quesada C. Lopez-Pedrera E. Collantes Estevez Y. Saeys M. Alarcon-Riquelme C. Marañón GENYO Centre for Genomics and Oncological Research Pfizer/University of Granada/Andalusian Regional Government PTS Genomic Medicine Granada Spain VIB Center for Inflammation Research Data Mining and Modeling for Biomedicine Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Maimonides Institute for Research in Biomedicine of Cordoba (IMIBIC) Reina Sofia University Hospital/University of Córdoba Córdoba Spain Servicio de Medicina Interna. Unidad de Enfermedades Autoinmunes Sistémicas Departamento de Medicina Universidad de Granada. Hospital Universitario San Cecilio P.T.S. Granada Granada Spain Servicio de Reumatología Hospital Universitario San Cecilio P.T.S Granada Spain Biobanco del Sistema Sanitario Público de Andalucía Andalusian Public Health System Biobank Granada Spain

Background Systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), systemic sclerosis (SSC), Sjögren's syndrome (SJS), mixed connective tissue disease (MCTD), primary antiphospholipid syndrome (PAPS) and undifferentiated connective tissue disease (UCTD) are classified as systemic autoimmune diseases (SADs). They are diagnosed based on different clinical and laboratory criteria. Due to their high internal heterogeneity and overlapping symptoms, SADs are difficult to diagnose. Therefore, molecular and cellular-based studies need to be undertaken to precisely classify the patients. Mass cytometry is a single-cell proteomics technology that measures approximately 50 markers per cell, thus it is a suitable tool to perform deep-phenotyping studies in SADs. Objectives Explore differences and similarities between SADs and build reclassification framework using high-dimensional cytometry data. Methods The whole blood samples collected from 129 individuals, including patients and controls were stained with a 39-plex antibody panel and acquired in 9 batches on a CyTOF (HELIOS) instrument. Data were cleaned, and normalized for batch effects using semi-automated cytof analysis pipeline. Cell frequencies and median signal intensities (MSI) for each population were extracted using FlowSOM for mononuclear cells (PBMC) and Phenograph for granulocytes. Secretion of 44 cytokines and chemokines were analyzed using a multiplexed luminex assay. Diseases were compared by Kruskal-Wallis analysis and hierarchical clustering and reclassification was done using unsupervised k-means clustering. Cytokine analysis across clusters was performed using Kruskal-Wallis test. Results Differently expressed features were observed between patient groups, regarding frequency of classical monocytes, B and T cells subpopulations, mature and immature granulocytes and intensities of CD38, HLA-DR and CD95 across various populations. However, none of them were disease specific. K-means clustering identif

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and Systems 2023年 729 LNNS卷 v-vi页

作者： Vasant, Pandian Weber, Gerhard-Wilhelm Arefin, Mohammad Shamsul Rodriguez-Aguilar, Roman Panchenko, Vladimir Munapo, Elias Thomas, J. Joshua Faculty of Electrical and Electronics Engineering Modeling Evolutionary Algorithms Simulation and Artificial Intelligence Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Computer Science Chittagong University of Engineering and Technology Chittagong Bangladesh Laboratory of Non-traditional Energy Systems Department of Theoretical and Applied Mechanics Federal Scientific Agroengineering Center VIM Russian University of Transport Moscow Russia Department of Computer Science UOW Malaysia KDU Penang University College George Town Malaysia Faculty of Engineering Management Poznań University of Technology Poznan Poland School of Economics and Decision Sciences North West University Mmabatho South Africa Facultad de Ciencias Económicas y Empresariales School of Economic and Business Sciences Universidad Panamericana Mexico City Mexico

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One pathogen does not an epidemic make: A review of interacting contagions, diseases, beliefs, and stories

arXiv

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arXiv 2025年

作者： Hébert-Dufresne, Laurent Ahn, Yong-Yeol Allard, Antoine Crothers, Jessica W. Dodds, Peter Sheridan Galesic, Mirta Ghanbarnejad, Fakhteh Gravel, Dominique Hammond, Ross A. Lerman, Kristina Lovato, Juniper Openshaw, John J. Redner, S. Scarpino, Samuel V. St-Onge, Guillaume Tangherlini, Timothy R. Young, Jean-Gabriel Vermont Complex Systems Institute University of Vermont BurlingtonVT United States Translational Global Infectious Disease Research Center University of Vermont BurlingtonVT United States Department of Computer Science University of Vermont BurlingtonVT United States Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Département de physique de génie physique et d’optique Université Laval QuébecQC Canada Department of Pathology and Laboratory Medicine Robert Larner M.D. College of Medicine University of Vermont BurlingtonVT United States Santa Fe Institute Santa FeNM United States Complexity Science Hub Vienna Austria School of Technology and Architecture SRH University of Applied Sciences Heidelberg Leipzig Germany Département de biologie Université de Sherbrooke SherbrookeQC Canada Brown School Washington University in St. Louis St. LouisMO United States The Brookings Institution WashingtonDC United States Information Sciences Institute University of Southern California United States Division of Infectious Diseases and Geographic Medicine Stanford University StanfordCA United States Institute for Experiential AI Northeastern University BostonMA United States Laboratory for the Modeling of Biological and Socio-technical Systems Northeastern University BostonMA United States The Roux Institute Northeastern University PortlandME United States Department of Scandinavian School of Information University of California Berkeley BerkeleyCA United States Department of Mathematics & Statistics University of Vermont BurlingtonVT United States

From pathogens and computer viruses to genes and memes, contagion models have found widespread utility across the natural and social sciences. Despite their success and breadth of adoption, the approach and structure of these models remain surprisingly siloed by field. Given the siloed nature of their development and widespread use, one persistent assumption is that a given contagion can be studied in isolation, independently from what else might be spreading in the population. In reality, countless contagions of biological and social nature interact within hosts (interacting with existing beliefs, or the immune system) and across hosts (interacting in the environment, or affecting transmission mechanisms). Additionally, from a modeling perspective, we know that relaxing these assumptions has profound effects on the physics and translational implications of the models. Here, we review mechanisms for interactions in social and biological contagions, as well as the models and frameworks developed to include these interactions in the study of the contagions. We highlight existing problems related to the inference of interactions and to the scalability of mathematical models and identify promising avenues of future inquiries. In doing so, we highlight the need for interdisciplinary efforts under a unified science of contagions and for removing a common dichotomy between social and biological contagions. Copyright © 2025, The Authors. All rights reserved.

关键词： Histology

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A cellular hierarchy in melanoma uncouples growth and metastasis

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四川生理科学杂志 2022年第9期44卷 1633-1633页

作者： Panagiotis Karras Laboratory for Molecular Cancer Biology Center for Cancer Biology VIB Leuven Belgium Department of Oncology KU Leuven Leuven Belgium Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK The Wellcome Trust/CRUK Gurdon Institute University of Cambridge Cambridge UK Department of General Medical Oncology UZ Leuven Leuven Belgium BGI-Shenzhen Shenzhen China VIB BioImaging Core VIB Center for Brain and Disease Research Leuven Belgium VIB Bioimaging Core VIB Center for Inflammation Research Ghent Belgium Department of Biomedical Molecular Biology Ghent University Ghent Belgium FACS Expertise Center Center for Cancer Biology VIB Leuven Belgium Laboratory of Tumor Microenvironment and Therapeutic Resistance Center for Cancer Biology VIB Leuven Belgium Center for Brain & Disease Research VIB-KU Leuven Leuven Belgium Department of Human Genetics KU Leuven Leuven Belgium Data Mining and Modeling for Biomedicine Group VIB Center for Inflammation Research Ghent Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Laboratory for Translational Cell and Tissue Research Department of Imaging and Pathology KU Leuven Leuven Belgium Laboratory of Translational Genetics Center for Cancer Biology VIB Leuven Belgium Laboratory of Translational Genetics Center for Human Genetics KU Leuven Leuven Belgium Herbert Irving Comprehensive Center Columbia University Irving Medical Center New York USA Laboratory of Stem Cells and Cancer Université Libre de Bruxelles (ULB) Brussels Belgium Wellcome Trust-MRC Stem Cell Institute University of Cambridge Cambridge UK

Although melanoma is notorious for its high degree of heterogeneity and plasticity1,2,the origin and magnitude of cell-state diversity remains poorly ***,it is unclear whether growth and metastatic dissemination are supported by overlapping or distinct melanoma ***,by combining mouse genetics,single-cell and spatial transcriptomics,lineage tracing and quantitative modelling,we provide evidence of a hierarchical model of tumour growth that mirrors the cellular and molecular logic underlying the cell-fate specification and differentiation of the embryonic neural *** show that tumorigenic competence is associated with a spatially localized perivascular niche,a phenotype acquired through an intercellular communication pathway established by endothelial *** with a model in which only a fraction of cells are fated to fuel growth,temporal single-cell tracing of a population of melanoma cells with a mesenchymal-like state revealed that these cells do not contribute to primary tumour growth but,instead,constitute a pool of metastatic initiating cells that switch cell identity while disseminating to secondary *** data provide a spatially and temporally resolved map of the diversity and trajectories of melanoma cell states and suggest that the ability to support growth and metastasis are limited to distinct pools of *** observation that these phenotypic competencies can be dynamically acquired after exposure to specific niche signals warrant the development of therapeutic strategies that interfere with the cancer cell reprogramming activity of such microenvironmental cues.

关键词： melanoma metastasis organs

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DeePMD-kit v2: A software package for Deep Potential models

arXiv

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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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Hybrid no-signaling-quantum correlations

arXiv

引用

arXiv 2021年

作者： Banacki, Micha Mironowicz, Piotr Ramanathan, Ravishankar Horodecki, Pawe International Centre for Theory of Quantum Technologies University of Gdánsk Wita Stwosza 63 Gdańsk80-308 Poland Institute of Theoretical Physics and Astrophysics Faculty of Mathematics Physics and Informatics University of Gdánsk Wita Stwosza 57 Gdańsk80-308 Poland Department of Algorithms and System Modeling Faculty of Electronics Telecommunications and Informatics Gdánsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Physics Stockholm University StockholmS-10691 Sweden Department of Computer Science The University of Hong Kong Pokfulam Road Hong Kong Faculty of Applied Physics and Mathematics Gdánsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland

Fundamental investigations in non-locality have shown that while the no-signaling principle alone is not sufficient to single out the set of quantum non-local correlations, local quantum mechanics and no-signaling together exactly reproduce the set of quantum correlations in the two-party Bell scenario. Here, we introduce and study an intermediate hybrid no-signaling quantum set of non-local correlations that we term HNSQ in the multi-party Bell scenario where some subsystems are locally quantum while the remaining subsystems are only constrained by the no-signaling principle. Specifically, the set HNSQ is a super-quantum set of correlations derived from no-signaling assemblages by performing quantum measurements on the trusted subsystems. As a tool for optimization over the set HNSQ, we introduce an outer hierarchy of semi-definite programming approximations to the set following an approach put forward by Doherty-Parillo-Spedalieri. We then show that in contrast to the set NS of no-signaling correlations, there exist extreme points of HNSQ in the tripartite Bell scenario that admit quantum realization. We perform an extensive numerical analysis of the maximal violation of the facet Bell inequalities in the three-party binary input-output scenario and study the corresponding self-testing properties. In contrast to the usual no-signaling correlations, the new set allows for simple security proofs of (one-sided)-device-independent applications against super-quantum adversaries. Copyright © 2021, The Authors. All rights reserved.

关键词： Signaling

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