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Classification with a disordered dopant-atom network in silicon (vol 577, pg 341, 2020)

NATURE 2025年第8056期639卷 E22-E22页

作者： Chen, Tao van Gelder, Jeroen van de Ven, Bram Amitonov, Sergey V. de Wilde, Bram Euler, Hans-Christian Ruiz Broersma, Hajo Bobbert, Peter A. Zwanenburg, Floris A. van der Wiel, Wilfred G. NanoElectronics Group MESA+ Institute for Nanotechnology and BRAINS Center for Brain-Inspired Nano Systems University of Twente Enschede The Netherlands Molecular Materials and Nanosystems and Center for Computational Energy Research Department of Applied Physics Eindhoven University of Technology Eindhoven The Netherlands Programmable Nanosystems and Formal Methods and Tools MESA+ Institute for Nanotechnology DSI Digital Society Institute and BRAINS Center for Brain-Inspired Nano Systems University of Twente Enschede The Netherlands

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sbi reloaded: a toolkit for simulation-based inference workflows

arXiv

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arXiv 2024年

作者： Boelts, Jan Deistler, Michael Gloeckler, Manuel Tejero-Cantero, Álvaro Lueckmann, Jan-Matthis Moss, Guy Steinbach, Peter Moreau, Thomas Muratore, Fabio Linhart, Julia Durkan, Conor Vetter, Julius Miller, Benjamin Kurt Herold, Maternus Ziaeemehr, Abolfazl Pals, Matthijs Gruner, Theo Bischoff, Sebastian Krouglova, Anastasia N. Gao, Richard Lappalainen, Janne K. Mucsányi, Bálint Pei, Felix Schulz, Auguste Stefanidi, Zinovia Rodrigues, Pedro L.C. Schröder, Cornelius Zaid, Faried Abu Beck, Jonas Kapoor, Jaivardhan Greenberg, David S. Gonçalves, Pedro J. Macke, Jakob H. Machine Learning in Science University of Tübingen Germany Tübingen AI Center Germany TransferLab AppliedAI Institute for Europe Germany ML Colab Cluster ML in Science University of Tübingen Germany Google Research United States Helmholtz-Zentrum Dresden-Rossendorf Germany Université Paris-Saclay INRIA CEA Palaiseau France Robert Bosch GmbH Germany School of Informatics University of Edinburgh United Kingdom University of Amsterdam Netherlands Research and Innovation Center BMW Group Germany Institute for Applied Mathematics and Scientific Computing University of the Bundeswehr Munich Germany Aix Marseille INSERM INS France TU Darmstadt Hessian.AI Germany University Hospital Tübingen M3 Research Center Germany Faculty of Science KU Leuven B-3000 Belgium Imec Belgium Methods of Machine Learning University of Tübingen Germany Neuroscience Institute Carnegie Mellon University United States Université Grenoble Alpes INRIA CNRS Grenoble INP LJK France Hertie Institute for AI in Brain Health University of Tübingen Germany Institute of Coastal Systems - Analysis and Modeling Helmholtz AI Germany Departments of Computer Science Electrical Engineering KU Leuven Belgium Department Empirical Inference Max Planck Institute for Intelligent Systems Tübingen Germany

Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a significant challenge. Simulation-based inference (SBI) addresses this by enabling Bayesian inference for simulators, identifying parameters that match observed data and align with prior knowledge. Unlike traditional Bayesian inference, SBI only needs access to simulations from the model and does not require evaluations of the likelihood-function. In addition, SBI algorithms do not require gradients through the simulator, allow for massive parallelization of simulations, and can perform inference for different observations without further simulations or training, thereby amortizing inference. Over the past years, we have developed, maintained, and extended sbi, a PyTorch-based package4 that implements Bayesian SBI algorithms based on neural networks. The sbi toolkit implements a wide range of inference methods, neural network architectures, sampling methods, and diagnostic tools. In addition, it provides well-tested default settings but also offers flexibility to fully customize every step of the simulation-based inference workflow. Taken together, the sbi toolkit enables scientists and engineers to apply state-of-the-art SBI methods to black-box simulators, opening up new possibilities for aligning simulations with empirically observed data. © 2024, CC BY-SA.

关键词： Neural networks

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Identification and Cross-Platform Validation of Sparse Molecular Classifiers for Antibody-mediated and T-Cell–Mediated Rejection After Kidney Transplantation

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Kidney International Reports 2025年第6期10卷 1806-1818页

作者： Callemeyn, Jasper Nava-Sedeño, Josué Manik Anglicheau, Dany Beadle, Jack Bräsen, Jan Hinrich Clahsen-van Groningen, Marian C. Cristoferi, Iacopo de Loor, Henriette Deutsch, Andreas Essig, Marie Gwinner, Wilfried Halloran, Philip F. Hesselink, Dennis A. Koshy, Priyanka Kuypers, Dirk Lerut, Evelyne Marquet, Pierre Minnee, Robert C. Roufosse, Candice Sprangers, Ben Van Craenenbroeck, Amaryllis H. Hatzikirou, Haralampos Naesens, Maarten Nephrology and Renal Transplantation Research Group Department of Microbiology Immunology and Transplantation KU Leuven Leuven Belgium Department of Nephrology and Renal Transplantation University Hospitals Leuven Leuven Belgium Zentrum für Informationsdienste und Hochleistungsrechnen Innovative Methods of Computing Technische Universität Dresden Dresden Germany Department of Mathematics Faculty of Sciences Universidad Nacional Autónoma de México Mexico City Mexico Université de Paris Paris France Institut Necker-Enfants Malades INSERM Paris U1151 France Department of Nephrology and Kidney Transplantation Necker-Enfants Malades Hospital Assistance Publique-Hôpitaux de Paris Paris France Centre for Inflammatory Disease Department of Immunology and Inflammation Imperial College London United Kingdom Imperial College Renal and Transplant Centre Imperial College NHS Trust London United Kingdom Nephropathology Unit Institute of Pathology Hannover Medical School Hannover Germany Department of Pathology and Clinical Bioinformatics Erasmus MC University Medical Center Rotterdam Rotterdam Netherlands Erasmus MC Transplant Institute Erasmus MC University Medical Center Rotterdam Rotterdam Netherlands Department of Medicine 2 Medical Faculty RWTH Aachen University Hospital Aachen Germany Division of HPB and Transplant Surgery Department of Surgery Erasmus MC University Medical Center Rotterdam Rotterdam Netherlands AP-HP Paris Saclay Ambroise Pare Hospital Department of Nephrology Boulogne Billancourt France Nephrology Internal Medicine Hannover Medical School Hannover Germany Department of Medicine Division of Nephrology and Transplant Immunology University of Alberta Edmonton AB Canada Department of Internal Medicine Division of Nephrology and Transplantation Erasmus MC University Medical Center Rotterdam Netherlands Department of Morphology and Molecular Pathology University Hospitals Leuven Leuven Belgium Pharmacology and Transplantation INSERM

Introduction: Molecular classifiers are a promising tool to refine the diagnosis of antibody-mediated rejection (ABMR) and T-cell–mediated rejection (TCMR) after kidney transplantation. Despite this potential, the integration of molecular classifiers in transplant clinics has been slow, in part because of the complexity of current assays and lack of a consensus platform. Herein, we aimed to develop and validate sparse molecular classifiers for ABMR and TCMR using allograft tissue. methods: In a discovery cohort of 224 kidney transplant biopsies, lasso regression was applied on microarray gene expression data to derive a molecular classifier for ABMR and TCMR, respectively. Results: A 2-gene classifier for ABMR (PLA1A, GNLY) and a 2-gene classifier for TCMR (IL12RB1, ARPC1B) were identified. External validation (n = 403 biopsies) demonstrated preserved diagnostic accuracy for ABMR (area under the receiver operating characteristic curve [ROC-AUC]: 0.80, 95% confidence interval [CI]: 0.75–0.85) and TCMR (ROC-AUC: 0.83, 95% CI: 0.77–0.89), with the possibility to discriminate between pure and mixed rejection phenotypes. Complementary to their diagnostic potential, the molecular classifiers associated with accelerated graft loss in a second validation cohort (n = 282 biopsies) and identified allografts at risk for failure with histological lesions that did not reach the Banff thresholds for rejection. The computational approach was further validated using the Banff Human Organ Transplant (B-HOT) gene panel in 2 independent biopsy cohorts that were analyzed on the Nanostring nCounter platform (n = 66 and n = 80, respectively). Conclusion: Rigid variable selection strategies can yield sparse molecular classifiers for allograft rejection phenotypes with preserved accuracy and prognostic value across different molecular diagnostic platforms, which may facilitate their interpretation and clinical implementation. © 2025 International Society of Nephrology

关键词： antibody-mediated rejection diagnosis gene expression kidney transplantation T-cell–mediated rejection transcriptomics

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AFast Solver for an H_(1) Regularized PDE-Constrained Optimization Problem

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Communications in Computational Physics 2016年第1期19卷 143-167页

作者： Andrew T.Barker Tyrone Rees Martin Stoll Center for Applied Scientific Computing Lawrence Livermore National LaboratoryMail Stop L-561LivermoreCA 94551USA Numerical Analysis Group Scientific Computing DepartmentRutherford Appleton LaboratoryChiltonDidcotOxfordshireOX110QXUnited Kingdom Computational Methods in Systems and Control Theory Max Planck Institute for Dynamics of Complex Technical SystemsSandtorstr.139106MagdeburgGermany

In this paperwe consider PDE-constrained optimization problemswhich incorporate an H_(1)regularization control *** focus on a time-dependent PDE,and consider both distributed and boundary *** problems we consider include bound constraints on the state,and we use a Moreau-Yosida penalty function to handle *** propose Krylov solvers and Schur complement preconditioning strategies for the different problems and illustrate their performance with numerical examples.

关键词： Preconditioning Krylov methods PDE-constrained optimization optimal control of PDEs

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Time-dependent analysis of attacks

Time-dependent analysis of attacks

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3rd International Conference on Principles of Security and Trust, POST 2014 - Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2014

作者： Arnold, Florian Hermanns, Holger Pulungan, Reza Stoelinga, Mariëlle Formal Methods and Tools Group Department of Computer Science University of Twente P.O. Box 217 7500 AE Enschede Netherlands Dependable Systems and Software Saarland University 66123 Saarbrücken Germany Jurusan Ilmu Komputer Dan Elektronika Universitas Gadjah Mada Indonesia

ISBN: (纸本)9783642547911

The success of a security attack crucially depends on time: the more time available to the attacker, the higher the probability of a successful attack;when given enough time, any system can be compromised. Insight in time-dependent behaviors of attacks and the evolution of the attacker's success as time progresses is therefore a key for effective countermeasures in securing systems. This paper presents an efficient technique to analyze attack times for an extension of the prominent formalism of attack trees. If each basic attack step, i.e., each leaf in an attack tree, is annotated with a probability distribution of the time needed for this step to be successful, we show how this information can be propagated to an analysis of the entire tree. In this way, we obtain the probability distribution for the entire system to be attacked successfully as time progresses. For our approach to be effective, we take great care to always work with the best possible compression of the representations of the probability distributions arising. This is achieved by an elegant calculus of acyclic phase type distributions, together with an effective compositional compression technique. We demonstrate the effectiveness of this approach on three case studies, exhibiting orders of magnitude of compression. © 2014 Springer-Verlag.

关键词： Probability distributions

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Model-driven design plus artificial intelligence for wireless sensor networks software development

Model-driven design plus artificial intelligence for wireles...

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2nd International Workshop on Software Engineering for Sensor Network Applications, SESENA 2011, in Conjunction with the International Conference on Software Engineering, ICSE 2011

作者： Mülders, Peter Gruner, Stefan Thang, Nguyen Xuan Research Group for Software Science and Formal Methods Department of Computer Science Universiteit Van Pretoria Lynnwood Road 0002 Pretoria South Africa Distributed Systems Group Faculty of Electr. and Inform. University of Kassel Wilhelmshöher Allee 73 34121 Kassel Germany

ISBN: (纸本)9781450305839

To date, software development for wireless sensor network nodes is still characterized by low-level ad-hoc programming to a large extent. This short-paper argues for a methodologically more systematic approach on the basis of Model-Driven Engineering with CASE-Tool support, in combination with AI-based design choice optimizations during the MDE-steps of model-refinement and information-enrichment. Our work in this project is ongoing. © 2011 ACM.

关键词： Genetic algorithms

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One Day Smart Grid Supersession - Analytics and Integration

One Day Smart Grid Supersession - Analytics and Integration

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General Meeting of the IEEE-Power-and-Energy-Society (PES)

作者： Liu, W-H Edwin McArthur, Stephen Giri, Jay Miu, Karen Pratt, Rob Wen, Jun Uluski, Bob Dow, Luther Labut, Gerard IEEE Computing and Analytical Methods Subcommittee United States IEEE PES Intelligent Systems Subcommittee United States IEEE PES Multi-Agent System Working Group United States Power Systems Technology and Strategic Initiatives ALSTOM Grid Redmond WA United States Electrical and Computer Engineering Department Drexel University United States Pacific Northwest National Laboratory's (PNNL's) Smart Grid R and D Program U.S. Department of Energy United States Transmission and Interconnection Planning Group Southern California Edison (SCE) Company United States WECC Modeling and Validation Working Group United States EPRI Power Delivery and Utilization Area United States Distribution and West Coast Operations Quanta Technology United States Distribution Operations SmartCurrentssm Program Strategy DTE Energy Detroit MI United States

ISBN: (纸本)9781457710018

The effort of "Smart Grid" is to modernize grid infrastructure and build-in intelligence to power grids and delivery systems, and their interfaces to customer premises. However, the perspectives range from an emphasis on infrastructure to an emphasis on new paradigm-shifting applications. Alternatively, the smart grid can be thought of as the advanced information technologies that enable the desired analytical applications. As a general understanding, we believe that Smart Grid needs to integrate power system analysis, computing and economics to enhance grid reliability, efficiency, and security, and contributes to the climate change strategic goal. In this Supersession, the analytics that empowers smart grid applications is discussed;and the practical implementation and integration challenges will be presented.

关键词： smart grid analytical applications system integration

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Comparison of Graph Clustering Algorithms for Recovering Software Architecture Module Views

Comparison of Graph Clustering Algorithms for Recovering Sof...

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(CSMR) European Conference on Software Maintenance and Reengineering

作者： Roberto Almeida Bittencourt Dalton Dario Serey Guerrero Formal Methods Group-Department of Systems and Computing UFCG Federal University of Campina Grande Campina Grande Paraiba Brazil UEFS-State University of Feira de Santana Feira de Santana Bahia Brazil

In the domain of software architecture recovery, classical clustering algorithms have been used to recover module views, while new ones have been proposed to tackle specific software architecture issues. Nonetheless, little information concerning their empirical evaluation in this context is presently available. This paper presents an empirical study that evaluates four clustering algorithms according to three previously proposed criteria: extremity of cluster distribution, authoritativeness, and stability, which were measured against consecutive releases of four different systems. Our results suggest that the k-means algorithm performs best in terms of authoritativeness and extremity and that the modularization quality algorithm produces more stable clusters. They also point out that fully automated clustering techniques alone cannot recover module views in a sensible way, but may provide a reasonable first step to speed up an expert-assisted architecture recovery process.

关键词： Clustering algorithms Software algorithms Software architecture Partitioning algorithms Extremities Stability criteria Algorithm design and analysis Software maintenance Data mining Computer architecture

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Untitled

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE computing 2008年第1期5卷 4-5页

作者： Gligor, Virgil D. Formal Methods Section Center for High Assurance Computer Systems Naval Research Laboratory NRL Protocol Analyzer Internet Engineering Task Force IFIP Working Group 1.7 on Foundations of Security Analysis and Design University of Texas Austin United States Boston University IFIP WG 10.4 on Dependability Microsoft's Trustworthy Computing Academic Advisory Board

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