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Distributed Semi-Supervised Sparse Statistical Inference

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arXiv 2023年

作者： Tu, Jiyuan Liu, Weidong Mao, Xiaojun Xu, Mingyue School of Mathematics Shanghai University of Finance and Economics Shanghai200433 China School of Mathematical Sciences MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Shanghai200240 China School of Mathematical Sciences Ministry of Education Key Laboratory of Scientific and Engineering Computing Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science Purdue University West LafayetteIN47906 United States

The debiased estimator is a crucial tool in statistical inference for high-dimensional model parameters. However, constructing such an estimator involves estimating the high-dimensional inverse Hessian matrix, incurring significant computational costs. This challenge becomes particularly acute in distributed setups, where traditional methods necessitate computing a debiased estimator on every machine. This becomes unwieldy, especially with a large number of machines. In this paper, we delve into semi-supervised sparse statistical inference in a distributed setup. An efficient multi-round distributed debiased estimator, which integrates both labeled and unlabelled data, is developed. We will show that the additional unlabeled data helps to improve the statistical rate of each round of iteration. Our approach offers tailored debiasing methods for M-estimation and generalized linear models according to the specific form of the loss function. Our method also applies to a non-smooth loss like absolute deviation loss. Furthermore, our algorithm is computationally efficient since it requires only one estimation of a high-dimensional inverse covariance matrix. We demonstrate the effectiveness of our method by presenting simulation studies and real data applications that highlight the benefits of incorporating unlabeled data. © 2023, CC BY.

关键词： Covariance matrix

A Performance Analysis of Task Scheduling for UQ Workflows on HPC Systems

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arXiv 2025年

作者： Loi, Chung Ming Reinarz, Anne Lykkegaard, Mikkel Hornsby, William Buchanan, James Seelinger, Linus Danish Technological Institute Aarhus Denmark UKAEA-CCFE Abingdon United Kingdom Scientific Computing Center Karlsruhe Institute of Technology Karlsruhe Germany Department of Computer Science Durham University Durham United Kingdom

Uncertainty Quantification (UQ) workloads are becoming increasingly common in science and engineering. They involve the submission of thousands or even millions of similar tasks with potentially unpredictable runtimes, where the total number is usually not known a priori. A static one-size-fits-all batch script would likely lead to suboptimal scheduling, and native schedulers installed on High-Performance computing (HPC) systems such as SLURM often struggle to efficiently handle such workloads. In this paper, we introduce a new load balancing approach suitable for UQ workflows. To demonstrate its efficiency in a real-world setting, we focus on the GS2 gyrokinetic plasma turbulence simulator. Individual simulations can be computationally demanding, with runtimes varying significantly—from minutes to hours—depending on the high-dimensional input parameters. Our approach uses the UQ and Modelling Bridge, which offers a language-agnostic interface to a simulation model, combined with HyperQueue which works alongside the native scheduler. In particular, deploying this framework on HPC systems does not require system-level changes. We benchmark our proposed framework against a standalone SLURM approach using GS2 and a Gaussian Process surrogate thereof. Our results demonstrate a reduction in scheduling overhead by up to three orders of magnitude and a maximum reduction of 38% in CPU time for long-running simulations compared to the naïve SLURM approach, while making no assumptions about the job submission patterns inherent to UQ workflows. © 2025, CC BY.

关键词： Fusion reactions

Data without software are just numbers

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Data science Journal 2020年第1期19卷

作者： Davenport, James Harold Grant, James Jones, Catherine Mary Computer Science University of Bath Bath United Kingdom Computing Services University of Bath Bath United Kingdom Scientific Computing Department Science and Technology Facilities Council Oxford United Kingdom

Great strides have been made to encourage researchers to archive data created by research and provide the necessary systems to support their storage. Additionally it is recognised that data are meaningless unless their provenance is preserved, through appropriate meta-data. Alongside this is a pressing need to ensure the quality and archiving of the software that generates data, through simulation, control of experiment or data-collection and that which analyses, modifies and draws value from raw data. In order to meet the aims of reproducibility we argue that data management alone is insufficient: it must be accompanied by good software practices, the training to facilitate it and the support of stakeholders, including appropriate recognition for software as a research output. © 2020 The Author(s).

关键词： Information management

What if?.. Fabulating African HCI Futures within the Veil of HCI 23

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What if?.. Fabulating African HCI Futures within the Veil of...

Proceedings of the 4th African Human computer Interaction Conference

作者： Shaimaa Lazem Muhammad Sadi Adamu Yasmeen Abdrabou Chris Muashekele Sheena Erete Jakita Thomas Hafeni Mthoko Heike Winschiers-Teophilus Nicola Bidwell Aderonke Sakpere Anicia Peters City of Scientific Research and Technology Applications (SRTA-City) Egypt Imagination Lancaster Lancaster University United Kingdom School of Computing and Communications Lancaster University United Kingdom Aalborg University Denmark College of Information Studeies University of Maryland USA Department of Computer science and software Engineering (CSSE) Auburn University USA NurtureID8 Namibia Department of Software Engineering Namibia University of Science and Technology Namibia Intenational Uiversity of Management Namibia and University of Melbourne Australia University of Ibadan Nigeria National Commission of Research Sicence and Technology (NCRST) Namibia

ISBN: (纸本)9798400708879

This workshop aims to bring together researchers and practitioners (African and otherwise) engaging with diverse communities and industries to discuss the geopolitics of knowledge production in computing research and design. Our proposal builds on existing works that have explored the modalities of the knowledge economy - particularly as knowledge is created, legitimized, and circulated as objective truth via the exercise of power. For example, engaging indigenous communities in conferences and knowledge fairs have sought to challenge disciplinary silos and boundaries imposed on non-Western academics and practitioners. This workshop, as a platform for interaction and discussion with no “sage on the stage”, is the first of its kind within AfriCHI and it seeks to explore and promote more subtle discussions about HCI knowledge production and dissemination practices.

关键词： African HCI Critical fabulation Decolonizing HCI Indigenous knowledge alt.HCI

Dynamical reduced basis methods for hamiltonian systems

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arXiv 2020年

作者： Pagliantini, Cecilia Centre for Analysis Scientific Computing and Applications Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands

We consider model order reduction of parameterized Hamiltonian systems describing nondissipative phenomena, like wave-type and transport dominated problems. The development of reduced basis methods for such models is challenged by two main factors: the rich geometric structure encoding the physical and stability properties of the dynamics and its local low-rank nature. To address these aspects, we propose a nonlinear structure-preserving model reduction where the reduced phase space evolves in time. In the spirit of dynamical low-rank approximation, the reduced dynamics is obtained by a symplectic projection of the Hamiltonian vector field onto the tangent space of the approximation manifold at each reduced state. A priori error estimates are established in terms of the projection error of the full model solution onto the reduced manifold. For the temporal discretization of the reduced dynamics we employ splitting techniques. The reduced basis satisfies an evolution equation on the manifold of symplectic and orthogonal rectangular matrices having one dimension equal to the size of the full model. We recast the problem on the tangent space of the matrix manifold and develop intrinsic temporal integrators based on Lie group techniques together with explicit Runge-Kutta (RK) schemes. The resulting methods are shown to converge with the order of the RK integrator and their computational complexity depends only linearly on the dimension of the full model, provided the evaluation of the reduced flow velocity has a comparable cost. Copyright © 2020, The Authors. All rights reserved.

关键词： Phase space methods

Learning constitutive models from microstructural simulations via a non-intrusive reduced basis method: Extension to geometrical parameterizations

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arXiv 2022年

作者： Guo, Theron Silva, Francesco A.B. Rokoš, Ondřej Veroy, Karen Centre for Analysis Scientific Computing and Applications Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven5612 AZ Netherlands Mechanics of Materials Department of Mechanical Engineering Eindhoven University of Technology Eindhoven5612 AZ Netherlands Institute for Complex Molecular Systems Eindhoven University of Technology Eindhoven5612 AZ Netherlands

Two-scale simulations are often employed to analyze the effect of the microstructure on a component's macroscopic properties. Understanding these structure-property relations is essential in the optimal design of materials for specific applications. However, these two-scale simulations are typically computationally expensive and infeasible in multi-query contexts such as optimization and material design. To make such analyses amenable, the microscopic simulations can be replaced by inexpensive-to-evaluate surrogate models. Such surrogate models must be able to handle microstructure parameters in order to be used for material design. A previous work focused on the construction of an accurate surrogate model for microstructures under varying loading and material parameters by combining proper orthogonal decomposition and Gaussian process regression. However, that method works only for a fixed geometry, greatly limiting the design space. This work hence focuses on extending the methodology to treat geometrical parameters. To this end, a method that transforms different geometries onto a parent domain is presented, that then permits existing methodologies to be applied. We propose to solve an auxiliary problem based on linear elasticity to obtain the geometrical transformations. The method has a good reducibility and can therefore be quickly solved for many different geometries. Using these transformations, combined with the nonlinear microscopic problem, we derive a fast-to-evaluate surrogate model with the following key features: (1) the predictions of the effective quantities are independent of the auxiliary problem, (2) the predicted stress fields automatically fulfill the microscopic balance laws and are periodic, (3) the method is non-intrusive, (4) the stress field for all geometries can be recovered, and (5) the sensitivities are available and can be readily used for optimization and material design. The proposed methodology is tested on several composite micros

关键词： Geometry

Sparse-grid Discontinuous Galerkin Methods for the Vlasov–Poisson–Lenard–Bernstein Model

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arXiv 2024年

作者： Schnake, Stefan Kendrick, Coleman Endeve, Eirik Stoyanov, Miroslav Hahn, Steven Hauck, Cory D. Green, David L. Snyder, Phil Canik, John Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Fusion Energy Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Physics and Astronomy University of Tennessee KnoxvilleTN37996 United States Department of Mathematics University of Tennessee KnoxvilleTN37996 United States Type One Energy Group Oak RidgeTN37830 United States Commonwealth Scientific and Industrial Research Organisation Mayfield WestNSW2304 Australia

Sparse-grid methods have recently gained interest in reducing the computational cost of solving high-dimensional kinetic equations. In this paper, we construct adaptive and hybrid sparse-grid methods for the Vlasov–Poisson–Lenard–Bernstein (VPLB) model. This model has applications to plasma physics and is simulated in two reduced geometries: a 0x3v space homogeneous geometry and a 1x3v slab geometry. We use the discontinuous Galerkin (DG) method as a base discretization due to its high-order accuracy and ability to preserve important structural properties of partial differential equations. We utilize a multiwavelet basis expansion to determine the sparse-grid basis and the adaptive mesh criteria. We analyze the proposed sparse-grid methods on a suite of three test problems by computing the savings afforded by sparse-grids in comparison to standard solutions of the DG method. The results are obtained using the adaptive sparse-grid discretization library ASGarD. Copyright © 2024, The Authors. All rights reserved.

关键词： Integral equations

Group Spam Identification in IOT Home Automation Using Tracking Time Metadata

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Group Spam Identification in IOT Home Automation Using Track...

Communication, computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Achilov Docent Saurabh Dhanik Pardeep Kumar Jindal Saif O. Husain Diwakar Bhardwaj Varsha Mittal Department of Physics and Chemistry Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan Scientific Researcher Western Caspian University Baku Azerbaijan School of Computing Graphic Era Hill University Bhimtal India Department of Electrical and Communication Engineering Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computer Engineering and Applications GLA University Mathura India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

With the stark increase due to smarter homes using online of Things, or IoT devices exchanging info far more frequently than we used to — all wirelessly. IoT devices are affected by several issues like possible cyberattacks, the unpredictability of network connectivity and data leakage since there are many types of IoT devices being used today. OBJECTIVES: To improve the privacy of data provided from web of Things (IoT) smart home devices by using machine training and computational modeling to detect anomalies. At its core the experiment contribution is how reliable Web of Things (WoT), i.e. IoT technologies transferring values from or to home things, reasons are taking different features such as average sq rate error origin and feature value in account of evaluations about internet-connected device efficacy. It is a metric that seems so important when we consider our dependency on every attached Internet of Things (IoT) device, in the network's architecture. The proposed method is applied and validated using a dataset of smart homes along with real temperature information collected for public access. Using Time series evidence collected by iot devices, the suggested method works extremely well as it can detect any anomaly or spam. This technique helps with the general health and safety of your entire structure by creating a solid base for evaluating the acceptability of Sea-of-things (SoT) gadgets in just a smart home network..

关键词： Training Data privacy Time series analysis Smart homes Smart meters Telecommunication computing Internet of Things Reliability Object recognition Tuning

A Scalable Real-Time Data Assimilation Framework for Predicting Turbulent Atmosphere Dynamics

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arXiv 2024年

作者： Yin, Junqi Liang, Siming Liu, Siyan Bao, Feng Chipilski, Hristo G. Lu, Dan Zhang, Guannan National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Mathematics Florida State University TallahasseeFL32306 United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Scientific Computing Florida State University TallahasseeFL32306 United States Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

The weather and climate domains are undergoing a significant transformation thanks to advances in AI-based foundation models such as FourCastNet, GraphCast, ClimaX and Pangu-Weather. While these models show considerable potential, they are not ready yet for operational use in weather forecasting or climate prediction. This is due to the lack of a data assimilation method as part of their workflow to enable the assimilation of incoming Earth system observations in real time. This limitation affects their effectiveness in predicting complex atmospheric phenomena such as tropical cyclones and atmospheric rivers. To overcome these obstacles, we introduce a generic real-time data assimilation framework and demonstrate its end-to-end performance on the Frontier supercomputer. This framework comprises two primary modules: an ensemble score filter (EnSF), which significantly outperforms the state-of-the-art data assimilation method, namely, the Local Ensemble Transform Kalman Filter (LETKF);and a vision transformer-based surrogate capable of real-time adaptation through the integration of observational data. The ViT surrogate can represent either physics-based models or AI-based foundation models. We demonstrate both the strong and weak scaling of our framework up to 1024 GPUs on the Exascale supercomputer, Frontier. Our results not only illustrate the framework's exceptional scalability on high-performance computing systems, but also demonstrate the importance of supercomputers in real-time data assimilation for weather and climate predictions. Even though the proposed framework is tested only on a benchmark surface quasigeostrophic (SQG) turbulence system, it has the potential to be combined with existing AI-based foundation models, making it suitable for future operational implementations. Copyright © 2024, The Authors. All rights reserved.

关键词： Supercomputers