检索结果-内蒙古大学图书馆

Improved Metaheuristics with Machine Learning Enabled Medical Decision Support System

computers, Materials & Continua 2022年第11期73卷 2423-2439页

作者： Sara A.Althubiti JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Romany F.Mansour Fayadh Alenezi Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Research Center-CIENS Escuela Naval de Suboficiales A.R.C.“Barranquilla”BarranquillaColombia Electronics and Telecommunications Engineering Program Universidad Autónoma del CaribeBarranquilla080001Colombia Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080001Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga 72511Egypt Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia

Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things(IoT),sensor technologies,cloud computing,and ***,the latest advances of Artificial Intelligence(AI)tools find helpful for decision-making in innovative healthcare to diagnose several *** Cancer(OC)is a kind of cancer that affects women’s ovaries,and it is tedious to identify OC at the primary stages with a high mortality *** OC data produced by the Internet of Medical Things(IoMT)devices can be utilized to differentiate *** this aspect,this paper introduces a new quantum black widow optimization with a machine learningenabled decision support system(QBWO-MLDSS)for smart *** primary intention of the QBWO-MLDSS technique is to detect and categorize the OC rapidly and ***,the QBWO-MLDSS model involves a Z-score normalization approach to pre-process the *** addition,the QBWO-MLDSS technique derives a QBWO algorithm as a feature selection to derive optimum feature ***,symbiotic organisms search(SOS)with extreme learning machine(ELM)model is applied as a classifier for the detection and classification of ELM model,thereby improving the overall classification *** design of QBWO and SOS for OC detection and classification in the smart healthcare environment shows the study’s *** experimental result analysis of the QBWO-MLDSS model is conducted using a benchmark dataset,and the comparative results reported the enhanced outcomes of the QBWO-MLDSS model over the recent approaches.

关键词： Ovarian cancer decision support system smart healthcare IoMT deep learning feature selection

来源：评论

学校读者我要写书评

暂无评论

Unifying Spatial Keyword Indexing in Continuous Search on Dynamic Objects

Unifying Spatial Keyword Indexing in Continuous Search on Dy...

引用

Future of Information and Communication Conference, FICC 2021

作者： Kato, Yasuyuki Chen, Hanxiong Furuse, Kazutaka Kitagawa, Hiroyuki Department of Computer Science University of Tsukuba Tsukuba Japan Center for Computational Sciences University of Tsukuba Tsukuba Japan Hakuou University Oyama Japan

ISBN: (纸本)9783030731021

People now access the web using mobile devices such as smartphones and tablets. Because of the prevalence of SNS and GPS, mobile users can send and receive information regarding both their locations and the content they are interested in. In this study, we consider a query problem of continuously monitoring dynamic spatial keyword objects, where each query continuously monitors its Top-k objects. We introduce a new concept called ranking-equivalence. Subsequently, on the basis of this concept, we propose a new similarity formula that unifies both spatial and keyword information in the same space. Using our similarity formula, all objects and queries can be treated in a high-dimensional space, where the spatial and keyword information become the spatial and keyword dimensions, respectively. In this unified space, an index can be developed to estimate the similarity between queries and objects, in advance. In addition, it is possible to conduct the filtering technique using a distance axiom to exclude the queries avoiding meaningless updating Top-k objects, thereby improving the efficiency. Theoretical analysis and experiments confirm the efficiency of our proposed techniques. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Efficiency

来源：评论

学校读者我要写书评

暂无评论

Approximate contraction of arbitrary tensor networks with a flexible and efficient density matrix algorithm

arXiv

引用

arXiv 2024年

作者： Ma, Linjian Fishman, Matthew T. Stoudenmire, E.M. Solomonik, Edgar Department of Computer Science University of Illinois Urbana-Champaign UrbanaIL61801 United States Center for Computational Quantum Physics Flatiron Institute New YorkNY10010 United States

Tensor network contractions are widely used in statistical physics, quantum computing, and computer science. We introduce a method to efficiently approximate tensor network contractions using low-rank approximations, where each intermediate tensor generated during the contractions is approximated as a low-rank binary tree tensor network. The proposed algorithm has the flexibility to incorporate a large portion of the environment when performing low-rank approximations, which can lead to high accuracy for a given rank. Here, the environment refers to the remaining set of tensors in the network, and low-rank approximations with larger environments can generally provide higher accuracy. For contracting tensor networks defined on lattices, the proposed algorithm can be viewed as a generalization of the standard boundary-based algorithms. In addition, the algorithm includes a cost-efficient density matrix algorithm for approximating a tensor network with a general graph structure into a tree structure, whose computational cost is asymptotically upper-bounded by that of the standard algorithm that uses canonicalization. Experimental results indicate that the proposed technique outperforms previously proposed approximate tensor network contraction algorithms for multiple problems in terms of both accuracy and efficiency. © 2024, CC BY.

关键词： Binary trees

来源：评论

学校读者我要写书评

暂无评论

Boosting Earth System Model Outputs And Saving PetaBytes in their Storage Using Exascale Climate Emulators

arXiv

引用

arXiv 2024年

作者： Abdulah, Sameh Baker, Allison H. Bosilca, George Cao, Qinglei Castruccio, Stefano Genton, Marc G. Keyes, David E. Khalid, Zubair Ltaief, Hatem Song, Yan Stenchikov, Georgiy L. Sun, Ying Extreme Computing & Statistics & Earth Science King Abdullah University of Science and Technology KSA Saudi Arabia Computational and Information Sciences Lab NSF National Center for Atmospheric Research United States NVIDIA United States Department of Computer Science Saint Louis University United States Department of Applied and Computational Mathematics and Statistics University of Notre Dame United States

We present the design and scalable implementation of an exascale climate emulator for addressing the escalating computational and storage requirements of high-resolution Earth System Model simulations. We utilize the spherical harmonic transform to stochastically model spatio-temporal variations in climate data. This provides tunable spatio-temporal resolution and significantly improves the fidelity and granularity of climate emulation, achieving an ultra-high spatial resolution of 0.034◦ (∼3.5 km) in space. Our emulator, trained on 318 billion hourly temperature data points from a 35-year and 31 billion daily data points from an 83-year global simulation ensemble, generates statistically consistent climate emulations. We extend linear solver software to mixed-precision arithmetic GPUs, applying different precisions within a single solver to adapt to different correlation strengths. The PaRSEC runtime system supports efficient parallel matrix operations by optimizing the dynamic balance between computation, communication, and memory requirements. Our BLAS3-rich code is optimized for systems equipped with four different families and generations of GPUs, scaling well to achieve 0.976 EFlop/s on 9,025 nodes (36,100 AMD MI250X multi-chip module (MCM) GPUs) of Frontier (nearly full system), 0.739 EFlop/s on 1,936 nodes (7,744 Grace-Hopper Superchips (GH200)) of Alps, 0.243 EFlop/s on 1,024 nodes (4,096 A100 GPUs) of Leonardo, and 0.375 EFlop/s on 3,072 nodes (18,432 V100 GPUs) of Summit. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Multirate Time-Integration Based on Dynamic Ode Partitioning Through Adaptively Refined Meshes for Compressible Fluid Dynamics

SSRN

引用

SSRN 2024年

作者： Doehring, Daniel Schlottke-Lakemper, Michael Gassner, Gregor J. Torrilhon, Manuel Applied and Computational Mathematics RWTH Aachen University Schinkelstrasse 2 North Rhine-Westphalia Aachen52062 Germany University of Stuttgart Germany Department of Mathematics and Computer Science Center for Data and Simulation Science University of Cologne Germany

In this paper, we apply the Paired-Explicit Runge-Kutta (P-ERK) schemes by Vermeire et. al. (2019, 2022) to dynamically partitioned systems arising from adaptive mesh refinement. The P-ERK schemes enable multirate time-integration with no changes in the spatial discretization methodology, making them readily implementable in existing codes that employ a method-of- lines *** show that speedup compared to a range of state of the art Runge-Kutta methods can be realized, despite additional overhead due to the dynamic re-assignment of flagging variables and restricting nonlinear stability properties. The effectiveness of the approach is demonstrated for a range of simulation setups for viscous and inviscid convection-dominated compressible flows for which we provide a reproducibility *** addition, we perform a thorough investigation of the nonlinear stability properties of the Paired-Explicit Runge-Kutta schemes regarding limitations due to the violation of monotonicity properties of the underlying spatial discretization. Furthermore, we present a novel approach for estimating the relevant eigenvalues of large Jacobians required for the optimization of stability polynomials . © 2024, The Authors. All rights reserved.

关键词： Runge Kutta methods

来源：评论

学校读者我要写书评

暂无评论

Rank-Normalization, Folding, and Localization: An Improved (Formula presented) for Assessing Convergence of MCMC (with Discussion)*†

引用

Bayesian Analysis 2021年第2期16卷 667-718页

作者： Vehtarh, Aki Gelman, Andrew Simpson, Daniel Carpenter, Bob Burkner, Paul-Christian Department of Computer Science Aalto University Finland Department of Statistics Columbia University New York Department of Statistical Sciences University of Toronto Canada Center for Computational Mathematics Flatiron Institute New York

Markov chain Monte Carlo is a key computational tool in Bayesian statistics, but it can be challenging to monitor the convergence of an iterative stochastic algorithm. In this paper we show that the convergence diagnostic R of Gelman and Rubin (1992) has serious flaws. Traditional R will fail to correctly diagnose convergence failures when the chain has a heavy tail or when the variance varies across the chains. In this paper we propose an alternative rank-based diagnostic that fixes these problems. We also introduce a collection of quantile-based local efficiency measures, along with a practical approach for computing Monte Carlo error estimates for quantiles. We suggest that common trace plots should be replaced with rank plots from multiple chains. Finally, we give recommendations for how these methods should be used in practice. © 2021 International Society for Bayesian Analysis. All Rights Reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Compounded Burr Probability Distribution for Fitting Heavy-Tailed Data with Applications to Biological Networks

arXiv

引用

arXiv 2024年

作者： Chakraborty, Tanujit Chattopadhyay, Swarup Das, Suchismita Naik, Shraddha M. Hens, Chittaranjan SAFIR Sorbonne University Abu Dhabi United Arab Emirates Sorbonne Center for Artificial Intelligence Sorbonne University Paris France Department of Computer Science & Engineering XIM University Bhubaneswar India Department of Data Science SP Jain School of Global Management Mumbai India Department of Computer Science Khalifa University United Arab Emirates Center for Computational Natural Science and Bioinformatics International Institute of Information Technology Hyderabad India

Complex biological networks, encompassing metabolic pathways, gene regulatory systems, and protein–protein interaction networks, often exhibit scale-free structures characterized by heavy-tailed degree distributions. However, empirical studies reveal significant deviations from ideal power-law behavior, underscoring the need for more flexible and accurate probabilistic models. In this work, we propose the Compounded Burr (CBurr) distribution, a novel four-parameter family derived by compounding the Burr distribution with a discrete mixing process. This model is specifically designed to capture both the body and tail behavior of real-world network degree distributions with applications to biological networks. We rigorously derive its statistical properties, including moments, hazard and risk functions, and tail behavior, and develop an efficient maximum likelihood estimation framework. The CBurr model demonstrates broad applicability to networks with complex connectivity patterns, particularly in biological, social, and technological domains. Extensive experiments on large-scale biological network datasets show that CBurr consistently outperforms classical power-law, log-normal, and other heavy-tailed models across the full degree spectrum. By providing a statistically grounded and interpretable framework, the CBurr model enhances our ability to characterize the structural heterogeneity of biological networks. © 2024, CC BY.

关键词： Maximum likelihood estimation

来源：评论

学校读者我要写书评

暂无评论

Toward a 2D Local Implementation of Quantum Low-Density Parity-Check Codes

引用

PRX Quantum 2025年第1期6卷 010306-010306页

作者： Noah Berthusen Dhruv Devulapalli Eddie Schoute Andrew M. Childs Michael J. Gullans Alexey V. Gorshkov Daniel Gottesman Joint Center for Quantum Information and Computer Science National Institute of Standards and Technology (NIST)–University of Maryland College Park Maryland 20742 USA Computer Computational and Statistical Sciences Division Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Department of Computer Science and Institute for Advanced Computer Studies University of Maryland College Park Maryland 20742 USA Joint Quantum Institute National Institute of Standards and Technology (NIST)–University of Maryland College Park Maryland 20742 USA

Geometric locality is an important theoretical and practical factor for quantum low-density parity-check (qLDPC) codes that affects code performance and ease of physical realization. For device architectures restricted to two-dimensional (2D) local gates, naively implementing the high-rate codes suitable for low-overhead fault-tolerant quantum computing incurs prohibitive overhead. In this work, we present an error-correction protocol built on a bilayer architecture that aims to reduce operational overheads when restricted to 2D local gates by measuring some generators less frequently than others. We investigate the family of bivariate-bicycle qLDPC codes and show that they are well suited for a parallel syndrome-measurement scheme using fast routing with local operations and classical communication (LOCC). Through circuit-level simulations, we find that in some parameter regimes, bivariate-bicycle codes implemented with this protocol have logical error rates comparable to the surface code while using fewer physical qubits.

关键词： Quantum computers

来源：评论

学校读者我要写书评

暂无评论

Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems:A Review

引用

IEEE/CAA Journal of Automatica Sinica 2023年第6期10卷 1361-1387页

作者： Sibo Cheng César Quilodrán-Casas Said Ouala Alban Farchi Che Liu Pierre Tandeo Ronan Fablet Didier Lucor Bertrand Iooss Julien Brajard Dunhui Xiao Tijana Janjic Weiping Ding Yike Guo Alberto Carrassi Marc Bocquet Rossella Arcucci Data Science Institute Department of ComputingImperial College LondonSW72AZ London Department of Earth Science and Engineering Imperial College LondonSW72AZ London Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China the IMT Atlantique Lab-STICCUMR CNRS 6285France and OdysseyInria/IMTFrance.P.Tandeo is also with RIKEN Center for Computational ScienceKobeJapan the CEREA École des Ponts and EDF R&Dîle-de-FranceFrance the Laboratoire Interdisciplinaire des Sciences du Numérique CNRSParis-Saclay UniversityF-91403OrsayFrance the Electricitéde France(EDF) 78401 ChatouFranceInstitut de Mathématiques de Toulouse31062 ToulouseFrance and SINCLAIR AI LabSaclayFrance the Sorbonne University ParisFranceand also with Nansen Environmental and Remote Sensing Center(NERSC)BergenNorway the School of Mathematical Sciences Tongji UniversityShanghai 200092China the Mathematical Institute for Machine Learning and Data Science KU Eichstaett-IngolstadtBavariaGermany the School of Information Science and Technology Nantong UniversityNantong 226019China the Department of Physics and Astronomy“Augusto Righi” University of Bologna40124 BolognaItaly

Data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal *** applications span from computational fluid dynamics(CFD)to geoscience and climate ***,much effort has been given in combining DA,UQ and machine learning(ML)*** research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive *** paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of *** review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models,but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their ***,this article has a special focus on how ML methods can overcome the existing limits of DA and UQ,and vice *** exciting perspectives of this rapidly developing research field are also *** Terms-Data assimilation(DA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).

关键词： assimilation overcome apply

来源：评论

学校读者我要写书评

暂无评论

AstroMLab 2: AstroLLaMA-2-70B Model and Benchmarking Specialised LLMs for Astronomy 24

AstroMLab 2: AstroLLaMA-2-70B Model and Benchmarking Special...

引用

Proceedings of the SC '24 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis

作者： Rui Pan Tuan Dung Nguyen Hardik Arora Alberto Accomazzi Tirthankar Ghosal Yuan-Sen Ting Department of Computer Science University of Illinois Urbana-Champaign Urbana USA Department of Computer and Information Science University of Pennsylvania Philadelphia PA USA Indian Institute of Technology Patna India Center for Astrophysics Harvard University Cambridge USA National Center for Computational Sciences Oak Ridge National Laboratory Oak Ridge TN USA Department of Astronomy The Ohio State University Columbus USA

ISBN: (纸本)9798350355543

Continual pretraining of large language models on domain-specific data has been proposed to enhance performance on downstream tasks. In astronomy, the previous absence of astronomy-focused benchmarks has hindered objective evaluation of these specialized LLM models. Leveraging a recent initiative to curate high-quality astronomical MCQs, this study aims to quantitatively assess specialized LLMs in astronomy. We find that the previously released AstroLLaMA series, based on LLaMA-2-7B, underperforms compared to the base model. We demonstrate that this performance degradation can be partially mitigated by utilizing high-quality data for continual pretraining, such as summarized text from arXiv. Despite the observed catastrophic forgetting in smaller models, our results indicate that continual pretraining on the 70B model can yield improvements. However, the current supervised fine-tuning dataset still constrains the performance of instruct models. In conjunction with this study, we introduce a new set of models, AstroLLaMA-3-8B and AstroLLaMA-2-70B, building upon the previous AstroLLaMA series.

关键词： astronomy-tailored LLMs

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：