检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Tang, Yingheng Xu, Wenbin Cao, Jie Gao, Weilu Farrell, Steven Erichson, Benjamin Mahoney, Michael W. Nonaka, Andy Yao, Zhi Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA United States NSF National AI Institute for Student-AI Teaming University of Colorado at Boulder Boulder United States Department of Electrical and Computer Engineering The University of Utah Salt Lake CityUT United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA United States International Computer Science Institute BerkeleyCA United States Department of Statistics University of California at Berkeley BerkeleyCA United States

Understanding and predicting the properties of inorganic materials is crucial for accelerating advancements in materials science and driving applications in energy, electronics, and beyond. Integrating material structure data with language-based information through multi-modal large language models (LLMs) offers great potential to support these efforts by enhancing human–AI interaction. However, a key challenge lies in integrating atomic structures at full resolution into LLMs. In this work, we introduce MatterChat, a versatile structure-aware multi-modal LLM that unifies material structural data and textual inputs into a single cohesive model. MatterChat employs a bridging module to effectively align a pretrained universal machine learning interatomic potential with a pretrained LLM, reducing training costs and enhancing flexibility. Our results demonstrate that MatterChat significantly improves performance in material property prediction and human-AI interaction, surpassing general-purpose LLMs such as GPT-4. We also demonstrate its usefulness in applications such as more advanced scientific reasoning and step-by-step material synthesis. © 2025, CC BY.

关键词： Structure (composition)

来源：评论

学校读者我要写书评

暂无评论

Relative magnetic field and slipping effect on Casson dusty fluid of two phase fluctuating flow over inclined parallel plate

引用

South African Journal of Chemical Engineering 2023年 44卷 135-146页

作者： khan, Dolat Kumam, Poom Kumam, Wiyada Suttiarporn, Panawan Rehman, Ali Fixed Point Research Laboratory Fixed Point Theory and Applications Research Group Center of Excellence in Theoretical and Computational Science (TaCS-CoE) Faculty of Science King Mongkut's University of Technology Thonburi (KMUTT) 126 Pracha Uthit Rd. Bang Mod Bangkok Thung Khru 10140 Thailand Center of Excellence in Theoretical and Computational Science (TaCS-CoE) Faculty of Science King Mongkut's University of Technology Thonburi (KMUTT) 126 Pracha Uthit Rd. Bang Mod Bangkok Thung Khru 10140 Thailand Applied Mathematics for Science and Engineering Research Unit (AMSERU) Program in Applied Statistics Department of Mathematics and Computer Science Faculty of Science and Technology Rajamangala University of Technology Thanyaburi (RMUTT) Pathum Thani 12110 Thailand Faculty of Science Energy and Environment King Mongkut's University of Technology North Bangkok Rayong Campus Rayong 21120 Thailand Special Interest Group for Modelling and Data Analytics Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Kuala Nerus 21030 Malaysia

Due to its significant applications in magnetic devices for cell separation, magnetic drugs for cancer tumor treatment, blood flow adjustment during surgery, magnetic endoscopy, and fluid pumping in industrial and engineering processes, magnetohydrodynamics (MHD) has a wide range of applications. Therefore, the aim of this study to investigate the Relative Magnetic Field and slipping effect on Casson dusty fluid of two-phase fluctuating flow over inclined parallel plate. More exactly, the relative magnetic phenomena are fixed relatively to the plate (MFFRP) or fixed relatively to the fluid (MFFRF). The mathematical modeling was established as the set of partial differential equations for dusty Casson fluid. Buckingham's pi theorem is used to find out the dimensionless variables, to make our system dimensionless. The solution in perturb form is to find out by incorporating Poincare–Lighthill perturbation technique (PLPT). To know in-depth different parameters the graphical results for both velocities are plotted with the help of software Mathcad- 15. It is clearly detected that the relative magnetic field performs a vital part in the fluid as well as particle motion. Moreover, the relative magnetic field play a vital role in the decreasing of skin friction of both plates. © 2023

关键词： Brinkman fluid Dust particles Fluctuating vertical plate Inclined relative magnetic field PLPT

来源：评论

学校读者我要写书评

暂无评论

COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

引用

computers, Materials & Continua 2021年第7期68卷 391-407页

作者： Aqib Ali Wali Khan Mashwani Samreen Naeem Muhammad Irfan Uddin Wiyada Kumam Poom Kumam Hussam Alrabaiah Farrukh Jamal Christophe Chesneau Department of Computer Science Concordia College BahawalpurBahawalpur63100Pakistan Department of Computer Science&IT Glim Institute of Modern StudiesBahawalpur63100Pakistan Institute of Numerical Sciences Kohat University of Science&TechnologyKohat26000Pakistan Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Program in Applied Statistics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology ThanyaburiThanyaburi12110Thailand Departments of Mathematics Faculty of ScienceCenter of Excellence in Theoretical and Computational Science(TaCS-CoE)&KMUTT Fixed Point Research LaboratoryRoom SCL 802 Fixed Point LaboratoryScience Laboratory BuildingKing Mongkut’s University of Technology Thonburi(KMUTT)Bangkok10140Thailand Department of Medical Research China Medical University HospitalTaichung40402Taiwan College of Engineering Al Ain UniversityAl Ain64141United Arab Emirates Department of Mathematics Tafila Technical UniversityTafila66110Jordan Department of Statistics The Islamia University of BahawalpurBahawalpur63100Pakistan 11Department of MathematicsUniversitéde CaenLMNOCaen14032France

The purpose of this research is the segmentation of lungs computed tomography(CT)scan for the diagnosis of COVID-19 by using machine learning *** dataset contains data from patients who are prone to the *** contains three types of lungs CT images(Normal,Pneumonia,and COVID-19)collected from two different sources;the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur,Pakistan,and the second one is a publicly free available medical imaging database known as *** the preprocessing,a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an automated region of interest(ROIs)and acquire 52 hybrid statistical features for each ***,12 optimized statistical features are selected via the chi-square feature reduction *** the classification,five machine learning classifiers named as deep learning J4,multilayer perceptron,support vector machine,random forest,and naive Bayes are deployed to optimize the hybrid statistical features *** is observed that the deep learning J4 has promising results(sensitivity and specificity:0.987;accuracy:98.67%)among all the deployed *** a complementary study,a statistical work is devoted to the use of a new statistical model to fit the main datasets of COVID-19 collected in Pakistan.

关键词： COVID-19 machine learning fuzzy c-mean deep learning J4

来源：评论

学校读者我要写书评

暂无评论

computer Vision for Primate Behavior Analysis in the Wild

arXiv

引用

arXiv 2024年

作者： Vogg, Richard Lüddecke, Timo Henrich, Jonathan Dey, Sharmita Nuske, Matthias Hassler, Valentin Murphy, Derek Fischer, Julia Ostner, Julia Schülke, Oliver Kappeler, Peter M. Fichtel, Claudia Gail, Alexander Treue, Stefan Scherberger, Hansjörg Wörgötter, Florentin Ecker, Alexander S. Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Chairs of Statistics and Econometrics Faculty of Economics University of Göttingen Germany Department for Computational Neuroscience Third Physics Institute University of Göttingen Germany Cognitive Ethology Laboratory German Primate Center Leibniz Institute for Primate Research Göttingen Germany Department for Primate Cognition Johann-Friedrich-Blumenbach Institute University of Göttingen Germany Leibniz ScienceCampus German Primate Center Leibniz Institute for Primate Research Göttingen Germany Bernstein Center for Computational Neuroscience University of Göttingen Germany Behavioral Ecology Department University of Göttingen Germany Social Evolution in Primates Group German Primate Center Leibniz Institute for Primate Research Göttingen Germany Behavioral Ecology & Sociobiology Unit German Primate Center Leibniz Institute for Primate Research Göttingen Germany Department of Sociobiology/Anthropology University of Göttingen Germany Cognitive Neuroscience Laboratory German Primate Center Leibniz Institute for Primate Research Göttingen Germany Georg-Elias-Müller-Institute of Psychology University of Göttingen Germany Faculty of Biology and Psychology University of Göttingen Germany Neurobiology Laboratory German Primate Center Leibniz Institute for Primate Research Göttingen Germany Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Advances in computer vision as well as increasingly widespread video-based behavioral monitoring have great potential for transforming how we study animal cognition and behavior. However, there is still a fairly large gap between the exciting prospects and what can actually be achieved in practice today, especially in videos from the wild. With this perspective paper, we want to contribute towards closing this gap, by guiding behavioral scientists in what can be expected from current methods and steering computer vision researchers towards problems that are relevant to advance research in animal behavior. We start with a survey of the state-of-the-art methods for computer vision problems that are directly relevant to the video-based study of animal behavior, including object detection, multi-individual tracking, individual identification, and (inter)action recognition. We then review methods for effort-efficient learning, which is one of the biggest challenges from a practical perspective. Finally, we close with an outlook into the future of the emerging field of computer vision for animal behavior, where we argue that the field should develop approaches to unify detection, tracking, identification and (inter)action recognition in a single, video-based framework. Copyright © 2024, The Authors. All rights reserved.

关键词： Invertebrates

来源：评论

学校读者我要写书评

暂无评论

Predicting convection configurations in coupled fluid-porous systems

arXiv

引用

arXiv 2022年

作者： McCurdy, Matthew Moore, Nicholas J. Wang, Xiaoming Department of Mathematics and Computer Science Ohio Wesleyan University DelawareOH43015 United States Department of Mathematics Colgate University HamiltonNY13346 United States Department of Mathematics SUSTech International Center for Mathematics National Center for Applied Mathematics Shenzhen Guangdong Provincial Key Laboratory of Computational Science and Material Design Southern University of Science and Technology Shenzhen518055 China Department of of Mathematics and Statistics Missouri University of Science and Technology RollaMO65409 United States

A ubiquitous arrangement in nature is a free-flowing fluid coupled to a porous medium, for example a river or lake lying above a porous bed. Depending on the environmental conditions, thermal convection can occur and may be confined to the clear fluid region, forming shallow convection cells, or it can penetrate into the porous medium, forming deep cells. Here, we combine three complementary approaches - linear stability analysis, fully nonlinear numerical simulations, and a coarse-grained model - to determine the circumstances that lead to each configuration. The coarse-grained model yields an explicit formula for the transition between deep and shallow convection in the physically relevant limit of small Darcy number. Near the onset of convection, all three of the approaches agree, validating the predictive capability of the explicit formula. The numerical simulations extend these results into the strongly nonlinear regime, revealing novel hybrid configurations in which the flow exhibits a dynamic shift from shallow to deep convection. This hybrid shallow-to-deep convection begins with small, random initial data, progresses through a metastable shallow state, and arrives at the preferred steady-state of deep convection. We construct a phase diagram that incorporates information from all three approaches and depicts the regions in parameter space that give rise to each convective *** Codes 76S05, 82B26, 76M10 © 2022, CC BY.

关键词： Porous materials

来源：评论

学校读者我要写书评

暂无评论

AmberTools

引用

Journal of Chemical Information and Modeling 2023年第20期63卷 6183-6191页

作者： Case, David A. Aktulga, Hasan Metin Belfon, Kellon Cerutti, David S. Cisneros, G. Andrés Cruzeiro, Vinícius Wilian D. Forouzesh, Negin Giese, Timothy J. Götz, Andreas W. Gohlke, Holger Izadi, Saeed Kasavajhala, Koushik Kaymak, Mehmet C. King, Edward Kurtzman, Tom Lee, Tai-Sung Li, Pengfei Liu, Jian Luchko, Tyler Luo, Ray Manathunga, Madushanka Machado, Matias R. Nguyen, Hai Minh O’Hearn, Kurt A. Onufriev, Alexey V. Pan, Feng Pantano, Sergio Qi, Ruxi Rahnamoun, Ali Risheh, Ali Schott-Verdugo, Stephan Shajan, Akhil Swails, Jason Wang, Junmei Wei, Haixin Wu, Xiongwu Wu, Yongxian Zhang, Shi Zhao, Shiji Zhu, Qiang Cheatham, Thomas E. Roe, Daniel R. Roitberg, Adrian Simmerling, Carlos York, Darrin M. Nagan, Maria C. Merz, Kenneth M. Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Computer Science and Engineering Michigan State University East LansingMI48824-1322 United States FOG Pharmaceuticals Inc. CambridgeMA02140 United States Psivant 451 D Street Suite 205 BostonMA02210 United States Department of Physics Department of Chemistry and Biochemistry University of Texas at Dallas RichardsonTX75801 United States Department of Chemistry and The PULSE Institute Stanford University StanfordCA94305 United States Department of Computer Science California State University Los AngelesCA90032 United States Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States San Diego Supercomputer Center University of California San Diego La Jolla CA92093-0505 United States Institute for Pharmaceutical and Medicinal Chemistry Heinrich Heine University Düsseldorf Düsseldorf40225 Germany Forschungszentrum Jülich GmbH Jülich52425 Germany Pharmaceutical Development Genentech Inc. South San FranciscoCA94080 United States Laufer Center for Physical and Quantitative Biology Department of Chemistry Stony Brook University Stony BrookNY11794 United States Departments of Molecular Biology and Biochemistry Chemical and Biomolecular Engineering Materials Science and Engineering and Biomedical Engineering Graduate Program in Chemical and Materials Physics University of California IrvineCA92697 United States Ph.D. Programs in Chemistry Biochemistry and Biology The Graduate Center of the City University of New York 365 Fifth Avenue New YorkNY10016 United States Department of Chemistry Lehman College 250 Bedford Park Blvd West BronxNY10468 United States Department of Chemistry and Biochemistry Loyola University Chicago ChicagoIL60660 United States Beijing National Laboratory for Molecular Science

AmberTools is a free and open-source collection of programs used to set up, run, and analyze molecular simulations. The newer features contained within AmberTools23 are briefly described in this Application note. ... 详细信息

关键词： Free energy Ligands Molecular mechanics Molecular modeling QM/MM

来源：评论

学校读者我要写书评

暂无评论

Current and future directions in network biology

arXiv

引用

arXiv 2023年

作者： Zitnik, Marinka Li, Michelle M. Wells, Aydin Glass, Kimberly Gysi, Deisy Morselli Krishnan, Arjun Murali, T.M. Radivojac, Predrag Roy, Sushmita Baudot, Anaïs Bozdag, Serdar Chen, Danny Z. Cowen, Lenore Devkota, Kapil Gitter, Anthony Gosline, Sara Gu, Pengfei Guzzi, Pietro H. Huang, Heng Jiang, Meng Kesimoglu, Ziynet Nesibe Koyuturk, Mehmet Ma, Jian Pico, Alexander R. Pržulj, Nataša Przytycka, Teresa M. Raphael, Benjamin J. Ritz, Anna Sharan, Roded Shen, Yang Singh, Mona Slonim, Donna K. Tong, Hanghang Yang, Xinan Holly Yoon, Byung-Jun Yu, Haiyuan Milenković, Tijana Department of Biomedical Informatics Harvard University United States Department of Computer Science and Engineering University of Notre Dame United States Lucy Family Institute for Data and Society University of Notre Dame United States Eck Institute for Global Health University of Notre Dame United States Channing Division of Network Medicine Brigham and Women’s Hospital Harvard Medical School United States Department of Statistics Federal University of Paraná Brazil Department of Physics Northeastern University United States Department of Biomedical Informatics University of Colorado United States Department of Computer Science Virginia Tech United States Khoury College of Computer Sciences Northeastern University United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison United States Wisconsin Institute for Discovery United States Aix Marseille Université INSERM MMG Marseille France Department of Computer Science and Engineering University of North Texas United States Department of Mathematics University of North Texas United States Department of Computer Science Tufts University United States Morgridge Institute for Research United States Pacific Northwest National Laboratory United States Department of Medical and Surgical Sciences University Magna Graecia of Catanzaro Italy Department of Computer Science University of Maryland College Park United States National Center of Biotechnology Information National Library of Medicine National Institutes of Health Department of Computer and Data Sciences Case Western Reserve University United States Computational Biology Department School of Computer Science Carnegie Mellon University United States Institute of Data Science and Biotechnology Gladstone Institutes Department of Computer Science University College London United Kingdom ICREA Catalan Institution for Research and Advanced Studies Spain Department of Computer Science Princeton University United States Departmen

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These challenges stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology and highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on the future directions of network biology. Additionally, we offer insights into scientific communities, educational initiatives, and the importance of fostering diversity within the field. This paper establishes a roadmap for an immediate and long-term vision for network biology. © 2023, CC BY.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Probabilistic load-margin assessment using vine copula and gaussian process emulation

Probabilistic load-margin assessment using vine copula and g...

引用

2020 IEEE Power and Energy Society General Meeting, PESGM 2020

作者： Xu, Yijun Karra, Kiran Mili, Lamine Korkali, Mert Chen, Xiao Hu, Zhixiong Virginia Tech Northern Virginia Center Bradley Department of Electrical and Computer Engineering Falls ChurchVA22043 United States Johns Hopkins University Applied Physics Lab BaltimoreMD21218 United States Lawrence Livermore National Laboratory Computational Engineering Division LivermoreCA94550 United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94550 United States University of California-Santa Cruz Department of Statistics Santa CruzCA95064 United States

ISBN: (纸本)9781728155081

The increasing penetration of renewable energy along with the variations of the loads bring large uncertainties in the power system states that are threatening the security of power system planning and operation. Facing these challenges, this paper proposes a cost-effective, nonparametric method to quantity the impact of uncertain power injections on the load margins. First, we propose to generate system uncertain inputs via a novel vine copula due to its capability in simulating complex multivariate highly dependent model inputs. Furthermore, to reduce the prohibitive computational time required in the traditional Monte-Carlo method, we propose to use a nonparametric, Gaussian-process-emulator-based reduced-order model to replace the original complicated continuation power-flow model. This emulator allows us to execute the time-consuming continuation power-flow solver at the sampled values with a negligible computational cost. The simulations conducted on the IEEE 57-bus system, to which correlated renewable generation are attached, reveal the excellent performance of the proposed method. © 2020 IEEE.

关键词： Monte Carlo methods

来源：评论

学校读者我要写书评

暂无评论

A spacetime finite elements method to solve the dirac equation

arXiv

引用

arXiv 2021年

作者： Sundermann, Rylee Lim, Hyun Waybright, Jace Kimn, Jung-Han Department of Mathematics and Statistics South Dakota State University BrookingsSD57007 United States Computational Physics and Methods Group Los Alamos National Laboratory Los AlamosNM87545 United States Center for Theoretical Astrophysics Los Alamos National Laboratory Los AlamosNM87545 United States Applied Computer Science Group Los Alamos National Laboratory Los AlamosNM87545 United States Princeton Plasma Physics Laboratory Princeton University PrincetonNJ08543 United States

In this work, a fully implicit numerical approach based on space-time finite element method is presented to solve the Dirac equation in 1 (space) + 1 (time), 2 + 1, and 3 + 1 dimensions. We utilize PETSc/Tao library to implement our linear system and for using Krylov subspace based solvers such as GMRES. We demonstrate our method by analyzing several different cases including plane wave solution, Zitterbewegung, and Klein paradox. Parallel performance of this implementation is also presented. © 2021, CC BY.

关键词： Finite element method

来源：评论

学校读者我要写书评

暂无评论

The Resume Paradox: Greater Language Differences, Smaller Pay Gaps

arXiv

引用

arXiv 2023年

作者： Minot, Joshua R. Maier, Marc Demarest, Bradford Cheney, Nicholas Danforth, Christopher M. Dodds, Peter Sheridan Frank, Morgan R. Computational Story Lab The University of Vermont BurlingtonVT05401 United States Vermont Complex Systems Center The University of Vermont BurlingtonVT05401 United States Department of Computer Science The University of Vermont BurlingtonVT05401 United States MassMutual Data Science AmherstMA01002 United States Neurobotics Lab The University of Vermont BurlingtonVT05401 United States Department of Mathematics and Statistics The University of Vermont BurlingtonVT05401 United States Department of Informatics and Networked Systems University of Pittsburgh PittsburghPA15260 United States Digital Economy Lab Stanford University StanfordCA94305 United States Media Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States

Over the past decade, the gender pay gap has remained steady with women earning 84 cents for every dollar earned by men on average. Many studies explain this gap through demand-side bias in the labor market represented through employers' job postings. However, few studies analyze potential bias from the worker supply-side. Here, we analyze the language in millions of US workers' resumes to investigate how differences in workers' self-representation by gender compare to differences in earnings. Across US occupations, language differences between male and female resumes correspond to 11% of the variation in gender pay gap. This suggests that females' resumes that are semantically similar to males' resumes may have greater wage parity. However, surprisingly, occupations with greater language differences between male and female resumes have lower gender pay gaps. A doubling of the language difference between female and male resumes results in an annual wage increase of $2,797 for the average female worker. This result holds with controls for gender-biases of resume text and we find that per-word bias poorly describes the variance in wage gap. The results demonstrate that textual data and self-representation are valuable factors for improving worker representations and understanding employment inequities. Copyright © 2023, The Authors. All rights reserved.

关键词： Employment

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：