检索结果-内蒙古大学图书馆

NAVAL ENGINEERS JOURNAL 1989年第5期101卷 53-58页

作者： FRINK, JG VERVEN, DM John G. Frink is an assistant group supervisor in the Fleet Systems Department of The Johns Hopkins University Applied Physics Laboratory. He received his B.S. degree in physics from Michigan State University in 1968 and the M.S. degree in computer science from the University of Maryland in 1971. Mr. Frink worked at the Naval Surface Weapons Center White Oak Laboratory on a variety of weapons systems before joining the Applied Physics Laboratory in 1978. Mr. Frink has supervised the development of a large scale battle group simulation that successfully employs the receiver selection paradigm described herein on a hardware suite composed of an extensible number of (currently 15) processors. David M. Verven is a senior electrical engineer in the Fleet Systems Department of The Johns Hopkins University Applied Physics Laboratory. He received the B.S. degree in electrical engineering from the University of Maryland College Park in 1979 and the M. S. degree in the same discipline from The Johns Hopkins University in 1984. Mr. Verven joined the Applied Physics Laboratory in 1979 and initially worked on analysis of Navy sensor systems. In 1981 he was the AN/SPS-49 test team leader for the New Threat Upgrade (NTU) combat system land-based test. In 1982 he became involved in the development of the BBWRS system. Currently he is involved in real-time computer program development for the CEP project.

This paper documents an experiment performed by The Johns Hopkins University Applied Physics Laboratory to measure the effect of inserting a data bus into a combat system. The experiment was conducted at the Aegis computer Center located at the Naval Surface Weapons Center in Dahlgren, Virginia (NSWC/DL). The purpose of the experiment was to determine whether or not the Aegis Weapon System (the core of the Aegis Combat System) could be operated with a portion of its point-to-point interelement cables replaced by a data bus. The data bus chosen for the experiment employs message broadcasting with receiver selection. A primary goal of the experiment was to minimize the amount of Aegis computer program changes required to accommodate the data bus. The results presented in this paper will show that the experiment was a success. Key certification tests were passed with no computer program changes to the tactical elements and minimal changes in the Aegis tactical executive (ATES) program (less than 110 words changed).

关键词： Data Transmission Equipment

来源：评论

学校读者我要写书评

暂无评论

What happened to the system perspective in reliability?

引用

Quality and Reliability engineering International 1992年第5期8卷 391-412页

作者： Halverson, Mark Ozdes, Demir Mark Halverson:received a BSEE from the University of Michigan in 1972 followed by an MS in Computer Information and Control System Engineering in 1973 also from the University of Michigan. Mr. Halverson has worked predominantly on military and aerospace design projects for such companies as Litton and Northrop. He is a specialist in inertial navigation and control systems. He has also worked with PLG Inc. as a risk analyst on various projects including commercial military and space applications. Since 1986 Mr. Halverson is an aerospace consultant working in Europe where he among other activities codeveloped the BRAT probabilistic risk analysis program. Demir Ozdes:received his BSEE from Purdue University in 1958 and his MSEE from the University of Arizona in 1963. He has over 35 years of experience in electronic design system engineering optimal control system design and analysis as well as in the design analysis and simulation of synergistic navigation systems. He has worked at Northrop Teledyne SPC TRW and Litton. Since 1984 he has been a private consultant in the aerospace field in Europe and is co-holder of patents as well as being a co-developer of the BRAT probabilistic risk analysis program.

This paper takes a broad overview of the reliability and safety engineering practice as it is commonly exercised in the aerospace and military industries today, especially with respect to complex electronic equipment which typically includes considerable software content. A general deficiency is noted with regard to the system-level approach to reliability. It would appear that the system-level reliability activities are given less attention than is needed, while excessive emphasis is placed on component micro- physics, component failure models, and various component-oriented analyses. systems-level reliability topics, which predominate in the equipment reliability which is actually observed, are sometimes ignored by reliability engineers. Methods and practical philosophies to re-establish a solid system engineering foundation for contemporary reliability practice, which have been developed through the authors' experience in the field, are presented. Finally, an example analysis is shown to illustrate the concepts.

关键词： Reliability

来源：评论

学校读者我要写书评

暂无评论

Direct Comparison of RT-ddPCR and Targeted Amplicon Sequencing for SARS-CoV-2 Mutation Monitoring in Wastewater

SSRN

引用

SSRN 2022年

作者： Lou, Esther G. Sapoval, Nicolae McCall, Camille Bauhs, Lauren Carlson-Stadler, Russell Kalvapalle, Prashant Lai, Yanlai Palmer, Kyle Penn, Ryker Rich, Whitney Wolken, Madeline Ensor, Katherine B. Hopkins, Loren Treangen, Todd J. Stadler, Lauren B. Department of Civil and Environmental Engineering Rice University 6100 Main Street MS 519 HoustonTX77005 United States Department of Computer Science Rice University 6100 Main Street HoustonTX77005 United States Systems Synthetic and Physical Biology Graduate Program Rice University 6100 Main Street HoustonTX77005 United States Houston Health Department 8000 N. Stadium Dr. HoustonTX77054 United States Department of Statistics Rice University 6100 Main Street MS 138 HoustonTX77005 United States

Over the course of the COVID-19 pandemic, variants of SARS-CoV-2 have emerged that are more contagious and more likely to cause breakthrough infections. Targeted amplicon sequencing approach is a gold standard for identification and analysis of variants. However, when applied to environmental samples such as wastewater, it remains unclear how sensitive this method is for detecting variant-associated mutations in environmental samples. Here we directly compare a targeted amplicon sequencing approach (using ARTIC v3;hereafter referred to as sequencing) with RT-ddPCR quantification for the detection of five mutations that are characteristic of variants of concern (VoCs) in wastewater samples. In total, 547 wastewater samples were analyzed using both methods in parallel. When we observed positive mutation detections by RT-ddPCR, 42.6% of the detection events were missed by sequencing, due to negative detection or the limited read coverage at the mutation position. Further, when sequencing reported negative or depth-limited mutation detections, 26.7% of those events were instead positive detections by RT-ddPCR, highlighting the relatively poor sensitivity of sequencing. No or weak associations were observed between quantitative measurements of target mutations determined by RT-ddPCR and sequencing. These findings caution the use of quantitative measurements of SARS-CoV-2 variants in wastewater samples determined solely based on sequencing. © 2022, The Authors. All rights reserved.

关键词： SARS

来源：评论

学校读者我要写书评

暂无评论

Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model

arXiv

引用

arXiv 2024年

作者： Zhou, Peng Wang, Jianmin Li, Chunyan Wang, Zixu Liu, Yiping Sun, Siqi Lin, Jianxin Wei, Leyi Cai, Xibao Lai, Houtim Liu, Wei Wang, Longyue Liu, Yuansheng Zeng, Xiangxiang College of Information Science and Engineering Hunan University Changsha China The Interdisciplinary Graduate Program in Integrative Biotechnology Yonsei University Incheon21983 Korea Republic of School of Informatics Yunnan Normal University Kunming650500 China Department of Computer Science University of Tsukuba Tsukuba3058577 Japan Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China Shanghai AI Laboratory Shanghai200232 China Centre for Artificial Intelligence driven Drug Discovery Faculty of Applied Science Macao Polytechnic University China School of Informatics Xiamen University Xiamen China Tencent AI Lab China

While various models and computational tools have been proposed for structure and property analysis of molecules, generating molecules that conform to all desired structures and properties remains a challenge. Here, we introduce a multi-constraint molecular generation large language model, TSMMG, which, akin to a student, incorporates knowledge from various small models and tools, namely, the 'teachers'. To train TSMMG, we construct a large set of text-molecule pairs by extracting molecular knowledge from these 'teachers', enabling it to generate novel molecules that conform to the descriptions through various text prompts. We experimentally show that TSMMG remarkably performs in generating molecules meeting complex, natural language-described property requirements across two-, three-, and four-constraint tasks, with an average molecular validity of over 99% and success ratio of 82.58%, 68.03%, and 67.48%, respectively. The model also exhibits adaptability through zero-shot testing, creating molecules that satisfy combinations of properties that have not been encountered. It can comprehend text inputs with various language styles, extending beyond the confines of outlined prompts, as confirmed through empirical validation. Additionally, the knowledge distillation feature of TSMMG contributes to the continuous enhancement of small models, while the innovative approach to dataset construction effectively addresses the issues of data scarcity and quality, which positions TSMMG as a promising tool in the domains of drug discovery and materials science. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Book reviews

引用

Minds and Machines 1996年第2期6卷 239-285页

作者： Abbott, Barbara Herskovits, Annette Peterson, Philip L. Mele, Alfred R. Cole, David J. Crevier, Daniel Pelletier, Francis Jeffry Berkeley, Istvan S. N. Kitts, Brendan J. Brown, Mike Paliouras, George Department of Linguistics and Languages Michigan State University East Lansing USA Department of Philosophy Michigan State University East Lansing USA Program in Applied Linguistics Boston University Boston USA Department of Philosophy Syracuse University Syracuse USA Department of Philosophy Davidson College Davidson USA Department of Philosophy University of Minnesota Duluth USA Department of Electrical Engineering École de Technologie Supérieure Université du Québec Montréal Canada Department of Philosophy University of Alberta Edmonton Canada Department of Computing Science University of Alberta Edmonton Canada Biological Computation Project University of Alberta Edmonton Canada Center for Complex Systems Brandeis University Waltham USA Department of Computer Science University of Manchester Manchester U.K.

来源：评论

学校读者我要写书评

暂无评论

SRBench++: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation

引用

IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： de Franca, F.O. Virgolin, M. Kommenda, M. Majumder, M.S. Cranmer, M. Espada, G. Ingelse, L. Fonseca, A. Landajuela, M. Petersen, B. Glatt, R. Mundhenk, N. Lee, C.S. Hochhalter, J.D. Randall, D.L. Kamienny, P. Zhang, H. Dick, G. Simon, A. Burlacu, B. Kasak, Jaan Machado, Meera Wilstrup, Casper Cavaz, W.G.L. Federal University of ABC Santo Andre Brazil Evolutionary Intelligence group Centrum Wiskunde and Informatica Science Park Amsterdam Netherlands University of Applied Sciences Upper Austria Hagenberg Austria Computational Health Informatics Program Boston Children’s Hospital Harvard Medical School Boston USA Center for Computational Astrophysics Flatiron Institute and Department of Astrophysical Sciences of Princeton University USA LASIGE Faculdade de Ciências Universidade de Lisboa Lisboa Portugal Computational Engineering Division Lawrence Livermore National Laboratory Livermore USA Department of Mechanical Engineering University of Utah Utah USA Meta FAIR France Victoria University of Wellington School of Engineering and Computer Science New Zealand University of otago Department of Information Science New Zealand Institut für Angewandte Physik Universität TübingenMax Planck Institute for Intelligent Systems Tübingen Germany Abzu AI Orient Plads 1 Nordhavn Denmark

Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main promise of this approach is that it may return an interpretable model that can be insightful to users, while maintaining high accuracy. The current standard for benchmarking these algorithms is SRBench, which evaluates methods on hundreds of datasets that are a mix of real-world and simulated processes spanning multiple domains. At present, the ability of SRBench to evaluate interpretability is limited to measuring the size of expressions on real-world data, and the exactness of model forms on synthetic data. In practice, model size is only one of many factors used by subject experts to determine how interpretable a model truly is. Furthermore, SRBench does not characterize algorithm performance on specific, challenging sub-tasks of regression such as feature selection and evasion of local minima. In this work, we propose and evaluate an approach to benchmarking SR algorithms that addresses these limitations of SRBench by 1) incorporating expert evaluations of interpretability on a domain-specific task, and 2) evaluating algorithms over distinct properties of data science tasks. We evaluate 12 modern symbolic regression algorithms on these benchmarks and present an in-depth analysis of the results, discuss current challenges of symbolic regression algorithms and highlight possible improvements for the benchmark itself. Authors

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

EXTENSION AND APPLICATION OF SHIP DESIGN OPTIMIZATION CODE (SHIPDOC)

引用

NAVAL ENGINEERS JOURNAL 1984年第3期96卷 177-190页

作者： RICHARDSON, WM WHITE, WN William M:. Richardsonhas been employed as a naval architect in the Surface Effect Ship Division of the David Taylor Naval Ship Research and Development Center (DTNSRDC) where he has worked since 1975 in the areas of SES structural loads estimation and optimal ship design. Upon first coming to DTNSRDC he worked in the Ship Dynamics Simulation Branch on ACV submarine and hydrofoil simulations. While at Mare Island Naval Shipyard (MINS) he was a member of the Engineering Computer Applications Branch where he was responsible for the development of algorithms and programs for the solution of naval architectural problems including ship design flooding effects and submarine overhaul scheduling. Before coming to MINS he was employed as a naval architect in the Hull Scientific Branch of the Boston Naval Shipyard's Design Division. He obtained an B.S. degree in Naval Architecture and Marine Engineering from M.I.T. and is a member of SNAME. William N:. Whiteis a senior naval architect in the Advance Vehicles Branch of the Naval Sea Systems Command where he is responsible for the design of all surface effect ships and air cushion vehicles. Previous to his current assignment he was with PMS-304 the Navy's Surface Effect Ship Project Office. There he was the manager for machinery and system integration for the 3000 ton SES acquisition program. His earliest advanced ship experience was acquired while employed by the David Taylor Naval Ship Research and Development Center starting in 1970. Here he worked on the Navy's hydrofoil and air cushion vehicle programs. Mr. White started his career as a student trainee at the Boston Naval Shipyard in 1961 and transferred to Mare Island Naval Shipyard in 1964 where he worked on the Navy's Polaris program. He graduated from the University of Michigan with a Master's Degree in Naval Architecture and is a member of the SNAME and ASNE.

An existing nonlinear ship design optimization program designated SHIPDOC has been extended and a new surface effect ship (SES) description input file is being developed under the sponsorship of the Naval Sea systems Command's surface ship concept formulation (CONFORM) program. SHIPDOC is currently being used in support of several feasibility and preliminary level SES designs. The program has several novel features including an open-ended, user-created ship description and the potential to model all types of ships both enhanced and conventional, surfaced and submerged. This capability has been combined with a nonlinear optimization algorithm that solves for the minimum weight ship subject to user controlled constraints. This paper presents a brief overall description of SHIPDOC and its current capabilities followed by a synopsis of ship types, both surface and submerged, which have been designed with the program in order to illustrate SHIPDOC's practical design capability.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Ten simple rules for reproducible research in jupyter notebooks

arXiv

引用

arXiv 2018年

作者： Rule, Adam Birmingham, Amanda Zuniga, Cristal Altintas, Ilkay Huang, Shih-Cheng Knight, Rob Moshiri, Niema Nguyen, Mai H. Rosenthal, Sara Brin Pérez, Fernando Rose, Peter W. Design Lab UC San Diego San DiegoCA United States Center for Computational Biology and Bioinformatics UC San Diego San DiegoCA United States Department of Pediatrics UC San Diego San DiegoCA United States Data Science Hub San Diego Supercomputer Center UC San Diego San DiegoCA United States Departments of Bioengineering and Computer Science and Engineering Center for Microbiome Innovation UC San Diego San DiegoCA United States Bioinformatics and Systems Biology Graduate Program UC San Diego San DiegoCA United States Department of Statistics Berkeley Institute for Data Science UC Berkeley Lawrence Berkeley National Laboratory BerkeleyCA United States Biomedical Informatics Graduate Program Stanford University StanfordCA United States

Reproducibility of computational studies is a hallmark of scientific methodology. It enables researchers to build with confidence on the methods and findings of others, reuse and extend computational pipelines, and thereby drive scientific progress. Since many experimental studies rely on computational analyses, biologists need guidance on how to set up and document reproducible data analyses or simulations. In this paper, we address several questions about reproducibility. For example, what are the technical and non-technical barriers to reproducible computational studies? What opportunities and challenges do computational notebooks offer to overcome some of these barriers? What tools are available and how can they be used effectively? We have developed a set of rules to serve as a guide to scientists with a specific focus on computational notebook systems, such as Jupyter Notebooks, which have become a tool of choice for many applications. Notebooks combine detailed workflows with narrative text and visualization of results. Combined with software repositories and open source licensing, notebooks are powerful tools for transparent, collaborative, reproducible, and reusable data analyses. Copyright © 2018, The Authors. All rights reserved.

关键词： Digital storage

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Derived Optimal Aviation Strategies to Control Pandemics

arXiv

引用

arXiv 2022年

作者： Rizvi, Syed Awasthi, Akash Peláez, Maria J. Wang, Zhihui Cristini, Vittorio Van Nguyen, Hien Dogra, Prashant Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States Mathematics in Medicine Program Department of Medicine Houston Methodist Research Institute HoustonTX77030 United States Department of Physiology and Biophysics Weill Cornell Medical College New YorkNY10065 United States Neal Cancer Center Houston Methodist Research Institute HoustonTX77030 United States Department of Imaging Physics The University of Texas M.D. Anderson Cancer Center HoustonTX77230 United States Physiology Biophysics and Systems Biology Program Graduate School of Medical Sciences Weill Cornell Medicine New YorkNY10065 United States Research in Physiology and Biophysics Department of Physiology and Biophysics Weill Cornell Medical College New YorkNY United States

The COVID-19 pandemic has affected countries across the world, demanding drastic public health policies to mitigate the spread of infection, leading to economic crisis as a collateral damage. In this work, we investigated the impact of human mobility (described via international commercial flights) on COVID-19 infection dynamics at the global scale. For this, we developed a graph neural network-based framework referred to as Dynamic Connectivity GraphSAGE (DCSAGE), which operates over spatiotemporal graphs and is well-suited for dynamically changing adjacency information. To obtain insights on the relative impact of different geographical locations, due to their associated air traffic, on the evolution of the pandemic, we conducted local sensitivity analysis on our model through node perturbation experiments. From our analyses, we identified Western Europe, North America, and Middle East as the leading geographical locations fueling the pandemic, attributed to the enormity of air traffic originating or transiting through these regions. We used these observations to identify tangible air traffic reduction strategies that can have a high impact on controlling the pandemic, with minimal interference to human mobility. Our work provides a robust deep learning-based tool to study global pandemics and is of key relevance to policy makers to take informed decisions regarding air traffic restrictions during future outbreaks. © 2022, CC BY-NC-SA.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

The association for the advancement of artificial intelligence 2020 workshop program

The association for the advancement of artificial intelligen...

引用

作者： Bang, Grace Barash, Guy Bea, Ryan Cali, Jacques Castillo-Effen, Mauricio Chen, Xin Cynthia Chhaya, Niyati Dhoopar, Rohan Dumancic, Sebastijan Espinoza, Huáscar Farchi, Eitan Fioretto, Ferdinando Fuentetaja, Raquel Galley, Michel Geib, Christopher Hernández-Orallo, José Huang, Xiaowei Keren, Sarah Kim, Seokhwan Jaidka, Kokil Liu, Xiaomo Lu, Tyler Ma, Zhiqiang Mallah, Richard McDermid, John Michalowski, Martin Mirsky, Reuth Héigeartaigh, Seán Ó. Ramachandran, Deepak Segovia-Aguas, Javier Shaban-Nejad, Arash Shehory, Onn Shwartz, Vered Srivastava, Siddharth Talamadupula, Kartik Tang, Jian Zhang, Dell Zhang, Jian S&P Global. Western Digital University of Southampton United Kingdom Blue Prism Group PLC Lockheed Martin's Advanced Technology Laboratories ArlingtonVA United States University of Hong Kong Hong Kong Adobe Research Google Research Katholieke Universiteit Leuven Belgium Commissariat à ĺÉnergie Atomique France IBM Research Department of Electrical Engineering and Computer Science Syracuse University United States Universidad Carlos III de Madrid Spain Microsoft Research AI SIFT LLC Universitat Politècnica de València Spain Leverhulme Centre for the Future of Intelligence United Kingdom Department of Computer Science University of Liverpool United Kingdom National University of Singapore Singapore Harvard School of Engineering and Applied Sciences in the of Fields of Multiagent Environment Design and Goal Recognition Design Amazon Alexa AI Future of Life Institute United States University of York Assuring Autonomy International Program United Kingdom University of Minnesota School of Nursing MinneapolisMN United States University of Texas at Austin AustinTX United States Institut de Robòtica i Informàtica Industrial Spanish Council for Scientific Research Technical University of Catalonia Spain Oak Ridge National Laboratory Center for Biomedical Informatics Department of Pediatrics University of Tennessee Health Science Center United States Bar Ilan University Israel University of Washington SeattleWA United States Autonomous Agents and Intelligent Robots lab of the School of Computing Informatics Decision Systems Arizona State University United States DiDi AI Labs School of Industrial and System Engineering Georgia Institute of Technology United States University of London United Kingdom Microsoft Azure

The Association for the Advancement of Artificial Intelligence 2020 Workshop program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but not all, of the workshop chairs. Copyright © 2020, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词：

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：