检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Nixon, Michelle Pistner McGovern, Kyle C. Letourneau, Jeffrey David, Lawrence A. Lazar, Nicole A. Mukherjee, Sayan Silverman, Justin D. College of Information Sciences and Technology The Pennsylvania State University United States Program in Bioinformatics and Genomics The Pennsylvania State University United States Department of Molecular Genetics and Microbiology Duke University United States Department of Statistics The Pennsylvania State University United States Huck Institutes for the Life Sciences The Pennsylvania State University United States Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University United States Center for Scalable Data Analytics and Artificial Intelligence University of Leipzig Germany Max Planck Institute for Mathematics in the Natural Sciences Germany Department of Medicine The Pennsylvania State University United States

Many scientific fields, including human gut microbiome science, collect multivariate count data where the sum of the counts is unrelated to the scale of the underlying system being measured (e.g., total microbial load in a subject’s colon). This disconnect complicates downstream analyses such as differential analysis in case-control studies. This article is motivated by a novel study of in vitro human gut microbiome models. Popular tools for analyzing these data led to dramatically elevated rates of both false positives and false negatives. To understand those failures, we provide a formal problem statement that frames these challenges of scale in terms of the classical theory of identifiability. We call this the problem of Scale Reliant Inference (SRI). We use this formulation to prove fundamental limits on SRI in terms of criteria such as consistency and type-I error control. We show that the failures of existing methods stem from a fundamental failure to properly quantify uncertainty in the system scale. We demonstrate that a particular type of Bayesian model called a Bayesian Partially Identified Model (PIMs) can correctly quantify uncertainty in SRI. We introduce Scale Simulation Random Variables (SSRVs) as a flexible and efficient approach to specifying and inferring Bayesian PIMs. In the context of both real and simulated data, we find SSRVs drastically decrease type-I and type-II error rates. © 2022, CC BY.

关键词： Bayesian networks

来源：评论

学校读者我要写书评

暂无评论

Glaucoma identification with retinal fundus images using deep learning: Systematic review

引用

Informatics in Medicine Unlocked 2025年 56卷

作者： Meedeniya, Dulani Shyamalee, Thisara Lim, Gilbert Yogarajah, Pratheepan Department of Computer Science and Engineering University of Moratuwa Moratuwa 10400 Sri Lanka First-Year Division Faculty of Computing Sri Lanka Institute of Information Technology Malabe 10115 Sri Lanka SingHealth AI Health Program SingHealth Group Singapore 168582 Singapore School of Computing Engineering and Intelligent System Ulster University Londonderry BT48 7JL United Kingdom

Glaucoma is a leading cause of blindness, affecting millions of people worldwide. It is a chronic eye condition that damages the optic nerve and, if left untreated, can lead to vision loss and a decreased quality of life. Therefore, there is a need to explore practical and reliable mechanisms for glaucoma identification. This study systematically reviews deep-learning approaches for glaucoma identification using retinal fundus images from 2018 to 2024. Compared to existing survey studies, we cover the latest research, including several public retinal fundus image datasets, and focus on segmentation, classification based on convolutional neural networks and vision transformers, and explainability. The findings of this study, including comparisons of existing methods and key insights, will assist researchers and developers in identifying the most suitable techniques for glaucoma detection. © 2025 The Authors

关键词： Artificial intelligence Classification Deep learning Explainability Glaucoma Segmentation Trustworthiness

来源：评论

学校读者我要写书评

暂无评论

Bulk-plasmon-mediated free-electron radiation beyond the conventional formation time

arXiv

引用

arXiv 2022年

作者： Tay, Fuyang Lin, Xiao Shi, Xihang Chen, Hongsheng Kaminer, Ido Zhang, Baile Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJUHangzhou Global Science and Technology Innovation Center College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley–Curl Institute Rice University HoustonTX77005 United States International Joint Innovation Center The Electromagnetics Academy Zhejiang University Zhejiang University Haining314400 China Department of Electrical Engineering Technion-Israel Institute of Technology Haifa32000 Israel Jinhua Institute of Zhejiang University Zhejiang University Jinhua321099 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore Centre for Disruptive Photonic Technologies Nanyang Technological University 637371 Singapore

Free-electron radiation is a fundamental photon emission process that is induced by fast-moving electrons interacting with optical media. Historically, it has been understood that, just like any other photon emission process, free-electron radiation must be constrained within a finite time interval known as the "formation time", whose concept is applicable to both Cherenkov radiation and transition radiation, the two basic mechanisms describing radiation from a bulk medium and from an interface, respectively. Here we reveal an alternative mechanism of free-electron radiation far beyond the previously defined formation time. It occurs when a fast electron crosses the interface between vacuum and a plasmonic medium supporting bulk plasmons. While emitted continuously from the crossing point on the interface — thus consistent with the features of transition radiation — the extra radiation beyond the conventional formation time is supported by a long tail of bulk plasmons following the electron’s trajectory deep into the plasmonic medium. Such a plasmonic tail mixes surface and bulk effects, and provides a sustained channel for electron-interface interaction. These results also settle the historical debate in Ferrell radiation, regarding whether it is a surface or bulk effect, from transition radiation or plasmonic oscillation. Copyright © 2022, The Authors. All rights reserved.

关键词： Plasmonics

来源：评论

学校读者我要写书评

暂无评论

The Impact of the COVID-19 Pandemic in Indonesia (Face to face versus Online Learning)

The Impact of the COVID-19 Pandemic in Indonesia (Face to fa...

引用

Vocational Education and Electrical Engineering, International Conference on

作者： Dina Fitria Murad Rosilah Hassan Yaya Heryadi Bambang Dwi Wijanarko Titan BINUS Online Learning Bina Nusantara University Jakarta Indonesia Center for Cyber Security Faculty of Information Science & Technology Universiti Kebangsaan Malaysia UKM Bangi Selangor Malaysia BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728174341

ISBN: (纸本)9781728174358

Covid-19 pandemic is an international disaster that is experienced by almost all countries in the world. This has an impact on all lines of the life of each country. Among them is the education sector. Aside from efforts to solve this co-19 problem, the state must continue to maintain the stability and sustainability of the learning process that is the right of all citizens. Indonesia experienced the same thing. face to face learning “shock” and immediately take the fastest action by utilizing existing technology, but not all of them are ready. Using survey methods and adopting the theories of Delon and McLean, this study aims to determine the readiness of organizers, lectures, and students for current conditions, their readiness in undergoing the learning process while maintaining the quality of education and user satisfaction (instructors and students) towards learning. The results of this study prove that we all tend to be unprepared but strangely, on the other hand, the fact is that the positive things from this pandemic prove that education practitioners in Indonesia are better prepared by online learning because they are more comfortable and satisfied with online learning while supported by the government and a good system (96% of respondents) compared to face to face (4% of respondents).

关键词： COVID-19 Pandemics Education Media Tools Sustainable development Faces

来源：评论

学校读者我要写书评

暂无评论

Advising big data transfer over dedicated connections based on profiling optimization

Advising big data transfer over dedicated connections based ...

引用

作者： Yun, Daqing Wu, Chase Q. Rao, Nageswara S. V. Kettimuthu, Rajkumar Computer and Information Sciences Program Harrisburg University HarrisburgPA17101 United States Department of Computer Science New Jersey Institute of Technology NewarkNJ07102 United States Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Data Science and Learning Division Argonne National Laboratory ArgonneIL60439 United States

Big data transfer in next-generation scientific applications is now commonly carried out over dedicated channels in high-performance networks (HPNs), where transport protocols play a critical role in maximizing application-level throughput. Optimizing the performance of these protocols is challenging: i) transport protocols perform differently in various network environments, and the protocol choice is not straightforward;ii) even for a given protocol in a given environment, different parameter settings of the protocol may lead to significantly different performance and oftentimes the default setting does not yield the best performance. However, it is prohibitively time-consuming to conduct exhaustive transport profiling due to the large parameter space. In this paper, we propose a PRofiling Optimization Based DAta Transfer Advisor (ProbData) to help end users determine the most effective transport method with the most appropriate parameter settings to achieve satisfactory performance for big data transfer over dedicated connections in HPNs. ProbData employs a fast profiling scheme based on the Simultaneous Perturbation Stochastic Approximation algorithm, namely, FastProf, to accelerate the exploration of the optimal operational zones of various transport methods to improve profiling efficiency. We first present a theoretical background of the optimized profiling approach in ProbData and then detail its design and implementation. The advising procedure and performance benefits of FastProf and ProbData are illustrated and evaluated by both extensive emulations based on real-life performance measurements and experiments over various physical connections in existing production HPNs. © 1993-2012 IEEE.

关键词： Data transfer

来源：评论

学校读者我要写书评

暂无评论

Reliable Collaborative Semi-infrastructure Vehicle-to-Vehicle Communication for Local File Sharing 15th

Reliable Collaborative Semi-infrastructure Vehicle-to-Vehicl...

引用

15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Mokhtar, Bassem Azab, Mohamed Fathalla, Efat Ghourab, Esraa M. Magdy, Mohamed Eltoweissy, Mohamed Electrical Engineering Department Alexandria University Alexandria Egypt Computer Engineering Program School of Engineering and Applied Sciences Nile University Giza Egypt Computer and Information Sciences Virginia Military Institute LexingtonVA24450 United States Department of Electrical and Computer Engineering Old Dominion University NorfolkVA United States College of Computing and Information Technology Arab Academy for Science Technology and Maritime Transport Alexandria Egypt

ISBN: (纸本)9783030301453

Recently, Vehicular Cloud Communication (VCC) has been gaining momentum targeting intelligent and efficient data transmission. VCC is a type of mobile ad-hoc network comprising heterogeneous vehicles sharing their resources to perform collaborative activities. In this paper, we propose a new semi-infrastructure file-browsing in order to provide Network as a service (NaaS) enabling internet-independent browsing. In our scenario, a central management platform plays the role of controlling and managing the selection of relaying vehicles supporting the source to destination file transmission procedure. Nagel-Schreckenberg rules for traffic cellular automata (CA) are used as the basis for our scenario simulation. Nagel-Schreckenberg rules simulate the behavior of a group of hypothetical vehicles moving across a highway. We study the reliability and efficiency of file transfer in such settings. Simulation results show that the number of selected relays required to establish the network highly impacts the probability of successfully sending the requested files. In addition, the distances between the selected relays influence the network throughput and the probability of network failure. Moreover, the density of relays strongly affects the overall delay that occurs due to the continuous re-transmission of the selected files among different hops. © 2019, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Vehicle to vehicle communications

来源：评论

学校读者我要写书评

暂无评论

Ccdh: Complexity Based Causal Discovery of Hidden Common Cause (and Other Beasts) in Time Series

SSRN

引用

SSRN 2022年

作者： Varga, Bálint Stippinger, Marcell Benkő, Zsigmond Fabó, Dániel Halász, Péter Erőss, Loránd Somogyvári, Zoltán Telcs, András Department of Computational Sciences Wigner Research Centre for Physics BudapestH-1121 Hungary János Szentágothai Doctoral School of Neurosciences Semmelweis University BudapestH-1085 Hungary Epilepsy Center Juhász Pál National Institute of Clinical Neurosciences BudapestH-1145 Hungary EPI-HELP BudapestH-1012 Hungary Department of Functional Neurosurgery National Institute of Clinical Neurosciences BudapestH-1145 Hungary Faculty of Information Technology and Bionics Péter Pázmány Catholic University BudapestH-1083 Hungary Neuromicrosystems ltd. BudapestH-1113 Hungary Department of Computer Science and Information Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics BudapestH-1111 Hungary Department of Quantitative Methods University of Pannonia Faculty of Business and Economics VeszprémH-8200 Hungary

Non-interventional causal discovery is a challenging task. This is particularly true for causal relations of dynamic systems with chaotic attractors. The known solutions are data-intensive and cannot detect a hidden common driver (common cause or confounder). We propose a new method which eliminates both *** method is based on Takens' embedding theorem and complexity of the rank order patterns of the embedded series measured by the compressibility usingnon-sequential recursive pair substitution. The method works on shortsamples and is capable of detecting all kinds of causal connection (including hidden common cause). The newmethod is validated on synthetic data sets and applied to two types of humanelectrophysiological data from epileptic patients. In bothcases, the method revealed self-consistent, contradiction-free and biologically relevant causal connections between the investigatedareas. Thus the method may provide additional indication for the location ofintervention for the decision making medical panel. Given the method advantages and simplicity it might be applied in many other scientific and practical areas with success. © 2022, The Authors. All rights reserved.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

City scale energy demand forecasting using machine learning based models: A comparative study 2

City scale energy demand forecasting using machine learning ...

引用

2nd International Conference on Data science, E-Learning and information Systems, DATA 2019

作者： Al-Bayaty, Hussein Mohammed, Tareq Wang, Weimin Ghareeb, Ahmed Engineering College University of Kirkuk Kirkuk Iraq College of Computer Science and Information Technology University of Kirkuk Kirkuk Iraq Department of Engineering Technology University of North Carolina at Charlotte Charlotte United States Program University of North Carolina at Charlotte Charlotte United States

ISBN: (纸本)9781450372848

The rapid change during the last decades in the activities of people and the global energy trends in the usage of daily equipment, led to massive shortage not just in the energy sector, but almost in all of the related sectors, especially in the developing countries. Consequently, predicting energy demand is a vital issue in order to fulfill the future strategy of any country. In this study, four machine learning algorithms have been deployed in order to forecast the short-term demand load for the city of Kirkuk based on hourly recorded real data. This data-driven study has taken in count the metrological weather data as a function of electrical energy demand. The stages of developing the models are discussed, and the forecasting models validated using 6 months of out-of-sample data. Among the four (Machine Learning (ML) models, two predictive algorithms (i.e. Artificial Neural Network (ANN) and Decision Trees (DTs)) showed better predictive capabilities achieving lower error rates. © 2019 Association for Computing Machinery. ACM

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

Governing AI-Driven Health Research: Are IRBs Up to the Task?

引用

Ethics & human research 2021年第2期43卷 35-42页

作者： Friesen, Phoebe Douglas-Jones, Rachel Marks, Mason Pierce, Robin Fletcher, Katherine Mishra, Abhishek Lorimer, Jessica Véliz, Carissa Hallowell, Nina Graham, Mackenzie Chan, Mei Sum Davies, Huw Sallamuddin, Taj Assistant professor in the Biomedical Ethics Unit and the Department of Social Studies of Medicine at McGill University Associate professor of anthropological approaches to data and infrastructure head of the Technologies in Practice research group the codirector of the ETHOS Lab at the IT University of Copenhagen Assistant professor of law at Gonzaga University and the Edmond J. Safra/Petrie-Flom Centers Joint Fellow-in-Residence at Harvard University Technology and Society at the Tilburg Law School at Tilburg University Coordinator of Cyber Security Oxford and the founding administrator of the Computer Science Department Research Ethics Committee in the Department of Computer Science at the University of Oxford DPhil candidate at the Uehiro Centre for Practical Ethics at the University of Oxford DPhil candidate on the NEUROSEC team in the Department of Psychiatry at the University of Oxford Associate professor in the Faculty of Philosophy at the Institute for Ethics in AI as well as a tutorial fellow at Hertford College at the University of Oxford Codirector of the EPSRC Centre for Doctoral Training in Health Data Science and a professor at the Ethox Centre and Wellcome Centre for Ethics and Humanities at the University of Oxford Senior research fellow in data ethics at the Wellcome Centre for Ethics and Humanities in the Nuffield Department of Population Health at the University of Oxford DPhil student in the Nuffield Department of Population Health at the University of Oxford and a research fellow in the Department of Applied Health Research at University College London Lecturer in education at the School of Education and Sport at the University of Edinburgh Data protection and information lawyer

Many are calling for concrete mechanisms of oversight for health research involving artificial intelligence (AI). In response, institutional review boards (IRBs) are being turned to as a familiar model of governance. Here, we examine the IRB model as a form of ethics oversight for health research that uses AI. We consider the model's origins, analyze the challenges IRBs are facing in the contexts of both industry and academia, and offer concrete recommendations for how these committees might be adapted in order to provide an effective mechanism of oversight for health-related AI research. © 2021 by The Hastings Center. All rights reserved.

关键词： AI research oversight artificial intelligence ethics committees health-related AI research institutional review boards research ethics

来源：评论

学校读者我要写书评

暂无评论

Mobile application to track people in covid19 monitoring and patients under covid19 supervision

引用

IOP Conference Series: Earth and Environmental science 2021年第1期729卷

作者： A Chrysler H L H S Warnars W H Utomo Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Department of Magister Science of Information Technology Faculty of Computing President University Bekasi Indonesia 17550

It is crucial for the community including the government and health workers to collaborate to halt the spread of Covid-19. The idea of developing the mobile application surfaced from the previous findings. Previous researches have implemented and developed different features to better the application. The development of a mobile application to provide a platform that will assist people with information regarding patients that are around the perimeter of users to help notify them. With the help of the notification, users will be able to avoid the chances of them being in contact with the people (ODP), patients that are under surveillance (PDP) confirmed patients. All patients including ODP and PDP are required to have the application therefore, the Minister of Health will be able to track the geolocation of patients. Moreover, people will be able to be aware of patients that are around their perimeter. Therefore, with the help of the application, it will be able to help assist the community for them to be aware of and to be able to avoid being in contact with the infected patients.

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：