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SSRN 2023年

作者： Wheeler, Kathryn I. Dietze, Michael C. LeBauer, David Peters, Jody Richardson, Andrew D. Ross, Arun A. Thomas, R. Quinn Zhu, Kai Bhat, Uttam Munch, Stephan Buzbee, Raphaela Floreani Chen, Min Goldstein, Benjamin Guo, Jessica Hao, Dalei Jones, Chris Kelly-Fair, Mira Liu, Haoran Malmborg, Charlotte Neupane, Naresh Pal, Debasmita Shirey, Vaughn Song, Yiluan Steen, McKalee Vance, Eric A. Woelmer, Whitney M. Wynne, Jacob Zachmann, Luke Department of Earth and Environment Boston University 685 Commonwealth Avenue BostonMA02215 United States Cooperative Programs for the Advancement of Earth System Science University Corporation for Atmospheric Research Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States National Oceanic and Atmospheric Administration Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology 15 Vassar St CambridgeMA02142 United States Arizona Experiment Station University of Arizona 1230 N Cherry Ave TucsonAZ85721 United States Department of Biological Sciences University of Notre Dame 100 Galvin Notre DameIN46556 United States School of Informatics Computing and Cyber Systems Northern Arizona University 1295 Knoles Dr. FlagstaffAZ86011 United States Center for Ecosystem Science and Society Northern Arizona University PO Box 5620 FlagstaffAZ86011 United States Department of Computer Science and Engineering Michigan State University 428 South Shaw Lane East LansingMI48824 United States Department of Forest Resources and Environmental Conservation Virginia Polytechnic Instit 310 West Campus Drive BlacksburgVA24061 United States Department of Biological Sciences Virginia Tech 926 West Campus Drive BlacksburgVA24061 United States Institute for Global Change Biology University of Michigan 440 Church Street Ann ArborMI48109 United States School for Environment and Sustainability University of Michigan 440 Church Street Ann ArborMI48109 United States Institute of Marine Sciences University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Applied Mathematics University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Environmental Science Policy and Management University of California Berkeley 4004 Valley Life Sciences Building Universi

Accurate models are important to predict how global climate change will continue to alter plant phenology and near-term ecological forecasts can be used to iteratively improve models and evaluate predictions that are made a priori. The Ecological Forecasting Initiative’s National Ecological Observatory Network (NEON) Forecasting Challenge, is an open challenge to the community to forecast daily greenness values, measured through digital images collected by the PhenoCam Network at NEON sites before the data are collected. For the first round of the challenge, which is presented here, we forecasted canopy greenness throughout the spring at eight deciduous broadleaf sites to investigate what factors impact current predictive skill of phenology forecasts. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that overall forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Predicting Spring Phenology in Deciduous Broadleaf Forests: An Open Community Forecast Challenge

SSRN

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SSRN 2023年

作者： Wheeler, Kathryn I. Dietze, Michael C. LeBauer, David Peters, Jody Richardson, Andrew D. Thomas, R. Quinn Zhu, Kai Bhat, Uttam Munch, Stephan Buzbee, Raphaela Floreani Chenp, Min Goldstein, Benjamin Guo, Jessica S. Haoq, Dalei Jones, Chris Kelly-Fair, Mira Liup, Haoran Malmborg, Charlotte Neupane, Naresh Pal, Debasmita Ross, Arun A. Shirey, Vaughn Song, Yiluan Steen, McKalee Vance, Eric A. Woelmer, Whitney M. Wynne, Jacob Zachmann, Luke Department of Earth and Environment Boston University 685 Commonwealth Avenue BostonMA02215 United States Cooperative Programs for the Advancement of Earth System Science University Corporation for Atmospheric Research Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States National Oceanic and Atmospheric Administration Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology 15 Vassar St CambridgeMA02142 United States Arizona Experiment Station University of Arizona 1230 N Cherry Ave TucsonAZ85721 United States Department of Biological Sciences University of Notre Dame 100 Galvin Notre DameIN46556 United States School of Informatics Computing and Cyber Systems Northern Arizona University 1295 Knoles Dr. FlagstaffAZ86011 United States Center for Ecosystem Science and Society Northern Arizona University PO Box 5620 FlagstaffAZ86011 United States Department of Forest Resources and Environmental Conservation Virginia Polytechnic Institute State University 310 West Campus Drive BlacksburgVA24061 United States Department of Biological Sciences Virginia Polytechnic Institute State University 926 West Campus Drive BlacksburgVA24061 United States Institute for Global Change Biology University of Michigan 440 Church Street Ann ArborMI48109 United States School for Environment and Sustainability University of Michigan 440 Church Street Ann ArborMI48109 United States Institute of Marine Sciences University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Applied Mathematics University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Environmental Science Policy and Management University of California Berkeley 4004 Valley Life Sciences Building University of California Berkeley BerkeleyCA94720 United States Department of For

Accurate phenology models are important to predict how global climate change will continue to alter the timing of plant phenological events, such as spring greenup in deciduous broadleaf forests. While there is merit in long-term predictions, investigating how models can predict near-term (1– 35 days) canopy greenness throughout the spring allows us to validate performance and understanding now. The Ecological Forecasting Initiative’s NEON Forecasting Challenge, is an open challenge to the community to predict daily greenness values, measured through digital images collected by the PhenoCam Network at National Ecological Observatory Network (NEON) sites. For the first round of the challenge, which is presented here, teams were tasked to forecast phenology at eight deciduous broadleaf sites. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts, which future rounds will continue to investigate. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Sparse data driven mesh deformation

arXiv

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arXiv 2017年

作者： Gao, Lin Lai, Yu-Kun Yang, Jie Zhang, Ling-Xiao Kobbelt, Leif Xia, Shihong Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Informatics Cardiff University Rwth Aachen University

Example-based mesh deformation methods are powerful tools for realistic shape editing. However, existing techniques typically combine all the example deformation modes, which can lead to overftting, i.e. using a overly complicated model to explain the user-specifed deformation. This leads to implausible or unstable deformation results, including unexpected global changes outside the region of interest. To address this fundamental limitation, we propose a sparse blending method that automatically selects a smaller number of deformation modes to compactly describe the desired deformation. This along with a suitably chosen deformation basis including spatially localized deformation modes leads to signifcant advantages, including more meaningful, reliable, and efcient deformations because fewer and localized deformation modes are applied. To cope with large rotations, we develop a simple but efective representation based on polar decomposition of deformation gradients, which resolves the ambiguity of large global rotations using an as-consistent-as-possible global optimization. This simple representation has a closed form solution for derivatives, making it efcient for sparse localized representation and thus ensuring interactive performance. Experimental results show that our method outperforms state-of-the-art data-driven mesh deformation methods, for both quality of results and efciency. Copyright © 2017, The Authors. All rights reserved.

关键词： Deformation

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Distributed Online Averaged One Dependence Estimator (DOAODE) Algorithm for Multi-class Classification of Network Anomaly Detection System

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IOP Conference Series: Materials Science and Engineering 2019年第1期557卷

作者： Mukrimah Nawir Amiza Amir Naimah Yaakob Ahmad R.Badlishah Anuar Mat Safar Mohd. Nazri Mohd. Warip I Zunaidi Embedded Network and Advanced Computing (ENAC) Research Cluster School of Computer and Communication Engineering Universiti Malaysia Perlis (UniMAP) Pauh Putra 02600 Perlis Faculty of Informatics and Computing Universiti Sultan Zainal Abidin (UniSZA) 22200 Besut Terengganu Faculty of Technology University of Sunderland St Peter's Campus Sunderland SR6 0DD United Kingdom

Network monitoring system consists of large data streams, distributed architecture, and multiple computers that are geographically located all over the world caused a difficulty to detect abnormalities in the system. In addition, when handling network traffic, the data in network is fast incoming and requires an online learning where immediately response and predict the pattern of network traffic for classification once there is an event or request occur. Therefore, this paper aims to develop an effective and efficient network anomaly detection system by using distributed online averaged one dependence estimator (DOAODE) classification algorithm for multi-class network data to overcome these issues. The finding of DOAODE algorithm for multi-class classification is high in accuracy with average 83% and fast to train the network traffic recorded less than ten seconds and takes shorter time when the number of nodes increases.

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Validating CRRM for aeronautical communications using a formal description technique 35

Validating CRRM for aeronautical communications using a form...

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35th AIAA International Communications Satellite Systems Conference, ICSSC 2017

作者： Hu, Yim Fun Pillai, Prashant Li, Jiangping Ayo, Babatope Samuel Faculty of Engineering and Informatics University of Bradford School of Electrical Engineering and Computer Science Richmond Road Bradford West Yorkshire United Kingdom Department of Computing and Communication Technologies Faculty of Technology Design and Environment Oxford Brookes University Wheatley Oxford United Kingdom

ISBN: (数字)9781624105180

ISBN: (纸本)9781624105180

This paper presents a Collaborative Radio Resource Management (CRRM) scheme to support seamless aeronautical networking. The CRRM exploits the IEEE 802.21 Media Independent Handover (MIH) framework and the ETSI Broadband Satellite Multimedia (BSM) SI-SAP concept to split the CRRM functions between the upper layers (layer 3 and above) and the lower layer (link layer and physical layer) of an aircraft terminal. Upper layer functions are managed by an Integrated Router (IR) on-board the aircraft and lower layer functions are provided by an on-board Integrated Modular Radio (IMR) consisting of heterogeneous radio access technologies. A Joint Radio Resource Manager (JRRM) provides the abstraction layer between the IR and IMR for mapping higher layer functions into lower layer functions to enable collaboration. The specification and design of the CRRM scheme is used to maintain a close collaboration to perform connection establishment functions and to support seamless handovers between different radio technologies. A detailed description of the CRRM mechanisms is presented. Message procedure charts are presented by combining MIH primitives and BSM primitives for session establishment and handover management. The design of this CRRM model is based on the Specification and Description Language (SDL) which is the most widely accepted formal description technique that enables model design validation and testing. The dynamic behavior and functionality of this CRRM scheme is validated using simulation in conjunction with the Message Sequence Chart (MSC). A set of validation scenarios have been simulated to show that the SDL model guarantees a good consistency with the designed target. © 2017, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Specifications

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HCI's making agendas

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Foundations and Trends in Human-Computer Interaction 2017年第3期11卷 126-200页

作者： Bardzell, Jeffrey Bardzell, Shaowen Lin, Cindy Lindtner, Silvia Toombs, Austin Indiana University School of Informatics Computing and Engineering United States University of Michigan School of Information United States Purdue University Department of Computer Graphics Technology United States

In this survey, we examine how making emerged as an interdisciplinary arena of scholarship, research and design that connects entrepreneurs, designers, researchers, critical theorists, historians, anthropologists, computer scientists and engineers. HCI is one among many other fields and domains that has declared having a stake in making. And yet, a lot of what and who defines making is happening outside the familiar research laboratory or design studio. We take this article as an opportunity to reflect on HCI's relationship to making and how this relationship has changed over the last years. Making, we argue, presents HCI with the opportunity to question and revisit underlying principles and long-held aspirations and values of the field. Exactly because HCI and making share some fundamental ideals such as user empowerment and the democratization of participation and technology production, making confronts us with both the potential and the unintended consequences of our own work. © 2017 J. Bardzell, S. Bardzell, C. Lin, S. Lindtner and H. Toombs.

关键词： Research laboratories

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Minimization Response Time Task scheduling Algorithm

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IOP Conference Series: Materials Science and Engineering 2019年第1期705卷

作者： Maria Maqsood Saima Anwar Lashari Murtaja Ali Saare Sari Ali Sari Yaqdhan Mahmood Hussein Hatem Oday Hatem Department of Computer Science University of Lahore Sargodha Campus Pakistan. College of Computing and Informatics Saudi Electronic University Dammam Kingdom of Saudi Arabia. Human-Centered Computing Research Lab School of Computing Universiti Utara Malaysia Sintok 06010 Kedah Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia 86400 Parit Raja Batu Pahat Johor Malaysia. Communication Engineering Department Faculty of Electrical Engineering University Technology Malaysia 81310 Johor Bahru Johor Malaysia

Cloud computing is a type of computing that base on shared computer resources for delivering information technology services. Distributed Computational system has been considered as best systematic model for sharing resources. Minimization of response time is the requirement in the distributed computational system. Previous Algorithm belongs to the optimization in which every new Virtual Machine reaching as a request to the cloud computing manager. This creates disturbance in the processing during life cycle of others Virtual Machines. For overcome this problem, there is a proposed an algorithm for minimization response time in distributed computational system by using the multi agents cloud structure. The overall system is architecture as a distributed over multi layered model. This leads to a situation that demands high care in allocating a Virtual Machine (VM) to a server machine in order to improve allocation time and efficiency of resources. Focus of this work remains to decrease time of allocation to optimize response time using a Virtual Machine (VM) placement and allocation algorithm that accounts Virtual. Machines (VM), their types and available resources among the data center server machines. Presented algorithm is implemented by using cloud simulator tool, through this tool, the calculated results of presented algorithm have been quite better with respect to traditional algorithm, and the difference presented with graphs for the both algorithms. Results support proposed methodology.

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Semi analytical solutions for fractional order singular partial differential equations with variable coefficients

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AIP Conference Proceedings 2019年第1期2116卷

作者： S. Iqbal Muhammad Rafiq Mufti Humaira Afzal S. Sarwar 1Department of Informatics and Systems School of Systems and Technology University of Management and Technology Lahore Pakistan 2Department of Computer Science COMSATS Institute of Information Technology Vehari Pakistan 3Institute of Computing Department of Computer Science BahauddinZakariya University Multan Pakistan 4Department of Mathematics Shanghai University 200444 P. R. China

Optimal homotopy asymptotic method (OHAM), a semi-analytical process is employed, for solving singular partial differential equations of fractional order to authenticate the usefulness and competence of the procedure. It is observed that the designed procedure is simple to implement, pretty important to knob singular phenomenon and capitulate superb results at low computational cost. The fractional derivatives are expressed in Caputo sense. Some examples with singularities are presented to exhibit the potential and practical utility of the method.

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A Unifying framework for UML profile-based cognitive modeling: Development and experience 17

A Unifying framework for UML profile-based cognitive modelin...

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10th EAI International Conference on Simulation Tools and Techniques, SIMUTOOLS 2017

作者： Zhu, Zhi Lei, Yonglin Zhu, Yifan Alshareef, Abdurrahman Sarjoughian, Hessam S. Institute of Simulation Engineering College of Information System and Management National University of Defense Technology Changsha410073 China Arizona Center for Integrated Modeling and Simulation School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States

ISBN: (纸本)9781450363884

To achieve model reuse, cognitive decision behaviors are usually implemented using a scripting language which is separate from the programming language used to implement simulation models. Therefore, it is desirable to establish a much better grounding for cognitive modeling. In the context of Domain-Specific Modeling (DSM), metamodeling from scratch for designing such a scripting language poses some limitations, among which is the issue of integrating various models that are represented by different customized languages, together with a large expenditure of designing, implementing, and maintaining these languages and their supporting resources. Instead, UML Profile-based metamodeling is adopted in this research, as a light weighted extension to capture the cognitive domain concepts, relationships, and constraints. Moreover, we propose a unifying framework for designing the domain specific profile where the expressiveness of cognitive domain specification is increased and the development cost and time decreased. Also, we present the development process by constructing a profile of Anti-Submarine Tactics (AST) and illustrate it by demonstrating an example with a scenario of armed escort. © 2017 Association for computing Machinery.

关键词： Submarines

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The medical segmentation decathlon

arXiv

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arXiv 2021年

作者： Antonelli, Michela Reinke, Annika Bakas, Spyridon Farahani, Keyvan Kopp-Schneider, Annette Landman, Bennett A. Litjens, Geert Menze, Bjoern Ronneberger, Olaf Summers, Ronald M. van Ginneken, Bram Bilello, Michel Bilic, Patrick Christ, Patrick F. Do, Richard K.G. Gollub, Marc J. Heckers, Stephan H. Huisman, Henkjan Jarnagin, William R. McHugo, Maureen K. Napel, Sandy Golia Pernicka, Jennifer S. Rhode, Kawal Tobon-Gomez, Catalina Vorontsov, Eugene Hulsmon, Hankan Meakin, James A. Ourselin, Sebastien Wiesenfarth, Manuel Arbeláez, Pablo Bae, Byeonguk Chen, Sihong Daza, Laura Feng, Jianjiang He, Baochun Isensee, Fabian Ji, Yuanfeng Jia, Fucang Kim, Namkug Kim, Ildoo Merhof, Dorit Pai, Akshay Park, Beomhee Perslev, Mathias Rezaiifar, Ramin Rippel, Oliver Sarasua, Ignacio Shen, Wei Son, Jaemin Wachinger, Christian Wang, Liansheng Wang, Yan Xia, Yingda Xu, Daguang Xu, Zhanwei Zheng, Yefeng Simpson, Amber L. Maier-Hein, Lena Cardoso, M. Jorge School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom Heidelberg Germany Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany University of Pennsylvania PhiladelphiaPA United States BethesdaMD United States Heidelberg Germany Electrical Engineering and Computer Science Vanderbilt University NashvilleTN United States Radboud University Medical Center Radboud Institute for Health Sciences Nijmegen Netherlands Quantitative Biomedicine University of Zurich Zurich Switzerland DeepMind London United Kingdom School of Computing Department of Biomedical and Molecular Sciences Queen’s University KingstonON Canada Department of Surgery Memorial Sloan Kettering Cancer Center New YorkNY United States BethesdaMD United States Department of Informatics Technische Universität München Germany Department of Radiology Memorial Sloan Kettering Cancer Center New YorkNY United States Department of Psychiatry & Behavioral Sciences Vanderbilt University Medical Center NashvilleTN United States Department of Radiology Stanford University StanfordCA United States Department of Computer Science and Software Engineering École Polytechnique de Montréal Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Medical Faculty University of Heidelberg Heidelberg Germany VUNO Inc. Seoul Korea Republic of Universidad de los Andes Colombia Department of Automation Tsinghua University Beijing China Heidelberg Germany Department of Computer Science Xiamen University Xiamen361005 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Kakao Brain Korea Republic of Institute of Imaging & Computer Vision RWTH Aachen University Aachen Germany Cere

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks;(2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability;(3) the training of accurate AI segmentation models is now commoditized to non AI *** Codes 68T07. © 2021, CC BY-NC-SA.

关键词： Image segmentation

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