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arXiv 2019年

作者： Cai, Bill Yang Alvarez, Ricardo Sit, Michelle Duarte, Fábio Ratti, Carlo Senseable City Lab Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Center for Computational Engineering Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States School of Architecture and Design Pontifical Catholic University of Paraná R. Imac. ConceiÃgÃ čo 1155 - Prado Velho Curitiba - PR80215-901 Brazil Department of Computer Science and Engineering University of California San Diego 9500 Gilman Drive La JollaCA92093 United States

Parking spaces are costly to build, parking payments are difficult to enforce, and drivers waste an excessive amount of time searching for empty lots. Accurate quantification would inform developers and municipalities in space allocation and design, while real-time measurements would provide drivers and parking enforcement with information that saves time and resources. In this paper, we propose an accurate and real-time video system for future Internet of Things (IoT) and smart cities applications. Using recent developments in deep convolutional neural networks (DCNNs) and a novel vehicle tracking filter, we combine information across multiple image frames in a video sequence to remove noise introduced by occlusions and detection failures. We demonstrate that our system achieves higher accuracy than pure image-based instance segmentation, and is comparable in performance to industry benchmark systems that utilize more expensive sensors such as radar. Furthermore, our system shows significant potential in its scalability to a city-wide scale and also in the richness of its output that goes beyond traditional binary occupancy statistics. Copyright © 2019, The Authors. All rights reserved.

关键词： Internet of things

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The influence of high-energy local orbitals and electron-phonon interactions on the band gaps and optical spectra of hexagonal boron nitride

arXiv

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arXiv 2020年

作者： Shen, Tong Zhang, Xiao-Wei Shang, Honghui Zhang, Min-Ye Wang, Xinqiang Wang, En-Ge Jiang, Hong Li, Xin-Zheng State Key Laboratory for Artificial Microstructure and Mesoscopic Physics Frontier Science Center for Nano-optoelectronics School of Physics Peking University Beijing China International Center for Quantum Materials and School of Physics Peking University Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Beijing National Laboratory for Molecular Sciences College of Chemistry and Molecular Engineering Peking University Beijing China Collaborative Innovation Center of Quantum Matter Peking University Beijing100871 China Ceramic Division Songshan Lake Lab Institute of Physics Chinese Academy of Sciences Guangdong China School of Physics Liaoning University Shenyang China Beijing National Laboratory for Molecular Sciences College of Chemistry and Molecular Engineering Peking University Beijing China Collaborative Innovation Center of Quantum Matter Peking University Beijing China

We report ab initio band diagram and optical absorption spectra of hexagonal boron nitride (h-BN), focusing on unravelling how the completeness of basis set for GW calculations and how electron-phonon interactions (EPIs) impact on them. The completeness of basis set, an issue which was seldom discussed in previous optical spectra calculations of h-BN, is found crucial in providing converged quasiparticle band gaps. In the comparison among three different codes, we demonstrate that by including high-energy local orbitals in the all-electron linearized augmented plane waves based GW calculations, the quasiparticle direct and fundamental indirect band gaps are widened by ∼0.2 eV, giving values of 6.81 eV and 6.25 eV respectively at the GW0 level. EPIs, on the other hand, reduce them to 6.62 eV and 6.03 eV respectively at 0 K, and 6.60 eV and 5.98 eV respectively at 300 K. With clamped crystal structure, the first peak of the absorption spectrum is at 6.07 eV, originating from the direct exciton contributed by electron transitions around K in the Brillouin zone. After including the EPIs-renormalized quasiparticles in the Bethe-Salpeter equation, the exciton-phonon coupling shifts the first peak to 5.83 eV at 300 K, lower than the experimental value of ∼6.00 eV. This accuracy is acceptable to an ab initio description of excited states with no fitting parameter. Copyright © 2020, The Authors. All rights reserved.

关键词： III-V semiconductors

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Extended Linear Mixing Model in an Ecosytem with High Spectral Variability

Extended Linear Mixing Model in an Ecosytem with High Spectr...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Edurne Ibarrola-Ulzurrun Lucas Drumetz Jocelyn Chanussot Consuelo Gonzalo-Martin Javier Marcello Department of Computer Architecture and Technology Universidad Politécnica de Madrid Boadilla del Monte Madrid Spain IMT Atlantique Lab-STICC Brest France CNRS GIPSA-Lab Grenoble France Universidad de Las Palmas de Gran Canaria Las Palmas de Gran Canaria Islas Canarias ES

Hyperspectral imagery (HSI) has become an important tool in ecosystem conservation due to its capability to perform accurate spectral unmixing, for vegetation mapping and ecosystem monitoring. An issue to be solved is the spectral variability of endmembers that can be induced by sensor noise and topographic changes. This spectral variability is considered by the Extended Linear Mixing Model (ELMM), which is applied to a mountainous ecosystem with high spectral variability and radiometric changes in each swath. The results obtained are very satisfactory, achieving reasonable abundance estimations and accurate characterization of the variability within the scene. ELMM allows studying the features of each pixel, including additional information about the characterization of the mixed pixels, in HSI by taking spectral variability into account. Moreover, it is observed that ELMM is robust to the absence of pure pixels as well as to noise.

关键词： Ecosystems Vegetation mapping Biological system modeling Hyperspectral imaging Radiometry Soil

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Visualisation of KPIs in zero emission neighbourhoods for improved stakeholder participation using Virtual Reality

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IOP Conference Series: Earth and Environmental Science 2019年第1期323卷

作者： A H Wiberg S Løvhaug M Mathisen B Tschoerner E Resch M Erdt E Prasolova-Førland The Research Centre for Zero Emission Neighbourhoods in Smart Cities (ZEN) Department of Architecture and Technology Faculty of Architecture and Design Norwegian University of Science and Technology Trondheim Norway Department of Computer Science Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim Norway Fraunhofer Singapore Nanyang Technical University 50 Nanyang Avenue Singapore 639798 VR/AR lab (IMTELlab) Department of Education and Lifelong Learning Faculty of Social and Educational Sciences Norwegian University of Science and Technology Trondheim Norway

This paper addresses the role of virtual reality in addressing the specific challenge of the increasing complexity and decreasing usability when dealing with the level of detail required to model a zero emission neighbourhood (ZEN).[1] In such neighbourhoods, there is a need to handle both 'top down' neighbourhood level data with 'bottom up' building and material level data. This can quickly become overwhelming particularly when dealing with non expert users such as planners, architects, researchers and citizens who play a key part in the design process of future ZENs. Visualisation is an invaluable means to communicate complex data in an interactive way that makes it easier for diverse stakeholders to engage in decision making early and throughout the design process. The main purpose of this work has been to make ZEN key performance indicators (KPIs) more easily comprehensible to a diverse set of stakeholders who need to be involved in the early design phase. The paper investigates how existing extended reality (XR) technologies, such as virtual reality, can be integrated with an existing dynamic LCA method in order to provide visualise feedback on KPIs in early phase design of sustainable neighbourhoods. This existing method provides a dynamic link between the REVIT Bim and the ZEB Tool using a Dynamo plugin.[2] The results presented in this paper demonstrate how virtual reality can help to improve stakeholder participation in the early design phase and more easily integrate science-based knowledge on GHG emissions and other KPIs into the further development of the user-centered architectural and urban ZEN toolbox for the design and planning, operation and monitoring of ZENs. [3]

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PHP: Power hungry pattern generation at higher abstraction level

PHP: Power hungry pattern generation at higher abstraction l...

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East-West Design & Test Symposium (EWDTS)

作者： Rohini Gulve Anshu Goel Virendra Singh Computer Architecture and Dependable Systems Lab Electrical Engineering Indian Institute of Technology Bombay India

The Performance, area, and power are most essential factors to be considered and optimize at every step in the design cycle. Design engineers often need to learn about these factors in order make right decisions on design strategies. Power analysis at lower levels of abstraction can provide more accurate analysis than higher levels. Worst case power can be estimated through high activity pattern generation. However, generation of such power hungry patterns (PHP) become challenging as the number of modules or design components increases. This process can be accelerated at higher abstraction levels by utilizing the available information. In this paper, we generate PHP by at higher abstraction level with significant speed up. A genetic algorithm is implemented to find out the global maximum power of designs. An experiment indicates that the process implemented is much faster and it finds about 10 % more power demanding PHP than random samples generated.

关键词： Mathematical model Genetic algorithms Integrated circuit modeling Logic gates Power demand Optimization Libraries

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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eQASM: An executable quantum instruction set architecture

arXiv

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arXiv 2018年

作者： Fu, X. Riesebos, L. Rol, M.A. van Straten, J. van Someren, J. Khammassi, N. Ashraf, I. Vermeulen, R.F.L. Newsum, V. Loh, K.K.L. de Sterke, J.C. Vlothuizen, W.J. Schouten, R.N. Almudever, C.G. DiCarlo, L. Bertels, K. QuTech Delft University of Technology P.O. Box 5046 Delft2600 GA Netherlands Quantum Computer Architecture Lab Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands Kavli Institute of Nanoscience Delft University of Technology P.O. Box 5046 Delft2600 GA Netherlands Computer Engineering Lab Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands P.O. Box 155 Delft2600 AD Netherlands Topic Embedded Systems B.V. P.O. Box 440 AK Best5680 Netherlands

A widely-used quantum programming paradigm comprises of both the data flow and control flow. Existing quantum hardware cannot well support the control flow, significantly limiting the range of quantum software executable on the hardware. By analyzing the constraints in the control microarchitecture, we found that existing quantum assembly languages are either too high-level or too restricted to support comprehensive flow control on the hardware. Also, as observed with the quantum microinstruction set QuMIS [1], the quantum instruction set architecture (QISA) design may suffer from limited scalability and flexibility because of microarchitectural constraints. It is an open challenge to design a scalable and flexible QISA which provides a comprehensive abstraction of the quantum hardware. In this paper, we propose an executable QISA, called eQASM, that can be translated from quantum assembly language (QASM), supports comprehensive quantum program flow control, and is executed on a quantum control microarchitecture. With efficient timing specification, single-operation-multiple-qubit execution, and a very-long-instruction-word architecture, eQASM presents better scalability than QuMIS. The definition of eQASM focuses on the assembly level to be expressive. Quantum operations are configured at compile time instead of being defined at QISA design time. We instantiate eQASM into a 32-bit instruction set targeting a seven-qubit superconducting quantum processor. We validate our design by performing several experiments on a two-qubit quantum processor. Copyright © 2018, The Authors. All rights reserved.

关键词： computer architecture

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EVALUATION OF HYPERSPECTRAL CLASSIFICATION MAPS IN HETEROGENEOUS ECOSYSTEM

EVALUATION OF HYPERSPECTRAL CLASSIFICATION MAPS IN HETEROGEN...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Edurne Ibarrola-Ulzurrun Javier Marcello Consuelo Gonzalo-Martin Jocelyn Chanussot Department of Computer Architecture and Technology Universidad Politécnica de Madrid Madrid Spain Instituto de Oceanografía y Cambio Global IOCAG Universidad de Las Palmas de Gran Canaria ULPGC Telde Las Palmas Spain Univ. Grenoble Alpes GIPSA-lab Grenoble France

Ecosystem management and monitoring are essential to preserve natural resources. Hyperspectral imagery (HSI) is a useful tool to obtain accurate classification maps, providing significant level of detail. Thus, traditional and novel methodologies based on pixel and object classification approaches are compared and evaluated in a homogeneous and mixed vulnerable ecosystem. Considering the challenging ecosystem, all classifications successfully resulted in high OA (higher than 82%), showing that HSI is very useful providing accurate vegetation maps to evaluate and monitor the ecosystems in a faster and economic way.

关键词： Support vector machines Hyperspectral imaging Ecosystems Image segmentation Vegetation mapping

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Taking the pulse of COVID-19: A spatiotemporal perspective

arXiv

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arXiv 2020年

作者： Yang, Chaowei Sha, Dexuan Liu, Qian Li, Yun Lan, Hai Guan, Weihe Wendy Hu, Tao Li, Zhenlong Zhang, Zhiran Thompson, John Hoot Wang, Zifu Wong, David Ruan, Shiyang Yu, Manzhu Richardson, Douglas Zhang, Luyao Hou, Ruizhi Zhou, You Zhong, Cheng Tian, Yifei Beaini, Fayez Carte, Kyla Flynn, Colin Liu, Wei Pfoser, Dieter Bao, Shuming Li, Mei Zhang, Haoyuan Liu, Chunbo Jiang, Jie Du, Shihong Zhao, Liang Lu, Mingyue Li, Lin Zhou, Huan NSF Spatiotemporal Innovation Center George Mason Univ. FairfaxVA22030 United States Department of Geography and GeoInformation Science George Mason Univ. FairfaxVA22030 United States Department of Geographical Sciences University of Maryland College ParkMD20742 United States Center for Geographic Analysis Harvard University CambridgeMA02138 United States Geoinformation and Big Data Research Lab Department of Geography University of South Carolina ColumbiaSC29208 United States Chinese Academy of Surveying and Mapping Beijing China Cloud Compute and Storage Operations Information Technology Services George Mason University FairfaxVA22030 United States Department of Geography Pennsylvania State University State CollegePA16801 United States School of Business Management East China Normal University Zhongshanbei Road Shanghai3663 China School of Mathematical Sciences East China Normal University Zhongshanbei Road Shanghai3663 China Department of Psychology George Mason University FairfaxVA22030 United States Department of Computer Science George Mason University FairfaxVA22030 United States Department of Information Technology George Mason University FairfaxVA22030 United States Department of Biology George Mason University FairfaxVA22030 United States College of Land Science and Technology China Agricultural University Beijing100083 China China Data Institute Ann ArborMI United States Institute of Remote Sensing and GIS Peking University Beijing100871 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and Architecture Beijing100044 China School of Geographical Sciences Nanjing University of Information Science & Technology 219 Ningliu Road Nanjing Jiangsu210044 China School of Resource and Environmental Sciences Wuhan University 129 Luoyu Rd Wuhan Hubei430079 China School of Geodesy a

The sudden outbreak of the Coronavirus disease (COVID-19) swept across the world in early 2020, triggering the lockdowns of several billion people across many countries, including China, Spain, India, the U.K., Italy, France, Germany, and most states of the U.S. The transmission of the virus accelerated rapidly with the most confirmed cases in the U.S., and New York City became an epicenter of the pandemic by the end of March. In response to this national and global emergency, the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implemented strategies to rapidly respond to this crisis, for supporting research, saving lives, and protecting the health of global citizens. This perspective paper presents our collective view on the global health emergency and our effort in collecting, analyzing, and sharing relevant data on global policy and government responses, geospatial indicators of the outbreak and evolving forecasts;in developing research capabilities and mitigation measures with global scientists, promoting collaborative research on outbreak dynamics, and reflecting on the dynamic responses from human societies. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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On the implementation of BRS codes in Ceph

On the implementation of BRS codes in Ceph

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IEEE International Conference on Big Data

作者： Jiyang Zhang Hanxu Hou Kedan Li Hui Li Shenzhen Key Lab of Information Theory & Future Internet Architecture Shenzhen Graduate School Pekine University Shenzhen China Dept. of Computer Science Dongguan University of Technology Dongguan China

Ceph is a reliable, scalable, unified distributed storage system, and recently has become one of OpenStack's standard open source storage solutions. For the reason of the low performance, Ceph Filesystem (Cephfs) cannot employ erasure codes directly, especially for binary Reed-Solomon (BRS) codes, whose size of the parity block is larger than that of the data block. To address these problems, we implemented efficient BRS codes, and filled the gap between BRS codes and Cephfs by a simple conversion. In addition to that, an efficient framework consisting of filesystem, cache tier and storage tier was adopted in Ceph to ensure that the file data is finally stored with the erasure coding technology. The experimental results show that such a design spends a small amount of additional cost but obtains much better effects.

关键词： Registers Encoding Hard disks Algorithm design and analysis Standards Big Data computer architecture

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