检索结果-内蒙古大学图书馆

Dual-Interlocked-Storage-Cell-Based Double-Node-Upset Self-Recoverable Flip-Flop Design for Safety-Critical Applications

学校读者我要写书评

暂无评论

Dual-Interlocked-Storage-Cell-Based Double-Node-Upset Self-R...

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Aibin Yan Zhelong Xu Jie Cui Zuobin Ying Zhengfeng Huang Huaguo Liang Patrick Girard Xiaoqing Wen School of Computer Science and Technology Anhui University / Anhui Engineering Laboratory of IoT Security Technologies Hefei China School of Electronic Science & Applied Physics Hefei University of Technology Hefei China Laboratory of Informatics Robotics and Microelectronics of Montpellier University of Montpellier / CNRS Montpellier France Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

This paper presents a novel dual-interlocked storage-cell (DICE)-based double-node-upset (DNU) self-recoverable, namely DURI-FF, in the nano-scale CMOS technology. The master latch of the DURI-FF cell consists of three transmission gates (TGs) and three interlocked DICEs with three common nodes. The common nodes are connected to TGs for value initialization. The slave latch of the DURI-FF cell comprises six TGs, six inverters and three interlocked DICEs. The outputs of the inverters respectively feed the internal nodes of the slave latch. The interlocked DICEs make the master latch and the slave latch DNU self-recoverable. Simulation results validate the DNU self-recoverability of the proposed DURI-FF cell. Moreover, compared with the state-of-the-art hardened flip-flop cells, the proposed DURI-FF cell achieves roughly 43% delay reduction at the cost of moderate silicon area and power dissipation.

关键词： Latches Inverters Flip-flops Logic gates CMOS technology Simulation Reliability

Erratum to:Influence of structural defects on charge density waves in 1T-TaS_(2)

学校读者我要写书评

暂无评论

Nano Research 2024年第3期17卷 2204-2204页

作者： Iaroslav Lutsyk Karol Szalowski Pawel Krukowski Pawel Dabrowski Maciej Rogala Witold Kozlowski Maxime Le Ster Michal Piskorski Dorota A.Kowalczyk Wojciech Rys Rafal Dunal Aleksandra Nadolska Klaudia Toczek Przemyslaw Przybysz Ewa Lacinska Johannes Binder Andrzej Wysmolek Natalia Olszowska Jacek J.Kolodziej Martin Gmitra Takuma Hattori Yuji Kuwahara Guang Bian Tai-Chang Chiang Pawel J.Kowalczyk Faculty of Physics and Applied Informatics University of LodzPomorska 149/15390-236 LodzPoland Faculty of Physics University of WarsawPasteura 502-093 WarsawPoland Faculty of Physics Astronomyand Applied Computer ScienceJagiellonian UniversityLojasiewicza 1130-348 KrakowPoland National Synchrotron Radiation Centre SOLARIS Jagiellonian UniversityCzerwone Maki 9830-392 KrakówPoland Institute of Physics Faculty of SciencePavol JozefŠafárik University in KošicePark Angelinum 904001 KošiceSlovakia Institute of Experimental Physics Slovak Academy of SciencesWatsonova 4704001 KošiceSlovakia Department of Precision Engineering Graduate School of EngineeringOsaka University2-1 Yamada-okaSuita 565-0871Japan Department of Physics and Astronomy University of MissouriColumbiaMissouri 65211USA Department of Physics and Frederick Seitz Materials Research Laboratory University of Illinois at Urbana-ChampaignUrbanaIllinois 61801-3080USA

The name of the second author in original paper was unfortunately *** should be“Karol Szalowski”,instead of“Karoli Szalowski”.

关键词： structural charge defects

IoT sensors in sea water environment: Ahoy! Experiences from a short summer trial

学校读者我要写书评

暂无评论

Electronic Notes in Theoretical computer Science 2019年 343卷 117-130页

作者： Chrysanthi Tziortzioti Dimitrios Amaxilatis Irene Mavrommati Ioannis Chatzigiannakis Hellenic Open University School of Applied Arts Patras Greece Computer Technology Institute and Press “Diophantus” & Computer Engineering and Informatics Department University of Patras Patras Greece Hellenic Open University School of Applied Arts & Computer Technology Institute and Press “Diophantus” Patras Greece Sapienza University of Rome Italy Computer Technology Institute and Press “Diophantus” Patras Greece

IoT sensors for measuring various sea water parameters, are explored here, aiming towards an educational context, in order to lead to a deeper understanding of the use of aquatic environments as natural resources, and towards the adoption of environmentally friendly behaviors. Sea-water sensing via IoT has not been extensively explored, due to practical difficulties in deployment, and the same applies to devising appropriate scenaria for understanding aquatic parameters in STEM education. A short hands-on IoT sensing trial, that has been conducted in various location of the Aegean sea, is reported in this paper. This research set out to gain insight into real data sets on which to base observations for devising realistic educational scenaria pertaining aquatic parameters. The results of this experiment are meant to guide research further, by shedding light into the IoT sensing issues that are involved in an educational scientific context. The goal is conducting broader research in the area of IoT water sensing towards its further utilization in STEM education.

关键词： IoT water sensors Science Technology engineering and Mathematics education (STEM) crowd sensing

CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Graham, Simon Vu, Quoc Dang Jahanifar, Mostafa Weigert, Martin Schmidt, Uwe Zhang, Wenhua Zhang, Jun Yang, Sen Xiang, Jinxi Wang, Xiyue Rumberger, Josef Lorenz Baumann, Elias Hirsch, Peter Liu, Lihao Hong, Chenyang Aviles-Rivero, Angelica I. Jain, Ayushi Ahn, Heeyoung Hong, Yiyu Azzuni, Hussam Xu, Min Yaqub, Mohammad Blache, Marie-Claire Piégu, Benoît Vernay, Bertrand Scherr, Tim Böhland, Moritz Löffler, Katharina Li, Jiachen Ying, Weiqin Wang, Chixin Snead, David Raza, Shan E. Ahmed Minhas, Fayyaz Rajpoot, Nasir M. Tissue Image Analytics Centre University of Warwick Coventry United Kingdom Histofy Ltd Birmingham United Kingdom Institute of Bioengineering School of Life Sciences Epfl Lausanne Switzerland Dresden Germany The Department of Computer Science The University of Hong Kong Hong Kong Tencent Ai Lab Shenzhen China College of Biomedical Engineering Sichuan University Chengdu China Department of Precision Instruments Tsinghua University Beijing China College of Computer Science Sichuan University Chengdu China Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association Berlin Germany Humboldt University of Berlin Faculty of Mathematics and Natural Sciences Berlin Germany Charité University Medicine Berlin Germany University of Bern Bern Switzerland Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Softsensor.ai BridgewaterNJ United States PRR.ai TX United States Department of R&d Center Arontier Co. Ltd Seoul Korea Republic of Computer Vision Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Cnrs Ifce Inrae Université de Tours Prc Nouzilly3780 France Institut de Génétique et de Biologie Moléculaire et Cellulaire Illkirch France Centre National de la Recherche Scientifique UMR7104 Illkirch France Institut National de la Santé et de la Recherche Médicale Inserm U1258 Illkirch France Université de Strasbourg Strasbourg France Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany School of Software Engineering South China University of Technology Guangzhou China Department of Pathology University Hospitals Coventry Warwickshire Nhs Trust Coventry United Kingdom Division of Biomedical Sciences Warwick Medical School University of Warwick Coventry United Kingdom

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge's improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery. © 2023, CC BY-NC-SA.

关键词： Grading

Unraveling somatotopic organization in the human brain using machine learning and adaptive supervoxel-based parcellations

学校读者我要写书评

暂无评论

NeuroImage 2021年 245卷 118710-118710页

作者： See, Kyle B. Arpin, David J. Vaillancourt, David E. Fang, Ruogu Coombes, Stephen A. J. Crayton Pruitt Family Department of Biomedical Engineering Smart Medical Informatics Learning and Evaluation Lab College of Engineering University of Florida PO Box 116131 Gainesville FL United States Laboratory for Rehabilitation Neuroscience Department of Applied Physiology and Kinesiology University of Florida PO Box 118206 Gainesville FL United States Center for Cognitive Aging and Memory McKnight Brain Institute University of Florida Gainesville United States Department of Electrical and Computer Engineering University of Florida Gainesville FL United States

In addition to the well-established somatotopy in the pre- and post-central gyrus, there is now strong evidence that somatotopic organization is evident across other regions in the sensorimotor network. This raises several experimental questions: To what extent is activity in the sensorimotor network effector-dependent and effector-independent? How important is the sensorimotor cortex when predicting the motor effector? Is there redundancy in the distributed somatotopically organized network such that removing one region has little impact on classification accuracy? To answer these questions, we developed a novel experimental approach. fMRI data were collected while human subjects performed a precisely controlled force generation task separately with their hand, foot, and mouth. We used a simple linear iterative clustering (SLIC) algorithm to segment whole-brain beta coefficient maps to build an adaptive brain parcellation and then classified effectors using extreme gradient boosting (XGBoost) based on parcellations at various spatial resolutions. This allowed us to understand how data-driven adaptive brain parcellation granularity altered classification accuracy. Results revealed effector-dependent activity in regions of the post-central gyrus, precentral gyrus, and paracentral lobule. SMA, regions of the inferior and superior parietal lobule, and cerebellum each contained effector-dependent and effector-independent representations. Machine learning analyses showed that increasing the spatial resolution of the data-driven model increased classification accuracy, which reached 94% with 1755 supervoxels. Our SLIC-based supervoxel parcellation outperformed classification analyses using established brain templates and random simulations. Occlusion experiments further demonstrated redundancy across the sensorimotor network when classifying effectors. Our observations extend our understanding of effector-dependent and effector-independent organization within the human br

关键词：

Quantum speedup for track reconstruction in particle accelerators

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Magano, Duarte Kumar, Akshat Kālis, Mārtiņš Locāns, Andris Glos, Adam Pratapsi, Sagar Quinta, Gonçalo Dimitrijevs, Maksims Rivošs, Aleksander Bargassa, Pedrame Seixas, João Ambainis, Andris Omar, Yasser Physics of Information and Quantum Technologies Group Instituto de Telecomunicações Portugal Instituto Superior Técnico Universidade de Lisboa Portugal Department of Mathematics Clarkson University United States Center for Quantum Computer Science Faculty of Computing University of Latvia Latvia Institute of Theoretical and Applied Informatics Polish Academy of Sciences Poland Portuguese Quantum Institute Portugal Laboratório de Instrumentação e Física Experimental de Partículas Portugal Center for Physics and Engineering of Advanced Materials Portugal

To investigate the fundamental nature of matter and its interactions, particles are accelerated to very high energies and collided inside detectors, producing a multitude of other particles that are scattered in all directions. As charged particles traverse the detector, they leave signals of their passage. The problem of track reconstruction is to recover the original trajectories from these signals. This challenging data analysis task will become even more demanding as the luminosity of future accelerators increases, leading to collision events with a more complex structure. We identify four fundamental routines present in every local tracking method and analyse how they scale in the context of a standard tracking algorithm. We show that for some of these routines we can reach a lower computational complexity with quantum search algorithms. Although the found quantum speedups are mild, this constitutes, to the best of our knowledge, the first rigorous evidence of a quantum advantage for a high-energy physics data processing task. Copyright © 2021, The Authors. All rights reserved.

关键词： Charged particles

REAL-TIME GEOINFORMATION SYSTEMS TO IMPROVE THE QUALITY, SCALABILITY, AND COST OF INTERNET OF THINGS FOR AGRI-ENVIRONMENT RESEARCH

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Runck, Bryan C. Schulz, Bobby Bishop, Jeff Carlson, Nathan Chantigian, Bryan Deters, Gary Erdmann, Jesse Ewing, Patrick M. Felzan, Michael Fu, Xiao Greyling, Jan Hogan, Christopher J. Hollman, Andrew Joglekar, Ali Junker, Kris Kantar, Michael Kaunda, Lumbani Krishna, Mohana Lynch, Benjamin Marchetto, Peter Marsolek, Megan McKay, Troy Morris, Brad Niaghi, Ali Rashid Pamulaparthy, Keerthi Pardey, Philip Piotrowski, Ann Poudyal, Christina Prather, Tom Raghavan, Barath Reiter, Maggie Rosen, Lucas Salazar, Benjamin Scobbie, Andrew Sharma, Vasudha Silverstein, Kevin A.T. Singh, Gurparteet Strock, Jeff Subedi, Samikshya Tang, Evan Turturillo, Gianna Watkins, Eric Webster, Blake Wilgenbusch, James GEMS Informatics Center University of Minnesota Twin Cities United States Department of Geography Environment and Society University of Minnesota Twin Cities United States Minnesota Supercomputing Institute University of Minnesota United States Department of Electrical Engineering University of Minnesota Twin Cities United States Department of Applied Economics University of Minnesota Twin Cities United States Department of Horticultural Science University of Minnesota Twin Cities United States Department of Computer Science University of Southern California United States Sensing Inc. Conservify Graphspan LLC USDA-ARS Food Systems Research Unit BurlingtonVT05405 United States Amazon United States Department of Soil Water and Climate University of Minnesota Twin Cities United States Department of Computer Science and Engineering University of Minnesota Twin Cities United States Departmnet of Agricultural Education Communication and Marketing University of Minnesota Twin Cities United States Benson Hill United States Southwest Research and Outreach Center University of Minnesota Twin Cities United States Code Weavers Department of Mechanical Engineering University of Minnesota Twin Cities United States University of Hawaii at Manoa HonoluluHI United States Minnesota Agricultural Experiment Station University of Minnesota Twin Cities United States Sunday Stellenbosch AgroInformatics Initiative Stellenbosch University South Africa Ameriprise Financial Services LLC Optum University of Minnesota Twin Cities United States

With the increasing emphasis on machine learning and artificial intelligence to drive knowledge discovery in the agricultural sciences, spatial internet of things (IoT) technologies have become increasingly important for collecting real-time, high resolution data for these models. However, managing large fleets of devices while maintaining high data quality remains an ongoing challenge as scientists iterate from prototype to mature end-to-end applications. Here, we provide a set of case studies using the framework of technology readiness levels for an open source spatial IoT system. The spatial IoT systems underwent 3 major and 14 minor system versions, had over 2,727 devices manufactured both in academic and commercial contexts, and are either in active or planned deployment across four continents. Our results show the evolution of a generalizable, open source spatial IoT system designed for agricultural scientists, and provide a model for academic researchers to overcome the challenges that exist in going from one-off prototypes to thousands of internet-connected devices. © 2024, CC BY-NC-ND.

关键词： Internet of things

SELFIES and the future of molecular string representations

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Krenn, Mario Ai, Qianxiang Barthel, Senja Carson, Nessa Frei, Angelo Frey, Nathan C. Friederich, Pascal Gaudin, Théophile Gayle, Alberto Alexander Jablonka, Kevin Maik Lameiro, Rafael F. Lemm, Dominik Lo, Alston Moosavi, Seyed Mohamad Nápoles-Duarte, José Manuel Nigam, AkshatKumar Pollice, Robert Rajan, Kohulan Schatzschneider, Ulrich Schwaller, Philippe Skreta, Marta Smit, Berend Strieth-Kalthoff, Felix Sun, Chong Tom, Gary von Rudorff, Guido Falk Wang, Andrew White, Andrew Young, Adamo Yu, Rose Aspuru-Guzik, Alán Erlangen Germany Department of Chemistry Fordham University The Bronx New York United States Department of Mathematics Vrije Universiteit Amsterdam Amsterdam Netherlands Syngenta Jealott’s Hill International Research Centre Bracknell Berkshire United Kingdom Department of Chemistry Imperial College London Molecular Sciences Research Hub White City Campus Wood Lane London United Kingdom Massachusetts Institute of Technology CambridgeMA United States Institute of Theoretical Informatics Karlsruhe Institute of Technology Karlsruhe Germany Institute of Nanotechnology Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany Department of Computer Science University of Toronto Canada IBM Research Europe Zürich Switzerland Unaffiliated Sapporo Japan Sion Valais Switzerland Medicinal and Biological Chemistry Group São Carlos Institute of Chemistry University of São Paulo São Paulo Brazil Faculty of Physics University of Vienna Vienna Austria Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany Facultad de Ciencias Químicas Universidad Autónoma de Chihuahua Chihuahua Mexico Department of Computer Science Stanford University CA United States Chemical Physics Theory Group Department of Chemistry University of Toronto Canada Institute for Inorganic and Analytical Chemistry Friedrich-Schiller Universität Jena Jena Germany Institut für Anorganische Chemie Julius-Maximilians-Universität Würzburg Würzburg Germany Lausanne Switzerland Lausanne Switzerland Vector Institute for Artificial Intelligence Toronto Canada Solar Fuels Group Department of Chemistry University of Toronto Canada Department of Chemical Engineering University of Rochester United States Department of Computer Science and Engineering University of California San Diego United States Department of Chemical Engineering and Applied Chemistry University of Toronto Canada Department of Materials Science University of Toronto Canada Toronto

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, SMILES, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, SMILES has several shortcomings – most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete Future Projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science. © 2022, CC BY.

关键词： Molecules

Corrigendum to “Szeged and Mostar root-indices of graphs”

学校读者我要写书评

暂无评论

applied Mathematics and Computation 2025年 499卷

作者： Simon Brezovnik Matthias Dehmer Niko Tratnik Petra Žigert Pleteršek University of Ljubljana Faculty of Mechanical Engineering Slovenia Institute of Mathematics Physics and Mechanics Ljubljana Slovenia Swiss Distance University of Applied Sciences Brig Switzerland Department for Biomedical Computer Science and Mechatronics UMIT-Private University for Health Sciences Medical Informatics and Technology Eduard-Wallnfer-Zentrum 1 6060 Hall in Tyrol Tyrol Austria College of Artificial Intelligence Nankai University Tianjin China School of Science Xian Technological University Xian Shaanxi 710021 China University of Maribor Faculty of Natural Sciences and Mathematics Slovenia University of Maribor Faculty of Chemistry and Chemical Engineering Slovenia