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arXiv 2021年

作者： Tahir, Muhammad N. Li, Jia Zhu, Tong Shang, Honghui Blum, Volker Ren, Xinguo Guangdong Provincial Key Laboratory of Thermal Management Engineering and Materials Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Department of Chemistry Duke University DurhamNC27708 United States State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China

We develop and implement a formalism which enables calculating the analytical gradients of particle-hole random-phase approximation (RPA) ground-state energy with respect to the atomic positions within the atomic orbital basis set framework. Our approach is based on a localized resolution of identity (LRI) approximation for evaluating the two-electron Coulomb integrals and their derivatives, and the density functional perturbation theory for computing the first-order derivatives of the Kohn-Sham (KS) orbitals and orbital energies. Our implementation allows one to relax molecular structures at the RPA level using both Gaussian-type orbitals (GTOs) and numerical atomic orbitals (NAOs). Benchmark calculations show that our approach delivers high numerical precision compared to previous implementations. A careful assessment of the quality of RPA geometries for small molecules reveals that post-KS RPA systematically overestimates the bond lengths. We furthermore optimized the geometries of the four low-lying water hexamers – cage, prism, cyclic and book isomers, and determined the energy hierarchy of these four isomers using RPA. The obtained RPA energy ordering is in good agreement with that yielded by the coupled cluster method with single, double and perturbative triple excitations, despite that the dissociation energies themselves are appreciably underestimated. The underestimation of the dissociation energies by RPA is well corrected by the renormalized single excitation correction. © 2021, CC BY.

关键词： Approximation algorithms

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Preface

Smart Innovation, Systems and Technologies

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Smart Innovation, Systems and Technologies 2021年 197卷 vii-viii页

作者： Mealha, Óscar Rehm, Matthias Rebedea, Traian Department of Communication and Art Digital Media and Interaction Research Center University of Aveiro Aveiro Portugal Department of Computer Science University Politehnica of Bucharest Bucharest Romania Department of Architecture Design and Media Technology Aalborg University Aalborg Denmark

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Transformer-Based Deep Learning for Mesoscale Eddy Detection in Sea Surface Temperature Maps

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Ji, Chen Xu, Wenyang Zheng, Xiangtian Ahmed, Yasmeen Jamal, Saad Ahmed Imam, Fakhar Ali, Mohammed Saleh Muthanna, Maha Ibrahim Ullah, Sajid Kucher, Dmitry E. Nanjing University School of Geography and Ocean Science Nanjing210046 China Nanjing Institute of Technology Nanjing211167 China Mississippi State University Department of Building Construction Science College of Architecture Art & Design MS39762 United States Paris Lodron Universitat Salzburg Institute for Research and Advanced Studies Universidade de Evora Department of Computer Science University of South Brittany Department of Geoinformatics PortugalZ_GIS France Austria University of the Punjab Lahore Pakistan Tashkent State University of Economics Department of International Business Management Tashkent Uzbekistan Nangarhar University Department of Water Resources and Environmental Engineering Nangarhar 2600 Afghanistan RUDN University Department of Environmental Management Institute of Environmental Engineering Moscow117198 Russia

Mesoscale eddies are dynamic oceanic phenomena significantly influencing marine ecosystems' energy transfer, nutrients, and biogeochemical cycles. These eddies' precise identification and categorization can improve climate modeling, ocean circulation research, and environmental surveillance. This study presents an innovative methodology for mesoscale eddy detection utilizing Transformer-based deep learning models, namely Swin Transformer U-Net (Swin-Unet) and SegFormer, to categorize ocean eddies from Sea Surface Temperature (SST) maps sourced from the Copernicus Marine Environment Monitoring Service (CMEMS). In contrast to traditional convolutional neural networks (CNNs) that have prevailed in the domain, Transformer-based models provide superior global attention mechanisms, facilitating greater feature extraction and segmentation precision. The models are trained on labeled SST datasets and assessed using Intersection over Union (IoU), Dice coefficient, precision, recall, and F1-score. Experimental findings demonstrate that Transformer-based designs surpass conventional CNN-based techniques, yielding enhanced generalization and superior accuracy in classifying cyclonic and anticyclonic eddies. This study illustrates the efficacy of attention based segmentation algorithms for resilient oceanographic applications. © 2008-2012 IEEE.

关键词： Semantic Segmentation

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Monitoring System for Prediction and Detection of Epilepsy Seizure

Monitoring System for Prediction and Detection of Epilepsy S...

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International Conference on Advances in Computational Tools for Engineering Applications, ACTEA

作者： Nashaat el Halabi Roy Abi Zeid Daou Roger Achkar Ali Hayek Josef Börcsök Department of Computer and Communications Engineering American University of Science and Technology Beirut Lebanon MART Learning Education and Research Center Chananir Lebanon Department of Computer Architecture and System Programming University of Kassel Germany

Epilepsy is a neurological disorder associated with abnormal electrical activity in the brain, which causes seizures. The occurrence of seizure is not predictable; the duration between seizures, as well as the symptoms, varies from patient to another. Since the seizures are not predictable, and most of epileptic patients suffer from physical risky symptoms during the seizure, such patients are not able to perform daily work activities. The objective of this project is to design and implement a monitoring system for epileptic patients; the system should continuously check some vital signs, analyze the measurements, and decide whether the patient is nearly to have a seizure or not. Whenever a seizure is predicted, the system initiates an alarm. In addition, a notification should be sent to the health care responsible, as well as one preferred contact. By implementing the monitoring system, people who suffer from epilepsy will have more chance to work and live a normal life. Thus, this paper presents the concept of the overall system and shows results of the implemented systems: EEG, ECG and Fall Detection system. Results have shown that the fall detection accuracy reached 99.89% whereas the accuracy of the prediction using the ANN was about 97.34%.

关键词： Electroencephalography Epilepsy Feature extraction Tools Monitoring Artificial neural networks Support vector machines

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Harnessing artificial intelligence to fill global shortfalls in biodiversity knowledge

Nature Reviews Biodiversity

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Nature Reviews Biodiversity 2025年第3期1卷 166-182页

作者： Laura J. Pollock David Rolnick Justin Kitzes Sara Beery Kaitlyn M. Gaynor Marta A. Jarzyna Tanya Berger-Wolf Oisin Mac Aodha Bernd Meyer Graham W. Taylor Devis Tuia Department of Biology McGill University Montréal Quebec Canada Mila—Quebec AI Institute Montréal Quebec Canada School of Computer Science McGill University Montréal Quebec Canada Department of Biological Sciences University of Pittsburgh Pittsburgh PA USA Faculty of AI and Decision Making Massachusetts Institute of Technology Cambridge MA USA Department of Botany University of British Columbia Vancouver British Columbia Canada Department of Zoology University of British Columbia Vancouver British Columbia Canada Department of Evolution Ecology and Organismal Biology The Ohio State University Columbus OH USA Translational Data Analytics Institute The Ohio State University Columbus OH USA Department of Computer Science and Engineering The Ohio State University Columbus OH USA Department of Electrical & Computer Engineering The Ohio State University Columbus OH USA School of Informatics University of Edinburgh Edinburgh UK Department of Data Science and AI Monash University Clayton Victoria Australia School of Engineering University of Guelph Guelph Ontario Canada Vector Institute Toronto Ontario Canada School of Architecture Civil and Environmental Engineering Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland

Large, well described gaps exist in both what we know and what we need to know to address the biodiversity crisis. Artificial intelligence (AI) offers new potential for filling these knowledge gaps, but where the biggest and most influential gains could be made remains unclear. To date, biodiversity-related uses of AI have largely focused on tracking and monitoring of wildlife populations. Rapid progress is being made in the use of AI to build phylogenetic trees and species distribution models. However, AI also has considerable unrealized potential in the re-evaluation of important ecological questions, especially those that require the integration of disparate and inherently complex data types, such as images, video, text, audio and DNA. This Review describes the current and potential future use of AI to address seven clearly defined shortfalls in biodiversity knowledge. Recommended steps for AI-based improvements include the re-use of existing image data and the development of novel paradigms, including the collaborative generation of new testable hypotheses. The resulting expansion of biodiversity knowledge could lead to science spanning from genes to ecosystems — advances that might represent our best hope for meeting the rapidly approaching 2030 targets of the Global Biodiversity Framework.

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When Deep Learning Meets the Edge: Auto-Masking Deep Neural Networks for Efficient Machine Learning on Edge Devices

When Deep Learning Meets the Edge: Auto-Masking Deep Neural ...

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IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Ning Lin Hang Lu Xing Hu Jingliang Gao Mingzhe Zhang Xiaowei Li State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Department of Electrical and Computer Engineering University of California Santa Barbara

Deep neural network (DNN) has demonstrated promising performance in various machine learning tasks. Due to the privacy issue and the unpredictable transmission latency, inferring DNN models directly on edge devices trends the development of intelligent systems, like self-driving cars, smart Internet-of-Things (IoTs) and autonomous robotics. The on-device DNN model is obtained by expensive training via vast volumes of high-quality training data in the cloud datacenter, and then deployed into these devices, expecting it to work effectively at the edge. However, edge device always deals with low-quality images caused by compression or environmental noise pollutions. The well-trained model, though could work perfectly on the cloud, cannot adapt to these edge-specific conditions without remarkable accuracy drop. In this paper, we propose an automated strategy, called "AutoMask", to embrace effective machine learning and accelerate DNN inference on edge devices. AutoMask comprises end-to-end trainable software strategies and cost-effective hardware accelerator architecture to improve the adaptability of the device without compromising the constrained computation and storage resources. Extensive experiments, over ImageNet dataset and various state-of-the-art DNNs, show that AutoMask achieves significant inference acceleration and storage reduction while maintains comparable accuracy level on embedded Xilinx Z7020 FPGA, as well as NVIDIA Jetson TX2.

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Practical Stabilization of Passive Nonlinear Systems with Limited Control

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IFAC-PapersOnLine 2019年第16期52卷 460-465页

作者： B. Jayawardhana M.Z. Almuzakki A. Tanwani Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen The Netherlands Department of Computer Science Faculty of Science and Computer Universitas Pertamina Jakarta Indonesia Laboratory for Analyses and Architecture of Systems (LAAS) –CNRS Université de Toulouse France

This article addresses output feedback stabilization of continuous-time nonlinear systems by choosing control actions from a finite set. Working under the assumption that the system under consideration is passive and large-time norm observable, we propose a static feedback mapping, from the output space to the finite set of control actions, which is shown to be practically stabilizing if the convex hull of certain control actions (in the chosen finite set) contains the origin in its interior. Consequently, to construct this stabilizing feedback, it suffices to have, in addition to a zero symbol, another m + 1 elements in the control set which form an m -simplex in ℝ m (the input, and output space).

关键词： Nonlinear passive systems finite control set output feedback binary control

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A Non-Stop Double Buffering Mechanism for Dataflow architecture

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Journal of computer Science & technology 2018年第1期33卷 145-157页

作者： Xu Tan Xiao-Wei Shen Xiao-Chun Ye Da Wang Dong-Rui Fan Lunkai Zhang Wen-Ming Li Zhi-Min Zhang Zhi-Min Tang State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100049 China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125 China Department of Computer Science The University of Chicago Chicago IL 60637 U.S.A.

Double buffering is an effective mechanism to hide the latency of data transfers between on-chip and off-chip memory. However, in dataflow architecture, the swapping of two buffers during the execution of many tiles decreases the performance because of repetitive filling and draining of the dataflow accelerator. In this work, we propose a non-stop double buffering mechanism for dataflow architecture. The proposed non-stop mechanism assigns tiles to the processing element array without stopping the execution of processing elements through optimizing control logic in dataflow architecture. Moreover, we propose a work-flow program to cooperate with the non-stop double buffering mechanism. After optimizations both on control logic and on work-flow program, the filling and draining of the array needs to be done only once across the execution of all tiles belonging to the same dataflow graph. Experimental results show that the proposed double buffering mechanism for dataftow architecture achieves a 16.2% average efficiency improvement over that without the optimization.

关键词： non-stop double buffering dataflow architecture high-performance computing

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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 2023年第6期20卷 824-835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

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Energy-consumption prediction of genetic programming algorithms using a fuzzy rule-based system 18

Energy-consumption prediction of genetic programming algorit...

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2018 Genetic and Evolutionary Computation Conference, GECCO 2018

作者： Chávez, F. Fdez De Vega, F. Díaz, J. García, J.A. Rodriguez, F.J. Castillo, P.A. Centro Universitario de Mérida Universidad de Extremadura Spain Department of Architecture and Computer Technology University of Granada Spain

ISBN: (纸本)9781450357647

Energy awareness has gained momentum over the last decade in the software industry, as well as in environmentally concious society. Thus, algorithm designers and programmers are paying increasing attention this issue, particularly when handheld devices are considered, given their battery-consuming characteristics. When we focus on Evolutionary Algorithms, few works have attempted to study the relationship between the main features of the algorithm, the problem to be solved and the underlying hardware where it runs. This work presents a preliminary analysis and modeling of energy consumption of EAs. We try to predict it by means of a fuzzy rule-based system, so that different devices are considered as well as a number of problems and Genetic Programming parameters. Experimental results performed show that the proposed model can predict energy consumption with very low error values. © 2018 Copyright held by the owner/author(s).

关键词： Genetic programming

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