检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Cheng, Yuan Cao, Xu-Bing Liu, Jiu-Meng Yu, Qin-Qin Zhang, Qiang He, Ke-Bin State Key Laboratory of Urban Water Resource and Environment School of Environment Harbin Institute of Technology Harbin150090 China Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System Science Tsinghua University Beijing100084 China State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Tsinghua University Beijing100084 China

Brown carbon (BrC) represents not only a major component of haze pollution but also a non-negligible contributor to positive radiative forcing, making it a key species for coordinating air quality and climate policies. In China, field observations on BrC remain limited given the highly variable emission sources and meteorological conditions across different regions. Here we focused on the optical properties of BrC in a distinct but largely unexplored megacity in Northeast China, which is within a major agricultural region and experiences extremely cold winter. Agricultural fires were evident in April of 2021 and the fall of 2020, despite a toughest-ever policy on open burning. Such emissions enhanced BrC's mass absorption efficiency at 365 nm (MAE365), more efficiently by the fall fires which were inferred to have relatively high combustion efficiencies (CE). After taking CE into consideration, the relationships between MAE365 and the levoglucosan to organic carbon ratio (a measure of the significance of agricultural fire influence) roughly converged for the fire episodes of different seasons, including those identified in February and March of 2019 by a previous campaign. Agricultural fires also influenced the determination of absorption Ångström exponent (AAE), by resulting in non-linearity for BrC's absorption spectra shown on ln-ln scale. Based on three indicators developed by this study, the non-linearity was inferred to be caused by similar chromophores although the fires were characterized by various CE levels in different seasons. In addition, for the samples without significant influence of open burning, coal combustion emissions were identified as the dominant influencing factor for MAE365, whereas none solid link was found between the solution-based AAE and aerosol source. © 2023, The Authors. All rights reserved.

关键词： Light absorption

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Deep Modular Co-Attention networks for visual question answering

arXiv

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arXiv 2019年

作者： Yu, Zhou Yu, Jun Cui, Yuhao Tao, Dacheng Tian, Qi Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University UBTECH Sydney AI Centre School of Computer Science FEIT University of Sydney Noah's Ark Lab Huawei

Visual Question Answering (VQA) requires a finegrained and simultaneous understanding of both the visual content of images and the textual content of questions. Therefore, designing an effective 'co-attention' model to associate key words in questions with key objects in images is central to VQA performance. So far, most successful attempts at co-attention learning have been achieved by using shallow models, and deep co-attention models show little improvement over their shallow counterparts. In this paper, we propose a deep Modular Co-Attention Network (MCAN) that consists of Modular Co-Attention (MCA) layers cascaded in depth. Each MCA layer models the self-attention of questions and images, as well as the guided-attention of images jointly using a modular composition of two basic attention units. We quantitatively and qualitatively evaluate MCAN on the benchmark VQA-v2 dataset and conduct extensive ablation studies to explore the reasons behind MCAN's effectiveness. Experimental results demonstrate that MCAN significantly outperforms the previous state-ofthe- A rt. Our best single model delivers 70.63% overall accuracy on the test-dev set. Code is available at http://***/MILVLG/mcan-vqa. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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A Synchronous Parallel Method with Parameters Communication Prediction for Distributed Machine Learning 19th

A Synchronous Parallel Method with Parameters Communication...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Zeng, Yanguo Xue, Meiting Xu, Peiran Shi, Yukun Zeng, Kaisheng Zhang, Jilin Yue, Lupeng School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China National University of Defense Technology Changsha China School of Cyberspace HangZhou Dianzi University Hangzhou China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9783031545306

With the development of machine learning technology in various fields, such as medical care, smart manufacturing, etc., the data has exploded. It is a challenge to train a deep learning model for different application domains with large-scale data and limited resources of a single device. The distributed machine-learning technology, which uses a parameter server and multiple clients to train a model collaboratively, is an excellent method to solve this problem. However, it needs much communication between different devices with limited communication resources. The stale synchronous parallel method is a mainstream communication method to solve this problem, but it always leads to high synchronization delay and low computing efficiency as the inappropriate delay threshold value set by the user based on experience. This paper proposes a synchronous parallel method with parameters communication prediction for distributed machine learning. It predicts the optimal timing for synchronization, which can solve the problem of long synchronization waiting time caused by the inappropriate threshold settings in the stale synchronous parallel method. Moreover, it allows fast nodes to continue local training while performing global synchronization, which can improve the resource utilization of work nodes. Experimental results show that compared with the delayed synchronous parallel method, the training time and quality, and resource usage of our method are both significantly improved. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Forecasting

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MobileEdge: Enhancing On-Board Vehicle Computing Units Using Mobile Edges for CAVs

MobileEdge: Enhancing On-Board Vehicle Computing Units Using...

引用

International Conference on Parallel and Distributed systems (ICPADS)

作者： Lin Wang Qingyang Zhang Youhuizi Li Hong Zhong Weisong Shi School of Computer Science and Technology Anhui University Hefei China Key Laboratory of Complex Systems Modeling and Simulation Hangzhou Dianzi University Hangzhou China Department of Computer Science Wayne State University Detroit MI USA

ISBN: (纸本)9781728125848

As the rapid growth of connected and autonomous vehicles (CAVs) and 5G intensifies, more third-party applications are increasingly being deployed on CAVs. They not only improve user experience but also provide more helpful services, for example, enhancing public safety by recognizing criminals in real-time videos. Current CAVs prefer to process collected data on the vehicle to avoid long transmission latency and extra network cost. However, due to the limitations of the on-board vehicle computing unit (VCU) and increasing use of computing-intensive in-vehicle applications, the burden of on-board VCU has sharply increased, which may affect driving safety. In particular, for existing vehicles on the road, adding more computing devices is a challenge if not impossible due to cost concerns. Inspired by edge computing, we propose a novel platform, MobileEdge, to enhance the computing capability of the unchangeable on-board VCU, which leverages mobile devices as edge nodes, e.g., the passengers' smartphones, by offloading computing tasks to them for collaboratively computing. Moreover, MobileEdge provides the dynamic management of mobile devices, monitoring device status and interfaces for customizable task offloading strategies and eventually achieves optimal task scheduling. We build a prototype to demonstrate the designed platform and evaluate three task offloading strategies which were implemented based on the developed interfaces. The results show that MobileEdge significantly reduces the application response latency. Compared with the baseline which does not employ task offloading, the response latency is almost near real-time when more computing resources are available. In addition, the proposed shortest response latency strategy outperforms the best overall task scheduling among the three strategies.

关键词： Edge computing Vehicular data analysis Distributed computing Connected and autonomous vehicles Distributed computing TASK SCHEDULING Response Latency UNLOADING vehicle vinyl chloride-urethane copolymer Mobile devices unchangeable quasi-realtime language Public safety

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Insights of carbon reduction practices from Winter Olympics 2022

引用

Science Bulletin 2024年第13期69卷 2034-2037页

作者： Xiaoyong Liao Shuai Zhong Jie Huang Zhu Liu Ayman Elshkaki You Li Lei Shen Yingli He Li An Yidong Zhu Wenhao Sun Tao Liang Jiaoe Wang Jinwei Dong Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Key Laboratory for Resources Use and Environmental Remediation Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Key Laboratory of Regional Sustainable Development Modeling Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China Department of Earth System Science Tsinghua UniversityBeijing 100084China

Concerns about the carbon emissions of mega-events revolve,mainly due to the impactful demonstration effects they ***,accurate monitoring of these emissions is crucial during such events[1].The Olympic and Paralympic Winter Games Beijing 2022(hereinafter“Winter Olympics 2022”)are recognized as the first Games to pay close attention to a wide range of emissions associated with all stages of the *** of emerging carbon reduction technologies,such as low-carbon materials and technologies,have shown promising effects on efforts to achieve carbon neutrality[2].The impacts of individual measures,such as Olympic infrastructure construction.

关键词： Winter emissions carbon

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A Model Based Heterogeneous Data Collaboration Method

A Model Based Heterogeneous Data Collaboration Method

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IEEE International Conference on Electronics and Communication Engineering (ICECE)

作者： Junjie Xue Junhua Zhou Guoqiang Shi Yanhong Jiang Bowen Wei Hongyan Quan Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing Simulation Center Beijing China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing China Beijing Simulation Center Science and Technology on Space System Simulation Laboratory Beijing China School of Software Engineering East China Normal University Shanghai China School of Computer Science and Technology East China Normal University Shanghai China

ISBN: (数字)9781728192581

ISBN: (纸本)9781728192598

Model based heterogeneous data collaboration is important in the integrated design and manufacturing technology. In this work, we propose a systematic method of collaborative design method for heterogeneous data sources. In our method, we use the Open Model Based Engineering Environment (OpenMBEE) to study. In our work, we setup environment and construct the integrated multi-tools achieving consistency design, and then we successfully design and implement a reasonable scheme to parameterize the model in requirement analysis and store the extracted parameters in Model Management system (MMS) for transporting to the visualization tool for further simulation. We integrate the mathematical analysis tools of MATLAB, and successfully design the interface in the tool integration, and realize the model consistency in the problem of integrating multiple tools in the design based on model center. The effectiveness of the proposed integration strategies is verified by some practical instances. That is verified that our provided study can an effective design idea for complex production.

关键词： Analytical models Collaboration Production Tools Data models Mathematical model Matlab

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A Comprehensive Evaluation Framework for Deep Model Robustness

arXiv

引用

arXiv 2021年

作者： Guo, Jun Bao, Wei Wang, Jiakai Ma, Yuqing Gao, Xinghai Xiao, Gang Liu, Aishan Dong, Jian Liu, Xianglong Wu, Wenjun State Key Lab of Software Development Environment Beihang University Beijing China China Electronics Standardization Institute Beijing China Zhongguancun Laboratory Beijing China Institute of Unmanned System Beihang University Beijing China National Key Laboratory for Complex Systems Simulation Beijing China

Deep neural networks (DNNs) have achieved remarkable performance across a wide range of applications, while they are vulnerable to adversarial examples, which motivates the evaluation and benchmark of model robustness. However, current evaluations usually use simple metrics to study the performance of defenses, which are far from understanding the limitation and weaknesses of these defense methods. Thus, most proposed defenses are quickly shown to be attacked successfully, which results in the "arm race" phenomenon between attack and defense. To mitigate this problem, we establish a model robustness evaluation framework containing 23 comprehensive and rigorous metrics, which consider two key perspectives of adversarial learning (i.e., data and model). Through neuron coverage and data imperceptibility, we use data-oriented metrics to measure the integrity of test examples;by delving into model structure and behavior, we exploit model-oriented metrics to further evaluate robustness in the adversarial setting. To fully demonstrate the effectiveness of our framework, we conduct large-scale experiments on multiple datasets including CIFAR-10, SVHN, and Im-ageNet using different models and defenses with our open-source platform. Overall, our paper provides a comprehensive evaluation framework, where re-searchers could conduct comprehensive and fast evaluations using the open-source toolkit, and the analytical results could inspire deeper understanding and further improvement to the model robustness. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm 29

An Auto-Parallel Method for Deep Learning Models Based on Ge...

引用

29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Zeng, Yan Huang, Chengchuang Ni, Yijie Zhou, Chunbao Zhang, Jilin Wang, Jue Zhou, Mingyao Xue, Meiting Zhang, Yunquan Hangzhou Dianzi University School of Computer Science and Technology Hangzhou310018 China Ministry of Education Key Laboratory for Modeling and Simulation of Complex Systems Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Hangzhou Dianzi University School of ITMO Joint Institute Hangzhou310018 China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing100086 China HuaWei China Institute of Computing Technology of the Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing100086 China

ISBN: (纸本)9798350330717

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network's dataflow graph and device cluster's topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method. © 2023 IEEE.

关键词： Auto-Parallel Distributed machine learning Genetic algorithm Integer linear programming Model parallelism

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A system-of-systems architecture-driven modeling method for combat system effectiveness simulation

A system-of-systems architecture-driven modeling method for ...

引用

IEEE International Symposium on systems Engineering (ISSE)

作者： Xiaobo Li Weiping Wang Zhe Shu Ning Zhu Hua He Tianjun Liao Beijing Institute of System Engineering Beijing CN College of Information Systems and Management National University of Defense Technology Changsha Hunan P. R. China State Key Laboratory of Complex System Simulation Beijing Institute of Systems engineering Beijing China

ISBN: (纸本)9781509007943

Under the background of system-of-systems (SoS) counterwork, it is more apparent that the simulation modeling of the equipment should be domain-specific, formal, automatic and composable. Current equipment effectiveness simulation modeling approaches are mainly based on the methods and techniques from the modeling and simulation (M&S) community and software engineering community, and they are not capable to satisfy these four requirements systematically. Thus the systems engineering architecture model and domain knowledge should be used to support effectiveness simulation modeling more effectively. In this research, we propose an architecture-driven modeling method which conforms to the principle of architecture-driven development, uses ontology techniques to build equipment SoS architecture model and sub-domain ontology. This method performs architecture driven simulation modeling to realize the transformation from architecture models to simulation model frameworks; employs ontological metamodeling to design domain-specific modeling languages (DSML) based on the comprehensive usage of architecture models, sub-domain ontologies and M&S formalisms; integrates domain-specific simulation models from various domains using the model framework, and supports the composable development of simulation applications.

关键词： Computational modeling Ontologies Computer architecture Analytical models Mathematical model

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Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question Answering

arXiv

引用

arXiv 2023年

作者： Yu, Zhou Ouyang, Xuecheng Shao, Zhenwei Wang, Meng Yu, Jun The Key Laboratory of Complex Systems Modeling and Simulation the School of Computer Science Hangzhou Dianzi University China The HDU-ITMO Joint Institute Hangzhou Dianzi University China The School of Computer Science and Information Engineering Hefei University of Technology China

Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. Early studies retrieve required knowledge from explicit knowledge bases (KBs), which often introduces irrelevant information to the question, hence restricting the performance of their models. Recent works have resorted to using a powerful large language model (LLM) as an implicit knowledge engine to acquire the necessary knowledge for answering. Despite the encouraging results achieved by these methods, we argue that they have not fully activated the capacity of the blind LLM as the provided textual input is insufficient to depict the required visual information to answer the question. In this paper, we present Prophet—a conceptually simple, flexible, and general framework designed to prompt LLM with answer heuristics for knowledge-based VQA. Specifically, we first train a vanilla VQA model on a specific knowledge-based VQA dataset without external knowledge. After that, we extract two types of complementary answer heuristics from the VQA model: answer candidates and answer-aware examples. Finally, the two types of answer heuristics are jointly encoded into a formatted prompt to facilitate the LLM’s understanding of both the image and question, thus generating a more accurate answer. By incorporating the state-of-the-art LLM GPT-3 [1], Prophet significantly outperforms existing state-of-the-art methods on four challenging knowledge-based VQA datasets. To demonstrate the generality of our approach, we instantiate Prophet with the combinations of different VQA models (i.e., both discriminative and generative ones) and different LLMs (i.e., both commercial and open-source ones). © 2023, CC BY.

关键词： Knowledge based systems

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