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arXiv 2023年

作者： Kechrakos, Dimitris Puliafito, Vito Riveros, Alejandro Liu, Jiahao Jiang, Wanjun Carpentieri, Mario Tomasello, Riccardo Finocchio, Giovanni Athens Greece Department of Electrical and Information Engineering Technical University of Bari Bari70125 Italy Escuela de Ingeniería Universidad Central de Chile Santiago8330601 Chile Department of Mathematical and Computer Sciences Physical Sciences and Earth Sciences University of Messina MessinaI-98166 Italy State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China Frontier Science Center for Quantum Information Tsinghua University Beijing100084 China College of Advanced Interdisciplinary Studies & Hunan Provincial Key Laboratory of Novel Nano-Optoelectronic Information Materials and Devices National University of Defense Technology Hunan Changsha410073 China

The dynamical properties of skyrmions can be exploited to build devices with new functionalities. Here, we first investigate a skyrmion-based ring-shaped device by means of micromagnetic simulations and Thiele’s equation. We subsequently show three applications scenarios: (1) a clock with tunable frequency that is biased with an electrical current having a radial spatial distribution, (2) an alternator, where the skyrmion circular motion driven by an engineered anisotropy gradient is converted into an electrical signal, and (3) an energy harvester, where the skyrmion motion driven by a thermal gradient is converted into an electrical signal, thus providing a heat recovery operation. We also show how to precisely tune the frequency and amplitude of the output electrical signals by varying material parameters, geometrical parameters, number and velocity of skyrmions, and we further prove the correct device functionality under realistic conditions given by room temperature and internal material defects. Our results open a new route for the realization of energy efficient nanoscale clocks, generators, and energy harvesters. © 2023, CC BY.

关键词： Clocks

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Graph-Order Optimization Algorithm Based on Equal-in-Space Distance Model for High-Resolution Image Matting

Graph-Order Optimization Algorithm Based on Equal-in-Space D...

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IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Fujian Feng Han Huang Yihui Liang School of Software Engineering South China University of Technology Guangzhou China School of Data Science and Information Engineering Guizhou Minzu University Guiyang China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education Guangzhou China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China

ISBN: (纸本)9781665441506

Image matting is an essential image processing technology. optimized-based image matting methods can significantly improve the alpha matte quality of high-resolution images. However, the local information of the foreground may be similar to the background, which causes the inversion problem of the alpha matte in the single-point optimized. In this paper, we propose an image matting mathematical model of the equal-in-space distance. The model transforms the high-resolution image matting problem into several small-scale combinatorial optimization problems according to the similarity among pixel features. Inspired by spanning tree, we propose a graph-order optimization strategy, which generates the optimization sequence of small-scale optimization problems according to the edge weight among graph nodes. In addition, we designed a graph-order optimization algorithm based on optimized information transfer to solve each node in the graph. Experimental results show that the proposed model solves the alpha matte inversion problem of single-point optimization matting. Besides, the proposed algorithm outperforms the state-of-the-art optimization algorithms for the high-resolution image matting problem.

关键词： Cloud computing Image processing Image edge detection Conferences Computational modeling Transforms Information processing

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DKPLM: Decomposable Knowledge-Enhanced Pre-trained Language Model for Natural Language Understanding

arXiv

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arXiv 2021年

作者： Zhang, Taolin Wang, Chengyu Hu, Nan Qiu, Minghui Tang, Chengguang He, Xiaofeng Huang, Jun School of Software Engineering East China Normal University China Alibaba Group China Shanghai Key Laboratory of Trsustworthy Computing China NPPA Key Laboratory of Publishing Integration Development ECNUP China School of Computer Science and Technology East China Normal University China

Knowledge-Enhanced Pre-trained Language Models (KEPLMs) are pre-trained models with relation triples injecting from knowledge graphs to improve language understanding *** show that our model outperforms other KEPLMs significantly over zero-shot knowledge probing tasks and multiple knowledge-aware language understanding tasks. To guarantee effective knowledge injection, previous studies integrate models with knowledge encoders for representing knowledge retrieved from knowledge graphs. The operations for knowledge retrieval and encoding bring significant computational burdens, restricting the usage of such models in real-world applications that require high inference speed. In this paper, we propose a novel KEPLM named DKPLM that decomposes knowledge injection process of the pre-trained language models in pre-training, fine-tuning and inference stages, which facilitates the applications of KEPLMs in real-world scenarios. Specifically, we first detect knowledge-aware long-tail entities as the target for knowledge injection, enhancing the KEPLMs’ semantic understanding abilities and avoiding injecting redundant information. The embeddings of long-tail entities are replaced by "pseudo token representations" formed by relevant knowledge triples. We further design the relational knowledge decoding task for pre-training to force the models to truly understand the injected knowledge by relation triple reconstruction. Experiments show that our model outperforms other KEPLMs significantly over zero-shot knowledge probing tasks and multiple knowledge-aware language understanding tasks. We further show that DKPLM has a higher inference speed than other competing models due to the decomposing mechanism. © 2021, CC BY.

关键词： Knowledge graph

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Application of ontology and multivariate decision diagram in cloud monitor systems

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International Journal of Performability Engineering 2019年第12期15卷 3227-3236页

作者： Xu, Han Chung Chu, William Cheng Luo, Jie School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Department of Computer Science Tunghai University Taiwan40704 Taiwan School of Computer Science and Engineering Beihang University Beijing100191 China State Key Laboratory of Software Development Environmen Beihang University Beijing100191 China

Cloud computing is different from distributed computing and grid computing and has its own characteristics. The existing cloud system is not sufficient in the unified identification of cloud resources and the dynamic joining management of new resources. According to the characteristics of cloud computing, this paper introduces the idea of ontology on cloud monitor system (CMS) based on bionic autonomic nervous system (BANS) and uses ontology web language (OWL) language to describe the resources of the system. It also establishes a reusable extended resource expression model. At the same time, the use of the third-party tool Jena for OWL semantic query also gives the monitoring system the characteristics of a rapid semantic query, which further enhances the convenience of cloud resource management. In addition, based on the application of ontology, we also introduce multivariate decision diagram (MDD) multi-valued decision graph technology, which allows B-CMS to self-diagnose complex system faults. The combination of ontology and MDD greatly simplifies the monitoring and management of large-scale systems, providing a fast and standardized means for the intelligent diagnosis of systems. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Ontology

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Compressed Gradient Tracking Algorithms for Distributed Nonconvex Optimization

arXiv

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arXiv 2023年

作者： Xu, Lei Yi, Xinlei Wen, Guanghui Shi, Yang Johansson, Karl H. Yang, Tao The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The Lab for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Systems Science School of Mathematics Southeast University Nanjing210096 China The Department of Mechanical Engineering University of Victoria VictoriaBCV8W 2Y2 Canada The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden

In this paper, we study the distributed nonconvex optimization problem, aiming to minimize the average value of the local nonconvex cost functions using local information exchange. To reduce the communication overhead, we introduce three general classes of compressors, i.e., compressors with bounded relative compression error, compressors with globally bounded absolute compression error, and compressors with locally bounded absolute compression error. By integrating them, respectively, with the distributed gradient tracking algorithm, we then propose three corresponding compressed distributed nonconvex optimization algorithms. Motivated by the state-of-the-art BEER algorithm proposed in [1], which is an efficient compressed algorithm integrating gradient tracking with biased and contractive compressors, our first proposed algorithm extends this algorithm to accommodate both biased and non-contractive compressors. For each algorithm, we design a novel Lyapunov function to demonstrate its sublinear convergence to a stationary point if the local cost functions are smooth. Furthermore, when the global cost function satisfies the Polyak–Lojasiewicz (P–L) condition, we show that our proposed algorithms linearly converge to a global optimal point. It is worth noting that, for compressors with bounded relative compression error and globally bounded absolute compression error, our proposed algorithms’ parameters do not require prior knowledge of the P–L constant. The theoretical results are illustrated by numerical examples, demonstrating the effectiveness of the proposed algorithms in significantly reducing the communication burden while maintaining the convergence performance. Moreover, simulation results show that the proposed algorithms outperform the state-of-the-art compressed distributed nonconvex optimization algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost functions

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Highly efficient difference-frequency generation for mid-infrared pulses by passively synchronous seeding

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High Power Laser science and Engineering 2021年第1期9卷 42-47页

作者： Kun Huang Yinqi Wang Jianan Fang Huaixi Chen Minghang Xu Qiang Hao Ming Yan Heping Zeng State Key Laboratory of Precision Spectroscopy East China Normal UniversityShanghai 200062China Key Laboratory of Optoelectronic Materials Chemistry and Physics Fujian Institute of Research on the Structure of MatterChinese Academy of SciencesFuzhou 350002China University of Chinese Academy of Sciences Beijing 100049China Shanghai Key Laboratory of Modern Optical System and Engineering Research Center of Optical Instrument and SystemMinistry of EducationSchool of Optical Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China Jinan Institute of Quantum Technology Jinan 250101China CAS Center for Excellence in Ultra-intense Laser Science Shanghai 201800China Shanghai Research Center for Quantum Sciences Shanghai 201315China

We have proposed and experimentally demonstrated a novel scheme for efficient mid-infrared difference-frequency generation based on passively synchronized fiber lasers. The adoption of coincident seeding pulses in the nonlinear conversion process could substantially lower the pumping threshold for mid-infrared parametric emission. Consequently,a picosecond mid-infrared source at 3.1 μm was prepared with watt-level average power, and a maximum power conversion efficiency of 77% was realized from pump to down-converted light. Additionally, the long-term stability of generated power was manifested with a relative fluctuation as low as 0.17% over one hour. Thanks to the all-optical passive synchronization and all-polarization-maintaining fiber architecture, the implemented laser system was also featured with simplicity, compactness and robustness, which would favor subsequent applications beyond laboratory operation.

关键词： mid-infrared fiber laser nonlinear optics difference-frequency generation

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In-situ data scheduling optimization based on rainbow DQN for IIoT

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Future Generation computer Systems 2026年 174卷

作者： Peiying Zhang Siyi Li Lizhuang Tan Neeraj Kumar Jian Wang Kai Liu Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao 266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao 266580 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 China Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala 147004 India Department of Computer Science University of Economics and Human Sciences Warszawa 01-043 Poland Department of Networks and Communications College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam 31441 Saudi Arabia College of Science China University of Petroleum (East China) Qingdao 266580 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing 100084 China State Key Laboratory of Space Network and Communications Tsinghua University Beijing 100084 China

In industrial systems, in-situ computing refers to a mode of local or proximate data processing within the cloud–edge-terminal architecture. In the Industrial Internet of Things (IIoT) environment, servers are typically deployed near terminal devices to coordinate and manage the scheduling of in-situ data, forming a comprehensive in-situ server system. With the rapid growth of IIoT terminal devices, challenges such as high processing delays and severe demand congestion arise in the in-situ data scheduling process. To address this challenge, this paper proposes a scheduling optimization scheme based on Rainbow DQN. The scheme designs a scheduling buffer and classification strategy to distinguish in-situ data based on storage demand characteristics, thereby optimizing the scheduling process. At the same time, a multi-level storage structure is introduced to store in-situ data classified according to the scheduling strategy. Additionally, the Rainbow DQN algorithm is utilized to assist in the scheduling decision-making of in-situ data retrieval, effectively alleviating system congestion while ensuring certain throughput performance. Extensive simulation experiments show that the Rainbow DQN algorithm achieves a reduction of 60.82% in average waiting delay, 62.94% in average unprocessed demand, and a 58.27% reduction in average completion delay.

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HetVis: A Visual Analysis Approach for Identifying Data Heterogeneity in Horizontal Federated Learning

arXiv

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arXiv 2022年

作者： Wang, Xumeng Chen, Wei Xia, Jiazhi Wen, Zhen Zhu, Rongchen Schreck, Tobias TMCC CS Nankai University China State Key Lab of CAD&CG Zhejiang University China Laboratory of Art and Archaeology Image Zhejiang University Ministry of Education China School of Computer Science and Engineering Central South University China Graz University of Technology Austria

Horizontal federated learning (HFL) enables distributed clients to train a shared model and keep their data privacy. In training high-quality HFL models, the data heterogeneity among clients is one of the major concerns. However, due to the security issue and the complexity of deep learning models, it is challenging to investigate data heterogeneity across different clients. To address this issue, based on a requirement analysis we developed a visual analytics tool, HetVis, for participating clients to explore data heterogeneity. We identify data heterogeneity through comparing prediction behaviors of the global federated model and the stand-alone model trained with local data. Then, a context-aware clustering of the inconsistent records is done, to provide a summary of data heterogeneity. Combining with the proposed comparison techniques, we develop a novel set of visualizations to identify heterogeneity issues in HFL. We designed three case studies to introduce how HetVis can assist client analysts in understanding different types of heterogeneity issues. Expert reviews and a comparative study demonstrate the effectiveness of HetVis. Copyright © 2022, The Authors. All rights reserved.

关键词： Cluster analysis

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Crossover-net: leveraging the vertical-horizontal crossover relation for robust segmentation

arXiv

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arXiv 2020年

作者： Yu, Qian Shi, Yinghuan Zheng, Yefeng Gao, Yang Zhu, Jianbing Dai, Yakang State Key Laboratory for Novel Software Technology Nanjing University China School of Data and Computer Science Shandong Women’s University China Youtu Lab Tencent China Suzhou Science and Technology Town Hospital China Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences China

—Robust segmentation for non-elongated tissues in medical images is hard to realize due to the large variation of the shape, size, and appearance of these tissues in different patients. In this paper, we present an end-to-end trainable deep segmentation model termed Crossover-Net for robust segmentation in medical images. Our proposed model is inspired by an insightful observation: during segmentation, the representation from the horizontal and vertical directions can provide different local appearance and orthogonality context information, which helps enhance the discrimination between different tissues by simultaneously learning from these two directions. Specifically, by converting the segmentation task to a pixel/voxel-wise prediction problem, firstly, we originally propose a cross-shaped patch, namely crossover-patch, which consists of a pair of (orthogonal and overlapped) vertical and horizontal patches, to capture the orthogonal vertical and horizontal relation. Then, we develop the Crossover-Net to learn the vertical-horizontal crossover relation captured by our crossover-patches. To achieve this goal, for learning the representation on a typical crossover-patch, we design a novel loss function to (1) impose the consistency on the overlap region of the vertical and horizontal patches and (2) preserve the diversity on their non-overlap regions. We have extensively evaluated our method on CT kidney tumor, MR cardiac, and X-ray breast mass segmentation tasks. Promising results are achieved according to our extensive evaluation and comparison with the state-of-the-art segmentation *** Codes 14J60 (Primary) 14F05, 14J26 (Secondary) Copyright © 2020, The Authors. All rights reserved.

关键词： Tissue

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A Probabilistic Logic for Verifying Continuous-time Markov Chains

arXiv

引用

arXiv 2020年

作者： Guan, Ji Yu, Nengkun State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Centre for Quantum Software and Information University of Technology Sydney Sydney Australia

A continuous-time Markov chain (CTMC) execution is a continuous class of probability distributions over states. This paper proposes a probabilistic linear-time temporal logic, namely continuous-time linear logic (CLL), to reason about the probability distribution execution of CTMCs. We define the syntax of CLL on the space of probability distributions. The syntax of CLL includes multiphase timed until formulas, and the semantics of CLL allows time reset to study relatively temporal properties. We derive a corresponding model-checking algorithm for CLL formulas. The correctness of the model-checking algorithm depends on Schanuel's conjecture, a central open problem in transcendental number theory. Furthermore, we provide a running example of CTMCs to illustrate our method. © 2020, CC BY.

关键词： Continuous time systems

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