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Asian Control Conference

作者： Ting Bai Alexander Johansson Shaoyuan Li Karl Henrik Johansson Jonas Må rtensson Integrated Transport Research Lab Digital Futures and Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai China

We model a platooning system including trucks and a third-party service provider that performs platoon coordination, distributes the platooning profit within platoons, and charges the trucks in exchange for its services. This paper studies one class of pricing rules, where the third-party service provider keeps part of the platooning profit each time a platoon is formed. Furthermore, we propose a platoon coordination solution based on distributed model predictive control in which the pricing rule is integrated. To evaluate the effect of the pricing on the platooning system, we perform a simulation over the Swedish road network. The simulation shows that the platooning rate and profit highly depend on the pricing. This suggests that pricing needs to be set carefully to obtain a satisfactory platooning system in the future.

关键词： Roads Pricing Predictive control

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Three Dimensional Synthetic Non-Ellipsoidal Nuclei Volume Generation Using Bézier Curves

Three Dimensional Synthetic Non-Ellipsoidal Nuclei Volume Ge...

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IEEE International Symposium on Biomedical Imaging

作者： Alain Chen Liming Wu Shuo Han Paul Salama Kenneth W. Dunn Edward J. Delp Video and Image Processing Laboratory School of Electrical and Computer Engineering Purdue University West Lafayette Indiana Indiana University-Purdue University Indianapolis Indiana Division of Nephrology School of Medicine Indiana University Indianapolis Indiana

ISBN: (纸本)9781665412469;9781665429474

Automated segmentation of cell nuclei is used to analyze individual cells to determine the number of nuclei in a 3D volume. Deep learning approaches that segment nuclei require large amounts of annotated (ground truth) microscopy volumes for training. In many cases acquiring large amounts of annotated volumes may not be possible and data augmentation methods must be used. One approach has been the use of synthetic volumes for training. Alternate methods employ spherical and ellipsoidal nuclear models for synthetic ground truth generation, resulting in segmentation that does not accurately match nuclei morphology. In this paper, we present a technique to generate synthetic non-ellipsoidal nuclei microscopy images using Bézier Curves. We test our approach by training a modified 3D U-Net. Our results indicates that our synthetic non-ellipsoidal nuclei approach achieves improved segmentation on volumes with irregularly shaped nuclei.

关键词： Training Deep learning Image segmentation Three-dimensional displays Microscopy Morphology Generative adversarial networks

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Fast Outage Analysis of Large-Scale Production Clouds with Service Correlation Mining

Fast Outage Analysis of Large-Scale Production Clouds with S...

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International Conference on Software engineering (ICSE)

作者： Yaohui Wang Guozheng Li Zijian Wang Yu Kang Yangfan Zhou Hongyu Zhang Feng Gao Jeffrey Sun Li Yang Pochian Lee Zhangwei Xu Pu Zhao Bo Qiao Liqun Li Xu Zhang Qingwei Lin School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing China School of Electronics Engineering and Computer Science Peking University Beijing China Microsoft Research Beijing China School of Electrical Engineering and Computing The University of Newcastle Australia Microsoft Azure Redmond USA

Cloud-based services are surging into popularity in recent years. However, outages, i.e., severe incidents that always impact multiple services, can dramatically affect user experience and incur severe economic losses. Locating the root-cause service, i.e., the service that contains the root cause of the outage, is a crucial step to mitigate the impact of the outage. In current industrial practice, this is generally performed in a bootstrap manner and largely depends on human efforts: the service that directly causes the outage is identified first, and the suspected root cause is traced back manually from service to service during diagnosis until the actual root cause is found. Unfortunately, production cloud systems typically contain a large number of interdependent services. Such a manual root cause analysis is often time-consuming and labor-intensive. In this work, we propose COT, the first outage triage approach that considers the global view of service correlations. COT mines the correlations among services from outage diagnosis data. After learning from historical outages, COT can infer the root cause of emerging ones accurately. We implement COT and evaluate it on a real-world dataset containing one year of data collected from Microsoft Azure, one of the representative cloud computing platforms in the world. Our experimental results show that COT can reach a triage accuracy of 82.1%-83.5%, which outperforms the state-of-the-art triage approach by 28.0%-29.7%.

关键词： Cloud computing Root cause analysis Correlation Systematics Production User experience Software engineering

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Multi-Slot Distributed Measurement Selection: A Sparsity Learning Approach

Multi-Slot Distributed Measurement Selection: A Sparsity Lea...

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作者： Xia, Qian Wang, Wei Ran, Rong Gong, Yi Zhang, Zhaoyang College of Information Science and Electronic Engineering Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Zhejiang University Hangzhou310027 China School of Electrical and Computer Engineering Ajou University Suwon16499 Korea Republic of Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China

When only a part of measurements are collected sequentially in large-scale Internet of Things (IoT) networks, it is motivated to extract significant information from the collected data for utilizing the communication resources over incoming slots more efficiently. In this article, we study the successive measurement selection problem for sparse parameter estimation and its distributed implementation in large-scale networks. Specifically, we transform the intractable multi-slot measurement selection problem into per-slot subproblems, and in each slot we select the uncollected measurements that are more 'useful' for parameter estimation based on sparse Bayesian learning. In order to deal with the combinatorial problem with distributed implementation, we relax the subproblem and propose an enhanced subgradient method to solve the Boolean constraint. The proposed distributed scheme needs only a common information broadcast by the centralized controller, where the communication overheads only depend on the number of sparse active sources rather than the number of sensors which is always huge. Simulation results demonstrate the performance gain of our proposed measurement selection method compared to the conventional schemes. When the total communication overheads are limited with the network scale, our scheme presents extreme practicability and applicability for the large-scale networks. © 2015 IEEE.

关键词： Wireless sensor networks

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Fast outage analysis of large-scale production clouds with service correlation mining

arXiv

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arXiv 2021年

作者： Wang, Yaohui Li, Guozheng Wang, Zijian Kang, Yu Zhou, Yangfan Zhang, Hongyu Gao, Feng Sun, Jeffrey Yang, Li Lee, Pochian Xu, Zhangwei Zhao, Pu Qiao, Bo Li, Liqun Zhang, Xu Lin, Qingwei School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing China School of Electronics Engineering and Computer Science Peking University Beijing China Microsoft Research Beijing China School of Electrical Engineering and Computing The University of Newcastle Australia Microsoft Azure Redmond United States

Cloud-based services are surging into popularity in recent years. However, outages, i.e., severe incidents that always impact multiple services, can dramatically affect user experience and incur severe economic losses. Locating the root-cause service, i.e., the service that contains the root cause of the outage, is a crucial step to mitigate the impact of the outage. In current industrial practice, this is generally performed in a bootstrap manner and largely depends on human efforts: the service that directly causes the outage is identified first, and the suspected root cause is traced back manually from service to service during diagnosis until the actual root cause is found. Unfortunately, production cloud systems typically contain a large number of interdependent services. Such a manual root cause analysis is often time-consuming and labor-intensive. In this work, we propose COT, the first outage triage approach that considers the global view of service correlations. COT mines the correlations among services from outage diagnosis data. After learning from historical outages, COT can infer the root cause of emerging ones accurately. We implement COT and evaluate it on a real-world dataset containing one year of data collected from Microsoft Azure, one of the representative cloud computing platforms in the world. Our experimental results show that COT can reach a triage accuracy of 82.1%∼83.5%, which outperforms the state-of-the-art triage approach by 28.0%∼29.7%. Copyright © 2021, The Authors. All rights reserved.

关键词： Cloud computing

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1D quasi-bound states in the continuum with large operation bandwidth in theω~k space for nonlinear optical applications

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Photonics Research 2022年第7期10卷 1575-1581页

作者： KAILI SUN HUI JIANG DMITRY ABYKOV VIEN VAN URIEL LEVY YANGJIAN CAI ZHANGHUA HAN Shandong Provincial Key Laboratory of Optics and Photonic Devices Center of Light Manipulation and ApplicationsSchool of Physics and ElectronicsShandong Normal UniversityJinan 250358China Image Processing Systems Institute—Branch of the Federal Scientific Research Centre“Crystallography and Photonics”of Russian Academy of Sciences Samara 443001Russia Department of Electrical and Computer Engineering University of AlbertaEdmontonAlberta T6G 2V4Canada Department of Applied Physics and Center for Nanoscience and NanotechnologyThe Hebrew University of JerusalemJerusalemIsrael

The phenomenon of bound state in the continuum(BIC)with an infinite quality factor and lifetime has emerged in recent years in photonics as a new tool to manipulate light-matter ***,most of the investigated structures only support BIC resonances at very few discrete points in theω~k *** when the BIC is switched to a quasi-BIC(QBIC)resonance through perturbation,its frequency will still be located within a narrow spectral band close to that of the original BIC,restricting their applications in many fields where random or multiple input frequencies beyond the narrow band are *** this work,we demonstrate that a new set of QBIC resonances can be supported by using a special binary grating consisting of two alternatingly aligned ridge arrays with the same period and zero-approaching ridge width difference on a slab *** QBIC resonances are distributed continuously over a broad band along a line in theω~k space and can thus be considered as 1 D *** the Q factors generally affected by the ridge difference,it is now possible to arbitrarily choose any frequencies on the dispersion line to achieve significantly enhanced light-matter interactions,facilitating many applications where multiple input wavelengths are required;e.g.,sum or difference frequency generations in nonlinear optics.

关键词： quasi nonlinear narrow

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Profiling HPC Applications with Low Overhead and High Accuracy

Profiling HPC Applications with Low Overhead and High Accura...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Jingyuan Zhao Xin Liu Yao Liu Penglong Jiao Jinshuo Liu Wei Xue School of Data Science & Engineering East China Normal University Shanghai China School of Cyber Science and Engineering Wuhan University Wuhan China National Research Centre of Parallel Computer Engineering and Technology Beijing China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China Tsinghua University Beijing China

As the parallel scale of HPC applications represented by earth system models becomes larger and the computing cost becomes higher, the performance of HPC applications is increasingly critical. Profiling HPC applications accurately helps to model the applications and find the performance bottlenecks. However, due to the complexity of HPC applications, the diversity of programming languages, the differences of individual programming habits, and multiple architectures, accurate profiling becomes very tough. In this paper, we propose LPerf: a low-overhead and high-accuracy profiler for HPC applications. To reduce the profiling overhead and improve the profiling accuracy, we propose a preprocessing method which can automatically instrument with tunable granularity thus significantly reducing the run-time overhead of profiling, an aggregated caller-callee relationship which is used to locate relationship of functions efficiently, and a profiling-aware method which can precisely calculate running time of functions. The experimental results show that the error rate of profiling reaches 0.02%, and the overhead reaches 1.6%, in the earth system model named CAS-ESM. Compared with the baselines, the precision, accuracy, and overhead of LPerf have reached the state of the art.

关键词： Earth Analytical models Schedules computer languages Costs Error analysis Computational modeling

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Asymmetry Total Variation and Framelet Regularized Nonconvex Low-Rank Tensor Completion

SSRN

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SSRN 2022年

作者： Chen, Yongyong Zhao, Xiaojia Xu, Tingting Zeng, Haijin Xu, Yanhui Chen, Junxing The School of Computer Science and Technology Harbin Institute of Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen518055 China Image Processing and Interpretation Imec Research Group Ghent University Belgium Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China The College of Medicine and Biological Information Engineering Northeastern University Shenyang110004 China

The low-rank tensor representation has shown enormous potential and advantages in diverse applications. However, (1) due to the inherent limitations of the low-rank tensor model, the tensor nuclear norm is utilized yet as a biased estimate of the tensor rank, which limits the image recovery performance. (2) most current low-rank approximation-based methods focus on the preservation of global information, while ignoring the local details preservation such as the spatial piece-wise smoothness and sharp edges. To overcome he above obstacles, we propose a novel asymmetry three-dimensional total variation and framelet regularized nonconvex low-rank tensor completion (ATV-FNTC) model, which integrates the nonconvex penalty function and asymmetric three-dimensional total variation into one unified model. Specifically, ATV-FNTC introduces one nonconvex penalty function as a tighter regularizer to approximate the tensor rank. Different from existing methods, the asymmetric three-dimensional total variation regularization is developed to achieve more accurate recovery of detailed information and flexibly control the smoothing strength of different dimensions of tensor data. Furthermore, we design an iterative algorithm to solve the nonconvex ATV-FNTC model by the alternating direction methods of multipliers. Experimental results reveal the superiority of the proposed method compared with several state-of-the-art methods when handling different completion tasks. © 2022, The Authors. All rights reserved.

关键词： Tensors

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Magnetic plasmon resonances in nanostructured topological insulators for strongly enhanced light-MoS_(2) interactions

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Light(Science & Applications) 2020年第1期9卷 155-164页

作者： Hua Lu Zengji Yue Yangwu Li Yinan Zhang Mingwen Zhang Wei Zeng Xuetao Gan Dong Mao Fajun Xiao Ting Mei Weiyao Zhao Xiaolin Wang Min Gu Jianlin Zhao MOE Key Laboratory of Material Physics and Chemistry under Extraordinary Conditions and Shaanxi Key Laboratory of Optical Information TechnologySchool of Physical Science and TechnologyNorthwestern Polytechnical University710129 Xi’anChina Institute for Superconducting&Electronic Materials and ARC Centre of Excellence in Future Low-Energy Electronics University of WollongongNorth WollongongNSW 2500Australia Center for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology200093 ShanghaiChina Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications Institute of Photonics TechnologyJinan University510632 GuangzhouChina State Key Laboratory of Solidification Processing School of Materials Science and EngineeringNorthwestern Polytechnical University710072 Xi’anChina

Magnetic resonances not only play crucial roles in artificial magnetic materials but also offer a promising way for light control and interaction with ***,magnetic resonance effects have attracted special attention in plasmonic systems for overcoming magnetic response saturation at high frequencies and realizing high-performance optical *** novel states of matter,topological insulators(TIs)present topologically protected conducting surfaces and insulating bulks in a broad optical range,providing new building blocks for ***,until now,high-frequency(*** range)magnetic resonances and related applications have not been demonstrated in TI ***,we report for the first time,to our knowledge,a kind of visible range magnetic plasmon resonances(MPRs)in TI structures composed of nanofabricated Sb_(2)Te_(3) *** experimental results show that the MPR response can be tailored by adjusting the nanogroove height,width,and pitch,which agrees well with the simulations and theoretical ***,we innovatively integrated monolayer MoS_(2) onto a TI nanostructure and observed strongly reinforced light-MoS_(2) interactions induced by a significant MPR-induced electric field enhancement,remarkable compared with TI-based electric plasmon resonances(EPRs).The MoS_(2) photoluminescence can be flexibly tuned by controlling the incident light *** results enrich TI optical physics and applications in highly efficient optical functionalities as well as artificial magnetic materials at high frequencies.

关键词： topological resonance visible

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Deep Learning Methods for Calibrated Photometric Stereo and Beyond

arXiv

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arXiv 2022年

作者： Ju, Yakun Lam, Kin-Man Xie, Wuyuan Zhou, Huiyu Dong, Junyu Shi, Boxin School of Electrical and Electronic Engineering Nanyang Technological University Singapore The Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong The Research Institute for Future Media Computing Shenzhen University Shenzhen China The Department of Informatics University of Leicester Leicester United Kingdom The Faculty of Information Science and Engineering The Institute for Advanced Ocean Study Ocean University of China Qingdao China The National Key Laboratory for Multimedia Information Processing National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China

Photometric stereo recovers the surface normals of an object from multiple images with varying shading cues, i.e., modeling the relationship between surface orientation and intensity at each pixel. Photometric stereo prevails in superior per-pixel resolution and fine reconstruction details. However, it is a complicated problem because of the non-linear relationship caused by non-Lambertian surface reflectance. Recently, various deep learning methods have shown a powerful ability in the context of photometric stereo against non-Lambertian surfaces. This paper provides a comprehensive review of existing deep learning-based calibrated photometric stereo methods utilizing orthographic cameras and directional light sources. We first analyze these methods from different perspectives, including input processing, supervision, and network architecture. We summarize the performance of deep learning photometric stereo models on the most widely-used benchmark data set. This demonstrates the advanced performance of deep learning-based photometric stereo methods. Finally, we give suggestions and propose future research trends based on the limitations of existing models. Copyright © 2022, The Authors. All rights reserved.

关键词： Light sources

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