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第40届中国控制会议

作者： Yixuan Huang Qingjiang Xiao Shiqiang Du College of Mathematics and Computer Science Northwest Minzu University Key Laboratory of Chinas Ethnic Languages and Information Technology of Ministry of Education Chinese National Information Technology Research Institute Northwest Minzu University

The multi-view algorithm based on graph learning pays attention to the manifold structure of data and shows the good performance in clustering task. However, multi-view data usually contains noise, which reduces the robustness of multi-view clustering algorithm. In order to solve this problem, we propose a novel multi-view clustering model,namely robust graph learning for multi-view clustering（RGLMC）. RGLMC eliminates noise and errors from the original data and employs the adaptive graph, which characterizes the relationship between clusters, as the new input of the algorithm. Our model can be optimized efficiently by utilizing the Augmented Lagrangian Multiplier with Alternating Direction Minimization（ALM-ADM） algorithm. Extensive experimental results on six benchmark datasets verify the superiority of the proposed method in clustering task.

关键词： Multi-view clustering manifold structure graph learning Markov chain

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ZnG: Architecting GPU Multi-Processors with New Flash for Scalable Data Analysis

ZnG: Architecting GPU Multi-Processors with New Flash for Sc...

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Annual International Symposium on computer architecture, ISCA

作者： Jie Zhang Myoungsoo Jung Computer Architecture and Memory Systems Laboratory Korea Advanced Institute of Science and Technology (KAIST)

ISBN: (数字)9781728146614

ISBN: (纸本)9781728146621

We propose ZnG, a new GPU-SSD integrated architecture, which can maximize the memory capacity in a GPU and address performance penalties imposed by an SSD. Specifically, ZnG replaces all GPU internal DRAMs with an ultra-low-latency SSD to maximize the GPU memory capacity. ZnG further removes performance bottleneck of the SSD by replacing its flash channels with a high-throughput flash network and integrating SSD firmware in the GPU's MMU to reap the benefits of hardware accelerations. Although flash arrays within the SSD can deliver high accumulated bandwidth, only a small fraction of such bandwidth can be utilized by GPU's memory requests due to mismatches of their access granularity. To address this, ZnG employs a large L2 cache and flash registers to buffer the memory requests. Our evaluation results indicate that ZnG can achieve 7.5× higher performance than prior work.

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Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report

Power Efficient Video Super-Resolution on Mobile NPUs with...

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17th European Conference on computer Vision, ECCV 2022

作者： Ignatov, Andrey Timofte, Radu Chiang, Cheng-Ming Kuo, Hsien-Kai Xu, Yu-Syuan Lee, Man-Yu Lu, Allen Cheng, Chia-Ming Chen, Chih-Cheng Yong, Jia-Ying Shuai, Hong-Han Cheng, Wen-Huang Jia, Zhuang Xu, Tianyu Zhang, Yijian Bao, Long Sun, Heng Zhang, Diankai Gao, Si Liu, Shaoli Wu, Biao Zhang, Xiaofeng Zheng, Chengjian Lu, Kaidi Wang, Ning Sun, Xiao Wu, HaoDong Liu, Xuncheng Zhang, Weizhan Yan, Caixia Du, Haipeng Zheng, Qinghua Wang, Qi Chen, Wangdu Duan, Ran Sun, Mengdi Zhu, Dan Chen, Guannan Cho, Hojin Kim, Steve Yue, Shijie Li, Chenghua Zhuge, Zhengyang Chen, Wei Wang, Wenxu Zhou, Yufeng Cai, Xiaochen Cai, Hengxing Xu, Kele Liu, Li Cheng, Zehua Lian, Wenyi Lian, Wenjing Computer Vision Lab ETH Zurich Zürich Switzerland AI Witchlabs Zürich Switzerland University of Wuerzburg Würzburg Germany MediaTek Inc. Hsinchu Taiwan National Yang Ming Chiao Tung University Hsinchu Taiwan Video Algorithm Group Camera Department Xiaomi Inc. Beijing China Audio & Video Technology Platform Department ZTE Corporation Shenzhen China Beijing China School of Computer Science and Technology Xi’an Jiaotong University Xi’an China MIGU Video Co. Ltd. Beijing China BOE Technology Group Co. Ltd. Beijing China GenGenAI Seoul Korea Republic of North China University of Technology Beijing China Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology Beijing China 4Paradigm Inc. Beijing China National University of Defense Technology Changsha China University of Oxford Oxford United Kingdom Uppsala University Uppsala Sweden Northeastern University Shenyang China

ISBN: (纸本)9783031250651

Video super-resolution is one of the most popular tasks on mobile devices, being widely used for an automatic improvement of low-bitrate and low-resolution video streams. While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices. In this Mobile AI challenge, we address this problem and propose the participants to design an end-to-end real-time video super-resolution solution for mobile NPUs optimized for low energy consumption. The participants were provided with the REDS training dataset containing video sequences for a 4X video upscaling task. The runtime and power efficiency of all models was evaluated on the powerful MediaTek Dimensity 9000 platform with a dedicated AI processing unit capable of accelerating floating-point and quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 500 FPS rate and 0.2 [Watt/30 FPS] power consumption. A detailed description of all models developed in the challenge is provided in this paper. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Energy utilization

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Training, testing and benchmarking medical AI models using Clinical AIBench

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BenchCouncil Transactions on Benchmarks, Standards and Evaluations 2022年第1期2卷 100037-100037页

作者： Huang, Yunyou Miao, Xiuxia Zhang, Ruchang Ma, Li Liu, Wenjing Zhang, Fan Guan, Xianglong Liang, Xiaoshuang Lu, Xiangjiang Tang, Suqing Zhang, Zhifei Guangxi Key Lab of Multi-Source Information Mining & Security School of Computer Science and Engineering & School of Software Guangxi Normal University Guilin China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Guilin Medical University Guilin China Department of Physiology and Pathophysiology Capital Medical University Beijing China Faculty of Education Guangxi Normal University Guilin China International Open Benchmark Council Beijing China

AI technology has been used in many clinical research fields, but most AI technologies are difficult to land in real-world clinical settings. In most current clinical AI research settings, the diagnosis task is to identify different types of diseases among the given ones. However, the diagnosis in real-world settings needs dynamically developing inspection strategies based on the existing resources of medical institutions and identifying different kinds of diseases out of many possibilities. To promote the development of different clinical AI technologies and the implementation of clinical applications, we propose a benchmark named Clinical AIBench for developing, verifying, and evaluating clinical AI technologies in real-world clinical settings. Specifically, Clinical AIBench can be used for: (1) Model training and testing: Researchers can use the data to train and test their models. (2)Model evaluation: Researchers can use Clinical AIBench to objectively, fairly, and comparably evaluate various models of different researchers. (3) Clinical value evaluation: Researchers can use the clinical indicators provided by Clinical AIBench to evaluate the clinical value of models, which will be applied in real-world clinical settings. For convenience, Clinical AIBench provides three different levels of clinical settings: restricted clinical setting, which is named closed clinical setting, data island clinical setting, and real-world clinical setting, which is called open clinical setting. In addition, Clinical AIBench covers three diseases: Alzheimer's disease, COVID-19, and dental. Clinical AIBench provides python APIs to researchers. The data and source code are publicly available from the project website https://***/clinical_aibench/. © 2022 The Authors

关键词： COVID-19

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Scalable and probabilistically complete planning for robotic spatial extrusion

arXiv

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arXiv 2020年

作者： Garrett, Caelan Reed Huang, Yijiang Lozano-Pérez, Tomás Mueller, Caitlin Tobin Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Department of Architecture Massachusetts Institute of Technology

There is increasing demand for automated systems that can fabricate 3D structures. Robotic spatial extrusion has become an attractive alternative to traditional layer-based 3D printing due to a manipulator's flexibility to print large, directionally-dependent structures. However, existing extrusion planning algorithms require a substantial amount of human input, do not scale to large instances, and lack theoretical guarantees. In this work, we present a rigorous formalization of robotic spatial extrusion planning and provide several efficient and probabilistically complete planning algorithms. The key planning challenge is, throughout the printing process, satisfying both stiffness constraints that limit the deformation of the structure and geometric constraints that ensure the robot does not collide with the structure. We show that, although these constraints often conflict with each other, a greedy backward state-space search guided by a stiffness-aware heuristic is able to successfully balance both constraints. We empirically compare our methods on a benchmark of over 40 simulated extrusion problems. Finally, we apply our approach to 3 real-world extrusion problems. Copyright © 2020, The Authors. All rights reserved.

关键词： Extrusion

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CertainOdom: Uncertainty Weighted Multi-task Learning Model for LiDAR Odometry Estimation

CertainOdom: Uncertainty Weighted Multi-task Learning Model ...

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IEEE International Conference on Robotics and Biomimetics

作者： Leyuan Sun Guanqun Ding Yusuke Yoshiyasu Fumio Kanehiro Department of Intelligent and Mechanical Interaction Systems Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan CNRS-AIST Joint Robotics Laboratory (JRL) IRL National Institute of Advanced Industrial Science and Technology (AIST). Digital Architecture Research Center (DARC) National Institute of Advanced Industrial Science and Technology (AIST) Tokyo Japan Computer Vision Research Team Artificial Intelligence Research Center (AIRC) National Institute of Advanced Industrial Science and Technology (AIST) Japan

ISBN: (纸本)9781665481106

As a basic and indispensable module, LiDAR odom-etry estimation is widely used in robotics. In recent years, learning-based modeling approaches for odometry estimation have been validated to be feasible. However, it is necessary to consider security factors as the highest priorities when we apply the learning-based model to certain high-risk real-world scenarios, such as autonomous driving. The odometry uncertainty estimation provides more valuable information for downstream tasks, such as route planning and navigation. In this paper, we propose an end-to-end neural network (namely CertainOdom) to solve odometry and uncertainty estimation tasks by applying multi-task learning. Instead of using the manually-tuned hyper-parameters, we employ the learnable uncertainties to weigh the balance between the error of translation and orientation in the loss function. We evaluate the estimated trajectory and uncertainty on KITTI dataset. We also compare the robustness against the traditional geometry-based methods on our artificially degraded KITTI LiDAR dataset. Extensive experimental results show that our model with uncertainty weighted loss achieves competitive performance in LiDAR odometry estimation. We also explain our uncertainties qualitatively and quantitatively.

关键词： Uncertainty Laser radar Biological system modeling Estimation Multitasking Robustness Trajectory

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Multi-view Spectral Clustering Based on Low-rank Tensor Decomposition

Multi-view Spectral Clustering Based on Low-rank Tensor Deco...

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第33届中国控制与决策会议

作者： Qingjiang Xiao Shiqiang Du Yixuan Huang Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Chinese National Information Technology Research InstituteNorthwest Minzu University College of Mathematics and Computer Science Northwest Minzu University

Recently,graph-based multi-view clustering has received widespread attention due to its simplicity and *** we all know,clustering results are highly dependent on data similarity *** the constructed similarity graph is poor-quality,the generated clustering results may also be *** multi-view learning algorithms only focus on extracting common information from multi views and ignore the unique information and high-level correlations of each *** overcome these shortcomings,in this paper,we propose a multi-view spectral clustering based on dynamic nearest neighbor learning and low-rank tensor decomposition(DNLTSC).Specifically,on the one hand,our model learns view-specific affinity matrix by assigning the adaptive and optimal neighbors for each data point based on the local *** the other hand,our model constructs a tensor containing the affinity matrix of each view,thereby retaining the information of each view.A tensor nuclear norm based on tensor singular value decomposition(t-S VD) is used to constrain the rank of the target *** minimizing the tensor nuclear norm,a tensor that contains the shared information of each view and has high-level correlation is *** alternation direction method of multipliers(ADMM) is used to optimize our proposed *** experiments on six datasets corresponding to four different types and compared with several state-of-the-art multi-view clustering algorithms,the proposed algorithm can effectively enhance the complementarity and high-order correlation between views,and has good accuracy and robustness.

关键词： Dynamic Nearest Neighbor Learning Multi-view Clustering Mainfold Learning Sparse Representation Tensor SVD

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A unified generative framework for various ner subtasks

arXiv

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arXiv 2021年

作者： Yan, Hang Gui, Tao Dai, Junqi Guo, Qipeng Zhang, Zheng Qiu, Xipeng Shanghai Key Laboratory of Intelligent Information Processing Fudan University School of Computer Science Fudan University Institute of Modern Languages and Linguistics Fudan University New York University Pazhou Lab Guangzhou China

Named Entity Recognition (NER) is the task of identifying spans that represent entities in sentences. Whether the entity spans are nested or discontinuous, the NER task can be categorized into the flat NER, nested NER, and discontinuous NER subtasks. These subtasks have been mainly solved by the token-level sequence labelling or span-level classification. However, these solutions can hardly tackle the three kinds of NER subtasks concurrently. To that end, we propose to formulate the NER subtasks as an entity span sequence generation task, which can be solved by a unified sequence-to-sequence (Seq2Seq) framework. Based on our unified framework, we can leverage the pre-trained Seq2Seq model to solve all three kinds of NER subtasks without the special design of the tagging schema or ways to enumerate spans. We exploit three types of entity representations to linearize entities into a sequence. Our proposed framework is easy-to-implement and achieves state-of-the-art (SoTA) or near SoTA performance on eight English NER datasets, including two flat NER datasets, three nested NER datasets, and three discontinuous NER datasets 1 Copyright © 2021, The Authors. All rights reserved.

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PRF: a process-RAM-feedback performance model to reveal bottlenecks and propose optimizations

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High Technology Letters 2020年第3期26卷 285-298页

作者： Xie Zhen Tan Guangming Liu Weifeng Sun Ninghui Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190P.R.China University of Chinese Academy of Sciences Beijing 100190P.R.China College of Information Science and Engineering China University of PetroleumBeijing 102249P.R.China

Performance models provide insightful perspectives to predict performance and to propose optimization *** there has been much researches,pinpointing bottlenecks of various memory access patterns and reaching high accurate prediction of both regular and irregular programs on various hardware configurations are still not *** work proposes a novel model called process-RAM-feedback(PRF)to quantify the overhead of computation and data transmission time on general-purpose multi-core *** PRF model predicts the cost of instruction for singlecore by a directed acyclic graph(DAG)and the transmission time of memory access between each memory hierarchy through a newly designed cache *** using performance modeling and feedback optimization method,this paper uses PRF model to analyze and optimize convolution,sparse matrix-vector multiplication and sn-sweep as case study for covering with typical regular kernel to irregular and data *** the PRF model,it obtains optimization guidance with various sparsity structures,algorithm designs,and instruction sets support on different data sizes.

关键词： performance model feedback optimization convolution sparse matrix-vector multiplication sn-sweep

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements

A Multi-Player Potential Game Approach for Sensor Network Lo...

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IEEE Conference on Decision and Control

作者： Gehui Xu Guanpu Chen Baris Fidan Yiguang Hong Hongsheng Qi Thomas Parisini Karl H. Johansson Department of Electrical and Electronic Engineering Imperial College London London UK Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Department of Control Science and Engineering Tongji University shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China Department of Electronic Systems Aalborg University Denmark Department of Engineering and Architecture University of Trieste Italy

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multiplayer non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies.

关键词： Location awareness Noise Games Position measurement Nash equilibrium Distance measurement Noise measurement

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