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arXiv 2020年

作者： Jiang, Xinhe Wang, Kun Qian, Kaiyi Chen, Zhaozhong Chen, Zhiyu Lu, Liangliang Xia, Lijun Song, Fangmin Zhu, Shining Ma, Xiaosong National Laboratory of Solid-state Microstructures School of Physics Collaborative Innovation Center of Advanced Microstructures State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing210093 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Quantum devices for generating entangled states have been extensively studied and widely used. As so, it becomes necessary to verify that these devices truly work reliably and efficiently as they are specified. Here, we experimentally realize the recently proposed two-qubit entangled state verification strategies using both local measurements (nonadaptive) and active feed-forward operations (adaptive) with a photonic platform. About 1650/268 number of copies (N) are required to achieve a 90% confidence to verify the target quantum state for nonadaptive/adaptive strategies. These optimal strategies provide the Heisenberg scaling of the infidelity Ε as a function of N (Ε∼Nr) with the parameter r = −1, exceeding the standard quantum limit with r = −0.5. We experimentally obtain the scaling parameter of r = −0.88±0.03 and −0.78±0.07 for nonadaptive and adaptive strategies, respectively. Our experimental work could serve as a standardized procedure for the verification of quantum states. Copyright © 2020, The Authors. All rights reserved.

关键词： Optimal systems

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Service Function Chaining with Deterministic Fault Tolerance in Optical Edge Networks

TechRxiv

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TechRxiv 2021年

作者： Zheng, Danyang Shen, Gangxiang Li, Yongcheng Cao, Xiaojun Mukherjee, Biswanath Suzhou Key Laboratory of Advanced Optical Communication Network Technology The School of Electronic and Information Engineering Soochow University Suzhou215006 China The Department of Computer Science Georgia State University AtlantaGA30302 United States

In the upcoming 5G-and-beyond era, ultra-reliable low-latency communication (URLLC) services will be ubiquitous in edge networks. To improve network performance and quality of service (QoS), URLLC services could be delivered via a sequence of software-based network functions, also known as service function chains (SFCs). Towards reliable SFC delivery, it is imperative to incorporate deterministic fault tolerance during SFC deployment. However, deploying an SFC with deterministic fault tolerance is challenging because the protection mechanism needs to consider protection against physical/virtual network failures and hardware/software failures jointly. Against multiple and diverse failures, this work investigates how to effectively deliver an SFC in optical edge networks with deterministic fault tolerance while minimizing wavelength resource consumption. We introduce a protection augmented graph, called k-connected service function slices layered graph (KC-SLG), protecting against k-1 fiber link failures and k-1 server failures. We formulate a novel problem called deterministic-fault-tolerant SFC embedding and propose an effective algorithm, called most candidate first SF slices layered graph embedding (MCF-SE). MCF-SE employs two proposed techniques: k-connected network slicing (KC-NS) and kconnected function slicing (KC-FS). Through thorough mathematical proof, we show that KC-NS is 2-approximate. For KC-FS, we demonstrate that k = 3 provides the best cost-efficiency. Our experimental results also show that the proposed MCF-SE achieves deterministic-fault-tolerant service delivery and performs better than the schemes directly extended from existing work regarding survivability and average cost-efficiency. © 2021, CC BY-SA.

关键词： Fault tolerance

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TPFIANet: Three path feature progressive interactive attention learning network for medical image segmentation

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Knowledge-Based Systems 2025年 323卷

作者： Zhao, Yawu Wang, Shudong Ren, Yande Wang, Jiehuan Wang, Shaoqiang Qiao, Sibo Liu, Tiyao Pang, Shanchen Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum Qingdao Shandong China Department of Medical Imaging Center Affiliated Hospital of Qingdao University Qingdao Shandong China Department of Medical Imaging Affiliated Hospital of Jining Medical University Jining Shandong China School of Information and Control Engineering Qingdao University of Technology Qingdao Shandong China School of Software Tiangong University Tianjin China

In the field of medical image segmentation, medical image segmentation methods with high accuracy and generalization capabilities are crucial for driving personalized healthcare solutions and enhancing patient experience. To address the inherent challenges of difficulty in acquiring complex structural features and weak generalization ability in medical image segmentation, we propose a three-path spatial learning (TPSL) module, which takes advantage of convolutional neural networks (CNNs) to skillfully learn specific details at different scales to acquire better feature representations, and then, we propose a progressive feature cross-fusion (PFCF) module, which aims to dynamically learn feature information under different levels in the encoder, which enhances the feature representation. Finally, we propose the feature interactive attention fusion (FIAF) module, which mixes features from different scales and channels, adaptively learns feature weights and enhances the connection between channel and local spatial information. Based on the above modules, we design a three path feature progressive interactive attention learning network (TPFIANet), which captures image features at different levels and scales. We demonstrate the segmentation performance of TPFIANet on three public datasets and three self-organized datasets. Notably, our TPFIANet also demonstrates amiable segmentation performance on datasets of different modalities while maintaining a lighter architecture. © 2025

关键词： Image segmentation

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Local and global structure-aware entropy regularized mean teacher model for 3d left atrium segmentation 23rd

Local and global structure-aware entropy regularized mean te...

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Hang, Wenlong Feng, Wei Liang, Shuang Yu, Lequan Wang, Qiong Choi, Kup-Sze Qin, Jing School of Computer Science and Technology Nanjing Tech University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Department of Radiation Oncology Stanford University StanfordCA94305 United States CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China School of Nursing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783030597092

Emerging self-ensembling methods have achieved promising semi-supervised segmentation performances on medical images through forcing consistent predictions of unannotated data under different perturbations. However, the consistency only penalizes on independent pixel-level predictions, making structure-level information of predictions not exploited in the learning procedure. In view of this, we propose a novel structure-aware entropy regularized mean teacher model to address the above limitation. Specifically, we firstly introduce the entropy minimization principle to the student network, thereby adjusting itself to produce high-confident predictions of unannotated images. Based on this, we design a local structural consistency loss to encourage the consistency of inter-voxel similarities within the same local region of predictions from teacher and student networks. To further capture local structural dependencies, we enforce the global structural consistency by matching the weighted self-information maps between two networks. In this way, our model can minimize the prediction uncertainty of unannotated images, and more importantly that it can capture local and global structural information and their complementarity. We evaluate the proposed method on a publicly available 3D left atrium MR image dataset. Experimental results demonstrate that our method achieves outstanding segmentation performances than the state-of-the-art approaches in scenes with limited annotated images. © Springer Nature Switzerland AG 2020.

关键词： Forecasting

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Quantum Algorithm for Lexicographically Minimal String Rotation

arXiv

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arXiv 2020年

作者： Wang, Qisheng Ying, Mingsheng The Graduate School of Mathematics Nagoya University Nagoya Japan The Department of Computer Science and Technology Tsinghua University Beijing China The State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

Lexicographically minimal string rotation (LMSR) is a problem to find the minimal one among all rotations of a string in the lexicographical order, which is widely used in equality checking of graphs, polygons, automata and chemical structures. In this paper, we propose an O(n3/4) quantum query algorithm for LMSR. In particular, the algorithm has average-case query complexity O(√n log n), which is shown to be asymptotically optimal up to a polylogarithmic factor, compared to its Ω (pn/ log n ) lower bound. Furthermore, we show that our quantum algorithm outperforms any (classical) randomized algorithms in both worst and average cases. As an application, it is used in benzenoid identification and disjoint-cycle automata minimization. Copyright © 2020, The Authors. All rights reserved.

关键词： Rotation

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Provably robust metric learning 20

Provably robust metric learning

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Proceedings of the 34th International Conference on Neural Information Processing Systems

作者： Lu Wang Xuanqing Liu Jinfeng Yi Yuan Jiang Cho-Jui Hsieh National Key Laboratory for Novel Software Technology Nanjing University Nanjing China and *** Beijing China Department of Computer Science University of California Los Angeles CA *** Beijing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9781713829546

Metric learning is an important family of algorithms for classification and similarity search, but the robustness of learned metrics against small adversarial perturbations is less studied. In this paper, we show that existing metric learning algorithms, which focus on boosting the clean accuracy, can result in metrics that are less robust than the Euclidean distance. To overcome this problem, we propose a novel metric learning algorithm to find a Mahalanobis distance that is robust against adversarial perturbations, and the robustness of the resulting model is certifiable. Experimental results show that the proposed metric learning algorithm improves both certified robust errors and empirical robust errors (errors under adversarial attacks). Furthermore, unlike neural network defenses which usually encounter a trade-off between clean and robust errors, our method does not sacrifice clean errors compared with previous metric learning methods.

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Preface to special topic on quantum computing

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National science Review 2019年第1期6卷 20页

作者： Guang-Can Guo Mingsheng Ying Key Laboratory of Quantum Information Chinese Academy of SciencesUniversity of Science and Technology of China Synergetic Innovation Center of Quantum Information & Quantum Physics University of Science and Technology of China Centre for Quantum Software and Information University of Technology Sydney State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Department of Computer Science and Technology Tsinghua University

At the beginning of the 1980s, quantum computing was born to noble parents—Quantum Mechanics and computer science,two of the greatest intellectual triumphs of the twentieth century—with Richard Feynman and David Deutsch among its midwives. Theoretical research over the last 40 years revealed that quantum computers can outperform current computers in a number of important computational tasks, particularly given the discovery of several celebrated quantum algorithms,such as Shor’s for factoring, Grover’s for searching, quantum

关键词： Preface to special topic on quantum computing

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Preface

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Journal of computer science & technology 2022年第1期37卷 1-3页

作者： Tao Xie Shengchao Qin Wenhui Zhang Jun Sun Lei Bu Ge Li School of Computer Science Peking UniversityBeijing Huawei Hong Kong Research Centre Hong KongChina Institute of Software Chinese Academy of SciencesBeijing Singapore Management University Singapore Department of Computer Science and Technology&State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing

As a continuation of previous years’special section on software systems,this special section encourages and promotes research to address challenges from the perspective of software *** goal of this special section is to present state-of-the-art and high-quality original research in the area of software *** special section is split in two parts,with the first part published earlier in the last issue in November 2021 and the second part published in this current issue.

关键词： systems. continuation section

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Granger-Causality Mining in Atmospheric Visibility Based on Deep Learning 9th

Granger-Causality Mining in Atmospheric Visibility Based on ...

引用

9th International Conference on Frontier Computing, FC 2019

作者： Liu, Bo He, Xi Li, Jianqiang Qu, Guangzhi Lang, Jianlei Gu, Rentao Faculty of Information Technology School of Software Engineering Beijing University of Technology Beijing100124 China Computer Science and Engineering Department Oakland University RochesterMI48309 United States Key Laboratory of Beijing on Regional Air Pollution Control College of Environmental and Energy Engineering Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9789811532498

Causal relationship mining of multi-dimensional meteorological time series data can reveal the potential connection between visibility and other influencing factors, and it is meaningful in terms of environmental management, air pollution chasing, and haze control. However, because causality analysis based on statistical methods or traditional machine learning techniques cannot represent the complex relationship between visibility and its influencing factors, as an extension of traditional Granger-causality analysis, we propose a causality mining method based on the seq2seq-LSTM deep learning model. The method can profoundly reveal the hidden relationship between different features and visibility and the regular pattern of bad weather, which can provide theoretical support for air pollution control. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Time series

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ExchNet: A unified Hashing network for large-scale fine-grained image retrieval

arXiv

引用

arXiv 2020年

作者： Cui, Quan Jiang, Qing-Yuan Wei, Xiu-Shen Li, Wu-Jun Yoshie, Osamu Graduate School of IPS Waseda University Japan National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China Megvii Research Nanjing Megvii Technology China

Retrieving content relevant images from a large-scale fine-grained dataset could suffer from intolerably slow query speed and highly redundant storage cost, due to high-dimensional real-valued embeddings which aim to distinguish subtle visual differences of fine-grained objects. In this paper, we study the novel fine-grained hashing topic to generate compact binary codes for fine-grained images, leveraging the search and storage efficiency of hash learning to alleviate the aforementioned problems. Specifically, we propose a unified end-to-end trainable network, termed as ExchNet. Based on attention mechanisms and proposed attention constraints, it can firstly obtain both local and global features to represent object parts and whole fine-grained objects, respectively. Furthermore, to ensure the discriminative ability and semantic meaning’s consistency of these part-level features across images, we design a local feature alignment approach by performing a feature exchanging operation. Later, an alternative learning algorithm is employed to optimize the whole ExchNet and then generate the final binary hash codes. Validated by extensive experiments, our proposal consistently outperforms state-of-the-art generic hashing methods on five fine-grained datasets, which shows our effectiveness. Moreover, compared with other approximate nearest neighbor methods, ExchNet achieves the best speed-up and storage reduction, revealing its efficiency and practicality. Copyright © 2020, The Authors. All rights reserved.

关键词： Nearest neighbor search

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