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Quantum noise protects quantum classifiers against adversaries

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arXiv 2020年

作者： Du, Yuxuan Hsieh, Min-Hsiu Liu, Tongliang Tao, Dacheng Liu, Nana UBTECH Sydney AI Centre School of Computer Science Faculty of Engineering University of Sydney Australia Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney Australia John Hopcroft Center for Computer Science Shanghai Jiao Tong University China

Noise in quantum information processing is often viewed as a disruptive and difficult-to-avoid feature, especially in near-term quantum technologies. However, noise has often played beneficial roles, from enhancing weak signals in stochastic resonance to protecting the privacy of data in differential privacy. It is then natural to ask, can we harness the power of quantum noise that is beneficial to quantum computing? An important current direction for quantum computing is its application to machine learning, such as classification problems. One outstanding problem in machine learning for classification is its sensitivity to adversarial examples. These are small, undetectable perturbations from the original data where the perturbed data is completely misclassified in otherwise extremely accurate classifiers. They can also be considered as ‘worst-case’ perturbations by unknown noise sources. We show that by taking advantage of depolarisation noise in quantum circuits for classification, a robustness bound against adversaries can be derived where the robustness improves with increasing noise. This robustness property is intimately connected with an important security concept called differential privacy which can be extended to quantum differential privacy. For the protection of quantum data, this is the first quantum protocol that can be used against the most general adversaries. Furthermore, we show how the robustness in the classical case can be sensitive to the details of the classification model, but in the quantum case the details of classification model are absent, thus also providing a potential quantum advantage for classical data that is independent of quantum speedups. This opens the opportunity to explore other ways in which quantum noise can be used in our favour, as well as identifying other ways quantum algorithms can be helpful that is independent of quantum speedups. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum noise

Segmentation of lesions in skin image based on salient object detection with deeply supervised learning 4

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Segmentation of lesions in skin image based on salient objec...

4th IEEE International Conference on computer and Communications, ICCC 2018

作者： Ji, Weijia Cai, Lizhi Chen, Wei Chen, Mingang Chai, Gang Laboratory of Computer Software Testing Evaluating Shanghai Development Center of Computer Software Technology Shanghai China Department of Plastic and Reconstructive Surgery Shanghai Ninth People's Hospital Shanghai JiaoTong University School of Medicine Shanghai China

ISBN: (纸本)9781538683392

In this paper, a binary segmentation method HCDS based on salient object detection is proposed to solve the segmentation of lesions in skin images. The original U-Net model encounters some problems in its application to skin image segmentation, one of which is the non-pathological region information remained in the segmentation results. Our HCDS method improves its basic structure by adding a hybrid convolution module to the direct connection between the down-sampling and up-sampling. It deepens the abstract understanding of the input features in shallow layers and further eliminate the information interference of the non-pathological regions. Besides, the method employs a deeply supervised structure in each stage of up-sampling to learn from the output feature and ground truth. Finally, the HCDS integrates the multi-path outputs to obtain the best result. The experimental results show that the proposed method HCDS can effectively segment the lesion regions from the skin images. Our evaluation metrics about the HCDS are obviously superior to the original U-Net's. © 2018 IEEE.

关键词： Image segmentation

Conditional Generative Modeling for Amorphous Multi-Element Materials

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arXiv 2025年

作者： Li, Honglin Liu, Chuhao Guo, Yongfeng Luo, Xiaoshan Chen, Yijie Liu, Guangsheng Li, Yu Wang, Ruoyu Wang, Zhenyu Wu, Jianzhuo Ma, Cheng Xie, Zhuohang Lv, Jian Ding, Yufei Zhang, Huabin Luo, Jian Zhong, Zhicheng Li, Mufan Wang, Yanchao Li, Wan-Lu Key Laboratory of Material Simulation Methods and Software Ministry of Education College of Physics Jilin University Changchun China Aiiso Yufeng Li Family Department of Chemical and Nano Engineering University of California La Jolla San DiegoCA United States Institute of Molecular Engineering Plus College of Chemistry Fuzhou University Fuzhou China College of Chemistry and Molecular Engineering Peking University Beijing China School of Physics Nankai University Tianjin China Institute of Modern Physics Fudan University Shanghai China Program in Materials Science and Engineering University of California La Jolla San DiegoCA United States School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China International Center of Future Science Jilin University Changchun China Department of Computer Science and Engineering University of California La Jolla San DiegoCA United States Center for Renewable Energy and Storage Technologies Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Suzhou Lab Suzhou215123 China

Amorphous multi-element materials offer unprecedented tunability in composition and properties, yet their rational design remains challenging due to the lack of predictive structure-property relationships and the vast configurational space. Traditional modeling struggles to capture the intricate short-range order that dictates their stability and functionality. We here introduce ApolloX, a pioneering predictive framework for amorphous multi-element materials, establishing a new paradigm by integrating physics-informed generative modeling with particle swarm optimization, using chemical short-range order as an explicit constraint. By systematically navigating the disordered energy landscape, ApolloX enables the targeted design of thermodynamically stable amorphous configurations. It accurately predicts atomic-scale arrangements, including composition-driven metal clustering and amorphization trends, which are well-validated by experiments, while also guiding synthesis by leveraging sluggish diffusion to control elemental distribution and disorder. The resulting structural evolution, governed by composition, directly impacts catalytic performance, leading to improved activity and stability with increasing amorphization. This predictive-experimental synergy transforms the discovery of amorphous materials, unlocking new frontiers in catalysis, energy storage, and functional disordered systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Amorphization

Investigating Bi-level optimization for learning and vision from a unified perspective: A survey and beyond

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arXiv 2021年

作者： Liu, Risheng Gao, Jiaxin Zhang, Jin Meng, Deyu Lin, Zhouchen DUT-RU International School of Information Science & Engineering Dalian University of Technology Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Dalian116024 China Department of Mathematics Southern University of Science and Technology National Center for Applied Mathematics Shenzhen China School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an Shaanxi China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Cooperative Medianet Innovation Center Shanghai Jiao Tong University Shanghai200240 China

Bi-Level Optimization (BLO) is originated from the area of economic game theory and then introduced into the optimization community. BLO is able to handle problems with a hierarchical structure, involving two levels of optimization tasks, where one task is nested inside the other. In machine learning and computer vision fields, despite the different motivations and mechanisms, a lot of complex problems, such as hyper-parameter optimization, multi-task and meta learning, neural architecture search, adversarial learning and deep reinforcement learning, actually all contain a series of closely related subproblms. In this paper, we first uniformly express these complex learning and vision problems from the perspective of BLO. Then we construct a best-response-based single-level reformulation and establish a unified algorithmic framework to understand and formulate mainstream gradient-based BLO methodologies, covering aspects ranging from fundamental automatic differentiation schemes to various accelerations, simplifications, extensions and their convergence and complexity properties. Last but not least, we discuss the potentials of our unified BLO framework for designing new algorithms and point out some promising directions for future research. © 2021, CC BY.

关键词： Deep learning

Tuning parallel symbolic execution engine for better performance

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Frontiers of computer Science 2018年第1期12卷 86-100页

作者： Anil Kumar KARNA Jinbo DU Haihao SHEN Hao ZHONG Jiong GONG Haibo YU Xiangning MA: Jianjun ZHAO Department of Computer Science Shanghai Jiao Tong University Shanghai 200240 China School of Software Shanghai Jiao Tong University Shanghai 200240 China Intel Asia-Pacific Research and Development Ltd. Shanghai 200240 China Department of Advanced Information Technology Kyushu University Fukuoka 819-0395 Japan

Symbolic execution is widely used in many code analysis, testing, and verification tools. As symbolic execu- tion exhaustively explores all feasible paths, it is quite time consuming. To handle the problem, researchers have par- alleled existing symbolic execution tools （e.g., KLEE）. In particular, Cloud9 is a widely used paralleled symbolic exe- cution tool, and researchers have used the tool to analyze real code. However, researchers criticize that tools such as Cloud9 still cannot analyze large scale code. In this paper, we con- duct a field study on Cloud9, in which we use KLEE and Cloud9 to analyze benchmarks in C. Our results confirm the criticism. Based on the results, we identify three bottlenecks that hinder the performance of Cloud9： the communication time gap, the job transfer policy, and the cache management of the solved constraints. To handle these problems, we tune the communication time gap with better parameters, modify the job transfer policy, and implement an approach for cache management of solved constraints. We conduct two evalua- tions on our benchmarks and a real application to understand our improvements. Our results show that our tuned Cloud9 reduces the execution time significantly, both on our bench- marks and the real application. Furthermore, our evaluation results show that our tuning techniques improve the effective- ness on all the devices, and the improvement can be achievedupto five times, depending upon a tuning value of our ap- proach and the behaviour of program under test.

关键词： code analysis symbolic execution parallelizingsymbolic execution KLEE Ctoud9

Matrix Classifier On Dynamic Functional Connectivity For Mci Identification

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Matrix Classifier On Dynamic Functional Connectivity For Mci...

IEEE International Conference on Image Processing

作者： Lei Zhou Liang Zhang Xiao Bai Jun Zhou School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Software Development Environment Beihang University Beijing School of Information and Communication Technology Griffith University Nathan

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

One of the most popular method for Alzheimer's disease (AD) diagnosis is exploring the Brain functional connectivity (FC) from resting-state functional magnetic resonance imaging (RS-fMRI). To early prevent AD, it is crucial to distinguish AD and and its preclinical stage, mild cognitive impairment (MCI) and early MCI (eMCI). In many existing works, dynamic functional connectivity (dFC) which contains rich spatiotemporal information has been exploited for the MCI and eMCI identification. However, most of these dFC based methods only consider the correlation between discrete brain status while ignore the valuable spatiotemporal information contained in dFC. To overcome this limitation, we propose a matrix classifier based method on the dFC signal for MCI and eMCI identification. Specifically, we first represent the dFC correlations by matrix features which contain rich spatiotemporal information and then learn the support matrix machines (SMM) to classify AD and its preclinical stage. Experiments on 600 real people data provide by the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate that our proposed matrix classifier based method outperforms other FC and dFC based methods for both normal controls (NC)/MCI identification and NC/eMCI identification.

关键词： Feature extraction Correlation Dementia Microsoft Windows Spatiotemporal phenomena Training

Joint prediction and time estimation of COVID-19 developing severe symptoms using chest CT scan

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arXiv 2020年

作者： Zhu, Xiaofeng Song, Bin Shi, Feng Chen, Yanbo Hu, Rongyao Gan, Jiangzhang Zhang, Wenhai Li, Man Wang, Liye Gao, Yaozong Shan, Fei Shen, Dinggang Center for Future Media and school of computer science and technology University of Electronic Science and Technology of China Chengdu611731 China Department of Radiology Sichuan University West China Hospital Chengdu610041 China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai200232 China Department of Radiology Shanghai Public Health Clinical Center Fudan University Shanghai China

With the rapidly worldwide spread of Coronavirus disease (COVID-19), it is of great importance to conduct early diagnosis of COVID-19 and predict the time that patients might convert to the severe stage, for designing effective treatment plan and reducing the clinicians’ workloads. In this study, we propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time, and if yes, predict the possible conversion time that the patient would spend to convert to the severe stage. To do this, the proposed method takes into account 1) the weight for each sample to reduce the outliers’ influence and explore the problem of imbalance classification, and 2) the weight for each feature via a sparsity regularization term to remove the redundant features of high-dimensional data and learn the shared information across the classification task and the regression task. To our knowledge, this study is the first work to predict the disease progression and the conversion time, which could help clinicians to deal with the potential severe cases in time or even save the patients’ lives. Experimental analysis was conducted on a real data set from two hospitals with 422 chest computed tomography (CT) scans, where 52 cases were converted to severe on average 5.64 days and 34 cases were severe at admission. Results show that our method achieves the best classification (e.g., 85.91% of accuracy) and regression (e.g., 0.462 of the correlation coefficient) performance, compared to all comparison methods. Moreover, our proposed method yields 76.97% of accuracy for predicting the severe cases, 0.524 of the correlation coefficient, and 0.55 days difference for the converted time. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

Dynamic Processing while Transmitting for SDN-based Space-Terrestrial Integrated Networks

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Dynamic Processing while Transmitting for SDN-based Space-Te...

IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Guangxiong Wu Feilong Tang Lijun Cao Ping Han Yanqin Yang Wenchao Xu Xingjun Zhang Shanghai Jiao Tong University Shanghai China Innovation Resource Center KEDACOM Co. Ltd China The Sci-Tech Information Office of Xuzhou Public Security Bureau Jiangsu Province China School of Computer Science and Software Engineering East China Normal University Shanghai China School of Computer Science and Technology Xi’an Jiaotong UniversityXian China

ISBN: (纸本)9781728176505

The space-terrestrial information network integrates the characteristics of satellite networks and terrestrial networks and can support diversified space network requirements, which has also become a new trend in future network development. In the process of coordinated transmitting between space and terrestrial, satellite may generate a data flow with a set of tasks to be processed while transmitting. Because of the dynamic changes in the links between nodes in the satellite network, how to effectively place the tasks will be huge challenges. In this paper, we considere the traffic changes brought about by task processing, the effectiveness of links, and combined with the operation laws of satellite network and have studied the placement of tasks in dynamic transmitting and processing scenarios. First, we formulate the Task Placement In Dynamic Transmitting-Processing (TPDTP) problem, and then divide the problem into three parts: flow path selection, task allocation between planes, and task placement within plane. For flow path selection, we proposed the Waiting-Time Reducing Path Select (WTRPS) algorithm. For the task allocation problem between planes, we proposed the Time-Constrained Task Placement (TCTP) algorithm and Waiting-Time Utilization Task Placement(WTUTP) algorithm. Finally, we propose Intra-plane Task Placement (IPTP) algorithm to solve the problem of task placement within plane which comprehensive consider the changes in traffic rate and the processing performance of nodes. We evaluate the proposed algorithms using simulations and present evaluation results to show the effectiveness of our design.

关键词： Satellites Heuristic algorithms Computational modeling High performance computing Low earth orbit satellites Focusing Market research

Generating Synthetic Computed Tomography for Radiotherapy: SynthRAD2023 Challenge Report

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arXiv 2024年

作者： Huijben, Evi M.C. Terpstra, Maarten L. Galapon, Arthur Pai, Suraj Thummerer, Adrian Koopmans, Peter Afonso, Manya van Eijnatten, Maureen Gurney-Champion, Oliver Chen, Zeli Zhang, Yiwen Zheng, Kaiyi Li, Chuanpu Pang, Haowen Ye, Chuyang Wang, Runqi Song, Tao Fan, Fuxin Qiu, Jingna Huang, Yixing Ha, Juhyung Park, Jong Sung Alain-Beaudoin, Alexandra Bériault, Silvain Yu, Pengxin Guo, Hongbin Huang, Zhanyao Li, Gengwan Zhang, Xueru Fan, Yubo Liu, Han Xin, Bowen Nicolson, Aaron Zhong, Lujia Deng, Zhiwei Müller-Franzes, Gustav Khader, Firas Li, Xia Zhang, Ye Hémon, Cédric Boussot, Valentin Zhang, Zhihao Wang, Long Bai, Lu Wang, Shaobin Mus, Derk Kooiman, Bram Sargeant, Chelsea A.H. Henderson, Edward G.A. Kondo, Satoshi Kasai, Satoshi Karimzadeh, Reza Ibragimov, Bulat Helfer, Thomas Dafflon, Jessica Chen, Zijie Wang, Enpei Perko, Zoltan Maspero, Matteo Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands Radiotherapy Department University Medical Center Utrecht Utrecht Netherlands Computational Imaging Group for MR Diagnostics & Therapy University Medical Center Utrecht Utrecht Netherlands Department of Radiation Oncology University Medical Center Groningen University of Groningen Groningen Netherlands GROW School for Oncology Maastricht University Medical Centre Maastricht Netherlands Department of Radiation Oncology LMU University Hospital LMU Munich Munich Germany Department of Radiation Oncology Radboud University Medical Center Nijmegen Netherlands Wageningen University & Research Wageningen Plant Research Wageningen Netherlands Department of Radiology and Nuclear Medicine Amsterdam UMC Location University of Amsterdam Amsterdam Netherlands Cancer Center Amsterdam Imaging and Biomarkers Amsterdam Netherlands School of Biomedical Engineering Southern Medical University Guangzhou China School of Integrated Circuits and Electronics Beijing Institute of Technology Beijing China School of Biomedical Engineering ShanghaiTech University Shanghai China Fudan University Shanghai China Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Indiana University Bloomington United States Advanced Development Engineering Elekta Ltd Montreal Canada Infervision Medical Technology Co. Ltd. Beijing China Department of Biomedical Engineering Shantou University China Independent researchers Department of Computer Science Vanderbilt University Nashville United States Australian e-Health Research Centre CSIRO HerstonQLD Australia Los AngelesCA United States University Hospital Aachen Aachen Germany Center for Proton Therapy Paul Scherrer Institut Villigen Switzerland Department of Computer Science ETH Zurich Zurich Switzerland University Rennes 1 CLCC Eugène Marquis INSERM LTSI Rennes France Subtle Medical Shanghai China MedMind Technology Co.

Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it lacks electron density information, while cone beam CT (CBCT) lacks direct electron density calibration and is mainly used for patient positioning. Adopting MRI-only or CBCT-based adaptive radiotherapy eliminates the need for CT planning but presents challenges. Synthetic CT (sCT) generation techniques aim to address these challenges by using image synthesis to bridge the gap between MRI, CBCT, and CT. The SynthRAD2023 challenge was organized to compare synthetic CT generation methods using multi-center ground truth data from 1080 patients, divided into two tasks: 1) MRI-to-CT and 2) CBCT-to-CT. The evaluation included image similarity and dose-based metrics from proton and photon plans. The challenge attracted significant participation, with 617 registrations and 22/17 valid submissions for tasks 1/2. Top-performing teams achieved high structural similarity indices (≥ 0.87/0.90) and gamma pass rates for photon (≥ 98.1%/99.0%) and proton (≥ 97.3%/97.0%) plans. However, no significant correlation was found between image similarity metrics and dose accuracy, emphasizing the need for dose evaluation when assessing the clinical applicability of sCT. SynthRAD2023 facilitated the investigation and benchmarking of sCT generation techniques, providing insights for developing MRI-only and CBCT-based adaptive radiotherapy. It showcased the growing capacity of deep learning to produce high-quality sCT, reducing reliance on conventional CT for treatment planning. © 2024, CC

关键词： Radiotherapy