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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

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Dynamics and Resonance Fluorescence from a Superconducting Artificial Atom Doubly Driven by Quantized and Classical Fields

arXiv

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arXiv 2024年

作者： Ruan, Xinhui Wang, Jia-Heng He, Dong Song, Pengtao Li, Shengyong Zhao, Qianchuan Kuang, L.M. Tsai, Jaw-Shen Zou, Chang-Ling Zhang, Jing Zheng, Dongning Astafiev, O.V. liu, Yu-Xi Peng, Zhihui Department of Automation Tsinghua University Beijing100084 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center of Quantum Effects and Applications Hunan Normal University Changsha410081 China School of Integrated Circuits Tsinghua University Beijing100084 China Institute of Physics Chinese Academy of Sciences Beijing100190 China School of Physical Sciences University of Chinese Academy of Sciences Beijing100190 China Center for Quantum Computing RIKEN Saitama351-0198 Japan Graduate School of Science Tokyo University of Science 1-3 Kagurazaka Shinjuku Tokyo162-0825 Japan CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei Anhui230026 China Hefei National Laboratory Hefei230088 China School of Automation Science and Engineering Xi'an Jiaotong University Xi’an710049 China MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi’an710049 China Skolkovo Institute of Science and Technology Nobel str. 3 Moscow143026 Russia Moscow Institute of Physics and Technology Institutskiy Pereulok 9 Dolgoprudny141701 Russia Royal Holloway University of London Surrey EghamTW20 0EX United Kingdom

We report an experimental demonstration of resonance fluorescence in a two-level superconducting artificial atom under two driving fields coupled to a detuned cavity. One of the fields is classical and the other is varied from quantum (vacuum fluctuations) to classical one by controlling the photon number inside the cavity. The device consists of a transmon qubit strongly coupled to a one-dimensional transmission line and a coplanar waveguide resonator. We observe a sideband anti-crossing and asymmetry in the emission spectra of the system through a one-dimensional transmission line, which is fundamentally different from the weak coupling case. By changing the photon number inside the cavity, the emission spectrum of our doubly driven system approaches to the case when the atom is driven by two classical bichromatic fields. We also measure the dynamical evolution of the system through the transmission line and study the properties of the first-order correlation function, Rabi oscillations and energy relaxation in the system. The study of resonance fluorescence from an atom driven by two fields promotes understanding decoherence in superconducting quantum circuits and may find applications in superconducting quantum computing and quantum networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Electric lines

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ZSTAD: Zero-Shot Temporal Activity Detection

ZSTAD: Zero-Shot Temporal Activity Detection

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Lingling Zhang Xiaojun Chang Jun liu Minnan Luo Sen Wang Zongyuan Ge Alexander Hauptmann School of Computer Science and Technology Xi'an Jiaotong University Xian China Ministry of Education Key Lab For Intelligent Networks and Network Security Xian China Faculty of Information Technology Monash University Australia National Engineering Lab for Big Data Analytics Xi'an Jiaotong University Xian China School of Information Technology and Electrical Engineering The University of Queensland Australia School of Computer Science Carnegie Mellon University USA

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos. Currently, the most effective methods of temporal activity detection are based on deep learning, and they typically perform very well with large scale annotated videos for training. However, these methods are limited in real applications due to the unavailable videos about certain activity classes and the time-consuming data annotation. To solve this challenging problem, we propose a novel task setting called zero-shot temporal activity detection (ZSTAD), where activities that have never been seen in training can still be detected. We design an end-to-end deep network based on R-C3D as the architecture for this solution. The proposed network is optimized with an innovative loss function that considers the embeddings of activity labels and their super-classes while learning the common semantics of seen and unseen activities. Experiments on both the THUMOS'14 and the Charades datasets show promising performance in terms of detecting unseen activities.

关键词： Semantics Proposals Training Task analysis Feature extraction Microsoft Windows Three-dimensional displays

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ZSTAD: Zero-shot temporal activity detection

arXiv

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arXiv 2020年

作者： Zhang, Lingling Chang, Xiaojun liu, Jun Luo, Minnan Wang, Sen Ge, Zongyuan Hauptmann, Alexander School of Computer Science and Technology Xian Jiaotong University Xian China Ministry of Education Key Lab For Intelligent Networks and Network Security Xian China Faculty of Information Technology Monash University Australia National Engineering Lab for Big Data Analytics Xian Jiaotong University Xian China School of Information Technology and Electrical Engineering University of Queensland Australia School of Computer Science Carnegie Mellon University United States

An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos. Currently, the most effective methods of temporal activity detection are based on deep learning, and they typically perform very well with large scale annotated videos for training. However, these methods are limited in real applications due to the unavailable videos about certain activity classes and the time-consuming data annotation. To solve this challenging problem, we propose a novel task setting called zero-shot temporal activity detection (ZSTAD), where activities that have never been seen in training can still be detected. We design an end-to-end deep network based on R-C3D as the architecture for this solution. The proposed network is optimized with an innovative loss function that considers the embeddings of activity labels and their super-classes while learning the common semantics of seen and unseen activities. Experiments on both the THUMOS14 and the Charades datasets show promising performance in terms of detecting unseen activities. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Quantum Acoustics with Superconducting Qubits in the Multimode Transition-Coupling Regime

arXiv

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arXiv 2025年

作者： Li, Li Ruan, Xinhui Zhao, Si-Lu Chen, Bing-Jie Liang, Gui-Han liu, Yu Deng, Cheng-Lin Yuan, Wei-Ping Song, Jia-Cheng liu, Zheng-He Li, Tian-Ming Shi, Yun-Hao Zhang, He Han, Ming Guo, Jin-Ming Guo, Xue-Yi Zhao, Qianchuan Zhang, Jing Song, Pengtao Song, Xiaohui Xu, Kai Fan, Heng liu, Yu-Xi Peng, Zhihui Xiang, Zhongcheng Zheng, Dongning Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China School of Physical Sciences University of Chinese Academy of Sciences Beijing100049 China Department of Automation Tsinghua University Beijing100084 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Key Laboratory for Matter Microstructure and Function of Hunan Province Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China Beijing Academy of Quantum Information Sciences Beijing100193 China School of Automation Science and Engineering Xi’an Jiaotong University Xi’an710049 China MOE Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China Hefei National Laboratory Hefei230088 China School of Integrated Circuits Tsinghua University Beijing100084 China

Hybrid mechanical-superconducting systems for quantum information processing have attracted significant attention due to their potential applications. In such systems, the weak coupling regime, dominated by dissipation, has been extensively studied. The strong coupling regime, where coherent energy exchange exceeds losses, has also been widely explored. However, the transition-coupling regime, which lies between the above two and exhibits rich, unique physics, remains underexplored. In this study, we fabricate a tunable coupling device to investigate the coupling of a superconducting transmon qubit to a seven-mode surface acoustic wave resonator (SAWR), with a particular focus on the transition-coupling regime. Through a series of phonon oscillation experiments and studies in the dispersive regime, we systematically characterize the performance of the SAWR. We then explore the complex dynamics of energy exchange between the qubit and the mechanical modes, highlighting the interplay between dissipation and coherence. Finally, we propose a protocol for qubit readout and fast reset with a multimode mechanical cavity using one mode for readout and another mode for reset. We have demonstrated in simulation that the qubit achieves both fast reset and high coherence performance when the qubit is coupled to the reset mode in the transition-coupling regime. © 2025, CC BY.

关键词： Qubits

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Is AI Robust Enough for Scientific Research?

arXiv

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arXiv 2024年

作者： Zhang, Jun-Jie Song, Jiahao Wang, Xiu-Cheng Li, Fu-Peng liu, Zehan Chen, Jian-Nan Dang, Haoning Wang, Shiyao Zhang, Yiyan Xu, Jianhui Shi, Chunxiang Wang, Fei Pang, Long-Gang Cheng, Nan Zhang, Weiwei Zhang, Duo Meng, Deyu Northwest Institute of Nuclear Technology No. 28 Pingyu Road Shaanxi Xi’an710024 China School of Telecommunications Engineering Xidian University No. 2 South Taibai Road Shaanxi Xi’an710071 China State Key Laboratory of ISN No. 2 South Taibai Road Shaanxi Xi’an710071 China Institute of Particle Physics Central China Normal University No. 152 Luoyu Road Hubei Wuhan30079 China School of Computer Science and Technology Xi’an Jiaotong University No. 28 Xianning West Road Shaanxi Xi’an710049 China Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University No. 28 Xianning West Road Shaanxi Xi’an710049 China Guangzhou Institute of Geography Academy of Sciences No. 100 Xianlie Road Guangdong Guangzhou510070 China National Meteorological Information Center Beijing100044 China MDX Research Center for Element Strategy Institute of Integrated Research Institute of Science Tokyo Midori-ku Yokohama226-8503 Japan School of Physics and Information Technology Shaanxi Normal University No. 620 West Chang’an Avenue Shaanxi Xi’an710119 China School of Aeronautics Northwestern Polytechnical University No. 127 West Youyi Road Shaanxi Xi’an710072 China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China School of Mathematics and Statistics Xi’an Jiaotong University No. 28 Xianning West Road Shaanxi Xi’an710049 China

We uncover a phenomenon largely overlooked by the scientific community utilizing AI: neural networks exhibit high susceptibility to minute perturbations, resulting in significant deviations in their outputs. Through an analysis of five diverse application areas—weather forecasting, chemical energy and force calculations, fluid dynamics, quantum chromodynamics, and wireless communication—we demonstrate that this vulnerability is a broad and general characteristic of AI systems. This revelation exposes a hidden risk in relying on neural networks for essential scientific computations, calling further studies on their reliability and security. © 2024, CC0.

关键词： Weather forecasting

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A Co-Evolutionary Model for Inferring Online Social Network User Behaviors

A Co-Evolutionary Model for Inferring Online Social Network ...

引用

2018 International Conference on security, Pattern Analysis, and Cybernetics, SPAC 2018

作者： liu, Xiaoming Shen, Chao Fan, Yingyue liu, Xiaozi Zhou, Yadong Guan, Xiaohong Xi'An Jiaotong University Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an710049 China College of Economics and Management Northwest A and F University Xianyang Shanxi712100 China Tsinghua University Center for Intelligent and Networked Systems TNLIST Lab Beijing100084 China

ISBN: (纸本)9781728105512

Accurate online social network user behavior inference can improve the performance of the other applications significantly, such as friend commendation, hot topic prediction, and personal website assistant. Previous works mainly focus on the trend analysis of user behaviors or adopt the method to fit the supposed user-behavior distribution, but they ignore the dynamic mutual influence among the users and behaviors on social networks. This paper proposes a co-evolutionary model to formulate the interaction pattern among the users and behaviors, in which a systematic method is used for embedding the distinctiveness and permanence properties of the users and behaviors into latent features. This model could naturally capture the dynamic evolving process of the user behaviors with the time. What's more, we also take into account the following relationship to depict the interaction information among users. Extensive experiments show that our algorithm achieves 0.024 of the MAE (hour) in the crime time inference, and 0.506 and 0.579 of the accuracy in the user and behavior inference, which surpass the state-of-arts more than 7.19×, 1.12× and 1.14×, respectively. Additional experiments on different training echoes of our model are provided to further explore its effectiveness and scalability. © 2018 IEEE.

关键词： Social networking (online)

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Robust Transmission Capacity Margin Evaluation Based on All-Scenario-Feasible Optimization 36

Robust Transmission Capacity Margin Evaluation Based on All-...

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第36届中国控制会议

作者： Weisheng liu Xiaohong Guan Jiang Wu Feilong Song Dong Xie Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University

ISBN: (纸本)9781538629185

A robust and effective evaluation method for the safety of optimal generation scheduling has great potential on optimizing operation of electrical power and energy *** on the concept of transmission capacity margin,a robust model of transmission capacity margin evaluation（TCME） is established to assess the overload risk of transmission lines when loads *** TCME method can provide the system operators with useful information such as possibly overloaded lines and time periods when lines are potentially overloaded under the uncertainties of nodal ***,TCME formulation has a multi-level（min-max） structure and the solution methods are usually *** order to overcome the computational complexity of original problem,an All-Scenario-Feasible optimization is *** is established based on very limited scenarios while the solution is feasible to all *** proposed formulation is single-level rather than *** no iterative algorithms are needed and commercial solver like CPLEX could be directly *** order to use the proposed formulation for large-scale power grid,considering that the transmission capacity constraint is the most important one in all problem constraints,we further reduce the number of *** testing is also performed and the results show that the proposed formulation is effective.

关键词： robust transmission capacity margin All-Scenario-Feasible

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A Wind Power Time Series Modeling Method Based on Stochastic Burgers Equation 36

A Wind Power Time Series Modeling Method Based on Stochastic...

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第36届中国控制会议

作者： Xie Dong Wu Jiang Song Feilong liu Weisheng Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University

ISBN: (纸本)9781538629185

With more wind power integrating into power grid,a lot of issues are caused by its uncertainty and *** order to analysis wind characters,this paper proposes a method to model wind *** method bases on stochastic Burgers *** turns wind power model from three dimension to one dimension in order to simplify the physical model and generate more wind speed data to improve *** uses Gaussian distribution to fit the *** uses the Park model to calculate the wake *** it turns the wind speed data to wind power ***,the scheme is applied to a 30-turbines case.

关键词： wind power stochastic Burgers equation Park model

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Hybrid Features based K-means Clustering Algorithm for use in Electricity Customer Load Pattern Analysis

Hybrid Features based K-means Clustering Algorithm for use i...

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第37届中国控制会议

作者： Pengyuan liu Chenye Yang Jiang Wu Xingbo Fu Ruanming Huang Yichao Huang Fei Fei Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University State Grid Shanghai Electric Economic and Technology Research Institute

In the power system, the load pattern of users is important for electricity price formulation, abnormal electricity consumption detection and load forecasting. Accurately identifying the load patterns effectively improve the overall load forecasting performance of the power grid. However, due to many factors affecting the load, identifying load patterns is not a simple job. In this paper, the features of load data are extracted and different load patterns of users are identified. In the pattern classification, K-means clustering is used. In feature extraction, PCA and prior knowledge were respectively used for dimensionality reduction to extract new features in the past. Combining the advantages of PCA and prior knowledge, a new method, namely, weighted combination of the obtained features from two former methods, is adopted to extract new features and identify the user’s load pattern. In the similarity measure, the variance of each feature is taken as its weight, and the clustering result is superior to that of PCA and prior knowledge. In the numerical simulation, it is found that the clustering results using the new method in this paper are better than those using only PCA to reduce dimensionality or using prior knowledge to reduce dimensionality. In the experiment, the users in a certain area were divided into four categories according to the clustering result of load data: peak electricity type, partial peak electricity type I, partial peak electricity type II and abnormal electricity type.

关键词： K-means PCA Priori Knowledge User Classification Load Pattern

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