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Visualizing nonlinear resonance in nanomechanical systems via single-electron tunneling

Nano Research 2021年第4期14卷 1156-1161页

作者： Xinhe Wang Lin Cong Dong Zhu Zi Yuan Xiaoyang Lin Weisheng Zhao Zaiqiao Bai Wenjie Liang Ximing Sun Guang-Wei Deng Kaili Jiang Fert Beijing Research Institute School of Microelectronics&Beijing Advanced Innovation Centre for Big Data and Brain Computing(BDBC)Beihang UniversityBeijing 100191China State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics and Tsinghua-Foxconn Nanotechnology Research CenterTsinghua UniversityBeijing 100084China Key Laboratory of Quantum Information CASUniversity of Science and Technology of ChinaHefei 230026China Department of Physics Beijing Normal UniversityBeijing 100875China Institute of Physics Chinese Academy of SciencesBeijing 100080China Institute of Nuclear and New Energy Technology Collaborative Innovation Center of Advanced Nuclear Energy TechnologyKey Laboratory of Advanced Reactor EngineeringTsinghua UniversityBeijing 100084China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 610054China

Numerous reports have elucidated the importance of mechanical resonators comprising quantum-dot-embedded carbon nanotubes(CNTs)for studying the effects of single-electron ***,there is a need to investigate the single-electron transport that drives a large amplitude into a nonlinear ***,a CNT hybrid device has been investigated,which comprises a gate-defined quantum dot that is embedded into a mechanical resonator under strong actuation *** Coulomb peak positions synchronously oscillate with the mechanical vibrations,enabling a single-electron Chopper*1 ***,the vibration amplitude of the CNT versus its frequency can be directly visualized via detecting the time-averaged single-electron tunneling *** understand this phenomenon,a general formula is derived for this time-averaged single-electron tunneling current,which agrees well with the experimental *** using this visualization method,a variety of nonlinear motions of a CNT mechanical oscillator have been directly recorded,such as Duffing nonlinearity,parametric resonance,and double-,fractional-,mixed-frequency *** approach opens up burgeoning opportunities for investigating and understanding the nonlinear motion of a nanomechanical system and its interactions with electron transport in quantum regimes.

关键词： carbon nanotube mechanical resonator quantum dot nonlinear coupling

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Multi-objective Firefly Algorithm for Test Data Generation with Surrogate Model

Multi-objective Firefly Algorithm for Test Data Generation w...

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2021国际计算机前沿大会

作者： Wenning Zhang Qinglei Zhou Chongyang Jiao Ting Xu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhongyuan University of Technology Zhengzhou University

To solve the emerging complex optimization problems,multi objective optimization algorithms are *** introducing the surrogate model for approximate fitness calculation,the multi objective firefly algorithm with surrogate model(MOFA-SM) is proposed in this ***,the population was initialized according to the chaotic ***,the external archive was constructed based on the preference sorting,with the lightweight clustering pruning *** the process of evolution,the elite solutions selected from archive were used to guide the movement to search optimal *** results show that the proposed algorithm can achieve better performance in terms of convergence iteration and stability.

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Outsourced secure ID3 decision tree algorithm over horizontally partitioned datasets with consortium blockchain 10

Outsourced secure ID3 decision tree algorithm over horizonta...

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10th International Conference on Communication and Network Security, ICCNS 2020

作者： Yang, Ming Hu, Xuexian Wei, Jianghog Zhang, Qihui Liu, Wenfen State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan China Guangxi Key Laboratory of Cryptography and Information Security Guilin Guangxi China

ISBN: (纸本)9781450389037

Due to the capacity of storing massive data and providing huge computing resources, cloud computing has been a desirable platform to assist machine learning in multiple-data-owners scenarios. However, the issue of data privacy is far from being well solved and thus has been a general concern in the cloud-assisted machine learning. For example, in the existing cloud-assisted decision tree classification algorithms, it is very hard to guarantee data privacy since all data owners have to aggregate their data to the cloud platform for model training. In this paper, we investigate the possibility of training a decision tree in the scenario that the distributed data are stored locally in each data owner, where the privacy of the original data can be guaranteed in a more intuitive approach. Specifically, we present a positive answer to the above issue by presenting a privacy-preserving ID3 training scheme using Gini index over horizontally partitioned datasets by multiple data owners. Since each data owner cannot directly divide the local dataset according to the best attributes selected, a consortium blockchain and a homomorphic encryption algorithm are employed to ensure the privacy and usability of the distributed data. Security analysis indicates that our scheme can preserve the privacy of the original data and the intermediate values. Moreover, extensive experiments show that our scheme can achieve the same result compared with the original ID3 decision tree algorithm while additionally preserving data privacy, and calculation time overhead and communication time overhead on data owners decrease greatly. © 2020 ACM.

关键词： Blockchain

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Open Set Recognition for Encrypted Traffic using Intra-class Partition and Boundary Sample Generation

Open Set Recognition for Encrypted Traffic using Intra-class...

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IEEE advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Xue Li Jinlong Fei Zan Qi Zhuo Lv Heng Jiang State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Information Engineering University Zhengzhou China National Digital Switching System Engineering Technological Research Center PLA Information Engineering University Zhengzhou China

ISBN: (纸本)9781665479691

Existing deep learning-based encrypted traffic recognition methods can achieve high precision identification performance while protecting user privacy, but almost all of them focus on closed sets, in which training data and test data come from the same label space. However, the real network environment is open, and it is necessary for traffic identification systems to deal with previously unseen categories. To classify known categories at a fine granularity and identify unknown ones, we propose a novel open-set recognition method for encrypted traffic. The decision boundary sample generation method based on constrained Generative Adversarial Networks is presented to generate samples from low-density regions in the known data space. Furthermore, an end-to-end open-set classifier is built using the accurate and diverse simulative samples of unknown classes through intra-class partition and boundary sample generation. Finally, the effectiveness of the proposed method is validated through numerical experiments.

关键词： Data privacy Automation Training data Generative adversarial networks Robustness Information management Cryptography

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Efficient Cache Strategy for Face Recognition System 20

Efficient Cache Strategy for Face Recognition System

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5th International Conference on Big Data and computing, ICBDC 2020

作者： Zong, Guoxiao Li, Qingbao Zhang, Ping Zhang, Guimin Zhu, Xinbing State Key Laboratory of Mathematical Engineering and Advanced Computing NO.62 Science Avenue ZhengzhouCN 450001 China

ISBN: (纸本)9781450375474

Recently, the need for real-time deep learning applications, such as face recognition system, implement on the embedded devices is increasing. At the system level, caching system is one of the most effective ways to reduce response time. However, embedded front-end devices can hardly guarantee effective system overhead for complex cache system while deploying deep learning model. In this paper, this paper presented an efficient cache strategy for deep learning applications such as face recognition system. This paper first established a distributed embedded system model to describe to functions of the face recognition systems. Then, this paper figures out the goal and idea of optimizing the cache design theoretically. Finally, our cache strategy is experimented and verified better than the traditional methods. © 2020 ACM.

关键词： Internet of things

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Self-recoverable Adversarial Examples: A New Effective Protection Mechanism in Social Networks

arXiv

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arXiv 2022年

作者： Zhang, Jiawei Wang, Jinwei Wang, Hao Luo, Xiangyang Nanjing University of Information Science and Technology Nanjing China Nanjing University of Science and Technology Nanjing China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhenzhou China

Malicious intelligent algorithms greatly threaten the security of social users’ privacy by detecting and analyzing the uploaded photos to social network platforms. The destruction to DNNs brought by the adversarial attack sparks the potential that adversarial examples serve as a new protection mechanism for privacy security in social networks. However, the existing adversarial example does not have recoverability for serving as an effective protection mechanism. To address this issue, we propose a recoverable generative adversarial network to generate sel f -recouerable aduersarial examples. By modeling the adversarial attack and recovery as a united task, our method can minimize the error of the recovered examples while maximizing the attack ability, resulting in better recoverability of adversarial examples. To further boost the recoverability of these examples, we exploit a dimension reducer to optimize the distribution of adversarial perturbation. The experimental results prove that the adversarial examples generated by the proposed method present superior recoverability, attack ability, and robustness on different datasets and network architectures, which ensure its effectiveness as a protection mechanism in social networks. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Entirety of Quantum Uncertainty and Its Experimental Verification

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Chinese Physics Letters 2021年第7期38卷 11-16页

作者： Jie Xie Li Zhou Aonan Zhang Huichao Xu Man-Hong Yung Ping Xu Nengkun Yu Lijian Zhang National Laboratory of Solid State Microstructures College of Engineering and Applied Sciences and School of PhysicsNanjing UniversityNanjing 210093China Collaborative Innovation Center of Advanced Microstructures Nanjing UniversityNanjing 210093China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Shenzhen Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Institute for Quantum Information&State Key Laboratory of High Performance Computing College of ComputerNational University of Defense TechnologyChangsha 410073China Centre for Quantum Software and Information School of SoftwareFaculty of Engineering and Information TechnologyUniversity of Technology SydneyNSWAustralia

As a foundation of quantum physics,uncertainty relations describe ultimate limit for the measurement uncertainty of incompatible ***,uncertainty relations are formulated by mathematical bounds for a specific *** we present a method for geometrically characterizing uncertainty relations as an entire area of variances of the observables,ranging over all possible input *** find that for the pair of position and momentum operators,Heisenberg's uncertainty principle points exactly to the attainable area of the variances of position and ***,for finite-dimensional systems,we prove that the corresponding area is necessarily semialgebraic;in other words,this set can be represented via finite polynomial equations and inequalities,or any finite union of such *** particular,we give the analytical characterization of the areas of variances of(a)a pair of one-qubit observables and(b)a pair of projective observables for arbitrary dimension,and give the first experimental observation of such areas in a photonic system.

关键词： observable union operators

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NEXT:a neural network framework for next POI recommendation

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Frontiers of Computer Science 2020年第2期14卷 314-333页

作者： Zhiqian ZHANG Chenliang LI Zhiyong WU Aixin SUN Dengpan YE Xiangyang LUO Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of EducationSchool of Cyber Science and EngineeringWuhan UniversityWuhan 430072China Department of Computer Science The University of Hong KongPokfulam RoadHong Kong 999077China School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore State Key Lab of Mathematical Engineering and Advanced Computing Zhengzhou 450001China

The task of next POI recommendations has been studied extensively in recent ***,developing a unified recommendation framework to incorporate multiple factors associated with both POIs and users remains challenging,because of the heterogeneity nature of these ***,effective mechanisms to smoothly handle cold-start cases are also a difficult *** by the recent success of neural networks in many areas,in this paper,we propose a simple yet effective neural network framework,named NEXT,for next POI *** is a unified framework to learn the hidden intent regarding user's next move,by incorporating different factors in a unified ***,in NEXT,we incorporate meta-data information,e.g.,user friendship and textual descriptions of POIs,and two kinds of temporal contexts(i.e.,time interval and visit time).To leverage sequential relations and geographical influence,we propose to adopt DeepWalk,a network representation learning technique,to encode such *** evaluate the effectiveness of NEXT against other state-of-the-art alternatives and neural networks based *** results on three publicly available datasets demonstrate that NEXT significantly outperforms baselines in real-time next POI *** experiments show inherent ability of NEXT in handling cold-start.

关键词： POI neural networks POI recommendation

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A Privacy Preserving and Byzantine Robust Collaborative Federated Learning Method Design

A Privacy Preserving and Byzantine Robust Collaborative Fede...

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IEEE International Conference on Communications (ICC)

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Changchuan Yin Christopher G. Brinton Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Collaborative federated learning (CFL) enables device cooperation in training shared machine learning models without reliance on a parameter server. However, the absence of a parameter server also impacts vulnerabilities associated with adversarial attacks, including privacy inference and Byzantine attacks. In this context, this paper introduces a novel CFL framework that enables each device to individually determine the subset of devices to transmit FL parameters to over the wireless network, based on its neighboring devices' location, current loss, and connection information, to achieve privacy protection and robust aggregation. This is formulated as an optimization problem whose goal is to minimize CFL training loss while satisfying the privacy preservation, robust aggregation, and transmission delay requirements. To solve this problem, a proximal policy optimization (PPO)-based reinforcement learning (RL) algorithm integrated with a graph neural network (GNN) is proposed. Compared to traditional algorithms that use global information with high computational complexity, the proposed GNN-RL method can be deployed on devices based on neighboring information with lower computational overhead. Simulation results show that the proposed algorithm can protect data privacy and increase identification accuracy by 15% compared to an algorithm in which devices are partially clustered for model aggregation.

关键词： Training Privacy Federated learning Simulation Collaboration Clustering algorithms Propagation losses

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KANsformer for Scalable Beamforming

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IEEE Transactions on Vehicular Technology 2025年

作者： Xie, Xinke Lu, Yang Chi, Chong-Yung Chen, Wei Ai, Bo Niyato, Dusit Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China National Tsing Hua University Institute of Communications Engineering Department of Electrical Engineering Hsinchu30013 Taiwan Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

This paper proposes an unsupervised deep-learning (DL) approach by integrating Transformer and Kolmogorov-Arnold networks (KAN) termed KANsformer to realize scalable beamforming for mobile communication systems. Specifically, we consider a classic multi-input single-output energy efficiency maximization problem subject to the total power budget. The proposed KANsformer first extracts hidden features via a multi-head self-attention mechanism and then reads out the desired beamforming design via KAN. Numerical results are provided to evaluate the KANsformer in terms of generalization performance, transfer learning and ablation experiment. Overall, the KANsformer outperforms the existing benchmark DL approaches, and is adaptable to the variation in the number of mobile users with real-time and near-optimal inference. © 1967-2012 IEEE.

关键词： Budget control

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