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arXiv 2025年

作者： Basilakos, Spyros Paliathanasis, Andronikos Saridakis, Emmanuel N. National Observatory of Athens Lofos Nymfon Athens11852 Greece Academy of Athens Research Center for Astronomy and Applied Mathematics Soranou Efesiou 4 Athens11527 Greece School of Sciences European University Cyprus Diogenes Street Engomi Nicosia1516 Cyprus Institute of Systems Science Department of Mathematics Faculty of Applied Sciences Durban University of Technology Durban4000 South Africa School for Data Science and Computational Thinking Stellenbosch University 44 Banghoek Rd Stellenbosch7600 South Africa Departamento de Matemáticas Universidad Católica del Norte Avda. Angamos 0610 Casilla Antofagasta1280 Chile CAS Key Laboratory for Researches in Galaxies and Cosmology Department of Astronomy University of Science and Technology of China Anhui Hefei230026 China

We show that f(Q) cosmology with a non-trivial connection, namely the Connection II of the literature, is dynamically equivalent with a quintom-like model. In particular, we show that the scalar field arising from the non-linear f(Q) form, and the scalar field associated to the non-trivial connection, are combined to provide one canonical and one phantom field in the minisuperspace Lagrangian. Hence, the combination of two well-behaved fields appears effectively as a phantom field. This is fundamentally different from the usual approach in phantom and quintom cosmology, in which the phantom field is introduced ad hoc, and thus it may open a new way to handle the phantom fields, namely as effective realizations of the connection-related canonical fields. Copyright © 2025, The Authors. All rights reserved.

关键词： Phantoms

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Efficient Federated Learning with Spike Neural networks for Traffic Sign Recognition

arXiv

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arXiv 2022年

作者： Xie, Kan Zhang, Zhe Li, Bo Kang, Jiawen Niyato, Dusit Xie, Shengli Wu, Yi The School of Automation Guangdong University of Technology Guangzhou510006 China The Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China The Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China The Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China The School of Data Science and Technology Heilongjiang University Harbin150080 China The Institute for Cryptology & Network Security Heilongjiang University Harbin150080 China School of Computer Science and Engineering Nanyang Technological University Singapore

With the gradual popularization of self-driving, it is becoming increasingly important for vehicles to smartly make the right driving decisions and autonomously obey traffic rules by correctly recognizing traffic signs. However, for machine learning-based traffic sign recognition on the Internet of Vehicles (IoV), a large amount of traffic sign data from distributed vehicles is needed to be gathered in a centralized server for model training, which brings serious privacy leakage risk because of traffic sign data containing lots of location privacy information. To address this issue, we first exploit privacy-preserving federated learning to perform collaborative training for accurate recognition models without sharing raw traffic sign data. Nevertheless, due to the limited computing and energy resources of most devices, it is hard for vehicles to continuously undertake complex artificial intelligence tasks. Therefore, we introduce powerful Spike Neural networks (SNNs) into traffic sign recognition for energy-efficient and fast model training, which is the next generation of neural networks and is practical and well-fitted to IoV scenarios. Furthermore, we design a novel encoding scheme for SNNs based on neuron receptive fields to extract information from the pixel and spatial dimensions of traffic signs to achieve high-accuracy training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well. Copyright © 2022, The Authors. All rights reserved.

关键词： Traffic signs

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Long-Distance Entanglement Purification for Quantum Communication

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Physical Review Letters 2021年第1期126卷 010503-010503页

作者： Xiao-Min Hu Cen-Xiao Huang Yu-Bo Sheng Lan Zhou Bi-Heng Liu Yu Guo Chao Zhang Wen-Bo Xing Yun-Feng Huang Chuan-Feng Li Guang-Can Guo CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei 230026 People’s Republic of China CAS Center For Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei 230026 People’s Republic of China Institute of Quantum Information and Technology Nanjing University of Posts and Telecommunications Nanjing 210003 People’s Republic of China School of Science Nanjing University of Posts and Telecommunications Nanjing 210003 People’s Republic of China Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Nanjing 210003 People’s Republic of China

High-quality long-distance entanglement is essential for both quantum communication and scalable quantum networks. Entanglement purification is to distill high-quality entanglement from low-quality entanglement in a noisy environment and it plays a key role in quantum repeaters. The previous significant entanglement purification experiments require two pairs of low-quality entangled states and were demonstrated in tabletop. Here we propose and report a high-efficiency and long-distance entanglement purification using only one pair of hyperentangled state. We also demonstrate its practical application in entanglement-based quantum key distribution (QKD). One pair of polarization spatial-mode hyperentanglement was distributed over 11 km multicore fiber (noisy channel). After purification, the fidelity of polarization entanglement arises from 0.771 to 0.887 and the effective key rate in entanglement-based QKD increases from 0 to 0.332. The values of Clauser-Horne-Shimony-Holt inequality of polarization entanglement arises from 1.829 to 2.128. Moreover, by using one pair of hyperentanglement and deterministic controlled-NOT gates, the total purification efficiency can be estimated as 6.6×103 times than the experiment using two pairs of entangled states with spontaneous parametric down-conversion sources. Our results offer the potential to be implemented as part of a full quantum repeater and large-scale quantum network.

关键词： Entanglement manipulation Optical quantum information processing Quantum communication Quantum optics Quantum protocols

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Accuracy Indoor Localization Based on Fuzzy Transfer Learning Model

Accuracy Indoor Localization Based on Fuzzy Transfer Learnin...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Sheng Wu Yanhui Ji Licai Zhu Liang Zhao Hao Yang Nanjing Tech University Yancheng Teachers University Nanjing China Yancheng Teachers University Nanjing Tech University Yancheng China Shenyang Aerospace University Shenyang China Yancheng Teachers University Yancheng China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu China

Location-based services greatly facilitate people’s daily life, which puts forward higher requirements for the location calculation of target objects in different environments. Since the fingerprint positioning method does not require additional special equipment and easy to implement, it has become one of the most attractive solutions. In order to ensure the positioning accuracy, this method requires complete sampling of the fingerprints of the positioning area, so a lot of sampling costs are required. In particular, when sampling buildings with multiple floors, the labor and time of the entire sampling process will increase dramatically. At the same time, certain floors or rooms may not be allowed to open, so their fingerprints cannot be sampled. In fact, the floor structures of buildings are mostly similar or the same, such as office buildings, hotels. Therefore, this paper proposes a fuzzy transfer learning model and builds the corresponding prototype system FTLoc. On the premise of ensuring the positioning accuracy of different floors, the system greatly reduces the sampling cost of the entire building. First, for the complete fingerprint data (source domain) of a certain floor, we mine the fingerprint features fine-grained to generate a short-term feature set for each sampling point. Then, according to the sparsity and timing of short-term features, we design an optimized SELSTM, and obtain an effective localization model as the PreModel for transfer learning. Finally, fuzzy clustering is used to add category labels to the source domain data and target domain data, and input them into PreModel to realize the localization model transfer, so as to avoid their data distribution differences affecting the transfer effect as much as possible. FTLoc is fully validated in a multi-storey building. According to the experimental results, when using the first floor sampling data as the source domain, the errors of the FTLoc system on the adjacent floor (second floor) are

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An interactive viral genome evolution network analysis system enabling rapid large-scale molecular tracing of SARS-CoV-2

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science Bulletin 2022年第7期67卷 665-669页

作者： Yunchao Ling Ruifang Cao Jiaqiang Qian Jiefu Li Haokui Zhou Liyun Yuan Zhen Wang Liangxiao Ma Guangyong Zheng Guoping Zhao Zefeng Wang Guoqing Zhang Yixue Li CAS Key Laboratory of Computational Biology Bio-Med Big Data CenterShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China Institute of Synthetic Biology Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Hangzhou Institute for Advanced Study University of Chinese Academy of SciencesHangzhou 310024China Collaborative Innovation Center for Genetics and Development Fudan UniversityShanghai 200433China CAS Center for Excellence in Molecular Cell Science Chinese Academy of SciencesShanghai 200031China School of Life Sciences and Biotechnology Shanghai Jiao Tong UniversityShanghai 200240China Guangzhou Laboratory Guangzhou 510005China

The comprehensive analyses of the SARS-CoV-2 genomes could provide a global picture of how the virus was transmitted among different populations,which may help predict the oncoming trends of the *** main approach for ... 详细信息

关键词：快速分析方法 thoroughly among

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Task-Oriented Over-the-Air Computation for Multi-Device Edge AI

arXiv

引用

arXiv 2022年

作者： Wen, Dingzhu Jiao, Xiang Liu, Peixi Zhu, Guangxu Shi, Yuanming Huang, Kaibin Network Intelligence Center School of Information Science and Technology ShanghaiTech University Shanghai China Shenzhen Research Institute of Big Data Shenzhen China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University China Departement of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

Edge inference refers to the use of artificial intelligent (AI) models at the network edge to provide mobile devices inference services and thereby enable intelligent services such as auto-driving and Metaverse towards 6G. However, departing from the classic paradigm of data-centric designs, the 6G networks for supporting edge AI features task-oriented techniques that focus on effective and efficient execution of AI task. Targeting end-to-end system performance, such techniques are sophisticated as they aim to seamlessly integrate sensing (data acquisition), communication (data transmission), and computation (data processing). Aligned with the paradigm shift, a task-oriented over-the-air computation (AirComp) scheme is proposed in this paper for multi-device split-inference system. In the considered system, local feature vectors, which are extracted from the real-time noisy sensory data on devices, are aggregated over-the-air by exploiting the waveform superposition in a multiuser channel. Then the aggregated features as received at a server are fed into an inference model with the result used for decision making or control of actuators. To design inference-oriented AirComp, the transmit precoders at edge devices and receive beamforming at edge server are jointly optimized to rein in the aggregation error and maximize the inference accuracy. The problem is made tractable by measuring the inference accuracy using a surrogate metric called discriminant gain, which measures the discernibility of two object classes in the application of object/event classification. It is discovered that the conventional AirComp beamforming design for minimizing the mean square error in generic AirComp with respect to the noiseless case may not lead to the optimal classification accuracy. The reason is due to the overlooking of the fact that feature dimensions have different sensitivity towards aggregation errors and are thus of different importance levels for classification. This issue

关键词： Beamforming

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Constructing Efficient Set of APs Via Spatial Discrimination and Localization Difference

Constructing Efficient Set of APs Via Spatial Discrimination...

引用

Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Yanhui Ji Sheng Wu Licai Zhu Liang Zhao Hao Yang Nanjing Tech University Yancheng Teachers University Nanjing China Yancheng Teachers University Nanjing Tech University Yancheng China Shenyang Aerospace University Shenyang China Yancheng Teachers University Yancheng China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu China

RSSI fingerprint-based localization has been one of the most attractive solutions, since it is free of extra infrastructure and specialized hardware. As wireless networks become more widely used, however, the number of APs for localization has been increasing and then results in "invalid APs", nearly APs that are redundant or have a negative impact on locating, and raises the computational overhead in the process of localization. To solve these problems, the previous literatures focused on selecting some efficient APs for localization. These approaches could remove a few of redundant APs and reduce the localization overhead to a certain extent, but the performance of locating will fluctuate significantly. In this paper, we propose a constructing efficient set of APs via spatial discrimination and localization difference, ESAP. First, for a single AP, we use the information gain rate to evaluate its spatial resolution ability, which effectively reflects the difference between the maps constructed by the fingerprints of each AP at different sampling points, and shows the AP’s discrimination of spatial locations. Moreover, we calculate the localization capability of APs at different sampling points, and design an incremental clustering algorithm based on the information gain rate of APs by using the localization differences of each AP at each sampling point. The algorithm generates various candidate sets according to the geographical diversity of AP’s fingerprint map, and is more robust in actual applications. Finally, an effective set of APs algorithm based on the minimum-maximum principle is proposed. In the process of constructing the effective set, suitable APs are chosen on the basis of the clustering results and the predefined requirements. Extensive experiments in real large-scale environment validates that the performance of our ESAP outperforms the existing AP selection methods, Info-Gain, ESC and SDN. When reducing APs by 40%, the accuracy of ESAP is better t

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ICTNET at Trec 2019 Decision Track 28

ICTNET at Trec 2019 Decision Track

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28th Text REtrieval Conference, TREC 2019

作者： Cui, Wanqing Jiang, Yan Tao, Shuchang Guo, Hanzhang CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

In this paper we report on our participation in the Trec 2019 Decision Track[1] which aims to provide a venue for research on retrieval methods that promote better decision making with search engines and develop new online and offline evaluation methods to predict the decision making quality induced by search results. We convert this task into a standard information retrieval task and use traditional IR model. Finally we give a summary for the solution of our work. © 2019 28th Text REtrieval Conference, TREC 2019 - Proceedings. All Rights Reserved.

关键词： Decision making

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Graph wavelet neural network 7

Graph wavelet neural network

引用

7th International Conference on Learning Representations, ICLR 2019

作者： Xu, Bingbing Shen, Huawei Cao, Qi Qiu, Yunqi Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China

We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcomings of previous spectral graph CNN methods that depend on graph Fourier transform. Different from graph Fourier transform, graph wavelet transform can be obtained via a fast algorithm without requiring matrix eigendecomposition with high computational cost. Moreover, graph wavelets are sparse and localized in vertex domain, offering high efficiency and good interpretability for graph convolution. The proposed GWNN significantly outperforms previous spectral graph CNNs in the task of graph-based semi-supervised classification on three benchmark datasets: Cora, Citeseer and Pubmed. © 7th International Conference on Learning Representations, ICLR 2019. All Rights Reserved.

关键词： Wavelet transforms

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Federated learning with class imbalance reduction

arXiv

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arXiv 2020年

作者： Yang, Miao Wong, Akitanoshou Zhu, Hongbin Wang, Haifeng Qian, Hua School of Information Science and Technology ShanghaiTech University China China Key Laboratory of Wireless Sensor Network Communication CAS

Federated learning (FL) is a promising technique that enables a large amount of edge computing devices to collaboratively train a global learning model. Due to privacy concerns, the raw data on devices could not be available for centralized server. Constrained by the spectrum limitation and computation capacity, only a subset of devices can be engaged to train and transmit the trained model to centralized server for aggregation. Since the local data distribution varies among all devices, class imbalance problem arises along with the unfavorable client selection, resulting in a slow converge rate of the global model. In this paper, an estimation scheme is designed to reveal the class distribution without the awareness of raw data. Based on the scheme, a device selection algorithm towards minimal class imbalance is proposed, thus can improve the convergence performance of the global model. Simulation results demonstrate the effectiveness of the proposed algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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