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arXiv 2024年

作者： Jiao, Yuhong Miao, Jiaqing Gong, Jinnan He, Hui Liang, Ping Luo, Cheng Tan, Ying The Key Laboratory for Computer Systems of State Ethnic Affairs Commission Southwest Minzu University Chengdu610041 China School of computing and software Chengdu Neusoft University Chengdu611844 China School of Computer Science Chengdu University of Information Technology Chengdu610225 China The Key Laboratory for NeuroInformation of Ministry of Education High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province Center for Information in Medicine University of Electronic Science and Technology of China Chengdu610054 China

Schizophrenia is a serious psychiatric disorder. Its pathogenesis is not completely clear, making it difficult to treat patients precisely. Because of the complicated non-Euclidean network structure of the human brain, learning critical information from brain networks remains difficult. To effectively capture the topological information of brain neural networks, a novel multimodal graph attention network based on sparse interaction mechanism (Multi-SIGATnet) was proposed for SZ classification was proposed for SZ classification. Firstly, structural and functional information were fused into multimodal data to obtain more comprehensive and abundant features for patients with SZ. Subsequently, a sparse interaction mechanism was proposed to effectively extract salient features and enhance the feature representation capability. By enhancing the strong connections and weakening the weak connections between feature information based on an asymmetric convolutional network, high-order interactive features were captured. Moreover, sparse learning strategies were designed to filter out redundant connections to improve model performance. Finally, local and global features were updated in accordance with the topological features and connection weight constraints of the higher-order brain network, the features being projected to the classification target space for disorder classification. The effectiveness of the model is verified on the Center for Biomedical Research Excellence (COBRE) and University of California Los Angeles (UCLA) datasets, achieving 81.9\% and 75.8\% average accuracy, respectively, 4.6\% and 5.5\% higher than the graph attention network (GAT) method. Experiments showed that the Multi-SIGATnet method exhibited good performance in identifying SZ. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

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Local Search For Satisfiability Modulo Integer Arithmetic Theories

arXiv

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arXiv 2022年

作者： Cai, Shaowei Li, Bohan Zhang, Xindi State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Satisfiability Modulo Theories (SMT) refers to the problem of deciding the satisfiability of a formula with respect to certain background first-order theories. In this paper, we focus on Satisfiablity Modulo Integer Arithmetic, which is referred to as SMT(IA), including both linear and non-linear integer arithmetic theories. Dominant approaches to SMT rely on calling a CDCL-based SAT solver, either in a lazy or eager flavour. Local search, a competitive approach to solving combinatorial problems including SAT, however, has not been well studied for SMT. We develop the first local-search algorithm for SMT(IA) by directly operating on variables, breaking through the traditional framework. We propose a local-search framework by considering the distinctions between Boolean and integer variables. Moreover, we design a novel operator and scoring functions tailored for integer arithmetic, as well as a two-level operation selection heuristic. Putting these together, we develop a local search SMT(IA) solver called LocalSMT. Experiments are carried out to evaluate LocalSMT on benchmark sets from SMT-LIB. The results show that LocalSMT is competitive and complementary with state-of-the-art SMT solvers, and performs particularly well on those formulae with only integer variables. A simple sequential portfolio with Z3 improves the state-of-the-art on satisfiable benchmark sets from SMT-LIB. © 2022, CC BY.

关键词： Local search (optimization)

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Efficient algorithms for generalized linear bandits with heavy-tailed rewards 23

Efficient algorithms for generalized linear bandits with hea...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Bo Xue Yimu Wang Yuanyu Wan Jinfeng Yi Lijun Zhang Department of Computer Science City University of Hong Kong Hong Kong China and The City University of Hong Kong Shenzhen Research Institute Shenzhen China Cheriton School of Computer Science University of Waterloo Waterloo Canada School of Software Technology Zhejiang University Ningbo China JD AI Research Beijing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China and Peng Cheng Laboratory Shenzhen China

This paper investigates the problem of generalized linear bandits with heavy-tailed rewards, whose (1 + ε)-th moment is bounded for some ε ∈ (0,1]. Although there exist methods for generalized linear bandits, most of them focus on bounded or sub-Gaussian rewards and are not well-suited for many real-world scenarios, such as financial markets and web-advertising. To address this issue, we propose two novel algorithms based on truncation and mean of medians. These algorithms achieve an almost optimal regret bound of $\widetilde{O}(dT^{\frac{1}{1+\epsilon}})$, where d is the dimension of contextual information and T is the time horizon. Our truncation-based algorithm supports online learning, distinguishing it from existing truncation-based approaches. Additionally, our mean-of-medians-based algorithm requires only O(log T) rewards and one estimator per epoch, making it more practical. Moreover, our algorithms improve the regret bounds by a logarithmic factor compared to existing algorithms when ε =1. Numerical experimental results confirm the merits of our algorithms.

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A Single-Bit Reconfigurable Folded Reflectarray/Transmitarray Antenna

IEEE Open Journal of Antennas and Propagation

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IEEE Open Journal of Antennas and Propagation 2025年

作者： Wang, Manting Du, Jiachen Liao, Dashuang Chan, Chi Hou City University of Hong Kong Department of Electrical Engineering The State Key Laboratory of Terahertz and Millimeter Waves Hong Kong Hong Kong Southern University of Science and Technology State Key Laboratory of Optical Fiber and Cable Manufacture Technology Department of Electronic and Electrical Engineering Shenzhen China Anhui Medical University Department of Computer Science China

In this study, a novel receiving-transmitting (RA-TA) metasurface unit cell is introduced for folded transmitarray/reflectarray antennas (FTAs/FRAs). The unit cell comprises two substrates with an air gap to ensure optimal transmission and integration of PIN diodes. Polarizer grids are employed for selecting desired polarization while each grid metal provides DC bias independently to a single unit cell with two PIN diodes surface-mounted and their orientation perpendicular to each other, i.e., along 1450, respectively, enabling 1-bit phase compensation and polarization conversion. Thus, the proposed antenna achieves 1-bit reconfigurability and maintains polarization consistency with the feeding source. Additionally, unlike conventional FTAs and FRAs that necessitate a "±1"voltage configuration, the proposed unit cell offers costeffectiveness and design simplicity by independently providing a positive voltage to each PIN diode. Thus, the unit cell finds application in FTA for 1-bit beam scanning and FRA for dual-beam operation in both the x-and y-directions, depending on voltage configurations. A 15 W 15 FRA array is fabricated and experimentally demonstrated to validate the proposed concept. This design provides potential applications in wireless communications, particularly in multiple input and multiple output (MIMO) systems. © 2020 IEEE.

关键词： Rectennas

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TNANet: A Temporal-Noise-Aware Neural Network for Suicidal Ideation Prediction with Noisy Physiological Data

arXiv

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arXiv 2024年

作者： Liu, Niqi Liu, Fang Ji, Wenqi Du, Xinxin Liu, Xu Zhao, Guozhen Mu, Wenting Liu, Yong-Jin BNRist Department of Computer Science and Technology MOE-Key Laboratory of Pervasive Computing Tsinghua University China State Key Laboratory of Media Convergence and Communication Communication University of China China Multimodal Sensing and Computing Laboratory Beijing China CAS Key Laboratory of Behavioral Science Institute of Psychology China Department of Psychology Tsinghua University China

The robust generalization of deep learning models in the presence of inherent noise remains a significant challenge, especially when labels are subjective and noise is indiscernible in natural settings. This problem is particularly pronounced in many practical applications. In this paper, we address a special and important scenario of monitoring suicidal ideation, where time-series data, such as photoplethysmography (PPG), is susceptible to such noise. Current methods predominantly focus on image and text data or address artificially introduced noise, neglecting the complexities of natural noise in time-series analysis. To tackle this, we introduce a novel neural network model tailored for analyzing noisy physiological time-series data, named TNANet, which merges advanced encoding techniques with confidence learning, enhancing prediction accuracy. Another contribution of our work is the collection of a specialized dataset of PPG signals derived from real-world environments for suicidal ideation prediction. Employing this dataset, our TNANet achieves the prediction accuracy of 63.33% in a binary classification task, outperforming state-of-the-art models. Furthermore, comprehensive evaluations were conducted on three other well-known public datasets with artificially introduced noise to rigorously test the TNANet's capabilities. These tests consistently demonstrated TNANet's superior performance by achieving an accuracy improvement of more than 10% compared to baseline methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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A Phase Estimation Algorithm for Quantum Speed-Up Multi-Party Computing

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computers, Materials & Continua 2021年第4期67卷 241-252页

作者： Wenbin Yu Hao Feng Yinsong Xu Na Yin Yadang Chen Zhi-Xin Yang Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Jiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Department of Computer Science and Engineering Michigan State UniversityEast Lansing48824MIUSA State Key Laboratory of Internet of Things for Smart City and Department of Electromechanical Engineering University of Macao999078Macao

Security and privacy issues have attracted the attention of researchers in the field of IoT as the information processing scale grows in sensor *** computing,theoretically known as an absolutely secure way to store and transmit information as well as a speed-up way to accelerate local or distributed classical algorithms that are hard to solve with polynomial complexity in computation or *** this paper,we focus on the phase estimation method that is crucial to the realization of a general multi-party computing model,which is able to be accelerated by quantum algorithms.A novel multi-party phase estimation algorithm and the related quantum circuit are proposed by using a distributed Oracle operator with *** proved theoretical communication complexity of this algorithm shows it can give the phase estimation before applying multi-party computing efficiently without increasing any additional ***,a practical problem of multi-party dating investigated shows it can make a successful estimation of the number of solution in advance with zero communication complexity by utilizing its special statistic *** simulations present the correctness,validity and efficiency of the proposed estimation method.

关键词： Edge computing security multi-party computing quantum algorithm phase estimation communication complexity

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Dmnet: Difference minimization network for semi-supervised segmentation in medical images 23rd

Dmnet: Difference minimization network for semi-supervised s...

引用

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

作者： Fang, Kang Li, Wu-Jun National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University National Institute of Healthcare Data Science at Nanjing University Nanjing China

ISBN: (纸本)9783030597092

Semantic segmentation is an important task in medical image analysis. In general, training models with high performance needs a large amount of labeled data. However, collecting labeled data is typically difficult, especially for medical images. Several semi-supervised methods have been proposed to use unlabeled data to facilitate learning. Most of these methods use a self-training framework, in which the model cannot be well trained if the pseudo masks predicted by the model itself are of low quality. Co-training is another widely used semi-supervised method in medical image segmentation. It uses two models and makes them learn from each other. All these methods are not end-to-end. In this paper, we propose a novel end-to-end approach, called difference minimization network(DMNet), for semi-supervised semantic segmentation. To use unlabeled data, DMNet adopts two decoder branches and minimizes the difference between soft masks generated by the two decoders. In this manner, each decoder can learn under the supervision of the other decoder, thus they can be improved at the same time. Also, to make the model generalize better, we force the model to generate low-entropy masks on unlabeled data so the decision boundary of model lies in low-density regions. Meanwhile, adversarial training strategy is adopted to learn a discriminator which can encourage the model to generate more accurate masks. Experiments on a kidney tumor dataset and a brain tumor dataset show that our method can outperform the baselines, including both supervised and semi-supervised ones, to achieve the best performance. © Springer Nature Switzerland AG 2020.

关键词： Semantic Segmentation

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Approximate Equivalence Checking of Noisy Quantum Circuits 21

Approximate Equivalence Checking of Noisy Quantum Circuits

引用

Proceedings of the 58th Annual ACM/IEEE Design Automation Conference

作者： Xin Hong Mingsheng Ying Yuan Feng Xiangzhen Zhou Sanjiang Li Centre for Quantum Software and Information University of Technology Sydney Australia Centre for Quantum Software and Information University of Technology Sydney Australia and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China and Department of Computer Science and Technology Tsinghua University China Centre for Quantum Software and Information University of Technology Sydney Australia and State Key Lab of Millimeter Waves Southeast University China

ISBN: (纸本)9781665432740

We study the fundamental design automation problem of equivalence checking in the NISQ (Noisy Intermediate-Scale Quantum) computing realm where quantum noise is present inevitably. The notion of approximate equivalence of (possibly noisy) quantum circuits is defined based on the Jamiolkowski fidelity which measures the average distance between output states of two super-operators when the input is chosen at random. By employing tensor network contraction, we present two algorithms, aiming at different situations where the number of noises varies, for computing the fidelity between an ideal quantum circuit and its noisy implementation. The effectiveness of our algorithms is demonstrated by experimenting on benchmarks of real NISQ circuits. When compared with the state-of-the-art implementation incorporated in Qiskit, experimental results show that the proposed algorithms outperform in both efficiency and scalability.

关键词： quantum computing

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An Efficient Quantum Private Comparison Protocol Based on Cluster state and Bell state 8th

An Efficient Quantum Private Comparison Protocol Based on Cl...

引用

8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Li, Chaoyang Qing, Hua Xu, Gang Chen, Xiubo Xin, Xiangjun Dong, Mianxiong Ota, Kaoru College of Software Engineering Zhengzhou University of Light Industry Zhengzhou450002 China School of Computing Science and Technology North China University of Technology Beijing100144 China Information Security Centre State Key Laboratory of Networking and Switching Technology Beijing University of Post and Telecommunications Beijing100876 China Department of Sciences and Informatics Muroran Institution of Technology Muroran050-8585 Japan

ISBN: (纸本)9783031067631

Quantum private comparison (QPC) is the quantum method that can compare the equality of the distrustful participants’ private information without leaking them. The 4-particle cluster state has a strong violation of local reality and shows to be robust against decoherence than any other 4-particle state. In this paper, a new efficient QPC protocol based on the entanglement swapping between the 4-particle cluster state and Bell state has been proposed. With the pre-shared secret key sequence between the two participants, the semi-honest third party (TP) can only execute the protocol’s processes without obtaining information about the participants’ secrets and the final comparison results. Then, we show that the proposed QPC protocol is secure against outside or participant attacks and more efficient than similar literature. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Quantum cryptography

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A Many-Objective Evolutionary Algorithm Assisted by Ideal Hyperplane

SSRN

引用

SSRN 2022年

作者： Zhang, Zhixia Wen, Jie Wang, Hui Cai, Xingjuan Cui, Zhihua Zhang, Wensheng School of Computer Science and Technology Taiyuan University of Science and Technology China School of Information Engineering Nanchang Institute of Technology Nanchang330099 China State Key Laboratory of Intelligent Control and Management of Complex System Institute of Automation Chinese Academy of Sciences China State Key Lab. for Novel Software Technology Nanjing University China

In many-objective optimization problems (MaOPs), it is a difficult task for evolutionary algorithms to balance convergence and diversity while rapidly converging to the Pareto front. As the number of objectives increases, the difficulty of evolutionary algorithms increases with individual selection pressure. To address above problems, we propose a many-objective evolution algorithm assisted by an ideal hyperplane (MaOEA-IH). Firstly, the ideal hyperplane as a reference surface to guide the individual to search the Pareto front, which is established from the extremum of each dimension of the objective space. Secondly, a parallel p -norm mating selection strategy is proposed to improve convergence. In addition, two factors are considered in this strategy: 1) different p -norm are used to measure different spatial scales;2) individual selection uncertainty is defined by incorporating a probabilistic perturbation mechanism. Thirdly, the improved shift-based density estimation strategy is used as the evaluation criterion for environmental selection to improve the convergence and diversity of solution sets and enhance selection pressure. To verify the effectiveness of the proposed algorithm, MAOEA-IH is compared with seven excellent algorithms in three benchmark sets (DTLZ, WFG and MaF). The results show that the proposed MAOEA-IH is highly competitive in solving MaOPs. © 2022, The Authors. All rights reserved.

关键词： Geometry

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