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Cu-catalyzed enantioselective assembly of axially chiral allylic boronates

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Science China Chemistry 2024年

作者： Shuai Wang Jianbo Liu Wenyue Ma Qiuling Song Key Laboratory of molecule Synthesis and Function Discovery Fujian Province University College of Chemistry at Fuzhou University State Key Laboratory of Organometallic Chemistry Shanghai Institute of Organic Chemistry Chinese Academy of Sciences School of Chemistry and Chemical Engineering Henan Normal University

Highly enantioselective construction of axially chiral cyclohexylidenes is difficult and challenging due to their structural peculiarities. The development of efficient method for the assembly of novel axially chiral cyclohexylidenes becomes particularly important in axial chirality. Chiral allylborons have become a focus of interest in chemical community as an important synthon as well as a backbone structure, yet an efficient and practical synthetic method for the synthesis of axially chiral allylborons have not been disclosed so far. Herein, we report a copper-catalyzed strategy which could readily access enantiopure axially chiral allylborons under mild conditions. The obtained axially chiral allylborons can also be subjected to a number of subsequent transformations as well as late modification of drug molecules, thus demonstrating the validity of the developed method.

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Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

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Chinese Physics B 2024年第4期33卷 418-433页

作者：周新卫蒋东华 Jean De Dieu Nkapkop Musheer Ahmad Jules Tagne Fossi Nestor Tsafack 吴建华 Department of Information Engineering Gongqing CollegeNanchang UniversityJiujiang 332020China School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 511400China Department of Electrical Engineering and Industrial computing University Institute of TechnologyDoualaCameroon Department of Computer Engineering Jamia Millia IslamiaNew Delhi 110025India Department of Physics Faculty of ScienceUniversity of YaoundeCameroon Electrical Engineering Department and Industrial computing of ISTAMA University of DoualaDoualaCameroon

Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first proposed in this ***,a novel four-dimensional fractional-order memristive cellular neural network(FO-MCNN)model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its ***,its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation ***,it is used toward secure communication application *** it as the pseudo-random number generator(PRNG),a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing(ASR-CS)***,the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.

关键词： cellular neural network memristor hardware circuit compressive sensing privacy data protection

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Theoretical and Experimental Performance Analysis on Error-Bounded Quantum Search Process

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Journal of Systems Science & Complexity 2024年

作者： FU Jianling XU Ming QIAN Haifeng LI Zhibin Shanghai institute for AI Education East China Normal University Shanghai Key Laboratory of Trustworthy computing East China Normal University

Grover's algorithm(a. k. a. quantum search process) is one of the most distinguished quantum algorithms, addressing the fundamental problem — How to find goal records from a huge but unstructured database efficiently. It can achieve a relatively optimal success probability after a few Grover iterations for amplitude amplification, which results in a quadratic speed-up in the whole process in terms of the size of database. However, it does not guarantee to achieve that probability within a user-specified error tolerance. So error-bounded quantum search process comes into being. The existing methods introduce extra qubits to meet that tolerance, or exploit high-precision(nonbasic) gates,each of which would be converted to a sequence of basic gates and thus costs much more computational units. In this paper, the authors employ the strategy of performing more Grover iterations expecting one of a series of local optima to meet the tolerance. To ensure that it works rigorously, the authors identify and exclude three exceptional instances by algebraic number theory, which is reported for the first time. Then the authors analyze the theoretical complexity of the employed method. It turns out to be in time exponential in the encoding size of the tolerance using basic gates, comparable to the existing method's, while extra space consumption is saved. The experimental performance is validated by extensive examples from real-world data sets ASRS and public Amazon review.

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Improving the utility of locally differentially private protocols for longitudinal and multidimensional frequency estimates

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Digital Communications and Networks 2024年第2期10卷 369-379页

作者： Héber H.Arcolezi Jean-François Couchot Bechara Al Bouna Xiaokui Xiao Inria andÉcole Polytechnique(IPP) PalaiseauFrance Femto-ST institute Univ.Bourg.Franche-ComtéUBFCCNRSBelfortFrance TICKET Lab. Antonine University Hadat-BaabdaBaabdaLebanon School of computing National University of SingaporeSingaporeSingapore

This paper investigates the problem of collecting multidimensional data throughout time(i.e.,longitudinal studies)for the fundamental task of frequency estimation under Local Differential Privacy(LDP)*** to frequency estimation of a single attribute,the multidimensional aspect demands particular attention to the privacy ***,when collecting user statistics longitudinally,privacy progressively ***,the“multiple”settings in combination(i.e.,many attributes and several collections throughout time)impose several challenges,for which this paper proposes the first solution for frequency estimates under *** tackle these issues,we extend the analysis of three state-of-the-art LDP protocols(Generalized Randomized Response–GRR,Optimized Unary Encoding–OUE,and Symmetric Unary Encoding–SUE)for both longitudinal and multidimensional data *** the known literature uses OUE and SUE for two rounds of sanitization(***),i.e.,L-OUE and L-SUE,respectively,we analytically and experimentally show that starting with OUE and then with SUE provides higher data utility(i.e.,L-OSUE).Also,for attributes with small domain sizes,we propose Longitudinal GRR(L-GRR),which provides higher utility than the other protocols based on unary ***,we also propose a new solution named Adaptive LDP for LOngitudinal and Multidimensional FREquency Estimates(ALLOMFREE),which randomly samples a single attribute to be sent with the whole privacy budget and adaptively selects the optimal protocol,i.e.,either L-GRR or *** shown in the results,ALLOMFREE consistently and considerably outperforms the state-of-the-art L-SUE and L-OUE protocols in the quality of the frequency estimates.

关键词： Local differential privacy Discrete distribution estimation Frequency estimation Multidimensional data Longitudinal studies

FedQMIX:Communication-efficient federated learning via multi-agent reinforcement learning

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High-Confidence Computing

High-Confidence computing 2024年第2期4卷 96-104页

作者： Shaohua Cao Hanqing Zhang Tian Wen Hongwei Zhao Quancheng Zheng Weishan Zhang Danyang Zheng Qingdao institute of Software College of Computer Science and TechnologyChina University of Petroleum(East China)Qingdao 266580China School of computing and Artificial Intelligence Xipu CampusSouthwest Jiaotong UniversityChengdu 611756China

Since the data samples on client devices are usually non-independent and non-identically distributed(non-IID),this will challenge the convergence of federated learning(FL)and reduce communication *** paper proposes FedQMIX,a node selection algorithm based on multi-agent reinforcement learning(MARL),to address these ***,we observe a connection between model weights and data distribution,and a clustering algorithm can group clients with similar data distribution into the same ***,we propose a QMIX-based mechanism that learns to select devices from clustering results in each communication round to maximize the reward,penalizing the use of more communication rounds and thereby improving the communication efficiency of ***,experiments show that FedQMIX can reduce the number of communication rounds by 11%and 30%on the MNIST and CIFAR-10 datasets,respectively,compared to the baseline algorithm(Favor).

关键词： Communication efficient Federated learning MARL

Maximizing Submodular+Supermodular Functions Subject to a Fairness Constraint

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Tsinghua Science and Technology 2024年第1期29卷 46-55页

作者： Zhenning Zhang Kaiqiao Meng Donglei Du Yang Zhou Beijing institute for Scientific and Engineering computing Beijing University of TechnologyBeijing 100124China Faculty of Management University of New BrunswickFredericton E3B 5A3Canada School of Mathematics and Statistics Shandong Normal UniversityJinan 250014China

We investigate the problem of maximizing the sum of submodular and supermodular functions under a fairness *** sum function is non-submodular in *** an offline model,we introduce two approximation algorithms:A greedy algorithm and a threshold greedy *** a streaming model,we propose a one-pass streaming *** also analyze the approximation ratios of these algorithms,which all depend on the total curvature of the supermodular *** total curvature is computable in polynomial time and widely utilized in the literature.

关键词： submodular function supermodular function fairness constraint greedy algorithm threshold greedy algorithm streaming algorithm

Quantum self-attention neural networks for text classification

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Science China(Information Sciences) 2024年第4期67卷 301-313页

作者： Guangxi LI Xuanqiang ZHAO Xin WANG institute for Quantum computing Baidu Research Centre for Quantum Software and Information University of Technology Sydney QICI Quantum Information and Computation Initiative Department of Computer ScienceThe University of Hong Kong Thrust of Artificial Intelligence Information HubHong Kong University of Science and Technology(Guangzhou)

An emerging direction of quantum computing is to establish meaningful quantum applications in various fields of artificial intelligence, including natural language processing(NLP). Although some efforts based on syntactic analysis have opened the door to research in quantum NLP(QNLP), limitations such as heavy syntactic preprocessing and syntax-dependent network architecture make them impracticable on larger and real-world data sets. In this paper, we propose a new simple network architecture, called the quantum selfattention neural network(QSANN), which can compensate for these limitations. Specifically, we introduce the self-attention mechanism into quantum neural networks and then utilize a Gaussian projected quantum self-attention serving as a sensible quantum version of self-attention. As a result, QSANN is effective and scalable on larger data sets and has the desirable property of being implementable on near-term quantum devices. In particular, our QSANN outperforms the best existing QNLP model based on syntactic analysis as well as a simple classical self-attention neural network in numerical experiments of text classification tasks on public data sets. We further show that our method exhibits robustness to low-level quantum noises and showcases resilience to quantum neural network architectures.

关键词： quantum neural networks self-attention natural language processing text classification parameterized quantum circuits

An evolutionary multiobjective method based on dominance and decomposition for feature selection in classification

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Science China(Information Sciences) 2024年第2期67卷 5-19页

作者： Jing LIANG Yuyang ZHANG Ke CHEN Boyang QU Kunjie YU Caitong YUE Ponnuthurai Nagaratnam SUGANTHAN School of Electrical and Information Engineering Zhengzhou University State Key Laboratory of Intelligent Agricultural Power Equipment School of Electrical Engineering and Automation Henan Institute of Technology School of Electronics and Information Zhongyuan University of Technology KINDI Center for computing Research College of EngineeringQatar University

Feature selection in classification can be considered a multiobjective problem with the objectives of increasing classification accuracy and decreasing the size of the selected feature subset. Dominance-based and decomposition-based multiobjective evolutionary algorithms(MOEAs) have been extensively used to address the feature selection problem due to their strong global search capability. However, most of them face the problem of not effectively balancing convergence and diversity during the evolutionary process. In addressing the aforementioned issue, this study proposes a unified evolutionary framework that combines two search forms of dominance and decomposition. The advantages of the two search methods assist one another in escaping the local optimum and inclining toward a balance of convergence and diversity. Specifically, an improved environmental selection strategy based on the distributions of individuals in the objective space is presented to avoid duplicate feature subsets. Furthermore, a novel knowledge transfer mechanism that considers evolutionary characteristics is developed, allowing for the effective implementation of positive knowledge transfer between dominance-based and decomposition-based feature selection methods. The experimental results demonstrate that the proposed algorithm can evolve feature subsets with good convergence and diversity in a shorter time compared with 9 state-of-the-art feature selection methods on 20 classification problems.

关键词： evolutionary algorithms feature selection multiobjective optimization knowledge transfer classification

Nonlinear Extrapolation Estimates of π

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Acta Mathematicae Applicatae Sinica 2024年第1期40卷 91-108页

作者： Wen-qing Xu Sha-sha Wang Da-chuan Xu Beijing institute for Scientific and Engineering computing Beijing University of TechnologyBeijing100124China Department of Mathematics and Statistics California State UniversityLong BeachCalifornia90840USA Department of Mathematics and Physics Shijiazhuang Tiedao UniversityShijiazhuang050043China

The classical Archimedean approximation of π uses the semiperimeter or area of regular polygons inscribed in or circumscribed about a unit circle in R~2 and it is well-known that by using linear combinations of these basic estimates,modern extrapolation techniques can greatly speed up the approximation ***,when n vertices are randomly selected on the circle,the semiperimeter and area of the corresponding random inscribed and circumscribing polygons are known to converge to π almost surely as n→∞,and by further applying extrapolation processes,faster convergence rates can also be achieved through similar linear combinations of the semiperimeter and area of these random *** this paper,we further develop nonlinear extrapolation methods for approximating π through certain nonlinear functions of the semiperimeter and area of such *** focus on two types of extrapolation estimates of the forms χ_n=S_n~αA_n~β and Y_n(p)=(αS_n~p+βA_n~p)~(1/p) where α+β=1,p≠0,and Sn and An respectively represents the semiperimeter and area of a random n-gon inscribed in the unit circle in R~2,and Xn may be viewed as the limit of Y_n(p) when p→*** deriving probabilistic asymptotic expansions with carefully controlled error estimates for Xn and Y_n(p),we show that the choice α=4/3,β=-1/3 minimizes the approximation error in both cases,and their distributions are also asymptotically normal.

关键词： Archimedean polygons random polygons approximations ofπ extrapolation processes asymptotic convergence

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