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arXiv 2022年

作者： Li, Renwang Guo, Bei Tao, Meixia Liu, Ya-Feng Yu, Wei Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical and Engineering University of Toronto TorontoONM5S 3G4 Canada

This paper considers a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) downlink communication system where hybrid analog-digital beamforming is employed at the base station (BS). We formulate a power minimization problem by jointly optimizing hybrid beamforming at the BS and the response matrix at the RIS, under the signalto-interference-plus-noise ratio (SINR) constraints at all users. The problem is highly challenging to solve due to the nonconvex SINR constraints as well as the unit-modulus phase shift constraints for both the RIS reflection coefficients and the analog beamformer. A two-layer penalty-based algorithm is proposed to decouple variables in SINR constraints, and manifold optimization is adopted to handle the non-convex unitmodulus *** also propose a low-complexity sequential optimization method, which optimizes the RIS reflection coefficients, the analog beamformer, and the digital beamformer sequentially without iteration. Furthermore, the relationship between the power minimization problem and the max-min fairness (MMF) problem is discussed. Simulation results show that the proposed penalty-based algorithm outperforms the stateof-the-art semidefinite relaxation (SDR)-based algorithm. Results also demonstrate that the RIS plays an important role in the power reduction. Copyright © 2022, The Authors. All rights reserved.

关键词： Millimeter waves

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Diffusion transients in motility-induced phase separation

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Physical Review Research 2025年第1期7卷 013153-013153页

作者： Shubhadip Nayak Poulami Bag Pulak K. Ghosh Yunyun Li Yuxin Zhou Qingqing Yin Fabio Marchesoni Franco Nori Department of Chemistry Presidency University Kolkata 700073 India MOE Key Laboratory of Advanced Micro-Structured Materials School of Physics Science and Engineering Tongji University Shanghai 200092 China Dipartimento di Fisica Università di Camerino I-62032 Camerino Italy Theoretical Quantum Physics Laboratory Quantum Computing Center RIKEN Wako-shi Saitama 351-0198 Japan Physics Department University of Michigan Ann Arbor Michigan 48109-1040 USA

We numerically investigate normal diffusion in a two-dimensional athermal suspension of active particles undergoing motility-induced phase separation. The particles are modeled as achiral Janus disks with fixed self-propulsion speed and weakly fluctuating orientation. When plotted versus the overall suspension packing fraction, the relevant diffusion constant traces a hysteresis loop with sharp jumps in correspondence with the binodal and spinodal of the gaseous phase. No hysteresis loop is observed between the spinodal and binodal of the dense phase, as they appear to overlap. Moreover, even under steady-state phase separation, the particle displacement distributions exhibit non-Gaussian normal diffusion with transient fat (thin) tails in the presence (absence) of phase separation.

关键词： Hysteresis

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Efficient learning of continuous-variable quantum states

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Physical Review Research 2024年第3期6卷 033280页

作者： Ya-Dong Wu Yan Zhu Giulio Chiribella Nana Liu John Hopcroft Center for Computer Science QICI Quantum Information and Computation Initiative Department of Computer Science The Department of Computer Science for Theoretical Physics Waterloo Ontario N2L 2Y5 Canada Institute of Natural Sciences Ministry of Education Key Laboratory in Scientific and Engineering Computing School of Mathematical Sciences University of Michigan-

The characterization of continuous-variable quantum states is crucial for applications in quantum communication, sensing, simulation, and computing. However, a full characterization of multimode quantum states requires a number of experiments that grows exponentially with the number of modes. Here we propose an alternative approach where the goal is not to reconstruct the full quantum state, but rather to estimate its characteristic function at a given set of points. For multimode states with reflection symmetry, we show that the characteristic function at M points can be estimated using only O(logM) copies of the state, independently of the number of modes. When the characteristic function is known to be positive, as in the case of squeezed vacuum states, the estimation is achieved by an experimentally friendly setup using only beamsplitters and homodyne measurements.

关键词： Machine learning Quantum information processing with continuous variables Quantum tomography

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Efficient explicit circuit for quantum state preparation of piece-wise continuous functions

arXiv

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arXiv 2024年

作者： Guseynov, Nikita Liu, Nana University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai200240 China Institute of Natural Sciences School of Mathematical Sciences Shanghai Jiao Tong University Shanghai200240 China Ministry of Education Key Laboratory in Scientific and Engineering Computing Shanghai Jiao Tong University Shanghai200240 China Shanghai Artificial Intelligence Laboratory Shanghai China

The ability to effectively upload data onto quantum states is an important task with broad applications in quantum computing. Numerous quantum algorithms heavily rely on the ability to efficiently upload information onto quantum states, without which those algorithms cannot achieve quantum advantage. In this paper, we address this challenge by proposing a method to upload a polynomial function f(x) on the interval x ∈ (a,b) onto a pure quantum state consisting of qubits, where a discretised f(x) is the amplitude of this state. The preparation cost has quadratic scaling in the number of qubits n and linear scaling with the degree of the polynomial Q. This efficiency allows the preparation of states whose amplitudes correspond to high-degree polynomials, enabling the approximation of almost any continuous function. We introduce an explicit algorithm for uploading such functions using four real polynomials that meet specific parity and boundedness conditions. We also generalize this approach to piece-wise polynomial functions, with the algorithm scaling linearly with the number of piecewise parts. Our method achieves efficient quantum circuit implementation and we present detailed gate counting and resource *** Codes 68Q12 © 2024, CC BY.

关键词： Qubits

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A penalty-free infeasible approach for a class of nonsmooth optimization problems over the Stiefel manifold

arXiv

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arXiv 2021年

作者： Xiao, Nachuan Liu, Xin Yuan, Ya-Xiang State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences China State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences China

Transforming into an exact penalty function model with convex compact constraints yields efficient infeasible approaches for optimization problems with orthogonality constraints. For smooth and 2,1-norm regularized cases, these infeasible approaches adopt simple and orthonormalizationfree updating schemes and show high efficiency in some numerical experiments. However, to avoid orthonormalization while enforcing the feasibility of the final solution, these infeasible approaches introduce a quadratic penalty term,where an inappropriate penalty parameter can lead to numerical inefficiency. Inspired by penalty-free approaches for smooth optimization problems, we proposed a sequential linearized proximal gradient method (SLPG) for a class of optimization problems with orthogonality constraints and nonsmooth regularization term. This approach alternatively takes tangential steps and normal steps to improve the optimality and feasibility respectively. In SLPG, the orthonormalization process is invoked only once at the last step if high precision for feasibility is needed, showing that main iterations in SLPG are orthonormalization-free. Besides, both the tangential steps and normal steps do not involve the penalty parameter, and thus SLPG is penaltyfree and avoids the inefficiency caused by possible inappropriate penalty parameter. We analyze the global convergence properties of SLPG where the tangential steps are inexactly computed. By inexactly computing tangential steps, for smooth cases and 2,1-norm regularized cases, SLPG has a closed-form updating scheme, which leads to cheap tangential steps. Numerical experiments illustrate the advantages of SLPG when compared with existing first-ordermethods. Copyright © 2021, The Authors. All rights reserved.

关键词： Gradient methods

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Research on patent recommendation method based on graph neural network

Research on patent recommendation method based on graph neur...

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International Conference on Computer Supported Cooperative Work in Design

作者： Enchao Zhang Weidong Liu Shi Liu Yan Cao College of Computer Science Inner Mongolia University China Institute of Scientific and Technical Information of China China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Social Computing and Data Processing China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

Intellectual property transactions have shown a strong growth momentum in recent years, but the patent transaction market has been plagued by the matching degree of consumers and sellers, resulting in frequent problems such as low patent transformation efficiency and poor transaction quality. This paper proposes a method of recommending patents to consumers by experts to improve the environment of patent transactions. Through the analysis of the past transaction information of the patent, the effective path information of the target is extracted. The graph neural network is used to describe the characteristics and semantics among experts, patents and consumers, and then capture the potential weight among them through the common attention mechanism, and then dynamically integrate them to predict the occurrence of recommendation behavior. The paper makes reasonable use of social information and expert information in the transaction, which significantly improves the rationality and accuracy of expert recommendation.

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Learning-rate-free Momentum SGD with Reshuffling Converges in Nonsmooth Nonconvex Optimization

arXiv

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arXiv 2024年

作者： Hu, Xiaoyin Xiao, Nachuan Liu, Xin Toh, Kim-Chuan School of Computer and Computing Science Hangzhou City University Hangzhou310015 China Institute of Operational Research and Analytics National University of Singapore Singapore State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences China Department of Mathematics Institute of Operations Research and Analytics National University of Singapore Singapore119076 Singapore

In this paper, we propose a generalized framework for developing learning-rate-free momentum stochastic gradient descent (SGD) methods in the minimization of nonsmooth nonconvex functions, especially in training nonsmooth neural networks. Our framework adaptively generates learning rates based on the historical data of stochastic subgradients and iterates. Under mild conditions, we prove that our proposed framework enjoys global convergence to the stationary points of the objective function in the sense of the conservative field, hence providing convergence guarantees for training nonsmooth neural networks. Based on our proposed framework, we propose a novel learning-rate-free momentum SGD method (LFM). Preliminary numerical experiments reveal that LFM performs comparably to the state-of-the-art learning-rate-free methods (which have not been shown theoretically to be convergence) across well-known neural network training benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Momentum

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THE QUADRATIC SPECHT TRIANGLE

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Journal of Computational Mathematics 2020年第1期38卷 103-124页

作者： Hongliang Li Pingbing Ming Zhongci Shi Department of Mathematics Sichuan Normal UniversityChengdu 610066China The State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems ScienceChinese Academy of SciencesNo.55East Road ZhongGuanCunBeijing 100190China

We propose a class of 12 degrees of freedom triangular plate bending elements with quadratic rate of *** may be viewed as the second order Specht triangle,while the Specht triangle is one of the best first order plate bending *** convergence result is proved under minimal smoothness assumption on the *** results for both the smooth solution and nonsmmoth solution confirm the theoretical prediction.

关键词： Specht triangle Plate bending element Basis functions

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Exploring Patent Transformation Event: Forecasting Patent Transfer Time

Exploring Patent Transformation Event: Forecasting Patent Tr...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Jinchen Huo Weidong Liu Yang Li Yan Cao College of Computer Science Inner Mongolia University China Institute of Scientific and Technical Information of China China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Social Computing and Data Processing China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

As the largest source of technical information around the world, patents are regarded as an essential crystallization and carrier of knowledge and technological innovation. Patent transformation is conducive not only to enhancing economic efficiency, but also to improving productivity and the rational utilization of resources. There is an imbalance between high patent ownership and low transformation rates. We try to predict the occurrence of transformation events from the patent assignment. However, there are some challenges in predicting patent transformation: (1) how to capture transformation features of patents, especially combined with the transfer time factor. (2) how to predict patent transfer time effectively. To address these challenges, a Patent Transfer Time Forecasting Model (PTTFM) is proposed. The model includes: (1) extraction of time-varying features of patents. (2) the patent transfer time is forecast using a Neural Temporal Point Process. By testing the model on patents under different classifications, the experimental results are obtained to show that the proposed model is applicable to predict the timing of patent assignment within a certain time frame, especially one month. Our work may facilitate patent transformation while interpretability is ensured for transformation events.

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Pseudo complementary measurement for traditional single-pixel cameras

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Chinese Physics B 2020年第11期29卷 315-322页

作者： Qian Dong Xu-Ri Yao Xin Liu Bing Liu Guang-Jie Zhai State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China Key Laboratory of Electronics and Information Technology for Space Systems National Space Science CenterChinese Academy of SciencesBeijing 100190China State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

A traditional single-pixel camera needs a large number of measurements to reconstruct the object with compressive sensing *** with the 1/0 matrices in classical measurement,the 1/-1 matrices in the complementary measurement has better property for reconstruction computation and returns better reconstruction ***,each row of the 1/-1 matrices needs two measurements with the traditional single-pixel camera which results into double measurements compared with the 1/0 *** this paper,we consider the pseudo complementary measurement which only takes the same amount of measurements with the row number of some properly designed 1/0 matrix to compute the total luminous flux of the objective and derives the measurement data of the corresponding 1/-1 matrix in a mathematical *** numerical simulation and experimental result show that the pseudo complementary measurement is an efficient tool for the traditional single-pixel camera imaging under low measurement rate,which can combine the advantages of the classical and complementary measurements and significantly improve the peak signal-to-noise ratio.

关键词： computational imaging single-pixel camera

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