arXiv subjects

Ying Cui

Publications and source records attributed to Ying Cui.

At least 109 records · Page 6Linked to original sources

arXiv2019

$Δ$-mass dependence of the M-matrix in the calculation of $\mbox{N}Δ\to \mbox{NN}$ cross sections

Within the one boson exchange model, $Δ$-mass dependent M-matrix and its influence on the calculation of $NΔ\to NN$ cross sections are investigated. Our calculations show that the $m_Δ$ dependence of $|\textbf{p}_{NΔ}|$ and $|\mathcal{M}|^2$ has effects on the calculations of $σ_{NΔ\to NN}$, especially around the threshold energy. We finally provide a table of accurate $σ_{NΔ\to NN}$ which can be used in the transport models.

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arXiv2019

Joint Design of Measurement Matrix and Sparse Support Recovery Method via Deep Auto-encoder

Sparse support recovery arises in many applications in communications and signal processing. Existing methods tackle sparse support recovery problems for a given measurement matrix, and cannot flexibly exploit the properties of sparsity patterns for improving performance. In this letter, we propose a data-driven approach to jointly design the measurement matrix and support recovery method for complex sparse signals, using auto-encoder in deep learning. The proposed architecture includes two components, an auto-encoder and a hard thresholding module. The proposed auto-encoder successfully handles complex signals using standard auto-encoder for real numbers. The proposed approach can effectively exploit properties of sparsity patterns, and is especially useful when these underlying properties do not have analytic models. In addition, the proposed approach can achieve sparse support recovery with low computational complexity. Experiments are conducted on an application ex-ample, device activity detection in grant-free massive access for massive machine type communications (mMTC). Numerical results show that the proposed approach achieves significantly better performance with much less computation time than classic methods, in the presence of extra structures in sparsity patterns.

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arXiv2019

Statistical Analysis of Stationary Solutions of Coupled Nonconvex Nonsmooth Empirical Risk Minimization

This paper has two main goals: (a) establish several statistical properties---consistency, asymptotic distributions, and convergence rates---of stationary solutions and values of a class of coupled nonconvex and nonsmoothempirical risk minimization problems, and (b) validate these properties by a noisy amplitude-based phase retrieval problem, the latter being of much topical interest.Derived from available data via sampling, these empirical risk minimization problems are the computational workhorse of a population risk model which involves the minimization of an expected value of a random functional. When these minimization problems are nonconvex, the computation of their globally optimal solutions is elusive. Together with the fact that the expectation operator cannot be evaluated for general probability distributions, it becomes necessary to justify whether the stationary solutions of the empirical problems are practical approximations of the stationary solution of the population problem. When these two features, general distribution and nonconvexity, are coupled with nondifferentiability that often renders the problems "non-Clarke regular", the task of the justification becomes challenging. Our work aims to address such a challenge within an algorithm-free setting. The resulting analysis is therefore different from the much of the analysis in the recent literature that is based on local search algorithms. Furthermore, supplementing the classical minimizer-centric analysis, our results offer a first step to close the gap between computational optimization and asymptotic analysis of coupled nonconvex nonsmooth statistical estimation problems, expanding the former with statistical properties of the practically obtained solution and providing the latter with a more practical focus pertaining to computational tractability.

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arXiv2019

Analysis and Optimization of Successful Symbol Transmission Rate for Grant-free Massive Access with Massive MIMO

Grant-free massive access is an important technique for supporting massive machine-type communications (mMTC) for Internet-of-Things (IoT). Two important features in grant-free massive access are low-complexity devices and short-packet data transmission, making the traditional performance metric, achievable rate, unsuitable in this case. In this letter, we investigate grant-free massive access in a massive multiple-input multiple-output (MIMO) system. We consider random access control, and adopt approximate message passing (AMP) for user activity detection and channel estimation in the pilot transmission phase and small phase-shift-keying (PSK) modulation in the data transmission phase. We propose a more reasonable performance metric, namely successful symbol transmission rate (SSTR), for grant-free massive access. We obtain closed-form approximate expressions for the asymptotic SSTR in the cases of maximal ratio combining (MRC) and zero forcing (ZF) beamforming at the base station (BS), respectively. We also maximize the asymptotic SSTR with respect to the access parameter and pilot length.

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arXiv2019

Analysis and Optimization of Outage Probability in Multi-Intelligent Reflecting Surface-Assisted Systems

Intelligent reflecting surface (IRS) is envisioned to be a promising solution for designing spectral and energy efficient wireless systems. In this letter, we study a multi-IRS-assisted system under Rician fading where the phase shifts adapt to only the line of sight (LoS) components. First, we analyze and optimize the outage probability of the multi-IRS-assisted system in the slow fading scenario for the non-LoS (NLoS) components. We also show that the optimal outage probability decreases with the numbers of IRSs and elements of each IRS when the LoS components are stronger than the NLoS ones. Then, we characterize the asymptotically optimal outage probability in the high signal-to-noise ratio (SNR) regime, and show that it decreases with the powers of the LoS components. To the best of our knowledge, this is the first work that studies the outage probability in multi-IRS-assisted systems.

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arXiv2019

Stochastic Successive Convex Approximation for General Stochastic Optimization Problems

One key challenge for solving a general stochastic optimization problem with expectations in the objective and constraint functions using ordinary stochastic iterative methods lies in the infeasibility issue caused by the randomness over iterates. This letter aims to address this main challenge. First, we obtain an equivalent stochastic optimization problem which is to minimize the weighted sum of the original objective and the penalty for violating the original constraints. Then, we propose a stochastic successive convex approximation (SSCA) method to obtain a stationary point of the original stochastic optimization problem. Using similar techniques, we propose a parallel SSCA method to obtain a stationary point of a special case of the general stochastic optimization problem which has decoupled constraint functions. We also provide application examples of the proposed methods in power control for interference networks. The proposed SSCA and parallel SSCA methods achieve empirically higher convergence rates and lower computational complexities than existing ones, benefiting from the elegant way of balancing the objective minimization and constraint satisfaction over random iterates.

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arXiv2019

Estimation of Individualized Decision Rules Based on an Optimized Covariate-Dependent Equivalent of Random Outcomes

Recent exploration of optimal individualized decision rules (IDRs) for patients in precision medicine has attracted a lot of attention due to the heterogeneous responses of patients to different treatments. In the existing literature of precision medicine, an optimal IDR is defined as a decision function mapping from the patients' covariate space into the treatment space that maximizes the expected outcome of each individual. Motivated by the concept of Optimized Certainty Equivalent (OCE) introduced originally in \cite{ben1986expected} that includes the popular conditional-value-of risk (CVaR) \cite{rockafellar2000optimization}, we propose a decision-rule based optimized covariates dependent equivalent (CDE) for individualized decision making problems. Our proposed IDR-CDE broadens the existing expected-mean outcome framework in precision medicine and enriches the previous concept of the OCE. Numerical experiments demonstrate that our overall approach outperforms existing methods in estimating optimal IDRs under heavy-tail distributions of the data.

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arXiv2019

Nonsmooth Composite Matrix Optimization: Strong Regularity, Constraint Nondegeneracy and Beyond

The nonsmooth composite matrix optimization problem (CMatOP), in particular, the matrix norm minimization problem, is a generalization of the matrix conic programming problem with wide applications in numerical linear algebra, computational statistics and engineering. This paper is devoted to the characterization of the strong regularity for the CMatOP via the generalized strong second order sufficient condition and constraint nondegeneracy for problems with nonsmooth objective functions. The derived result supplements the existing characterization of the strong regularity for the constrained optimization problems with twice continuously differentiable data.

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arXiv2019

Low Complexity Algorithms for Robust Multigroup Multicast Beamforming

Existing methods for robust multigroup multicast beamforming obtain feasible points using semidefinite relaxation (SDR) and Gaussian randomization, and have high computational complexity. In this letter, we consider the robust multigroup multicast beamforming design to minimize the sum power (SP) or per-antenna power (PAP) under the signal-to-interference-plus-noise ratio (SINR) constraints and to maximize the worst-case SINR under the SP constraint or PAP constraints, respectively. The resulting optimization problems are challenging non-convex problems with infinitely many constraints. For each problem, using the majorization-minimization (MM) approach, we propose an iterative algorithm to obtain a feasible solution which is shown to be a stationary point under certain conditions. We also show that the proposed algorithms have much lower computational complexity than existing SDR-based algorithms.

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arXiv2019

Optimization-based Decentralized Coded Caching for Files and Caches with Arbitrary Size

Existing decentralized coded caching solutions cannot guarantee small loads in the general scenario with arbitrary file sizes and cache sizes. In this paper, we propose an optimization framework for decentralized coded caching in the general scenario to minimize the worst-case load and average load (under an arbitrary file popularity), respectively. Specifically, we first propose a class of decentralized coded caching schemes for the general scenario, which are specified by a general caching parameter and include several known schemes as special cases. Then, we optimize the caching parameter to minimize the worst-case load and average load, respectively. Each of the two optimization problems is a challenging nonconvex problem with a nondifferentiable objective function. For each optimization problem, we develop an iterative algorithm to obtain a stationary point using techniques for solving Complementary Geometric Programming (GP). We also obtain a low-complexity approximate solution by solving an approximate problem with a differentiable objective function which is an upper bound on the original nondifferentiable one, and characterize the performance loss caused by the approximation. Finally, we present two information-theoretic converse bounds on the worst-case load and average load (under an arbitrary file popularity) in the general scenario, respectively. To the best of our knowledge, this is the first work that provides optimization-based decentralized coded caching schemes and information-theoretic converse bounds for the general scenario.

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arXiv2019

Optimal Caching Designs for Perfect, Imperfect and Unknown File Popularity Distributions in Large-Scale Multi-Tier Wireless Networks

Most existing caching solutions for wireless networks rest on an unrealistic assumption that the file popularity distribution is perfectly known. In this paper, we consider optimal caching designs for perfect, imperfect and unknown file popularity distributions in large-scale multi-tier wireless networks. First, in the case of perfect file popularity distribution, we formulate the optimization problem to maximize the successful transmission probability (STP), which is a nonconvex problem. We develop an efficient parallel iterative algorithm to obtain a stationary point using parallel successive convex approximation (SCA). Then, in the case of imperfect file popularity distribution, we formulate the robust optimization problem to maximize the worst-case STP. To solve this challenging maximin problem, we transform it to an equivalent complementary geometric programming (CGP), and develop an efficient iterative algorithm which is shown to converge to a stationary point using SCA. To the best of our knowledge, this is the first work explicitly considering the estimation error of file popularity distribution in the optimization of caching design. Next, in the case of unknown file popularity distribution, we formulate the stochastic optimization problem to maximize the stochastic STP (i.e., the STP in the stochastic form), which is a challenging nonconvex stochastic optimization problem. Based on stochastic parallel SCA, we develop an efficient iterative algorithm to obtain a stationary point, by exploiting structural properties of the stochastic STP and making full use of instantaneous file requests. As far as we know, this is the first work considering stochastic optimization in a large-scale multi-tier wireless network. Finally, by numerical results, we show that the proposed solutions achieve significant gains over existing schemes in all three cases.

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arXiv2019

End-to-end feature fusion siamese network for adaptive visual tracking

According to observations, different visual objects have different salient features in different scenarios. Even for the same object, its salient shape and appearance features may change greatly from time to time in a long-term tracking task. Motivated by them, we proposed an end-to-end feature fusion framework based on Siamese network, named FF-Siam, which can effectively fuse different features for adaptive visual tracking. The framework consists of four layers. A feature extraction layer is designed to extract the different features of the target region and search region. The extracted features are then put into a weight generation layer to obtain the channel weights, which indicate the importance of different feature channels. Both features and the channel weights are utilized in a template generation layer to generate a discriminative template. Finally, the corresponding response maps created by the convolution of the search region features and the template are applied with a fusion layer to obtain the final response map for locating the target. Experimental results demonstrate that the proposed framework achieves state-of-the-art performance on the popular Temple-Color, OTB50 and UAV123 benchmarks.

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arXiv2019

Analysis and Optimization of Caching and Multicasting for Multi-Quality Videos in Large-Scale Wireless Networks

Efficient dissemination of videos is an important problem for mobile telecom carriers. In this paper, to facilitate massive video dissemination, we study joint caching and multicasting for multi-quality videos encoded using two video encoding techniques, namely scalable video coding (SVC), and HEVC or H.264 as in dynamic adaptive streaming over HTTP (DASH) respectively, in a large-scale wireless network. First, for each type of videos, we propose a random caching and multicasting scheme, carefully reflecting the relationship between layers of an SVC-based video or descriptions of a DASH-based video. Then, for each type of videos, we derive tractable expressions for the successful transmission probability in the general and high user density regions, respectively, utilizing tools from stochastic geometry. The analytical results reveal that in the high user density region, the marginal increase of the successful transmission probability with respect to the caching probability of a video with a certain quality reduces when the caching probability increases. Next, for each type of videos, we consider the maximization of the successful transmission probability in the high user density region, which is a convex problem with an exceedingly large number of optimization variables. We propose a two-stage optimization method to obtain a low-complexity near optimal solution by solving a relaxed convex problem and a related packing problem. The optimization results reveal the impact of the caching gain of a layer for an SVC-based video or a description for a DASH-based video on its caching probability. Finally, we show that the proposed solutions for SVC-based and DASH-based videos achieve significant performance gains over baseline schemes in the general and high user density regions, and demonstrate their respective operating regions, using numerical results based on real video sequences.

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arXiv2019

Optimal Multi-Quality Multicast for 360 Virtual Reality Video

A 360 virtual reality (VR) video, recording a scene of interest in every direction, provides VR users with immersive viewing experience. However, transmission of a 360 VR video which is of a much larger size than a traditional video to mobile users brings a heavy burden to a wireless network. In this paper, we consider multi-quality multicast of a 360 VR video from a single server to multiple users using time division multiple access (TDMA). To improve transmission efficiency, tiling is adopted, and each tile is pre-encoded into multiple representations with different qualities. We optimize the quality level selection, transmission time allocation and transmission power allocation to maximize the total utility of all users under the transmission time and power allocation constraints as well as the quality smoothness constraints for mixed-quality tiles. The problem is a challenging mixed discrete-continuous opti-mization problem. We propose two low-complexity algorithms to obtain two suboptimal solutions, using continuous relaxation and DC programming, respectively. Finally, numerical results demonstrate the advantage of the proposed solutions.

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arXiv2018

In-medium $\mbox{NN}\to \mbox{N}Δ$ cross section and its dependence on effective Lagrange parameters in isospin-asymmetric nuclear matter

The in-medium $NN\rightarrow NΔ$ cross section and its differential cross section in isospin asymmetric nuclear medium are investigated in the framework of the one-boson exchange model by including the isovector mesons, i.e., $δ$ and $ρ$ mesons. Our results show that the in-medium $NN\rightarrow NΔ$ cross sections are suppressed with density increasing, and the differential cross sections become isotropic with the density increasing at the beam energy around the $Δ$ threshold energy. The isospin splitting on the medium correction factor, $R=σ_{ NN\rightarrow NΔ}^*/σ_{NN\rightarrow NΔ}^{\text{free}}$ is observed for different channels of $NN\to NΔ$, especially around the threshold energy for all the effective Lagrangian parameters. By analyzing the selected effective Lagrangian parameters, our results show that the larger effective mass is, the weaker medium correction $R$ is.

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arXiv2018

Optimal Hybrid Beamforming for Multiuser Massive MIMO Systems With Individual SINR Constraints

In this letter, we consider optimal hybrid beamforming design to minimize the transmission power under individual signal-to-interference-plus-noise ratio (SINR) constraints in a multiuser massive multiple-input-multiple-output (MIMO) system. This results in a challenging non-convex optimization problem. We consider two cases. In the case where the number of users is smaller than or equal to that of radio frequency (RF) chains, we propose a low-complexity method to obtain a globally optimal solution and show that it achieves the same transmission power as an optimal fully-digital beamformer. In the case where the number of users is larger than that of RF chains, we propose a low-complexity globally convergent alternating algorithm to obtain a stationary point.

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arXiv2018

Effect of energy conservation on in-medium $\mbox{NN}\to \mbox{N}Δ$ cross section in isospin-asymmetric nuclear matter

In this paper, the in-medium $NN\rightarrow NΔ$ cross section is calculated in the framework of the one-boson exchange model by including the isovector mesons, i.e. $δ$ and $ρ$ mesons. Due to the isospin exchange in the $NN\rightarrow NΔ$ process, the vector self-energies of the outgoing particles are modified relative to the incoming particles in isospin asymmetric nuclear matter, and it leads to the effective energies of the incoming $NN$ pair being different from the outgoing $NΔ$ pair. This effect is investigated in the calculation of the in-medium $NN\rightarrow NΔ$ cross section. With the corrected energy conservation, the cross sections of the $Δ^{++}$ and $Δ^+$ channels are suppressed, and the cross sections of the $Δ^0$ and $Δ^-$ channels are enhanced relative to the results obtained without properly considering the potential energy changes. Our results further confirm the dependence of medium correction factor, $R=σ_{ NN\rightarrow NΔ}^*/σ_{NN\rightarrow NΔ}^{\text{free}}$, on the charge state of $NN\rightarrow NΔ$ especially around the threshold energy, but the isospin splitting of medium correction factor $R$ becomes weak at high beam energies.

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arXiv2018

Composite Difference-Max Programs for Modern Statistical Estimation Problems

Many modern statistical estimation problems are defined by three major components: a statistical model that postulates the dependence of an output variable on the input features; a loss function measuring the error between the observed output and the model predicted output; and a regularizer that controls the overfitting and/or variable selection in the model. We study the sampling version of this generic statistical estimation problem where the model parameters are estimated by empirical risk minimization, which involves the minimization of the empirical average of the loss function at the data points weighted by the model regularizer. In our setup we allow all three component functions discussed above to be of the difference-of-convex (dc) type and illustrate them with a host of commonly used examples, including those in continuous piecewise affine regression and in deep learning (where the activation functions are piecewise affine). We describe a nonmonotone majorization-minimization (MM) algorithm for solving the unified nonconvex, nondifferentiable optimization problem which is formulated as a specially structured composite dc program of the pointwise max type, and present convergence results to a directional stationary solution. An efficient semismooth Newton method is proposed to solve the dual of the MM subproblems. Numerical results are presented to demonstrate the effectiveness of the proposed algorithm and the superiority of continuous piecewise affine regression over the standard linear model.

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