arXiv subjects

Quan Zhang

Publications and source records attributed to Quan Zhang.

At least 73 records · Page 4Linked to original sources

arXiv2022

ET White Paper: To Find the First Earth 2.0

We propose to develop a wide-field and ultra-high-precision photometric survey mission, temporarily named "Earth 2.0 (ET)". This mission is designed to measure, for the first time, the occurrence rate and the orbital distributions of Earth-sized planets. ET consists of seven 30cm telescopes, to be launched to the Earth-Sun's L2 point. Six of these are transit telescopes with a field of view of 500 square degrees. Staring in the direction that encompasses the original Kepler field for four continuous years, this monitoring will return tens of thousands of transiting planets, including the elusive Earth twins orbiting solar-type stars. The seventh telescope is a 30cm microlensing telescope that will monitor an area of 4 square degrees toward the galactic bulge. This, combined with simultaneous ground-based KMTNet observations, will measure masses for hundreds of long-period and free-floating planets. Together, the transit and the microlensing telescopes will revolutionize our understandings of terrestrial planets across a large swath of orbital distances and free space. In addition, the survey data will also facilitate studies in the fields of asteroseismology, Galactic archeology, time-domain sciences, and black holes in binaries.

astro-ph.IM↗

arXiv2022

Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power System

The real-time transient stability assessment (TSA) plays a critical role in the secure operation of the power system. Although the classic numerical integration method, \textit{i.e.} time-domain simulation (TDS), has been widely used in industry practice, it is inevitably trapped in a high computational complexity due to the high latitude sophistication of the power system. In this work, a data-driven power system estimation method is proposed to quickly predict the stability of the power system before TDS reaches the end of simulating time windows, which can reduce the average simulation time of stability assessment without loss of accuracy. As the topology of the power system is in the form of graph structure, graph neural network based representation learning is naturally suitable for learning the status of the power system. Motivated by observing the distribution information of crucial active power and reactive power on the power system's bus nodes, we thus propose a distribution-aware learning~(DAL) module to explore an informative graph representation vector for describing the status of a power system. Then, TSA is re-defined as a binary classification task, and the stability of the system is determined directly from the resulting graph representation without numerical integration. Finally, we apply our method to the online TSA task. The case studies on the IEEE 39-bus system and Polish 2383-bus system demonstrate the effectiveness of our proposed method.

eess.SY↗

arXiv2021

Efficient QAM Signal Detector for Massive MIMO Systems via PS-ADMM Approach

In this paper, we design an efficient quadrature amplitude modulation (QAM) signal detector for massive multiple-input multiple-output (MIMO) communication systems via the penalty-sharing alternating direction method of multipliers (PS-ADMM). Its main content is as follows: first, we formulate QAM-MIMO detection as a maximum-likelihood optimization problem with bound relaxation constraints. Decomposing QAM signals into a sum of multiple binary variables and exploiting introduced binary variables as penalty functions, we transform the detection optimization model to a non-convex sharing problem; second, a customized ADMM algorithm is presented to solve the formulated non-convex optimization problem. In the implementation, all variables can be solved analytically and in parallel; third, it is proved that the proposed PS-ADMM algorithm converges under mild conditions. Simulation results demonstrate the effectiveness of the proposed approach.

eess.SP↗

arXiv2021

Agent-Based Campus Novel Coronavirus Infection and Control Simulation

Corona Virus Disease 2019 (COVID-19), due to its extremely high infectivity, has been spreading rapidly around the world and bringing huge influence to socioeconomic development as well as people's daily life. Taking for example the virus transmission that may occur after college students return to school, we analyze the quantitative influence of the key factors on the virus spread, including crowd density and self-protection. One Campus Virus Infection and Control Simulation model (CVICS) of the novel coronavirus is proposed in this paper, fully considering the characteristics of repeated contact and strong mobility of crowd in the closed environment. Specifically, we build an agent-based infection model, introduce the mean field theory to calculate the probability of virus transmission, and micro-simulate the daily prevalence of infection among individuals. The experimental results show that the proposed model in this paper efficiently simulate how the virus spread in the dense crowd in frequent contact under closed environment. Furthermore, preventive and control measures such as self-protection, crowd decentralization and isolation during the epidemic can effectively delay the arrival of infection peak and reduce the prevalence, and finally lower the risk of COVID-19 transmission after the students return to school.

cs.SI↗

arXiv2021

A Low-Complexity ADMM-based Massive MIMO Detectors via Deep Neural Networks

An alternate direction method of multipliers (ADMM)-based detectors can achieve good performance in both small and large-scale multiple-input multiple-output (MIMO) systems. However, due to the difficulty of choosing the optimal penalty parameters, their performance is limited. This paper presents a deep neural network (DNN)-based massive MIMO detection method which can overcome the above limitation. It exploits the unfolding technique and learns to estimate the penalty parameters. Additionally, a computationally cheaper detector is also proposed. The proposed methods can handle the higher-order modulation signals. Numerical results are presented to demonstrate the performances of the proposed methods compared with the existing works.

eess.SP↗

arXiv2021

RNN-Test: Towards Adversarial Testing for Recurrent Neural Network Systems

While massive efforts have been investigated in adversarial testing of convolutional neural networks (CNN), testing for recurrent neural networks (RNN) is still limited and leaves threats for vast sequential application domains. In this paper, we propose an adversarial testing framework RNN-Test for RNN systems, focusing on the main sequential domains, not only classification tasks. First, we design a novel search methodology customized for RNN models by maximizing the inconsistency of RNN states to produce adversarial inputs. Next, we introduce two state-based coverage metrics according to the distinctive structure of RNNs to explore more inference logics. Finally, RNN-Test solves the joint optimization problem to maximize state inconsistency and state coverage, and crafts adversarial inputs for various tasks of different kinds of inputs. For evaluations, we apply RNN-Test on three sequential models of common RNN structures. On the tested models, the RNN-Test approach is demonstrated to be competitive in generating adversarial inputs, outperforming FGSM-based and DLFuzz-based methods to reduce the model performance more sharply with 2.78% to 32.5% higher success (or generation) rate. RNN-Test could also achieve 52.65% to 66.45% higher adversary rate on MNIST-LSTM model than relevant work testRNN. Compared with the neuron coverage, the proposed state coverage metrics as guidance excel with 4.17% to 97.22% higher success (or generation) rate.

cs.CL↗

arXiv2020

Designing Massive MIMO Detector via PS-ADMM approach

In this paper, we develop an efficient detector for massive multiple-input multiple-output (MIMO) communication systems via penalty-sharing alternating direction method of multipliers (PS-ADMM). Its main content are as follows: first, we formulate the MIMO detection as a maximum-likelihood optimization problem with bound relaxation constraints. Then, the higher modulation signals are decomposed into a sum of multiple binary variables through their inherent structures, by exploiting introduced binary variables as penalty functions, the detection optimization model is equivalent to a nonconvex sharing minimization problem. Second, a customized ADMM algorithm is presented to solve the formulated nonconvex optimization problem. In the implementation, all variables can be solved analytically and parallelly. Third, it is proved that the proposed PS-ADMM algorithm converges if proper parameters are chosen. Simulation results demonstrate the effectiveness of the proposed approach.

eess.SP↗

arXiv2019

Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks

We propose Lomax delegate racing (LDR) to explicitly model the mechanism of survival under competing risks and to interpret how the covariates accelerate or decelerate the time to event. LDR explains non-monotonic covariate effects by racing a potentially infinite number of sub-risks, and consequently relaxes the ubiquitous proportional-hazards assumption which may be too restrictive. Moreover, LDR is naturally able to model not only censoring, but also missing event times or event types. For inference, we develop a Gibbs sampler under data augmentation for moderately sized data, along with a stochastic gradient descent maximum a posteriori inference algorithm for big data applications. Illustrative experiments are provided on both synthetic and real datasets, and comparison with various benchmark algorithms for survival analysis with competing risks demonstrates distinguished performance of LDR.

stat.ME↗

arXiv2018

Research on Limited Buffer Scheduling Problems in Flexible Flow Shops with Setup Times

In order to solve the limited buffer scheduling problems in flexible flow shops with setup times, this paper proposes an improved whale optimization algorithm (IWOA) as a global optimization algorithm. Firstly, this paper presents a mathematic programming model for limited buffer in flexible flow shops with setup times, and applies the IWOA algorithm as the global optimization algorithm. Based on the whale optimization algorithm (WOA), the improved algorithm uses Levy flight, opposition-based learning strategy and simulated annealing to expand the search range, enhance the ability for jumping out of local extremum, and improve the continuous evolution of the algorithm. To verify the improvement of the proposed algorithm on the optimization ability of the standard WOA algorithm, the IWOA algorithm is tested by verification examples of small-scale and large-scale flexible flow shop scheduling problems, and the imperialist competitive algorithm (ICA), bat algorithm (BA), and whale optimization algorithm (WOA) are used for comparision. Based on the instance data of bus manufacturer, simulation tests are made on the four algorithms under variouis of practical evalucation scenarios. The simulation results show that the IWOA algorithm can better solve this type of limited buffer scheduling problem in flexible flow shops with setup times compared with the state of the art algorithms.

cs.AI↗

arXiv2018

Permuted and Augmented Stick-Breaking Bayesian Multinomial Regression

To model categorical response variables given their covariates, we propose a permuted and augmented stick-breaking (paSB) construction that one-to-one maps the observed categories to randomly permuted latent sticks. This new construction transforms multinomial regression into regression analysis of stick-specific binary random variables that are mutually independent given their covariate-dependent stick success probabilities, which are parameterized by the regression coefficients of their corresponding categories. The paSB construction allows transforming an arbitrary cross-entropy-loss binary classifier into a Bayesian multinomial one. Specifically, we parameterize the negative logarithms of the stick failure probabilities with a family of covariate-dependent softplus functions to construct nonparametric Bayesian multinomial softplus regression, and transform Bayesian support vector machine (SVM) into Bayesian multinomial SVM. These Bayesian multinomial regression models are not only capable of providing probability estimates, quantifying uncertainty, increasing robustness, and producing nonlinear classification decision boundaries, but also amenable to posterior simulation. Example results demonstrate their attractive properties and performance.

stat.ME↗

arXiv2018

Tunable fluid-solid metamaterials for manipulation of elastic wave propagation in broad frequency range

Current strategies for designing tunable locally resonant metamaterials are based on tuning the stiffness of the resonator; however, this approach presents a major shortcoming as the effective mass density is constant at high frequency. Here, this paper reports a type of tunable locally elastic metamaterial-'called tunable fluid-solid composite'-inspired by the functions of heart and vessels in animals and humans. The proposed metamaterial consists of several liquid or gas inclusions in a solid matrix, controlled through a pair of embedded pumps. Both the band gap and effective mass density at high frequency can be tuned by controlling the liquid distribution in the unit cell, as demonstrated through a combination of theoretical analysis, numerical simulation, and experimental testing. Finally, we show that the tunable fluid-solid metamaterial can be utilized to manipulate wave propagation over a broad frequency range, providing new avenues for vibration isolation and wave guiding.

physics.app-ph↗

arXiv2017

Pressure and strain effects on the optical properties of K4 phosphorus

An investigation of the mechanical, electronic, and optical properties of the recently reported material K4 phosphorus was made in this work. The K4 phosphorus has been proved to be mechanically and dynamically stable up to 7 GPa under hydrostatic pressure. We compared the elastic anisotropy, average acoustic velocity and Debye temperature of K4 phosphorus at 0 and 7 GPa. The ideal tensile at large strains of K4 phosphorus was also examined, with the results showing that it would cleave under the tensile strength of 8.5 GPa with the strain of 0.3. In addition, the effect of tensile strain and pressure on optical properties and band gap were studied.

cond-mat.mtrl-sci↗

arXiv2014

Preserving Privacy of Mobile Reader Holders in Server-less RFID Authentication and Searching Protocols

Along with the development of internet of things and pervasive computing, researchers are increasingly focusing on server-less RFID authentication and searching protocols, which utilize mobile RFID readers. However, revealing privacy of mobile reader holders is a widely neglected problem in current research. This paper concentrates on preserving privacy of mobile reader holders in server-less RFID authentication and searching protocols. We propose a detailed requirement as a principle for future protocol designs, and a scheme to enhance most current protocols. We apply our scheme to two classical protocols. The comparisons between the original and our enhanced protocols show that our scheme is secure and effective.

cs.CR↗

arXiv2014

RFID Authentication Against an Unsecure Backend Server

This paper address a new problem in RFID authentication research for the first time. That is, existing RFID authentication schemes generally assume that the backend server is absolutely secure, however, this assumption is rarely tenable in practical conditions. It disables existing RFID authentication protocols from being safely applied to a reallife scenario in which the backend server is actually vulnerable, compromised or even malicious itself. We propose an RFID authentication scheme against an unsecure backend server. It is based on hash chain, searching over encrypted data, and coprivacy, defending against the privacy revealing to the backend server. The proposed scheme is scalable, resistant to desynchronization attacks, and provides mutual authentication in only three frontend communication steps. Moreover, it is the first scheme meeting the special security and privacy requirement for a cloud-based RFID authentication scenario in which the backend server is untrustworthy to readers held by cloud clients.

cs.CR↗

arXiv2013

Smectic order, pinning, and phase transition in a smectic liquid crystal cell with a random substrate

We study smectic-liquid-crystal order in a cell with a heterogeneous substrate imposing surface random positional and orientational pinnings. Proposing a minimal random elastic model, we demonstrate that, for a thick cell, the smectic state without a rubbed substrate is always unstable at long scales and, for weak random pinning, is replaced by a smectic glass state. We compute the statistics of the associated substrate-driven distortions and the characteristic smectic domain size on the heterogeneous substrate and in the bulk. We find that for weak disorder, the system exhibits a three-dimensional temperature-controlled phase transition between a weakly and strongly pinned smectic glass states akin to the Cardy-Ostlund phase transition. We explore experimental implications of the predicted phenomenology and suggest that it provides a plausible explanation for the experimental observations on polarized light microscopy and x-ray scattering.

cond-mat.soft↗

arXiv2012

Smectic-glass transition in a liquid crystal cell with a "dirty" substrate

We explore the smectic liquid crystal order in a cell with a "dirty" substrate imposing random pinnings. Within harmonic elasticity we find a subtle three-dimensional surface disorder-driven transition into a pinned smectic-glass, controlled by a three-dimensional Cardy-Ostlund-like fixed line, akin to a super-rough phase of a two-dimensional xy model. We compute the associated random substrate-driven distortions of the smectic-glass state, identify the characteristic length scales on the heterogeneous substrate and in the bulk, and discuss a variety of experimental signatures.

cond-mat.soft↗

arXiv2011

Security proof of Counterfactual Quantum Cryptography against General Intercept-resend Attacks and Its Vulnerability

Counterfactual quantum cryptography (CQC), recently proposed by Noh, is featured with no transmission of signal particles. This exhibits evident security advantage, such as its immunity to the well known PNS attack. In this paper, the theoretical security of CQC protocol against the general intercept-resend attacks is proved by bounding the information of an eavesdropper Eve more tightly than in Yin's proposal[Phys. Rev. A 82, 042335 (2010)]. It is also showed that practical CQC implementations may be vulnerable when equipped with imperfect apparatuses, by proving that a negative key rate can be achieved when Eve launches a time-shift attack based on imperfect detector efficiency.

quant-ph↗

arXiv2011

Semiquantum secret sharing using two-particle entangled state

Recently, Boyer et al. presented a novel semiquantum key distribution protocol [M. Boyer, D. Kenigsberg, and T. Mor, Phys. Rev. Lett. 99, 140501 (2007)], in which quantum Alice shares a secret key with classical Bob. Li et al. proposed two semiquantum secret sharing protocols [Q. Li, W. H. Chan, and D. Y. Long, Phys. Rev. A 82, 022303 (2010)] by using maximally entangled Greenberger-Horne-Zeilinger states. In this paper, we present a semiquantum secret sharing protocol by using two-particle entangled states in which quantum Alice shares a secret key with two classical parties, Bob and Charlie. Classical Bob and Charlie are restricted to performing measurement in the computational basis, preparing a particle in the computational basis, or reflecting the particles. None of them can acquire the secret unless they collaborate. We also show the protocol is secure against eavesdropping.

quant-ph↗