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Zhixin Liu

Publications and source records attributed to Zhixin Liu.

49 records · Page 3Linked to original sources
arXiv2022

Convergence of the Distributed SG Algorithm Under Cooperative Excitation Condition

In this paper, a distributed stochastic gradient (SG) algorithm is proposed where the estimators are aimed to collectively estimate an unknown time-invariant parameter from a set of noisy measurements obtained by distributed sensors. The proposed distributed SG algorithm combines the consensus strategy of the estimation of neighbors with the diffusion of regression vectors. A cooperative excitation condition is introduced, under which the convergence of the distributed SG algorithm can be obtained without relying on the independency and stationarity assumptions of regression vectors which are commonly used in existing literature. Furthermore, the convergence rate of the algorithm can be established. Finally, we show that all sensors can cooperate to fulfill the estimation task even though any individual sensor can not by a simulation example.

eess.SY↗
arXiv2021

Distributed order estimation of ARX model under cooperative excitation condition

In this paper, we consider the distributed estimation problem of a linear stochastic system described by an autoregressive model with exogenous inputs (ARX) when both the system orders and parameters are unknown. We design distributed algorithms to estimate the unknown orders and parameters by combining the proposed local information criterion (LIC) with the distributed least squares method. The simultaneous estimation for both the system orders and parameters brings challenges for the theoretical analysis. Some analysis techniques, such as double array martingale limit theory, stochastic Lyapunov functions, and martingale convergence theorems are employed. For the case where the upper bounds of the true orders are available, we introduce a cooperative excitation condition, under which the strong consistency of the estimation for the orders and parameters is established. Moreover, for the case where the upper bounds of true orders are unknown, similar distributed algorithm is proposed to estimate both the orders and parameters, and the corresponding convergence analysis for the proposed algorithm is provided. We remark that our results are obtained without relying on the independency or stationarity assumptions of regression vectors, and the cooperative excitation conditions can show that all sensors can cooperate to fulfill the estimation task even though any individual sensor can not.

eess.SY↗
arXiv2021

Securing Federated Learning: A Covert Communication-based Approach

Federated Learning Networks (FLNs) have been envisaged as a promising paradigm to collaboratively train models among mobile devices without exposing their local privacy data. Due to the need for frequent model updates and communications, FLNs are vulnerable to various attacks (e.g., eavesdropping attacks, inference attacks, poisoning attacks, and backdoor attacks). Balancing privacy protection with efficient distributed model training is a key challenge for FLNs. Existing countermeasures incur high computation costs and are only designed for specific attacks on FLNs. In this paper, we bridge this gap by proposing the Covert Communication-based Federated Learning (CCFL) approach. Based on the emerging communication security technique of covert communication which hides the existence of wireless communication activities, CCFL can degrade attackers' capability of extracting useful information from the FLN training protocol, which is a fundamental step for most existing attacks, and thereby holistically enhances the privacy of FLNs. We experimentally evaluate CCFL extensively under real-world settings in which the FL latency is optimized under given security requirements. Numerical results demonstrate the significant effectiveness of the proposed approach in terms of both training efficiency and communication security.

cs.CR↗
arXiv2020

Experimental Analysis on Variations and Accuracy of Crosstalk in Trench-Assisted Multi-core Fibers

Space division multiplexing using multi-core fiber (MCF) is a promising solution to cope with the capacity crunch in standard single-mode fiber based optical communication systems. Nevertheless, the achievable capacity of MCF is limited by inter-core crosstalk (IC-XT). Many existing researches treat IC-XT as a static interference, however, recent research shows that IC-XT varies with time, wavelength and baud rate. This inherent stochastic feature requires a comprehensive characterization of the behaviour of MCF to its application in practical transmission systems and the theoretical understanding of IC-XT phenomenon. In this paper, we experimentally investigate the IC-XT behaviour of an 8-core trench-assisted MCF in a temperature-controlled environment, using popular modulation formats. We compare the measured results with the theoretical prediction to validate the analytical IC-XT models previously developed. Moreover, we explore the effects of the measurement configurations on the IC-XT accuracy and present an analysis on the IC-XT step distribution. Our results indicate that a number of transmission parameters have significant influence on the strength and volatility of IC-XT. Moreover, the averaging time of the power meter and the observation time window can affect the value of the observed IC-XT, the degrees of the effects vary with the type of the source signals.

eess.SP↗
arXiv2020

Learning-based Quantum Robust Control: Algorithm, Applications and Experiments

Robust control design for quantum systems has been recognized as a key task in quantum information technology, molecular chemistry and atomic physics. In this paper, an improved differential evolution algorithm, referred to as \emph{msMS}\_DE, is proposed to search robust fields for various quantum control problems. In \emph{msMS}\_DE, multiple samples are used for fitness evaluation and a mixed strategy is employed for the mutation operation. In particular, the \emph{msMS}\_DE algorithm is applied to the control problems of (i) open inhomogeneous quantum ensembles and (ii) the consensus goal of a quantum network with uncertainties. Numerical results are presented to demonstrate the excellent performance of the improved machine learning algorithm for these two classes of quantum robust control problems. Furthermore, \emph{msMS}\_DE is experimentally implemented on femtosecond laser control applications to optimize two-photon absorption and control fragmentation of the molecule $\text{CH}_2\text{BrI}$. Experimental results demonstrate excellent performance of \emph{msMS}\_DE in searching for effective femtosecond laser pulses for various tasks.

quant-ph↗
arXiv2020

ASR-Free Pronunciation Assessment

Most of the pronunciation assessment methods are based on local features derived from automatic speech recognition (ASR), e.g., the Goodness of Pronunciation (GOP) score. In this paper, we investigate an ASR-free scoring approach that is derived from the marginal distribution of raw speech signals. The hypothesis is that even if we have no knowledge of the language (so cannot recognize the phones/words), we can still tell how good a pronunciation is, by comparatively listening to some speech data from the target language. Our analysis shows that this new scoring approach provides an interesting correction for the phone-competition problem of GOP. Experimental results on the ERJ dataset demonstrated that combining the ASR-free score and GOP can achieve better performance than the GOP baseline.

eess.AS↗
arXiv2020

Dynamic Partial Removal: A Neural Network Heuristic for Large Neighborhood Search

This paper presents a novel neural network design that learns the heuristic for Large Neighborhood Search (LNS). LNS consists of a destroy operator and a repair operator that specify a way to carry out the neighborhood search to solve the Combinatorial Optimization problems. The proposed approach in this paper applies a Hierarchical Recurrent Graph Convolutional Network (HRGCN) as a LNS heuristic, namely Dynamic Partial Removal, with the advantage of adaptive destruction and the potential to search across a large scale, as well as the context-awareness in both spatial and temporal perspective. This model is generalized as an efficient heuristic approach to different combinatorial optimization problems, especially to the problems with relatively tight constraints. We apply this model to vehicle routing problem (VRP) in this paper as an example. The experimental results show that this approach outperforms the traditional LNS heuristics on the same problem as well. The source code is available at \href{https://github.com/water-mirror/DPR}{https://github.com/water-mirror/DPR}.

cs.NE↗
arXiv2020

Learn to Design the Heuristics for Vehicle Routing Problem

This paper presents an approach to learn the local-search heuristics that iteratively improves the solution of Vehicle Routing Problem (VRP). A local-search heuristics is composed of a destroy operator that destructs a candidate solution, and a following repair operator that rebuilds the destructed one into a new one. The proposed neural network, as trained through actor-critic framework, consists of an encoder in form of a modified version of Graph Attention Network where node embeddings and edge embeddings are integrated, and a GRU-based decoder rendering a pair of destroy and repair operators. Experiment results show that it outperforms both the traditional heuristics algorithms and the existing neural combinatorial optimization for VRP on medium-scale data set, and is able to tackle the large-scale data set (e.g., over 400 nodes) which is a considerable challenge in this area. Moreover, the need for expertise and handcrafted heuristics design is eliminated due to the fact that the proposed network learns to design the heuristics with a better performance. Our implementation is available online.

cs.NE↗
arXiv2019

Multiband superconductivity and possible nodal gap in RbCr$_{3}$As$_{3}$ revealed by Andreev reflection and single-particle tunneling measurements

By measuring point-contact Andreev reflection (PCAR) spectra in the newly discovered chromium-based quasi-one-dimensional superconductor RbCr$_{3}$As$_{3}$, we find clear evidence of two superconducting components, i.e., one having a gap value of about 1.8 meV and another with a gap value of about 5 meV. Since the current injection may have components in both the direction parallel and perpendicular to the [(Cr$_{3}$As$_{3}$)$^{-}$]$_\infty$ chains in the PCAR measurements, it naturally explains the two-component feature observed in this multi-band superconductor. Detailed analysis shows that the larger gap may have an $s$-wave nature. We then carry out the single-particle tunneling measurements based on a scanning tunneling spectroscope by using the needle-like sample as the tip, and in this case the measured current is mainly parallel to the [(Cr$_{3}$As$_{3}$)$^{-}$]$_\infty$ chains. The single-particle tunneling spectra show only one gap feature with a gap value of about 1.8 meV. Fitting to the single particle tunneling spectra indicates that the gap should have a large anisotropy or even node(s). We argue that the absence of the larger gap may be related to the direction of the injecting current. Therefore, our combined experiments show the multiband superconductivity with one gap being nodal or highly anisotropic.

cond-mat.supr-con↗
arXiv2019

Dual Frequency Comb Assisted Analog-to-Digital Conversion of Subcarrier Modulated Signals

Photonic analog to digital conversion offers promise to overcome the signal-to-noise ratio (SNR) and sample rate trade-off in conventional analog to digital converters (ADCs), critical for modern digital communications and signal analysis. We propose using phase-stable dual frequency combs with a fixed frequency spacing offset to downconvert spectral slices of a broadband signal and enable high resolution parallel digitization. To prove the concept of our proposed method, we demonstrate the detection of a 10-GHz subcarrier modulated (SCM) signal using 500-MHz bandwidth ADCs by optically converting the SCM signal to ten 1-GHz bandwidth signals that can be processed in parallel for full signal detection and reconstruction. Using sinusoidal wave based standard ADC testing, we demonstrate a spurious-free dynamic range (SFDR) of >45dB and signal-to-noise-and-distortion (SINAD) of >20dB, only limited by the receiver front-end design. Our experimental investigation reveals that this SINAD limitation can be overcome by improved receiver design, promising high resolution ADC for broadband signals.

eess.SP↗
arXiv2016

Distributed sampled-data control of nonholonomic multi-robot systems with proximity networks

This paper considers the distributed sampled-data control problem of a group of mobile robots connected via distance-induced proximity networks. A dwell time is assumed in order to avoid chattering in the neighbor relations that may be caused by abrupt changes of positions when updating information from neighbors. Distributed sampled-data control laws are designed based on nearest neighbour rules, which in conjunction with continuous-time dynamics results in hybrid closed-loop systems. For uniformly and independently initial states, a sufficient condition is provided to guarantee synchronization for the system without leaders. In order to steer all robots to move with the desired orientation and speed, we then introduce a number of leaders into the system, and quantitatively establish the proportion of leaders needed to track either constant or time-varying signals. All these conditions depend only on the neighborhood radius, the maximum initial moving speed and the dwell time, without assuming a prior properties of the neighbor graphs as are used in most of the existing literature.

eess.SY↗