arXiv subjects

Fei Chen

Publications and source records attributed to Fei Chen.

At least 145 records · Page 8Linked to original sources

arXiv2020

Delay and Packet-Drop Tolerant Multi-Stage Distributed Average Tracking in Mean Square

This paper studies the distributed average tracking problem pertaining to a discrete-time linear time-invariant multi-agent network, which is subject to, concurrently, input delays, random packet-drops, and reference noise. The problem amounts to an integrated design of delay and packet-drop tolerant algorithm and determining the ultimate upper bound of the tracking error between agents' states and the average of the reference signals. The investigation is driven by the goal of devising a practically more attainable average tracking algorithm, thereby extending the existing work in the literature which largely ignored the aforementioned uncertainties. For this purpose, a blend of techniques from Kalman filtering, multi-stage consensus filtering, and predictive control is employed, which gives rise to a simple yet comepelling distributed average tracking algorithm that is robust to initialization error and allows the trade-off between communication/computation cost and stationary-state tracking error. Due to the inherent coupling among different control components, convergence analysis is significantly challenging. Nevertheless, it is revealed that the allowable values of the algorithm parameters rely upon the maximal degree of an expected network, while the convergence speed depends upon the second smallest eigenvalue of the same network's topology. The effectiveness of the theoretical results is verified by a numerical example.

eess.SY↗

arXiv2020

Uncovering the influence of common nonmetal impurities on the stability and strength of a $Σ$5 (310) grain boundary in Cu

Impurities are often driven to segregate to grain boundaries, which can significantly alter a material's thermal stability and mechanical behavior. To provide a comprehensive picture of this issue, the influence of a wide variety of common nonmetal impurities (H, B, C, N, O, Si, P and S) incorporated during service or materials processing are studied using first-principles simulations, with a focus on identifying changes to the energetics and mechanical strength of a Cu $Σ$5 (310) grain boundary. Changes to the grain boundary energy are found to be closely correlated with the covalent radii of the impurities and the volumetric deformations of polyhedra at the interface. The strengthening energies of each impurity are evaluated as a function of covalent radius and electronegativity, followed by first-principles-based tensile tests on selected impurities. The strengthening of a B-doped grain boundary comes from an enhancement of the charge density among the adjacent Cu atoms, which improves the connection between the two grains. Alternatively, the detrimental effect of O results from the reduction of charge density between the Cu atoms. This work deepens the understanding of the possible beneficial and harmful effects of impurities on grain boundaries, providing a guide for materials processing studies.

cond-mat.mtrl-sci↗

arXiv2020

Combined effects of nonmetallic impurities and planned metallic dopants on grain boundary energy and strength

Most research on nanocrystalline alloys has been focused on planned doping of metals with other metallic elements, but nonmetallic impurities are also prevalent in the real world. In this work, we report on the combined effects of metallic dopants and nonmetallic impurities on grain boundary energy and strength using first-principles calculations, with a $Σ$5 (310) grain boundary in Cu chosen as a model system. We find a clear correlation between the grain boundary energy and the change in excess free volume of doped grain boundaries. A combination of a larger substitutional dopant and an interstitial impurity can fill the excess free volume more efficiently and further reduce the grain boundary energy. We also find that the strengthening effects of dopants and impurities are dominated by the electronic interactions between the host Cu atoms and the two types of dopant elements. For example, the significant competing effects of metal dopants such as Zr, Nb, and Mo with impurities on the grain boundary strength are uncovered from the density of states of the d electrons. As a whole, this work deepens the field's understanding of the interaction between metallic dopants and nonmetallic impurities on grain boundary properties, providing a guide for improving the thermal stability of materials while avoiding embrittling effects.

cond-mat.mtrl-sci↗

arXiv2019

Federated Meta-Learning with Fast Convergence and Efficient Communication

Statistical and systematic challenges in collaboratively training machine learning models across distributed networks of mobile devices have been the bottlenecks in the real-world application of federated learning. In this work, we show that meta-learning is a natural choice to handle these issues, and propose a federated meta-learning framework FedMeta, where a parameterized algorithm (or meta-learner) is shared, instead of a global model in previous approaches. We conduct an extensive empirical evaluation on LEAF datasets and a real-world production dataset, and demonstrate that FedMeta achieves a reduction in required communication cost by 2.82-4.33 times with faster convergence, and an increase in accuracy by 3.23%-14.84% as compared to Federated Averaging (FedAvg) which is a leading optimization algorithm in federated learning. Moreover, FedMeta preserves user privacy since only the parameterized algorithm is transmitted between mobile devices and central servers, and no raw data is collected onto the servers.

cs.LG↗

arXiv2019

Consensus Control for Leader-follower Multi-agent Systems under Prescribed Performance Guarantees

This paper addresses the problem of distributed control for leader-follower multi-agent systems under prescribed performance guarantees. Leader-follower is meant in the sense that a group of agents with external inputs are selected as leaders in order to drive the group of followers in a way that the entire system can achieve consensus within certain prescribed performance transient bounds. Under the assumption of tree graphs, a distributed control law is proposed when the decay rate of the performance functions is within a sufficient bound. Then, two classes of tree graphs that can have additional followers are investigated. Finally, several simulation examples are given to illustrate the results.

cs.MA↗

arXiv2018

Controllability of Directed Heterogeneous Networked MIMO Systems

This paper studies the controllability of networked multi-input-multi-output (MIMO) systems, in which the network topology is weighted and directed, and the nodes are heterogeneous higher-dimensional linear time-invariant (LTI) dynamical systems. The primary objective is to search for controllability criteria beyond those already known for homogeneous networks. The focus is on the effects of the network topology, node dynamics, external control inputs, as well as the inner interactions on the network controllability. It is found that a network of heterogeneous systems can be controllable even if the corresponding homogeneous network topology is uncontrollable. The finding thus unravels another fundamental property that affects the network controllability---the heterogeneity of the node dynamics. A necessary and sufficient condition is derived for the controllability of heterogeneous networked MIMO LTI systems. For some typical cases, necessary and/or sufficient controllability conditions are specified and presented on the node dynamics, inner interactions, as well as the network topology.

math.OC↗

arXiv2018

Speech Dereverberation Based on Integrated Deep and Ensemble Learning Algorithm

Reverberation, which is generally caused by sound reflections from walls, ceilings, and floors, can result in severe performance degradation of acoustic applications. Due to a complicated combination of attenuation and time-delay effects, the reverberation property is difficult to characterize, and it remains a challenging task to effectively retrieve the anechoic speech signals from reverberation ones. In the present study, we proposed a novel integrated deep and ensemble learning algorithm (IDEA) for speech dereverberation. The IDEA consists of offline and online phases. In the offline phase, we train multiple dereverberation models, each aiming to precisely dereverb speech signals in a particular acoustic environment; then a unified fusion function is estimated that aims to integrate the information of multiple dereverberation models. In the online phase, an input utterance is first processed by each of the dereverberation models. The outputs of all models are integrated accordingly to generate the final anechoic signal. We evaluated the IDEA on designed acoustic environments, including both matched and mismatched conditions of the training and testing data. Experimental results confirm that the proposed IDEA outperforms single deep-neural-network-based dereverberation model with the same model architecture and training data.

cs.SD↗

arXiv2017

Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

Few-shot learning is challenging for learning algorithms that learn each task in isolation and from scratch. In contrast, meta-learning learns from many related tasks a meta-learner that can learn a new task more accurately and faster with fewer examples, where the choice of meta-learners is crucial. In this paper, we develop Meta-SGD, an SGD-like, easily trainable meta-learner that can initialize and adapt any differentiable learner in just one step, on both supervised learning and reinforcement learning. Compared to the popular meta-learner LSTM, Meta-SGD is conceptually simpler, easier to implement, and can be learned more efficiently. Compared to the latest meta-learner MAML, Meta-SGD has a much higher capacity by learning to learn not just the learner initialization, but also the learner update direction and learning rate, all in a single meta-learning process. Meta-SGD shows highly competitive performance for few-shot learning on regression, classification, and reinforcement learning.

cs.LG↗

arXiv2017

Control refinement for discrete-time descriptor systems: a behavioural approach via simulation relations

The analysis of industrial processes, modelled as descriptor systems, is often computationally hard due to the presence of both algebraic couplings and difference equations of high order. In this paper, we introduce a control refinement notion for these descriptor systems that enables analysis and control design over related reduced-order systems. Utilising the behavioural framework, we extend upon the standard hierarchical control refinement for ordinary systems and allow for algebraic couplings inherent to descriptor systems.

eess.SY↗

arXiv2017

Electronic structure of FeS

Here we report the electronic structure of FeS, a recently identified iron-based superconductor. Our high-resolution angle-resolved photoemission spectroscopy studies show two hole-like ($α$ and $β$) and two electron-like ($η$ and $δ$) Fermi pockets around the Brillouin zone center and corner, respectively, all of which exhibit moderate dispersion along $k_z$. However, a third hole-like band ($γ$) is not observed, which is expected around the zone center from band calculations and is common in iron-based superconductors. Since this band has the highest renormalization factor and is known to be the most vulnerable to defects, its absence in our data is likely due to defect scattering --- and yet superconductivity can exist without coherent quasiparticles in the $γ$ band. This may help resolve the current controversy on the superconducting gap structure of FeS. Moreover, by comparing the $β$ bandwidths of various iron chalcogenides, including FeS, FeSe$_{1-x}$S$_x$, FeSe, and FeSe$_{1-x}$ Te$_x$, we find that the $β$ bandwidth of FeS is the broadest. However, the band renormalization factor of FeS is still quite large, when compared with the band calculations, which indicates sizable electron correlations. This explains why the unconventional superconductivity can persist over such a broad range of isovalent substitution in FeSe$_{1-x}$Te$_{x}$ and FeSe$_{1-x}$S$_{x}$.

cond-mat.supr-con↗

arXiv2017

Control refinement for DAE systems: A behavioral approach via simulation relations

The controller design of the so-called "difference algebraic equation" (DAE) systems that are frequently shown in industrial processes, tend to be challenging because of the combination of algebraic equations and high state dimensions. In this paper, we tackle this problem by developing control refinement approaches for DAE systems via the notions of (bi)simulation relations and approximate simulation relations from computer science. The quantified refinement accuracy is achieved by defining observation metrics over a general system framework named transition systems. We employ the behavioral theory to tackle dynamical systems and control problems in a more general framework. Due to the difficulty in dealing with a DAE system directly, we derive another system, which is behaviorally equivalent to the related DAE system and in standard state space form, to provide ease for further control refinement. Consequently, well-developed model reduction approaches can be applied to obtain an abstract simplified system, which can be rewritten into a DAE system again. Based on the (bi)simulation relations, approximate simulation relations and the initialization conditions, we show that for any given well-posed controller of the abstract model, we can always refine it to a controller for the concrete model such that the two systems have the same controlled output behavior or the distance between their output behavior is bounded.

eess.SY↗

arXiv2017

Automated scalable segmentation of neurons from multispectral images

Reconstruction of neuroanatomy is a fundamental problem in neuroscience. Stochastic expression of colors in individual cells is a promising tool, although its use in the nervous system has been limited due to various sources of variability in expression. Moreover, the intermingled anatomy of neuronal trees is challenging for existing segmentation algorithms. Here, we propose a method to automate the segmentation of neurons in such (potentially pseudo-colored) images. The method uses spatio-color relations between the voxels, generates supervoxels to reduce the problem size by four orders of magnitude before the final segmentation, and is parallelizable over the supervoxels. To quantify performance and gain insight, we generate simulated images, where the noise level and characteristics, the density of expression, and the number of fluorophore types are variable. We also present segmentations of real Brainbow images of the mouse hippocampus, which reveal many of the dendritic segments.

q-bio.NC↗

arXiv2016

Variable p norm constrained LMS algorithm based on gradient of root relative deviation.pdf

A new Lp-norm constraint least mean square (Lp-LMS) algorithm with new strategy of varying p is presented, which is applied to system identification in this letter. The parameter p is iteratively adjusted by the gradient method applied to the root relative deviation of the estimated weight vector. Numerical simulations show that this new algorithm achieves lower steady-state error as well as equally fast convergence compared with the traditional Lp-LMS and LMS algorithms in the application setting of sparse system identification in the presence of noise.

eess.SY↗

arXiv2015

Heteroscedasticity Testing for Regression Models: A Dimension Reduction-based Model Adaptive

Heteroscedasticity testing is of importance in regression analysis. Existing local smoothing tests suffer severely from curse of dimensionality even when the number of covariates is moderate because of use of nonparametric estimation. In this paper, a dimension reduction-based model adaptive test is proposed which behaves like a local smoothing test as if the number of covariates were equal to the number of their linear combinations in the mean regression function, in particular, equal to 1 when the mean function contains a single index. The test statistic is asymptotically normal under the null hypothesis such that critical values are easily determined. The finite sample performances of the test are examined by simulations and a real data analysis.

stat.ME↗

arXiv2015

Error Gradient-based Variable-Lp Norm Constraint LMS Algorithm for Sparse System Identification

Sparse adaptive filtering has gained much attention due to its wide applicability in the field of signal processing. Among the main algorithm families, sparse norm constraint adaptive filters develop rapidly in recent years. However, when applied for system identification, most priori work in sparse norm constraint adaptive filtering suffers from the difficulty of adaptability to the sparsity of the systems to be identified. To address this problem, we propose a novel variable p-norm constraint least mean square (LMS) algorithm, which serves as a variant of the conventional Lp-LMS algorithm established for sparse system identification. The parameter p is iteratively adjusted by the gradient descent method applied to the instantaneous square error. Numerical simulations show that this new approach achieves better performance than the traditional Lp-LMS and LMS algorithms in terms of steady-state error and convergence rate.

eess.SY↗

arXiv2015

Distributed Average Tracking for Double-integrator Multi-agent Systems with Reduced Requirement on Velocity Measurements

This paper addresses distributed average tracking for a group of physical double-integrator agents under an undirected graph with reduced requirement on velocity measurements. The idea is that multiple agents track the average of multiple time-varying input signals, each of which is available to only one agent, under local interaction with neighbors. We consider two cases. First, a distributed discontinuous algorithm and filter are proposed, where each agent needs the relative positions between itself and its neighbors and its neighbors' filter outputs obtained through communication but the requirement for either absolute or relative velocity measurements is removed. The agents' positions and velocities must be initialized correctly, but the algorithm can deal with a wide class of input signals with bounded acceleration deviations. Second, a distributed discontinuous algorithm and filter are proposed to remove the requirement for communication and accurate initialization. Here each agent needs to measure the relative position between itself and its neighbors and its own velocity but the requirement for relative velocity measurements between itself and its neighbors is removed. The algorithm can deal with the case where the input signals and their velocities and accelerations are all bounded. Numerical simulations are also presented to illustrate the theoretical results.

math.OC↗

arXiv2015

Crowdsourced Live Streaming over the Cloud

Empowered by today's rich tools for media generation and distribution, and the convenient Internet access, crowdsourced streaming generalizes the single-source streaming paradigm by including massive contributors for a video channel. It calls a joint optimization along the path from crowdsourcers, through streaming servers, to the end-users to minimize the overall latency. The dynamics of the video sources, together with the globalized request demands and the high computation demand from each sourcer, make crowdsourced live streaming challenging even with powerful support from modern cloud computing. In this paper, we present a generic framework that facilitates a cost-effective cloud service for crowdsourced live streaming. Through adaptively leasing, the cloud servers can be provisioned in a fine granularity to accommodate geo-distributed video crowdsourcers. We present an optimal solution to deal with service migration among cloud instances of diverse lease prices. It also addresses the location impact to the streaming quality. To understand the performance of the proposed strategies in the realworld, we have built a prototype system running over the planetlab and the Amazon/Microsoft Cloud. Our extensive experiments demonstrate that the effectiveness of our solution in terms of deployment cost and streaming quality.

cs.MM↗

arXiv2015

Superconductivity in Dense Rashba Semiconductor BiTeCl

Layered non-centrosymmetric bismuth tellurohalides are being examined as candidates for topological insulators. Pressure is believed to be essential for inducing and tuning topological order in these systems. Through electrical transport and Raman scattering measurements, we find superconductivity in two high-pressure phases of BiTeCl with the different normal state features, carrier characteristics, and upper critical field behaviors. Superconductivity emerges when the resistivity maximum or charge density wave is suppressed by the applied pressure and then persists till the highest pressure of 51 GPa measured. The huge enhancement of the resistivity with three magnitude of orders indicates the possible achievement of the topological order in the dense insulating phase. These findings not only enrich the superconducting family from topological insulators but also pave the road on the search of topological superconductivity in bismuth tellurohalides.

cond-mat.supr-con↗