arXiv subjects

Yan Zhang

Publications and source records attributed to Yan Zhang.

At least 595 records · Page 33Linked to original sources

arXiv2019

A Distributed Online Convex Optimization Algorithm with Improved Dynamic Regret

In this paper, we consider the problem of distributed online convex optimization, where a network of local agents aim to jointly optimize a convex function over a period of multiple time steps. The agents do not have any information about the future. Existing algorithms have established dynamic regret bounds that have explicit dependence on the number of time steps. In this work, we show that we can remove this dependence assuming that the local objective functions are strongly convex. More precisely, we propose a gradient tracking algorithm where agents jointly communicate and descend based on corrected gradient steps. We verify our theoretical results through numerical experiments.

math.OC↗

arXiv2019

Quantum phase transitions and critical behaviors in the two-mode three-level quantum Rabi model

We explore an extended quantum Rabi model describing the interaction between a two-mode bosonic field and a three-level atom. Quantum phase transitions of this few degree of freedom model is found when the ratio $η$ of the atom energy scale to the bosonic field frequency approaches infinity. An analytical solution is provided when the two lowest-energy levels are degenerate. According to it, we recognize that the phase diagram of the model consists of three regions: one normal phase and two superradiant phases. The quantum phase transitions between the normal phase and the two superradiant phases are of second order relating to the spontaneous breaking of the discrete $Z_{2}$ symmetry. On the other hand, the quantum phase transition between the two different superradiant phases is discontinuous with a phase boundary line relating to the continuous $U(1)$ symmetry. For a large enough but finite $η$, the scaling function and critical exponents are derived analytically and verified numerically, from which the universality class of the model is identified.

quant-ph↗

arXiv2019

Privacy-Preserving Collaborative Deep Learning with Unreliable Participants

With powerful parallel computing GPUs and massive user data, neural-network-based deep learning can well exert its strong power in problem modeling and solving, and has archived great success in many applications such as image classification, speech recognition and machine translation etc. While deep learning has been increasingly popular, the problem of privacy leakage becomes more and more urgent. Given the fact that the training data may contain highly sensitive information, e.g., personal medical records, directly sharing them among the users (i.e., participants) or centrally storing them in one single location may pose a considerable threat to user privacy. In this paper, we present a practical privacy-preserving collaborative deep learning system that allows users to cooperatively build a collective deep learning model with data of all participants, without direct data sharing and central data storage. In our system, each participant trains a local model with their own data and only shares model parameters with the others. To further avoid potential privacy leakage from sharing model parameters, we use functional mechanism to perturb the objective function of the neural network in the training process to achieve $ε$-differential privacy. In particular, for the first time, we consider the existence of~\textit{unreliable participants}, i.e., the participants with low-quality data, and propose a solution to reduce the impact of these participants while protecting their privacy. We evaluate the performance of our system on two well-known real-world datasets for regression and classification tasks. The results demonstrate that the proposed system is robust against unreliable participants, and achieves high accuracy close to the model trained in a traditional centralized manner while ensuring rigorous privacy protection.

cs.CR↗

arXiv2019

Secrecy and Covert Communications against UAV Surveillance via Multi-Hop Networks

The deployment of unmanned aerial vehicle (UAV) for surveillance and monitoring gives rise to the confidential information leakage challenge in both civilian and military environments. The security and covert communication problems for a pair of terrestrial nodes against UAV surveillance are considered in this paper. To overcome the information leakage and increase the transmission reliability, a multi-hop relaying strategy is deployed. We aim to optimize the throughput by carefully designing the parameters of the multi-hop network, including the coding rates, transmit power, and required number of hops. In the secure transmission scenario, the expressions of the connection probability and secrecy outage probability of an end-to-end path are derived and the closed-form expressions of the optimal transmit power, transmission and secrecy rates under a fixed number of hops are obtained. In the covert communication problem, under the constraints of the detection error rate and aggregate power, the sub-problem of transmit power allocation is a convex problem and can be solved numerically. Simulation shows the impact of network settings on the transmission performance. The trade-off between secrecy/covertness and efficiency of the multi-hop transmission is discussed which leads to the existence of the optimal number of hops.

cs.IT↗

arXiv2019

Two classes of $p$-ary linear codes and their duals

Let $\mathbb{F}_{p^m}$ be the finite field of order $p^m$, where $p$ is an odd prime and $m$ is a positive integer. In this paper, we investigate a class of subfield codes of linear codes and obtain the weight distribution of \begin{equation*} \begin{split} \mathcal{C}_k=\left\{\left(\left( {\rm Tr}_1^m\left(ax^{p^k+1}+bx\right)+c\right)_{x \in \mathbb{F}_{p^m}}, {\rm Tr}_1^m(a)\right) : \, a,b \in \mathbb{F}_{p^m}, c \in \mathbb{F}_p\right\}, \end{split} \end{equation*} where $k$ is a nonnegative integer. Our results generalize the results of the subfield codes of the conic codes in \cite{Hengar}. Among other results, we study the punctured code of $\mathcal{C}_k$, which is defined as $$\mathcal{\bar{C}}_k=\left\{\left( {\rm Tr}_1^m\left(a x^{{p^k}+1}+bx\right)+c\right)_{x \in \mathbb{F}_{p^m}} : \, a,b \in \mathbb{F}_{p^m}, \,\,c \in \mathbb{F}_p\right\}.$$ The parameters of these linear codes are new in some cases. Some of the presented codes are optimal or almost optimal. Moreover, let $v_2(\cdot)$ denote the 2-adic order function and $v_2(0)=\infty$, the duals of $\mathcal{C}_k$ and $\mathcal{\bar{C}}_k$ are optimal with respect to the Sphere Packing bound if $p>3$, and the dual of $\mathcal{\bar{C}}_k$ is an optimal ternary linear code for the case $v_2(m)\leq v_2(k)$ if $p=3$ and $m>1$.

cs.IT↗

arXiv2019

Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning

We focus on graph-to-sequence learning, which can be framed as transducing graph structures to sequences for text generation. To capture structural information associated with graphs, we investigate the problem of encoding graphs using graph convolutional networks (GCNs). Unlike various existing approaches where shallow architectures were used for capturing local structural information only, we introduce a dense connection strategy, proposing a novel Densely Connected Graph Convolutional Networks (DCGCNs). Such a deep architecture is able to integrate both local and non-local features to learn a better structural representation of a graph. Our model outperforms the state-of-the-art neural models significantly on AMRto-text generation and syntax-based neural machine translation.

cs.CL↗

arXiv2019

Flexible Auto-weighted Local-coordinate Concept Factorization: A Robust Framework for Unsupervised Clustering

Concept Factorization (CF) and its variants may produce inaccurate representation and clustering results due to the sensitivity to noise, hard constraint on the reconstruction error and pre-obtained approximate similarities. To improve the representation ability, a novel unsupervised Robust Flexible Auto-weighted Local-coordinate Concept Factorization (RFA-LCF) framework is proposed for clustering high-dimensional data. Specifically, RFA-LCF integrates the robust flexible CF by clean data space recovery, robust sparse local-coordinate coding and adaptive weighting into a unified model. RFA-LCF improves the representations by enhancing the robustness of CF to noise and errors, providing a flexible constraint on the reconstruction error and optimizing the locality jointly. For robust learning, RFA-LCF clearly learns a sparse projection to recover the underlying clean data space, and then the flexible CF is performed in the projected feature space. RFA-LCF also uses a L2,1-norm based flexible residue to encode the mismatch between the recovered data and its reconstruction, and uses the robust sparse local-coordinate coding to represent data using a few nearby basis concepts. For auto-weighting, RFA-LCF jointly preserves the manifold structures in the basis concept space and new coordinate space in an adaptive manner by minimizing the reconstruction errors on clean data, anchor points and coordinates. By updating the local-coordinate preserving data, basis concepts and new coordinates alternately, the representation abilities can be potentially improved. Extensive results on public databases show that RFA-LCF delivers the state-of-the-art clustering results compared with other related methods.

cs.CV↗

arXiv2019

Compositional Network Embedding

Network embedding has proved extremely useful in a variety of network analysis tasks such as node classification, link prediction, and network visualization. Almost all the existing network embedding methods learn to map the node IDs to their corresponding node embeddings. This design principle, however, hinders the existing methods from being applied in real cases. Node ID is not generalizable and, thus, the existing methods have to pay great effort in cold-start problem. The heterogeneous network usually requires extra work to encode node types, as node type is not able to be identified by node ID. Node ID carries rare information, resulting in the criticism that the existing methods are not robust to noise. To address this issue, we introduce Compositional Network Embedding, a general inductive network representation learning framework that generates node embeddings by combining node features based on the principle of compositionally. Instead of directly optimizing an embedding lookup based on arbitrary node IDs, we learn a composition function that infers node embeddings by combining the corresponding node attribute embeddings through a graph-based loss. For evaluation, we conduct the experiments on link prediction under four different settings. The results verified the effectiveness and generalization ability of compositional network embeddings, especially on unseen nodes.

cs.LG↗

arXiv2019

Effects of Surface Trapping and Contact Ion Pairing on Ion Transport in Nanopores

Ion transport in highly-confined space is important to various applications, such as biosensing and seawater desalination with nanopores. All-atom molecular dynamics simulations are conducted to investigate the transport of Na$^+$ and Cl$^-$ ions through nanopores with the diameter below 6 nm. It is found that the formation of the contact ion pair plays a critical role in reducing the ion mobility inside a nanopore without surface charges. The mobility for both cations and anions decreases with the reduced pore size because it is easier to form the contact ion pairs inside the neutral nanopore with smaller diameter. Inside a charged nanopores, besides the contact ion pair formation, the surface charges also play a significant role in reducing the counterion mobility through surface trapping. It is uncovered that the mobility of Na$^+$ ions increases first and then decreases with the surface charge density, while Cl$^-$ ions have the opposite trend. A modified first-passage time model is proposed to take into account the ion pair formation and the trapped ions inside a nanopore, which provides a clear picture in describing ion transport through a nanopore.

cond-mat.mes-hall↗

arXiv2019

Vehicular Edge Computing and Networking: A Survey

As one key enabler of Intelligent Transportation System (ITS), Vehicular Ad Hoc Network (VANET) has received remarkable interest from academia and industry. The emerging vehicular applications and the exponential growing data have naturally led to the increased needs of communication, computation and storage resources, and also to strict performance requirements on response time and network bandwidth. In order to deal with these challenges, Mobile Edge Computing (MEC) is regarded as a promising solution. MEC pushes powerful computational and storage capacities from the remote cloud to the edge of networks in close proximity of vehicular users, which enables low latency and reduced bandwidth consumption. Driven by the benefits of MEC, many efforts have been devoted to integrating vehicular networks into MEC, thereby forming a novel paradigm named as Vehicular Edge Computing (VEC). In this paper, we provide a comprehensive survey of state-of-art research on VEC. First of all, we provide an overview of VEC, including the introduction, architecture, key enablers, advantages, challenges as well as several attractive application scenarios. Then, we describe several typical research topics where VEC is applied. After that, we present a careful literature review on existing research work in VEC by classification. Finally, we identify open research issues and discuss future research directions.

eess.SP↗

arXiv2019

Electric field-induced chiral d+id superconducting state in AA-stacked bilayer graphene: A quantum Monte Carlo study

Using constrained-path quantum Monte Carlo method, we systematically study the Hubbard model on AA-stacked honeycomb lattices with electric field. Our simulation demonstrates a dominant chiral d+id wave pairing induced by the electric field at half filling. In particular, as the on-site Coulomb interaction increases, the effective pairing correlation of chiral d+id superconducting state exhibits increasing behavior. We attribute the electric field induced d+id superconductivity to an increased density of states near the Fermi energy and an suppressed antiferromagnetic spin correlation after turning on the electric field. Our results strongly suggest the AA-stacked graphene system with electric field is a good candidate for chiral d+id superconductors.

cond-mat.supr-con↗

arXiv2019

Augmented Lagrangian Optimization under Fixed-Point Arithmetic

In this paper, we propose an inexact Augmented Lagrangian Method (ALM) for the optimization of convex and nonsmooth objective functions subject to linear equality constraints and box constraints where errors are due to fixed-point data. To prevent data overflow we also introduce a projection operation in the multiplier update. We analyze theoretically the proposed algorithm and provide convergence rate results and bounds on the accuracy of the optimal solution. Since iterative methods are often needed to solve the primal subproblem in ALM, we also propose an early stopping criterion that is simple to implement on embedded platforms, can be used for problems that are not strongly convex, and guarantees the precision of the primal update. To the best of our knowledge, this is the first fixed-point ALM that can handle non-smooth problems, data overflow, and can efficiently and systematically utilize iterative solvers in the primal update. Numerical simulation studies on a utility maximization problem are presented that illustrate the proposed method.

math.OC↗

arXiv2019

A New Automatic Tool for CME Detection and Tracking with Machine Learning Techniques

With the accumulation of big data of CME observations by coronagraphs, automatic detection and tracking of CMEs has proven to be crucial. The excellent performance of convolutional neural network in image classification, object detection and other computer vision tasks motivates us to apply it to CME detection and tracking as well. We have developed a new tool for CME Automatic detection and tracking with MachinE Learning (CAMEL) techniques. The system is a three-module pipeline. It is first a supervised image classification problem. We solve it by training a neural network LeNet with training labels obtained from an existing CME catalog. Those images containing CME structures are flagged as CME images. Next, to identify the CME region in each CME-flagged image, we use deep descriptor transforming to localize the common object in an image set. A following step is to apply the graph cut technique to finely tune the detected CME region. To track the CME in an image sequence, the binary images with detected CME pixels are converted from cartesian to polar coordinate. A CME event is labeled if it can move in at least two frames and reach the edge of coronagraph field of view. For each event, a few fundamental parameters are derived. The results of four representative CMEs with various characteristics are presented and compared with those from four existing automatic and manual catalogs. We find that CAMEL can detect more complete and weaker structures, and has better performance to catch a CME as early as possible.

astro-ph.SR↗

arXiv2019

Semi-Associative $3$-Algebras

A new 3-ary non-associative algebra, which is called a semi-associative $3$-algebra, is introduced, and the double modules and double extensions by cocycles are provided. Every semi-associative $3$-algebra $(A, \{ , , \})$ has an adjacent 3-Lie algebra $(A, [ , , ]_c)$. From a semi-associative $3$-algebra $(A, \{, , \})$, a double module $(ϕ, ψ, M)$ and a cocycle $θ$, a semi-direct product semi-associative $3$-algebra $A\ltimes_{ϕψ} M $ and a double extension $(A\dot+A^*, \{ , , \}_θ)$ are constructed, and structures are studied.

math-ph↗

arXiv2019

Permissioned Blockchain for Efficient and Secure Resource Sharing in Vehicular Edge Computing

With the fast expanding scale of vehicular networks, vehicular edge computing (VEC) has emerged and attracted growing attention from both industry and academia. Parked vehicles (PVs) have great potential to join vehicular networks for sharing their idle computing and networking resources. However, due to the underlying security and privacy threats, it is challenging to fairly motivate PVs for resource sharing in an efficient and secure way. In this paper, we propose a permissioned vehicular blockchain for secure and efficient resource sharing in VEC, namely, Parkingchain. We first design smart contract to achieve secure resource sharing and efficient service provisioning between PVs and service requesters (SRs). A multi-weight subjective logic based delegated Byzantine Fault Tolerance (DBFT) consensus mechanism is presented to improve the consensus process in Parkingchain. Further, we design a contract theory-based incentive mechanism to model the interactions between SR and PVs under asymmetric information scenario. Finally, numerical results demonstrate that the proposed incentive mechanism is effective and efficient compared with existing schemes.

cs.NI↗

arXiv2019

Control contribution identifies top driver nodes in complex networks

We propose a new measure to quantify the impact of a node $i$ in controlling a directed network. This measure, called `control contribution' $\mathcal{C}_{i}$, combines the probability for node $i$ to appear in a set of driver nodes and the probability for other nodes to be controlled by $i$. To calculate $\mathcal{C}_{i}$, we propose an optimization method based on random samples of minimum sets of drivers. Using real-world and synthetic networks, we find very broad distributions of $C_{i}$. Ranking nodes according to their $C_{i}$ values allows us to identify the top driver nodes that control most of the network. We show that this ranking is superior to rankings based on control capacity or control range. We find that control contribution indeed contains new information that cannot be traced back to degree, control capacity or control range of a node.

cs.SI↗

arXiv2019

Efficient Broadband Terahertz Generation from Organic Crystal BNA Using Near Infrared Pump

We are reporting on terahertz generation from organic crystal BNA using a 1.15 to 1.55 um near infrared pump. We observed a very large emission spectrum extending up to 7 THz, compared to 2.5 THz from Ti:Sa pump in previous reports. The maximum optical-to-THz conversion efficiency in our experiment was 0.8% at 1 kHz repetition rate and without saturation leading to a peak electric field of 1 GV/m. Our results show pronounced phase matching privilege for intense terahertz generation using a pump in the 1.15 to 1.25 um range where high energy near infrared pump sources operate.

cond-mat.mtrl-sci↗

arXiv2019

Decentralized Deep Reinforcement Learning for Delay-Power Tradeoff in Vehicular Communications

This paper targets at the problem of radio resource management for expected long-term delay-power tradeoff in vehicular communications. At each decision epoch, the road side unit observes the global network state, allocates channels and schedules data packets for all vehicle user equipment-pairs (VUE-pairs). The decision-making procedure is modelled as a discrete-time Markov decision process (MDP). The technical challenges in solving an optimal control policy originate from highly spatial mobility of vehicles and temporal variations in data traffic. To simplify the decision-making process, we first decompose the MDP into a series of per-VUE-pair MDPs. We then propose an online long short-term memory based deep reinforcement learning algorithm to break the curse of high dimensionality in state space faced by each per-VUE-pair MDP. With the proposed algorithm, the optimal channel allocation and packet scheduling decision at each epoch can be made in a decentralized way in accordance with the partial observations of the global network state at the VUE-pairs. Numerical simulations validate the theoretical analysis and show the effectiveness of the proposed online learning algorithm.

eess.SP↗