arXiv subjects

Lin Wang

Publications and source records attributed to Lin Wang.

At least 415 records · Page 23Linked to original sources

arXiv2020

A Class of Multilevel Nonregular Designs for Studying Quantitative Factors

Fractional factorial designs are widely used for designing screening experiments. Nonregular fractional factorial designs can have better properties than regular designs, but their construction is challenging. Current research on the construction of nonregular designs focuses on two-level designs. We provide a novel class of multilevel nonregular designs by permuting levels of regular designs. We develop a theory illustrating how levels can be permuted without computer search and accordingly propose a sequential method for constructing nonregular designs. Compared to regular designs, these nonregular designs can provide more accurate estimations on factorial effects and more efficient screening for experiments with quantitative factors. We further explore the space-filling property of the obtained designs and demonstrate their superiority.

stat.ME↗

arXiv2020

Predicting Network Controllability Robustness: A Convolutional Neural Network Approach

Network controllability measures how well a networked system can be controlled to a target state, and its robustness reflects how well the system can maintain the controllability against malicious attacks by means of node-removals or edge-removals. The measure of network controllability is quantified by the number of external control inputs needed to recover or to retain the controllability after the occurrence of an unexpected attack. The measure of the network controllability robustness, on the other hand, is quantified by a sequence of values that record the remaining controllability of the network after a sequence of attacks. Traditionally, the controllability robustness is determined by attack simulations, which is computationally time consuming. In this paper, a method to predict the controllability robustness based on machine learning using a convolutional neural network is proposed, motivated by the observations that 1) there is no clear correlation between the topological features and the controllability robustness of a general network, 2) the adjacency matrix of a network can be regarded as a gray-scale image, and 3) the convolutional neural network technique has proved successful in image processing without human intervention. Under the new framework, a fairly large number of training data generated by simulations are used to train a convolutional neural network for predicting the controllability robustness according to the input network-adjacency matrices, without performing conventional attack simulations. Extensive experimental studies were carried out, which demonstrate that the proposed framework for predicting controllability robustness of different network configurations is accurate and reliable with very low overheads.

eess.SY↗

arXiv2020

Towards Optimal Robustness of Network Controllability: An Empirical Necessary Condition

To better understand the correlation between network topological features and the robustness of network controllability in a general setting, this paper suggests a practical approach to searching for optimal network topologies with given numbers of nodes and edges. Since theoretical analysis seems impossible at least in the present time, exhaustive search based on optimization techniques is employed, firstly for a group of small-sized networks that are realistically workable, where \textit{exhaustive} means 1) all possible network structures with the given numbers of nodes and edges are computed and compared, and 2) all possible node-removal sequences are considered. A main contribution of this paper is the observation of an empirical necessary condition (ENC) from the results of exhaustive search, which shrinks the search space to quickly find an optimal solution. ENC shows that the maximum and minimum in- and out-degrees of an optimal network structure should be almost identical, or within a very narrow range, i.e., the network should be extremely homogeneous. Edge rectification towards the satisfaction of the ENC is then designed and evaluated. Simulation results on large-sized synthetic and real-world networks verify the effectiveness of both the observed ENC and the edge rectification scheme. As more operations of edge rectification are performed, the network is getting closer to exactly satisfying the ENC, and consequently the robustness of the network controllability is enhanced towards optimum.

eess.SY↗

arXiv2020

Comparison of Modeling SPARC spiral galaxies' rotation curves: halo models vs MOND

We investigate a sub-sample of the rotation curves consisting of 45 HSB non-bulgy spiral galaxies selected from SPARC (Spitzer Photometry and Accurate Rotation Curves) database by using two dark halo models (NFW and Burkert) and MOdified Newtonian Dynamics (MOND) theory. Among these three models, the core-dominated Burkert halo model provides a better description of the observed data ($χ_ν^2$ = 0.33) than Navarro, Frenk and White (NFW, $χ_ν^2$= 0.45) and MOND model ($χ_ν^2$ = 0.58). So our results show that, for dark halo models, the selected 45 HSB non-bulgy spiral galaxies prefer a cored density profile to the cuspy one (NFW); We also positively find that there is a correlation between $ρ_0$ and $r_0$ in Burkert model. For MOND fits, when we take $a_0$ as a free parameter, there is no obvious correlation between $a_0$ and disk central surface brightness at 3.6 $μm$ of these HSB spiral galaxies, which is in line with the basic assumption of MOND that $a_0$ should be a universal constant. Interestingly, our fittings gives $a_0$ an average value of $(0.74 \pm 0.45) \times 10^{- 8}\rm {cm\ s^{- 2}}$ if we exclude the three highest values in the sample, which is smaller than the standard value ($1.21 \times 10^{-8}\rm {cm\ s^{- 2}}$).

astro-ph.GA↗

arXiv2020

A Framework of Hierarchical Attacks to Network Controllability

Network controllability robustness reflects how well a networked dynamical system can maintain its controllability against destructive attacks. This paper investigates the network controllability robustness from the perspective of a malicious attack. A framework of hierarchical attack is proposed, by means of edge- or node-removal attacks. Edges (or nodes) in a target network are classified hierarchically into categories, with different priorities to attack. The category of critical edges (or nodes) has the highest priority to be selected for attack. Extensive experiments on nine synthetic networks and nine real-world networks show the effectiveness of the proposed hierarchical attack strategies for destructing the network controllability. From the protection point of view, this study suggests that the critical edges and nodes should be hidden from the attackers. This finding helps better understand the network controllability and better design robust networks.

physics.soc-ph↗

arXiv2020

On the vanishing discount problem from the negative direction

It has been proved in [10] that the unique viscosity solution of \begin{equation}\label{abs}\tag{*} λu_λ+H(x,d_x u_λ)=c(H)\qquad\hbox{in $M$}, \end{equation} uniformly converges, for $λ\rightarrow 0^+$, to a specific solution $u_0$ of the critical equation \[ H(x,d_x u)=c(H)\qquad\hbox{in $M$}, \] where $M$ is a closed and connected Riemannian manifold and $c(H)$ is the critical value. In this note, we consider the same problem for $λ\rightarrow 0^-$. In this case, viscosity solutions of equation \eqref{abs} are not unique, in general, so we focus on the asymptotics of the minimal solution $u_λ^-$ of \eqref{abs}. Under the assumption that constant functions are subsolutions of the critical equation, we prove that the $u_λ^-$ also converges to $u_0$ as $λ\rightarrow 0^-$. Furthermore, we exhibit an example of $H$ for which equation \eqref{abs} admits a unique solution for $λ<0$ as well.

math.AP↗

arXiv2020

A wolbachia infection model with free boundary

Scientists have been seeking ways to use Wolbachia to eliminate the mosquitoes that spread human diseases. Could Wolbachia be the determining factor in controlling the mosquito-borne infectious diseases? To answer this question mathematically, we develop a reaction-diffusion model with free boundary in a one-dimensional environment. We divide the female mosquito population into two groups: one is the uninfected mosquito population that grows in the whole region while the other is the mosquito population infected with Wolbachia that occupies a finite small region and invades the environment with a spreading front governed by a free boundary satisfying the well-known one-phase Stefan condition. For the resulting free boundary problem, we establish criteria under which spreading and vanishing occur. Our results provide useful insights on designing a feasible mosquito releasing strategy to invade the whole mosquito population with Wolbachia infection and thus eventually eradicate the mosquito-borne diseases.

math.AP↗

arXiv2020

Weak KAM solutions of Hamilton-Jacobi equations with decreasing dependence on unknown functions

We consider the Hamilton-Jacobi equation \[{H}(x,u,Du)=0,\quad x\in M, \] where $M$ is a connected, closed and smooth Riemannian manifold, ${H}(x,u,p)$ satisfies Tonelli conditions with respect to $p$ and certain decreasing condition with respect to $u$. Based on a dynamical approach developed in \cite{WWY,WWY1,WWY2}, we obtain a series of properties for weak KAM solutions (equivalently, viscosity solutions) of the stationary equation and the long time behavior of viscosity solutions of the evolutionary equation on the Cauchy problem \begin{equation*} \begin{cases} w_t+{H}(x,w,w_x)=0,\quad (x,t)\in M\times (0,+\infty),\\ w(x,0)=φ(x), \quad x\in M. \end{cases} \end{equation*}

math.DS↗

arXiv2020

Dimensionality Reduction for Sentiment Classification: Evolving for the Most Prominent and Separable Features

In sentiment classification, the enormous amount of textual data, its immense dimensionality, and inherent noise make it extremely difficult for machine learning classifiers to extract high-level and complex abstractions. In order to make the data less sparse and more statistically significant, the dimensionality reduction techniques are needed. But in the existing dimensionality reduction techniques, the number of components needs to be set manually which results in loss of the most prominent features, thus reducing the performance of the classifiers. Our prior work, i.e., Term Presence Count (TPC) and Term Presence Ratio (TPR) have proven to be effective techniques as they reject the less separable features. However, the most prominent and separable features might still get removed from the initial feature set despite having higher distributions among positive and negative tagged documents. To overcome this problem, we have proposed a new framework that consists of two-dimensionality reduction techniques i.e., Sentiment Term Presence Count (SentiTPC) and Sentiment Term Presence Ratio (SentiTPR). These techniques reject the features by considering term presence difference for SentiTPC and ratio of the distribution distinction for SentiTPR. Additionally, these methods also analyze the total distribution information. Extensive experimental results exhibit that the proposed framework reduces the feature dimension by a large scale, and thus significantly improve the classification performance.

cs.IR↗

arXiv2020

CoAug-MR: An MR-based Interactive Office Workstation Design System via Augmented Multi-Person Collaboration

Digital prototyping and evaluation using 3D modeling and digital human models are becoming more practical for customizing products to the preference of a user. However, the 3D modeling is less accessible to casual users, and digital human models suffer from insufficient body data and less intuitive illustration on how people use the product or how it accommodates to their body. Recently, VR-supported 'Do It Yourself' design has achieved real-time ergonomic evaluation with users themselves by capturing their poses, however, it lacks reliability and quality of design. In this paper, we explore a multi-person interactive design approach that enables designers, users, and even ergonomists to collaborate to achieve effective and reliable design and prototyping tasks. Mixed Reality that utilizes Hololens and motion tracking devices had been developed to provide instant design feedback and evaluation and to experience prototyping in physical space. We evaluate the system based on the usability study, where casual users and designers are engaged in the interactive process of designing items with respect to the body information, the preference, and the environment.

cs.HC↗

arXiv2020

Dipolar spin waves in uniaxial easy-axis antiferromagnets: A natural topological nodal-line semimetal

The existence of the magnetostatic surface spin waves in ferromagnets, known as Damon-Eshbach mode, was recently demonstrated to originate from the topology of the dipole-dipole interaction. In this work, we study the topological characteristics of magnons in easy-axis antiferromagnets with uniaxial anisotropy. The dipolar spin waves are found to be, driven by the dipole-dipole interaction, in a topological nodal-line semimetal phase, which hosts Damon-Eshbach-type surface modes due to the bulk-edge correspondence. The long wavelength character of dipolar spin waves makes our proposal valid for any natural uniaxial easy-axis antiferromagnet, and thus enriches the candidates of topological magnonic materials. In contrast to the nonreciprocal property in ferromagnetic case, the surface modes with opposite momentum coexist at each surface, but with different chiralities. Such a chirality-momentum or spin-momentum locking, similar to that of electronic surface states in topological insulators, offers the opportunity to design novel chirality-based magnonic devices in antiferromagnets.

cond-mat.mes-hall↗

arXiv2020

Smith-Purcell radiation from a charge moving above a finite-length grating with rectangular profiles

Smith-Purcell radiation is generated by a charged particle beam passing close to the surface of a diffraction grating which has a strong dependency of the emitted radiation intensity on the form of the grating profile. For relativistic electron beam, it is important to take into account the number of grating periods in practical SPR setups. In this paper, the theoretical investigations of Smith-Purcell radiation from a three-dimensional bunch of relativistic electrons that moves at constant speed parallel to an electrically perfectly conducting grating with finite rectangular grooves are carried out by using the modal matching method. This model may offer a new efficient tool for terahertz production by SPR interaction and for nondestructive bunch-length measurements by SPR.

physics.acc-ph↗

arXiv2020

An Energy Stable Linear Diffusive Crank-Nicolson Scheme for the Cahn-Hilliard Gradient Flow

We propose and analyze a linearly stabilized semi-implicit diffusive Crank--Nicolson scheme for the Cahn--Hilliard gradient flow. In this scheme, the nonlinear bulk force is treated explicitly with two second-order stabilization terms. This treatment leads to linear elliptic system with constant coefficients and provable discrete energy dissipation. Rigorous error analysis is carried out for the fully discrete scheme. When the time step-size and the space step-size are small enough, second order accuracy in time is obtained with a prefactor controlled by some lower degree polynomial of $1/\varepsilon$. {Here $\varepsilon$ is the thickness of the interface}. Numerical results together with an adaptive time stepping are presented to verify the accuracy and efficiency of the proposed scheme.

math.NA↗

arXiv2020

EventSR: From Asynchronous Events to Image Reconstruction, Restoration, and Super-Resolution via End-to-End Adversarial Learning

Event cameras sense intensity changes and have many advantages over conventional cameras. To take advantage of event cameras, some methods have been proposed to reconstruct intensity images from event streams. However, the outputs are still in low resolution (LR), noisy, and unrealistic. The low-quality outputs stem broader applications of event cameras, where high spatial resolution (HR) is needed as well as high temporal resolution, dynamic range, and no motion blur. We consider the problem of reconstructing and super-resolving intensity images from LR events, when no ground truth (GT) HR images and down-sampling kernels are available. To tackle the challenges, we propose a novel end-to-end pipeline that reconstructs LR images from event streams, enhances the image qualities and upsamples the enhanced images, called EventSR. For the absence of real GT images, our method is primarily unsupervised, deploying adversarial learning. To train EventSR, we create an open dataset including both real-world and simulated scenes. The use of both datasets boosts up the network performance, and the network architectures and various loss functions in each phase help improve the image qualities. The whole pipeline is trained in three phases. While each phase is mainly for one of the three tasks, the networks in earlier phases are fine-tuned by respective loss functions in an end-to-end manner. Experimental results show that EventSR reconstructs high-quality SR images from events for both simulated and real-world data.

cs.CV↗

arXiv2020

AGL: a Scalable System for Industrial-purpose Graph Machine Learning

Machine learning over graphs have been emerging as powerful learning tools for graph data. However, it is challenging for industrial communities to leverage the techniques, such as graph neural networks (GNNs), and solve real-world problems at scale because of inherent data dependency in the graphs. As such, we cannot simply train a GNN with classic learning systems, for instance parameter server that assumes data parallel. Existing systems store the graph data in-memory for fast accesses either in a single machine or graph stores from remote. The major drawbacks are in three-fold. First, they cannot scale because of the limitations on the volume of the memory, or the bandwidth between graph stores and workers. Second, they require extra development of graph stores without well exploiting mature infrastructures such as MapReduce that guarantee good system properties. Third, they focus on training but ignore the optimization of inference over graphs, thus makes them an unintegrated system. In this paper, we design AGL, a scalable, fault-tolerance and integrated system, with fully-functional training and inference for GNNs. Our system design follows the message passing scheme underlying the computations of GNNs. We design to generate the $k$-hop neighborhood, an information-complete subgraph for each node, as well as do the inference simply by merging values from in-edge neighbors and propagating values to out-edge neighbors via MapReduce. In addition, the $k$-hop neighborhood contains information-complete subgraphs for each node, thus we simply do the training on parameter servers due to data independency. Our system AGL, implemented on mature infrastructures, can finish the training of a 2-layer graph attention network on a graph with billions of nodes and hundred billions of edges in 14 hours, and complete the inference in 1.2 hour.

cs.SI↗

arXiv2020

DSSLP: A Distributed Framework for Semi-supervised Link Prediction

Link prediction is widely used in a variety of industrial applications, such as merchant recommendation, fraudulent transaction detection, and so on. However, it's a great challenge to train and deploy a link prediction model on industrial-scale graphs with billions of nodes and edges. In this work, we present a scalable and distributed framework for semi-supervised link prediction problem (named DSSLP), which is able to handle industrial-scale graphs. Instead of training model on the whole graph, DSSLP is proposed to train on the \emph{$k$-hops neighborhood} of nodes in a mini-batch setting, which helps reduce the scale of the input graph and distribute the training procedure. In order to generate negative examples effectively, DSSLP contains a distributed batched runtime sampling module. It implements uniform and dynamic sampling approaches, and is able to adaptively construct positive and negative examples to guide the training process. Moreover, DSSLP proposes a model-split strategy to accelerate the speed of inference process of the link prediction task. Experimental results demonstrate that the effectiveness and efficiency of DSSLP in serval public datasets as well as real-world datasets of industrial-scale graphs.

cs.SI↗

arXiv2020

The FAST discovery of an Eclipsing Binary Millisecond Pulsar in the Globular Cluster M92 (NGC 6341)

We report the discovery of an eclipsing binary millisecond pulsar in the globular cluster M92 (NGC6341) with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). PSR J1717+4308A, or M92A, has a pulse frequency of 316.5~Hz (3.16~ms) and a dispersion measure of 35.45 pc cm$^{-3}$. The pulsar is a member of a binary system with an orbital period of 0.20~days around a low-mass companion which has a median mass of $\sim$0.18~\Ms. From observations so far, at least two eclipsing events have been observed in each orbit. The longer one lasted for ~5000~s in the orbital phase range 0.1--0.5. The other lasted for ~500~s and occurred between 1000--2000~s before or after the longer eclipsing event. The lengths of these two eclipsing events also change. These properties suggest that J1717+4308A is a ``red-back'' system with a low-mass main sequence or sub-giant companion. Timing observations of the pulsar and further searches of the data for additional pulsars are ongoing.

astro-ph.HE↗

arXiv2020

Discovery and timing of pulsars in the globular cluster M13 with FAST

We report the discovery of a binary millisecond pulsar (namely PSR J1641+3627F or M13F) in the globular cluster M13 (NGC 6205) and timing solutions of M13A to F using observations made with the Five-hundred-metre Aperture Spherical radio Telescope (FAST). PSR J1641+3627F has a spin period of 3.00 ms and an orbital period of 1.4 days. The most likely companion mass is 0.16 M$_{\odot}$. M13A to E all have short spin periods and small period derivatives. We also confirm that the binary millisecond pulsar PSR J1641$+$3627E (also M13E) is a black widow with a companion mass around 0.02 M$_{\odot}$. We find that all the binary systems have low eccentricities compared to those typical for globular cluster pulsars and that they decrease with distance from the cluster core. This is consistent with what is expected as this cluster has a very low encounter rate per binary.

astro-ph.HE↗