arXiv subjects

Shuang Liu

Publications and source records attributed to Shuang Liu.

At least 145 records · Page 8Linked to original sources

arXiv2020

Multiple solutions of Kazdan-Warner equation on graphs in the negative case

Let $G=(V,E)$ be a finite connected graph, and let $κ: V\rightarrow \mathbb{R}$ be a function such that $\int_Vκdμ<0$. We consider the following Kazdan-Warner equation on $G$:\[Δu+κ-K_λe^{2u}=0,\] where $K_λ=K+λ$ and $K: V\rightarrow \mathbb{R}$ is a non-constant function satisfying $\max_{x\in V}K(x)=0$ and $λ\in \mathbb{R}$. By a variational method, we prove that there exists a $λ^*>0$ such that when $λ\in(-\infty,λ^*]$ the above equation has solutions, and has no solution when $λ\geq λ^\ast$. In particular, it has only one solution if $λ\leq 0$; at least two distinct solutions if $0<λ<λ^*$; at least one solution if $λ=λ^\ast$. This result complements earlier work of Grigor'yan-Lin-Yang \cite{GLY16}, and is viewed as a discrete analog of that of Ding-Liu \cite{DL95} and Yang-Zhu \cite{YZ19} on manifolds.

math.AP↗

arXiv2020

From Rayleigh-Bénard convection to porous-media convection: how porosity affects heat transfer and flow structure

We perform a numerical study of the heat transfer and flow structure of Rayleigh-Bénard (RB) convection in (in most cases regular) porous media, which are comprised of circular, solid obstacles located on a square lattice. This study is focused on the role of porosity $ϕ$ in the flow properties during the transition process from the traditional RB convection with $ϕ=1$ (so no obstacles included) to Darcy-type porous-media convection with $ϕ$ approaching 0. Simulations are carried out in a cell with unity aspect ratio, for the Rayleigh number $Ra$ from $10^5$ to $10^{10}$ and varying porosities $ϕ$, at a fixed Prandtl number $Pr=4.3$, and we restrict ourselves to the two dimensional case. For fixed $Ra$, the Nusselt number $Nu$ is found to vary non-monotonously as a function of $ϕ$; namely, with decreasing $ϕ$, it first increases, before it decreases for $ϕ$ approaching 0. The non-monotonous behaviour of $Nu(ϕ)$ originates from two competing effects of the porous structure on the heat transfer. On the one hand, the flow coherence is enhanced in the porous media, which is beneficial for the heat transfer. On the other hand, the convection is slowed down by the enhanced resistance due to the porous structure, leading to heat transfer reduction. For fixed $ϕ$, depending on $Ra$, two different heat transfer regimes are identified, with different effective power-law behaviours of $Nu$ vs $Ra$, namely, a steep one for low $Ra$ when viscosity dominates, and the standard classical one for large $Ra$. The scaling crossover occurs when the thermal boundary layer thickness and the pore scale are comparable. The influences of the porous structure on the temperature and velocity fluctuations, convective heat flux, and energy dissipation rates are analysed, further demonstrating the competing effects of the porous structure to enhance or reduce the heat transfer.

physics.flu-dyn↗

arXiv2020

Selected topics on reaction-diffusion-advection models from spatial ecology

We discuss the effects of movement and spatial heterogeneity on population dynamics via reaction-diffusion-advection models, focusing on the persistence, competition, and evolution of organisms in spatially heterogeneous environments. Topics include Lokta-Volterra competition models, river models, evolution of biased movement, phytoplankton growth, and spatial spread of epidemic disease. Open problems and conjectures are presented.

math.AP↗

arXiv2020

APPCorp: A Corpus for Android Privacy Policy Document Structure Analysis

With the increasing popularity of mobile devices and the wide adoption of mobile Apps, an increasing concern of privacy issues is raised. Privacy policy is identified as a proper medium to indicate the legal terms, such as GDPR, and to bind legal agreement between service providers and users. However, privacy policies are usually long and vague for end users to read and understand. It is thus important to be able to automatically analyze the document structures of privacy policies to assist user understanding. In this work we create a manually labelled corpus containing $167$ privacy policies (of more than $447$K words and $5,276$ annotated paragraphs). We report the annotation process and details of the annotated corpus. We also benchmark our data corpus with $4$ document classification models, thoroughly analyze the results and discuss challenges and opportunities for the research committee to use the corpus. We release our labelled corpus as well as the classification models for public access.

cs.CY↗

arXiv2020

Stacked invasion waves in a competition-diffusion model with three species

We investigate the spreading properties of a three-species competition-diffusion system, which is non-cooperative. We apply the Hamilton-Jacobi approach, due to Freidlin, Evans and Souganidis, to establish upper and lower estimates of spreading speed of the slowest species, in terms of the spreading speed of two faster species, and show that the estimates are sharp in some situations. The spreading speed will first be characterized as the free boundary point of the viscosity solution for certain variational inequality cast in the space of speeds. Its exact formulas will then be derived by solving the variational inequality explicitly. To the best of our knowledge, this is the first theoretical result on three-species competition system in unbounded domains.

math.AP↗

arXiv2020

Asymptotic spreading of interacting species with multiple fronts II: Exponentially decaying initial data

This is part two of our study on the spreading properties of the Lotka-Volterra competition-diffusion systems with a stable coexistence state. We focus on the case when the initial data are exponential decaying. By establishing a comparison principle for Hamilton-Jacobi equations, we are able to apply the Hamilton-Jacobi approach for Fisher-KPP equation due to Freidlin, Evans and Souganidis. As a result, the exact formulas of spreading speeds and their dependence on initial data are derived. Our results indicate that sometimes the spreading speed of the slower species is nonlocally determined. Connections of our results with the traveling profile due to Tang and Fife, as well as the more recent spreading result of Girardin and Lam, will be discussed.

math.AP↗

arXiv2020

High-throughput injection-acceleration of electron bunches from a linear accelerator to a laser wakefield accelerator

Plasma-based accelerators (PBAs) driven by either intense lasers (laser wakefield accelerators, LWFAs) or particle beams (plasma wakefield accelerators, PWFAs), can accelerate charged particles at extremely high gradients compared to conventional radio-frequency (RF) accelerators. In the past two decades, great strides have been made in this field, making PBA a candidate for next-generation light sources and colliders. However, these challenging applications necessarily require beams with good stability, high quality, controllable polarization and excellent reproducibility. To date, such beams are generated only by conventional RF accelerators. Therefore, it is important to demonstrate the injection and acceleration of beams first produced using a conventional RF accelerator, by a PBA. In some recent studies on LWFA staging and external injection-acceleration in PWFA only a very small fraction (from below 0.1% to few percent) of the injected charge (the coupling efficiency) was accelerated. For future colliders where beam energy will need to be boosted using multiple stages, the coupling efficiency per stage must approach 100%. Here we report the first demonstration of external injection from a photocathode-RF-gun-based conventional linear accelerator (LINAC) into a LWFA and subsequent acceleration without any significant loss of charge or degradation of quality, which is achieved by properly shaping and matching the beam into the plasma structure. This is an important step towards realizing a high-throughput, multi-stage, high-energy, hybrid conventional-plasma accelerator.

physics.plasm-ph↗

arXiv2020

Automated synthesis of local time requirement for service composition

Service composition aims at achieving a business goal by composing existing service-based applications or components. The response time of a service is crucial especially in time critical business environments, which is often stated as a clause in service level agreements between service providers and service users. To meet the guaranteed response time requirement of a composite service, it is important to select a feasible set of component services such that their response time will collectively satisfy the response time requirement of the composite service. In this work, we use the BPEL modeling language, that aims at specifying Web services. We extend it with timing parameters, and equip it with a formal semantics. Then, we propose a fully automated approach to synthesize the response time requirement of component services modeled using BPEL, in the form of a constraint on the local response times. The synthesized requirement will guarantee the satisfaction of the global response time requirement, statically or dynamically. We implemented our work into a tool, Selamat, and performed several experiments to evaluate the validity of our approach.

cs.SE↗

arXiv2020

Generalized L$_p$-norm joint inversion of gravity and magnetic data using cross-gradient constraint

A generalized unifying approach for $L_{p}$-norm joint inversion of gravity and magnetic data using the cross-gradient constraint is presented. The presented framework incorporates stabilizers that use $L_{0}$, $L_{1}$, and $L_{2}$-norms of the model parameters, and/or the gradient of the model parameters. Furthermore, the formulation is developed from standard approaches for independent inversion of single data sets, and, thus, also facilitates the inclusion of necessary model and data weighting matrices that provide, for example, depth weighting and imposition of hard constraint data. The developed efficient algorithm can, therefore, be employed to provide physically-relevant smooth, sparse, or blocky target(s) which are relevant to the geophysical community. Here, the nonlinear objective function, that describes the inclusion of all stabilizing terms and the fit to data measurements, is minimized iteratively by imposing stationarity on the linear equation that results from applying linearization of the objective function about a starting model. To numerically solve the resulting linear system, at each iteration, the conjugate gradient algorithm is used. The general framework is then validated for three-dimensional synthetic models for both sparse and smooth reconstructions, and the results are compared with those of individual gravity and magnetic inversions. It is demonstrated that the presented joint inversion algorithm is practical and significantly improves reconstructed models obtained by independent inversion.

physics.geo-ph↗

arXiv2019

There is Limited Correlation between Coverage and Robustness for Deep Neural Networks

Deep neural networks (DNN) are increasingly applied in safety-critical systems, e.g., for face recognition, autonomous car control and malware detection. It is also shown that DNNs are subject to attacks such as adversarial perturbation and thus must be properly tested. Many coverage criteria for DNN since have been proposed, inspired by the success of code coverage criteria for software programs. The expectation is that if a DNN is a well tested (and retrained) according to such coverage criteria, it is more likely to be robust. In this work, we conduct an empirical study to evaluate the relationship between coverage, robustness and attack/defense metrics for DNN. Our study is the largest to date and systematically done based on 100 DNN models and 25 metrics. One of our findings is that there is limited correlation between coverage and robustness, i.e., improving coverage does not help improve the robustness. Our dataset and implementation have been made available to serve as a benchmark for future studies on testing DNN.

cs.LG↗

arXiv2019

Asymptotic spreading of interacting species with multiple fronts I: A geometric optics approach

We establish spreading properties of the Lotka-Volterra competition-diffusion system. When the initial data vanish on a right half-line, we derive the exact spreading speeds and prove the convergence to homogeneous equilibrium states between successive invasion fronts. Our method is inspired by the geometric optics approach for Fisher-KPP equation due to Freidlin, Evans and Souganidis. Our main result settles an open question raised by Shigesada et al. in 1997, and shows that one of the species spreads to the right with a nonlocally pulled front.

math.AP↗

arXiv2019

Bias-free reconfigurable magnonic phase shifter based on a spin-current controlled ferromagnetic resonator

Controllable phase modulation plays a pivotal role in the researches of magnonic logic gates. Here we propose a reconfigurable spin-current controlled magnonic phase shifter based on a ferromagnetic resonator. The proposed phase shifter requires no magnetic bias field during operation. The device is directly configured over the waveguide while keeping the original structure of the waveguide unaffected. Numerical micromagnetic simulations show that the phase shifter could yield either a π-phase or no shift depending on the magnetization status of the resonator, which can be controlled by a current pulse. Moreover, the phase-shifting operation could be affected by spin current. At different input current density, the device could be either used as a dynamic controlled phase shifter or a spin-wave valve. Finally, a XNOR magnonic logic gate is demonstrated using the proposed phase shifter. Our work can be a beneficial step to enhance the functionality and compatibility of the magnonic logic circuits.

physics.app-ph↗

arXiv2019

Improving the use of the randomized singular value decomposition for the inversion of gravity and magnetic data

The large-scale focusing inversion of gravity and magnetic potential field data using $L_1$-norm regularization is considered. The use of the randomized singular value decomposition methodology facilitates tackling the computational challenge that arises in the solution of these large-scale inverse problems. As such the powerful randomized singular value decomposition is used for the numerical solution of all linear systems required in the algorithm. A comprehensive comparison of the developed methodology for the inversion of magnetic and gravity data is presented. These results indicate that there is generally an important difference between the gravity and magnetic inversion problems. Specifically, the randomized singular value decomposition is dependent on the generation of a rank $q$ approximation to the underlying model matrix, and the results demonstrate that $q$ needs to be larger, for equivalent problem sizes, for the magnetic problem as compared to the gravity problem. Without a relatively large $q$ the dominant singular values of the magnetic model matrix are not well-approximated. The comparison also shows how the use of the power iteration embedded within the randomized algorithm is used to improve the quality of the resulting dominant subspace approximation, especially in magnetic inversion, yielding acceptable approximations for smaller choices of $q$. The price to pay is the trade-off between approximation accuracy and computational cost. The algorithm is applied for the inversion of magnetic data obtained over a portion of the Wuskwatim Lake region in Manitoba, Canada

physics.geo-ph↗

arXiv2019

Monotonicity of the principal eigenvalue for a linear time-periodic parabolic operator

We investigate the effect of frequency on the principal eigenvalue of a time-periodic parabolic operator with Dirichlet, Robin or Neumann boundary conditions. The monotonicity and asymptotic behaviors of the principal eigenvalue with respect to the frequency parameter are established. Our results prove a conjecture raised by Hutson, Michaikow and Poláčik [2001 J. Math. Biol.].

math.AP↗

arXiv2018

Buser's inequality on infinite graphs

In this paper, we establish Buser type inequalities, i.e., upper bounds for eigenvalues in terms of Cheeger constants. We prove the Buser's inequality for an infinite but locally finite connected graph with Ricci curvature lower bounds. Furthermore, we derive that the graph with positive curvature is finite, especially for unbounded Laplacians. By proving Poincaré inequality, we obtain a lower bound on Cheeger constant in terms of positive curvature.

math.DG↗

arXiv2018

The Inductive Bias of Restricted f-GANs

Generative adversarial networks are a novel method for statistical inference that have achieved much empirical success; however, the factors contributing to this success remain ill-understood. In this work, we attempt to analyze generative adversarial learning -- that is, statistical inference as the result of a game between a generator and a discriminator -- with the view of understanding how it differs from classical statistical inference solutions such as maximum likelihood inference and the method of moments. Specifically, we provide a theoretical characterization of the distribution inferred by a simple form of generative adversarial learning called restricted f-GANs -- where the discriminator is a function in a given function class, the distribution induced by the generator is restricted to lie in a pre-specified distribution class and the objective is similar to a variational form of the f-divergence. A consequence of our result is that for linear KL-GANs -- that is, when the discriminator is a linear function over some feature space and f corresponds to the KL-divergence -- the distribution induced by the optimal generator is neither the maximum likelihood nor the method of moments solution, but an interesting combination of both.

cs.LG↗

arXiv2018

Falsification of Cyber-Physical Systems Using Deep Reinforcement Learning

With the rapid development of software and distributed computing, Cyber-Physical Systems (CPS) are widely adopted in many application areas, e.g., smart grid, autonomous automobile. It is difficult to detect defects in CPS models due to the complexities involved in the software and physical systems. To find defects in CPS models efficiently, robustness guided falsification of CPS is introduced. Existing methods use several optimization techniques to generate counterexamples, which falsify the given properties of a CPS. However those methods may require a large number of simulation runs to find the counterexample and is far from practical. In this work, we explore state-of-the-art Deep Reinforcement Learning (DRL) techniques to reduce the number of simulation runs required to find such counterexamples. We report our method and the preliminary evaluation results.

cs.SE↗

arXiv2018

Li-Yau inequality for unbounded Laplacian on graphs

In this paper, we derive Li-Yau inequality for unbounded Laplacian on complete weighted graphs with the assumption of the curvature-dimension inequality $CDE'(n,K)$, which can be regarded as a notion of curvature on graphs. Furthermore, we obtain some applications of Li-Yau inequality, including Harnack inequality, heat kernel bounds and Cheng's eigenvalue estimate. These are first kind of results on this direction for unbounded Laplacian on graphs.

math.DG↗