arXiv subjects

Shuang Liu

Publications and source records attributed to Shuang Liu.

At least 109 records · Page 6Linked to original sources

arXiv2022

Integration factor combined with level set method for reaction-diffusion systems with free boundary in high spatial dimensions

For reaction-diffusion equations in irregular domain with moving boundaries, the numerical stability constraints from the reaction and diffusion terms often require very restricted time step size, while complex geometries may lead to difficulties in accuracy when discretizing the high-order derivatives on grid points near the boundary. It is very challenging to design numerical methods that can efficiently and accurately handle both difficulties. Applying an implicit scheme may be able to remove the stability constraints on the time step, however, it usually requires solving a large global system of nonlinear equations for each time step, and the computational cost could be significant. Integration factor (IF) or exponential differencing time (ETD) methods are one of the popular methods for temporal partial differential equations (PDEs) among many other methods. In our paper, we couple ETD methods with an embedded boundary method to solve a system of reaction-diffusion equations with complex geometries. In particular, we rewrite all ETD schemes into a linear combination of specific ϕ-functions and apply one start-of-the-art algorithm to compute the matrix-vector multiplications, which offers significant computational advantages with adaptive Krylov subspaces. In addition, we extend this method by incorporating the level set method to solve the free boundary problem. The accuracy, stability, and efficiency of the developed method are demonstrated by numerical examples.

math.NA↗

arXiv2022

Micro-Vibration Modes Reconstruction Based on Micro-Doppler Coincidence Imaging

Micro-vibration, a ubiquitous nature phenomenon, can be seen as a characteristic feature on the objects, these vibrations always have tiny amplitudes which are much less than the wavelengths of the sensing systems, thus these motions information can only be reflected in the phase item of echo. Normally the conventional radar system can detect these micro vibrations through the time frequency analyzing, but these vibration characteristics can only be reflected by time-frequency spectrum, the spatial distribution of these micro vibrations can not be reconstructed precisely. Ghost imaging (GI), a novel imaging method also known as Coincidence Imaging that originated in the quantum and optical fields, can reconstruct unknown images using computational methods. To reconstruct the spatial distribution of micro vibrations, this paper proposes a new method based on a coincidence imaging system. A detailed model of target micro-vibration is created first, taking into account two categories: discrete and continuous targets. We use the first-order field correlation feature to obtain objective different micro vibration distribution based on the complex target models and time-frequency analysis in this work.

eess.SP↗

arXiv2022

Cell Polarity and Movement with Reaction-Diffusion and Moving Boundary: Rigorous Modeling and Robust Simulations

Cell polarity and movement are fundamental to many biological functions. Experimental and theoretically studies have indicated that interactions of certain proteins lead to the cell polarization which plays a key role in controlling the cell movement. We study the cell polarity and movement based on a class of biophysical models that consist of reaction-diffusion equations for different proteins and the dynamics of moving cell boundary. Such a moving boundary is often simulated by a phase-filed model. We first apply the matched asymptotic analysis to give a rigorous derivation of the sharp-interface model of the cell boundary from a phase-field model. We then develop a robust numerical approach that combines the level-set method to track the sharp boundary of a moving cell and accurate discretization techniques for solving the reaction-diffusion equations on the moving cell region. Our extensive numerical simulations predict the cell polarization under various kinds of stimulus, and capture both the linear and circular trajectories of a moving cell for a long period of time. In particular, we have identified some key parameters controlling different cell trajectories that are less accurately predicted by reduced models. Our work has linked different models and also developed tools that can be adapted for the challenging three-dimensional simulations.

math.DS↗

arXiv2022

Explicit-Solute Implicit-Solvent Molecular Simulation with Binary Level-Set, Adaptive-Mobility, and GPU

Coarse-grained modeling and efficient computer simulations are critical to the study of complex molecular processes with many degrees of freedom and multiple spatiotemporal scales. Variational implicit-solvent model (VISM) for biomolecular solvation is such a modeling framework, and its initial success has been demonstrated consistently. In VISM, an effective free-energy functional of solute-solvent interfaces is minimized, and the surface energy is a key component of the free energy. In this work, we extend VISM to include the solute mechanical interactions, and develop fast algorithms and GPU implementation for the extended variational explicit-solute implicit-solvent (VESIS) molecular simulations to determine the underlying molecular equilibrium conformations. We employ a fast binary level-set method for minimizing the solvation free energy of solute-solvent interfaces and construct an adaptive-mobility gradient descent method for solute atomic optimization. We also implement our methods in GPU. Numerical tests and applications to several molecular systems verify the accuracy, stability, and efficiency of our methods and algorithms. It is found that our new methods and GPU implementation improve the efficiency of the molecular simulation significantly over the CPU implementation. Our fast computational techniques may enable us to simulate very large systems such as protein-protein interactions and membrane dynamics for which explicit-solvent all-atom molecular dynamics simulations can be very expensive.

physics.chem-ph↗

arXiv2022

The Physical Properties of Star-Forming Galaxies with Strong [O III] Lines at z=3.25

We present an analysis of physical properties of 34 [O III] emission-line galaxies (ELGs) at z=3.254$\pm$0.029 in the Extended Chandra Deep Field South (ECDFS). These ELGs are selected from deep narrow H2S(1) and broad Ks imaging of 383 arcmin$^{2}$ obtained with CFHT/WIRCam. We construct spectral energy distributions (SEDs) from U to Ks to derive the physical properties of ELGs. These [O III] ELGs are identified as starburst galaxies with strong [O III] lines of L([O III]) ~ 10$^{42.6}$ - 10$^{44.2}$ erg s$^{-1}$, and have stellar masses of M* ~ 10$^{9.0}$-10$^{10.6}$ M$_\odot$ and star formation rates of ~ 10-210 M$_\odot$ yr$^{-1}$. Our results show that 24% of our sample galaxies are dusty with Av > 1 mag and EW(OIII)$_{rest}$ ~ 70-500 $Å$, which are often missed in optically selected [O III] ELG samples. Their rest-frame UV and optical morphologies from HST/ACS and HST/WFC3 deep imaging reveal that these [O III] ELGs are mostly multiple-component systems (likely mergers) or compact. And 20% of them are nearly invisible in the rest-frame UV owing to heavy dust attenuation. Interestingly, we find that our samples reside in an overdensity consisting of two components: one southeast (SE) with an overdensity factor of $δ_{gal}$ ~ 41 over a volume of 13$^{3}$ cMpc$^{3}$ and the other northwest (NW) with $δ_{gal}$ ~ 38 over a volume of 10$^{3}$ cMpc$^{3}$. The two overdense substructures are expected to be virialized at z=0 with a total mass of ~ 1.1 x 10$^{15}$ M$_\odot$ and ~ 4.8 x 10$^{14}$ M$_\odot$, and probably merge into a Coma-like galaxy cluster.

astro-ph.GA↗

arXiv2022

Achieve Optimal Adversarial Accuracy for Adversarial Deep Learning using Stackelberg Game

Adversarial deep learning is to train robust DNNs against adversarial attacks, which is one of the major research focuses of deep learning. Game theory has been used to answer some of the basic questions about adversarial deep learning such as the existence of a classifier with optimal robustness and the existence of optimal adversarial samples for a given class of classifiers. In most previous work, adversarial deep learning was formulated as a simultaneous game and the strategy spaces are assumed to be certain probability distributions in order for the Nash equilibrium to exist. But, this assumption is not applicable to the practical situation. In this paper, we give answers to these basic questions for the practical case where the classifiers are DNNs with a given structure, by formulating the adversarial deep learning as sequential games. The existence of Stackelberg equilibria for these games are proved. Furthermore, it is shown that the equilibrium DNN has the largest adversarial accuracy among all DNNs with the same structure, when Carlini-Wagner's margin loss is used. Trade-off between robustness and accuracy in adversarial deep learning is also studied from game theoretical aspect.

cs.LG↗

arXiv2022

Observation of enhanced spin-spin correlations at triple point in 2D ferromagnetic Cr2X2Te6 (X=Si, Ge)

The domain dynamics and spin-spin correlation of 2D ferromagnets Cr2X2Te6 (X=Si, Ge) are investigated by a composite magnetoelectric method. The magnetic field-temperature phase diagrams for both in-plane and out-of-plane magnetic fields disclose a triple point around TC and 1 kOe, where ferromagnetic, paramagnetic, and spin-polarized phases coexist. The magnetoelectric signal shows peak features at the phase boundaries and reaches the maximum at the triple point, suggesting significant enhancement of spin-spin correlations at this point. A comparison between two systems reveals that the spin-spin correlations in Cr2Si2Te6 are stronger than that in Cr2Ge2Te6.

cond-mat.mtrl-sci↗

arXiv2022

Improving Policy Optimization with Generalist-Specialist Learning

Generalization in deep reinforcement learning over unseen environment variations usually requires policy learning over a large set of diverse training variations. We empirically observe that an agent trained on many variations (a generalist) tends to learn faster at the beginning, yet its performance plateaus at a less optimal level for a long time. In contrast, an agent trained only on a few variations (a specialist) can often achieve high returns under a limited computational budget. To have the best of both worlds, we propose a novel generalist-specialist training framework. Specifically, we first train a generalist on all environment variations; when it fails to improve, we launch a large population of specialists with weights cloned from the generalist, each trained to master a selected small subset of variations. We finally resume the training of the generalist with auxiliary rewards induced by demonstrations of all specialists. In particular, we investigate the timing to start specialist training and compare strategies to learn generalists with assistance from specialists. We show that this framework pushes the envelope of policy learning on several challenging and popular benchmarks including Procgen, Meta-World and ManiSkill.

cs.LG↗

arXiv2022

Provably Efficient Kernelized Q-Learning

We propose and analyze a kernelized version of Q-learning. Although a kernel space is typically infinite-dimensional, extensive study has shown that generalization is only affected by the effective dimension of the data. We incorporate such ideas into the Q-learning framework and derive regret bounds for arbitrary kernels. In particular, we provide concrete bounds for linear kernels and Gaussian RBF kernels; notably, the latter bound looks almost identical to the former, only that the actual dimension is replaced by a different notion of dimensionality. Finally, we test our algorithm on a suite of classic control tasks; remarkably, under the Gaussian RBF kernel, it achieves reasonably good performance after only 1000 environmental steps, while its neural network counterpart, deep Q-learning, still struggles.

cs.LG↗

arXiv2022

First-Principles investigation of the First-order Phase Transition in Kagome Na$_2$Ti$_3$Cl$_8$

The antiferromagnetic kagome material Na2Ti3Cl8 exhibits a first-order phase transition; the low-temperature phase is characterized by the trimerizations of Ti ions. In this work, we carry out first-principles calculations on the crystal and electronic structure of Na$_2$Ti$_3$Cl$_8$ at the high- and low-temperature phases. The crystal structures, including the lattice constant and the trimerization of Ti ions, are well reproduced by taking account a small Hubbard correction of 1 eV. The calculated total energy landscape reveals a first-order phase transition with the total energy barrier of 80 meV per formula unit. Analysis of the electronic structure indicates a direct metal bonding among Ti in the trimierized low-temperature phase.

cond-mat.mtrl-sci↗

arXiv2022

Universal AMG Accelerated Embedded Boundary Method Without Small Cell Stiffness

We develop a universally applicable embedded boundary finite difference method, which results in a symmetric positive definite linear system and does not suffer from small cell stiffness. Our discretization is efficient for the wave, heat and Poisson's equation with Dirichlet boundary conditions. When the system needs to be inverted we can use the conjugate gradient method, accelerated by algebraic multigrid techniques. A series of numerical tests for the wave, heat and Poisson's equation and applications to shape optimization problems verify the accuracy, stability, and efficiency of our method. Our fast computational techniques can be extended to moving boundary problems (e.g. Stefan problem), to the Navier-Stokes equations, and to the Grad-Shafranov equations for which problems are posed on domains with complex geometry and fast simulations are very important.

math.NA↗

arXiv2022

Submillimetre galaxies in two massive protoclusters at z = 2.24: witnessing the enrichment of extreme starbursts in the outskirts of HAE density peaks

Submillimetre galaxies represent a rapid growth phase of both star formation and massive galaxies. Mapping SMGs in galaxy protoclusters provides key insights into where and how these extreme starbursts take place in connections with the assembly of the large-scale structure in the early Universe. We search for SMGs at 850$\,μm$ using JCMT/SCUBA-2 in two massive protoclusters at $z=2.24$, BOSS1244 and BOSS1542, and detect 43 and 54 sources with $S_{850}>4\,$mJy at the $4σ$ level within an effective area of 264$\,$arcmin$^2$, respectively. We construct the intrinsic number counts and find that the abundance of SMGs is $2.0\pm0.3$ and $2.1\pm0.2$ times that of the general fields, confirming that BOSS1244 and BOSS1542 contain a higher fraction of dusty galaxies with strongly enhanced star formation. The volume densities of the SMGs are estimated to be $\sim15-$30 times the average, significantly higher than the overdensity factor ($\sim 6$) traced by H$α$ emission-line galaxies (HAEs). More importantly, we discover a prominent offset between the spatial distributions of the two populations in these two protoclusters -- SMGs are mostly located around the high-density regions of HAEs, and few are seen inside these regions. This finding may have revealed for the first time the occurrence of violent star formation enhancement in the outskirts of the HAE density peaks, likely driven by the boosting of gas supplies and/or starburst triggering events. Meanwhile, the lack of SMGs inside the most overdense regions at $z\sim2$ implies a transition to the environment disfavouring extreme starbursts.

astro-ph.GA↗

arXiv2022

Unified Line and Paragraph Detection by Graph Convolutional Networks

We formulate the task of detecting lines and paragraphs in a document into a unified two-level clustering problem. Given a set of text detection boxes that roughly correspond to words, a text line is a cluster of boxes and a paragraph is a cluster of lines. These clusters form a two-level tree that represents a major part of the layout of a document. We use a graph convolutional network to predict the relations between text detection boxes and then build both levels of clusters from these predictions. Experimentally, we demonstrate that the unified approach can be highly efficient while still achieving state-of-the-art quality for detecting paragraphs in public benchmarks and real-world images.

cs.CV↗

arXiv2022

Dynamics of finite-size spheroids in turbulent flow: the roles of flow structures and particle boundary layers

We study the translational and rotational dynamics of neutrally-buoyant finite-size spheroids in hydrodynamic turbulence by means of fully resolved numerical simulations. We examine axisymmetric shapes, from oblate to prolate, and the particle volume dependences. We show that the accelerations and rotations experienced by non-spherical inertial-scale particles result from volume filtered fluid forces and torques, similar to spherical particles. However, the particle orientations carry signatures of preferential alignments with the surrounding flow structures, which is reflected in distinct axial and lateral fluctuations for accelerations and rotation rates. The randomization of orientations does not occur even for particles with volume equivalent diameter size in the inertial range, here up to 60 $η$ at $Re_λ=120$. Additionally, we demonstrate that the role of fluid boundary layers around the particles cannot be neglected to reach a quantitative understanding of particle statistical dynamics, as they affect the intensities of angular velocities, and the relative importance of tumbling with respect to spinning rotations. This study brings to the fore the importance of inertial-scale flow structures in homogeneous and isotropic turbulence and their impacts on the transport of neutrally-buoyant bodies with size in the inertial range.

physics.flu-dyn↗

arXiv2022

Systematic biases in determining dust attenuation curves through galaxy SED fitting

While the slope of the dust attenuation curve ($δ$) is found to correlate with effective dust attenuation ($A_V$) as obtained through spectral energy distribution (SED) fitting, it remains unknown how the fitting degeneracies shape this relation. We examine the degeneracy effects by fitting SEDs of a sample of local star-forming galaxies (SFGs) selected from the Galaxy And Mass Assembly survey, in conjunction with mock galaxy SEDs of known attenuation parameters. A well-designed declining starburst star formation history is adopted to generate model SED templates with intrinsic UV slope ($β_0$) spanning over a reasonably wide range. The best-fitting $β_0$ for our sample SFGs shows a wide coverage, dramatically differing from the limited range of $β_0<-2.2$ for a starburst of constant star formation. Our results show that strong degeneracies between $β_0$, $δ$, and $A_V$ in the SED fitting induce systematic biases leading to a false $A_V$--$δ$ correlation. Our simulation tests reveal that this relationship can be well reproduced even when a flat $A_V$--$δ$ relation is taken to build the input model galaxy SEDs. The variations in best-fitting $δ$ are dominated by the fitting errors. We show that assuming a starburst with constant star formation in SED fitting will result in a steeper attenuation curve, smaller degeneracy errors, and a stronger $A_V$--$δ$ relation. Our findings confirm that the $A_V$--$δ$ relation obtained through SED fitting is likely driven by the systematic biases induced by the fitting degeneracies between $β_0$, $δ$, and $A_V$.

astro-ph.GA↗

arXiv2021

Symmetry-aware Neural Architecture for Embodied Visual Navigation

Visual exploration is a task that seeks to visit all the navigable areas of an environment as quickly as possible. The existing methods employ deep reinforcement learning (RL) as the standard tool for the task. However, they tend to be vulnerable to statistical shifts between the training and test data, resulting in poor generalization over novel environments that are out-of-distribution (OOD) from the training data. In this paper, we attempt to improve the generalization ability by utilizing the inductive biases available for the task. Employing the active neural SLAM (ANS) that learns exploration policies with the advantage actor-critic (A2C) method as the base framework, we first point out that the mappings represented by the actor and the critic should satisfy specific symmetries. We then propose a network design for the actor and the critic to inherently attain these symmetries. Specifically, we use $G$-convolution instead of the standard convolution and insert the semi-global polar pooling (SGPP) layer, which we newly design in this study, in the last section of the critic network. Experimental results show that our method increases area coverage by $8.1 m^2$ when trained on the Gibson dataset and tested on the MP3D dataset, establishing the new state-of-the-art.

cs.CV↗

arXiv2021

The cosmic environment overtakes the local density in shaping galaxy star formation

The gas supply from the cosmic web is the key to sustain star formation in galaxies. It remains to be explored how the cosmic large-scale structure (LSS) effects on galaxy evolution at given local environments. We examine galaxy specific star formation rate as a function of local density in a LSS at $z=0.735$ in the Extended Chandra Deep Field South. The LSS is mapped by 732 galaxies with $R<24$\,mag and redshift at $0.72\le z \le 0.75$ collected from the literature and our spectroscopic observations with Magellan/IMACS, consisting of five galaxy clusters/groups and surrounding filaments over an area of $23.9 \times22.7$\,co-moving\,Mpc$^2$. The spread of spectroscopic redshifts corresponds a velocity dispersion of 494\,km\,s$^{-1}$, indicating the LSS likely to be a thin sheet with a galaxy density $\gtrsim 3.9$ times that of the general field. These clusters/groups in this LSS mostly exhibit elongated morphologies and multiple components connected with surrounding filaments. Strikingly, we find that star-forming galaxies in the LSS keep star formation at the same level as field, and show no dependence on local density but stellar mass. Meanwhile, an increasing fraction of quiescent galaxies is detected at increasing local density in both the LSS and the field, consistent with the expectation that galaxy mass and local dense environment hold the key to quench star formation. Combined together, we conclude that the cosmic environment of the LSS overtakes the local environment in remaining galaxy star formation to the level of the field.

astro-ph.GA↗

arXiv2021

A Comprehensive Study on Learning-Based PE Malware Family Classification Methods

Driven by the high profit, Portable Executable (PE) malware has been consistently evolving in terms of both volume and sophistication. PE malware family classification has gained great attention and a large number of approaches have been proposed. With the rapid development of machine learning techniques and the exciting results they achieved on various tasks, machine learning algorithms have also gained popularity in the PE malware family classification task. Three mainstream approaches that use learning based algorithms, as categorized by the input format the methods take, are image-based, binary-based and disassembly-based approaches. Although a large number of approaches are published, there is no consistent comparisons on those approaches, especially from the practical industry adoption perspective. Moreover, there is no comparison in the scenario of concept drift, which is a fact for the malware classification task due to the fast evolving nature of malware. In this work, we conduct a thorough empirical study on learning-based PE malware classification approaches on 4 different datasets and consistent experiment settings. Based on the experiment results and an interview with our industry partners, we find that (1) there is no individual class of methods that significantly outperforms the others; (2) All classes of methods show performance degradation on concept drift (by an average F1-score of 32.23%); and (3) the prediction time and high memory consumption hinder existing approaches from being adopted for industry usage.

cs.CR↗