arXiv subjects

Shuang Liu

Publications and source records attributed to Shuang Liu.

At least 127 records · Page 7Linked to original sources

arXiv2021

Morphological Transformation and Star Formation Quenching of Massive Galaxies at 0.5 < z < 2.5 in 3D-HST/CANDELS

To figure out the effect of stellar mass and local environment on morphological transformation and star formation quenching in galaxies, we use the massive ($M_* \geq 10^{10} M_{\odot}$) galaxies at $0.5 \leq z \leq 2.5$ in five fields of 3D-HST/CANDELS. Based on the {\it UVJ} diagnosis and the possibility of possessing spheroid, our sample of massive galaxies are classified into four populations: quiescent early-type galaxies (qEs), quiescent late-type galaxies (qLs), star-forming early-type galaxies (sEs), and star-forming late-type galaxies (sLs). It is found that the quiescent fraction is significantly elevated at the high ends of mass and local environmental overdensity, which suggests a clear dependence of quenching on both mass and local environment. Over cosmic time, the mass dependence of galaxy quiescence decreases while the local environment dependence increases. The early-type fraction is found to be larger only at high-mass end, indicating a evident mass dependence of morphological transformation. This mass dependence becomes more significant at lower redshifts. Among the four populations, the fraction of active galactic nucleus (AGN) in the qLs peaks at $2<z \leq 2.5$, and rapidly declines with cosmic time. The sEs are found to have higher AGN fractions of $20-30\%$ at $0.5\leq z<2$ . The redshift evolution of AGN fractions in the qLs and sEs suggests that the AGN feedback could have played important roles in the formation of the qLs and sEs.

astro-ph.GA↗

arXiv2021

Evidences for the existence of the ultimate regime in supergravitational turbulent thermal convection

What is the final state of turbulence when the driving parameter approaches to infinity? For thermal turbulence, in 1962, Kraichnan proposed a so-called ultimate scaling dependence of the heat transport (quantified by the Nusselt number $\text{Nu}$) on the Rayleigh number ($\text{Ra}$), which can be extrapolated to arbitrarily high $\text{Ra}$. The existence of Kraichnan ultimate scaling has been intensively debated in the past decades. In this work, using a supergravitational thermal convection system, with an effective gravity up to 100 times the Earth's gravity, both Rayleigh number and shear Reynolds number can be boosted due to the increase of the buoyancy driving and the additional Coriolis forces. Over a decade of $\text{Ra}$ range, we demonstrate the existence of Kraichnan-like ultimate regime with four direct evidences: the ultimate scaling dependence of $\text{Nu}$ versus $\text{Ra}$; the appearance of turbulent velocity boundary layer profile; the enhanced strength of the shear Reynolds number; the new statistical properties of local temperature fluctuations. The present findings will greatly improve the understanding of the flow dynamics in geophysical and astrophysical flows.

physics.flu-dyn↗

arXiv2021

The effect of environment on star formation activity and morphology at 0.5 < z < 2.5 in CANDELS

To explore the effect of environment on star-formation and morphological transformation of high-redshift galaxies, we present a robust estimation of localized galaxy overdensity using a density estimator within the Bayesian probability framework.The maps of environmental overdensity at $0.5< z< 2.5$ are constructed for the five CANDELS fields. In general, the quiescent fraction increases with overdensity and stellar mass. Stellar mass dominates the star formation quenching for massive galaxies, while environmental quenching tends to be more effective for the low-mass galaxies at $0.5< z < 1$. For the most massive galaxies ($M_* > 10^{10.8} M_{\odot}$), the effect of environmental quenching is still significant up to $z \sim 2.5$. No significant environmental dependence is found in the distributions of Sérsic index and effective radius for SFGs and QGs separately. The primary role of environment might be to control the quiescent fraction. And the morphological parameters are primarily connected with star formation status. The similarity in the trends of quiescent fraction and Sérsic index along with stellar mass indicates that morphological transformation is accompanied with star formation quenching.

astro-ph.GA↗

arXiv2021

Task Guided Compositional Representation Learning for ZDA

Zero-shot domain adaptation (ZDA) methods aim to transfer knowledge about a task learned in a source domain to a target domain, while data from target domain are not available. In this work, we address learning feature representations which are invariant to and shared among different domains considering task characteristics for ZDA. To this end, we propose a method for task-guided ZDA (TG-ZDA) which employs multi-branch deep neural networks to learn feature representations exploiting their domain invariance and shareability properties. The proposed TG-ZDA models can be trained end-to-end without requiring synthetic tasks and data generated from estimated representations of target domains. The proposed TG-ZDA has been examined using benchmark ZDA tasks on image classification datasets. Experimental results show that our proposed TG-ZDA outperforms state-of-the-art ZDA methods for different domains and tasks.

cs.CV↗

arXiv2021

A parallel cut-cell algorithm for the free-boundary Grad-Shafranov problem

A parallel cut-cell algorithm is described to solve the free-boundary problem of the Grad-Shafranov equation. The algorithm reformulates the free-boundary problem in an irregular bounded domain and its important aspects include a searching algorithm for the magnetic axis and separatrix, a surface integral along the irregular boundary to determine the boundary values, an approach to optimize the coil current based on a targeting plasma shape, Picard iterations with Aitken's acceleration for the resulting nonlinear problem, and a Cartesian grid embedded boundary method to handle the complex geometry. The algorithm is implemented in parallel using a standard domain-decomposition approach and a good parallel scaling is observed. Numerical results verify the accuracy and efficiency of the free-boundary Grad-Shafranov solver.

math.NA↗

arXiv2021

GQE-PRF: Generative Query Expansion with Pseudo-Relevance Feedback

Query expansion with pseudo-relevance feedback (PRF) is a powerful approach to enhance the effectiveness in information retrieval. Recently, with the rapid advance of deep learning techniques, neural text generation has achieved promising success in many natural language tasks. To leverage the strength of text generation for information retrieval, in this article, we propose a novel approach which effectively integrates text generation models into PRF-based query expansion. In particular, our approach generates augmented query terms via neural text generation models conditioned on both the initial query and pseudo-relevance feedback. Moreover, in order to train the generative model, we adopt the conditional generative adversarial nets (CGANs) and propose the PRF-CGAN method in which both the generator and the discriminator are conditioned on the pseudo-relevance feedback. We evaluate the performance of our approach on information retrieval tasks using two benchmark datasets. The experimental results show that our approach achieves comparable performance or outperforms traditional query expansion methods on both the retrieval and reranking tasks.

cs.IR↗

arXiv2021

Cross-View Tracking for Multi-Human 3D Pose Estimation at over 100 FPS

Estimating 3D poses of multiple humans in real-time is a classic but still challenging task in computer vision. Its major difficulty lies in the ambiguity in cross-view association of 2D poses and the huge state space when there are multiple people in multiple views. In this paper, we present a novel solution for multi-human 3D pose estimation from multiple calibrated camera views. It takes 2D poses in different camera coordinates as inputs and aims for the accurate 3D poses in the global coordinate. Unlike previous methods that associate 2D poses among all pairs of views from scratch at every frame, we exploit the temporal consistency in videos to match the 2D inputs with 3D poses directly in 3-space. More specifically, we propose to retain the 3D pose for each person and update them iteratively via the cross-view multi-human tracking. This novel formulation improves both accuracy and efficiency, as we demonstrated on widely-used public datasets. To further verify the scalability of our method, we propose a new large-scale multi-human dataset with 12 to 28 camera views. Without bells and whistles, our solution achieves 154 FPS on 12 cameras and 34 FPS on 28 cameras, indicating its ability to handle large-scale real-world applications. The proposed dataset is released at https://github.com/longcw/crossview_3d_pose_tracking.

cs.CV↗

arXiv2021

Regret Bounds for Discounted MDPs

Reinforcement learning (RL) has traditionally been understood from an episodic perspective; the concept of non-episodic RL, where there is no restart and therefore no reliable recovery, remains elusive. A fundamental question in non-episodic RL is how to measure the performance of a learner and derive algorithms to maximize such performance. Conventional wisdom is to maximize the difference between the average reward received by the learner and the maximal long-term average reward. In this paper, we argue that if the total time budget is relatively limited compared to the complexity of the environment, such comparison may fail to reflect the finite-time optimality of the learner. We propose a family of measures, called $γ$-regret, which we believe to better capture the finite-time optimality. We give motivations and derive lower and upper bounds for such measures. Note: A follow-up work (arXiv:2010.00587) has improved both our lower and upper bound, the gap is now closed at $\tildeΘ\left(\frac{\sqrt{SAT}}{(1 - γ)^{\frac{1}{2}}}\right)$.

cs.LG↗

arXiv2021

Asymptotics of the principal eigenvalue for a linear time-periodic parabolic operator I: Large advection

We investigate the effects of advection on the principal eigenvalues of linear time-periodic parabolic operators with zero Neumann boundary conditions. Various asymptotic behaviors of the principal eigenvalues, when advection coefficient approaches infinity, are established in heterogeneous environments, where spatial or temporal degeneracy could occur in the advection term. Our findings partially extend the existing results in Chen-Lou [2008 Indiana Univ. Math. J.] and Peng-Zhou [2018 Indiana Univ. Math. J.] for elliptic operators and those in Peng-Zhao [2015 Calc. Var. Partial Diff.] for parabolic operators.

math.AP↗

arXiv2021

Lagrangian dynamics and heat transfer in porous media convection

We report a numerical study of Rayleigh--Bénard convection through random porous media using pore-scale modelling, focusing on the Lagrangian dynamics of fluid particles and heat transfer for varied porosities $ϕ$. Due to the interaction between the porous medium and the coherent flow structures, the flow is found to be highly heterogeneous, consisting of convection channels with strong flow strength and stagnant regions with low velocities. The modifications of flow field due to porous structure have a significant influence on the dynamics of fluid particles. Evaluation of the particle displacement along the trajectory reveals the emergence of anomalous transport for long times as $ϕ$ is decreased, which is associated with the long-time correlation of Lagrangian velocity of the fluid. As porosity is decreased, the cross-correlation between the vertical velocity and temperature fluctuation is enhanced, which reveals a mechanism to enhance the heat transfer in porous media convection.

physics.flu-dyn↗

arXiv2021

Effects of radius ratio on annular centrifugal Rayleigh-Bénard convection

We report on a three-dimensional direct numerical simulation study of flow structure and heat transport in the annular centrifugal Rayleigh-Bénard convection (ACRBC) system, with cold inner and hot outer cylinders corotating axially, for the Rayleigh number range Ra $ \in [{10^6},{10^8}]$ and radius ratio range $η= {R_i}/{R_o} \in [0.3,0.9]$. This study focuses on the dependence of flow properties on the radius ratio $η$. The temperature and velocity fields reveal that different curvatures of the inner and outer cylinders of the ACRBC system lead to asymmetric movements of hot and cold plumes under the action of Coriolis force, resulting in the formation of zonal flow. The physical mechanism of zonal flow is verified by the dependence of the drift frequency of the large-scale circulation rolls and the space- and time-averaged azimuthal velocity on $η$. We find that the larger $η$ is, the weaker the zonal flow becomes. We show that the heat transport efficiency increases with $η$. It is also found that the bulk temperature deviates from the arithmetic mean temperature and the deviation increases as $η$ decreases. This effect can be explained by a simple model that accounts for the curvature effects and the radially-dependent centrifugal force in ACRBC.

physics.flu-dyn↗

arXiv2021

Asymptotics of the principal eigenvalue for a linear time-periodic parabolic operator II: Small diffusion

We investigate the effect of small diffusion on the principal eigenvalues of linear time-periodic parabolic operators with zero Neumann boundary conditions in one dimensional space. The asymptotic behaviors of the principal eigenvalues, as the diffusion coefficients tend to zero, are established for non-degenerate and degenerate spatial-temporally varying environments. A new finding is the dependence of these asymptotic behaviors on the periodic solutions of a specific ordinary differential equation induced by the drift. The proofs are based upon delicate constructions of super/sub-solutions and the applications of comparison principles.

math.AP↗

arXiv2020

Heat transfer enhancement in Rayleigh-Bénard convection using a single passive barrier

In this numerical study on Rayleigh-Bénard convection we seek to improve the heat transfer by passive means. To this end we introduce a single tilted conductive barrier centered in an aspect ratio one cell, breaking the symmetry of the geometry and to channel the ascending hot and descending cold plumes. We study the global and local heat transfer and the flow organization for Rayleigh numbers $10^5 \leq Ra \leq 10^9$ for a fixed Prandtl number of $Pr=4.3$. We find that the global heat transfer can be enhanced up to $18\%$, and locally around $800\%$. The averaged Reynolds number is always decreased when a barrier is introduced, even for those cases where the global heat transfer is increased. We map the entire parameter space spanned by the orientation and the size of a single barrier for $Ra=10^8$.

physics.flu-dyn↗

arXiv2020

SE-ECGNet: A Multi-scale Deep Residual Network with Squeeze-and-Excitation Module for ECG Signal Classification

The classification of electrocardiogram (ECG) signals, which takes much time and suffers from a high rate of misjudgment, is recognized as an extremely challenging task for cardiologists. The major difficulty of the ECG signals classification is caused by the long-term sequence dependencies. Most existing approaches for ECG signal classification use Recurrent Neural Network models, e.g., LSTM and GRU, which are unable to extract accurate features for such long sequences. Other approaches utilize 1-Dimensional Convolutional Neural Network (CNN), such as ResNet or its variant, and they can not make good use of the multi-lead information from ECG signals.Based on the above observations, we develop a multi-scale deep residual network for the ECG signal classification task. We are the first to propose to treat the multi-lead signal as a 2-dimensional matrix and combines multi-scale 2-D convolution blocks with 1-D convolution blocks for feature extraction. Our proposed model achieves 99.2% F1-score in the MIT-BIH dataset and 89.4% F1-score in Alibaba dataset and outperforms the state-of-the-art performance by 2% and 3%, respectively, view related code and data at https://github.com/Amadeuszhao/SE-ECGNet

cs.LG↗

arXiv2020

Rotational dynamics of bottom-heavy rods in turbulence from experiments and numerical simulations

We successfully perform the three-dimensional tracking in a turbulent fluid flow of small asymmetrical particles that are neutrally-buoyant and bottom-heavy, i.e., they have a non-homogeneous mass distribution along their symmetry axis. We experimentally show how a tiny mass inhomogeneity can affect the particle orientation along the preferred vertical direction and modify its tumbling rate. The experiment is complemented by a series of simulations based on realistic Navier-Stokes turbulence and on a point-like particle model that is capable to explore the full range of parameter space characterized by the gravitational torque stability number and by the particle aspect ratio. We propose a theoretical perturbative prediction valid in the high bottom-heaviness regime that agrees well with the observed preferential orientation and tumbling rate of the particles. We also show that the heavy-tail shape of the probability distribution function of the tumbling rate is weakly affected by the bottom-heaviness of the particles.

physics.flu-dyn↗

arXiv2020

Measurements of the growth and saturation of electron Weibel instability in optical-field ionized plasmas

The temporal evolution of the magnetic field associated with electron thermal Weibel instability in optical-field ionized plasmas is measured using ultrashort (1.8 ps), relativistic (45 MeV) electron bunches from a linear accelerator. The self-generated magnetic fields are found to self-organize into a quasi-static structure consistent with a helicoid topology within a few ps and such a structure lasts for tens of ps in underdense plasmas. The measured growth rate agrees well with that predicted by the kinetic theory of plasmas taking into account collisions. Magnetic trapping is identified as the dominant saturation mechanism.

physics.plasm-ph↗

arXiv2020

Visualization of Technical and Tactical Characteristics in Fencing

Fencing is a sport that relies heavily on the use of tactics. However, most existing methods for analyzing fencing data are based on statistical models in which hidden patterns are difficult to discover. Unlike sequential games, such as tennis and table tennis, fencing is a type of simultaneous game. Thus, the existing methods on the sports visualization do not operate well for fencing matches. In this study, we cooperated with experts to analyze the technical and tactical characteristics of fencing competitions. To meet the requirements of the fencing experts, we designed and implemented FencingVis, an interactive visualization system for fencing competition data.The action sequences in the bout are first visualized by modified bar charts to reveal the actions of footworks and bladeworks of both fencers. Then an interactive technique is provided for exploring the patterns of behavior of fencers. The different combinations of tactical behavior patterns are further mapped to the graph model and visualized by a tactical flow graph. This graph can reveal the different strategies adopted by both fencers and their mutual influence in one bout. We also provided a number of well-coordinated views to supplement the tactical flow graph and display the information of the fencing competition from different perspectives. The well-coordinated views are meant to organically integrate with the tactical flow graph through consistent visual style and view coordination. We demonstrated the usability and effectiveness of the proposed system with three case studies. On the basis of expert feedback, FencingVis can help analysts find not only the tactical patterns hidden in fencing bouts, but also the technical and tactical characteristics of the contestant.

cs.HC↗

arXiv2020

Multi-task Batch Reinforcement Learning with Metric Learning

We tackle the Multi-task Batch Reinforcement Learning problem. Given multiple datasets collected from different tasks, we train a multi-task policy to perform well in unseen tasks sampled from the same distribution. The task identities of the unseen tasks are not provided. To perform well, the policy must infer the task identity from collected transitions by modelling its dependency on states, actions and rewards. Because the different datasets may have state-action distributions with large divergence, the task inference module can learn to ignore the rewards and spuriously correlate $\textit{only}$ state-action pairs to the task identity, leading to poor test time performance. To robustify task inference, we propose a novel application of the triplet loss. To mine hard negative examples, we relabel the transitions from the training tasks by approximating their reward functions. When we allow further training on the unseen tasks, using the trained policy as an initialization leads to significantly faster convergence compared to randomly initialized policies (up to $80\%$ improvement and across 5 different Mujoco task distributions). We name our method $\textbf{MBML}$ ($\textbf{M}\text{ulti-task}$ $\textbf{B}\text{atch}$ RL with $\textbf{M}\text{etric}$ $\textbf{L}\text{earning}$).

cs.LG↗