arXiv subjects

Peng Wang

Publications and source records attributed to Peng Wang.

At least 919 records · Page 51Linked to original sources

arXiv2018

High-Order Implicit Hybridizable Discontinuous Galerkin Method for the Boltzmann Equation

The high-order hybridizable discontinuous Galerkin (HDG) method combining with an implicit iterative scheme is used to find the steady-state solution of the Boltzmann equation with full collision integral on two-dimensional triangular meshes. The velocity distribution function and its trace are approximated in the piecewise polynomial space of degree up to 4. The fast spectral method (FSM) is incorporated into the DG discretization to evaluate the collision operator. Specific polynomial approximation is proposed for the collision term to reduce the computational cost. The proposed scheme is proved to be accurate and efficient.

physics.flu-dyn↗

arXiv2018

Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network

Deep neural networks have achieved remarkable success in single image super-resolution (SISR). The computing and memory requirements of these methods have hindered their application to broad classes of real devices with limited computing power, however. One approach to this problem has been lightweight network architectures that bal- ance the super-resolution performance and the computation burden. In this study, we revisit this problem from an orthog- onal view, and propose a novel learning strategy to maxi- mize the pixel-wise fitting capacity of a given lightweight network architecture. Considering that the initial capacity of the lightweight network is very limited, we present an adaptive importance learning scheme for SISR that trains the network with an easy-to-complex paradigm by dynam- ically updating the importance of image pixels on the basis of the training loss. Specifically, we formulate the network training and the importance learning into a joint optimization problem. With a carefully designed importance penalty function, the importance of individual pixels can be gradu- ally increased through solving a convex optimization problem. The training process thus begins with pixels that are easy to reconstruct, and gradually proceeds to more complex pixels as fitting improves.

cs.CV↗

arXiv2018

Multi-carrier transport in ZrTe5 film

The single layer of Zirconium pentatelluride (ZrTe5) has been predicted to be a large-gap two-dimensional (2D) topological insulator, which has attracted particular attention in the topological phase transitions and potential device application. Here we investigated the transport properties in ZrTe5 films with the dependence of thickness from a few nm to several hundred nm. We find that the temperature of the resistivity anomaly's peak (Tp) is inclining to increase as the thickness decreases, and around a critical thickness of ~40 nm, the dominating carriers in the films change from n-type to p-type. With comprehensive studying of the Shubnikov-de Hass (SdH) oscillations and Hall resistance at variable temperatures, we demonstrate the multi-carrier transport instinct in the thin films. We extract the carrier densities and mobilities of two majority carriers using the simplified two-carrier model. The electron carriers can be attributed to the Dirac band with a non-trivial Berry's phase π, while the hole carriers may originate from the surface chemical reaction or unintentional doping during the microfabrication process. It is necessary to encapsulate ZrTe5 film in the inert or vacuum environment to make a substantial improvement in the device quality.

cond-mat.mes-hall↗

arXiv2018

Holographic DC Conductivity for Backreacted Nonlinear Electrodynamics with Momentum Dissipation

We consider a holographic model with the charge current dual to a general nonlinear electrodynamics (NLED) field. Taking into account the backreaction of the NLED field on the geometry and introducing axionic scalars to generate momentum dissipation, we obtain expressions for DC conductivities with a finite magnetic field. The properties of the in-plane resistance are examined in several NLED models. For Maxwell-Chern-Simons electrodynamics, negative magneto-resistance and Mott-like behavior could appear in some parameter space region. Depending on the sign of the parameters, we expect the NLED models to mimic some type of weak or strong interactions between electrons. In the latter case, negative magneto-resistance and Mott-like behavior can be realized at low temperatures. Moreover, the Mott insulator to metal transition induced by a magnetic field is also observed at low temperatures.

hep-th↗

arXiv2018

Matching Consecutive Subpatterns Over Streaming Time Series

Pattern matching of streaming time series with lower latency under limited computing resource comes to a critical problem, especially as the growth of Industry 4.0 and Industry Internet of Things. However, against traditional single pattern matching model, a pattern may contain multiple subpatterns representing different physical meanings in the real world. Hence, we formulate a new problem, called "consecutive subpatterns matching", which allows users to specify a pattern containing several consecutive subpatterns with various specified thresholds. We propose a novel representation Equal-Length Block (ELB) together with two efficient implementations, which work very well under all Lp-Norms without false dismissals. Extensive experiments are performed on synthetic and real-world datasets to illustrate that our approach outperforms the brute-force method and MSM, a multi-step filter mechanism over the multi-scaled representation by orders of magnitude.

cs.DB↗

arXiv2018

Full-sky ray-tracing simulation of weak lensing using ELUCID simulations: exploring galaxy intrinsic alignment and cosmic shear correlations

The intrinsic alignment of galaxies is an important systematic effect in weak-lensing surveys, which can affect the derived cosmological parameters. One direct way to distinguish different alignment models and quantify their effects on the measurement is to produce mocked weak-lensing surveys. In this work, we use full-sky ray-tracing technique to produce mock images of galaxies from the ELUCID $N$-body simulation run with the WMAP9 cosmology. In our model we assume that the shape of central elliptical galaxy follows that of the dark matter halo, and spiral galaxy follows the halo spin. Using the mocked galaxy images, a combination of galaxy intrinsic shape and the gravitational shear, we compare the predicted tomographic shear correlations to the results of KiDS and DLS. It is found that our predictions stay between the KiDS and DLS results. We rule out a model in which the satellite galaxies are radially aligned with the center galaxy, otherwise the shear-correlations on small scales are too high. Most important, we find that although the intrinsic alignment of spiral galaxies is very weak, they induce a positive correlation between the gravitational shear signal and the intrinsic galaxy orientation (GI). This is because the spiral galaxy is tangentially aligned with the nearby large-scale overdensity, contrary to the radial alignment of elliptical galaxy. Our results explain the origin of detected positive GI term from the weak-lensing surveys. We conclude that in future analysis, the GI model must include the dependence on galaxy types in more detail.

astro-ph.CO↗

arXiv2018

DeLS-3D: Deep Localization and Segmentation with a 3D Semantic Map

For applications such as autonomous driving, self-localization/camera pose estimation and scene parsing are crucial technologies. In this paper, we propose a unified framework to tackle these two problems simultaneously. The uniqueness of our design is a sensor fusion scheme which integrates camera videos, motion sensors (GPS/IMU), and a 3D semantic map in order to achieve robustness and efficiency of the system. Specifically, we first have an initial coarse camera pose obtained from consumer-grade GPS/IMU, based on which a label map can be rendered from the 3D semantic map. Then, the rendered label map and the RGB image are jointly fed into a pose CNN, yielding a corrected camera pose. In addition, to incorporate temporal information, a multi-layer recurrent neural network (RNN) is further deployed improve the pose accuracy. Finally, based on the pose from RNN, we render a new label map, which is fed together with the RGB image into a segment CNN which produces per-pixel semantic label. In order to validate our approach, we build a dataset with registered 3D point clouds and video camera images. Both the point clouds and the images are semantically-labeled. Each video frame has ground truth pose from highly accurate motion sensors. We show that practically, pose estimation solely relying on images like PoseNet may fail due to street view confusion, and it is important to fuse multiple sensors. Finally, various ablation studies are performed, which demonstrate the effectiveness of the proposed system. In particular, we show that scene parsing and pose estimation are mutually beneficial to achieve a more robust and accurate system.

cs.CV↗

arXiv2018

Terahertz streaking of few-femtosecond relativistic electron beams

Streaking of photoelectrons with optical lasers has been widely used for temporal characterization of attosecond extreme ultraviolet pulses. Recently, this technique has been adapted to characterize femtosecond x-ray pulses in free-electron lasers with the streaking imprinted by farinfrared and Terahertz (THz) pulses. Here, we report successful implementation of THz streaking for time-stamping of an ultrashort relativistic electron beam of which the energy is several orders of magnitude higher than photoelectrons. Such ability is especially important for MeV ultrafast electron diffraction (UED) applications where electron beams with a few femtosecond pulse width may be obtained with longitudinal compression while the arrival time may fluctuate at a much larger time scale. Using this laser-driven THz streaking technique, the arrival time of an ultrashort electron beam with 6 fs (rms) pulse width has been determined with 1.5 fs (rms) accuracy. Furthermore, we have proposed and demonstrated a non-invasive method for correction of the timing jitter with femtosecond accuracy through measurement of the compressed beam energy, which may allow one to advance UED towards sub-10 fs frontier far beyond the ~100 fs (rms) jitter.

physics.acc-ph↗

arXiv2018

Aberration-free three dimensional micromachining in glass with spatiotemporally shaped femtosecond laser pulses

We observe that focusing a femtosecond laser beam simultaneously chirped in time and space domains in glass can efficiently suppress the optical aberration caused by the refractive index mismatch at the interface of air and the glass sample. We then demonstrate three dimensional microprocessing in glass with a nearly invariant spatial resolution for a large range of penetration depth between 0.25 mm and 9 mm without any aberration correction.

physics.optics↗

arXiv2018

Classification of Homogeneous Willmore Surfaces in $S^N$

In this note we consider homogeneous Willmore surfaces in $S^{n+2}$. The main result is that a homogeneous Willmore two-sphere is conformally equivalent to a homogeneous minimal two-sphere in $S^{n+2}$, i.e., either a round two-sphere or one of the Borůvka-Veronese 2-spheres in $S^{2m}$. This entails a classification of all Willmore $\mathbb{C} P^1$ in $S^{2m}$. As a second main result we show that there exists no homogeneous Willmore upper-half plane in $S^{n+2}$ and we give, in terms of special constant potentials, a simple loop group characterization of all homogeneous surfaces which have an abelian transitive group.

math.DG↗

arXiv2018

Selection by Partitioning the Solution Paths

The performance of penalized likelihood approaches depends profoundly on the selection of the tuning parameter; however, there is no commonly agreed-upon criterion for choosing the tuning parameter. Moreover, penalized likelihood estimation based on a single value of the tuning parameter suffers from several drawbacks. This article introduces a novel approach for feature selection based on the entire solution paths rather than the choice of a single tuning parameter, which significantly improves the accuracy of the selection. Moreover, the approach allows for feature selection using ridge or other strictly convex penalties. The key idea is to classify variables as relevant or irrelevant at each tuning parameter and then to select all of the variables which have been classified as relevant at least once. We establish the theoretical properties of the method, which requires significantly weaker conditions than existing methods in the literature. We also illustrate the advantages of the proposed approach with simulation studies and a data example.

stat.ME↗

arXiv2018

Fast Channel Estimation for Millimetre Wave Wireless Systems Using Overlapped Beam Patterns

This paper is concerned with the channel estimation problem in millimetre wave (MMW) wireless systems with large antenna arrays. By exploiting the sparse nature of the MMW channel, we present an efficient estimation algorithm based on a novel overlapped beam pattern design. The performance of the algorithm is analyzed and an upper bound on the probability of channel estimation failure is derived. Results show that the algorithm can significantly reduce the number of required measurements in channel estimation (e.g., by 225% when a single overlap is used) when compared to the existing channel estimation algorithm based on non-overlapped beam patterns.

cs.IT↗

arXiv2018

Alignment between satellite and central galaxies in the SDSS DR7: dependence on large-scale environment

The alignment between satellites and central galaxies has been studied in detail both in observational and theoretical works. The widely accepted fact is that the satellites preferentially reside along the major axis of their central galaxy. However, the origin and large-scale environment dependence of this alignment are still unknown. In an attempt to figure out those, we use data constructed from SDSS DR7 to investigate the large-scale environmental dependence of this alignment with emphasis on examining the alignments' dependence on the colour of the central galaxy. We find a very strong large-scale environmental dependence of the satellite-central alignment in groups with blue centrals. Satellites of blue centrals in knots are preferentially located perpendicular to the major axis of the centrals, and the alignment angle decreases with environment namely when going from knots to voids. The alignment angle strongly depend on the ${}^{0.1}(g-r)$ colour of centrals. We suggest that the satellite-central alignment is the result of a competition between satellite accretion within large scale-structure and galaxy evolution inside host haloes. For groups containing red central galaxies, the satellite-central alignment is mainly determined by the evolution effect, while for blue central dominated groups, the effect of large-scale structure plays a more important role, especially in knots. Our results provide an explanation for how the satellite-central alignment forms within different large-scale environments. The perpendicular case in groups and knots with blue centrals may also provide insight into understanding similar polar arrangements such the formation of the Milky Way and Centaurus A's satellite system.

astro-ph.GA↗

arXiv2018

View Extrapolation of Human Body from a Single Image

We study how to synthesize novel views of human body from a single image. Though recent deep learning based methods work well for rigid objects, they often fail on objects with large articulation, like human bodies. The core step of existing methods is to fit a map from the observable views to novel views by CNNs; however, the rich articulation modes of human body make it rather challenging for CNNs to memorize and interpolate the data well. To address the problem, we propose a novel deep learning based pipeline that explicitly estimates and leverages the geometry of the underlying human body. Our new pipeline is a composition of a shape estimation network and an image generation network, and at the interface a perspective transformation is applied to generate a forward flow for pixel value transportation. Our design is able to factor out the space of data variation and makes learning at each step much easier. Empirically, we show that the performance for pose-varying objects can be improved dramatically. Our method can also be applied on real data captured by 3D sensors, and the flow generated by our methods can be used for generating high quality results in higher resolution.

cs.CV↗

arXiv2018

Occlusion Aware Unsupervised Learning of Optical Flow

It has been recently shown that a convolutional neural network can learn optical flow estimation with unsupervised learning. However, the performance of the unsupervised methods still has a relatively large gap compared to its supervised counterpart. Occlusion and large motion are some of the major factors that limit the current unsupervised learning of optical flow methods. In this work we introduce a new method which models occlusion explicitly and a new warping way that facilitates the learning of large motion. Our method shows promising results on Flying Chairs, MPI-Sintel and KITTI benchmark datasets. Especially on KITTI dataset where abundant unlabeled samples exist, our unsupervised method outperforms its counterpart trained with supervised learning.

cs.CV↗

arXiv2018

Visual Question Answering with Memory-Augmented Networks

In this paper, we exploit a memory-augmented neural network to predict accurate answers to visual questions, even when those answers occur rarely in the training set. The memory network incorporates both internal and external memory blocks and selectively pays attention to each training exemplar. We show that memory-augmented neural networks are able to maintain a relatively long-term memory of scarce training exemplars, which is important for visual question answering due to the heavy-tailed distribution of answers in a general VQA setting. Experimental results on two large-scale benchmark datasets show the favorable performance of the proposed algorithm with a comparison to state of the art.

cs.CV↗

arXiv2018

A high-order hybridizable discontinuous Galerkin method with fast convergence to steady-state solutions of the gas kinetic equation

The mass flow rate of Poiseuille flow of rarefied gas through long ducts of two-dimensional cross-sections with arbitrary shape are critical in the pore-network modeling of gas transport in porous media. In this paper, for the first time, the high-order hybridizable discontinuous Galerkin (HDG) method is used to find the steady-state solution of the linearized Bhatnagar-Gross-Krook equation on two-dimensional triangular meshes. The velocity distribution function and its traces are approximated in the piecewise polynomial space (of degree up to 4) on the triangular meshes and the mesh skeletons, respectively. By employing a numerical flux that is derived from the first-order upwind scheme and imposing its continuity on the mesh skeletons, global systems for unknown traces are obtained with a few coupled degrees of freedom. To achieve fast convergence to the steady-state solution, a diffusion-type equation for flow velocity that is asymptotic-preserving into the fluid dynamic limit is solved by the HDG simultaneously, on the same meshes. The proposed HDG-synthetic iterative scheme is proved to be accurate and efficient. Specifically, for flows in the near-continuum regime, numerical simulations have shown that, to achieve the same level of accuracy, our scheme could be faster than the conventional iterative scheme by two orders of magnitude, while it is faster than the synthetic iterative scheme based on the finite difference discretization in the spatial space by one order of magnitude. The HDG-synthetic iterative scheme is ready to be extended to simulate rarefied gas mixtures and the Boltzmann collision operator.

physics.comp-ph↗

arXiv2018

LEGO: Learning Edge with Geometry all at Once by Watching Videos

Learning to estimate 3D geometry in a single image by watching unlabeled videos via deep convolutional network is attracting significant attention. In this paper, we introduce a "3D as-smooth-as-possible (3D-ASAP)" prior inside the pipeline, which enables joint estimation of edges and 3D scene, yielding results with significant improvement in accuracy for fine detailed structures. Specifically, we define the 3D-ASAP prior by requiring that any two points recovered in 3D from an image should lie on an existing planar surface if no other cues provided. We design an unsupervised framework that Learns Edges and Geometry (depth, normal) all at Once (LEGO). The predicted edges are embedded into depth and surface normal smoothness terms, where pixels without edges in-between are constrained to satisfy the prior. In our framework, the predicted depths, normals and edges are forced to be consistent all the time. We conduct experiments on KITTI to evaluate our estimated geometry and CityScapes to perform edge evaluation. We show that in all of the tasks, i.e.depth, normal and edge, our algorithm vastly outperforms other state-of-the-art (SOTA) algorithms, demonstrating the benefits of our approach.

cs.CV↗