arXiv subjects

Yan Wang

Publications and source records attributed to Yan Wang.

At least 1,009 records · Page 56Linked to original sources

arXiv2016

Cosmic constraint on massive neutrinos in viable f(R) gravity with producing LCDM background expansion

Tensions between several cosmic observations were found recently, such as the inconsistent values of $H_{0}$ (or $σ_{8}$) were indicated by the different cosmic observations. Introducing the massive neutrinos in $Λ$CDM could potentially solve the tensions. Viable $f(R)$ gravity producing $Λ$CDM background expansion with massive neutrinos is investigated in this paper. We fit the current observational data: Planck-2015 CMB, RSD, BAO and SNIa to constrain the mass of neutrinos in viable $f(R)$ theory. The constraint results at 95\% confidence level are: $Σm_ν<0.202$ eV for the active neutrino case, $m_{ν, sterile}^{eff}<0.757$ eV with $N_{eff}<3.22$ for the sterile neutrino case. For the effects by the mass of neutrinos, the constraint results on model parameter at 95\% confidence level become $f_{R0}\times 10^{-6}> -1.89$ and $f_{R0}\times 10^{-6}> -2.02$ for two cases, respectively. It is also shown that the fitting values of several parameters much depend on the neutrino properties, such as the cold dark matter density, the cosmological quantities at matter-radiation equality, the neutrino density and the fraction of baryonic mass in helium. At last, the constraint result shows that the tension between direct and CMB measurements of $H_0$ gets slightly weaker in the viable $f(R)$ model than that in the base $Λ$CDM model.

astro-ph.CO↗

arXiv2016

Symmetric high order Gautschi-type exponential wave integrators pseudospectral method for the nonlinear Klein-Gordon equation in the nonrelativistic limit regime

A group of high order Gautschi-type exponential wave integrators (EWIs) Fourier pseudospectral method are proposed and analyzed for solving the nonlinear Klein-Gordon equation (KGE) in the nonrelativistic limit regime, where a parameter $0<\varepsilon\ll1$ which is inversely proportional to the speed of light, makes the solution propagate waves with wavelength $O(\varepsilon^2)$ in time and $O(1)$ in space. With the Fourier pseudospectral method to discretize the KGE in space, we propose a group of EWIs with designed Gautschi's type quadratures for the temporal integrations, which can offer any intended even order of accuracy provided that the solution is smooth enough, while all the current existing EWIs offer at most second order accuracy. The scheme is explicit, time symmetric and rigorous error estimates show the meshing strategy of the proposed method is time step $τ=O(\varepsilon^2)$ and mesh size $h=O(1)$ as $0<\varepsilon\ll1$, which is `optimal' among all classical numerical methods towards solving the KGE directly in the limit regime, and which also distinguish our methods from other high order approaches such as Runge-Kutta methods which require $τ=O(\varepsilon^3)$. Numerical experiments with comparisons are done to confirm the error bound and show the superiority of the proposed methods over existing classical numerical methods.

math.NA↗

arXiv2016

e-Distance Weighted Support Vector Regression

We propose a novel support vector regression approach called e-Distance Weighted Support Vector Regression (e-DWSVR).e-DWSVR specifically addresses two challenging issues in support vector regression: first, the process of noisy data; second, how to deal with the situation when the distribution of boundary data is different from that of the overall data. The proposed e-DWSVR optimizes the minimum margin and the mean of functional margin simultaneously to tackle these two issues. In addition, we use both dual coordinate descent (CD) and averaged stochastic gradient descent (ASGD) strategies to make e-DWSVR scalable to large scale problems. We report promising results obtained by e-DWSVR in comparison with existing methods on several benchmark datasets.

cs.LG↗

arXiv2016

A Simple, Fast and Highly-Accurate Algorithm to Recover 3D Shape from 2D Landmarks on a Single Image

Three-dimensional shape reconstruction of 2D landmark points on a single image is a hallmark of human vision, but is a task that has been proven difficult for computer vision algorithms. We define a feed-forward deep neural network algorithm that can reconstruct 3D shapes from 2D landmark points almost perfectly (i.e., with extremely small reconstruction errors), even when these 2D landmarks are from a single image. Our experimental results show an improvement of up to two-fold over state-of-the-art computer vision algorithms; 3D shape reconstruction of human faces is given at a reconstruction error < .004, cars at .0022, human bodies at .022, and highly-deformable flags at an error of .0004. Our algorithm was also a top performer at the 2016 3D Face Alignment in the Wild Challenge competition (done in conjunction with the European Conference on Computer Vision, ECCV) that required the reconstruction of 3D face shape from a single image. The derived algorithm can be trained in a couple hours and testing runs at more than 1, 000 frames/s on an i7 desktop. We also present an innovative data augmentation approach that allows us to train the system efficiently with small number of samples. And the system is robust to noise (e.g., imprecise landmark points) and missing data (e.g., occluded or undetected landmark points).

cs.CV↗

arXiv2016

Diphoton and Dark Matter from Cascade Decay

We propose a simplified model to study the possible new heavy diphoton resonance from cascade decay of a heavier particle at colliders, which may be related to the dark matter or other new physics beyond the standard model. Model-independent constraints and predictions on the allowed couplings for generating the observed diphoton data are studied in detail. We demonstrate that this scenario can be tested by the possible four-photon signal or the $WW/ZZ/Zγ$ resonances. Meanwhile, this cascade decay scenario also provides us with the dark matter candidates, which is consistent with the observed dark matter relic density.

hep-ph↗

arXiv2016

The Kelmans-Seymour conjecture III: 3-vertices in $K_4^-$

Let $G$ be a 5-connected nonplanar graph and let $x_1,x_2,y_1,y_2\in V(G)$ be distinct, such that $G[\{x_1,x_2,y_1,y_2\}]\cong K_4^-$ and $y_1y_2\notin E(G)$. We show that one of the following holds: $G-x_1$ contains $K_4^-$, or $G$ contains a $K_4^-$ in which $x_1$ is of degree 2, or $G$ contains a $TK_5$ in which $x_1$ is not a branch vertex, or $\{x_2,y_1,y_2\}$ may be chosen so that for any distinct $z_0, z_1\in N(x_1)-\{x_2,y_1,y_2\}$, $G-\{x_1v:v\notin \{z_0, z_1,x_2, y_1,y_2\}\}$ contains $TK_5$. This result will be used to prove the Kelmans-Seymour conjecture.

math.CO↗

arXiv2016

A parametric finite element method for solid-state dewetting problems with anisotropic surface energies

We propose an efficient and accurate parametric finite element method (PFEM) for solving sharp-interface continuum models for solid-state dewetting of thin films with anisotropic surface energies. The governing equations of the sharp-interface models belong to a new type of high-order (4th- or 6th-order) geometric evolution partial differential equations about open curve/surface interface tracking problems which include anisotropic surface diffusion flow and contact line migration. Compared to the traditional methods (e.g., marker-particle methods), the proposed PFEM not only has very good accuracy, but also poses very mild restrictions on the numerical stability, and thus it has significant advantages for solving this type of open curve evolution problems with applications in the simulation of solid-state dewetting. Extensive numerical results are reported to demonstrate the accuracy and high efficiency of the proposed PFEM.

math.NA↗

arXiv2016

Model-Driven Feed-Forward Prediction for Manipulation of Deformable Objects

Robotic manipulation of deformable objects is a difficult problem especially because of the complexity of the many different ways an object can deform. Searching such a high dimensional state space makes it difficult to recognize, track, and manipulate deformable objects. In this paper, we introduce a predictive, model-driven approach to address this challenge, using a pre-computed, simulated database of deformable object models. Mesh models of common deformable garments are simulated with the garments picked up in multiple different poses under gravity, and stored in a database for fast and efficient retrieval. To validate this approach, we developed a comprehensive pipeline for manipulating clothing as in a typical laundry task. First, the database is used for category and pose estimation for a garment in an arbitrary position. A fully featured 3D model of the garment is constructed in real-time and volumetric features are then used to obtain the most similar model in the database to predict the object category and pose. Second, the database can significantly benefit the manipulation of deformable objects via non-rigid registration, providing accurate correspondences between the reconstructed object model and the database models. Third, the accurate model simulation can also be used to optimize the trajectories for manipulation of deformable objects, such as the folding of garments. Extensive experimental results are shown for the tasks above using a variety of different clothing.

cs.CV↗

arXiv2016

Detection and localization of continuous gravitational waves with pulsar timing arrays: the role of pulsar terms

A pulsar timing array is a Galactic-scale detector of nanohertz gravitational waves (GWs). Its target signals contain two components: the `Earth term' and the `pulsar term' corresponding to GWs incident on the Earth and pulsar respectively. In this work we present a Frequentist method for the detection and localization of continuous waves that takes into account the pulsar term and is significantly faster than existing methods. We investigate the role of pulsar terms by comparing a full-signal search with an Earth-term-only search for non-evolving black hole binaries. By applying the method to synthetic data sets, we find that (i) a full-signal search can slightly improve the detection probability (by about five percent); (ii) sky localization is biased if only Earth terms are searched for and the inclusion of pulsar terms is critical to remove such a bias; (iii) in the case of strong detections (with signal-to-noise ratio $\gtrsim$ 30), it may be possible to improve pulsar distance estimation through GW measurements.

astro-ph.IM↗

arXiv2016

Orbital-selective Mott phases of a one-dimensional three-orbital Hubbard model studied using computational techniques

A recently introduced one-dimensional three-orbital Hubbard model displays orbital-selective Mott phases with exotic spin arrangements such as spin block states [J. Rincón {\em et al.}, Phys. Rev. Lett. \textbf{112}, 106405 (2014)]. In this publication we show that the Constrained-Path Quantum Monte Carlo (CPQMC) technique can accurately reproduce the phase diagram of this multiorbital one-dimensional model, paving the way to future CPQMC studies in systems with more challenging geometries, such as ladders and planes. The success of this approach relies on using the Hartree-Fock technique to prepare the trial states needed in CPQMC. We also study a simplified version of the model where the pair-hopping term is neglected and the Hund coupling is restricted to its Ising component. The corresponding phase diagrams are shown to be only mildly affected by the absence of these technically difficult-to-implement terms. This is confirmed by additional Density Matrix Renormalization Group and Determinant Quantum Monte Carlo calculations carried out for the same simplified model, with the latter displaying only mild Fermion sign problems. We conclude that these methods are able to capture quantitatively the rich physics of the several orbital-selective Mott phases (OSMP) displayed by this model, thus enabling computational studies of the OSMP regime in higher dimensions, beyond static or dynamic mean field approximations.

cond-mat.str-el↗

arXiv2016

A Powerful Generative Model Using Random Weights for the Deep Image Representation

To what extent is the success of deep visualization due to the training? Could we do deep visualization using untrained, random weight networks? To address this issue, we explore new and powerful generative models for three popular deep visualization tasks using untrained, random weight convolutional neural networks. First we invert representations in feature spaces and reconstruct images from white noise inputs. The reconstruction quality is statistically higher than that of the same method applied on well trained networks with the same architecture. Next we synthesize textures using scaled correlations of representations in multiple layers and our results are almost indistinguishable with the original natural texture and the synthesized textures based on the trained network. Third, by recasting the content of an image in the style of various artworks, we create artistic images with high perceptual quality, highly competitive to the prior work of Gatys et al. on pretrained networks. To our knowledge this is the first demonstration of image representations using untrained deep neural networks. Our work provides a new and fascinating tool to study the representation of deep network architecture and sheds light on new understandings on deep visualization.

cs.CV↗

arXiv2016

Ab initio Study of Cross-Interface Electron-Phonon Couplings in FeSe Thin Films on SrTiO$_3$ and BaTiO$_3$

We study the electron-phonon coupling strength near the interface of monolayer and bilayer FeSe thin films on SrTiO$_3$, BaTiO$_3$, and oxygen-vacant SrTiO$_3$ substrates, using ab initio methods. The calculated total electron-phonon coupling strength $λ=0.2\text{--}0.3$ cannot account for the high $T_c\sim 70$ K observed in these systems through the conventional phonon-mediated pairing mechanism. In all of these systems, however, we find that the coupling constant of a polar oxygen branch peaks at $\mathbf{q}=0$ with negligible coupling elsewhere, while the energy of this mode coincides with the offset energy of the replica bands measured recently by angle-resolved photoemission spectroscopy experiments. But the integrated coupling strength for this mode from our current calculations is still too small to produce the observed high $T_c$, even through the more efficient pairing mechanism provided by the forward scattering. We arrive at the same qualitative conclusion when considering a checkerboard antiferromagnetic configuration in the Fe layer. In light of the experimental observations of the replica band feature and the relatively high $T_c$ of FeSe monolayers on polar substrates, our results point towards a cooperative role for the electron-phonon interaction, where the cross-interface interaction acts in conjunction with a purely electronic interaction. We also discuss a few scenarios where the coupling strength obtained here may be enhanced.

cond-mat.supr-con↗

arXiv2016

Induced Forests in Bipartite Planar Graphs

Akiyama and Watanabe conjectured that every simple planar bipartite graph on $n$ vertices contains an induced forest on at least $5n/8$ vertices. We apply the discharging method to show that every simple bipartite planar graph on $n$ vertices contains an induced forest on at least $\lceil (4n+3)/7 \rceil$ vertices.

math.CO↗

arXiv2016

Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs

Object skeleton is a useful cue for object detection, complementary to the object contour, as it provides a structural representation to describe the relationship among object parts. While object skeleton extraction in natural images is a very challenging problem, as it requires the extractor to be able to capture both local and global image context to determine the intrinsic scale of each skeleton pixel. Existing methods rely on per-pixel based multi-scale feature computation, which results in difficult modeling and high time consumption. In this paper, we present a fully convolutional network with multiple scale-associated side outputs to address this problem. By observing the relationship between the receptive field sizes of the sequential stages in the network and the skeleton scales they can capture, we introduce a scale-associated side output to each stage. We impose supervision to different stages by guiding the scale-associated side outputs toward groundtruth skeletons of different scales. The responses of the multiple scale-associated side outputs are then fused in a scale-specific way to localize skeleton pixels with multiple scales effectively. Our method achieves promising results on two skeleton extraction datasets, and significantly outperforms other competitors.

cs.CV↗

arXiv2016

Accelerating Data Regeneration for Distributed Storage Systems with Heterogeneous Link Capacities

Distributed storage systems provide large-scale reliable data storage services by spreading redundancy across a large group of storage nodes. In such a large system, node failures take place on a regular basis. When a storage node breaks down, a replacement node is expected to regenerate the redundant data as soon as possible in order to maintain the same level of redundancy. Previous results have been mainly focused on the minimization of network traffic in regeneration. However, in practical networks, where link capacities vary in a wide range, minimizing network traffic does not always yield the minimum regeneration time. In this paper, we investigate two approaches to the problem of minimizing regeneration time in networks with heterogeneous link capacities. The first approach is to download different amounts of repair data from the helping nodes according to the link capacities. The second approach generalizes the conventional star-structured regeneration topology to tree-structured topologies so that we can utilize the links between helping nodes with bypassing low-capacity links. Simulation results show that the flexible tree-structured regeneration scheme that combines the advantages of both approaches can achieve a substantial reduction in the regeneration time.

cs.DC↗

arXiv2016

The Kelmans-Seymour conjecture II: 2-vertices in $K_4^-$

We use $K_4^-$ to denote the graph obtained from $K_4$ by removing an edge, and use $TK_5$ to denote a subdivision of $K_5$. Let $G$ be a 5-connected nonplanar graph and $\{x_1,x_2,y_1,y_2\}\subseteq V(G)$ such that $G[\{x_1,x_2,$ $y_1,y_2\}]\cong K_4^-$ with $y_1y_2\notin E(G)$. Let $w_1,w_2,w_3\in N(y_2)-\{x_1,x_2\}$ be distinct. We show that $G$ contains a $TK_5$ in which $y_2$ is not a branch vertex, or $G-y_2$ contains $K_4^-$, or $G$ has a special 5-separation, or $G-\{y_2v:v\notin \{w_1,w_2,w_3,x_1,x_2\}\}$ contains $TK_5$.

math.CO↗

arXiv2016

Coherent network analysis for continuous gravitational wave signals in a pulsar timing array: Pulsar phases as extrinsic parameters

Supermassive black hole binaries are one of the primary targets for gravitational wave searches using pulsar timing arrays. Gravitational wave signals from such systems are well represented by parametrized models, allowing the standard Generalized Likelihood Ratio Test (GLRT) to be used for their detection and estimation. However, there is a dichotomy in how the GLRT can be implemented for pulsar timing arrays: there are two possible ways in which one can split the set of signal parameters for semi-analytical and numerical extremization. The straightforward extension of the method used for continuous signals in ground-based gravitational wave searches, where the so-called pulsar phase parameters are maximized numerically, was addressed in an earlier paper (Wang et al. 2014). In this paper, we report the first study of the performance of the second approach where the pulsar phases are maximized semi-analytically. This approach is scalable since the number of parameters left over for numerical optimization does not depend on the size of the pulsar timing array. Our results show that, for the same array size (9 pulsars), the new method performs somewhat worse in parameter estimation, but not in detection, than the previous method where the pulsar phases were maximized numerically. The origin of the performance discrepancy is likely to be in the ill-posedness that is intrinsic to any network analysis method. However, scalability of the new method allows the ill-posedness to be mitigated by simply adding more pulsars to the array. This is shown explicitly by taking a larger array of pulsars.

astro-ph.IM↗

arXiv2016

Solid-State Dewetting and Island Morphologies in Strongly Anisotropic Materials

We propose a sharp-interface continuum model based on a thermodynamic variational approach to investigate the strong anisotropic effect on solid-state dewetting including contact line dynamics. For sufficiently strong surface energy anisotropy, we show that multiple equilibrium shapes may appear that can not be described by the widely employed Winterbottom construction, i.e., the modified Wulff construction for an island on a substrate. We repair the Winterbottom construction to include multiple equilibrium shapes and employ our evolution model to demonstrate that all such shapes are dynamically accessible.

cond-mat.mtrl-sci↗