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Yan Wu

Publications and source records attributed to Yan Wu.

At least 145 records · Page 8Linked to original sources
arXiv2018

What is parameterized $Om(z)$ diagnostics telling us in light of recent observations?

In this paper, we propose a new parametrization of $Om(z)$ diagnostics and show how the most recent and significantly improved observations concerning the $H(z)$ and SN Ia measurements can be used to probe the consistency or tension between $Λ$CDM model and observations. Our results demonstrates that $H_0$ plays a very important role in the consistency test of $Λ$CDM with the $H(z)$ data. Adopting the Hubble constant priors from \textit{Planck} 2013 and Riess (2016), one finds a considerable tension between the current $H(z)$ data and $Λ$CDM model and confirms the conclusions obtained previously by the others. However, with the Hubble constant prior taken from WMAP9, the discrepancy between $H(z)$ data and $Λ$CDM disappears, i.e., the current $H(z)$ observations still support the cosmological constant scenario. This conclusion is also supported by the results derived from the JLA SNe Ia sample. The best-fit Hubble constant from the combination of $H(z)$+JLA ($H_0=68.81^{+1.50}_{-1.49}$ km/s/Mpc) is well consistent with the results derived both by Planck 2013 and WMAP9, which is significantly different from the recent local measurement by Riess (2016).

astro-ph.CO↗
arXiv2018

Semantic Segmentation via Highly Fused Convolutional Network with Multiple Soft Cost Functions

Semantic image segmentation is one of the most challenged tasks in computer vision. In this paper, we propose a highly fused convolutional network, which consists of three parts: feature downsampling, combined feature upsampling and multiple predictions. We adopt a strategy of multiple steps of upsampling and combined feature maps in pooling layers with its corresponding unpooling layers. Then we bring out multiple pre-outputs, each pre-output is generated from an unpooling layer by one-step upsampling. Finally, we concatenate these pre-outputs to get the final output. As a result, our proposed network makes highly use of the feature information by fusing and reusing feature maps. In addition, when training our model, we add multiple soft cost functions on pre-outputs and final outputs. In this way, we can reduce the loss reduction when the loss is back propagated. We evaluate our model on three major segmentation datasets: CamVid, PASCAL VOC and ADE20K. We achieve a state-of-the-art performance on CamVid dataset, as well as considerable improvements on PASCAL VOC dataset and ADE20K dataset

cs.CV↗
arXiv2017

Spin Density wave instability in a ferromagnet

Ferromagnetic (FM) and incommensurate spin-density wave (ISDW) states are an unusual set of competing magnetic orders that are seldom observed in the same material without application of a polarizing magnetic field. We report, for the first time, the discovery of an ISDW state that is derived from a FM ground state through a Fermi surface (FS) instability in Fe$_3$Ga$_4$. This was achieved by combining neutron scattering experiments with first principles simulations. Neutron diffraction demonstrates that Fe$_3$Ga$_4$ is in an ISDW state at intermediate temperatures and that there is a conspicuous re-emergence of ferromagnetism above 360 K. First principles calculations show that the ISDW ordering wavevector is in excellent agreement with a prominent nesting condition in the spin-majority FS demonstrating the discovery of a novel instability for FM metals; ISDW formation due to Fermi surface nesting in a spin-polarized Fermi surface.

cond-mat.str-el↗
arXiv2017

Coarse-Grained Descriptions of Dynamics for Networks with both Intrinsic and Structural Heterogeneities

Finding accurate reduced descriptions for large, complex, dynamically evolving networks is a crucial enabler to their simulation, analysis, and, ultimately, design. Here we propose and illustrate a systematic and powerful approach to obtaining good collective coarse-grained observables-- variables successfully summarizing the detailed state of such networks. Finding such variables can naturally lead to successful reduced dynamic models for the networks. The main premise enabling our approach is the assumption that the behavior of a node in the network depends (after a short initial transient) on the node identity: a set of descriptors that quantify the node properties, whether intrinsic (e.g. parameters in the node evolution equations) or structural (imparted to the node by its connectivity in the particular network structure). The approach creates a natural link with modeling and "computational enabling technology" developed in the context of Uncertainty Quantification. In our case, however, we will not focus on ensembles of different realizations of a problem, each with parameters randomly selected from a distribution. We will instead study many coupled heterogeneous units, each characterized by randomly assigned (heterogeneous) parameter value(s). One could then coin the term Heterogeneity Quantification for this approach, which we illustrate through a model dynamic network consisting of coupled oscillators with one intrinsic heterogeneity (oscillator individual frequency) and one structural heterogeneity (oscillator degree in the undirected network). The computational implementation of the approach, its shortcomings and possible extensions are also discussed.

nlin.CD↗
arXiv2017

Chiral Magnetic Effect in a Lattice Model

We study analytically the one-loop contribution to the Chiral Magnetic Effect(CME) using lattice regularization with a Wilson fermion field. In the continuum limit, we find that the chiral magnetic current vanishes at nonzero temperature but emerges at zero temperature consistent with that found by Pauli-Villas regularization. For finite lattice size, however, the chiral magnetic current is nonvanishing at nonzero temperature. But the numerical vaule of the coefficient of CME current is very small compared with that extracted from the full QCD simulation for the same lattice parameters. The possibility of higher order corrections from QCD dynamics is also assessed.

hep-ph↗
arXiv2017

The Subtleties of the Wigner Function Formulation of the Chiral Magnetic Effect

We assess the applicability of the Wigner function formulation in its present form to the chiral Magnetic Effect and noted some issues regarding the conservation and the consistency of the electric current in the presence of an inhomogeneous and time dependent axial chemical potential. The problems are rooted in the ultraviolet divergence of the underlying field theory associated with the axial anomaly and can be fixed with the Pauli-Villars regularization of the Wigner function.

hep-ph↗
arXiv2016

Metric spaces with complexity of the smallest infinite ordinal number

In this paper, we are concerned with the study on metric spaces with complexity of the smallest infinite ordinal number. We give equivalent formulations of the definition of metric spaces with complexity of the smallest infinite ordinal number and prove that the exact complexity of the finite product of wreath product is the smallest infinite ordinal number. Consequently, we obtain the complexity of (ZwrZ)wrZ is w+1.

math.GR↗
arXiv2016

Holographic Schwinger effect in a confining D3-brane background with chemical potential

Using the AdS/CFT correspondence, we investigate the Schwinger effect in a confining D3-brane background with chemical potential. The potential between a test particle pair on the D3-brane in an external electric field is obtained. The critical field $E_c$ in this case is calculated. Also, we apply numerical method to evaluate the production rate for various cases. The results imply that the presence of chemical potential tends to suppress the pair production effect.

hep-ph↗
arXiv2016

R^2 corrections to the jet quenching parameter

A calculation of the $R^2$ corrections to the jet quenching parameter from AdS/CFT correspondence is presented. These corrections are related to curvature-squared corrections in the corresponding gravity dual. It is shown that the corrections will increase or decrease the jet quenching parameter depending on the coefficients of the high curvature terms.

hep-ph↗
arXiv2015

Melting temperature of heavy quarkonium with a holographic potential up to sub-leading order

A calculation of the melting temperatures of heavy quarkonium states with the holographic potential was introduced in a previous work. In this paper, we consider the holographic potential at sub-leading order, which permits finite coupling corrections to be taken into account. It is found that this correction lowers the dissociation temperatures of heavy quarkonium.

hep-ph↗
arXiv2015

Volterra-composition operators on the weighted Bergman space with exponential type weights

The properties of Volterra-composition operators on the weighted Bergman space with exponential type weights are investigated in this paper. We state some necessary and sufficient conditions that a Volterra-composition operator from the weighted Bergman space to Bloch type space ( or little Bloch type space ) must satisfy for the Volterra-composition operator to be bounded or compact.

math.FA↗
arXiv2015

Electrical Control of Strong Spin-Phonon Coupling in a Carbon Nanotube

We describe an approach to electrically control the strong interaction between a single electron spin and the vibrational motion of a suspended carbon nanotube resonator. The strength of the deflection-induced spin-phonon coupling is dependent on the wavefunction of the electron confined in a lateral carbon nanotube quantum dot. An electrical field along the nanotube shifts the effective center of the quantum dot, leading to the corresponding modification of the spin-phonon strength. Numerical simulations with experimentally reachable parameters show that high fidelity quantum state transfer between mechanical and spin qubits driven by electrical pulses is feasible. Our results form the basis for the fully electrical control of the coherent interconvertion between light and spin qubits and for manufacturing electrically driven quantum information processing systems.

quant-ph↗
arXiv2015

A Population Model for the Academic Ecosystem

In recent times, the academic ecosystem has seen a tremendous growth in number of authors and publications. While most temporal studies in this area focus on evolution of co-author and citation network structure, this systemic inflation has received very little attention. In this paper, we address this issue by proposing a population model for academia, derived from publication records in the Computer Science domain. We use a generalized branching process as an overarching framework, which enables us to describe the evolution and composition of the research community in a systematic manner. Further, the observed patterns allow us to shed light on researchers' lifecycle encompassing arrival, academic life expectancy, activity, productivity and offspring distribution in the ecosystem. We believe such a study will help develop better bibliometric indices which account for the inflation, and also provide insights into sustainable and efficient resource management for academia.

cs.SI↗
arXiv2014

Contractive De-noising Auto-encoder

Auto-encoder is a special kind of neural network based on reconstruction. De-noising auto-encoder (DAE) is an improved auto-encoder which is robust to the input by corrupting the original data first and then reconstructing the original input by minimizing the reconstruction error function. And contractive auto-encoder (CAE) is another kind of improved auto-encoder to learn robust feature by introducing the Frobenius norm of the Jacobean matrix of the learned feature with respect to the original input. In this paper, we combine de-noising auto-encoder and contractive auto- encoder, and propose another improved auto-encoder, contractive de-noising auto- encoder (CDAE), which is robust to both the original input and the learned feature. We stack CDAE to extract more abstract features and apply SVM for classification. The experiment result on benchmark dataset MNIST shows that our proposed CDAE performed better than both DAE and CAE, proving the effective of our method.

cs.LG↗
arXiv2014

Some Comments on the Holographic Heavy Quark Potential in a Thermal Bath

The heavy quark potential of a thermal Yang-Mills theory in strong coupling limit is explored in terms of the holographic principle. With a fairly general AdS/QCD metric the heavy quark potential displays a kink-like screening in the plasma phase. This behavior may conflict the causality of a field theory that is mathematically equivalent to the thermal Yang-Mills.

hep-ph↗
arXiv2013

Spectral Classification Using Restricted Boltzmann Machine

In this study, a novel machine learning algorithm, restricted Boltzmann machine (RBM), is introduced. The algorithm is applied for the spectral classification in astronomy. RBM is a bipartite generative graphical model with two separate layers (one visible layer and one hidden layer), which can extract higher level features to represent the original data. Despite generative, RBM can be used for classification when modified with a free energy and a soft-max function. Before spectral classification, the original data is binarized according to some rule. Then we resort to the binary RBM to classify cataclysmic variables (CVs) and non-CVs (one half of all the given data for training and the other half for testing). The experiment result shows state-of-the-art accuracy of 100%, which indicates the efficiency of the binary RBM algorithm.

cs.LG↗