arXiv subjects

Yue Wu

Publications and source records attributed to Yue Wu.

At least 415 records · Page 23Linked to original sources

arXiv2016

LAMOST observations in the Kepler field. Analysis of the stellar parameters measured with the LASP based on the low-resolution spectra

All of the 14 subfields of the Kepler field have been observed at least once with the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST, Xinglong Observatory, China) during the 2012-2014 observation seasons. There are 88,628 reduced spectra with SNR$_g$ (signal-to-noise ratio in g band) $\geq$ 6 after the first round (2012-2014) of observations for the LAMOST-Kepler project (LK-project). By adopting the upgraded version of the LAMOST Stellar Parameter pipeline (LASP), we have determined the atmospheric parameters ($T_{\rm eff}$ , $\log g$, and $\rm [Fe/H]$) and heliocentric radial velocity $v_{\rm rad}$ for 51,406 stars with 61,226 spectra. Compared with atmospheric parameters derived from both high-resolution spectroscopy and asteroseismology method for common stars in Huber et al. (2014), an external calibration of LASP atmospheric parameters was made, leading to the determination of external errors for the giants and dwarfs, respectively. Multiple spectroscopic observations for the same objects of the LK-project were used to estimate the internal uncertainties of the atmospheric parameters as a function of SNR$_g$ with the unbiased estimation method. The LASP atmospheric parameters were calibrated based on both the external and internal uncertainties for the giants and dwarfs, respectively. A general statistical analysis of the stellar parameters leads to discovery of 106 candidate metal-poor stars, 9 candidate very metal-poor stars, and 18 candidate high-velocity stars. Fitting formulae were obtained segmentally for both the calibrated atmospheric parameters of the LK-project and the KIC parameters with the common stars. The calibrated atmospheric parameters and radial velocities of the LK-project will be useful for studying stars in the Kepler field.

astro-ph.SR↗

arXiv2016

Moments of quantum Lévy areas using sticky shuffle Hopf algebras

We study a family of quantum analogs of Lévy's stochastic area for planar Brownian motion depending on a variance parameter $σ\geq 1$ which deform to the classical Lévy area as $σ\rightarrow\infty$. They are defined as second rank iterated stochastic integrals against the components of planar Brownian motion, which are one-dimensional Brownian motions satisfying Heisnberg-type commutation relations. Such iterated integrals can be multiplied using the sticky shuffle product determined by the underlying Itô algebra of stochastic differentials. We use the corresponding Hopf algebra structure to evaluate the moments of the quantum Lévy areas and study how they deform to their classical values, which are well known to be given essentially by the Euler numbers, in the infinite variance limit.

math.PR↗

arXiv2016

Anticipating Random Periodic Solutions--I. SDEs with Multiplicative Linear Noise

In this paper, we study the existence of random periodic solutions for semilinear stochastic differential equations. We identify them as solutions of coupled forward-backward infinite horizon stochastic integral equations (IHSIEs), using the "substitution theorem" of stochastic differential equations with anticipating initial conditions. In general, random periodic solutions and the solutions of IHSIEs, are anticipating. For the linear noise case, with the help of the exponential dichotomy given in the multiplicative ergodic theorem, we can identify them as the solutions of infinite horizon random integral equations (IHSIEs). We then solve a localised forward-backward IHRIE in $C(\mathbb{R}, L^2_{loc}(Ω))$ using an argument of truncations, the Malliavin calculus, the relative compactness of Wiener-Sobolev spaces in $C([0, T], L^2(Ω))$ and Schauder's fixed point theorem. We finally measurably glue the local solutions together to obtain a global solution in $C(\mathbb{R}, L^2(Ω))$. Thus we obtain the existence of a random periodic solution and a periodic measure.

math.PR↗

arXiv2016

Calibration of LAMOST Stellar Surface Gravities Using the Kepler Asteroseismic Data

Asteroseismology is a powerful tool to precisely determine the evolutionary status and fundamental properties of stars. With the unprecedented precision and nearly continuous photometric data acquired by the NASA Kepler mission, parameters of more than 10$^4$ stars have been determined nearly consistently. However, most studies still use photometric effective temperatures (Teff) and metallicities ([Fe/H]) as inputs, which are not sufficiently accurate as suggested by previous studies. We adopted the spectroscopic Teff and [Fe/H] values based on the LAMOST low-resolution spectra (R~1,800), and combined them with the global oscillation parameters to derive the physical parameters of a large sample of stars. Clear trends were found between Δlogg(LAMOST - seismic) and spectroscopic Teff as well as logg, which may result in an overestimation of up to 0.5 dex for the logg of giants in the LAMOST catalog. We established empirical calibration relations for the logg values of dwarfs and giants. These results can be used for determining the precise distances to these stars based on their spectroscopic parameters.

astro-ph.SR↗

arXiv2016

Facial Landmark Detection with Tweaked Convolutional Neural Networks

We present a novel convolutional neural network (CNN) design for facial landmark coordinate regression. We examine the intermediate features of a standard CNN trained for landmark detection and show that features extracted from later, more specialized layers capture rough landmark locations. This provides a natural means of applying differential treatment midway through the network, tweaking processing based on facial alignment. The resulting Tweaked CNN model (TCNN) harnesses the robustness of CNNs for landmark detection, in an appearance-sensitive manner without training multi-part or multi-scale models. Our results on standard face landmark detection and face verification benchmarks show TCNN to surpasses previously published performances by wide margins.

cs.CV↗

arXiv2016

FGK 22 $μ$m Excess Stars in LAMOST DR2 Stellar Catalog

Since the release of LAMOST (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope) catalog, we have the opportunity to use the LAMOST DR2 stellar catalog and \emph{WISE All-sky catalog} to search for 22 $μ$m excess candidates. In this paper, we present 10 FGK candidates which show an excess in the infrared (IR) at 22 $μ$m. The ten sources are all the newly identified 22 $μ$m excess candidates. Of these 10 stars, 5 stars are F type and 5 stars are G type. The criterion for selecting candidates is $K_s-[22]_{μm}\geq0.387$. In addition, we present the spectral energy distributions (SEDs) covering wavelength from optical to mid-infrared band. Most of them show an obvious excess from 12 $μ$m band and three candidates even show excess from 3.4 $μ$m. To characterize the amount of dust, we also estimate the fractional luminosity of ten 22 $μ$m excess candidates.

astro-ph.SR↗

arXiv2016

Spin-valley qubit in nanostructures of monolayer semiconductors: Optical control and hyperfine interaction

We investigate the optical control possibilities of spin-valley qubit carried by single electrons localized in nanostructures of monolayer TMDs, including small quantum dots formed by lateral heterojunction and charged impurities. The quantum controls are discussed when the confinement induces valley hybridization and when the valley hybridization is absent. We show that the bulk valley and spin optical selection rules can be inherited in different forms in the two scenarios, both of which allow the definition of spin-valley qubit with desired optical controllability. We also investigate nuclear spin induced decoherence and quantum control of electron-nuclear spin entanglement via intervalley terms of the hyperfine interaction. Optically controlled two-qubit operations in a single quantum dot are discussed.

cond-mat.mes-hall↗

arXiv2015

Large-scale Online Feature Selection for Ultra-high Dimensional Sparse Data

Feature selection with large-scale high-dimensional data is important yet very challenging in machine learning and data mining. Online feature selection is a promising new paradigm that is more efficient and scalable than batch feature section methods, but the existing online approaches usually fall short in their inferior efficacy as compared with batch approaches. In this paper, we present a novel second-order online feature selection scheme that is simple yet effective, very fast and extremely scalable to deal with large-scale ultra-high dimensional sparse data streams. The basic idea is to improve the existing first-order online feature selection methods by exploiting second-order information for choosing the subset of important features with high confidence weights. However, unlike many second-order learning methods that often suffer from extra high computational cost, we devise a novel smart algorithm for second-order online feature selection using a MaxHeap-based approach, which is not only more effective than the existing first-order approaches, but also significantly more efficient and scalable for large-scale feature selection with ultra-high dimensional sparse data, as validated from our extensive experiments. Impressively, on a billion-scale synthetic dataset (1-billion dimensions, 1-billion nonzero features, and 1-million samples), our new algorithm took only 8 minutes on a single PC, which is orders of magnitudes faster than traditional batch approaches. \url{http://arxiv.org/abs/1409.7794}

cs.LG↗

arXiv2015

LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks

Logo detection from images has many applications, particularly for brand recognition and intellectual property protection. Most existing studies for logo recognition and detection are based on small-scale datasets which are not comprehensive enough when exploring emerging deep learning techniques. In this paper, we introduce "LOGO-Net", a large-scale logo image database for logo detection and brand recognition from real-world product images. To facilitate research, LOGO-Net has two datasets: (i)"logos-18" consists of 18 logo classes, 10 brands, and 16,043 logo objects, and (ii) "logos-160" consists of 160 logo classes, 100 brands, and 130,608 logo objects. We describe the ideas and challenges for constructing such a large-scale database. Another key contribution of this work is to apply emerging deep learning techniques for logo detection and brand recognition tasks, and conduct extensive experiments by exploring several state-of-the-art deep region-based convolutional networks techniques for object detection tasks. The LOGO-net will be released at http://logo-net.org/

cs.CV↗

arXiv2015

The thermal and electrical properties of the promising semiconductor MXene Hf2CO2

In this work, we investigate the thermal and electrical properties of oxygen-functionalized M2CO2 (M = Ti, Zr, Hf) MXenes using first-principles calculations. Hf2CO2 is found to exhibit a thermal conductivity better than MoS2 and phosphorene. The room temperature thermal conductivity along the armchair direction is determined to be 86.25-131.2 Wm-1K-1 with a flake length of 5-100 um, and the corresponding value in the zigzag direction is approximately 42% of that in the armchair direction. Other important thermal properties of M2CO2 are also considered, including their specific heat and thermal expansion coefficients. The theoretical room temperature thermal expansion coefficient of Hf2CO2 is 6.094x10-6 K-1, which is lower than that of most metals. Moreover, Hf2CO2 is determined to be a semiconductor with a band gap of 1.657 eV and to have high and anisotropic carrier mobility. At room temperature, the Hf2CO2 hole mobility in the armchair direction (in the zigzag direction) is determined to be as high as 13.5x103 cm2V-1s-1 (17.6x103 cm2V-1s-1), which is comparable to that of phosphorene. Broader utilization of Hf2CO2 as a material for nanoelectronics is likely because of its moderate band gap, satisfactory thermal conductivity, low thermal expansion coefficient, and excellent carrier mobility. The corresponding thermal and electrical properties of Ti2CO2 and Zr2CO2 are also provided here for comparison. Notably, Ti2CO2 presents relatively low thermal conductivity and much higher carrier mobility than Hf2CO2, which is an indication that Ti2CO2 may be used as an efficient thermoelectric material.

cond-mat.mtrl-sci↗

arXiv2015

Determining the local dark matter density with LAMOST data

Measurement of the local dark matter density plays an important role in both Galactic dynamics and dark matter direct detection experiments. However, the estimated values from previous works are far from agreeing with each other. In this work, we provide a well-defined observed sample with 1427 G \& K type main-sequence stars from the LAMOST spectroscopic survey, taking into account selection effects, volume completeness, and the stellar populations. We apply a vertical Jeans equation method containing a single exponential stellar disk, a razor thin gas disk, and a constant dark matter density distribution to the sample, and obtain a total surface mass density of $\rm {78.7 ^{+3.9}_{-4.7}\ M_{\odot}\ pc^{-2}}$ up to 1 kpc and a local dark matter density of $0.0159^{+0.0047}_{-0.0057}\,\rm M_{\odot}\,\rm pc^{-3}$. We find that the sampling density (i.e. number of stars per unit volume) of the spectroscopic data contributes to about two-thirds of the uncertainty in the estimated values. We discuss the effect of the tilt term in the Jeans equation and find it has little impact on our measurement. Other issues, such as a non-equilibrium component due to perturbations and contamination by the thick disk population, are also discussed.

astro-ph.GA↗

arXiv2015

The K giant stars from the LAMOST survey data II: the Hercules stream in radial migration

We estimate the age for the individual stars located at the lower part of the red giant branch from the LAMOST DR2 K giant sample. Taking into account the selection effects and the volume completeness, the age--metallicity map for the stars located between 0.3 and 1.5 kpc from the Sun is obtained. A significant substructure (denoted as the \it{narrow stripe}) located from (age, [Fe/H])$\sim$(5, 0.4) to (10 Gyr, -0.4 dex) in the age--metallicity map is clearly identified. Moreover, the \it{narrow stripe} stars are found the dominate contributors to several velocity substructures, including the well-known Hercules stream. The substantially large difference between the observed guiding-center radii and the birth radii inferred from the age--metallicity relation is evident that the \it{narrow stripe} stars have been radially migrated from about R$\sim4$ kpc to the solar neighborhood. This implies that the Hercules stream may not be owe to the resonance associated with the bar, but may be the kinematic imprint of the inner disk and later moved out due to radial migration. We estimate that the traveling speed of the radial migration are roughly 1.1$\pm0.1$ kpc Gyr$^{-1}$, equivalent with about $1.1\pm0.1$ km s$^{-1}$. This is in agreement with the median $v_R$ of $2.6^{+1.8}_{-1.9}$ km s$^{-1}$ of the \it{narrow stripe}. We also obtain that about one third stars in the solar neighborhood are radially migrated from around 4 kpc. Finally, we find that the radial migration does not lead to additional disk thickening according to the distribution of $z_{max}$.

astro-ph.GA↗

arXiv2015

Validation of LAMOST Stellar Parameters with the PASTEL Catalog

Recently the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) published its first data release (DR1), which is ranked as the largest stellar spectra dataset in the world so far. We combine the PASTEL catalog and SIMBAD radial velocities as a testing standard to validate the DR1 stellar parameters (effective temperature $T_{\mathrm{eff}}$, surface gravity $\log g$, metallicity $\mathrm{[Fe/H]}$ and radial velocity $V_{\mathrm{r}}$). Through cross-identification of the DR1 catalogs and the PASTEL catalog, we obtain a preliminary sample of 422 stars. After removal of stellar parameter measurements from problematic spectra and applying effective temperature constraints to the sample, we compare the stellar parameters from DR1 with those from PASTEL and SIMBAD to prove that the DR1 results are reliable in restricted $T_{\mathrm{eff}}$ ranges. We derive standard deviations of 110 K, 0.19 dex, 0.11 dex and 4.91 $\mathrm{km\,s^{-1}}$ , for $T_{\mathrm{eff}}$, $\log g$, $[\mathrm{Fe/H}]$ when $T_{\mathrm{eff}}<8000\,\mathrm{K}$, and for $V_{\mathrm{r}}$ when $T_{\mathrm{eff}}<10000\,\mathrm{K}$, respectively. Systematic errors are negligible except for that of $V_{\mathrm{r}}$. Besides, metallicities in DR1 are systematically higher than those in PASTEL, in the range of PASTEL $[\mathrm{Fe/H}]<-1.5$.

astro-ph.SR↗

arXiv2015

The stellar kinematics in the solar neighborhood from LAMOST data

We use about 200,000 FGK type main-sequence stars from the LAMOST DR1 data to map the local stellar kinematics. With the velocity de-projection technique, we are able to derive the averaged 3 dimensional velocity and velocity ellipsoids using only the line-of-sight velocity for the stars with various effective temperatures within $100 < |z| < 500$ pc. Using the mean velocities of the cool stars, we derive the solar motion of ($U_{\!\odot}$, $V_{\!\odot}$, $W_{\!\odot}$)=(9.58$\pm2.39$, 10.52$\pm1.96$, 7.01$\pm1.67$)$km\,s^{-1}$ with respect to the local standard of rest. Moreover, we find that the stars with ${T}_{\rm eff}>6000$K show a net asymmetric motion of $\sim3 km\,s^{-1}$ in $\langle W\rangle$ compared to the stars with ${T}_{\rm eff}<6000$K. And their azimuthal velocity increases when $|z|$ increases. This peculiar motion in the warmer stars is likely because they are young and not completely relaxed, although other reasons, such as the resonance induced by the central rotating bar or the spiral structures, and the perturbation of the merging dwarf galaxies, can not be ruled out. The derived velocity dispersions and cross terms for the data are approximately consistent with previous studies. We also find that the vertical gradients of $σ_{U}$ and $σ_V$ are larger than that of $σ_W$ . And the vertical gradient of $σ_U$ shows clear correlation with ${T}_{\rm eff}$, while the other two do not. Finally, our sample shows vertex deviation of about 11$^\circ$, at $300 < |z| < 500$pc, but roughly zero at $100 < |z| < 300$pc.

astro-ph.GA↗

arXiv2015

Model-Free 3D Reconstruction of Weld Joint Using Laser Scanning

This article presents a novel utilization of the concept of entropy in information theory to model-free 3D reconstruction of weld joint in presence of noise. We show that our formulation attains its global minimum at the upper edge of this joint. This property significantly simplifies the extraction of this welding joint. Furthermore, we present an approach to compute the volume of this extracted space to facilitate the monitoring of the progress of the welding task. Moreover, we provide a preliminary analysis of the effect of variation of the noise on the extraction process of this space to realize the impact of this noise on the computation of its area and volume.

cs.RO↗

arXiv2015

An Adaptive Sampling Approach to 3D Reconstruction of Weld Joint

We present an adaptive sampling approach to 3D reconstruction of the welding joint using the point cloud that is generated by a laser sensor. We start with a randomized strategy to approximate the surface of the volume of interest through selection of a number of pivotal candidates. Furthermore, we introduce three proposal distributions over the neighborhood of each of these pivots to adaptively sample from their neighbors to refine the original randomized approximation to incrementally reconstruct this welding space. We prevent our algorithm from being trapped in a neighborhood via permanently labeling the visited samples. In addition, we accumulate the accepted candidates along with their selected neighbors in a queue structure to allow every selected sample to contribute to the evolution of the reconstructed welding space as the algorithm progresses. We analyze the performance of our adaptive sampling algorithm in contrast to the random sampling, with and without replacement, to show a significant improvement in total number of samples that are drawn to identify the region of interest, thereby expanding upon neighboring samples to extract the entire region in a fewer iterations and a shorter computation time.

cs.RO↗

arXiv2015

Member candidates of the star clusters from LAMOST DR2 data

In this work, we provide 2189 photometric- and kinematic-selected member candidates of 24 star clusters from the LAMOST DR2 catalog. We perform two-step membership identification: selection along the stellar track in the color-magnitude diagram, i.e., photometric identification, and the selection from the distribution of radial velocities, i.e. the kinematic identification. We find that the radial velocity from the LAMOST data are very helpful in the membership identification. The mean probability of membership is 40\% for the radial velocity selected sample. With these 24 star clusters, we investigate the performance of the radial velocity and metallicity estimated in the LAMOST pipeline. We find that the systematic offset in radial velocity and metallicity are $0.85\pm1.26$\,\kms\ and $-0.08\pm0.04$\,dex, with dispersions of $5.47_{-0.71}^{+1.16}$\,\kms\ and $0.13_{-0.02}^{+0.04}$\,dex, respectively. Finally, we propose that the photometric member candidates of the clusters covered by the LAMOST footprints should be assigned higher priority so that more member stars can be observed.

astro-ph.SR↗

arXiv2015

19 low mass hyper-velocity star candidates from the first data release of LAMOST survey

Hyper-velocity stars are believed to be ejected out from the Galactic center through dynamical interactions between (binary) stars and the central massive black hole(s). In this paper, we report 19 low mass F/G/K type hyper-velocity star candidates from over one mil- lion stars of the first data release of the LAMOST general survey. We determine the unbound probability for each candidate using a Monte-Carlo simulation by assuming a non-Gaussian proper-motion error distribution, Gaussian heliocentric distance and radial velocity error dis- tributions. The simulation results show that all the candidates have unbound possibilities over 50% as expected, and one of them may even exceed escape velocity with over 90% probabili- ty. In addition, we compare the metallicities of our candidates with the metallicity distribution functions of the Galactic bulge, disk, halo and globular cluster, and conclude that the Galactic bulge or disk is likely the birth place for our candidates.

astro-ph.GA↗