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Jun Yan

Publications and source records attributed to Jun Yan.

At least 289 records · Page 16Linked to original sources
arXiv2016

Automated Selection of r for the r Largest Order Statistics Approach with Adjustment for Sequential Testing

The r largest order statistics approach is widely used in extreme value analysis because it may use more information from the data than just the block maxima. In practice, the choice of r is critical. If r is too large, bias can occur; if too small, the variance of the estimator can be high. The limiting distribution of the r largest order statistics, denoted by GEVr, extends that of the block maxima. Two specification tests are proposed to select r sequentially. The first is a score test for the GEVr distribution. Due to the special characteristics of the GEVr distribution, the classical chi-square asymptotics cannot be used. The simplest approach is to use the parametric bootstrap, which is straightforward to implement but computationally expensive. An alternative fast weighted bootstrap or multiplier procedure is developed for computational efficiency. The second test uses the difference in estimated entropy between the GEVr and GEV(r-1) models, applied to the r largest order statistics and the r-1 largest order statistics, respectively. The asymptotic distribution of the difference statistic is derived. In a large scale simulation study, both tests held their size and had substantial power to detect various misspecification schemes. A new approach to address the issue of multiple, sequential hypotheses testing is adapted to this setting to control the false discovery rate or familywise error rate. The utility of the procedures is demonstrated with extreme sea level and precipitation data.

stat.ME↗
arXiv2016

Automated Threshold Selection for Extreme Value Analysis via Goodness-of-Fit Tests with Application to Batched Return Level Mapping

Threshold selection is a critical issue for extreme value analysis with threshold-based approaches. Under suitable conditions, exceedances over a high threshold have been shown to follow the generalized Pareto distribution (GPD) asymptotically. In practice, however, the threshold must be chosen. If the chosen threshold is too low, the GPD approximation may not hold and bias can occur. If the threshold is chosen too high, reduced sample size increases the variance of parameter estimates. To process batch analyses, commonly used selection methods such as graphical diagnosis are subjective and cannot be automated, while computational methods may not be feasible. We propose to test a set of thresholds through the goodness-of-fit of the GPD for the exceedances, and select the lowest one, above which the data provides adequate fit to the GPD. Previous attempts in this setting are not valid due to the special feature that the multiple tests are done in an ordered fashion. We apply two recently available stopping rules that control the false discovery rate or familywise error rate to ordered goodness-of-fit tests to automate threshold selection. Various model specification tests such as the Cramer-von Mises, Anderson-Darling, Moran's, and a score test are investigated. The performance of the method is assessed in a large scale simulation study that mimics practical return level estimation. This procedure was repeated at hundreds of sites in the western US to generate return level maps of extreme precipitation.

stat.ME↗
arXiv2016

Raman scattering and anomalous Stokes anti-Stokes ratio in MoTe2 atomic layers

Stokes and anti-Stokes Raman scattering are performed on atomic layers of hexagonal molybdenum ditelluride (MoTe2), a prototypical transition metal dichalcogenide (TMDC) semiconductor. The data reveal all six types of zone center optical phonons, along with their corresponding Davydov splittings, which have been challenging to see in other TMDCs. We discover that the anti-Stokes Raman intensity of the low energy layer-breathing mode becomes more intense than the Stokes peak under certain experimental conditions, and find the effect to be tunable by excitation frequency and number of atomic layers. These observations are interpreted as a result of resonance effects arising from the C excitons in the vicinity of the Brillouin zone center in the photon-electron-phonon interaction process.

cond-mat.mtrl-sci↗
arXiv2016

Inversion-symmetry-breaking-activated shear Raman bands in $T'$-MoTe$_2$

Type-II Weyl fermion nodes, located at the touching points between electron and hole pockets, have been recently predicted to occur in distorted octahedral ($T'$) transition metal dichalcogenide semimetals, contingent upon the condition that the layered crystal has the noncentrosymmetric orthorhombic ($T'_{or}$) stacking. Here, we report on the emergence of two shear Raman bands activated by inversion symmetry breaking in $T'$-MoTe$_2$ due to sample cooling. Polarization and crystal orientation resolved measurements further point to a phase transition from the monoclinic ($T'_{mo}$) structure to the desired $T'_{or}$ lattice. These results provide spectroscopic evidence that low-temperature $T'$-MoTe$_2$ is suitable for probing type-II Weyl physics.

cond-mat.mes-hall↗
arXiv2015

Statistical Methods and Computing for Big Data

Big data are data on a massive scale in terms of volume, intensity, and complexity that exceed the capacity of standard software tools. They present opportunities as well as challenges to statisticians. The role of computational statisticians in scientific discovery from big data analyses has been under-recognized even by peer statisticians. This article reviews recent methodological and software developments in statistics that address the big data challenges. Methodologies are grouped into three classes: subsampling-based, divide and conquer, and sequential updating for stream data. Software review focuses on the open source R and R packages, covering recent tools that help break the barriers of computer memory and computing power. Some of the tools are illustrated in a case study with a logistic regression for the chance of airline delay.

stat.CO↗
arXiv2015

A two-step approach to model precipitation extremes in California based on max-stable and marginal point processes

In modeling spatial extremes, the dependence structure is classically inferred by assuming that block maxima derive from max-stable processes. Weather stations provide daily records rather than just block maxima. The point process approach for univariate extreme value analysis, which uses more historical data and is preferred by some practitioners, does not adapt easily to the spatial setting. We propose a two-step approach with a composite likelihood that utilizes site-wise daily records in addition to block maxima. The procedure separates the estimation of marginal parameters and dependence parameters into two steps. The first step estimates the marginal parameters with an independence likelihood from the point process approach using daily records. Given the marginal parameter estimates, the second step estimates the dependence parameters with a pairwise likelihood using block maxima. In a simulation study, the two-step approach was found to be more efficient than the pairwise likelihood approach using only block maxima. The method was applied to study the effect of El Niño-Southern Oscillation on extreme precipitation in California with maximum daily winter precipitation from 35 sites over 55 years. Using site-specific generalized extreme value models, the two-step approach led to more sites detected with the El Niño effect, narrower confidence intervals for return levels and tighter confidence regions for risk measures of jointly defined events.

stat.ME↗
arXiv2015

Online Updating of Statistical Inference in the Big Data Setting

We present statistical methods for big data arising from online analytical processing, where large amounts of data arrive in streams and require fast analysis without storage/access to the historical data. In particular, we develop iterative estimating algorithms and statistical inferences for linear models and estimating equations that update as new data arrive. These algorithms are computationally efficient, minimally storage-intensive, and allow for possible rank deficiencies in the subset design matrices due to rare-event covariates. Within the linear model setting, the proposed online-updating framework leads to predictive residual tests that can be used to assess the goodness-of-fit of the hypothesized model. We also propose a new online-updating estimator under the estimating equation setting. Theoretical properties of the goodness-of-fit tests and proposed estimators are examined in detail. In simulation studies and real data applications, our estimator compares favorably with competing approaches under the estimating equation setting.

stat.CO↗
arXiv2015

Variational principle for contact Tonelli Hamiltonian systems

We establish an implicit variational principle for the equations of the contact flow generated by the Hamiltonian $H(x,u,p)$ with respect to the contact 1-form $α=du-pdx$ under Tonelli and Osgood growth assumptions. It is the first step to generalize Mather's global variational method from the Hamiltonian dynamics to the contact Hamiltonian dynamics.

math.DS↗
arXiv2015

Photometric Variability in the CSTAR Field: Results From the 2008 Data Set

The Chinese Small Telescope ARray (CSTAR) is the first telescope facility built at Dome A, Antarctica. During the 2008 observing season, the installation provided long-baseline and high-cadence photometric observations in the i-band for 18,145 targets within 20 deg2 CSTAR field around the South Celestial Pole for the purpose of monitoring the astronomical observing quality of Dome A and detecting various types of photometric variability. Using sensitive and robust detection methods, we discover 274 potential variables from this data set, 83 of which are new discoveries. We characterize most of them, providing the periods, amplitudes and classes of variability. The catalog of all these variables is presented along with the discussion of their statistical properties.

astro-ph.SR↗
arXiv2015

Eclipsing Binaries From the CSTAR Project at Dome A, Antarctica

The Chinese Small Telescope ARray (CSTAR) has observed an area around the Celestial South Pole at Dome A since 2008. About $20,000$ light curves in the i band were obtained lasting from March to July, 2008. The photometric precision achieves about 4 mmag at i = 7.5 and 20 mmag at i = 12 within a 30 s exposure time. These light curves are analyzed using Lomb--Scargle, Phase Dispersion Minimization, and Box Least Squares methods to search for periodic signals. False positives may appear as a variable signature caused by contaminating stars and the observation mode of CSTAR. Therefore the period and position of each variable candidate are checked to eliminate false positives. Eclipsing binaries are removed by visual inspection, frequency spectrum analysis and locally linear embedding technique. We identify 53 eclipsing binaries in the field of view of CSTAR, containing 24 detached binaries, 8 semi-detached binaries, 18 contact binaries, and 3 ellipsoidal variables. To derive the parameters of these binaries, we use the Eclipsing Binaries via Artificial Intelligence (EBAI) method. The primary and the secondary eclipse timing variations (ETVs) for semi-detached and contact systems are analyzed. Correlated primary and secondary ETVs confirmed by false alarm tests may indicate an unseen perturbing companion. Through ETV analysis, we identify two triple systems (CSTAR J084612.64-883342.9 and CSTAR J220502.55-895206.7). The orbital parameters of the third body in CSTAR J220502.55-895206.7 are derived using a simple dynamical model.

astro-ph.SR↗
arXiv2015

Antenna enhanced graphene THz emitter and detector

Recent intense electrical and optical studies of graphene have pushed the material to the forefront of optoelectronic research. Of particular interest is the few terahertz (THz) frequency regime where efficient light sources and highly sensitive detectors are very challenging to make. Here we present THz sources and detectors made with graphene field effect transistors (GFETs) enhanced by a double-patch antenna and an on-chip silicon lens. We report the first experimental observation of 1-3 THz radiation from graphene, as well as four orders of magnitude performance improvements in a GFET thermoelectric detector operating at ~2 THz. The quantitative analysis of the emitting power and its unusual charge density dependence indicate significant non-thermal contribution from the GFET. The polarization resolved detection measurements with different illumination geometries allow for detailed and quantitative analysis of various factors that contribute to the overall detector performance. Our experimental results represent a significant advance towards practically useful graphene THz devices.

cond-mat.mtrl-sci↗
arXiv2015

LINE: Large-scale Information Network Embedding

This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction. Most existing graph embedding methods do not scale for real world information networks which usually contain millions of nodes. In this paper, we propose a novel network embedding method called the "LINE," which is suitable for arbitrary types of information networks: undirected, directed, and/or weighted. The method optimizes a carefully designed objective function that preserves both the local and global network structures. An edge-sampling algorithm is proposed that addresses the limitation of the classical stochastic gradient descent and improves both the effectiveness and the efficiency of the inference. Empirical experiments prove the effectiveness of the LINE on a variety of real-world information networks, including language networks, social networks, and citation networks. The algorithm is very efficient, which is able to learn the embedding of a network with millions of vertices and billions of edges in a few hours on a typical single machine. The source code of the LINE is available online.

cs.LG↗
arXiv2015

Helicity resolved Raman scattering of MoS2, MoSe2, WS2 and WSe2 atomic layers

The two-fold valley degeneracy in two-dimensional (2D) semiconducting transition metal dichalcogenides (TMDCs) (Mo,W)(S,Se)2 is suitable for "valleytronics", the storage and manupulation of information utilizing the valley degree of freedom. The conservation of luminescent photon helicity in these 2D crystal monolayers has been widely regarded as a benchmark indicator for charge carrier valley polarization. Here we perform helicity-resolved Raman scattering of the TMDC atomic layers. In drastic contrast to luminescence, the dominant first-order zone-center Raman bands, including the low energy breathing and shear modes as well as the higher energy optical phonons, are found to either maintain or completely switch the helicity of incident photons. These experimental observations, in addition to providing a useful tool for characterization of TMDC atomic layers, shed new light on the connection between photon helicity and valley polarization.

cond-mat.mes-hall↗
arXiv2014

A Novel Compressed Sensing Based Model for Reconstructing Sparse Signals Using Phase Sparse Character

Phase modulation is a commonly used modulation mode in digital communication, which usually brings phase sparsity to digital signals. It is naturally to connect the sparsity with the newly emerged theory of compressed sensing (CS), which enables sub-Nyquist sampling of high-bandwidth to sparse signals. For the present, applications of CS theory in communication field mainly focus on spectrum sensing, sparse channel estimation etc. Few of current researches take the phase sparse character into consideration. In this paper, we establish the novel model of phase modulation signals based on phase sparsity, and introduce CS theory to the phase domain. According to CS theory, rather than the bandwidth, the sampling rate required here is scaling with the symbol rate, which is usually much lower than the Nyquist rate. In this paper, we provide analytical support for the model, and simulations verify its validity.

cs.IT↗
arXiv2014

Automatic Modulation Recognition of PSK Signals Using Nonuniform Compressive Samples Based on High Order Statistics

Phase modulation is a commonly used modulation mode in digital communication, which usually brings phase sparsity to digital signals. It is naturally to connect the sparsity with the newly emerged theory of compressed sensing (CS), which enables sub-Nyquist sampling of high-bandwidth to sparse signals. For the present, applications of CS theory in communication field mainly focus on spectrum sensing, sparse channel estimation etc. Few of current researches take the phase sparse character into consideration. In this paper, we establish the novel model of phase modulation signals based on phase sparsity, and introduce CS theory to the phase domain. According to CS theory, rather than the bandwidth, the sampling rate required here is scaling with the symbol rate, which is usually much lower than the Nyquist rate. In this paper, we provide analytical support for the model, and simulations verify its validity.

cs.IT↗
arXiv2014

Automatic Modulation Recognition of PSK Signals with Sub-Nyquist Sampling Based on High Order Statistics

Sampling rate required in the Nth Power Nonlinear Transformation (NPT) method is typically much greater than Nyquist rate, which causes heavy burden for the Analog to Digital Converter (ADC). Taking advantage of the sparse property of PSK signals' spectrum under NPT, we develop the NPT method for PSK signals with Sub-Nyquist rate samples. In this paper, combined the NPT method with Compressive Sensing (CS) theory, frequency spectrum reconstruction of the Nth power nonlinear transformation of PSK signals is presented, which can be further used for AMR and rough estimations of unknown carrier frequency and symbol rate.

cs.IT↗
arXiv2014

Sensitive Room-Temperature Terahertz Detection via Photothermoelectric Effect in Graphene

Terahertz (THz) radiation has uses from security to medicine; however, sensitive room-temperature detection of THz is notoriously difficult. The hot-electron photothermoelectric effect in graphene is a promising detection mechanism: photoexcited carriers rapidly thermalize due to strong electron-electron interactions, but lose energy to the lattice more slowly. The electron temperature gradient drives electron diffusion, and asymmetry due to local gating or dissimilar contact metals produces a net current via the thermoelectric effect. Here we demonstrate a graphene thermoelectric THz photodetector with sensitivity exceeding 10 V/W (700 V/W) at room temperature and noise equivalent power less than 1100 pW/Hz^1/2 (20 pW/Hz^1/2), referenced to the incident (absorbed) power. This implies a performance which is competitive with the best room-temperature THz detectors for an optimally coupled device, while time-resolved measurements indicate that our graphene detector is eight to nine orders of magnitude faster than those. A simple model of the response, including contact asymmetries (resistance, work function and Fermi-energy pinning) reproduces the qualitative features of the data, and indicates that orders-of-magnitude sensitivity improvements are possible.

cond-mat.mes-hall↗
arXiv2014

Weak KAM theory for general Hamilton-Jacobi equations III: the variational principle under Osgood conditions

We consider the following evolutionary Hamilton-Jacobi equation with initial condition: \begin{equation*} \begin{cases} \partial_tu(x,t)+H(x,u(x,t),\partial_xu(x,t))=0,\\ u(x,0)=ϕ(x), \end{cases} \end{equation*} where $ϕ(x)\in C(M,\mathbb{R})$. Under some assumptions on the convexity of $H(x,u,p)$ with respect to $p$ and the Osgood growth of $H(x,u,p)$ with respect to $u$, we establish an implicitly variational principle and provide an intrinsic relation between viscosity solutions and certain minimal characteristics. Moreover, we obtain a representation formula of the viscosity solution of the evolutionary Hamilton-Jacobi equation.

math.AP↗