arXiv subjects

Yang Tang

Publications and source records attributed to Yang Tang.

106 records · Page 6Linked to original sources

arXiv2019

Doping-dependent phonon anomaly and charge-order phenomena in the HgBa$_{2}$CuO$_{4+δ}$ and HgBa$_{2}$CaCu$_{2}$O$_{6+δ}$

Using resonant X-ray diffraction and Raman spectroscopy, we study charge correlations and lattice dynamics in two model cuprates, HgBa$_{2}$CuO$_{4+δ}$ and HgBa$_{2}$CaCu$_{2}$O$_{6+δ}$. We observe a maximum of the characteristic charge order temperature around the same hole concentration ($p \approx 0.09$) in both compounds, and concomitant pronounced anomalies in the lattice dynamics that involve the motion of atoms in and/or adjacent to the CuO$_2$ layers. These anomalies are already present at room temperature, and therefore precede the formation of the static charge correlations, and we attribute them to an instability of the CuO$_2$ layers. Our finding implies that the charge order in the cuprates is an emergent phenomenon, driven by a fundamental variation in both lattice and electronic properties as a function of doping.

cond-mat.supr-con↗

arXiv2019

Clustering Airbnb Reviews

In the last decade, online customer reviews increasingly exert influence on consumers' decision when booking accommodation online. The renewal importance to the concept of word-of mouth is reflected in the growing interests in investigating consumers' experience by analyzing their online reviews through the process of text mining and sentiment analysis. A clustering approach is developed for Boston Airbnb reviews submitted in the English language and collected from 2009 to 2016. This approach is based on a mixture of latent variable models, which provides an appealing framework for handling clustered binary data. We address here the problem of discovering meaningful segments of consumers that are coherent from both the underlying topics and the sentiment behind the reviews. A penalized mixture of latent traits approach is developed to reduce the number of parameters and identify variables that are not informative for clustering. The introduction of component-specific rate parameters avoids the over-penalization that can occur when inferring a shared rate parameter on clustered data. We divided the guests into four groups -- property driven guests, host driven guests, guests with recent overall negative stay and guests with some negative experiences.

stat.AP↗

arXiv2018

Text Assisted Insight Ranking Using Context-Aware Memory Network

Extracting valuable facts or informative summaries from multi-dimensional tables, i.e. insight mining, is an important task in data analysis and business intelligence. However, ranking the importance of insights remains a challenging and unexplored task. The main challenge is that explicitly scoring an insight or giving it a rank requires a thorough understanding of the tables and costs a lot of manual efforts, which leads to the lack of available training data for the insight ranking problem. In this paper, we propose an insight ranking model that consists of two parts: A neural ranking model explores the data characteristics, such as the header semantics and the data statistical features, and a memory network model introduces table structure and context information into the ranking process. We also build a dataset with text assistance. Experimental results show that our approach largely improves the ranking precision as reported in multi evaluation metrics.

cs.CL↗

arXiv2018

Flexible High-Dimensional Unsupervised Learning with Missing Data

The mixture of factor analyzers (MFA) model is a famous mixture model-based approach for unsupervised learning with high-dimensional data. It can be useful, inter alia, in situations where the data dimensionality far exceeds the number of observations. In recent years, the MFA model has been extended to non-Gaussian mixtures to account for clusters with heavier tail weight and/or asymmetry. The generalized hyperbolic factor analyzers (MGHFA) model is one such extension, which leads to a flexible modelling paradigm that accounts for both heavier tail weight and cluster asymmetry. In many practical applications, the occurrence of missing values often complicates data analyses. A generalization of the MGHFA is presented to accommodate missing values. Under a missing-at-random mechanism, we develop a computationally efficient alternating expectation conditional maximization algorithm for parameter estimation of the MGHFA model with different patterns of missing values. The imputation of missing values under an incomplete-data structure of MGHFA is also investigated. The performance of our proposed methodology is illustrated through the analysis of simulated and real data.

stat.CO↗

arXiv2018

Mixtures of Generalized Hyperbolic Distributions and Mixtures of Skew-t Distributions for Model-Based Clustering with Incomplete Data

Robust clustering from incomplete data is an important topic because, in many practical situations, real data sets are heavy-tailed, asymmetric, and/or have arbitrary patterns of missing observations. Flexible methods and algorithms for model-based clustering are presented via mixture of the generalized hyperbolic distributions and its limiting case, the mixture of multivariate skew-t distributions. An analytically feasible EM algorithm is formulated for parameter estimation and imputation of missing values for mixture models employing missing at random mechanisms. The proposed methodologies are investigated through a simulation study with varying proportions of synthetic missing values and illustrated using a real dataset. Comparisons are made with those obtained from the traditional mixture of generalized hyperbolic distribution counterparts by filling in the missing data using the mean imputation method.

stat.ME↗

arXiv2018

Growth and characterization of HgBa2CaCu2O6+d and HgBa2Ca2Cu3O8+d crystals

We report the successful synthesis of single-crystalline cuprate superconductors HgBa$_{2}$CaCu$_{2}$O$_{6+δ}$ and HgBa$_{2}$Ca$_{2}$Cu$_{3}$O$_{8+δ}$. These compounds are well-known for their high optimal superconducting critical temperatures of $T_\mathrm{c}$ = 128 K and 134 K at ambient pressure, respectively, and for their challenging synthesis. Using a conventional quartz-tube encapsulation method and a two-layer encapsulation method that utilizes custom-built high-pressure furnaces, we are able to grow single crystals with linear dimensions up to several millimeters parallel to the CuO$_2$ planes. Extended post-growth anneals are shown to lead to sharp superconducting transitions, indicative of high macroscopic homogeneity. X-ray diffraction and polarized Raman spectroscopy are identified as viable methods to resolve the seemingly unavoidable inter-growth of the two compounds. Our work helps to remove obstacles toward the study of these model cuprate systems with experimental probes that require sizable high-quality crystals.

cond-mat.supr-con↗

arXiv2018

Robust Model-Based Clustering of Voting Records

We explore the possibility of discovering extreme voting patterns in the U.S. Congressional voting records by drawing ideas from the mixture of contaminated normal distributions. A mixture of latent trait models via contaminated normal distributions is proposed. We assume that the low dimensional continuous latent variable comes from a contaminated normal distribution and, therefore, picks up extreme patterns in the observed binary data while clustering. We consider in particular such model for the analysis of voting records. The model is applied to a U.S. Congressional Voting data set on 16 issues. Note this approach is the first instance within the literature of a mixture model handling binary data with possible extreme patterns.

stat.ME↗

arXiv2018

Orientation of the intra-unit-cell magnetic moment in the high-Tc superconductor HgBa2CuO$_{4+δ}$

Polarized-neutron diffraction experiments (PND) have revealed that the pseudogap state of the cuprates exhibits unusual intra-unit-cell (IUC) magnetism. At a qualitative level, the data indicate a moment direction that is neither perpendicular nor parallel to the CuO2 layers, yet an accurate measurement of a structurally simple compound has been lacking. Here we report PND results with unprecedented accuracy for the IUC magnetic order in the simple-tetragonal single-CuO$_2$-layer compound HgBa2CuO$_{4+δ}$. At the transition temperature, we find evidence for magnetic critical scattering. Deep in the ordered state, we determine the moment direction to be 70° $\pm$ 10° away from the normal to the CuO$_2$ layers, which rules out both purely planar loop currents and high-symmetry Dirac multipoles, the two most prominent theoretical proposals for the microscopic origin of the IUC magnetism. However, the data are consistent with Dirac multipoles of lower symmetry or, alternatively, with a particular configuration of loop currents that flow on the faces of the CuO$_6$ octahedra.

cond-mat.supr-con↗

arXiv2018

Flexible Clustering for High-Dimensional Data via Mixtures of Joint Generalized Hyperbolic Models

A mixture of joint generalized hyperbolic distributions (MJGHD) is introduced for asymmetric clustering for high-dimensional data. The MJGHD approach takes into account the cluster-specific subspace, thereby limiting the number of parameters to estimate while also facilitating visualization of results. Identifiability is discussed, and a multi-cycle ECM algorithm is outlined for parameter estimation. The MJGHD approach is illustrated on two real data sets, where the Bayesian information criterion is used for model selection.

stat.ME↗

arXiv2017

Extending Growth Mixture Models Using Continuous Non-Elliptical Distributions

Growth mixture models (GMMs) incorporate both conventional random effects growth modeling and latent trajectory classes as in finite mixture modeling; therefore, they offer a way to handle the unobserved heterogeneity between subjects in their development. GMMs with Gaussian random effects dominate the literature. When the data are asymmetric and/or have heavier tails, more than one latent class is required to capture the observed variable distribution. Therefore, a GMM with continuous non-elliptical distributions is proposed to capture skewness and heavier tails in the data set. Specifically, multivariate skew-t distributions and generalized hyperbolic distributions are introduced to extend GMMs. When extending GMMs, four statistical models are considered with differing distributions of measurement errors and random effects. The mathematical development of GMMs with non-elliptical distributions relies on their expression as normal variance-mean mixtures and the resultant relationship with the generalized inverse Gaussian distribution. Parameter estimation is outlined within the expectation-maximization framework before the performance of our GMMs with non-elliptical distributions is illustrated on simulated and real data.

stat.ME↗

arXiv2017

Percolative nature of the dc paraconductivity in the cuprate superconductors

We present an investigation of the planar direct-current (dc) paraconductivity of the model cuprate material HgBa$_2$CuO$_{4+δ}$ in the underdoped part of the phase diagram. The simple quadratic temperature-dependence of the Fermi-liquid normal-state resistivity enables us to extract the paraconductivity above the macroscopic $T_c$ with great accuracy. The paraconductivity exhibits unusual exponential temperature dependence, with a characteristic temperature scale that is distinct from $T_c$. In the entire temperature range where it is discernable, the paraconductivity is quantitatively explained by a simple superconducting percolation model, which implies that underlying gap disorder dominates the emergence of superconductivity.

cond-mat.supr-con↗

arXiv2017

Robust Order Scheduling in the Fashion Industry: A Multi-Objective Optimization Approach

In the fashion industry, order scheduling focuses on the assignment of production orders to appropriate production lines. In reality, before a new order can be put into production, a series of activities known as pre-production events need to be completed. In addition, in real production process, owing to various uncertainties, the daily production quantity of each order is not always as expected. In this research, by considering the pre-production events and the uncertainties in the daily production quantity, robust order scheduling problems in the fashion industry are investigated with the aid of a multi-objective evolutionary algorithm (MOEA) called nondominated sorting adaptive differential evolution (NSJADE). The experimental results illustrate that it is of paramount importance to consider pre-production events in order scheduling problems in the fashion industry. We also unveil that the existence of the uncertainties in the daily production quantity heavily affects the order scheduling.

cs.AI↗

arXiv2015

Differential Evolution with Event-Triggered Impulsive Control

Differential evolution (DE) is a simple but powerful evolutionary algorithm, which has been widely and successfully used in various areas. In this paper, an event-triggered impulsive control scheme (ETI) is introduced to improve the performance of DE. Impulsive control, the concept of which derives from control theory, aims at regulating the states of a network by instantly adjusting the states of a fraction of nodes at certain instants, and these instants are determined by event-triggered mechanism (ETM). By introducing impulsive control and ETM into DE, we hope to change the search performance of the population in a positive way after revising the positions of some individuals at certain moments. At the end of each generation, the impulsive control operation is triggered when the update rate of the population declines or equals to zero. In detail, inspired by the concepts of impulsive control, two types of impulses are presented within the framework of DE in this paper: stabilizing impulses and destabilizing impulses. Stabilizing impulses help the individuals with lower rankings instantly move to a desired state determined by the individuals with better fitness values. Destabilizing impulses randomly alter the positions of inferior individuals within the range of the current population. By means of intelligently modifying the positions of a part of individuals with these two kinds of impulses, both exploitation and exploration abilities of the whole population can be meliorated. In addition, the proposed ETI is flexible to be incorporated into several state-of-the-art DE variants. Experimental results over the CEC 2014 benchmark functions exhibit that the developed scheme is simple yet effective, which significantly improves the performance of the considered DE algorithms.

cs.NE↗

arXiv2014

Model Based Clustering of High-Dimensional Binary Data

We propose a mixture of latent trait models with common slope parameters (MCLT) for model-based clustering of high-dimensional binary data, a data type for which few established methods exist. Recent work on clustering of binary data, based on a $d$-dimensional Gaussian latent variable, is extended by incorporating common factor analyzers. Accordingly, our approach facilitates a low-dimensional visual representation of the clusters. We extend the model further by the incorporation of random block effects. The dependencies in each block are taken into account through block-specific parameters that are considered to be random variables. A variational approximation to the likelihood is exploited to derive a fast algorithm for determining the model parameters. Our approach is demonstrated on real and simulated data.

stat.ME↗

arXiv2013

Driving Perpendicular Heat Flow: Ambipolar Transverse Thermoelectrics for Microscale and Cryogenic Peltier Cooling

Whereas thermoelectric performance is normally limited by the figure of merit ZT, transverse thermoelectrics can achieve arbitrarily large temperature differences in a single leg even with inferior ZT by being geometrically tapered. We introduce a band-engineered transverse thermoelectric with p-type Seebeck in one direction and n-type orthogonal, resulting in off-diagonal terms that drive heat flow transverse to electrical current. Such materials are advantageous for microscale devices and cryogenic temperatures -- exactly the regimes where standard longitudinal thermoelectrics fail. InAs/GaSb type II superlattices are shown to have the appropriate band structure for use as a transverse thermoelectric.

cond-mat.mtrl-sci↗

arXiv2011

Separating parallel conduction from two-dimensional magnetotransport in high mobility InP/InGaAs MOCVD-grown heterostructures

In this Letter, four-point magnetotransport of high mobility InGaAs/InP heterointerfaces is measured from 1.6 K to 300 K and from 0 to 15 T, and an analysis is shown whereby the mobility and density of the two-dimensional (2D) accumulation layer can be separately characterized from that of the parallel conducting dopant layer over all but a small intermediate temperature range. Standard magnetotransport regimes are defined as the temperature increases from 1.6 K to 300 K, namely quantum Hall (QH), Shubnikov de Haas (SdH), and Drude regimes (D), and in the QH and D regimes different analyses are applied to deduce densities and mobilities of both layers separately. Quantitative conditions for the intermediate SdH regime are defined, within which both QH and D analyses fail. The density and activation energy of unintentional donors at the InP epilayer/substrate interface is deduced. At base temperature, QH minima are resolved down to B = 0.4 T at nu = 20, revealing a mobility of mu = 160,000 cm^2/Vs. The 2D system maintains this high mobility up to at least 40 K in this high quality structure.

cond-mat.mes-hall↗