Empirical Likelihood Inference for the Area Under the ROC Curve

For a continuous-scale diagnostic test, the most commonly used summary index of the receiver operating characteristic (ROC) curve is the area under the curve (AUC) that measures the accuracy of the diagnostic test. In this paper we propose an empirical likelihood approach for the inference of AUC. We first define an empirical likelihood ratio for AUC and show that its limiting distribution is a scaled chi-square distribution. We then obtain an empirical likelihood based confidence interval for AUC using the scaled chi-square distribution. This empirical likelihood inference for AUC can be extended to stratified samples and the resulting limiting distribution is a weighted sum of independent chi-square distributions. We also conduct simulation studies to compare the relative performance of the proposed empirical likelihood based interval with the existing normal approximation based intervals and bootstrap intervals for AUC

A Review of Text Corpus-Based Tourism Big Data Mining

With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists’ opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-to-date review of text mining techniques that have been, or have the potential to be, applied to modern tourism big data analysis. We summarize and discuss different text representation strategies, text-based NLP techniques for topic extraction, text classification, sentiment analysis, and text clustering in the context of tourism text mining, and their applications in tourist profiling, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years

Iron sources and pathways into the Pacific Equatorial Undercurrent

Using a novel observationally constrained Lagrangian iron model forced by outputs from an eddy-resolving biogeochemical ocean model, we examine the sensitivity of the Equatorial Undercurrent (EUC) iron distribution to EUC source region iron concentrations. We find that elevated iron concentrations derived from New Guinea Coastal Undercurrent (NGCU) alone is insufficient to explain the high concentrations observed in the EUC. In addition, due to the spread in transit times, interannual NGCU iron pulses are scavenged, diluted, or eroded, before reaching the eastern equatorial Pacific. With an additional iron source from the nearby New Ireland Coastal Undercurrent, EUC iron concentrations become consistent with observations. Furthermore, as both the New Guinea and New Ireland Coastal Undercurrents strengthen during El Niño, increased iron input into the EUC can enhance the iron supply into the eastern equatorial Pacific. Notably, during the 1997/1998 El Niño, this causes a simulated 30% iron increase at a 13 month lag

A Review of Text Corpus-Based Tourism Big Data Mining

With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists’ opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-to-date review of text mining techniques that have been, or have the potential to be, applied to modern tourism big data analysis. We summarize and discuss different text representation strategies, text-based NLP techniques for topic extraction, text classification, sentiment analysis, and text clustering in the context of tourism text mining, and their applications in tourist profiling, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years

Duality between the deconfined quantum-critical point and the bosonic topological transition

Recently significant progress has been made in $(2+1)$-dimensional conformal field theories without supersymmetry. In particular, it was realized that different Lagrangians may be related by hidden dualities, i.e., seemingly different field theories may actually be identical in the infrared limit. Among all the proposed dualities, one has attracted particular interest in the field of strongly-correlated quantum-matter systems: the one relating the easy-plane noncompact CP$^1$ model (NCCP$^1$) and noncompact quantum electrodynamics (QED) with two flavors ($N = 2$) of massless two-component Dirac fermions. The easy-plane NCCP$^1$ model is the field theory of the putative deconfined quantum-critical point separating a planar (XY) antiferromagnet and a dimerized (valence-bond solid) ground state, while $N=2$ noncompact QED is the theory for the transition between a bosonic symmetry-protected topological phase and a trivial Mott insulator. In this work we present strong numerical support for the proposed duality. We realize the $N=2$ noncompact QED at a critical point of an interacting fermion model on the bilayer honeycomb lattice and study it using determinant quantum Monte Carlo (QMC) simulations. Using stochastic series expansion QMC, we study a planar version of the $S=1/2$ $J$-$Q$ spin Hamiltonian (a quantum XY-model with additional multi-spin couplings) and show that it hosts a continuous transition between the XY magnet and the valence-bond solid. The duality between the two systems, following from a mapping of their phase diagrams extending from their respective critical points, is supported by the good agreement between the critical exponents according to the proposed duality relationships.Comment: 14 pages, 9 figure

Axial gravitational quasinormal modes of a self-dual black hole in loop quantum gravity

We study the axial gravitational quasinormal modes of a self-dual black hole in loop quantum gravity. Considering the axial perturbation of the background spacetime, we obtain the Schr\"{o}dinger-like master equation. Then we calculate the quasinormal frequencies with the Wentzel-Kramers-Brillouin approximation and the asymptotic iteration method. We also investigate the numerical evolution of an initial wave packet on the self-dual black hole spacetime.~We find the quantum correction parameter $P$ positively affects the absolute values of both the real and imaginary parts of quasinormal frequencies. We derive the relation between the parameters of the circular null geodesics and quasinormal frequencies in the eikonal limit for the self-dual black hole, and numerically verify this relation.Comment: 13 pages, 5 figures, 5 table