Mining compact predictive pattern sets using classification model

In this paper, we develop a new framework for mining predictive patterns that aims to describe compactly the condition (or class) of interest. Our framework relies on a classification model that considers and combines various predictive pattern candidates and selects only those that are important for improving the overall class prediction performance. We test our approach on data derived from MIMIC-III EHR database, focusing on patterns predictive of sepsis. We show that using our classification approach we can achieve a significant reduction in the number of extracted patterns compared to the state-of-the-art methods based on minimum predictive pattern mining approach, while preserving the overall classification accuracy of the model

FIBS: A Generic Framework for Classifying Interval-based Temporal Sequences

We study the problem of classifying interval-based temporal sequences (IBTSs). Since common classification algorithms cannot be directly applied to IBTSs, the main challenge is to define a set of features that effectively represents the data such that classifiers can be applied. Most prior work utilizes frequent pattern mining to define a feature set based on discovered patterns. However, frequent pattern mining is computationally expensive and often discovers many irrelevant patterns. To address this shortcoming, we propose the FIBS framework for classifying IBTSs. FIBS extracts features relevant to classification from IBTSs based on relative frequency and temporal relations. To avoid selecting irrelevant features, a filter-based selection strategy is incorporated into FIBS. Our empirical evaluation on eight real-world datasets demonstrates the effectiveness of our methods in practice. The results provide evidence that FIBS effectively represents IBTSs for classification algorithms, which contributes to similar or significantly better accuracy compared to state-of-the-art competitors. It also suggests that the feature selection strategy is beneficial to FIBS's performance.Comment: In: Big Data Analytics and Knowledge Discovery. DaWaK 2020. Springer, Cha

Physical Aspects of Pseudo-Hermitian and PTPT-Symmetric Quantum Mechanics

For a non-Hermitian Hamiltonian H possessing a real spectrum, we introduce a canonical orthonormal basis in which a previously introduced unitary mapping of H to a Hermitian Hamiltonian h takes a simple form. We use this basis to construct the observables O of the quantum mechanics based on H. In particular, we introduce pseudo-Hermitian position and momentum operators and a pseudo-Hermitian quantization scheme that relates the latter to the ordinary classical position and momentum observables. These allow us to address the problem of determining the conserved probability density and the underlying classical system for pseudo-Hermitian and in particular PT-symmetric quantum systems. As a concrete example we construct the Hermitian Hamiltonian h, the physical observables O, the localized states, and the conserved probability density for the non-Hermitian PT-symmetric square well. We achieve this by employing an appropriate perturbation scheme. For this system, we conduct a comprehensive study of both the kinematical and dynamical effects of the non-Hermiticity of the Hamiltonian on various physical quantities. In particular, we show that these effects are quantum mechanical in nature and diminish in the classical limit. Our results provide an objective assessment of the physical aspects of PT-symmetric quantum mechanics and clarify its relationship with both the conventional quantum mechanics and the classical mechanics.Comment: 45 pages, 13 figures, 2 table

Coherent and squeezed states of quantum Heisenberg algebras

Starting from deformed quantum Heisenberg Lie algebras some realizations are given in terms of the usual creation and annihilation operators of the standard harmonic oscillator. Then the associated algebra eigenstates are computed and give rise to new classes of deformed coherent and squeezed states. They are parametrized by deformed algebra parameters and suitable redefinitions of them as paragrassmann numbers. Some properties of these deformed states also are analyzed.Comment: 32 pages, 3 figure

Accreting Black Holes

This chapter provides a general overview of the theory and observations of black holes in the Universe and on their interpretation. We briefly review the black hole classes, accretion disk models, spectral state classification, the AGN classification, and the leading techniques for measuring black hole spins. We also introduce quasi-periodic oscillations, the shadow of black holes, and the observations and the theoretical models of jets.Comment: 41 pages, 18 figures. To appear in "Tutorial Guide to X-ray and Gamma-ray Astronomy: Data Reduction and Analysis" (Ed. C. Bambi, Springer Singapore, 2020). v3: fixed some typos and updated some parts. arXiv admin note: substantial text overlap with arXiv:1711.1025

Predictors of Breast and Cervical Cancer Screening among Chamorro Women in Southern California

This study examined the role of sociodemographic characteristics, health insurance, cancer knowledge, perceived health risk, and having a recent physicians’ visit on breast and cervical cancer screening utilization among a randomly selected group of Chamorro women (n = 250) residing in San Diego, California. Data were collected by a telephone survey and analyzed using multiple logistic regression models. After adjusting for covariates, having a recent full exam was the strongest predictor of having had a Pap exam in the past 2 years for women 21 years and older and a clinical breast exam in the past 2 years for women 40 years and over