Inference in a Stationary/Nonstationary Autoregressive Time-Varying-Parameter Model

This paper considers nonparametric estimation and inference in first-order autoregressive (AR(1)) models with deterministically time-varying parameters. A key feature of the proposed approach is to allow for time-varying stationarity in some time periods, time-varying nonstationarity (i.e., unit root or local-to-unit root behavior) in other periods, and smooth transitions between the two. The estimation of the AR parameter at any time point is based on a local least squares regression method, where the relevant initial condition is endogenous. We obtain limit distributions for the AR parameter estimator and t-statistic at a given point τ in time when the parameter exhibits unit root, local-to-unity, or stationary/stationary-like behavior at time τ. These results are used to construct confidence intervals and median-unbiased interval estimators for the AR parameter at any specified point in time. The confidence intervals have correct asymptotic coverage probabilities with the coverage holding uniformly over stationary and nonstationary behavior of the observations

Inference in a Stationary/Nonstationary Autoregressive Time-Varying-Parameter Model

This paper considers nonparametric estimation and inference in first-order autoregressive (AR(1)) models with deterministically time-varying parameters. A key feature of the proposed approach is to allow for time-varying stationarity in some time periods, time-varying nonstationarity (i.e., unit root or local-to-unit root behavior) in other periods, and smooth transitions between the two. The estimation of the AR parameter at any time point is based on a local least squares regression method, where the relevant initial condition is endogenous. We obtain limit distributions for the AR parameter estimator and t-statistic at a given point τ in time when the parameter exhibits unit root, local-to-unity, or stationary/stationary-like behavior at time τ. These results are used to construct confidence intervals and median-unbiased interval estimators for the AR parameter at any specified point in time. The confidence intervals have correct uniform asymptotic coverage probability regardless of the time-varying stationarity/nonstationary behavior of the observations

A single photon produces general W state of N qubits and its application

Based on the Wu's scheme[1], We prepare the general N-qubit W state. We find that the concurrence of two qubits in general N-qubit W state is only related to their coefficients and we successfully apply the general N-qubit W state to quantum state transfer and quantum state prepare like that in two-qubit system

Parametric Analysis of Energy Absorption in Micro-particle Photophoresis in Absorbing Gaseous Media

The study deals with photophoresis of a spherical micro-particle suspended in absorbing gaseous media. Photophoretic motion of the particle stems from the asymmetric distribution of absorbed energy within the particle. By evaluating the so-called heat source function at various conditions, the study focuses on the effects of governing parameters on the energy distribution within the particle and their potential influences to the photophoresis. The results reveal that the increase in either particle size or absorptivity enhances the energy intensity on the illuminated (leading) side and tends to generate positive photophoresis. For a particle of low absorptivity, the energy distribution is dominated by particle refraction. Enhancing particle refractivity, the energy tends to be focused onto a certain spot area on the shaded (trailing) side and leads to a tendency of negative photophoresis. Increasing medium absorptivity significantly degrades the level of energy absorbed by the particle and in turn weakens the driving force of the particle photophoresis.Defence Science Journal, 2010, 60(3), pp.233-237, DOI:http://dx.doi.org/10.14429/dsj.60.34

Discussion on the paper 'Analyzing short time series data from periodically fluctuating rodent populations by threshold models: a nearest block bootstrap approach'

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AI deployment on GBM diagnosis: a novel approach to analyze histopathological images using image feature-based analysis

Background: Glioblastoma (GBM) is one of the most common malignant primary brain tumors, which accounts for 60–70% of all gliomas. Conventional diagnosis and the decision of post-operation treatment plan for glioblastoma is mainly based on the feature-based qualitative analysis of hematoxylin and eosin-stained (H&amp;E) histopathological slides by both an experienced medical technologist and a pathologist. The recent development of digital whole slide scanners makes AI-based histopathological image analysis feasible and helps to diagnose cancer by accurately counting cell types and/or quantitative analysis. However, the technology available for digital slide image analysis is still very limited. This study aimed to build an image feature-based computer model using histopathology whole slide images to differentiate patients with glioblastoma (GBM) from healthy control (HC). Method: Two independent cohorts of patients were used. The first cohort was composed of 262 GBM patients of the Cancer Genome Atlas Glioblastoma Multiform Collection (TCGA-GBM) dataset from the cancer imaging archive (TCIA) database. The second cohort was composed of 60 GBM patients collected from a local hospital. Also, a group of 60 participants with no known brain disease were collected. All the H&amp;E slides were collected. Thirty-three image features (22 GLCM and 11 GLRLM) were retrieved from the tumor volume delineated by medical technologist on H&amp;E slides. Five machine-learning algorithms including decision-tree (DT), extreme-boost (EB), support vector machine (SVM), random forest (RF), and linear model (LM) were used to build five models using the image features extracted from the first cohort of patients. Models built were deployed using the selected key image features for GBM diagnosis from the second cohort (local patients) as model testing, to identify and verify key image features for GBM diagnosis. Results: All five machine learning algorithms demonstrated excellent performance in GBM diagnosis and achieved an overall accuracy of 100% in the training and validation stage. A total of 12 GLCM and 3 GLRLM image features were identified and they showed a significant difference between the normal and the GBM image. However, only the SVM model maintained its excellent performance in the deployment of the models using the independent local cohort, with an accuracy of 93.5%, sensitivity of 86.95%, and specificity of 99.73%. Conclusion: In this study, we have identified 12 GLCM and 3 GLRLM image features which can aid the GBM diagnosis. Among the five models built, the SVM model proposed in this study demonstrated excellent accuracy with very good sensitivity and specificity. It could potentially be used for GBM diagnosis and future clinical application.</p

A novel quantum key distribution scheme with orthogonal product states

The general conditions for the orthogonal product states of the multi-state systems to be used in quantum key distribution (QKD) are proposed, and a novel QKD scheme with orthogonal product states in the 3x3 Hilbert space is presented. We show that this protocol has many distinct features such as great capacity, high efficiency. The generalization to nxn systems is also discussed and a fancy limitation for the eavesdropper's success probability is reached.Comment: 4 Pages, 3 Figure

Local Concurrent Error Detection and Correction in Data Structures Using Virtual Backpointers

Coordinated Science Laboratory was formerly known as Control Systems LaboratorySDIO/IST managed by the Office of Naval Research / N00014-86-K-0519National Aeronautics and Space Administration / NASA NAG 1-602Joint Services Electronics Program / N00014-84-C-014

Universal properties for linelike melting of the vortex lattice

Using numerical results obtained within two models describing vortex matter (interacting elastic lines (Bose model) and uniformly frustrated XY-model) we establish universal properties of the melting transition within the linelike regime. These properties, which are captured correctly by both models, include the scaling of the melting temperature with anisotropy and magnetic field, the effective line tension of vortices in the liquid regime, the latent heat, the entropy jump per entanglement length, and relative jump of Josephson energy at the transition as compared to the latent heat. The universal properties can serve as experimental fingerprints of the linelike regime of melting. Comparison of the models allows us to establish boundaries of the linelike regime in temperature and magnetic field.Comment: Revtex, 12 pages, 2 EPS figure