Central limit theorem of linear regression model under right censorship

In this paper, the estimation of joint distribution F(y,z) of (Y, Z) and the estimation in thelinear regression model Y = b′Z + ε for complete data are extended to that of the right censored data. Theregression parameter estimates of b and the variance of ε are weighted least square estimates with randomweights. The central limit theorems of the estimators are obtained under very weak conditions and the derivedasymptotic variance has a very simple form.

MODEL ANALYSIS AND PARAMETER EXTRACTION FOR MOS CAPACITOR INCLUDING QUANTUM MECHANICAL EFFECTS

The high frequency CV curves of MOS capacitor have been studied. It is shown that semiclassical model is a good approximation to quantum model and approaches to classical model when the oxide layer is thick. This conclusion provides us an efficient （semiclassical） model including quantum mechanical effects to do parameter extraction for ultrathin oxide device. Here the effective extracting strategy is designed and numerical experiments demonstrate the validity of the strategy.

SINGULARITY-FREE NUMERICAL SCHEME FOR THE STATIONARY WIGNER EQUATION

For the stationary Wigner equation with inflow boundary conditions, the numerical convergence with respect to the velocity mesh size are deteriorated due to the singularity at velocity zero. In this paper, using the fact that the solution of the stationary Wigner equation is subject to an integral constraint, we prove that the Wigner equation can be written into a form with a bounded operator B[V]. which is equivalent to the operatorA[V]=θ[V]/v in the original Wigner equation under some conditions. Then the discrete operators discretizing B[V] are proved to be uniformly bounded with respect to the mesh size. Basted on the thcoretical findings, a singularity-free numerical incthod is proposed. Numerical results are provided to show our improved numerical scheme performs much better in numerical convergence than the original scheme based on discretizing A[V].

A Spatiotemporal Causality Based Governance Framework for Noisy Urban Sensory Data

Urban sensing is one of the fundamental building blocks of urban computing.It uses various types of sensors deployed in different geospatial locations to continuously and cooperatively monitor the natural and cultural environment in urban areas.Nevertheless,issues such as uneven distribution,low sampling rate and high failure ratio of sensors often make their readings less reliable.This paper provides an innovative framework to detect the noise data as well as to repair them from a spatial-temporal causality perspective rather than to deal with them inclividually.This can be achieved by connecting data through monitored objects,using the Skip-gram model to estimate spatial correlation and long shortterm memory to estimate temporal correlation.The framework consists of three major modules:1)a space embedded Bidirectional Long Short-Term Memory(BiLSTM)-based sequence labeling module to detect the noise data and the latent missing data;2)a space embedded BiLSTM-based sequence predicting module calculating the value of the missing data;3)an object characteristics fusion repairing module to correct the spatial and temporal dislocation sensory data.The approach is evaluated with real-world data collected by over 3000 electronic traffic bayonet devices in a citywide scale of a medium-sized city in China,and the result is superior to those of several referenced approaches.With a 12.9%improvement,in data accuracy over the raw data,the proposed framework plays a significant,role in various real-world use cases in urban governance,such as criminal investigation,traffic violation monitoring,and equipment maintenance.