Variation of Entanglement Entropy in Scattering Process

In a scattering process, the final state is determined by an initial state and an S-matrix. We focus on two-particle scattering processes and consider the entanglement between these particles. For two types initial states; i.e., an unentangled state and an entangled one, we calculate perturbatively the change of entanglement entropy from the initial state to the final one. Then we show a few examples in a field theory and in quantum mechanics.Comment: 13 pages; v2: refs. adde

Variation aware analysis of bridging fault testing

This paper investigates the impact of process variation on test quality with regard to resistive bridging faults. The input logic threshold voltage and gate drive strength parameters are analyzed regarding their process variation induced influence on test quality. The impact of process variation on test quality is studied in terms of test escapes and measured by a robustness metric. It is shown that some bridges are sensitive to process variation in terms of logic behavior, but such variation does not necessarily compromise test quality if the test has high robustness. Experimental results of Monte-Carlo simulation based on recent process variation statistics are presented for ISCAS85 and -89 benchmark circuits, using a 45nm gate library and realistic bridges. The results show that tests generated without consideration of process variation are inadequate in terms of test quality, particularly for small test sets. On the other hand, larger test sets detect more of the logic faults introduced by process variation and have higher test quality

Understanding Behavioral Sources of Process Variation Following Enterprise System Deployment

This paper extends the current understanding of the time-sensitivity of intent and usage following large-scale IT implementation. Our study focuses on perceived system misfit with organizational processes in tandem with the availability of system circumvention opportunities. Case study comparisons and controlled experiments are used to support the theoretical unpacking of organizational and technical contingencies and their relationship to shifts in user intentions and variation in work-processing tactics over time. Findings suggest that managers and users may retain strong intentions to circumvent systems in the presence of perceived task-technology misfit. The perceived ease with which this circumvention is attainable factors significantly into the timeframe within which it is attempted, and subsequently impacts the onset of deviation from prescribed practice and anticipated dynamics

Process Variation as a Determinant of Service Quality and Bank Performance: Evidence from the Retail Banking Study

Conventional wisdom in retail banking states that firm performance is dependent on higher average process performance. This paper refutes conventional wisdom and provides empirical evidence, which demonstrates that low process variation contributes significantly to firm performance. More specifically, this paper examines the effect of process variation, caused by process variability, on service quality and financial performance, as measured by customer satisfaction and price-to-earnings ratio. This paper estimates process variation and reveals large variation in rocesses, reflecting large variation in firm strategy and process design. The data is from the

Gibbs point process approximation: Total variation bounds using Stein's method

We obtain upper bounds for the total variation distance between the distributions of two Gibbs point processes in a very general setting. Applications are provided to various well-known processes and settings from spatial statistics and statistical physics, including the comparison of two Lennard-Jones processes, hard core approximation of an area interaction process and the approximation of lattice processes by a continuous Gibbs process. Our proof of the main results is based on Stein's method. We construct an explicit coupling between two spatial birth-death processes to obtain Stein factors, and employ the Georgii-Nguyen-Zessin equation for the total bound.Comment: Published in at http://dx.doi.org/10.1214/13-AOP895 the Annals of Probability (http://www.imstat.org/aop/) by the Institute of Mathematical Statistics (http://www.imstat.org

Modeling the Impact of Process Variation on Resistive Bridge Defects

Recent research has shown that tests generated without taking process variation into account may lead to loss of test quality. At present there is no efficient device-level modeling technique that models the effect of process variation on resistive bridges. This paper presents a fast and accurate technique to model the effect of process variation on resistive bridge defects. The proposed model is implemented in two stages: firstly, it employs an accurate transistor model (BSIM4) to calculate the critical resistance of a bridge; secondly, the effect of process variation is incorporated in this model by using three transistor parameters: gate length (L), threshold voltage (V) and effective mobility (ueff) where each follow Gaussian distribution. Experiments are conducted on a 65-nm gate library (for illustration purposes), and results show that on average the proposed modeling technique is more than 7 times faster and in the worst case, error in bridge critical resistance is 0.8% when compared with HSPICE

Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models

The interpretation of complex high-dimensional data typically requires the use of dimensionality reduction techniques to extract explanatory low-dimensional representations. However, in many real-world problems these representations may not be sufficient to aid interpretation on their own, and it would be desirable to interpret the model in terms of the original features themselves. Our goal is to characterise how feature-level variation depends on latent low-dimensional representations, external covariates, and non-linear interactions between the two. In this paper, we propose to achieve this through a structured kernel decomposition in a hybrid Gaussian Process model which we call the Covariate Gaussian Process Latent Variable Model (c-GPLVM). We demonstrate the utility of our model on simulated examples and applications in disease progression modelling from high-dimensional gene expression data in the presence of additional phenotypes. In each setting we show how the c-GPLVM can extract low-dimensional structures from high-dimensional data sets whilst allowing a breakdown of feature-level variability that is not present in other commonly used dimensionality reduction approaches

Simulation of Laminated Object Manufacturing (LOM) with Variation of Process Parameters

A previously developed and verified thermal model for Laminated Object Manufacturing (LOM) was used to investigate the effects of various processing parameters on the temperature profile in a LOM part during the build cycle. The mathematical model, based on 3-dimensional transient heat conduction in a rectangular geometry LOM part, allows calculation ofthe transient temperature distribution within the part during the application of a new layer as well as during other periods ofthe LOM build cycle. The parameters roller temperature, roller speed, chamber air temperature, base plate temperature, and laser cutting time were independently varied, and the LOM process response simulated. The results were analyzed in order to gain insight into potential strategies for intelligent process control.Mechanical Engineerin

Identification of shift variation in bivariate process using pattern recognition technique

In quality control, the identification of unnatural variation in mean shifts is a challenge when dealing with two correlated quality characteristics (bivariate). Various schemes based on statistical process control pattern recognition (SPCPR) approach have been proposed to monitor the presence of unnatural variation and diagnose its sources. However, existing studies have focused either on limited sources of unnatural variation and/or lack in avoiding false alarm. In terms for unnatural variation, the classification of reciprocating patterns such as upward-downward and downward-upward shifts was less reported. In order to enhance its capability, this study aims at proposing an improved design of SPCPR scheme for enabling classification of nine bivariate patterns with high accuracy and reduced false alarm. There were two schemes investigated in this study: (i) Statistical Features-Multilayer Perceptron (SF-MLP) and (ii) Integrated Multivariate Exponentially Weighted Moving Average-Multilayer Perceptron (MEWMA-MLP). Input representation for the MLP recogniser in both schemes was designed based on summary statistical features and it was systematically selected using the design of experiments analysis. The average run lengths (ARL0, ARL1) and recognition accuracy percentage (RA) were used as the performance measures. In monitoring performance, the MEWMA-MLP provided a longer run of ARL0 (386.8 ~ 606.9) and ARL1 (2.76 ~ 9.63) compared to the SF-MLP (ARL0 = 160.8 ~529.3, ARL1 = 2.25 ~ 8.91). This result suggests that the MEWMA-MLP is better in avoiding false alarm, which exceeded the de facto level (ARL0 = 370) but requires a slightly longer run to detect the unnatural variation. In diagnosis performance, the MEWMA-MLP gave an improved range of recognition accuracy (RA= 86.5 ~ 99.3 %) compared to the SF-MLP (RA= 83.3 ~ 97.7 %) in classifying the sources of unnatural variation. Overall, this study opens a new perspective to enhance the capability of SPCPR scheme for the application of bivariate quality control

Weighted power variation of integrals with respect to a Gaussian process

We consider a stochastic process $Y$ defined by an integral in quadratic mean of a deterministic function $f$ with respect to a Gaussian process $X$, which need not have stationary increments. For a class of Gaussian processes $X$, it is proved that sums of properly weighted powers of increments of $Y$ over a sequence of partitions of a time interval converge almost surely. The conditions of this result are expressed in terms of the $p$-variation of the covariance function of $X$. In particular, the result holds when $X$ is a fractional Brownian motion, a subfractional Brownian motion and a bifractional Brownian motion.Comment: Published at http://dx.doi.org/10.3150/14-BEJ606 in the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm