Polyhedral Analysis using Parametric Objectives

The abstract domain of polyhedra lies at the heart of many program analysis techniques. However, its operations can be expensive, precluding their application to polyhedra that involve many variables. This paper describes a new approach to computing polyhedral domain operations. The core of this approach is an algorithm to calculate variable elimination (projection) based on parametric linear programming. The algorithm enumerates only non-redundant inequalities of the projection space, hence permits anytime approximation of the output

Parametric Schedulability Analysis of Fixed Priority Real-Time Distributed Systems

Parametric analysis is a powerful tool for designing modern embedded systems, because it permits to explore the space of design parameters, and to check the robustness of the system with respect to variations of some uncontrollable variable. In this paper, we address the problem of parametric schedulability analysis of distributed real-time systems scheduled by fixed priority. In particular, we propose two different approaches to parametric analysis: the first one is a novel technique based on classical schedulability analysis, whereas the second approach is based on model checking of Parametric Timed Automata (PTA). The proposed analytic method extends existing sensitivity analysis for single processors to the case of a distributed system, supporting preemptive and non-preemptive scheduling, jitters and unconstrained deadlines. Parametric Timed Automata are used to model all possible behaviours of a distributed system, and therefore it is a necessary and sufficient analysis. Both techniques have been implemented in two software tools, and they have been compared with classical holistic analysis on two meaningful test cases. The results show that the analytic method provides results similar to classical holistic analysis in a very efficient way, whereas the PTA approach is slower but covers the entire space of solutions.Comment: Submitted to ECRTS 2013 (http://ecrts.eit.uni-kl.de/ecrts13

Genome-wide search for strabismus susceptibility loci.

The purpose of this study was to search for chromosomal susceptibility loci for comitant strabismus. Genomic DNA was isolated from 10mL blood taken from each member of 30 nuclear families in which 2 or more siblings are affected by either esotropia or exotropia. A genome-wide search was performed with amplification by polymerase chain reaction of 400 markers in microsatellite regions with approximately 10 cM resolution. For each locus, non-parametric affected sib-pair analysis and non-parametric linkage analysis for multiple pedigrees (Genehunter software, http://linkage.rockefeller.edu/soft/) were used to calculate multipoint lod scores and non-parametric linkage (NPL) scores, respectively. In sib-pair analysis, lod scores showed basically flat lines with several peaks of 0.25 on all chromosomes. In non-parametric linkage analysis for multiple pedigrees, NPL scores showed one peak as high as 1.34 on chromosomes 1, 2, 4, 7, 10, 15, and 16, while 2 such peaks were found on chromosomes 3, 9, 11, 12, 18, and 20. Non-parametric linkage analysis for multiple pedigrees of 30 families with comitant strabismus suggested a number of chromosomal susceptibility loci. Our ongoing study involving a larger number of families will refine the accuracy of statistical analysis to pinpoint susceptibility loci for comitant strabismus.&#60;/P&#62;</p

Parametric stiffness analysis of the Orthoglide

This paper presents a parametric stiffness analysis of the Orthoglide. A compliant modeling and a symbolic expression of the stiffness matrix are conducted. This allows a simple systematic analysis of the influence of the geometric design parameters and to quickly identify the critical link parameters. Our symbolic model is used to display the stiffest areas of the workspace for a specific machining task. Our approach can be applied to any parallel manipulator for which stiffness is a critical issue

Statistical model on student performance in UTHM by using non-parametric, semi-parametric and parametric survival analysis

Student performance defined as students who are capable to success during their studies. This study explored the use of survival analysis to investigate the performance of Bachelor’s degree students in Universiti Tun Hussein Onn Malaysia (UTHM). The data was collected from the Academic Management Office (AMO), UTHM. The main objective of this study is to estimate the survival rates of students with different entrance qualifications. The study also aim to identify the covariates that dominate the student performance, investigate the performance of Cox model based on the violation of the Proportional Hazard (PH) assumption, compare the model performance by using the survival and Accelerated Failure Time (AFT) models and estimate the time ratio (TR) of student performance in accordance to the selected best model. The survival analysis considered the survival approach such as the Kaplan-Meier (KM) method in the non-parametric method, Cox model in semi-parametric model and survival and AFT models in parametric model. The results revealed that students with STPM-entrance qualification had the highest survival rate compared to Diploma and Matriculation holders. The Cox model in the semi-parametric model identified the GPA, entrance qualification and course as the significant covariates to be included in the study. Faculty covariate was excluded since the p-value insignificant at 90% significance level. The result provided by the Cox model violated the PH assumptions. Then, the performance of the Cox model is less accurate. The invalidation performance of Cox model prompted the need to conduct other parametric survival and AFT models to produce more precise results. As a conclusion, the Log-normal AFT model is the best alternative model to estimate student performance in UTHM and other similar higher educational institution

Semi-parametric analysis of multi-rater data

Datasets that are subjectively labeled by a number of experts are becoming more common in tasks such as biological text annotation where class definitions are necessarily somewhat subjective. Standard classification and regression models are not suited to multiple labels and typically a pre-processing step (normally assigning the majority class) is performed. We propose Bayesian models for classification and ordinal regression that naturally incorporate multiple expert opinions in defining predictive distributions. The models make use of Gaussian process priors, resulting in great flexibility and particular suitability to text based problems where the number of covariates can be far greater than the number of data instances. We show that using all labels rather than just the majority improves performance on a recent biological dataset

Parametric Analysis of the Selective Laser Sintering Process

Qualitative and quantitative analyses are required to develop Selective Laser Sintering into a viable Manufacturing process. A simplified mathematical model for sintering incorporating the heat tJ;ansfer equation. and the sintering rate equation, but using temperature independent thermal properties, is presented in this paper. A practical result is the calculation of sintering depthdeftned as the depth of powder where the void fraction is less than 0.1 as a function of control parameters, such as the laser power intensity, the laser scanning velocity, and the initial bedtemperature. We derive the general behavior of laser sintering. A comparison of model predictions with laser sinterlng tests is provided.Mechanical Engineerin