Design and Implementation of Statistical Rate Monotonic Scheduling in KURT Linux

Statistical Rate Monotonic Scheduling (SRMS) is a generalization of the classical RMS results of Liu and Layland [LL73] for periodic tasks with highly variable execution times and statistical QoS requirements. The main tenet of SRMS is that the variability in task resource requirements could be smoothed through aggregation to yield guaranteed QoS. This aggregation is done over time for a given task and across multiple tasks for a given period of time. Similar to RMS, SRMS has two components: a feasibility test and a scheduling algorithm. SRMS feasibility test ensures that it is possible for a given periodic task set to share a given resource without violating any of the statistical QoS constraints imposed on each task in the set. The SRMS scheduling algorithm consists of two parts: a job admission controller and a scheduler. The SRMS scheduler is a simple, preemptive, fixed-priority scheduler. The SRMS job admission controller manages the QoS delivered to the various tasks through admit/reject and priority assignment decisions. In particular, it ensures the important property of task isolation, whereby tasks do not infringe on each other. In this paper we present the design and implementation of SRMS within the KURT Linux Operating System [HSPN98, SPH 98, Sri98]. KURT Linux supports conventional tasks as well as real-time tasks. It provides a mechanism for transitioning from normal Linux scheduling to a mixed scheduling of conventional and real-time tasks, and to a focused mode where only real-time tasks are scheduled. We overview the technical issues that we had to overcome in order to integrate SRMS into KURT Linux and present the API we have developed for scheduling periodic real-time tasks using SRMS.

Statistical Properties of Single and Competing Non-Linear Fast-Slow Oscillators in Noise

Statistical properties offast-slow Ellias-Grossberg oscillators are studied in response to deterministic and noisy inputs. Oscillatory responses remain stable in noise due to the slow inhibitory variable, which establishes an adaptation level that centers the oscillatory responses of the fast excitatory variable to deterministic and noisy inputs. Competitive interactions between oscillators improve the stability in noise. Although individual oscillation amplitudes decrease with input amplitude, the average to'tal activity increases with input amplitude, thereby suggesting that oscillator output is evaluated by a slow process at downstream network sites.

Statistical modelling of cement slurries for self compacting SIFCON containing silica fume

This paper reports a study carried out to develop a self-compacting fibre reinforced concrete containing a high fibre content with slurry infiltrated fibre concrete (SIFCON). The SIFCON was developed with 10% of steel fibres which are infiltrated by self-compacting cement slurry without any vibration. Traditionally, the infiltration of the slurry into the layer of fibres is carried out under intensive vibration. A two-level fractional factorial design was used to optimise the properties of cement-based slurries with four independent variables, such as dosage of silica fume, dosage of superplasticiser, sand content, and water/cement ratio (W/C). Rheometer, mini-slump test, Lombardi plate cohesion meter, J-fibre penetration test, and induced bleeding were used to assess the behaviour of fresh cement slurries. The compressive strengths at 7 and 28 days were also measured. The statistical models are valid for slurries made with W/C of 0.40 to 0.50, 50 to 100% of sand by mass of cement, 5 to 10% of silica fume by mass of cement, and SP dosage of 0.6 to 1.2% by mass of cement. This model makes it possible to evaluate the effect of individual variables on measured parameters of fresh cement slurries. The proposed models offered useful information to understand trade-offs between mix variables and compare the responses obtained from various test methods in order to optimise self-compacting SIFCON.

Statistical evaluation of an acute dermal toxicity test using the dermal fixed dose procedure

The conventional method for the assessment of acute dermal toxicity (OECD Test Guideline 402, 1987) uses death of animals as an endpoint to identify the median lethal dose (LD50). A new OECD Testing Guideline called the dermal fixed dose procedure (dermal FDP) is being prepared to provide an alternative to Test Guideline 402. In contrast to Test Guideline 402, the dermal FDP does not provide a point estimate of the LD50, but aims to identify that dose of the substance under investigation that causes clear signs of nonlethal toxicity. This is then used to assign classification according to the new Globally Harmonised System of Classification and Labelling scheme (GHS). The dermal FDP has been validated using statistical modelling rather than by in vivo testing. The statistical modelling approach enables calculation of the probability of each GHS classification and the expected numbers of deaths and animals used in the test for imaginary substances with a range of LD50 values and dose-response curve slopes. This paper describes the dermal FDP and reports the results from the statistical evaluation. It is shown that the procedure will be completed with considerably less death and suffering than guideline 402, and will classify substances either in the same or a more stringent GHS class than that assigned on the basis of the LD50 value.

Statistical evaluation of the fixed concentration procedure for acute inhalation toxicity assessment

The conventional method for the assessment of acute inhalation toxicity (OECD Test Guideline 403, 1981) uses death of animals as an endpoint to identify the median lethal concentration (LC50). A new OECD Testing Guideline called the Fixed Concentration Procedure (FCP) is being prepared to provide an alternative to Test Guideline 403. Unlike Test Guideline 403, the FCP does not provide a point estimate of the LC50, but aims to identify an airborne exposure level that causes clear signs of nonlethal toxicity. This is then used to assign classification according to the new Globally Harmonized System of Classification and Labelling scheme (GHS). The FCP has been validated using statistical simulation rather than byin vivo testing. The statistical simulation approach predicts the GHS classification outcome and the numbers of deaths and animals used in the test for imaginary substances with a range of LC50 values and dose response curve slopes. This paper describes the FCP and reports the results from the statistical simulation study assessing its properties. It is shown that the procedure will be completed with considerably less death and suffering than Test Guideline 403, and will classify substances either in the same or a more stringent GHS class than that assigned on the basis of the LC50 value.

On Occurrence Of Plagiarism In Published Computer Science Thesis Reports At Swedish Universities

In recent years, it has been observed that software clones and plagiarism are becoming an increased threat for one?s creativity. Clones are the results of copying and using other?s work. According to the Merriam – Webster dictionary, “A clone is one that appears to be a copy of an original form”. It is synonym to duplicate. Clones lead to redundancy of codes, but not all redundant code is a clone.On basis of this background knowledge ,in order to safeguard one?s idea and to avoid intentional code duplication for pretending other?s work as if their owns, software clone detection should be emphasized more. The objective of this paper is to review the methods for clone detection and to apply those methods for finding the extent of plagiarism occurrence among the Swedish Universities in Master level computer science department and to analyze the results.The rest part of the paper, discuss about software plagiarism detection which employs data analysis technique and then statistical analysis of the results.Plagiarism is an act of stealing and passing off the idea?s and words of another person?s as one?s own. Using data analysis technique, samples(Master level computer Science thesis report) were taken from various Swedish universities and processed in Ephorus anti plagiarism software detection. Ephorus gives the percentage of plagiarism for each thesis document, from this results statistical analysis were carried out using Minitab Software.The results gives a very low percentage of Plagiarism extent among the Swedish universities, which concludes that Plagiarism is not a threat to Sweden?s standard of education in computer science.This paper is based on data analysis, intelligence techniques, EPHORUS software plagiarism detection tool and MINITAB statistical software analysis.

Report on the implementation of the 2009-2011 biennial programme of regional and international cooperation activities of the Statistical Conference of the Americas of ECLAC

Incluye Bibliografía

Types of discrepancy in Millennium Development Goal indicator values and measures for statistical reconciliation: Overall framework and implementation in selected thematic areas and indicators

Includes bibliography.

STATISTICAL METHODS FOR RARE AND COMMON VARIANT ASSOCIATION STUDIES

Complex human diseases are a major challenge for biological research. The goal of my research is to develop effective methods for biostatistics in order to create more opportunities for the prevention and cure of human diseases. This dissertation proposes statistical technologies that have the ability of being adapted to sequencing data in family-based designs, and that account for joint effects as well as gene-gene and gene-environment interactions in the GWA studies. The framework includes statistical methods for rare and common variant association studies. Although next-generation DNA sequencing technologies have made rare variant association studies feasible, the development of powerful statistical methods for rare variant association studies is still underway. Chapter 2 demonstrates two adaptive weighting methods for rare variant association studies based on family data for quantitative traits. The results show that both proposed methods are robust to population stratification, robust to the direction and magnitude of the effects of causal variants, and more powerful than the methods using weights suggested by Madsen and Browning [2009]. In Chapter 3, I extended the previously proposed test for Testing the effect of an Optimally Weighted combination of variants (TOW) [Sha et al., 2012] for unrelated individuals to TOW &ndash F, TOW for Family &ndash based design. Simulation results show that TOW &ndash F can control for population stratification in wide range of population structures including spatially structured populations, is robust to the directions of effect of causal variants, and is relatively robust to percentage of neutral variants. In GWA studies, this dissertation consists of a two &ndash locus joint effect analysis and a two-stage approach accounting for gene &ndash gene and gene &ndash environment interaction. Chapter 4 proposes a novel two &ndash stage approach, which is promising to identify joint effects, especially for monotonic models. The proposed approach outperforms a single &ndash marker method and a regular two &ndash stage analysis based on the two &ndash locus genotypic test. In Chapter 5, I proposed a gene &ndash based two &ndash stage approach to identify gene &ndash gene and gene &ndash environment interactions in GWA studies which can include rare variants. The two &ndash stage approach is applied to the GAW 17 dataset to identify the interaction between KDR gene and smoking status.

IDENTIFICATION OF GENES CONTROLLING BIOLOGICAL PROCESSES AND PATHWAYS THROUGH STATISTICAL ANALYSIS AND NETWORK RECONSTRUCTION

The developmental processes and functions of an organism are controlled by the genes and the proteins that are derived from these genes. The identification of key genes and the reconstruction of gene networks can provide a model to help us understand the regulatory mechanisms for the initiation and progression of biological processes or functional abnormalities (e.g. diseases) in living organisms. In this dissertation, I have developed statistical methods to identify the genes and transcription factors (TFs) involved in biological processes, constructed their regulatory networks, and also evaluated some existing association methods to find robust methods for coexpression analyses. Two kinds of data sets were used for this work: genotype data and gene expression microarray data. On the basis of these data sets, this dissertation has two major parts, together forming six chapters. The first part deals with developing association methods for rare variants using genotype data (chapter 4 and 5). The second part deals with developing and/or evaluating statistical methods to identify genes and TFs involved in biological processes, and construction of their regulatory networks using gene expression data (chapter 2, 3, and 6). For the first part, I have developed two methods to find the groupwise association of rare variants with given diseases or traits. The first method is based on kernel machine learning and can be applied to both quantitative as well as qualitative traits. Simulation results showed that the proposed method has improved power over the existing weighted sum method (WS) in most settings. The second method uses multiple phenotypes to select a few top significant genes. It then finds the association of each gene with each phenotype while controlling the population stratification by adjusting the data for ancestry using principal components. This method was applied to GAW 17 data and was able to find several disease risk genes. For the second part, I have worked on three problems. First problem involved evaluation of eight gene association methods. A very comprehensive comparison of these methods with further analysis clearly demonstrates the distinct and common performance of these eight gene association methods. For the second problem, an algorithm named the bottom-up graphical Gaussian model was developed to identify the TFs that regulate pathway genes and reconstruct their hierarchical regulatory networks. This algorithm has produced very significant results and it is the first report to produce such hierarchical networks for these pathways. The third problem dealt with developing another algorithm called the top-down graphical Gaussian model that identifies the network governed by a specific TF. The network produced by the algorithm is proven to be of very high accuracy.

Statistical methods for multi-marker testing in genetic association studies

As the development of genotyping and next-generation sequencing technologies, multi-marker testing in genome-wide association study and rare variant association study became active research areas in statistical genetics. This dissertation contains three methodologies for association study by exploring different genetic data features and demonstrates how to use those methods to test genetic association hypothesis. The methods can be categorized into in three scenarios: 1) multi-marker testing for strong Linkage Disequilibrium regions, 2) multi-marker testing for family-based association studies, 3) multi-marker testing for rare variant association study. I also discussed the advantage of using these methods and demonstrated its power by simulation studies and applications to real genetic data.