Evaluating Modeled Intra- to Multidecadal Climate Variability Using Running Mann–Whitney \u3cem\u3eZ\u3c/em\u3e Statistics

An analysis method previously used to detect observed intra- to multidecadal (IMD) climate regimes was adapted to compare observed and modeled IMD climate variations. Pending the availability of the more appropriate phase 5 Coupled Model Intercomparison Project (CMIP-5) simulations, the method is demonstrated using CMIP-3 model simulations. Although the CMIP-3 experimental design will almost certainly prevent these model runs from reproducing features of historical IMD climate variability, these simulations allow for the demonstration of the method and illustrate how the models and observations disagree. This method samples a time series’s data rankings over moving time windows, converts those ranking sets to a Mann–Whitney U statistic, and then normalizes the U statistic into a Z statistic. By detecting optimally significant IMD ranking regimes of arbitrary onset and varying duration, this process generates time series of Z values that are an adaptively low-passed and normalized transformation of the original time series. Principal component (PC) analysis of the Z series derived from observed annual temperatures at 92 U.S. grid locations during 1919–2008 shows two dominant modes: a PC1 mode with cool temperatures before the late 1960s and warm temperatures after the mid-1980s, and a PC2 mode indicating a multidecadal temperature cycle over the Southeast. Using a graphic analysis of a Z error metric that compares modeled and observed Z series, the three CMIP-3 model simulations tested here are shown to reproduce the PC1 mode but not the PC2 mode. By providing a way to compare grid-level IMD climate response patterns in observed and modeled data, this method can play a useful diagnostic role in future model development and decadal climate forecasting

Children's biobehavioral reactivity to challenge predicts DNA methylation in adolescence and emerging adulthood.

A growing body of research has documented associations between adverse childhood environments and DNA methylation, highlighting epigenetic processes as potential mechanisms through which early external contexts influence health across the life course. The present study tested a complementary hypothesis: indicators of children's early internal, biological, and behavioral responses to stressful challenges may also be linked to stable patterns of DNA methylation later in life. Children's autonomic nervous system reactivity, temperament, and mental health symptoms were prospectively assessed from infancy through early childhood, and principal components analysis (PCA) was applied to derive composites of biological and behavioral reactivity. Buccal epithelial cells were collected from participants at 15 and 18 years of age. Findings revealed an association between early life biobehavioral inhibition/disinhibition and DNA methylation across many genes. Notably, reactive, inhibited children were found to have decreased DNA methylation of the DLX5 and IGF2 genes at both time points, as compared to non-reactive, disinhibited children. Results of the present study are provisional but suggest that the gene's profile of DNA methylation may constitute a biomarker of normative or potentially pathological differences in reactivity. Overall, findings provide a foundation for future research to explore relations among epigenetic processes and differences in both individual-level biobehavioral risk and qualities of the early, external childhood environment

Online Signature Verification using SVD Method

Online signature verification rests on hypothesis which any writer has similarity among signature samples, with scale variability and small distortion. This is a dynamic method in which users sign and then biometric system recognizes the signature by analyzing its characters such as acceleration, pressure, and orientation. The proposed technique for online signature verification is based on the Singular Value Decomposition (SVD) technique which involves four aspects: I) data acquisition and preprocessing 2) feature extraction 3) matching (classification), 4) decision making. The SVD is used to find r-singular vectors sensing the maximal energy of the signature data matrix A, called principle subspace thus account for most of the variation in the original data. Having modeled the signature through its r-th principal subspace, the authenticity of the tried signature can be determined by calculating the average distance between its principal subspace and the template signature. The input device used for this signature verification system is 5DT Data Glove 14 Ultra which is originally design for virtual reality application. The output of the data glove, which captures the dynamic process in the signing action, is the data matrix, A to be processed for feature extraction and matching. This work is divided into two parts. In part I, we investigate the performance of the SVD-based signature verification system using a new matching technique, that is, by calculating the average distance between the different subspaces. In part IJ, we investigate the performance of the signature verification with reducedsensor data glove. To select the 7-most prominent sensors of the data glove, we calculate the F-value for each sensor and choose 7 sensors that gives the highest Fvalue

Monitoring Architecture for Real Time Systems

It can be hard to understand how an operating system - and software in general - reached a certain output just by looking at said output. A simple approach is to use loggers, or simple print statements on some specific critical areas, however that is an approach that does not scale very well in a consistent and manageable way. The purpose of this thesis is to propose and develop a tool - a Monitoring Tool - capable of capturing and recording the execution of a given application with minimal intrusion in the context of real-time embedded systems, namely using a space-qualified version of the RTEMS real-time operating system, and making that information available for further processing and analysis. Multicore environments are also considered. The current state of the art in monitoring and execution tracing is presented, featuring both a literature review and a discussion of existing tools and frameworks. Using an implementation of the proposed architecture, the tool was tested in both unicore and multicore configurations in both sparc and arm architectures, and was able to record execution data of a sample application, with varying degrees of verbosity.Nem sempre é fácil perceber como é que um sistema operativo - e software em geral - chegaram a determinado resultado apenas olhando para este. A abordagem normal é usar registos, ou pequenas impressões em locais estratégicos do código, no entanto esta abordagem não é escalável de forma consistente e sustentada. O propósito desta tese é o de propor e desenvolver uma ferramenta - uma ferramenta de monitorização - capaz de capturar e registar a execução de uma dada aplicação com o mínimo de impacto no contexto de sistemas embebidos de tempo-real, nomeadamente usando uma versão do sistema operativo de tempo-real Real-Time Executive for Multiprocessor Systems (RTEMS) qualificada para o espaço, e colocando essa informação à disposição para processamento e análise futura. Ambientes com múltiplos núcleos de processamento são também considerados. O atual estado da arte em monitorização e registo de execução de software é apresentado, destacando tanto exemplos da literatura como ferramentas e frameworks existentes. Usando uma implementação da arquitetura proposta, a ferramenta foi testada em configurações com um ou mais núcleos de processamento em arquiteturas sparc e arm, tendo sido capaz de registar e gravar dados da execução de uma aplicação de exemplo, como vários níveis de detalhe

Machine Learning for Biometrics

Biometrics aims at reliable and robust identification of humans from their personal traits, mainly for security and authentication purposes, but also for identifying and tracking the users of smarter applications. Frequently considered modalities are fingerprint, face, iris, palmprint and voice, but there are many other possible biometrics, including gait, ear image, retina, DNA, and even behaviours. This chapter presents a survey of machine learning methods used for biometrics applications, and identifies relevant research issues. We focus on three areas of interest: offline methods for biometric template construction and recognition, information fusion methods for integrating multiple biometrics to obtain robust results, and methods for dealing with temporal information. By introducing exemplary and influential machine learning approaches in the context of specific biometrics applications, we hope to provide the reader with the means to create novel machine learning solutions to challenging biometrics problems