PI: Frank mueller, production codes on supercomputers are struggling to remain scalable each time the processor core count increases by a factor of 10, even though they run efficiently at smaller scale. But root cause diagnosis fails at petascale since (1) symptoms of performance problems can be subtle, (2) only few metrics can be efficiently collected and (3) tools can only feasibly record a small subset of even these metrics. This work addresses these problems by creating a framework that allows application developers to focus on data analysis that drives customized data extraction combined with on-the-fly analysis specifically geared to their individual problems. This is accomplished by combining trace analysis and in-situ data analysis techniques at runtime, thereby lifting data reduction to a new level where it is analysis. With this approach, modular measurement and analysis components are combined to selectively extract representative data from production codes in a problem-specific manner, which enables root cause analysis. The work demonstrates the feasibility of customized data extraction and analysis at scale for root cause analysis on current and forthcoming multi-petascale supercomputers. It thus contributes to sustain scalable scientific computing into the future up to the largest scales.
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The ability to detect differentially abundant genes and control error rates is evaluated for eleven methods previously used in metagenomics. Resampled data from a large metagenomic data set is used to provide an unbiased basis for comparisons between methods. The number of replicates, the effect size and the gene abundance are all shown to have a large impact on the performance. The statistical characteristics of the evaluated methods can serve as a guide for the statistical analysis in future metagenomic studies. The second paper describes a new statistical method for the analysis of metagenomic data. The underlying model is formulated within the framework of a hierarchical bayesian generalized linear model. A joint prior is placed on the variance parameters and shared between all genes. We evaluate the model and show that it improves the ability to detect differentially abundant genes. This thesis underlines the importance of sound statistical analysis when the data is noisy and high-dimensional. It also demonstrates the potential of statistical modeling within metagenomics. ScalaJack: reason Scalable Trace-based tools for In-Situ data Analysis of hpc applications funded by: nsf ( award abstract ) funding level: 457,395 duration: - 05/31/2015 (no-cost extension until 05/31/2017).
Subject categories, statistics, mathematical statistics, bioinformatics and Systems biology, metagenomics is the study of microbial communities on the genome level by direct sequencing of environmental and clinical samples. Recently developed dna sequencing technologies have made metagenomics widely applicable and the field is growing rapidly. The statistical analysis is however challenging due to the high variability present in the data which stems from the underlying biological diversity and complexity of microbial communities. Metagenomic data is also high-dimensional and the number of replicates is typically few. Many management standard methods are therefore unsuitable and there is a need for developing new statistical procedures. This thesis contains two papers. In the first paper we perform an evaluation of statistical methods for comparative metagenomics.
The thesis describes the implementation of a set of statistical methods (the bootstrap, jackknife, case-deletion diagnostics, log-likelhood profiling and stepwise covariate model building made available through the software perl-speaks-nonmem (PsN). The appropriateness of the methods and the consistency of the software tools were evaluated using a large selection of clinical and nonclinical data. Criteria based on clinical relevance were found to be useful components in automated stepwise covariate model building. Their ability to restric the number of included parameter-covariate relationships while maintaining the predictive performance of the model was demonstrated using the antiarrythmic drug dofetilide. Log-likelihodd profiling was shown to be equivalent to the bootstrap for calculating confidence intervals for fixed-effects parameters if an appropriate estimation method was used. The condition number of the covariance matrix for the parameter estimates was shown to be a good indicator of how well resampling methods behave when applied to pharmacometric data analyses using nonmem. The software developed in this thesis equips modellers with an enhanced set of tools for efficient pharmacometric data analysis. Read the full thesis.
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Along with the spectral information, inclusion of book textural information in feature vectors is expected to increase the robustness of classification and information extraction capabilities of system. Point target behavior in high resolution sar images: time-frequency versus polarimetric analysis. Time-frequency analysis of point target behavior in high resolution single polarization sar images. Parametric versus non-parametric complex-values image analysis. Ieee international geoscience and Remote sensing Symposium, july 2009.
(yet to be published). Towards Intelligent Music Information retrieval. Ieee transactions on Multimedia, pages 564-574, june 2006. Pharmacometrics uses models based on pharmacology, physiology and disease for quantitative analysis of interactions between drugs and patients. The availability of software implementing modern statistical methods is important for efficient model building and evaluation throughout pharmacometric data analyses. The aim of this thesis was to facilitate the practical used of available and new statistical methods in the area of pharmacometric data analysis. This involved the development of suitable software tools that allows for efficient use of these methods, characterisation of basic properties and demonstration of their usefulness when applied in real world data.
1: Radar spectrogram (i) Optical image (ii) sar image and (iii) Radar spectogram of target. The other advantage of high resolution data is that it can be considered stationary when looked in short time. In discussed second techniques, non-linear short time fourier transform analysis carried out in the thesis try to exploit the local stationarity of the signal. Clustering results obtained from non-linear stft analysis are found to be very encouraging. The clustering by radar spectrogram was found to be very time consuming. Thus it can be attempted to extract individual target information using radar spectrogram technique, and combining it in the classification results obtained from non-linear stft technique.
2 shows the classification result obtained using non-linear stft technique. 2: K-means based classification using non-linear stft. Conclusion, it can be concluded that this research work provide new temptations to use complex valued data over detected data. This research work also provides a starting point in analyzing the high resolution sar images by using the spectral content. Thus providing a new dimension to the information extraction from high resolution sar data, leading to a more robust and accurate classification. Further extensions of the work are also possible. For example, now no contextual information is used in the analysis of targets. The inclusion of contextual information can be used to apply this method for content based image retrieval systems.
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Methodology, in this thesis, we have focused on spectral analysis of complex valued sar data for information extraction at finer level. We have mainly discussed two techniques based on spectral analysis. First technique based on spectral decomposition, called radar spectrogram was proposed by cnes nurse in 1 2 for the analysis of fine backscattering behavior of various targets. Radar spectogram, a time frequency analysis (TFA) approach can be assumed as a generalization of the azimuth splitting method. The cut of the spectrum allows the study of the phase responses of scatterers, which have been viewed by the antenna at different viewing angles. The principle exploits the holographic property of the spectrum at the cost of reducing resolution. As we can see from the presented results in Fig. 1, radar spectogram technique provides a very powerful tool for individual target analysis. Although radar spectogram is very complex in nature, but this complexity can be exploited for use of this method in applications such as change detection.
The problem of target analysis in Synthetic Aperture radar (SAR) is dealt differently as sar is not an imaging sensor but an active coherent device, which works on the principle of transmitting coherent electromagnetic pulses and recording the amplitude as well as phase information. Depending upon the requirement sar data is available in various formats, complex valued as well as detected format. Because of the availability of phase information in complex valued data, it is possible to do detail target analysis based on phase and amplitude information. In this thesis, it has been attempted to exploit the holographic property of sar signal for the fine backscattering analysis of targets and perform spectral analysis of complex sar data. A method based on the principle of short time plan fourier transform have been proposed for spectral analysis of complex sar data. The information extracted from this method, and compressed in the form of six non-linear feature vectors acts as input parameter in k-means and svm based clustering algorithms to obtain classification of image. The proposed feature vectors are motivated from timbral texture features used for music genre classification. Another method based on spectral decomposition denoted as radar spectrogram has also been analyzed in this thesis. Terrasar-x spotLight Single look complex data have been used as test data set for the mentioned analysis, and obviously the scheme may be extensible to sar sensors of similar resolution working on different frequency bands.
work is to generate temptations to use complex valued data over detected data and provide a new dimension to the high resolution sar image understanding. The goal of signal processing is to extract information embedded in the signals by various possible means. While dealing with complex signals, choosing tractable technique for analysis and information extraction from various possible options or developing a new method is not an easy task. In similar way, the information extraction from image data, a two dimensional signal is also a mammoth task, as analysis is dependent upon various parameters of the image such as sensor, resolution and data format etc. In case of satellite imagery, the automated method of information retrieval has always been a challenge. Moreover the ever increasing resolution in modern day satellite images has tremendously increased the complexity of problem. With low resolution images, interest was always in global criterion. But the increasing resolution has prompted to analyze targets at finer level.
Completion: July 2009, tutor: Prof. Mihai datcu (dlr, oberpfaffenhofen). Fritsch, abstract, data collected by sar sensor and processed by sar processor is inherently complex valued. For the ease of interpretation to user, melisande it is normally provided in detected format, but the phase information is lost during this process. The spectrum of complex valued sar data which contains this phase information has a special meaning. Thus an attempt has been made to develop and suggest methodologies to expolit this special meaning of spectrum to extract information from complex valued Terrasar-x images for classification. Two techniques, both based on spectral analysis, have been discussed in this thesis. First technique, based on spectral decomposition, denoted as radar spectrogram 1 2 has been found quite suitable for manual target analysis.
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First year Physics, z production cross section, the z-production cross section in proton kites proton collisions at 14 tev will be one of the first measurements done at lhc. It is one of the benchmark processes to understand the cms detector performance. It is an important background channel for Higgs searches. A detailed understanding of this process would allow a determination of the lhc luminosity. The offered Diploma-Thesis are focused on mc-studies and data analysis. Jagmal Singh, information Extraction for the Classification of Terrasar-x images. Duration of the Thesis: 6 months.