Managing Large Data Sets Using Support Vector Machines

Hundreds of Terabytes of CMS (Compact Muon Solenoid) data are being accumulated for storage day by day at the University of Nebraska-Lincoln, which is one of the eight US CMS Tier-2 sites. Managing this data includes retaining useful CMS data sets and clearing storage space for newly arriving data by deleting less useful data sets. This is an important task that is currently being done manually and it requires a large amount of time. The overall objective of this study was to develop a methodology to help identify the data sets to be deleted when there is a requirement for storage space. CMS data is stored using HDFS (Hadoop Distributed File System). HDFS logs give information regarding file access operations. Hadoop MapReduce was used to feed information in these logs to Support Vector Machines (SVMs), a machine learning algorithm applicable to classification and regression which is used in this Thesis to develop a classifier. Time elapsed in data set classification by this method is dependent on the size of the input HDFS log file since the algorithmic complexities of Hadoop MapReduce algorithms here are O(n). The SVM methodology produces a list of data sets for deletion along with their respective sizes. This methodology was also compared with a heuristic called Retention Cost which was calculated using size of the data set and the time since its last access to help decide how useful a data set is. Accuracies of both were compared by calculating the percentage of data sets predicted for deletion which were accessed at a later instance of time. Our methodology using SVMs proved to be more accurate than using the Retention Cost heuristic. This methodology could be used to solve similar problems involving other large data sets.

Phenothiazine based polymers for energy and data storage application

Die vorliegende Arbeit befasst sich mit der Synthese und Charakterisierung von Polymeren mit redox-funktionalen Phenothiazin-Seitenketten. Phenothiazin und seine Derivate sind kleine Redoxeinheiten, deren reversibles Redoxverhalten mit electrochromen Eigenschaften verbunden ist. Das besondere an Phenothiazine ist die Bildung von stabilen Radikalkationen im oxidierten Zustand. Daher können Phenothiazine als bistabile Moleküle agieren und zwischen zwei stabilen Redoxzuständen wechseln. Dieser Schaltprozess geht gleichzeitig mit einer Farbveränderung an her.rnrnIm Rahmen dieser Arbeit wird die Synthese neuartiger Phenothiazin-Polymere mittels radikalischer Polymerisation beschrieben. Phenothiazin-Derivate wurden kovalent an aliphatischen und aromatischen Polymerketten gebunden. Dies erfolgte über zwei unterschiedlichen synthetischen Routen. Die erste Route beinhaltet den Einsatz von Vinyl-Monomeren mit Phenothiazin Funktionalität zur direkten Polymerisation. Die zweite Route verwendet Amin modifizierte Phenothiazin-Derivate zur Funktionalisierung von Polymeren mit Aktivester-Seitenketten in einer polymeranalogen Reaktion. rnrnPolymere mit redox-funktionalen Phenothiazin-Seitenketten sind aufgrund ihrer Elektron-Donor-Eigenschaften geeignete Kandidaten für die Verwendung als Kathodenmaterialien. Zur Überprüfung ihrer Eignung wurden Phenothiazin-Polymere als Elektrodenmaterialien in Lithium-Batteriezellen eingesetzt. Die verwendeten Polymere wiesen gute Kapazitätswerte von circa 50-90 Ah/kg sowie schnelle Aufladezeiten in der Batteriezelle auf. Besonders die Aufladezeiten sind 5-10 mal höher als konventionelle Lithium-Batterien. Im Hinblick auf Anzahl der Lade- und Entladezyklen, erzielten die Polymere gute Werte in den Langzeit-Stabilitätstests. Insgesamt überstehen die Polymere 500 Ladezyklen mit geringen Veränderungen der Anfangswerte bezüglich Ladezeiten und -kapazitäten. Die Langzeit-Stabilität hängt unmittelbar mit der Radikalstabilität zusammen. Eine Stabilisierung der Radikalkationen gelang durch die Verlängerung der Seitenkette am Stickstoffatom des Phenothiazins und der Polymerhauptkette. Eine derartige Alkyl-Substitution erhöht die Radikalstabilität durch verstärkte Wechselwirkung mit dem aromatischen Ring und verbessert somit die Batterieleistung hinsichtlich der Stabilität gegenüber Lade- und Entladezyklen. rnrnDes Weiteren wurde die praktische Anwendung von bistabilen Phenothiazin-Polymeren als Speichermedium für hohe Datendichten untersucht. Dazu wurden dünne Filme des Polymers auf leitfähigen Substraten elektrochemisch oxidiert. Die elektrochemische Oxidation erfolgte mittels Rasterkraftmikroskopie in Kombination mit leitfähigen Mikroskopspitzen. Mittels dieser Technik gelang es, die Oberfläche des Polymers im nanoskaligen Bereich zu oxidieren und somit die lokale Leitfähigkeit zu verändern. Damit konnten unterschiedlich große Muster lithographisch beschrieben und aufgrund der Veränderung ihrer Leitfähigkeit detektiert werden. Der Schreibprozess führte nur zu einer Veränderung der lokalen Leitfähigkeit ohne die topographische Beschaffenheit des Polymerfilms zu beeinflussen. Außerdem erwiesen sich die Muster als besonders stabil sowohl mechanisch als auch über die Zeit.rnrnZum Schluss wurden neue Synthesestrategien entwickelt um mechanisch stabile als auch redox-funktionale Oberflächen zu produzieren. Mit Hilfe der oberflächen-initiierten Atomtransfer-Radikalpolymerisation wurden gepfropfte Polymerbürsten mit redox-funktionalen Phenothiazin-Seitenketten hergestellt und mittels Röntgenmethoden und Rasterkraftmikroskopie analysiert. Eine der Synthesestrategien geht von gepfropften Aktivesterbürsten aus, die anschließend in einem nachfolgenden Schritt mit redox-funktionalen Gruppen modifiziert werden können. Diese Vorgehensweise ist besonders vielversprechend und erlaubt es unterschiedliche funktionelle Gruppen an den Aktivesterbürsten zu verankern. Damit können durch Verwendung von vernetzenden Gruppen neben den Redoxeigenschaften, die mechanische Stabilität solcher Polymerfilme optimiert werden. rn rn

Towards Energy Consumption Measurement in a Cloud Computing Wireless Testbed

The evolution of the Next Generation Networks, especially the wireless broadband access technologies such as Long Term Evolution (LTE) and Worldwide Interoperability for Microwave Access (WiMAX), have increased the number of "all-IP" networks across the world. The enhanced capabilities of these access networks has spearheaded the cloud computing paradigm, where the end-users aim at having the services accessible anytime and anywhere. The services availability is also related with the end-user device, where one of the major constraints is the battery lifetime. Therefore, it is necessary to assess and minimize the energy consumed by the end-user devices, given its significance for the user perceived quality of the cloud computing services. In this paper, an empirical methodology to measure network interfaces energy consumption is proposed. By employing this methodology, an experimental evaluation of energy consumption in three different cloud computing access scenarios (including WiMAX) were performed. The empirical results obtained show the impact of accurate network interface states management and application network level design in the energy consumption. Additionally, the achieved outcomes can be used in further software-based models to optimized energy consumption, and increase the Quality of Experience (QoE) perceived by the end-users.

Cervical soft tissue imaging using a mobile CBCT scanner with a flat panel detector in comparison with corresponding CT and MRI data sets

OBJECTIVE: The aim of this study was to evaluate soft tissue image quality of a mobile cone-beam computed tomography (CBCT) scanner with an integrated flat-panel detector. STUDY DESIGN: Eight fresh human cadavers were used in this study. For evaluation of soft tissue visualization, CBCT data sets and corresponding computed tomography (CT) and magnetic resonance imaging (MRI) data sets were acquired. Evaluation was performed with the help of 10 defined cervical anatomical structures. RESULTS: The statistical analysis of the scoring results of 3 examiners revealed the CBCT images to be of inferior quality regarding the visualization of most of the predefined structures. Visualization without a significant difference was found regarding the demarcation of the vertebral bodies and the pyramidal cartilages, the arteriosclerosis of the carotids (compared with CT), and the laryngeal skeleton (compared with MRI). Regarding arteriosclerosis of the carotids compared with MRI, CBCT proved to be superior. CONCLUSIONS: The integration of a flat-panel detector improves soft tissue visualization using a mobile CBCT scanner.

Systemic analysis of microarray data sets towards identifying genes regulating root development

Nitrogen and water are essential for plant growth and development. In this study, we designed experiments to produce gene expression data of poplar roots under nitrogen starvation and water deprivation conditions. We found low concentration of nitrogen led first to increased root elongation followed by lateral root proliferation and eventually increased root biomass. To identify genes regulating root growth and development under nitrogen starvation and water deprivation, we designed a series of data analysis procedures, through which, we have successfully identified biologically important genes. Differentially Expressed Genes (DEGs) analysis identified the genes that are differentially expressed under nitrogen starvation or drought. Protein domain enrichment analysis identified enriched themes (in same domains) that are highly interactive during the treatment. Gene Ontology (GO) enrichment analysis allowed us to identify biological process changed during nitrogen starvation. Based on the above analyses, we examined the local Gene Regulatory Network (GRN) and identified a number of transcription factors. After testing, one of them is a high hierarchically ranked transcription factor that affects root growth under nitrogen starvation. It is very tedious and time-consuming to analyze gene expression data. To avoid doing analysis manually, we attempt to automate a computational pipeline that now can be used for identification of DEGs and protein domain analysis in a single run. It is implemented in scripts of Perl and R.

Survey on haptic rendering of data sets: Exploration of scalar and vector fields

Complementary to automatic extraction processes, Virtual Reality technologies provide an adequate framework to integrate human perception in the exploration of large data sets. In such multisensory system, thanks to intuitive interactions, a user can take advantage of all his perceptual abilities in the exploration task. In this context the haptic perception, coupled to visual rendering, has been investigated for the last two decades, with significant achievements. In this paper, we present a survey related to exploitation of the haptic feedback in exploration of large data sets. For each haptic technique introduced, we describe its principles and its effectiveness.

Influence of the sunspot cycle on the Northern Hemisphere wintertime circulation from long upper-air data sets

Here we present a study of the 11 yr sunspot cycle's imprint on the Northern Hemisphere atmospheric circulation, using three recently developed gridded upper-air data sets that extend back to the early twentieth century. We find a robust response of the tropospheric late-wintertime circulation to the sunspot cycle, independent from the data set. This response is particularly significant over Europe, although results show that it is not directly related to a North Atlantic Oscillation (NAO) modulation; instead, it reveals a significant connection to the more meridional Eurasian pattern (EU). The magnitude of mean seasonal temperature changes over the European land areas locally exceeds 1 K in the lower troposphere over a sunspot cycle. We also analyse surface data to address the question whether the solar signal over Europe is temporally stable for a longer 250 yr period. The results increase our confidence in the existence of an influence of the 11 yr cycle on the European climate, but the signal is much weaker in the first half of the period compared to the second half. The last solar minimum (2005 to 2010), which was not included in our analysis, shows anomalies that are consistent with our statistical results for earlier solar minima.

NodePM: A Remote Monitoring Alert System for Energy Consumption Using Probabilistic Techniques

In this paper, we propose an intelligent method, named the Novelty Detection Power Meter (NodePM), to detect novelties in electronic equipment monitored by a smart grid. Considering the entropy of each device monitored, which is calculated based on a Markov chain model, the proposed method identifies novelties through a machine learning algorithm. To this end, the NodePM is integrated into a platform for the remote monitoring of energy consumption, which consists of a wireless sensors network (WSN). It thus should be stressed that the experiments were conducted in real environments different from many related works, which are evaluated in simulated environments. In this sense, the results show that the NodePM reduces by 13.7% the power consumption of the equipment we monitored. In addition, the NodePM provides better efficiency to detect novelties when compared to an approach from the literature, surpassing it in different scenarios in all evaluations that were carried out.

Automated 3D Lumbar Intervertebral Disc Segmentation from MRI Data Sets

This paper proposed an automated 3D lumbar intervertebral disc (IVD) segmentation strategy from MRI data. Starting from two user supplied landmarks, the geometrical parameters of all lumbar vertebral bodies and intervertebral discs are automatically extracted from a mid-sagittal slice using a graphical model based approach. After that, a three-dimensional (3D) variable-radius soft tube model of the lumbar spine column is built to guide the 3D disc segmentation. The disc segmentation is achieved as a multi-kernel diffeomorphic registration between a 3D template of the disc and the observed MRI data. Experiments on 15 patient data sets showed the robustness and the accuracy of the proposed algorithm.

Advances in understanding energy consumption behavior and the governance of its change - Outline of an integrated framework

Transforming today’s energy systems in industrialized countries requires a substantial reduction of the total energy consumption at the individual level. Selected instruments have been found to be effective in changing people’s behavior in single domains. However, the so far weak success story on reducing overall energy consumption indicates that our understanding of the determining factors of individual energy consumption as well as of its change is far from being conclusive. Among others, the scientific state of the art is dominated by analyzing single domains of consumption and by neglecting embodied energy. It also displays strong disciplinary splits and the literature often fails to distinguish between explaining behavior and explaining change of behavior. Moreover, there are knowledge gaps regarding the legitimacy and effectiveness of the governance of individual consumption behavior and its change. Against this backdrop, the aim of this paper is to establish an integrated interdisciplinary framework that offers a systematic basis for linking the different aspects in research on energy related consumption behavior, thus paving the way for establishing a better evidence base to inform societal actions. The framework connects the three relevant analytical aspects of the topic in question: (1) It systematically and conceptually frames the objects, i.e. the energy consumption behavior and its change (explananda); (2) it structures the factors that potentially explain the energy consumption behavior and its change (explanantia); (3) it provides a differentiated understanding of change inducing interventions in terms of governance. Based on the existing states of the art approaches from different disciplines within the social sciences the proposed framework is supposed to guide interdisciplinary empirical research.