Parallel Support Vector Machines

The Support Vector Machine (SVM) is a supervised algorithm for the solution of classification and regression problems. SVMs have gained widespread use in recent years because of successful applications like character recognition and the profound theoretical underpinnings concerning generalization performance. Yet, one of the remaining drawbacks of the SVM algorithm is its high computational demands during the training and testing phase. This article describes how to efficiently parallelize SVM training in order to cut down execution times. The parallelization technique employed is based on a decomposition approach, where the inner quadratic program (QP) is solved using Sequential Minimal Optimization (SMO). Thus all types of SVM formulations can be solved in parallel, including C-SVC and nu-SVC for classification as well as epsilon-SVR and nu-SVR for regression. Practical results show, that on most problems linear or even superlinear speedups can be attained

Self-Learning Prediciton System for Optimisation of Workload Managememt in a Mainframe Operating System

We present a framework for extraction and prediction of online workload data from a workload manager of a mainframe operating system. To boost overall system performance, the prediction will be corporated into the workload manager to take preventive action before a bottleneck develops. Model and feature selection automatically create a prediction model based on given training data, thereby keeping the system flexible. We tailor data extraction, preprocessing and training to this specific task, keeping in mind the nonstationarity of business processes. Using error measures suited to our task, we show that our approach is promising. To conclude, we discuss our first results and give an outlook on future work

Investigating health impacts of natural resource extraction projects in Burkina Faso, Ghana, Mozambique, and Tanzania: protocol for a mixed methods study

Natural resource extraction projects offer both opportunities and risks for sustainable development and health in host communities. Often, however, the health of the community suffers. Health impact assessment (HIA) can mitigate the risks and promote the benefits of development but is not routinely done in the developing regions that could benefit the most.; Our study aims to investigate health and health determinants in regions affected by extractive industries in Burkina Faso, Ghana, Mozambique, and Tanzania. The evidence generated in our study will inform a policy dialogue on how HIA can be promoted as a regulatory approach as part of the larger research initiative called the HIA4SD (Health impact assessment for sustainable development) project.; The study is a concurrent triangulation, mixed methods, multi-stage, multi-focus project that specifically addresses the topics of governance and policy, social determinants of health, health economics, health systems, maternal and child health, morbidity and mortality, and environmental determinants, as well as the associated health outcomes in natural resource extraction project settings across four countries. To investigate each of these health topics, the project will (1) use existing population-level databases to quantify incidence of disease and other health outcomes and determinants over time using time series analysis; (2) conduct two quantitative surveys on mortality and cost of disease in producer regions; and (3) collect primary qualitative data using focus groups and key informant interviews describing community perceptions of the impacts of extraction projects on health and partnership arrangements between the projects and local and national governance. Differences in health outcomes and health determinants between districts with and without an extraction project will be analyzed using matched geographical analyses in quasi-Poisson regression models and binomial regression models. Costs to the health system and to the households from diseases found to be associated with projects in each country will be estimated retrospectively.; Fieldwork for the study began in February 2019 and concluded in February 2020. At the time of submission, qualitative data collection had been completed in all four study countries. In Burkina Faso, 36 focus group discussions and 74 key informant interviews were conducted in three sites. In Ghana, 34 focus group discussions and 64 key informant interviews were conducted in three sites. In Mozambique, 75 focus group discussions and 103 key informant interviews were conducted in four sites. In Tanzania, 36 focus group discussions and 84 key informant interviews were conducted in three sites. Quantitative data extraction and collection is ongoing in all four study countries. Ethical approval for the study was received in all four study countries prior to beginning the fieldwork. Data analyses are underway and results are expected to be published in 2020 and 2021.; Disentangling the complex interactions of resource extraction projects with their host communities requires an integrative approach drawing on many methodologies under the HIA umbrella. By using complementary data sources to address the question of population health in project areas from several angles, bias and missing data will be reduced, generating high-quality evidence to aid countries in moving toward sustainable development.; DERR1-10.2196/17138

Nachhaltigkeit im industriellen Umfeld

Im Rahmen der Lehrveranstaltung "Nachhaltigkeit im industriellen Umfeld" im Masterstudiengang Umwelt- und Verfahrenstechnik der Hochschulen Konstanz und Ravensburg-Weingarten wurde 2015 eine studentische Fachkonferenz durchgeführt. Die Studierenden entwickelten in Einzelarbeit oder als Zweierteam Konferenzbeiträge zu folgenden Themen: - Innovationen und Spannendes aus dem Bereich der Energieerzeugung und -wandlung - Aspekte der Schließung von Stoffkreisläufen und Vermeidung von Schadstoffeinträgen in die Umwelt - Chancen und Herausforderungen Nachwachsender Rohstoffe bei verschiedenen Einsatzmöglichkeiten sowie Themen der Nachhaltigkeit in der Landwirtschaft - verschiedene Blickwinkel auf das Thema Wasser (von der Abwasserreinigung bis zum Wasserkonsum der Konsumenten) - die Betrachtung spezifischer Industrien und Unternehmen sowie deren Werkzeuge zur Umsetzung von Nachhaltigkeit Die Ergebnisse der studentischen Fachkonferenz zur „Nachhaltigkeit im industriellen Umfeld“ werden in der vorliegenden Publikation präsentiert

Adaptive Mikrostimulation zur Stabilisierung evozierter kortikaler Potentiale

Die Wiederherstellung einer verlorenen Sinneswahrnehmung, wie zum Beispiel des Sehvermögens, ermöglichen kortikale Implantate. Künstliche Wahrnehmungen werden dabei durch direkte Stimulation des entsprechenden Gehirnareals über eine Reihe von Mikroelektroden hervorgerufen. Im Gegensatz zum Retina-Implantat können kortikale Implantate bei einer blinden Person sogar dann eingesetzt werden, wenn der Informationsfluss zwischen Rezeptor und Gehirn entlang der visuellen Leitungsbahn erst in späteren Stufen unterbrochen ist. Das Erzeugen stabiler Sinneseindrücke durch direkte Stimulation des Gehirns ist derzeit noch nicht möglich. Ein wesentliches Problem dabei ist die starke Schwankung evozierter Potentiale, die durch eine stetig fluktuierende kortikale Aktivität verursacht wird. Diese Dissertation hat sich mit dieser Schwierigkeit auseinandergesetzt und schlägt zur Stabilisierung evozierter kortikaler Potentiale eine adaptive Mikrostimulation vor, bei der die Intensität der Stromstöße ausgehend von der gegenwärtigen Gehirnaktivität fortlaufend angepasst wird. Um die Machbarkeit dieses Ansatzes zu untersuchen, wurde im Rahmen dieser Arbeit ein experimenteller Aufbau für eine gleichzeitige Aufnahme und Stimulation im Barrel Kortex anästhesierter Ratten entwickelt. Für die Steuerung der Intensitäten werden ein direkter und inverser Lösungsansatz vorgeschlagen und evaluiert, wobei die Schätzung der erforderlichen Funktionen auf Basis experimenteller Daten durch Support Vektor Regression erfolgt. Eine anwendungsspezifische Kern-Funktion, die unter Ausnutzung von Vorwissen über die zeitliche Struktur der Daten, eine Dekodierung der kortikalen Aktivität erlaubt, gehört zu den weiteren algorithmischen Entwicklungen. Im Vergleich mit üblichen Kern-Funktionen erzielt die weiterentwickelte Kern-Funktion eine höhere Präzision bei der Vorhersage der Stimulationsintensität. Die bei sieben Versuchstieren erhobenen experimentellen Ergebnisse zeigen erstmals, dass evozierte Potentiale durch adaptive Mikrostimulation stabilisiert werden können, falls die Stromstöße eine hinreichend geringe Intensität aufweisen. Allerdings schwankt der durch adaptive Mikrostimulation erreichte Effekt innerhalb weniger Minuten, was auf einen Verfall der durch Support Vektor Regression ermittelten Funktion zurückzuführen ist. Zur Vermeidung dieses Verfalls in zukünftigen Anwendungen adaptiver Mikrostimulation schlägt diese Arbeit einen neuen Algorithmus zum Online-Training der Support Vektor Regression vor. Der Algorithmus ist besonders für eine Aktualisierung der geschätzten Funktion in einer Echtzeit Umgebung geeignet und benötigt keine manuelle Einstellung einer Schrittweite. Mit dem neuen Algorithmus lässt sich, bei gleichem Zeitaufwand pro Iteration, im Vergleich mit anderen aktuellen Verfahren eine schnellere Konvergenz der Vorhersagefehlers auf verschiedenen Datensätzen erreichen. Zusammengenommen bestätigen die in dieser Arbeit vorgestellten Ergebnisse die Machbarkeit adaptiver Mikrostimulation. Darüber hinaus eröffnet sich die Perspektive zukünftig stabile Wahrnehmungen mit Hilfe kortikaler Implantate zu erzeugen.Cortical implants hold the promise to restore lost sensory perceptions, like vision, by using an array of microelectrodes to directly stimulate neural tissue in the corresponding area of the brain. In contrast to retinal implants, cortical implants can aid blind patients even when the information flow from receptors to brain is interrupted in later stages of the visual pathway. Unfortunately evoking stable perceptions by direct stimulation in cortex is currently not possible. One essential unsolved problem is the high variability of evoked cortical potentials caused by an incessantly fluctuating cortical state. This thesis deals with this problem and proposes to stabilize evoked cortical potentials by adaptive microstimulation, where the intensity of stimulation pulses is continuously adjusted based on the ongoing brain activity. To investigate the feasibility of this approach this work developed an experimental setup with simultaneous recording and stimulation in the barrel cortex of anesthetized rats. A direct and inverse solution using support vector regression is suggested to tackle the control problem associated with adaptive microstimulation. Further algorithmic developments include an application specific kernel function for decoding the cortical state which allows to exploit prior knowledge about the temporal structure of stimulation trials and outperforms other standard kernels. The experimental results recorded in seven animals show for the first time that adaptive microstimulation can stabilize evoked cortical potentials if intensities are chosen from a sub-threshold range. Unfortunately the size of the stabilization effect varies on a time scale of minutes which is due to invalidation of the function learnt by support vector regression. To eliminate the temporal variation in future applications of adaptive microstimulation this work proposes a novel online training algorithm for support vector regression which is suitable for updating the estimated function in a real-time environment and does not require manual tuning of a learning rate. The new algorithm is shown to perform better in terms of convergence speed in comparison to other state of the art algorithms on several benchmark data sets. Together the results presented in this work support the feasibility of adaptive microstimulation and open the perspective to reliably imprint brain activity in future cortical implants

Are spatial frequency cues used for whisker-based active discrimination?

Rats are highly skilled in discriminating objects and textures by palpatory movements of their whiskers. If they used spatial frequency cues, they would be able to optimize performance in a stimulus dependent way - by moving their whisker faster or slower across the texture surface, thereby shifting the frequency content of the neuronal signal toward an optimal working range for perception. We tested this idea by measuring discrimination performance of head-fixed rats that were trained to actively sample from virtual grids. The virtual grid mimicked discrete and repetitive whisker deflections generated by real objects (e.g. grove patterns) with single electrical microstimulation pulses delivered directly to the barrel cortex, and provided the critical advantage that stimuli could be controlled at highest precision. Surprisingly, rats failed to use the spatial frequency cue for discrimination as a matter of course, and also failed to adapt whisking patterns in order to optimally exploit frequency differences. In striking contrast they could be easily trained to discriminate stimuli differing in electrical pulse amplitudes, a stimulus property that is not malleable by whisking. Intermingling these 'easy-to-discriminate' discriminanda with others that solely offered frequency/positional cues, rats could be guided to base discrimination on frequency and/or position, albeit on a lower level of performance. Following this training, abolishment of electrical amplitude cues and reducing positional cues led to initial good performance which, however, was unstable and ran down to very low levels over the course of hundreds of trials. These results clearly demonstrate that frequency cues, while definitely perceived by rats, are of minor importance and they are not able to support consistent modulation of whisking patterns to optimize performance

Vibrotactile Discrimination in the Rat Whisker System is Based on Neuronal Coding of Instantaneous Kinematic Cues

Which physical parameter of vibrissa deflections is extracted by the rodent tactile system for discrimination? Particularly, it remains unclear whether perception has access to instantaneous kinematic parameters (i.e., the details of the trajectory) or relies on temporally integration of the movement trajectory such as frequency (e.g., spec-tral information) and intensity (e.g., mean speed). Here, we use a novel detection of change paradigm in head-fixed rats, which presents pulsatile vibrissa stimuli in seamless sequence for discrimination. This procedure ensures that processes of decision making can directly tap into sensory signals (no memory functions involved). We find that dis-crimination performance based on instantaneous kinematic cues far exceeds the ones provided by frequency and intensity. Neuronal mod-eling based on barrel cortex single units shows that small populations of sensitive neurons provide a transient signal that optimally fits the characteristic of the subject’s perception. The present study is the first to show that perceptual read-out is superior in situations allowing the subject to base perception on detailed trajectory cues, that is, instantaneous kinematic variables. A possible impact of this finding on tactile systems of other species is suggested by evidence for instantaneous coding also in primates