Hadronic accelerators in the universe

The search for the origin of charged cosmic rays remains one of the greatest challenges in astrophysics. Extremely accelerated particles propagate through the universe carrying the secrets of the most energetic cosmic phenomena. While neutral particles are not deflected by magnetic fields and point back to their sources, charged cosmic rays arrive on Earth as a diffuse flux, making it nearly impossible to identify their origin. The MAGIC telescopes, primarily designed to detect high-energetic gamma rays, also have the potential to study charged cosmic rays. This work presents the analysis chain to produce a proton spectrum from data measured with the MAGIC telescopes. The analysis chain includes data preparation, machine learning algorithms for particle reconstruction, and unfolding techniques which consider remaining background contributions. New simulations of air showers induced by charged cosmic rays are used in this analysis and tested accordingly. This work illustrates the potential of IACTs for the research of charged cosmic rays and provides the first proton spectrum of MAGIC, which constitutes a valuable addition to previous measurements by other cosmic-ray experiments.Die Suche nach dem Ursprung der geladenen kosmischen Strahlung ist nach wie vor eine der größten Herausforderungen der Astrophysik. Extrem beschleunigte Teilchen propagieren durch das Universum und tragen die Geheimnisse der höchstenergetischen kosmischen Phänomene in sich. Während neutrale Teilchen nicht von Magnetfeldern abgelenkt werden und zu ihren Quellen zurückweisen, erreicht geladene kosmische Strahlung die Erde als diffuser Teilchenstrom, was es nahezu unmöglich macht, ihren Ursprung zu bestimmen. Die MAGIC-Teleskope, die in erster Linie für die Untersuchung von hochenergetischer Gammastrahlung konzipiert sind, haben auch das Potenzial, geladene kosmische Strahlung zu untersuchen. In dieser Arbeit wird die Analysekette zur Erstellung eines Protonenspektrums aus den mit den MAGIC-Teleskopen gemessenen Daten entwickelt. Die Analysekette umfasst die Datenaufbereitung, Algorithmen für maschinelles Lernen zur Teilchenrekonstruk- tion, und Entfaltungstechniken unter Berücksichtigung von verbliebenen Untergrund- beiträgen. Neue Simulationen der durch geladene kosmische Strahlung induzierten Luftschauer werden in dieser Analyse verwendet und entsprechend getestet. Diese Arbeit veranschaulicht das Potenzial von IACTs für die Forschung im Bereich der geladenen kosmischen Strahlung und liefert das erste Protonenspektrum von MAGIC, welches eine wertvolle Ergänzung zu den bisherigen Messungen anderer Experimente für kosmische Strahlung bildet

An attention model and its application in man-made scene interpretation

The ultimate aim of research into computer vision is designing a system which interprets its surrounding environment in a similar way the human can do effortlessly. However, the state of technology is far from achieving such a goal. In this thesis different components of a computer vision system that are designed for the task of interpreting man-made scenes, in particular images of buildings, are described. The flow of information in the proposed system is bottom-up i.e., the image is first segmented into its meaningful components and subsequently the regions are labelled using a contextual classifier. Starting from simple observations concerning the human vision system and the gestalt laws of human perception, like the law of “good (simple) shape” and “perceptual grouping”, a blob detector is developed, that identifies components in a 2D image. These components are convex regions of interest, with interest being defined as significant gradient magnitude content. An eye tracking experiment is conducted, which shows that the regions identified by the blob detector, correlate significantly with the regions which drive the attention of viewers. Having identified these blobs, it is postulated that a blob represents an object, linguistically identified with its own semantic name. In other words, a blob may contain a window a door or a chimney in a building. These regions are used to identify and segment higher order structures in a building, like facade, window array and also environmental regions like sky and ground. Because of inconsistency in the unary features of buildings, a contextual learning algorithm is used to classify the segmented regions. A model which learns spatial and topological relationships between different objects from a set of hand-labelled data, is used. This model utilises this information in a MRF to achieve consistent labellings of new scenes

Deep learning in the wild

Invited paperDeep learning with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range of machine perception tasks. While this interest is fueled by beautiful success stories, practical work in deep learning on novel tasks without existing baselines remains challenging. This paper explores the specific challenges arising in the realm of real world tasks, based on case studies from research & development in conjunction with industry, and extracts lessons learned from them. It thus fills a gap between the publication of latest algorithmic and methodical developments, and the usually omitted nitty-gritty of how to make them work. Specifically, we give insight into deep learning projects on face matching, print media monitoring, industrial quality control, music scanning, strategy game playing, and automated machine learning, thereby providing best practices for deep learning in practice

ENHANCING NETWORK INTRUSION CLASSIfiCATION THROUGH THE KOLMOGOROV-SMIRNOV SPLITTING CRITERION

ABSTRACTOur investigation aims at detecting network intrusions using decision tree algorithms. Large differences in prior class probabilities of intrusion data have been reported to hinder the performance of decision trees. We propose to replace the Shannon entropy used in tree induction algorithms with a Kolmogorov Smirnov splitting criterion which locates a Bayes optimal cutpoint of attributes. The Kolmogorov-Smirnov distance based on the cumulative distributions is not degraded by class imbalance. Numerical test  results on the KDDCup99 dataset showed that our proposals are attractive to network intrusion detection tasks. The single decision tree gives best results for minority classes, cost metric and global accuracy compared with the bagged boosting of trees of the KDDCup’99 winner and classical decision tree algorithms using the Shannon entropy. In contrast to the complex model of KDDCup winner, our decision tree represents inductive rules (IF-THEN) that facilitate human interpretation.ABSTRACTOur investigation aims at detecting network intrusions using decision tree algorithms. Large differences in prior class probabilities of intrusion data have been reported to hinder the performance of decision trees. We propose to replace the Shannon entropy used in tree induction algorithms with a Kolmogorov Smirnov splitting criterion which locates a Bayes optimal cutpoint of attributes. The Kolmogorov-Smirnov distance based on the cumulative distributions is not degraded by class imbalance. Numerical test  results on the KDDCup99 dataset showed that our proposals are attractive to network intrusion detection tasks. The single decision tree gives best results for minority classes, cost metric and global accuracy compared with the bagged boosting of trees of the KDDCup’99 winner and classical decision tree algorithms using the Shannon entropy. In contrast to the complex model of KDDCup winner, our decision tree represents inductive rules (IF-THEN) that facilitate human interpretation

Reconstruction Methods for Semi-leptonic Decays of B-mesons with the Belle II Experiment

The Belle II detector located in Tsukuba, Japan, building on the work of its predecessor Belle, is scheduled for long-term data collection from electron-positron e+e- collisions commencing in early 2019, for the purpose of studying rare B-meson decays in the search for new physics beyond the Standard Model. The Belle II Analysis Software Framework (BASF2) has been developed for physics analyses, with the Full Event Interpretation (FEI) being one such method designed for the reconstruction of B-meson decays from detector information. The FEI must be trained on simulated Monte Carlo (MC) data and introduces a signal-specific training process that can be tailored for a particular decay of interest in an attempt to increase the performance over signal-independent training processes such as those employed in Full Reconstruction (FR) methods at Belle. This study investigates the performance of the signal-specific and signal-independent FEI algorithms in the context of rare semi-leptonic B-meson decays, in comparison to leptonic decays, with the respective modes B+→ρ0 µ+ νµ and B+ → τ+ ντ chosen as working examples. The relative performance of the FEI methods implemented is evaluated via a number of key performance indicators including the reconstruction efficiency and purity of the reconstructed ϒ(4S) event

Video metadata extraction in a videoMail system

Currently the world swiftly adapts to visual communication. Online services like YouTube and Vine show that video is no longer the domain of broadcast television only. Video is used for different purposes like entertainment, information, education or communication. The rapid growth of today’s video archives with sparsely available editorial data creates a big problem of its retrieval. The humans see a video like a complex interplay of cognitive concepts. As a result there is a need to build a bridge between numeric values and semantic concepts. This establishes a connection that will facilitate videos’ retrieval by humans. The critical aspect of this bridge is video annotation. The process could be done manually or automatically. Manual annotation is very tedious, subjective and expensive. Therefore automatic annotation is being actively studied. In this thesis we focus on the multimedia content automatic annotation. Namely the use of analysis techniques for information retrieval allowing to automatically extract metadata from video in a videomail system. Furthermore the identification of text, people, actions, spaces, objects, including animals and plants. Hence it will be possible to align multimedia content with the text presented in the email message and the creation of applications for semantic video database indexing and retrieving