The Topology ToolKit

This system paper presents the Topology ToolKit (TTK), a software platform designed for topological data analysis in scientific visualization. TTK provides a unified, generic, efficient, and robust implementation of key algorithms for the topological analysis of scalar data, including: critical points, integral lines, persistence diagrams, persistence curves, merge trees, contour trees, Morse-Smale complexes, fiber surfaces, continuous scatterplots, Jacobi sets, Reeb spaces, and more. TTK is easily accessible to end users due to a tight integration with ParaView. It is also easily accessible to developers through a variety of bindings (Python, VTK/C++) for fast prototyping or through direct, dependence-free, C++, to ease integration into pre-existing complex systems. While developing TTK, we faced several algorithmic and software engineering challenges, which we document in this paper. In particular, we present an algorithm for the construction of a discrete gradient that complies to the critical points extracted in the piecewise-linear setting. This algorithm guarantees a combinatorial consistency across the topological abstractions supported by TTK, and importantly, a unified implementation of topological data simplification for multi-scale exploration and analysis. We also present a cached triangulation data structure, that supports time efficient and generic traversals, which self-adjusts its memory usage on demand for input simplicial meshes and which implicitly emulates a triangulation for regular grids with no memory overhead. Finally, we describe an original software architecture, which guarantees memory efficient and direct accesses to TTK features, while still allowing for researchers powerful and easy bindings and extensions. TTK is open source (BSD license) and its code, online documentation and video tutorials are available on TTK's website

Automatic design of neuromarkers for obsessive compulsive disorder characterisation

This bacherlor thesis proposes a new paradigm to discover biomarkers capable of characterizing obsessive-compulsive disorder (OCD) by means of machine learning methods. These biomarkers, named neuromarkers, will be obtained through the analysis of sets of magnetic resonance images of the brains of OCD patients and healthy control subjects. The design of the neuromarkers stems from a method for the automatic discovery of clusters of voxels, distributed in separate brain regions, relevant to OCD. This method was recently published by Dr. Emilio Parrado Hernández, Dr. Vanessa Gómez Verdejo and Dr. Manel Martínez Ramón. With these clusters as a starting point, we will de ne the neuromarkers as a set of measurements describing features of these individual regions. Then we will perform a selection of these neuromarkers, using state of the art feature selection techniques, to arrive at a reduced, relevant and intuitive set. The results will be sent to Dr. Carles Soriano Mas at the Bellvitge University Hospital in Barcelona, Spain. His feedback will be used to determine the e cacy of our neuromarkers and their usefulness for psychiatric analysis. The main goal of the project is to come up with a set of neuromarkers for OCD characterisation that are easy to interpret and handle by the psychiatric community. A paper presenting the methods and results described in this bachelor thesis, of which the student is the main author, has been submitted and accepted for presentation in the 2014 European Congress of Machine Learning (ECML/PKDD 2014). The ECML reported a 23.8% paper acceptance rate for 2014.Ingeniería de Sistemas Audiovisuale

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Automated detection of depression from brain structural magnetic resonance imaging (sMRI) scans

 Automated sMRI-based depression detection system is developed whose components include acquisition and preprocessing, feature extraction, feature selection, and classification. The core focus of the research is on the establishment of a new feature selection algorithm that quantifies the most relevant brain volumetric feature for depression detection at an individual level

Interpretable Machine Learning Methods for Prediction and Analysis of Genome Regulation in 3D

With the development of chromosome conformation capture-based techniques, we now know that chromatin is packed in three-dimensional (3D) space inside the cell nucleus. Changes in the 3D chromatin architecture have already been implicated in diseases such as cancer. Thus, a better understanding of this 3D conformation is of interest to help enhance our comprehension of the complex, multipronged regulatory mechanisms of the genome. The work described in this dissertation largely focuses on development and application of interpretable machine learning methods for prediction and analysis of long-range genomic interactions output from chromatin interaction experiments. In the first part, we demonstrate that the genetic sequence information at the ge- nomic loci is predictive of the long-range interactions of a particular locus of interest (LoI). For example, the genetic sequence information at and around enhancers can help predict whether it interacts with a promoter region of interest. This is achieved by building string kernel-based support vector classifiers together with two novel, in- tuitive visualization methods. These models suggest a potential general role of short tandem repeat motifs in the 3D genome organization. But, the insights gained out of these models are still coarse-grained. To this end, we devised a machine learning method, called CoMIK for Conformal Multi-Instance Kernels, capable of providing more fine-grained insights. When comparing sequences of variable length in the su- pervised learning setting, CoMIK can not only identify the features important for classification but also locate them within the sequence. Such precise identification of important segments of the whole sequence can help in gaining de novo insights into any role played by the intervening chromatin towards long-range interactions. Although CoMIK primarily uses only genetic sequence information, it can also si- multaneously utilize other information modalities such as the numerous functional genomics data if available. The second part describes our pipeline, pHDee, for easy manipulation of large amounts of 3D genomics data. We used the pipeline for analyzing HiChIP experimen- tal data for studying the 3D architectural changes in Ewing sarcoma (EWS) which is a rare cancer affecting adolescents. In particular, HiChIP data for two experimen- tal conditions, doxycycline-treated and untreated, and for primary tumor samples is analyzed. We demonstrate that pHDee facilitates processing and easy integration of large amounts of 3D genomics data analysis together with other data-intensive bioinformatics analyses.Mit der Entwicklung von Techniken zur Bestimmung der Chromosomen-Konforma- tion wissen wir jetzt, dass Chromatin in einer dreidimensionalen (3D) Struktur in- nerhalb des Zellkerns gepackt ist. Änderungen in der 3D-Chromatin-Architektur sind bereits mit Krankheiten wie Krebs in Verbindung gebracht worden. Daher ist ein besseres Verständnis dieser 3D-Konformation von Interesse, um einen tieferen Einblick in die komplexen, vielschichtigen Regulationsmechanismen des Genoms zu ermöglichen. Die in dieser Dissertation beschriebene Arbeit konzentriert sich im Wesentlichen auf die Entwicklung und Anwendung interpretierbarer maschineller Lernmethoden zur Vorhersage und Analyse von weitreichenden genomischen Inter- aktionen aus Chromatin-Interaktionsexperimenten. Im ersten Teil zeigen wir, dass die genetische Sequenzinformation an den genomis- chen Loci prädiktiv für die weitreichenden Interaktionen eines bestimmten Locus von Interesse (LoI) ist. Zum Beispiel kann die genetische Sequenzinformation an und um Enhancer-Elemente helfen, vorherzusagen, ob diese mit einer Promotorregion von Interesse interagieren. Dies wird durch die Erstellung von String-Kernel-basierten Support Vector Klassifikationsmodellen zusammen mit zwei neuen, intuitiven Visual- isierungsmethoden erreicht. Diese Modelle deuten auf eine mögliche allgemeine Rolle von kurzen, repetitiven Sequenzmotiven (”tandem repeats”) in der dreidimensionalen Genomorganisation hin. Die Erkenntnisse aus diesen Modellen sind jedoch immer noch grobkörnig. Zu diesem Zweck haben wir die maschinelle Lernmethode CoMIK (für Conformal Multi-Instance-Kernel) entwickelt, welche feiner aufgelöste Erkennt- nisse liefern kann. Beim Vergleich von Sequenzen mit variabler Länge in überwachten Lernszenarien kann CoMIK nicht nur die für die Klassifizierung wichtigen Merkmale identifizieren, sondern sie auch innerhalb der Sequenz lokalisieren. Diese genaue Identifizierung wichtiger Abschnitte der gesamten Sequenz kann dazu beitragen, de novo Einblick in jede Rolle zu gewinnen, die das dazwischen liegende Chromatin für weitreichende Interaktionen spielt. Obwohl CoMIK hauptsächlich nur genetische Se- quenzinformationen verwendet, kann es gleichzeitig auch andere Informationsquellen nutzen, beispielsweise zahlreiche funktionellen Genomdaten sofern verfügbar. Der zweite Teil beschreibt unsere Pipeline pHDee für die einfache Bearbeitung großer Mengen von 3D-Genomdaten. Wir haben die Pipeline zur Analyse von HiChIP- Experimenten zur Untersuchung von dreidimensionalen Architekturänderungen bei der seltenen Krebsart Ewing-Sarkom (EWS) verwendet, welche Jugendliche betrifft. Insbesondere werden HiChIP-Daten für zwei experimentelle Bedingungen, Doxycyclin- behandelt und unbehandelt, und für primäre Tumorproben analysiert. Wir zeigen, dass pHDee die Verarbeitung und einfache Integration großer Mengen der 3D-Genomik- Datenanalyse zusammen mit anderen datenintensiven Bioinformatik-Analysen erle- ichtert

Application of advanced machine learning techniques to early network traffic classification

The fast-paced evolution of the Internet is drawing a complex context which imposes demanding requirements to assure end-to-end Quality of Service. The development of advanced intelligent approaches in networking is envisioning features that include autonomous resource allocation, fast reaction against unexpected network events and so on. Internet Network Traffic Classification constitutes a crucial source of information for Network Management, being decisive in assisting the emerging network control paradigms. Monitoring traffic flowing through network devices support tasks such as: network orchestration, traffic prioritization, network arbitration and cyberthreats detection, amongst others. The traditional traffic classifiers became obsolete owing to the rapid Internet evolution. Port-based classifiers suffer from significant accuracy losses due to port masking, meanwhile Deep Packet Inspection approaches have severe user-privacy limitations. The advent of Machine Learning has propelled the application of advanced algorithms in diverse research areas, and some learning approaches have proved as an interesting alternative to the classic traffic classification approaches. Addressing Network Traffic Classification from a Machine Learning perspective implies numerous challenges demanding research efforts to achieve feasible classifiers. In this dissertation, we endeavor to formulate and solve important research questions in Machine-Learning-based Network Traffic Classification. As a result of numerous experiments, the knowledge provided in this research constitutes an engaging case of study in which network traffic data from two different environments are successfully collected, processed and modeled. Firstly, we approached the Feature Extraction and Selection processes providing our own contributions. A Feature Extractor was designed to create Machine-Learning ready datasets from real traffic data, and a Feature Selection Filter based on fast correlation is proposed and tested in several classification datasets. Then, the original Network Traffic Classification datasets are reduced using our Selection Filter to provide efficient classification models. Many classification models based on CART Decision Trees were analyzed exhibiting excellent outcomes in identifying various Internet applications. The experiments presented in this research comprise a comparison amongst ensemble learning schemes, an exploratory study on Class Imbalance and solutions; and an analysis of IP-header predictors for early traffic classification. This thesis is presented in the form of compendium of JCR-indexed scientific manuscripts and, furthermore, one conference paper is included. In the present work we study a wide number of learning approaches employing the most advance methodology in Machine Learning. As a result, we identify the strengths and weaknesses of these algorithms, providing our own solutions to overcome the observed limitations. Shortly, this thesis proves that Machine Learning offers interesting advanced techniques that open prominent prospects in Internet Network Traffic Classification.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione

Event horizons, gravitational waves and astrophysical kicks in black-hole spacetimes

In this thesis we use computational techniques (numerical simulations) to study different stages of black hole mergers. A first project describes topological properties of the main performer of this play, the black hole and its event horizon. We investigate three configurations: a continuum ring singularity, a \u27discretized\u27 ring (black holes arranged on a ring), and a linear distribution of black holes. We evolve each of the corresponding spacetimes forward and then backwards in time, searching for the respective event horizons. We find some evidence, based on configurations of multiple BHs arranged in a ring, that this configuration leads to singular limit where the horizon width has zero size, possibly indicating the presence of a naked singularity, when the radius of the ring is sufficiently large. In a second project, we study the dynamics of a hydrodynamical accretion disk around a recoiling black hole, which models the behavior of an accretion disk around a binary just after the merger, using \u27smoothed-particle hydrodynamics\u27 techniques. We simulated different recoil angles between the accretion disk and the recoil velocity of the black hole. We find that for more vertical kicks (angles \u3c 30 degrees) a gap remains present in the inner disk, while for more oblique kicks (angles \u3e 45 degrees), matter rapidly accretes toward the black hole. There is a systematic trend for higher potential luminosities for more oblique kick angles for a given black hole mass, disk mass and kick velocity, and we find large amplitude oscillations in time in the case of a kick oriented 60 degrees from the vertical

A Dynamically Reconfigurable Parallel Processing Framework with Application to High-Performance Video Processing

Digital video processing demands have and will continue to grow at unprecedented rates. Growth comes from ever increasing volume of data, demand for higher resolution, higher frame rates, and the need for high capacity communications. Moreover, economic realities force continued reductions in size, weight and power requirements. The ever-changing needs and complexities associated with effective video processing systems leads to the consideration of dynamically reconfigurable systems. The goal of this dissertation research was to develop and demonstrate the viability of integrated parallel processing system that effectively and efficiently apply pre-optimized hardware cores for processing video streamed data. Digital video is decomposed into packets which are then distributed over a group of parallel video processing cores. Real time processing requires an effective task scheduler that distributes video packets efficiently to any of the reconfigurable distributed processing nodes across the framework, with the nodes running on FPGA reconfigurable logic in an inherently Virtual\u27 mode. The developed framework, coupled with the use of hardware techniques for dynamic processing optimization achieves an optimal cost/power/performance realization for video processing applications. The system is evaluated by testing processor utilization relative to I/O bandwidth and algorithm latency using a separable 2-D FIR filtering system, and a dynamic pixel processor. For these applications, the system can achieve performance of hundreds of 640x480 video frames per second across an eight lane Gen I PCIe bus. Overall, optimal performance is achieved in the sense that video data is processed at the maximum possible rate that can be streamed through the processing cores. This performance, coupled with inherent ability to dynamically add new algorithms to the described dynamically reconfigurable distributed processing framework, creates new opportunities for realizable and economic hardware virtualization.\u2