aus dem Inhalt: „Spätestens seit Ausbruch der Corona-Pandemie sind Datenvisualisierungen und Infografiken in aller Munde oder besser gesagt «in aller Augen ». Kaum ein News-Portal, kaum eine Online-Ausgabe renommierter Zeitungen kommt ohne die fast schon obligatorische interaktive Datenvisualisierung über den Verlauf der Pandemie, die Entwicklung der Infektionszahlen oder einen Ländervergleich aus.”

aus dem Inhalt: „Many aspects of society, science, business, finance, journalism, and everyday human activity, become ever more quantified. As a result, our world is awash with data of increasing amount and complexity. Still, we must keep afloat with our innate human abilities and limitations. Visualization is one way to manage this information overload: well-designed representations replace difficult cognitive calculations with simpler perceptual interpretations.”

aus dem Inhalt: „Leonardo da Vinci is worldwide considered to be one of the greatest geniuses in human history. The famous frescoes and paintings that we can still admire today are only a tiny fraction of what constitutes the gigantic heritage of Leonardo da Vinci’s significance. Part of his heritage is an incredible amount of sketches that survived the total dismemberment thanks to vicissitudes that comprehend legacies, lootings, millionaire purchases, and thefts.”

aus dem Inhalt: „Visuelle Formen der Information und Kommunikation dominieren heute nahezu alle Lebensbereiche. Sie haben lange schon unsere Erwartungsebene erreicht – man möchte keine langen Bedienungsanleitungen lesen, sondern intuitiv über eine visuelle Führung das Gerät verstehen oder über eine Lebensmittelampel auf den ersten Blick sehen, wie «gesund» ein Produkt ist. Werden Konsumenten/-innen auf diesem Weg abgeholt, ist der erste Pluspunkt auf der Ebene der User-Experience gesammelt. Visualisierungen werden vielfach erwartet, die Fähigkeit sie zu dechiffrieren wächst – umgekehrt sinkt der Wille und teils die Fähigkeit, textbasierte Information verarbeiten zu können.”

aus dem Inhalt: „Sprache stellt unsere Verbindung zur Welt dar – dazu, wie wir die Welt verstehen und mit ihr interagieren. Digitalisierung hat dazu geführt, dass Konsumenten Tag für Tag und in unterschiedlichsten Kanälen digitale, textbasierte Sprachspuren kreieren und hinterlassen.”

aus dem Inhalt: „Datenvisualisierung wird immer wichtiger in der Kommunikation. Gerade in der Zeit der Corona-Pandemie spielt Datenvisualisierung eine zentrale Rolle, um die Lage und Dynamik zu kommunizieren. Wenn Daten erhoben und mit immer neuen Methoden analysiert werden, ist es wichtig, diese Daten addressatengerecht aufzubereiten.”

Statistical inference is a tool for creating models that can accurately display real-world events. Special importance is given to the financial methods that model risk and large price movements. A parameter that describes tail heaviness, and risk overall, is α. This research finds a representative distribution that models α. The absolute value of standardized stock returns from the Center for Research on Security Prices are used in this research. The inference is performed using R. Approximations for α are found using the ptsuite package. The GAMLSS package employs maximum likelihood estimation to estimate distribution parameters using the CRSP data. The distributions are selected by using AIC and worm plots. The Skew t family is found to be representative for the parameter α based on subsets of the CRSP data. The Skew t type 2 distribution is robust for multiple subsets of values calculated from the CRSP stock return data.

<p>  </p> <p>In 2011, the Materials Genome Initiative (MGI) was founded as an effort to unite and drive materials design at an unprecedented pace. By linking computational tools with experimental data, and aligning their data structures to match and interact, scientists across the world have been able to change the way they do science at a fundamental level.</p> <p>The 3 Mission Statements of the Materials Genome Initiative include: 1) Developing a Materials Innovation Infrastructure 2) Achieving National Goals with Advanced Materials 3) Equipping the Next-Generation Materials Workforce. Since the inception of the MGI the Materials Engineering community has developed numerous cyberinfrastructure repositories for experimental, and varied levels of computational data. This practice aligns with a separate initiative for Findable, Accessible, Interoperable, and Reproducible (F.A.I.R.) principles for data handling and science. By integrating the cyberinfrastructure efforts with continued collaboration from experimental and computational scientists we push the field to evolve improved workflows for research.</p> <p>This thesis is a collection of applied solutions for materials design with atomistic modeling, and machine learning (ML). In Part 1, we will discuss bridges for the gaps between atomistic simulation and experiment, and what it means for material solutions. A showcase of combining experimental information with ab initio electronic transport calculations will be discussed, as well as the principles of density functional theory (DFT) and molecular dynamics (MD) simulations. In Part 2, our focus will shift to applications of machine learning and the use of composition and chemical featurizers for materials design. Here we leverage cyberinfrastructure efforts with APIs and ML with transfer and active learning for efficient high-dimensional space exploration. In Part 3 local atomic environments and configurations, associative fingerprinting solutions, and workflows for designing deep learning (DL) interatomic potentials for MD are discussed. Finally, a brief section will conclude with efforts made to align with F.A.I.R. principles for Materials Engineering research, and educational development for Mission Statement 3 of the MGI.</p>

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Les graphes, ou réseaux, sont des structures mathématiques représentant des relations entre des éléments. Ces systèmes peuvent être analysés dans le but d’extraire des informations sur la structure globale ou sur des composants individuels. L'analyse de graphe conduit souvent à des problèmes hautement complexes à résoudre. À grande échelle, le coût de calcul de la solution exacte est prohibitif. Heureusement, il est possible d’utiliser des méthodes d’approximations itératives pour parvenir à des estimations précises. Lesméthodes historiques adaptées à un petit nombre de variables ne conviennent pas aux matrices creuses de grande taille provenant des graphes. Par conséquent, la conception de solveurs fiables, évolutifs, et efficaces demeure un problème essentiel. L’émergence d'architectures parallèles telles que le GPU ouvre également de nouvelles perspectives avec des progrès concernant à la fois la puissance de calcul et l'efficacité énergétique. Nos travaux ciblent la résolution de problèmes de valeurs propres de grande taille provenant des méthodes d’analyse de graphe dans le but d'utiliser efficacement les architectures parallèles. Nous présentons le domaine de l'analyse spectrale de grands réseaux puis proposons de nouveaux algorithmes et implémentations parallèles. Les résultats expérimentaux indiquent des améliorations conséquentes dans des applications réelles comme la détection de communautés et les indicateurs de popularité / Graphs, or networks, are mathematical structures to represent relations between elements. These systems can be analyzed to extract information upon the comprehensive structure or the nature of individual components. The analysis of networks often results in problems of high complexity. At large scale, the exact solution is prohibitively expensive to compute. Fortunately, this is an area where iterative approximation methods can be employed to find accurate estimations. Historical methods suitable for a small number of variables could not scale to large and sparse matrices arising in graph applications. Therefore, the design of scalable and efficient solvers remains an essential problem. Simultaneously, the emergence of parallel architecture such as GPU revealed remarkable ameliorations regarding performances and power efficiency. In this dissertation, we focus on solving large eigenvalue problems a rising in network analytics with the goal of efficiently utilizing parallel architectures. We revisit the spectral graph analysis theory and propose novel parallel algorithms and implementations. Experimental results indicate improvements on real and large applications in the context of ranking and clustering problems