Scalable Algorithms for the Analysis of Massive Networks

Die Netzwerkanalyse zielt darauf ab, nicht-triviale Erkenntnisse aus vernetzten Daten zu gewinnen. Beispiele für diese Erkenntnisse sind die Wichtigkeit einer Entität im Verhältnis zu anderen nach bestimmten Kriterien oder das Finden des am besten geeigneten Partners für jeden Teilnehmer eines Netzwerks - bekannt als Maximum Weighted Matching (MWM). Da der Begriff der Wichtigkeit an die zu betrachtende Anwendung gebunden ist, wurden zahlreiche Zentralitätsmaße eingeführt. Diese Maße stammen hierbei aus Jahrzehnten, in denen die Rechenleistung sehr begrenzt war und die Netzwerke im Vergleich zu heute viel kleiner waren. Heute sind massive Netzwerke mit Millionen von Kanten allgegenwärtig und eine triviale Berechnung von Zentralitätsmaßen ist oft zu zeitaufwändig. Darüber hinaus ist die Suche nach der Gruppe von k Knoten mit hoher Zentralität eine noch kostspieligere Aufgabe. Skalierbare Algorithmen zur Identifizierung hochzentraler (Gruppen von) Knoten in großen Graphen sind von großer Bedeutung für eine umfassende Netzwerkanalyse. Heutigen Netzwerke verändern sich zusätzlich im zeitlichen Verlauf und die effiziente Aktualisierung der Ergebnisse nach einer Änderung ist eine Herausforderung. Effiziente dynamische Algorithmen sind daher ein weiterer wesentlicher Bestandteil moderner Analyse-Pipelines. Hauptziel dieser Arbeit ist es, skalierbare algorithmische Lösungen für die zwei oben genannten Probleme zu finden. Die meisten unserer Algorithmen benötigen Sekunden bis einige Minuten, um diese Aufgaben in realen Netzwerken mit bis zu Hunderten Millionen von Kanten zu lösen, was eine deutliche Verbesserung gegenüber dem Stand der Technik darstellt. Außerdem erweitern wir einen modernen Algorithmus für MWM auf dynamische Graphen. Experimente zeigen, dass unser dynamischer MWM-Algorithmus Aktualisierungen in Graphen mit Milliarden von Kanten in Millisekunden bewältigt.Network analysis aims to unveil non-trivial insights from networked data by studying relationship patterns between the entities of a network. Among these insights, a popular one is to quantify the importance of an entity with respect to the others according to some criteria. Another one is to find the most suitable matching partner for each participant of a network knowing the pairwise preferences of the participants to be matched with each other - known as Maximum Weighted Matching (MWM). Since the notion of importance is tied to the application under consideration, numerous centrality measures have been introduced. Many of these measures, however, were conceived in a time when computing power was very limited and networks were much smaller compared to today's, and thus scalability to large datasets was not considered. Today, massive networks with millions of edges are ubiquitous, and a complete exact computation for traditional centrality measures are often too time-consuming. This issue is amplified if our objective is to find the group of k vertices that is the most central as a group. Scalable algorithms to identify highly central (groups of) vertices on massive graphs are thus of pivotal importance for large-scale network analysis. In addition to their size, today's networks often evolve over time, which poses the challenge of efficiently updating results after a change occurs. Hence, efficient dynamic algorithms are essential for modern network analysis pipelines. In this work, we propose scalable algorithms for identifying important vertices in a network, and for efficiently updating them in evolving networks. In real-world graphs with hundreds of millions of edges, most of our algorithms require seconds to a few minutes to perform these tasks. Further, we extend a state-of-the-art algorithm for MWM to dynamic graphs. Experiments show that our dynamic MWM algorithm handles updates in graphs with billion edges in milliseconds

The multidisciplinary health care team in the management of stenosis in Crohn's disease.

BACKGROUND: Stricture formation is a common complication of Crohn's disease (CD), occurring in approximately one-third of all patients with this condition. Our aim was to summarize the available epidemiology data on strictures in patients with CD, to outline the principal evidence on diagnostic imaging, and to provide an overview of the current knowledge on treatment strategies, including surgical and endoscopic options. Overall, the unifying theme of this narrative review is the multidisciplinary approach in the clinical management of patients with stricturing CD. METHODS: A Medline search was performed, using "Inflammatory Bowel Disease", "stricture", "Crohn's Disease", "Ulcerative Colitis", "endoscopic balloon dilatation" and "strictureplasty" as keywords. A selection of clinical cohort studies and systematic reviews were reviewed. RESULTS: Strictures in CD are described as either inflammatory or fibrotic. They can occur de novo, at sites of bowel anastomosis or in the ileal pouch. CD-related strictures generally show a poor response to medical therapies, and surgical bowel resection or surgical strictureplasty are often required. Over the last three decades, the potential role of endoscopic balloon dilatation has grown in importance, and nowadays this technique is a valid option, complementary to surgery. CONCLUSION: Patients with stricturing CD require complex clinical management, which benefits from a multidisciplinary approach: gastroenterologists, pediatricians, radiologists, surgeons, specialist nurses, and dieticians are among the health care providers involved in supporting these patients throughout diagnosis, prevention of complications, and treatment

Velocity and acceleration statistics in particle-laden turbulent swirling flows

We present a comparison of different particles' velocity and acceleration statistics in two paradigmatic turbulent swirling flows: the von Kármán flow in a laboratory experiment and the Taylor-Green flow in direct numerical simulations. Tracers, as well as inertial particles, are considered. Results indicate that, in spite of the differences in boundary conditions and forcing mechanisms, scaling properties and statistical quantities reveal similarities between both flows, pointing to new methods to calibrate and compare models for particles dynamics in numerical simulations, as well as to characterize the dynamics of particles in simulations and experiments. The comparison also allows us to identify contributions of the mean flow to the inertial-range scaling of the particles' velocity structure functions.Fil: Angriman, Sofia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Mininni, Pablo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Cobelli, Pablo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentin

El riesgo en la actividad física- deportiva y sus límites

El riesgo es un concepto que se define en función de la probabilidad; por lo que una actividad es más arriesgada cuanto más probable sea la aparición de un accidente.Un accidente es un suceso imprevisto que puede ocurrir causando un daño físico o mental, aunque se minimicen los riesgos; mientras que un peligro representa una contingencia inminente o muy probable.El objetivo del presente artículo es a través de casos, visualizar cual es el límite del riesgo permitido para quien organiza actividades físicas guiadas o eventos deportivos y cuál es el riesgo aceptable por parte de quien los practica.Dicho de otra forma hasta donde el participante de dichas actividades asume un riesgo que le es propio desde comienza el de quien las organiza