Constraints on Meta-stable de Sitter Flux Vacua

We consider flux compactification of type IIB string theory as the orientifold limit of an F-theory on a Calabi-Yau fourfold. We show that when supersymmetry is dominantly broken by the axion-dilaton and the contributions of the F-terms associated with complex structure moduli are small, the Hessian of the flux potential always has tachyonic modes for de Sitter vacua. This implies that there exist no meta-stable de Sitter vacua in this limit. Moreover, we find that the stability requirement imposes a relation between the values of cosmological constant and the scale of supersymmetry breaking for non-supersymmetric anti de Sitter vacua in this limit. The proof is general and does not rely on the details of the geometry of the compact Calabi-Yau internal space. We finally analyze the consequences of these constraints on the statistics of meta-stable de Sitter vacua and address some other related issues.Comment: 22 pages, 2 figures, LaTeX; v2: typos corrected, references adde

Dynamic Views of Structure and Function during Heart Morphogenesis

Congenital heart defects remain the most common birth defect in humans, occurring in over 1% of live births. The high prevalence of cardiac malformations can be partially attributed to limited knowledge regarding the embryonic roots of the disease. A variety of congenital heart defects are thought to arise from combinations of genetic and epigenetic factors. In an effort to better understand this dynamic relationship, our study explores the structure and function of the developing heart and valves and examines hemodynamic factors influencing valvulogenesis. In order to study cardiac mechanics, we employed novel high-speed confocal microscopy and four-dimensional visualization techniques. A dynamic four-dimensional dataset describing heart and valve development along with blood flow patterns throughout cardiac morphogenesis is presented. Utilizing newly developed tools, we propose a novel pumping mechanism in the valveless embryonic heart tube via elastic wave propagation and reflection. We show that this form of pumping leads to oscillatory shear stresses in the developing atrio-ventricular canal, a phenomenon that had not previously been documented. An in vivo method to modulate trans-valvular oscillatory flows is described and used to test our hypothesis that oscillatory shear stress across the primitive valve cushions stimulates heart valve leaflet formation. Our results suggest hemodynamic forces contribute to valvulogenesis and enhance our understanding of normal and abnormal heart valve development

A Semi-automatic Method for Efficient Detection of Stories on Social Media

Twitter has become one of the main sources of news for many people. As real-world events and emergencies unfold, Twitter is abuzz with hundreds of thousands of stories about the events. Some of these stories are harmless, while others could potentially be life-saving or sources of malicious rumors. Thus, it is critically important to be able to efficiently track stories that spread on Twitter during these events. In this paper, we present a novel semi-automatic tool that enables users to efficiently identify and track stories about real-world events on Twitter. We ran a user study with 25 participants, demonstrating that compared to more conventional methods, our tool can increase the speed and the accuracy with which users can track stories about real-world events.Comment: ICWSM'16, May 17-20, Cologne, Germany. In Proceedings of the 10th International AAAI Conference on Weblogs and Social Media (ICWSM 2016). Cologne, German

Tweet Acts: A Speech Act Classifier for Twitter

Speech acts are a way to conceptualize speech as action. This holds true for communication on any platform, including social media platforms such as Twitter. In this paper, we explored speech act recognition on Twitter by treating it as a multi-class classification problem. We created a taxonomy of six speech acts for Twitter and proposed a set of semantic and syntactic features. We trained and tested a logistic regression classifier using a data set of manually labelled tweets. Our method achieved a state-of-the-art performance with an average F1 score of more than $0.70$. We also explored classifiers with three different granularities (Twitter-wide, type-specific and topic-specific) in order to find the right balance between generalization and overfitting for our task.Comment: ICWSM'16, May 17-20, Cologne, Germany. In Proceedings of the 10th AAAI Conference on Weblogs and Social Media (ICWSM 2016). Cologne, German