Chemistry and kinematics of the pre-stellar core L1544: Constraints from H2D+

This paper explores the sensitivity of line profiles of H2D+, HCO+ and N2H+, observed towards the center of L1544, to various kinematic and chemical parameters. The total width of the H2D+ line can be matched by a static model and by models invoking ambipolar diffusion and gravitational collapse. The derived turbulent line width is b=0.15 km/s for the static case and <~ 0.05 km/s for the collapse case. However, line profiles of HC18O+ and N2H+ rule out the static solution. The double-peaked H2D+ line shape requires either infall speeds in the center that are much higher than predicted by ambipolar diffusion models, or a shell-type distribution of H2D+, as is the case for HCO+ and N2H+. At an offset of ~20 arcsec from the dust peak, the H2D+ abundance drops by a factor of ~5.Comment: four pages, two colour figures; to appear in The Dense Interstellar Medium in Galaxies, proceedings of the fourth Cologne-Bonn-Zermatt Symposium, Sept 22-26, 200

Submillimeter Studies of Prestellar Cores and Protostars: Probing the Initial Conditions for Protostellar Collapse

Improving our understanding of the initial conditions and earliest stages of protostellar collapse is crucial to gain insight into the origin of stellar masses, multiple systems, and protoplanetary disks. Observationally, there are two complementary approaches to this problem: (1) studying the structure and kinematics of prestellar cores observed prior to protostar formation, and (2) studying the structure of young (e.g. Class 0) accreting protostars observed soon after point mass formation. We discuss recent advances made in this area thanks to (sub)millimeter mapping observations with large single-dish telescopes and interferometers. In particular, we argue that the beginning of protostellar collapse is much more violent in cluster-forming clouds than in regions of distributed star formation. Major breakthroughs are expected in this field from future large submillimeter instruments such as Herschel and ALMA.Comment: 12 pages, 9 figures, to appear in the proceedings of the conference "Chemistry as a Diagnostic of Star Formation" (C.L. Curry & M. Fich eds.

The Dynamical Structure and Evolution of Giant Molecular Clouds

Giant molecular clouds (GMCs) are the sites of star formation in the Galaxy. Many of their properties can be understood in terms of a model in which the GMCs and the star-forming clumps within them are in approximate pressure equilibrium, with turbulent motions treated as a separate pressure component

Detecting conversational groups in images and sequences: A robust game-theoretic approach

Detecting groups is becoming of relevant interest as an important step for scene (and especially activity) understanding. Differently from what is commonly assumed in the computer vision community, different types of groups do exist, and among these, standing conversational groups (a.k.a. F-formations) play an important role. An F-formation is a common type of people aggregation occurring when two or more persons sustain a social interaction, such as a chat at a cocktail party. Indeed, detecting and subsequently classifying such an interaction in images or videos is of considerable importance in many applicative contexts, like surveillance, social signal processing, social robotics or activity classification, to name a few. This paper presents a principled method to approach to this problem grounded upon the socio-psychological concept of an F-formation. More specifically, a game-theoretic framework is proposed, aimed at modeling the spatial structure characterizing F-formations. In other words, since F-formations are subject to geometrical configurations on how humans have to be mutually located and oriented, the proposed solution is able to account for these constraints while also statistically modeling the uncertainty associated with the position and orientation of the engaged persons. Moreover, taking advantage of video data, it is also able to integrate temporal information over multiple frames utilizing the recent notions from multi-payoff evolutionary game theory. The experiments have been performed on several benchmark datasets, consistently showing the superiority of the proposed approach over the state of the art, and its robustness under severe noise conditions.Detecting groups is becoming of relevant interest as an important step for scene (and especially activity) understanding. Differently from what is commonly assumed in the computer vision community, different types of groups do exist, and among these, standing conversational groups (a.k.a. F-formations) play an important role. An F-formation is a common type of people aggregation occurring when two or more persons sustain a social interaction, such as a chat at a cocktail party. Indeed, detecting and subsequently classifying such an interaction in images or videos is of considerable importance in many applicative contexts, like surveillance, social signal processing, social robotics or activity classification, to name a few. This paper presents a principled method to approach to this problem grounded upon the socio-psychological concept of an F-formation. More specifically, a game-theoretic framework is proposed, aimed at modeling the spatial structure characterizing F-formations. In other words, since F-formations are subject to geometrical configurations on how humans have to be mutually located and oriented, the proposed solution is able to account for these constraints while also statistically modeling the uncertainty associated with the position and orientation of the engaged persons. Moreover, taking advantage of video data, it is also able to integrate temporal information over multiple frames utilizing the recent notions from multi-payoff evolutionary game theory. The experiments have been performed on several benchmark datasets, consistently showing the superiority of the proposed approach over the state of the art, and its robustness under severe noise conditions. (C) 2015 Elsevier Inc. All rights reserved