Hydrodynamic Limit for an Hamiltonian System with Boundary Conditions and Conservative Noise

We study the hyperbolic scaling limit for a chain of N coupled anharmonic oscillators. The chain is attached to a point on the left and there is a force (tension) $\tau$ acting on the right. In order to provide good ergodic properties to the system, we perturb the Hamiltonian dynamics with random local exchanges of velocities between the particles, so that momentum and energy are locally conserved. We prove that in the macroscopic limit the distributions of the elongation, momentum and energy, converge to the solution of the Euler system of equations, in the smooth regime.Comment: New deeply revised version. 1 figure adde

Dense gas and star formation in individual Giant Molecular Clouds in M31

This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society © 2018 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.9 pages, 6 figures, accepted for publication in MNRASStudies both of entire galaxies and of local Galactic star formation indicate a dependency of a molecular cloud's star formation rate (SFR) on its dense gas mass. In external galaxies, such measurements are derived from HCN(1-0) observations, usually encompassing many Giant Molecular Clouds (GMCs) at once. The Andromeda galaxy (M31) is a unique laboratory to study the relation of the SFR and HCN emission down to GMC scales at solar-like metallicities. In this work, we correlate our composite SFR determinations with archival HCN, HCO+, and CO observations, resulting in a sample of nine reasonably representative GMCs. We find that, at the scale of individual clouds, it is important to take into account both obscured and unobscured star formation to determine the SFR. When correlated against the dense-gas mass from HCN, we find that the SFR is low, in spite of these refinements. We nevertheless retrieve an SFR - dense-gas mass correlation, confirming that these SFR tracers are still meaningful on GMC scales. The correlation improves markedly when we consider the HCN/CO ratio instead of HCN by itself. This nominally indicates a dependency of the SFR on the dense-gas fraction, in contradiction to local studies. However, we hypothesize that this partly reflects the limited dynamic range in dense-gas mass, and partly that the ratio of single-pointing HCN and CO measurements may be less prone to systematics like sidelobes. In this case, the HCN/CO ratio would importantly be a better empirical measure of the dense-gas content itself.Peer reviewedFinal Published versio

High-temperature measurements of Q-factor in rotated X-cut quartz resonators

The Q-factors of piezoelectric resonators fabricated from natural and synthetic quartz with a 34 deg rotated X-cut orientation were measured at temperatures up to 325 C. The synthetic material, which was purified by electrolysis, retains a higher enough Q to be suitable for high temperature pressure-transducer applications, whereas the natural quartz is excessively lossy above 200 C for this application. The results are compared to results obtained previously at AT-cut resonators

The Evolution of Spheroidal Galaxies in Different Environments

We analyse the kinematic and chemical evolution of 203 distant spheroidal (elliptical and S0) galaxies at 0.2<z<0.8 which are located in different environments (rich clusters, low-mass clusters and in the field). VLT/FORS and CAHA/MOSCA spectra with intermediate-resolution have been acquired to measure the internal kinematics and stellar populations of the galaxies. From HST/ACS and WFPC2 imaging, surface brightness profiles and structural parameters were derived for half of the galaxy sample. The scaling relations of the Faber-Jackson relation and Kormendy relation as well as the Fundamental Plane indicate a moderate evolution for the whole galaxy population in each density regime. In all environments, S0 galaxies show a faster evolution than elliptical galaxies. For the cluster galaxies a slight radial dependence of the evolution out to one virial radius is found. Dividing the samples with respect to their mass, a mass dependent evolution with a stronger evolution of lower-mass galaxies (M<2x10^{11} M_{\sun}) is detected. Evidence for recent star formation is provided by blue colours and weak OII emission or strong H\delta absorption features in the spectra. The results are consistent with a down-sizing formation scenario which is independent from the environment of the galaxies.Comment: 4 pages, 2 figures, to be published in Astronomische Nachrichten (proceedings of Symposium 6 of the JENAM 2008, Vienna

I Left My Dear Old Village Home For You

https://digitalcommons.library.umaine.edu/mmb-vp/6596/thumbnail.jp

Learning Manipulation under Physics Constraints with Visual Perception

Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing novel objects and their configurations. In this work, we consider the problem of autonomous block stacking and explore solutions to learning manipulation under physics constraints with visual perception inherent to the task. Inspired by the intuitive physics in humans, we first present an end-to-end learning-based approach to predict stability directly from appearance, contrasting a more traditional model-based approach with explicit 3D representations and physical simulation. We study the model's behavior together with an accompanied human subject test. It is then integrated into a real-world robotic system to guide the placement of a single wood block into the scene without collapsing existing tower structure. To further automate the process of consecutive blocks stacking, we present an alternative approach where the model learns the physics constraint through the interaction with the environment, bypassing the dedicated physics learning as in the former part of this work. In particular, we are interested in the type of tasks that require the agent to reach a given goal state that may be different for every new trial. Thereby we propose a deep reinforcement learning framework that learns policies for stacking tasks which are parametrized by a target structure