Thermodynamics of explosions

We present our first attempts to formulate a thermodynamics-like description of explosions. The motivation is partly a fundamental interest in non-equilibrium statistical physics, partly the resemblance of an explosion to the late stages of a heavy-ion collision. We perform numerical simulations on a microscopic model of interacting billiard-ball like particles, and we analyse the results of such simulations trying to identify collective variables describing the degree of equilibrium during the explosion.Comment: 6 pages. Talk presented at "Bologna 2000 - Structure of the nucleus" international conference, May 29 - June 3, Bologna, Italy. Shortened version, to appear in the Proceeding

Electrochemical control of quantum interference in anthraquinone-based molecular switches

Using first-principles calculations we analyze the electronic transport properties of a recently proposed anthraquinone based electrochemical switch. Robust conductance on/off ratios of several orders of magnitude are observed due to destructive quantum interference present in the anthraquinone, but absent in the hydroquinone molecular bridge. A simple explanation of the interference effect is achieved by transforming the frontier molecular orbitals into localized molecular orbitals thereby obtaining a minimal tight-binding model describing the transport in the relevant energy range in terms of hopping via the localized orbitals. The topology of the tight-binding model, which is dictated by the symmetries of the molecular orbitals, determines the amount of quantum interference.Comment: 6 pages, 6 figure

Beyond E11

We study the non-linear realisation of E11 originally proposed by West with particular emphasis on the issue of linearised gauge invariance. Our analysis shows even at low levels that the conjectured equations can only be invariant under local gauge transformations if a certain section condition that has appeared in a different context in the E11 literature is satisfied. This section condition also generalises the one known from exceptional field theory. Even with the section condition, the E11 duality equation for gravity is known to miss the trace component of the spin connection. We propose an extended scheme based on an infinite-dimensional Lie superalgebra, called the tensor hierarchy algebra, that incorporates the section condition and resolves the above issue. The tensor hierarchy algebra defines a generalised differential complex, which provides a systematic description of gauge invariance and Bianchi identities. It furthermore provides an E11 representation for the field strengths, for which we define a twisted first order self-duality equation underlying the dynamics.Comment: 97 pages. v2: Minor changes, references added. Published versio

Learning to Communicate with Deep Multi-Agent Reinforcement Learning

We consider the problem of multiple agents sensing and acting in environments with the goal of maximising their shared utility. In these environments, agents must learn communication protocols in order to share information that is needed to solve the tasks. By embracing deep neural networks, we are able to demonstrate end-to-end learning of protocols in complex environments inspired by communication riddles and multi-agent computer vision problems with partial observability. We propose two approaches for learning in these domains: Reinforced Inter-Agent Learning (RIAL) and Differentiable Inter-Agent Learning (DIAL). The former uses deep Q-learning, while the latter exploits the fact that, during learning, agents can backpropagate error derivatives through (noisy) communication channels. Hence, this approach uses centralised learning but decentralised execution. Our experiments introduce new environments for studying the learning of communication protocols and present a set of engineering innovations that are essential for success in these domains

CII, CI, and CO in the massive star forming region W3 Main

We have used the KOSMA 3m telescope to map the core 7'x5' of the Galactic massive star forming region W3Main in the two fine structure lines of atomic carbon and four mid-J transitions of CO and 13CO. In combination with a map of singly ionized carbon (Howe et al. 1991), and FIR fine structure line data observed by ISO/LWS at the center position, these data sets allow to study in detail the physical structure of the photon dominated cloud interface regions (PDRs) where the occurance of carbon changes from CII to CI, and to CO.Comment: 4 pages, 4 figures, to appear in "Proceedings of the 4th Cologne-Bonn-Zermatt-Symposium, The dense interstellar medium in galaxies", eds. S. Pfalzner, C. Kramer, C. Straubmeier, and A. Heithausen (Springer Verlag

The Carbon content in the Galactic CygnusX/DR21 star forming region

Observations of Carbon bearing species are among the most important diagnostic probes of ongoing star formation. CO is a surrogate for H$_2$ and is found in the vicinity of star formation sites. There, [CI] emission is thought to outline the dense molecular cores and extend into the lower density regions, where the impinging interstellar UV radiation field plays a critical role for the dissociation and ionization processes. Emission of ionized carbon ([CII]) is found to be even more extended than [CI] and is linking up with the ionized medium. These different tracers emphasize the importance of multi-wavelength studies to draw a coherent picture of the processes driving and driven by high mass star formation. Until now, large scale surveys were only done with low resolution, such as the COBE full sky survey, or were biased to a few selected bright sources (e.g. Yamamoto et al. 2001, Schneider et al. 2003). A broader basis of unbiased, high-resolution observations of [CI], CO, and [CII] may play a key role to probe the material processed by UV radiation.Comment: 4 pages, 4 figure, to appear in "Proceedings of the 4th Cologne-Bonn-Zermatt-Symposium", ed. S. Pfalzner, C. Kramer, C. Straubmeier, and A. Heithausen (Springer Verlag

Learning with Opponent-Learning Awareness

Multi-agent settings are quickly gathering importance in machine learning. This includes a plethora of recent work on deep multi-agent reinforcement learning, but also can be extended to hierarchical RL, generative adversarial networks and decentralised optimisation. In all these settings the presence of multiple learning agents renders the training problem non-stationary and often leads to unstable training or undesired final results. We present Learning with Opponent-Learning Awareness (LOLA), a method in which each agent shapes the anticipated learning of the other agents in the environment. The LOLA learning rule includes a term that accounts for the impact of one agent's policy on the anticipated parameter update of the other agents. Results show that the encounter of two LOLA agents leads to the emergence of tit-for-tat and therefore cooperation in the iterated prisoners' dilemma, while independent learning does not. In this domain, LOLA also receives higher payouts compared to a naive learner, and is robust against exploitation by higher order gradient-based methods. Applied to repeated matching pennies, LOLA agents converge to the Nash equilibrium. In a round robin tournament we show that LOLA agents successfully shape the learning of a range of multi-agent learning algorithms from literature, resulting in the highest average returns on the IPD. We also show that the LOLA update rule can be efficiently calculated using an extension of the policy gradient estimator, making the method suitable for model-free RL. The method thus scales to large parameter and input spaces and nonlinear function approximators. We apply LOLA to a grid world task with an embedded social dilemma using recurrent policies and opponent modelling. By explicitly considering the learning of the other agent, LOLA agents learn to cooperate out of self-interest. The code is at github.com/alshedivat/lola