5,087 research outputs found
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 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
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