559 research outputs found
Disentangling the timescales behind the non-perturbative heavy quark potential
The static part of the heavy quark potential has been shown to be closely
related to the spectrum of the rectangular Wilson loop. In particular the
lowest lying positive frequency peak encodes the late time evolution of the
two-body system, characterized by a complex potential. While initial studies
assumed a perfect separation of early and late time physics, where a simple
Lorentian (Breit-Wigner) shape suffices to describe the spectral peak, we argue
that scale decoupling in general is not complete. Thus early time, i.e.
non-potential effects, significantly modify the shape of the lowest peak. We
derive on general grounds an improved peak distribution that reflects this
fact. Application of the improved fit to non-perturbative lattice QCD spectra
now yields a potential that is compatible with a transition to a deconfined
screening plasma.Comment: 5 pages, 3 figure
Complex Heavy-Quark Potential at Finite Temperature from Lattice QCD
We calculate for the first time the complex potential between a heavy quark
and antiquark at finite temperature across the deconfinement transition in
lattice QCD. The real and imaginary part of the potential at each separation
distance is obtained from the spectral function of the thermal Wilson loop.
We confirm the existence of an imaginary part above the critical temperature
, which grows as a function of and underscores the importance of
collisions with the gluonic environment for the melting of heavy quarkonia in
the quark-gluon-plasma.Comment: 4 pages, 3 figures, to be published in PR
Light-cone Wilson loop in classical lattice gauge theory
The transverse broadening of an energetic jet passing through a non-Abelian
plasma is believed to be described by the thermal expectation value of a
light-cone Wilson loop. In this exploratory study, we measure the light-cone
Wilson loop with classical lattice gauge theory simulations. We observe, as
suggested by previous studies, that there are strong interactions already at
short transverse distances, which may lead to more efficient jet quenching than
in leading-order perturbation theory. We also verify that the asymptotics of
the Wilson loop do not change qualitatively when crossing the light cone, which
supports arguments in the literature that infrared contributions to jet
quenching can be studied with dimensionally reduced simulations in the
space-like domain. Finally we speculate on possibilities for full
four-dimensional lattice studies of the same observable, perhaps by employing
shifted boundary conditions in order to simulate ensembles boosted by an
imaginary velocity.Comment: 20 pages. v2: more elaboration on systematic errors; published
versio
Credit assignment in multiple goal embodied visuomotor behavior
The intrinsic complexity of the brain can lead one to set aside issues related to its relationships with the body, but the field of embodied cognition emphasizes that understanding brain function at the system level requires one to address the role of the brain-body interface. It has only recently been appreciated that this interface performs huge amounts of computation that does not have to be repeated by the brain, and thus affords the brain great simplifications in its representations. In effect the brain’s abstract states can refer to coded representations of the world created by the body. But even if the brain can communicate with the world through abstractions, the severe speed limitations in its neural circuitry mean that vast amounts of indexing must be performed during development so that appropriate behavioral responses can be rapidly accessed. One way this could happen would be if the brain used a decomposition whereby behavioral primitives could be quickly accessed and combined. This realization motivates our study of independent sensorimotor task solvers, which we call modules, in directing behavior. The issue we focus on herein is how an embodied agent can learn to calibrate such individual visuomotor modules while pursuing multiple goals. The biologically plausible standard for module programming is that of reinforcement given during exploration of the environment. However this formulation contains a substantial issue when sensorimotor modules are used in combination: The credit for their overall performance must be divided amongst them. We show that this problem can be solved and that diverse task combinations are beneficial in learning and not a complication, as usually assumed. Our simulations show that fast algorithms are available that allot credit correctly and are insensitive to measurement noise
Proper heavy-quark potential from a spectral decomposition of the thermal Wilson loop
We propose a non-perturbative and gauge invariant derivation of the static
potential between a heavy-quark () and an anti-quark () at finite
temperature. This proper potential is defined through the spectral function
(SPF) of the thermal Wilson loop and can be shown to satisfy the
Schr\"{o}dinger equation for the heavy pair in the thermal medium.
In general, the proper potential has a real and an imaginary part,corresponding
to the peak position and width of the SPF. The validity of using a
Schr\"{o}dinger equation for heavy can also be checked from the
structure of the SPF. To test this idea, quenched QCD simulations on
anisotropic lattices (, ) are performed. The real part of the proper
potential below the deconfinement temperature () exhibits the well
known Coulombic and confining behavior. At () we find that it
coincides with the Debye screened potential obtained from Polyakov-line
correlations in the color-singlet channel under Coulomb gauge fixing. The
physical meaning of the spectral structure of the thermal Wilson loop and the
use of the maximum entropy method (MEM) to extract the real and imaginary part
of the proper potential are also discussed.Comment: 7 pages, 8 figures, Talk given at the XXVII International Symposium
on Lattice Field Theory (LATTICE 2009), July 25-31, 2009, Beijing, Chin
Solving Bongard Problems with a Visual Language and Pragmatic Reasoning
More than 50 years ago Bongard introduced 100 visual concept learning
problems as a testbed for intelligent vision systems. These problems are now
known as Bongard problems. Although they are well known in the cognitive
science and AI communities only moderate progress has been made towards
building systems that can solve a substantial subset of them. In the system
presented here, visual features are extracted through image processing and then
translated into a symbolic visual vocabulary. We introduce a formal language
that allows representing complex visual concepts based on this vocabulary.
Using this language and Bayesian inference, complex visual concepts can be
induced from the examples that are provided in each Bongard problem. Contrary
to other concept learning problems the examples from which concepts are induced
are not random in Bongard problems, instead they are carefully chosen to
communicate the concept, hence requiring pragmatic reasoning. Taking pragmatic
reasoning into account we find good agreement between the concepts with high
posterior probability and the solutions formulated by Bongard himself. While
this approach is far from solving all Bongard problems, it solves the biggest
fraction yet
Bayesian Classifier Fusion with an Explicit Model of Correlation
Combining the outputs of multiple classifiers or experts into a single
probabilistic classification is a fundamental task in machine learning with
broad applications from classifier fusion to expert opinion pooling. Here we
present a hierarchical Bayesian model of probabilistic classifier fusion based
on a new correlated Dirichlet distribution. This distribution explicitly models
positive correlations between marginally Dirichlet-distributed random vectors
thereby allowing explicit modeling of correlations between base classifiers or
experts. The proposed model naturally accommodates the classic Independent
Opinion Pool and other independent fusion algorithms as special cases. It is
evaluated by uncertainty reduction and correctness of fusion on synthetic and
real-world data sets. We show that a change in performance of the fused
classifier due to uncertainty reduction can be Bayes optimal even for highly
correlated base classifiers.Comment: 12 pages, 4 figures, 1 table, revised title and Fig 2, added real
data set Bookies
Probabilistic inverse optimal control with local linearization for non-linear partially observable systems
Inverse optimal control methods can be used to characterize behavior in
sequential decision-making tasks. Most existing work, however, requires the
control signals to be known, or is limited to fully-observable or linear
systems. This paper introduces a probabilistic approach to inverse optimal
control for stochastic non-linear systems with missing control signals and
partial observability that unifies existing approaches. By using an explicit
model of the noise characteristics of the sensory and control systems of the
agent in conjunction with local linearization techniques, we derive an
approximate likelihood for the model parameters, which can be computed within a
single forward pass. We evaluate our proposed method on stochastic and
partially observable version of classic control tasks, a navigation task, and a
manual reaching task. The proposed method has broad applicability, ranging from
imitation learning to sensorimotor neuroscience
- …