969 research outputs found
Glauber - Gribov approach for DIS on nuclei in N=4 SYM
In this paper the Glauber-Gribov approach for deep-inelastic scattering (DIS)
with nuclei is developed in N=4 SYM. It is shown that the amplitude displays
the same general properties, such as geometrical scaling, as is the case in the
high density QCD approach. We found that the quantum effects leading to the
graviton reggeization, give rise to an imaginary part of the nucleon amplitude,
which makes the DIS in N=4 SYM almost identical to the one expected in high
density QCD. We concluded that the impact parameter dependence of the nucleon
amplitude is very essential for N=4 SYM, and the entire kinematic region can be
divided into three regions which are discussed in the paper. We revisited the
dipole description for DIS and proposed a new renormalized Lagrangian for the
shock wave formalism which reproduces the Glauber-Gribov approach in a certain
kinematic region. However the saturation momentum turns out to be independent
of energy, as it has been discussed by Albacete, Kovchegov and Taliotis. We
discuss the physical meaning of such a saturation momentum and argue
that one can consider only within the shock wave approximation.Comment: 40pp.,9 figures in eps file
Dark Energy Content of Nonlinear Electromagnetism
Quasi-constant external fields in nonlinear electromagnetism generate a
contribution to the energy-momentum tensor with the form of dark energy. To
provide a thorough understanding of the origin and strength of the effects, we
undertake a complete theoretical and numerical study of the energy-momentum
tensor for nonlinear electromagnetism. The Euler-Heisenberg
nonlinearity due to quantum fluctuations of spinor and scalar matter fields is
considered and contrasted with the properties of classical nonlinear
Born-Infeld electromagnetism. We also address modifications of charged particle
kinematics by strong background fields.Comment: 16 pages, 12 figures; reorganized introduction and sections 4 and 5,
added further numerical results and discussion, updated references, fixed
typo
Reconstruction of Bandlimited Functions from Unsigned Samples
We consider the recovery of real-valued bandlimited functions from the
absolute values of their samples, possibly spaced nonuniformly. We show that
such a reconstruction is always possible if the function is sampled at more
than twice its Nyquist rate, and may not necessarily be possible if the samples
are taken at less than twice the Nyquist rate. In the case of uniform samples,
we also describe an FFT-based algorithm to perform the reconstruction. We prove
that it converges exponentially rapidly in the number of samples used and
examine its numerical behavior on some test cases
Elevated B cell activating factor (BAFF) in patient plasma after allogeneic stem cell transplantation is a potential biomarker for chronic graft versus host disease
Towards heuristic algorithmic memory
We propose a long-term memory design for artificial general intelligence based on Solomonoff's incremental machine learning methods. We introduce four synergistic update algorithms that use a Stochastic Context-Free Grammar as a guiding probability distribution of programs. The update algorithms accomplish adjusting production probabilities, re-using previous solutions, learning programming idioms and discovery of frequent subprograms. A controlled experiment with a long training sequence shows that our incremental learning approach is effective. © 2011 Springer-Verlag Berlin Heidelberg
Origin of Intrinsic Josephson Coupling in the Cuprates and Its Relation to Order Parameter Symmetry: An Incoherent Hopping Model
Experiments on the cuprate superconductors demonstrate that these materials
may be viewed as a stack of Josephson junctions along the c-direction. In this
paper, we present a model which describes this intrinsic Josephson coupling in
terms of incoherent quasiparticle hopping along the c-axis arising from
wave-function overlap, impurity-assisted hopping, and boson-assisted hopping.
We use this model to compute the magnitude and temperature T dependence of the
resulting Josephson critical current j_c (T) for s- and d-wave superconductors.
Contrary to other approaches, d-wave pairing in this model is compatible with
an intrinsic Josephson effect at all hole concentrations and leads to j_c (T)
\propto T at low T. By parameterizing our theory with c-axis resistivity data
from YBCO, we estimate j_c (T) for optimally doped and underdoped members of
this family. Our estimates suggest that further experiments on this compound
would be of great help in elucidating the validity of our model in general and
the pairing symmetry in particular. We also discuss the implications of our
model for LSCO and BSCCO.Comment: 28 pages, REVTEX, 5 compressed PostScript figures. Substantially
expanded and revised from the earlier version. To appear in Physica
Leading strategies in competitive on-line prediction
We start from a simple asymptotic result for the problem of on-line
regression with the quadratic loss function: the class of continuous
limited-memory prediction strategies admits a "leading prediction strategy",
which not only asymptotically performs at least as well as any continuous
limited-memory strategy but also satisfies the property that the excess loss of
any continuous limited-memory strategy is determined by how closely it imitates
the leading strategy. More specifically, for any class of prediction strategies
constituting a reproducing kernel Hilbert space we construct a leading
strategy, in the sense that the loss of any prediction strategy whose norm is
not too large is determined by how closely it imitates the leading strategy.
This result is extended to the loss functions given by Bregman divergences and
by strictly proper scoring rules.Comment: 20 pages; a conference version is to appear in the ALT'2006
proceeding
Cutoff for the Ising model on the lattice
Introduced in 1963, Glauber dynamics is one of the most practiced and
extensively studied methods for sampling the Ising model on lattices. It is
well known that at high temperatures, the time it takes this chain to mix in
on a system of size is . Whether in this regime there is
cutoff, i.e. a sharp transition in the -convergence to equilibrium, is a
fundamental open problem: If so, as conjectured by Peres, it would imply that
mixing occurs abruptly at for some fixed , thus providing
a rigorous stopping rule for this MCMC sampler. However, obtaining the precise
asymptotics of the mixing and proving cutoff can be extremely challenging even
for fairly simple Markov chains. Already for the one-dimensional Ising model,
showing cutoff is a longstanding open problem.
We settle the above by establishing cutoff and its location at the high
temperature regime of the Ising model on the lattice with periodic boundary
conditions. Our results hold for any dimension and at any temperature where
there is strong spatial mixing: For this carries all the way to the
critical temperature. Specifically, for fixed , the continuous-time
Glauber dynamics for the Ising model on with periodic boundary
conditions has cutoff at , where is
the spectral gap of the dynamics on the infinite-volume lattice. To our
knowledge, this is the first time where cutoff is shown for a Markov chain
where even understanding its stationary distribution is limited.
The proof hinges on a new technique for translating to mixing
which enables the application of log-Sobolev inequalities. The technique is
general and carries to other monotone and anti-monotone spin-systems.Comment: 34 pages, 3 figure
Meshfree finite differences for vector Poisson and pressure Poisson equations with electric boundary conditions
We demonstrate how meshfree finite difference methods can be applied to solve
vector Poisson problems with electric boundary conditions. In these, the
tangential velocity and the incompressibility of the vector field are
prescribed at the boundary. Even on irregular domains with only convex corners,
canonical nodal-based finite elements may converge to the wrong solution due to
a version of the Babuska paradox. In turn, straightforward meshfree finite
differences converge to the true solution, and even high-order accuracy can be
achieved in a simple fashion. The methodology is then extended to a specific
pressure Poisson equation reformulation of the Navier-Stokes equations that
possesses the same type of boundary conditions. The resulting numerical
approach is second order accurate and allows for a simple switching between an
explicit and implicit treatment of the viscosity terms.Comment: 19 pages, 7 figure
Evaluating a reinforcement learning algorithm with a general intelligence test
In this paper we apply the recent notion of anytime universal intelligence tests to the evaluation of a popular reinforcement learning algorithm, Q-learning. We show that a general approach to intelligence evaluation of AI algorithms is feasible. This top-down (theory-derived) approach is based on a generation of environments under a Solomonoff universal distribution instead of using a pre-defined set of specific tasks, such as mazes, problem repositories, etc. This first application of a general intelligence test to a reinforcement learning algorithm brings us to the issue of task-specific vs. general AI agents. This, in turn, suggests new avenues for AI agent evaluation and AI competitions, and also conveys some further insights about the performance of specific algorithms. © 2011 Springer-Verlag.We are grateful for the funding from the Spanish MEC and MICINN for projects TIN2009-06078-E/TIN, Consolider-Ingenio CSD2007-00022 and TIN2010-21062-C02, for MEC FPU grant AP2006-02323, and Generalitat Valenciana for Prometeo/2008/051.Insa Cabrera, J.; Dowe, DL.; Hernández Orallo, J. (2011). Evaluating a reinforcement learning algorithm with a general intelligence test. En Advances in Artificial Intelligence. Springer Verlag (Germany). 7023:1-11. https://doi.org/10.1007/978-3-642-25274-7_1S1117023Dowe, D.L., Hajek, A.R.: A non-behavioural, computational extension to the Turing Test. In: Intl. Conf. on Computational Intelligence & multimedia applications (ICCIMA 1998), Gippsland, Australia, pp. 101–106 (1998)Genesereth, M., Love, N., Pell, B.: General game playing: Overview of the AAAI competition. AI Magazine 26(2), 62 (2005)Hernández-Orallo, J.: Beyond the Turing Test. J. Logic, Language & Information 9(4), 447–466 (2000)Hernández-Orallo, J.: A (hopefully) non-biased universal environment class for measuring intelligence of biological and artificial systems. In: Hutter, M., et al. (eds.) 3rd Intl. Conf. on Artificial General Intelligence, Atlantis, pp. 182–183 (2010)Hernández-Orallo, J.: On evaluating agent performance in a fixed period of time. In: Hutter, M., et al. (eds.) 3rd Intl. Conf. on Artificial General Intelligence, pp. 25–30. Atlantis Press (2010)Hernández-Orallo, J., Dowe, D.L.: Measuring universal intelligence: Towards an anytime intelligence test. Artificial Intelligence 174(18), 1508–1539 (2010)Legg, S., Hutter, M.: A universal measure of intelligence for artificial agents. Intl. Joint Conf. on Artificial Intelligence, IJCAI 19, 1509 (2005)Legg, S., Hutter, M.: Universal intelligence: A definition of machine intelligence. Minds and Machines 17(4), 391–444 (2007)Levin, L.A.: Universal sequential search problems. Problems of Information Transmission 9(3), 265–266 (1973)Li, M., Vitányi, P.: An introduction to Kolmogorov complexity and its applications, 3rd edn. Springer-Verlag New York, Inc. (2008)Sanghi, P., Dowe, D.L.: A computer program capable of passing IQ tests. In: Proc. 4th ICCS International Conference on Cognitive Science (ICCS 2003), Sydney, Australia, pp. 570–575 (2003)Solomonoff, R.J.: A formal theory of inductive inference. Part I. Information and Control 7(1), 1–22 (1964)Strehl, A.L., Li, L., Wiewiora, E., Langford, J., Littman, M.L.: PAC model-free reinforcement learning. In: Proc. of the 23rd Intl. Conf. on Machine Learning, ICML 2006, New York, pp. 881–888 (2006)Sutton, R.S., Barto, A.G.: Reinforcement learning: An introduction. The MIT press (1998)Turing, A.M.: Computing machinery and intelligence. Mind 59, 433–460 (1950)Veness, J., Ng, K.S., Hutter, M., Silver, D.: Reinforcement learning via AIXI approximation. In: Proc. 24th Conf. on Artificial Intelligence (AAAI 2010), pp. 605–611 (2010)Watkins, C.J.C.H., Dayan, P.: Q-learning. Machine learning 8(3), 279–292 (1992)Weyns, D., Parunak, H.V.D., Michel, F., Holvoet, T., Ferber, J.: Environments for multiagent systems state-of-the-art and research challenges. In: Weyns, D., Van Dyke Parunak, H., Michel, F. (eds.) E4MAS 2004. LNCS (LNAI), vol. 3374, pp. 1–47. Springer, Heidelberg (2005)Whiteson, S., Tanner, B., White, A.: The Reinforcement Learning Competitions. The AI magazine 31(2), 81–94 (2010)Woergoetter, F., Porr, B.: Reinforcement learning. Scholarpedia 3(3), 1448 (2008)Zatuchna, Z., Bagnall, A.: Learning mazes with aliasing states: An LCS algorithm with associative perception. Adaptive Behavior 17(1), 28–57 (2009
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