849 research outputs found
Event-based simulation of interference with alternatingly blocked particle sources
We analyze the predictions of an event-based corpuscular model for
interference in the case of two-beam interference experiments in which the two
sources are alternatingly blocked. We show that such experiments may be used to
test specific predictions of the corpuscular model.Comment: FPP6 - Foundations of Probability and Physics 6, edited by A.
Khrennikov et al., AIP Conference Proceeding
Computer simulation of Wheeler's delayed choice experiment with photons
We present a computer simulation model of Wheeler's delayed choice experiment
that is a one-to-one copy of an experiment reported recently (V. Jacques {\sl
et al.}, Science 315, 966 (2007)). The model is solely based on experimental
facts, satisfies Einstein's criterion of local causality and does not rely on
any concept of quantum theory. Nevertheless, the simulation model reproduces
the averages as obtained from the quantum theoretical description of Wheeler's
delayed choice experiment. Our results prove that it is possible to give a
particle-only description of Wheeler's delayed choice experiment which
reproduces the averages calculated from quantum theory and which does not defy
common sense.Comment: Europhysics Letters (in press
Logic, Probability and Learning, or an Introduction to Statistical Relational Learning
Probabilistic inductive logic programming (PILP), sometimes also called statistical relational learning, addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with first order logic representations and machine learning. A rich variety of different formalisms and learning techniques have been developed and they are being applied on applications in network analysis, robotics, bio-informatics, intelligent agents, etc. This tutorial starts with an introduction to probabilistic representations and machine learning, and then continues with an overview of the state-of-the-art in statistical relational learning. We start from classical settings for logic learning (or inductive logic programming) namely learning from entailment, learning from interpretations, and learning from proofs, and show how they can be extended with probabilistic methods. While doing so, we review state-of-the-art statistical relational learning approaches and show how they fit the discussed learning settings for probabilistic inductive logic programming.status: publishe
Decoherence by a spin thermal bath: Role of the spin-spin interactions and initial state of the bath
We study the decoherence of two coupled spins that interact with a spin-bath
environment. It is shown that the connectivity and the coupling strength
between the spins in the environment are of crucial importance for the
decoherence of the central system. For the anisotropic spin-bath, changing the
connectivity or coupling strenghts changes the decoherence of the central
system from Gaussian to exponential decay law. The initial state of the
environment is shown to affect the decoherence process in a qualitatively
significant manner.Comment: submitted to PR
Generalizing Refinement Operators to Learn Prenex Conjunctive Normal Forms
Inductive Logic Programming considers almost exclusively universally quantied theories. To add expressiveness, prenex conjunctive normal forms (PCNF) with existential variables should also be considered. ILP mostly uses learning with refinement operators. To extend refinement operators to PCNF, we should first do so with substitutions. However, applying a classic substitution to a PCNF with existential variables, one often obtains a generalization rather than a specialization. In this article we define substitutions that specialize a given PCNF and a weakly complete downward refinement operator. Moreover, we analyze the complexities of this operator in different types of languages and search spaces. In this way we lay a foundation for learning systems on PCNF. Based on this operator, we have implemented a simple learning system PCL on some type of PCNF.learning;PCNF;completeness;refinement;substitutions
Possible Experience: from Boole to Bell
Mainstream interpretations of quantum theory maintain that violations of the
Bell inequalities deny at least either realism or Einstein locality. Here we
investigate the premises of the Bell-type inequalities by returning to earlier
inequalities presented by Boole and the findings of Vorob'ev as related to
these inequalities. These findings together with a space-time generalization of
Boole's elements of logic lead us to a completely transparent Einstein local
counterexample from everyday life that violates certain variations of the Bell
inequalities. We show that the counterexample suggests an interpretation of the
Born rule as a pre-measure of probability that can be transformed into a
Kolmogorov probability measure by certain Einstein local space-time
characterizations of the involved random variables.Comment: Published in: EPL, 87 (2009) 6000
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