On curvature coupling and quintessence fine-tuning

We discuss the phenomenological model in which the potential energy of the quintessence field depends linearly on the energy density of the spatial curvature. We find that the pressure of the scalar field takes a different form when the potential of the scalar field also depends on the scale factor and the energy momentum tensor of the scalar field can be expressed as the form of a perfect fluid. A general coupling was proposed to explain the current accelerating expansion of the Universe and solve the fine-tuning problem.Comment: 5 pages, 1 figure, v2: correct the comment on astro-ph/0509177, v3: significant changes are made to better present the paper;v4: use epl style, add new contents, conclusion remains, accepted for publication by Europhys. Let

Neural Relational Learning Through Semi-Propositionalization of Bottom Clauses

Relational learning can be described as the task of learning first-order logic rules from examples. It has enabled a number of new machine learning applications, e.g. graph mining and link analysis in social networks. The CILP++ system is a neural-symbolic system which can perform efficient relational learning, by being able to process first-order logic knowledge into a neural network. CILP++ relies on BCP, a recently discovered propositionalization algorithm, to perform relational learning. However, efficient knowledge extraction from such networks is an open issue and features generated by BCP do not have an independent relational description, which prevents sound knowledge extraction from such networks. We present a methodology for generating independent propositional features for BCP by using semi-propositionalization of bottom clauses. Empirical results obtained in comparison with the original version of BCP show that this approach has comparable accuracy and runtimes, while allowing proper relational knowledge representation of features for knowledge extraction from CILP++ networks

Linear entropy fails to predict entanglement behavior in low-density fermionic systems

Entanglement is considered a fundamental ingredient for quantum technologies and condensed matter systems are among the good candidates for quantum devices. For bipartite pure states the von Neumann entropy is a proper measure of entanglement, while the linear entropy, associated to the mixedness of the reduced density matrices, is a simpler quantity to be obtained and is considered to be qualitatively equivalent to the von Neumann. Here we investigate both linear and von Neumann entropies for quantifying entanglement in homogeneous, superlattice and disordered Hubbard chains. We find regimes of parameters for which the linear entropy fails in reproducing the qualitative behavior of the von Neumann entropy. This then may lead to incorrect predictions i) of maximum and minimum entanglement states and ii) of quantum phase transitions

Coherent evolution via reservoir driven holonomy

We show that in the limit of strongly interacting environment a system initially prepared in a Decoherence Free Subspace (DFS) coherently evolves in time, adiabatically following the changes of the DFS. If the reservoir cyclicly evolves in time, the DFS states acquire an holonomy.Comment: 4 page

The Need for Knowledge Extraction: Understanding Harmful Gambling Behavior with Neural Networks

Responsible gambling is a field of study that involves supporting gamblers so as to reduce the harm that their gambling activity might cause. Recently in the literature, machine learning algorithms have been introduced as a way to predict potentially harmful gambling based on patterns of gambling behavior, such as trends in amounts wagered and the time spent gambling. In this paper, neural network models are analyzed to help predict the outcome of a partial proxy for harmful gambling behavior: when a gambler “self-excludes”, requesting a gambling operator to prevent them from accessing gambling opportunities. Drawing on survey and interview insights from industry and public officials as to the importance of interpretability, a variant of the knowledge extraction algorithm TREPAN is proposed which can produce compact, human-readable logic rules efficiently, given a neural network trained on gambling data. To the best of our knowledge, this paper reports the first industrial-strength application of knowledge extraction from neural networks, which otherwise are black-boxes unable to provide the explanatory insights which are crucially required in this area of application. We show that through knowledge extraction one can explore and validate the kinds of behavioral and demographic profiles that best predict self-exclusion, while developing a machine learning approach with greater potential for adoption by industry and treatment providers. Experimental results reported in this paper indicate that the rules extracted can achieve high fidelity to the trained neural network while maintaining competitive accuracy and providing useful insight to domain experts in responsible gambling

Geometric phase in open systems

We calculate the geometric phase associated to the evolution of a system subjected to decoherence through a quantum-jump approach. The method is general and can be applied to many different physical systems. As examples, two main source of decoherence are considered: dephasing and spontaneous decay. We show that the geometric phase is completely insensitive to the former, i.e. it is independent of the number of jumps determined by the dephasing operator.Comment: 4 pages, 2 figures, RevTe

Acceleration from M theory and Fine-tuning

The compactification of M theory with time dependent hyperbolic internal space gives an effective scalar field with exponential potential which provides a transient acceleration in Einstein frame in four dimensions. Ordinary matter and radiation are present in addition to the scalar field coming from compactification. We find that we have to fine-tune the initial conditions of the scalar field so that our Universe experiences acceleration now. During the evolution of our Universe, the volume of the internal space increases about 12 times. The time variation of the internal space results in a large time variation of the fine structure constant which violates the observational constraint on the variation of the fine structure constant. The large variation of the fine structure constant is a generic feature of transient acceleration models.Comment: 9 pages, 3 figures, use iopart, v2; references updated, accepted for publication in Class. Quantum Gra