No UV/IR Mixing in Unitary Space-Time Noncommutative Field Theory

In this article we calculate several divergent amplitudes in phi^4-theory on non-commutative space-time in the framework of Interaction Point Time Ordered Perturbation Theory (IPTOPT), continuing work done in hep-th/0209253. On the ground of these results we find corresponding Feynman rules which allow for a much easier diagrammatic calculation of amplitudes. The most important feature of the present theory is the lack of the UV/IR mixing problem in all amplitudes calculated so far. Although we are not yet able to give a rigorous proof, we provide a strong argument for this result to hold in general. Together with the found Feynman rules this opens promising vistas towards the systematic renormalization of non-commutative field theories.Comment: 23 pages, uses package feynmf, v2: typos, added reference, minor improvement

Single-particle machine for quantum thermalization

The long time accumulation of the \textit{random} actions of a single particle "reservoir" on its coupled system can transfer some temperature information of its initial state to the coupled system. This dynamic process can be referred to as a quantum thermalization in the sense that the coupled system can reach a stable thermal equilibrium with a temperature equal to that of the reservoir. We illustrate this idea based on the usual micromaser model, in which a series of initially prepared two-level atoms randomly pass through an electromagnetic cavity. It is found that, when the randomly injected atoms are initially prepared in a thermal equilibrium state with a given temperature, the cavity field will reach a thermal equilibrium state with the same temperature as that of the injected atoms. As in two limit cases, the cavity field can be cooled and "coherently heated" as a maser process, respectively, when the injected atoms are initially prepared in ground and excited states. Especially, when the atoms in equilibrium are driven to possess some coherence, the cavity field may reach a higher temperature in comparison with the injected atoms. We also point out a possible experimental test for our theoretical prediction based on a superconducting circuit QED system.Comment: 9 pages,4 figures

Enhancing Perceptual Attributes with Bayesian Style Generation

Deep learning has brought an unprecedented progress in computer vision and significant advances have been made in predicting subjective properties inherent to visual data (e.g., memorability, aesthetic quality, evoked emotions, etc.). Recently, some research works have even proposed deep learning approaches to modify images such as to appropriately alter these properties. Following this research line, this paper introduces a novel deep learning framework for synthesizing images in order to enhance a predefined perceptual attribute. Our approach takes as input a natural image and exploits recent models for deep style transfer and generative adversarial networks to change its style in order to modify a specific high-level attribute. Differently from previous works focusing on enhancing a specific property of a visual content, we propose a general framework and demonstrate its effectiveness in two use cases, i.e. increasing image memorability and generating scary pictures. We evaluate the proposed approach on publicly available benchmarks, demonstrating its advantages over state of the art methods.Comment: ACCV-201

Precise Formulation of Neutrino Oscillation in the Earth

We give a perturbation theory of neutrino oscillation in the Earth. The perturbation theory is valid for neutrinos with energy E \gsim 0.5 GeV. It is formulated using trajectory dependent average potential. Non-adiabatic contributions are included as the first order effects in the perturbation theory. We analyze neutrino oscillation with standard matter effect and with non-standard matter effect. In a three flavor analysis we show that the perturbation theory gives a precise description of neutrino conversion in the Earth. Effect of the Earth matter is substantially simplified in this formulation.Comment: References added, 21 pages, 10 figures, version to appear in PR

Stick-Breaking Policy Learning in Dec-POMDPs

Expectation maximization (EM) has recently been shown to be an efficient algorithm for learning finite-state controllers (FSCs) in large decentralized POMDPs (Dec-POMDPs). However, current methods use fixed-size FSCs and often converge to maxima that are far from optimal. This paper considers a variable-size FSC to represent the local policy of each agent. These variable-size FSCs are constructed using a stick-breaking prior, leading to a new framework called \emph{decentralized stick-breaking policy representation} (Dec-SBPR). This approach learns the controller parameters with a variational Bayesian algorithm without having to assume that the Dec-POMDP model is available. The performance of Dec-SBPR is demonstrated on several benchmark problems, showing that the algorithm scales to large problems while outperforming other state-of-the-art methods