Abstract: Tunnelling probabilities in field emission processes are usually calculated assuming flat emitting surfaces. This corresponds to consider the electric field as a constant along the potential barrier. Previous work have shown the importance of taking into account image effects

ABSTRACT. We report a new technique to measure direction and amplitude of electric fields generated by microelectrodes embedded in polar liquid environment, as often used in microfluidic devices. The method is based on optical tweezers which act as sensitive force transducer while a trapped charged

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potential and again find agreements between the gauge theory and the supergravity results. By turning on Ramond-Ramond zero-form potential, we also study θ vacuum angle dependence of the static potential. We finally discuss dynamical realization of loop equation via turning on local electric field

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. The latter relates to how data is observed and is problem domain dependent. The former depends on how various prior constraints are expressed. Markov Random Field Models (MRF) theory is a tool to encode contextual constraints into the prior probability. This paper presents a unified approach for MRF modeling

globally hyperbolic curved spacetimes, it is essential that the theory be formulated in an entirely local and covariant manner, without assuming the presence of a preferred state. We propose a new framework for quantum field theory, in which the existence of an Operator Product Expansion (OPE) is elevated

Conditional random fields for sequence labeling offer advantages over both generative models like HMMs and classifiers applied at each sequence position. Among sequence labeling tasks in language processing, shallow parsing has received much attention, with the development of standard

We show that the large N limit of certain conformal field theories in various dimensions include in their Hilbert space a sector describing supergravity on the product of AntideSitter spacetimes, spheres and other compact manifolds. This is shown by taking some branes in the full M/string theory

Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples