Search results for "Markov"
showing 10 items of 628 documents
Waiting time in quantum repeaters with probabilistic entanglement swapping
2019
The standard approach to realize a quantum repeater relies upon probabilistic but heralded entangled state manipulations and the storage of quantum states while waiting for successful events. In the literature on this class of repeaters, calculating repeater rates has typically depended on approximations assuming sufficiently small probabilities. Here we propose an exact and systematic approach including an algorithm based on Markov chain theory to compute the average waiting time (and hence the transmission rates) of quantum repeaters with arbitrary numbers of links. For up to four repeater segments, we explicitly give the exact rate formulae for arbitrary entanglement swapping probabiliti…
Multiscale Model Selection for High-Frequency Financial Data of a Large Tick Stock by Means of the Jensen–Shannon Metric
2014
Modeling financial time series at different time scales is still an open challenge. The choice of a suitable indicator quantifying the distance between the model and the data is therefore of fundamental importance for selecting models. In this paper, we propose a multiscale model selection method based on the Jensen–Shannon distance in order to select the model that is able to better reproduce the distribution of price changes at different time scales. Specifically, we consider the problem of modeling the ultra high frequency dynamics of an asset with a large tick-to-price ratio. We study the price process at different time scales and compute the Jensen–Shannon distance between the original…
A probabilistic approach to learning a visually grounded language model through human-robot interaction
2010
A Language is among the most fascinating and complex cognitive activities that develops rapidly since the early months of infants' life. The aim of the present work is to provide a humanoid robot with cognitive, perceptual and motor skills fundamental for the acquisition of a rudimentary form of language. We present a novel probabilistic model, inspired by the findings in cognitive sciences, able to associate spoken words with their perceptually grounded meanings. The main focus is set on acquiring the meaning of various perceptual categories (e. g. red, blue, circle, above, etc.), rather than specific world entities (e. g. an apple, a toy, etc.). Our probabilistic model is based on a varia…
Robust Estimation for Discrete Markov System with Time-Varying Delay and Missing Measurements
2013
This paper addresses theℋ∞filtering problem for time-delayed Markov jump systems (MJSs) with intermittent measurements. Within network environment, missing measurements are taken into account, since the communication channel is supposed to be imperfect. A Bernoulli process is utilized to describe the phenomenon of the missing measurements. The original system is transformed into an input-output form consisting of two interconnected subsystems. Based on scaled small gain (SSG) theorem and proposed Lyapunov-Krasovskii functional (LKF), the scaled small gains of the subsystems are analyzed, respectively. New conditions for the existence of theℋ∞filters are established, and the correspondingℋ∞f…
Hidden Markov Random Fields and Direct Search Methods for Medical Image Segmentation
2016
The goal of image segmentation is to simplify the representation of an image to items meaningful and easier to analyze. Medical image segmentation is one of the fundamental problems in image processing field. It aims to provide a crucial decision support to physicians. There is no one way to perform the segmentation. There are several methods based on HMRF. Hidden Markov Random Fields (HMRF) constitute an elegant way to model the problem of segmentation. This modelling leads to the minimization of an energy function. In this paper we investigate direct search methods that are Nelder-Mead and Torczon methods to solve this optimization problem. The quality of segmentation is evaluated on grou…
Sequence Q-learning: A memory-based method towards solving POMDP
2015
Partially observable Markov decision process (POMDP) models a control problem, where states are only partially observable by an agent. The two main approaches to solve such tasks are these of value function and direct search in policy space. This paper introduces the Sequence Q-learning method which extends the well known Q-learning algorithm towards the ability to solve POMDPs through adding a special sequence management framework by advancing from action values to “sequence” values and including the “sequence continuity principle”.
The Rail Quality Index as an Indicator of the “Global Comfort” in Optimizing Safety, Quality and Efficiency in Railway Rails
2012
AbstractThe proposed model uses the stochastic dynamic programming and in particular Markov decision processes applied to the Rail Quality Index (RQI - Italian Indice di Qualità del Binario, IQB).By performing the integrated analysis of the classes of variables which characterize the overall service quality (in terms of comfort and safety), the proposed mathematical approach allows to find the solutions to the decision-making process in function of the probability of deterioration of the state variables of the infrastructure over time and of the flow of available resources.
Is Admission-Controlled Traffic Self-Similar?
2002
It is widely recognized that the maximum number of heavy-tailed flows that can be admitted to a network link, while meeting QoS targets, can be much lower than in the case of markovian flows. In fact, the superposition of heavy-tailed flows shows long range dependence (self-similarity), which has a detrimental impact on network performance. In this paper, we show that long range dependence is significantly reduced when traffic is controlled by a Measurement-Based Admission Control (MBAC) algorithm. Our results appear to suggest that MBAC is a value added tool to improve performance in the presence of self-similar traffic, rather than a mere approximation for traditional (parameter-based) ad…
Fundamentals of a Generalized Measure Theory
1999
In this chapter, we try to present a coherent survey on some recent attempts in building a theory of generalized measures. Our main goal is to emphasize a minimal set of axioms both for the measures and their domains, and still to be able to prove significant results. Therefore we start with fairly general structures and enrich them with additional properties only if necessary.
Iodofluorination of alkenes and alkynes promoted by iodine and 4-iodotoluene difluoride
2006
It was found that a mixt. of mol. iodine and 4-iodotoluene difluoride are useful to generate in situ the electrophilic iodine/nucleophilic fluorine (IF) couple that was able to add in a Markovnikov fashion and with prevalent anti-stereoselectivity to various alkenes and alkynes.