This paper considers a network of energy harvesting wireless nodes transmitting simultaneously in a Gaussian interference channel and investigates a distributed power allocation algorithm that maximizes the sum-rate. ...
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This paper considers a network of energy harvesting wireless nodes transmitting simultaneously in a Gaussian interference channel and investigates a distributed power allocation algorithm that maximizes the sum-rate. The power consumption model is based on a series of step functions that allow to model, among others, radio frequency circuits being on/off and the startup power consumption of the transmitter. After showing that the sum-rate maximization problem is nonsmooth, nonconvex, and NP-hard, the Iterative Smooth and Convex approximation Algorithm (ISCA) is proposed, which successively approximates the step functions by proper smooth functions to obtain a sequence of smooth nonconvex problems that can be solved by means of the successive convex approximation method. It is demonstrated that the ISCA distributedly converges to a stationary solution of the sum-rate maximization problem. For the particular case of point to point communications, the numerical results show that the ISCA is able to avoid bad stationary solutions, performing close to the globally optimal solution. The performance of the ISCA is also evaluated in the interference channel and with real solar energy harvesting data.
In a recent work J. Sci. Comput. 16, 479 - 524 (2001), B. Despres and F. Lagoutiere introduced a new approach to derive numerical schemes for hyperbolic conservation laws. Its most important feature is the ability to ...
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In a recent work J. Sci. Comput. 16, 479 - 524 (2001), B. Despres and F. Lagoutiere introduced a new approach to derive numerical schemes for hyperbolic conservation laws. Its most important feature is the ability to perform an exact resolution for a single traveling discontinuity. However their scheme is not entropy satisfying and can keep nonentropic discontinuities. The purpose of our work is, starting from the previous one, to introduce a new class of schemes for monotone scalar conservation laws, that satisfy an entropy inequality, while still resolving exactly the single traveling shocks or contact discontinuities. We show that it is then possible to have an excellent resolution of rarefaction waves, and also to avoid the undesirable staircase effect. In practice, our numerical experiments show second-order accuracy.
The stability of several density profiles constructed from a linear combination of step functions in a vertically orientated two-dimensional porous medium are considered. A quasi-steady-state approximation is made so ...
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The stability of several density profiles constructed from a linear combination of step functions in a vertically orientated two-dimensional porous medium are considered. A quasi-steady-state approximation is made so that the initial stability of the system can be approximated. Using a similar approach to that of Tan and Homsy (Phys Fluids 29:3549-3556, 1986) a dispersion equation for each density profile is obtained analytically at time zero. Neutral stability curves are then obtained to allow the regions of the parameter space to be divided into stable and unstable regions.
Abstract: Let C denote the class of regulated real-valued functions on the unit interval vanishing at the origin, whose positive and negative jumps sum to infinity in every nontrivial subinterval of I. Goffman...
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Abstract: Let C denote the class of regulated real-valued functions on the unit interval vanishing at the origin, whose positive and negative jumps sum to infinity in every nontrivial subinterval of I. Goffman [2] showed that every f in C is (essentially) a sum $g + s$ where g is continuous and s is steplike. In this sense, a function in C is like a function of bounded variation, that has a unique such g and s. The import of this paper is that for f in C the representation $f = g + s$ is not only not unique, but by far the opposite holds: g can be chosen to be any continuous function on I vanishing at 0, at the expense of a rearrangement of s.
I analyze a model of a private value divisible good auction with different payment rules, standard rationing rule pro-rata on-the-margin and both with and without a restriction on the number of bids (steps) bidders ca...
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I analyze a model of a private value divisible good auction with different payment rules, standard rationing rule pro-rata on-the-margin and both with and without a restriction on the number of bids (steps) bidders can submit. I provide characterization of equilibrium bidding strategies in a model with restricted strategy sets and I show that these equilibria converge to an equilibrium of the model with unrestricted strategy sets as the restrictions are relaxed. However, not all equilibria in the unrestricted game can be achieved as limits of the equilibria of the restricted games. I demonstrate that the equilibrium conditions require that the Euler condition characterizing equilibrium in continuously differentiable strategies in the unrestricted model holds "on average" over the intervals defined by the length of each (price) step of the restricted strategy, where the average is taken with respect to the endogenous distribution of the market clearing price. The characterization from the restricted model also allows for a natural interpretation of the involved trade-offs. Adapting the argument of Chao and Wilson (1987) I also prove that the foregone surplus of a bidder from using K steps rather than a continuous bid is proportional to 1/k(2). (C) 2012 Elsevier B.V. All rights reserved.
Purpose Describing patterns of use, including changes in dose and interruptions is challenging. Group-based trajectory modelling (GBTM) can be used to identify individuals with similar dose patterns. We provide an int...
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Purpose Describing patterns of use, including changes in dose and interruptions is challenging. Group-based trajectory modelling (GBTM) can be used to identify individuals with similar dose patterns. We provide an intuitive graphical representation of dose patterns in groups identified using GBTM. We illustrate our approach using two drugs with different combinations of available dosages. Methods We drew data on patients with MS followed from 1977 to 2014 in Montreal using two sub-cohorts of subjects. A sub-cohort of patients taking interferon-beta-1a and another of patients taking amitriptyline were identified from the initial cohort. We use GBTM to identify groups of patients with homogeneous dose patterns for each of the two drugs. We compared the graphical representation obtained from the fitted values of GBTM with our proposed approach, which consisted of using step functions whose values corresponded to the mode. Differences in characteristics across groups were identified using chi-squares and analysis of variance, both weighted by the posterior probability of group membership. Results Seven patterns of dose were identified for interferon-beta-1a and five for amitriptyline. The graphical representations of the patterns of dose from GBTM included values outside of the prescribed doses and did not capture changes in dose as clearly as the proposed representation using step functions. Conclusion Our proposed approach which is based on the mode at each visit in each pattern provides an intuitive and realistic representation of dose patterns in groups identified with GBTM.
We present a Bayesian method of ion channel analysis and apply it to a simulated data set. An alternating renewal process prior is assigned to the signal, and an autoregressive process is fitted to the noise. After ch...
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We present a Bayesian method of ion channel analysis and apply it to a simulated data set. An alternating renewal process prior is assigned to the signal, and an autoregressive process is fitted to the noise. After choosing model hyperconstants to yield 'uninformative' priors on the parameters, the joint posterior distribution is computed by using the reversible jump Markov chain Monte Carte method. A novel form of simulated tempering is used to improve the mixing of the original sampler.
Various statistical models involve a certain function, say f, like the mean regression as a function of a covariate, the hazard rate as a function of time, the spectral density of a time series as a function of freque...
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Various statistical models involve a certain function, say f, like the mean regression as a function of a covariate, the hazard rate as a function of time, the spectral density of a time series as a function of frequency, or an intensity as a function of geographical position, etc. Such functions are often modelled parametrically, whether for frequentist or Bayesian uses, and under weak conditions there are so-called Bernshtein-von Mises theorems implying that these two approaches are large-sample equivalent. Results of this nature do not necessarily hold up in nonparametric and high-dimensional setups, however. The aim of the present paper is to exhibit a unified framework and methodology for both frequentist and Bayesian nonparametric analysis, involving priors that set f constant over windows, and where the number m of such windows grows with sample size n. Applications include nonparametric regression, maximum likelihood with nonparametrically varying parameter functions, hazard rates being functions of covariates, and nonparametric analysis of stationary time series. We work out conditions on the number and sizes of the windows under which Bernshtein-von Mises type theorems can be established, with the prior changing with sample size via the growing number of windows. These conditions entail e.g. that if m proportional to n(alpha), then alpha is an element of (1/4, 1/2) is required. illustrations of the general methodology are given, including setups with nonparametric regression, hazard rate estimation, and inference about frequency spectra for stationary time series. (C) 2015 Elsevier B.V. All rights reserved.
Biomedical data often exhibit jumps or abrupt changes. For example, women's basal body temperature may jump at ovulation, menstruation, implantation, and miscarriage. These sudden changes make these data challengi...
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Biomedical data often exhibit jumps or abrupt changes. For example, women's basal body temperature may jump at ovulation, menstruation, implantation, and miscarriage. These sudden changes make these data challenging to model: many methods will oversmooth the sharp changes or overfit in response to measurement error. We develop horseshoe process regression (HPR) to address this problem. We define a horseshoe process as a stochastic process in which each increment is horseshoe-distributed. We use the horseshoe process as a nonparametric Bayesian prior for modeling a potentially nonlinear association between an outcome and its continuous predictor, which we implement via Stan and in the R package HPR. We provide guidance and extensions to advance HPR's use in applied practice: we introduce a Bayesian imputation scheme to allow for interpolation at unobserved values of the predictor within the HPR;include additional covariates via a partial linear model framework;and allow for monotonicity constraints. We find that HPR performs well when fitting functions that have sharp changes. We apply HPR to model women's basal body temperatures over the course of the menstrual cycle.
A sufficient condition is obtained under which the sample paths of the two-parameter stochastic processes will be step functions. This result is similar to that for the one-parameter case.
A sufficient condition is obtained under which the sample paths of the two-parameter stochastic processes will be step functions. This result is similar to that for the one-parameter case.
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