Functional Multi-Layer Perceptron: a Nonlinear Tool for Functional Data Analysis

In this paper, we study a natural extension of Multi-Layer Perceptrons (MLP) to functional inputs. We show that fundamental results for classical MLP can be extended to functional MLP. We obtain universal approximation results that show the expressive power of functional MLP is comparable to that of numerical MLP. We obtain consistency results which imply that the estimation of optimal parameters for functional MLP is statistically well defined. We finally show on simulated and real world data that the proposed model performs in a very satisfactory way.Comment: http://www.sciencedirect.com/science/journal/0893608

BFpack: Flexible Bayes Factor Testing of Scientific Theories in R

There have been considerable methodological developments of Bayes factors for hypothesis testing in the social and behavioral sciences, and related fields. This development is due to the flexibility of the Bayes factor for testing multiple hypotheses simultaneously, the ability to test complex hypotheses involving equality as well as order constraints on the parameters of interest, and the interpretability of the outcome as the weight of evidence provided by the data in support of competing scientific theories. The available software tools for Bayesian hypothesis testing are still limited however. In this paper we present a new R package called BFpack that contains functions for Bayes factor hypothesis testing for the many common testing problems. The software includes novel tools for (i) Bayesian exploratory testing (e.g., zero vs positive vs negative effects), (ii) Bayesian confirmatory testing (competing hypotheses with equality and/or order constraints), (iii) common statistical analyses, such as linear regression, generalized linear models, (multivariate) analysis of (co)variance, correlation analysis, and random intercept models, (iv) using default priors, and (v) while allowing data to contain missing observations that are missing at random

Voice and rural wireless mesh community networks: a framework to quantify scalability and manage end-user smartphone battery consumption

Philosophiae Doctor - PhDCommunity wireless mesh initiatives are a pioneering option to cheap ‘last-mile’ access to network services for rural low-income regions primarily located in Sub-Saharan Africa and Developing Asia. However, researchers have criticized wireless mesh networks for their poor scalability; and scalability quantification research has mostly consisted of modularization of per-node throughput capacity behaviour. A scalability quantification model to design wireless mesh networks to provide adequate quality of service is lacking. However, scalability quantification of community mesh networks alone is inadequate because rural users need affordable devices for access; and they need to know how best to use them. Low-cost low-end smartphones offer handset affordability solutions but require smart management of their small capacity battery. Related work supports the usage of Wi-Fi for communication because it is shown to consume less battery than 2G, 3G or Bluetooth. However, a model to compare Wi-Fi battery consumption amongst different low-end smartphones is missing, as is a comparison of different over-the-top communication applications

A Confidence Paradigm for Classification Systems

There is no universally accepted methodology to determine how much confidence one should have in a classifier output. This research proposes a framework to determine the level of confidence in an indication from a classifier system where the output is or can be transformed into a posterior probability estimate. This is a theoretical framework that attempts to unite the viewpoints of the classification system developer (or engineer) and the classification system user (or war-fighter). The paradigm is based on the assumptions that the system confidence acts like, or can be modeled as a value and that indication confidence can be modeled as a function of the posterior probability estimates. The introduction of the non-declaration possibility induces the production of a higher-level value model that weighs the contribution of engineering confidence and associated non-declaration rate. Now, the task becomes to choose the appropriate threshold to maximize this overarching value function. This paradigm is developed in a setting considering only in-library problems, but it is applied to out-of-library problems as well. Introduction of out-of-library problems requires expansion of the overarching value model. This confidence measure is a direct link between traditional decision analysis techniques and traditional pattern recognition techniques. This methodology is applied to multiple data sets, and experimental results show the behavior that would be expected from a rational confidence paradigm

11th European Headache Federation Congress jointly with 31st Congress of the Italian Society for the Study of Headaches : Rome, Italy. 01-03 December 2017

. Aims of the study were explore the relationship between peripheral chromatic and central visual dysfunction evaluating also the presence of functional receptor impairment in patients with migraine, with and without aura examined interictally