Capturing personal health data from wearable sensors

Recently, there has been a significant growth in pervasive computing and ubiquitous sensing which strives to develop and deploy sensing technology all around us. We are also seeing the emergence of applications such as environmental and personal health monitoring to leverage data from a physical world. Most of the developments in this area have been concerned with either developing the sensing technologies, or the infrastructure (middleware) to gather this data and the issues which have been addressed include power consumption on the devices, security of data transmission, networking challenges in gathering and storing the data and fault tolerance in the event of network and/or device failure. Research is focusing on harvesting and managing data and providing query capabilities

Comparing Different Information Levels

Given a sequence of random variables ${\bf X}=X_1,X_2,\ldots$ suppose the aim is to maximize one's return by picking a `favorable' $X_i$. Obviously, the expected payoff crucially depends on the information at hand. An optimally informed person knows all the values $X_i=x_i$ and thus receives $E (\sup X_i)$. We will compare this return to the expected payoffs of a number of observers having less information, in particular $\sup_i (EX_i)$, the value of the sequence to a person who only knows the first moments of the random variables. In general, there is a stochastic environment (i.e. a class of random variables $\cal C$), and several levels of information. Given some ${\bf X} \in {\cal C}$, an observer possessing information $j$ obtains $r_j({\bf X})$. We are going to study `information sets' of the form $$R_{\cal C}^{j,k} = \{ (x,y) | x = r_j({\bf X}), y=r_k({\bf X}), {\bf X} \in {\cal C} \},$$ characterizing the advantage of $k$ relative to $j$. Since such a set measures the additional payoff by virtue of increased information, its analysis yields a number of interesting results, in particular `prophet-type' inequalities.Comment: 14 pages, 3 figure

Religion-based Urbanization Process in Italy: Statistical Evidence from Demographic and Economic Data

This paper analyzes some economic and demographic features of Italians living in cities containing a Saint name in their appellation (hagiotoponyms). Demographic data come from the surveys done in the 15th (2011) Italian Census, while the economic wealth of such cities is explored through their recent [2007-2011] aggregated tax income (ATI). This cultural problem is treated from various points of view. First, the exact list of hagiotoponyms is obtained through linguistic and religiosity criteria. Next, it is examined how such cities are distributed in the Italian regions. Demographic and economic perspectives are also offered at the Saint level, i.e. calculating the cumulated values of the number of inhabitants and the ATI, "per Saint", as well as the corresponding relative values taking into account the Saint popularity. On one hand, frequency-size plots and cumulative distribution function plots, and on the other hand, scatter plots and rank-size plots between the various quantities are shown and discussed in order to find the importance of correlations between the variables. It is concluded that rank-rank correlations point to a strong Saint effect, which explains what actually Saint-based toponyms imply in terms of comparing economic and demographic data.Comment: 55 pages, 70 refs., 21 figures, 15 tables; prepared for and to be published in Quantity & Qualit

Randomization does not help much, comparability does

Following Fisher, it is widely believed that randomization "relieves the experimenter from the anxiety of considering innumerable causes by which the data may be disturbed." In particular, it is said to control for known and unknown nuisance factors that may considerably challenge the validity of a result. Looking for quantitative advice, we study a number of straightforward, mathematically simple models. However, they all demonstrate that the optimism with respect to randomization is wishful thinking rather than based on fact. In small to medium-sized samples, random allocation of units to treatments typically yields a considerable imbalance between the groups, i.e., confounding due to randomization is the rule rather than the exception. In the second part of this contribution, we extend the reasoning to a number of traditional arguments for and against randomization. This discussion is rather non-technical, and at times even "foundational" (Frequentist vs. Bayesian). However, its result turns out to be quite similar. While randomization's contribution remains questionable, comparability contributes much to a compelling conclusion. Summing up, classical experimentation based on sound background theory and the systematic construction of exchangeable groups seems to be advisable

The effect of water stage on the infiltration rate for initially dry channels

Several hydrological models that are used for simulating the water flow in rivers and channels are based on the shallow water equations as in Copeland and El-Hanafy, (2006) or Saint Venant equations (El-Hanafy and Copeland, 2007a). Both the shallow water equations and the Saint Venant equations form a system of partial differential equations which presents mass and momentum conservation along the channel and include source terms for the bed slope and bed friction. This paper presents a staggered finite difference scheme for the channel routing based upon Saint Venant equations and the well know method of characteristics after modifying it to suit the case of a shallow water depth initially followed by a flood event (El-Hanafy and Copeland, 2007b). The modified method of characteristics is implemented to achieve a transparent down stream boundary. The relation between the water depth and the infiltration rate have been derived for Saint Venant equations and it is concluded that the effect of water stage have a positive effect on the infiltration rate as it was expected