Household food expenditure and its contribution to socioeconomic inequalities in purchasing foods consistent with Australian dietary guideline recommendations

Aim: To examine the amount of money spent on food by household income, and to ascertain whether food expenditure mediates the relationship between household income and the purchase of staple foods consistent with Australian dietary guideline recommendations. ----- ----- Methods: In face-to-face interviews (n = 1003, 66.4% response rate), households in Brisbane, Australia were asked about their purchasing choices for a range of staple foods, including grocery items, fruits and vegetables. For each participant, information was obtained about their total weekly household food expenditure, along with their sociodemographic and household characteristics. ----- ----- Results: Household income was significantly associated with food expenditure; participants residing in higher-income households spent more money on food per household member than those from lower-income households. Lower income households were less likely to make food purchasing choices of dietary staples that were consistent with recommendations. However, food expenditure did not attenuate the relationship between household income and the purchase of staple foods consistent with dietary guideline recommendations. ----- ----- Conclusions: The findings suggest that food expenditure may not contribute to income inequalities in purchasing staple foods consistent with dietary guideline recommendations: instead, other material or psychosocial factors not considered in the current study may be more important determinants of these inequalities. Further research should examine whether expenditure on non-staple items and takeaway foods is a larger contributor to socioeconomic inequalities in dietary behavior.

Connecting users and items with weighted tags for personalized item recommendations

Social tags are an important information source in Web 2.0. They can be used to describe users’ topic preferences as well as the content of items to make personalized recommendations. However, since tags are arbitrary words given by users, they contain a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise brings difficulties to improve the accuracy of item recommendations. To eliminate the noise of tags, in this paper we propose to use the multiple relationships among users, items and tags to find the semantic meaning of each tag for each user individually. With the proposed approach, the relevant tags of each item and the tag preferences of each user are determined. In addition, the user and item-based collaborative filtering combined with the content filtering approach are explored. The effectiveness of the proposed approaches is demonstrated in the experiments conducted on real world datasets collected from Amazon.com and citeULike website.

A hybrid recommender systems based on weighted tags

Social tags in web 2.0 are becoming another important information source to describe the content of items as well as to profile users’ topic preferences. However, as arbitrary words given by users, tags contains a lot of noise such as tag synonym and semantic ambiguity a large number personal tags that only used by one user, which brings challenges to effectively use tags to make item recommendations. To solve these problems, this paper proposes to use a set of related tags along with their weights to represent semantic meaning of each tag for each user individually. A hybrid recommendation generation approaches that based on the weighted tags are proposed. We have conducted experiments using the real world dataset obtained from Amazon.com. The experimental results show that the proposed approaches outperform the other state of the art approaches.

Adding a receding horizon to Locally Weighted Regression for learning robot control

There have been notable advances in learning to control complex robotic systems using methods such as Locally Weighted Regression (LWR). In this paper we explore some potential limits of LWR for robotic applications, particularly investigating its application to systems with a long horizon of temporal dependence. We define the horizon of temporal dependence as the delay from a control input to a desired change in output. LWR alone cannot be used in a temporally dependent system to find meaningful control values from only the current state variables and output, as the relationship between the input and the current state is under-constrained. By introducing a receding horizon of the future output states of the system, we show that sufficient constraint is applied to learn good solutions through LWR. The new method, Receding Horizon Locally Weighted Regression (RH-LWR), is demonstrated through one-shot learning on a real Series Elastic Actuator controlling a pendulum.

A systematic review of studies on socioeconomic inequalities in dietary intakes associated with weight gain and overweight/obesity conducted among European adults

This Review examined socioeconomic inequalities in intakes of dietary factors associated with weight gain, overweight/obesity among adults in Europe. Literature searches of studies published between 1990 and 2007 examining socioeconomic position (SEP) and the consumption of energy, fat, fibre, fruit, vegetables, energy-rich drinks and meal patterns were conducted. Forty-seven articles met the inclusion criteria. The direction of associations between SEP and energy intakes were inconsistent. Approximately half the associations examined between SEP and fat intakes showed higher total fat intakes among socioeconomically disadvantaged groups. There was some evidence that these groups consume a diet lower in fibre. The most consistent evidence of dietary inequalities was for fruit and vegetable consumption; lower socioeconomic groups were less likely to consume fruit and vegetables. Differences in energy, fat and fibre intakes (when found) were small-to-moderate in magnitude; however, differences were moderate-to-large for fruit and vegetable intakes. Socioeconomic inequalities in the consumption of energy-rich drinks and meal patterns were relatively under-studied compared with other dietary factors. There were no regional or gender differences in the direction and magnitude of the inequalities in the dietary factors examined. The findings suggest that dietary behaviours may contribute to socioeconomic inequalities in overweight/obesity in Europe. However, there is only consistent evidence that fruit and vegetables may make an important contribution to inequalities in weight status across European regions.

Fast rates for estimation error and oracle inequalities for model section

We consider complexity penalization methods for model selection. These methods aim to choose a model to optimally trade off estimation and approximation errors by minimizing the sum of an empirical risk term and a complexity penalty. It is well known that if we use a bound on the maximal deviation between empirical and true risks as a complexity penalty, then the risk of our choice is no more than the approximation error plus twice the complexity penalty. There are many cases, however, where complexity penalties like this give loose upper bounds on the estimation error. In particular, if we choose a function from a suitably simple convex function class with a strictly convex loss function, then the estimation error (the difference between the risk of the empirical risk minimizer and the minimal risk in the class) approaches zero at a faster rate than the maximal deviation between empirical and true risks. In this paper, we address the question of whether it is possible to design a complexity penalized model selection method for these situations. We show that, provided the sequence of models is ordered by inclusion, in these cases we can use tight upper bounds on estimation error as a complexity penalty. Surprisingly, this is the case even in situations when the difference between the empirical risk and true risk (and indeed the error of any estimate of the approximation error) decreases much more slowly than the complexity penalty. We give an oracle inequality showing that the resulting model selection method chooses a function with risk no more than the approximation error plus a constant times the complexity penalty.

Learning multidimensional joint control of a robot using receding horizon locally weighted regression

In this paper we explore the ability of a recent model-based learning technique Receding Horizon Locally Weighted Regression (RH-LWR) useful for learning temporally dependent systems. In particular this paper investigates the application of RH-LWR to learn control of Multiple-input Multiple-output robot systems. RH-LWR is demonstrated through learning joint velocity and position control of a three Degree of Freedom (DoF) rigid body robot.