An Angiosperm species dataset reveals relationships between seed size and two-dimensional shape

Datasets containing information on seed size have been published and are currently available. Nevertheless, there is a lack in the literature of a dataset dedicated to seed shape. We present a preliminary version for a dataset on seed morphology based on a comparison of seed shape with geometric figures. Similarity of the outline of seed images with geometric models is considered as a basis to classify seeds according to the geometric figures they resemble (e.g., ellipse, oval, cardioid). This allows, first, the classification of plant species according to their geometric type of seed, and second, seed shape quantification. For each seed image, the percent of similarity of their outline with a geometric figure can be calculated as a J index. Similarity in absolute terms is considered only when the J index >90. This criterion is important to avoid ambiguity and increase discrimination. The dataset opens the possibility of studying the relationship between seed shape and other variables such as seed size, genome complexity, life form or adaptive responses

Product Origin and Reputation for Quality: the Case of Organic Foods

Replaced with revised version of paper 12/30/09.Agribusiness, Demand and Price Analysis, Industrial Organization,

Enhancing new user cold-start based on decision trees active learning by using past warm-users predictions

The cold-start is the situation in which the recommender system has no or not enough information about the (new) users/items, i.e. their ratings/feedback; hence, the recommendations are not accurate. Active learning techniques for recommender systems propose to interact with new users by asking them to rate sequentially a few items while the system tries to detect her preferences. This bootstraps recommender systems and alleviate the new user cold-start. Compared to current state of the art, the presented approach takes into account the users' ratings predictions in addition to the available users' ratings. The experimentation shows that our approach achieves better performance in terms of precision and limits the number of questions asked to the users

Exploiting past users’ interests and predictions in an active learning method for dealing with cold start in recommender systems

This paper focuses on the new users cold-start issue in the context of recommender systems. New users who do not receive pertinent recommendations may abandon the system. In order to cope with this issue, we use active learning techniques. These methods engage the new users to interact with the system by presenting them with a questionnaire that aims to understand their preferences to the related items. In this paper, we propose an active learning technique that exploits past users’ interests and past users’ predictions in order to identify the best questions to ask. Our technique achieves a better performance in terms of precision (RMSE), which leads to learn the users’ preferences in less questions. The experimentations were carried out in a small and public dataset to prove the applicability for handling cold start issues

Positioning systems in Minkowski space-time: Bifurcation problem and observational data

In the framework of relativistic positioning systems in Minkowski space-time, the determination of the inertial coordinates of a user involves the {\em bifurcation problem} (which is the indeterminate location of a pair of different events receiving the same emission coordinates). To solve it, in addition to the user emission coordinates and the emitter positions in inertial coordinates, it may happen that the user needs to know {\em independently} the orientation of its emission coordinates. Assuming that the user may observe the relative positions of the four emitters on its celestial sphere, an observational rule to determine this orientation is presented. The bifurcation problem is thus solved by applying this observational rule, and consequently, {\em all} of the parameters in the general expression of the coordinate transformation from emission coordinates to inertial ones may be computed from the data received by the user of the relativistic positioning system.Comment: 10 pages, 7 figures. The version published in PRD contains a misprint in the caption of Figure 3, which is here amende

Compositional analysis of the association between mortality and 24-hour movement behaviour from NHANES

Aims:Previous prospective studies of the association between mortality and physical activity have generally not fully accounted for the interplay between movement behaviours. A compositional data modelling approach accounts for relative scale and co-dependency in time-use data across physical activity behaviours of the 24-hour day. Methods:A prospective analysis of the National Health and Nutrition Examination Survey 2005-2006 on N = 1468 adults (d = 135 deaths) in ages 50-79 years was undertaken using compositional Cox regression analysis. Daily time spent in sedentary behaviour, light intensity (LIPA) and moderate-to-vigorous physical activity (MVPA) was determined from waist-mounted accelerometer data (Actigraph 7164) and supplemented with self-reported sleep data to determine the daily time-use composition. Results:The composition of time spent in sedentary behaviour, LIPA, MVPA and sleep was associated with mortality rate after allowing for age and sex effects (p < 0.001), and remained significant when other lifestyle factors were added (p < 0.001). This was driven primarily by the preponderance of MVPA; however, significant changes are attributable to LIPA relative to sedentary behaviour and sleep, and sedentary behaviour relative to sleep. The final ratio ceased to be statistically significant after incorporating lifestyle factors. The preponderance of MVPA ceased to be statistically significant after incorporating health at outset and physical limitations on movement. Conclusions: An association is inferred between survival rate and the physical activity composition of the day. The MVPA time share is important, but time spent in LIPA relative to sedentary behaviour and sleep is also a significant factor. Increased preponderance of MVPA may have detrimental associations at higher levels of MVPA

An item/user representation for recommender systems based on bloom filters

This paper focuses on the items/users representation in the domain of recommender systems. These systems compute similarities between items (and/or users) to recommend new items to users based on their previous preferences. It is often useful to consider the characteristics (a.k.a features or attributes) of the items and/or users. This represents items/users by vectors that can be very large, sparse and space-consuming. In this paper, we propose a new accurate method for representing items/users with low size data structures that relies on two concepts: (1) item/user representation is based on bloom filter vectors, and (2) the usage of these filters to compute bitwise AND similarities and bitwise XNOR similarities. This work is motivated by three ideas: (1) detailed vector representations are large and sparse, (2) comparing more features of items/users may achieve better accuracy for items similarities, and (3) similarities are not only in common existing aspects, but also in common missing aspects. We have experimented this approach on the publicly available MovieLens dataset. The results show a good performance in comparison with existing approaches such as standard vector representation and Singular Value Decomposition (SVD)