DeepFactors: Real-time probabilistic dense monocular SLAM

The ability to estimate rich geometry and camera motion from monocular imagery is fundamental to future interactive robotics and augmented reality applications. Different approaches have been proposed that vary in scene geometry representation (sparse landmarks, dense maps), the consistency metric used for optimising the multi-view problem, and the use of learned priors. We present a SLAM system that unifies these methods in a probabilistic framework while still maintaining real-time performance. This is achieved through the use of a learned compact depth map representation and reformulating three different types of errors: photometric, reprojection and geometric, which we make use of within standard factor graph software. We evaluate our system on trajectory estimation and depth reconstruction on real-world sequences and present various examples of estimated dense geometry

Retrospective Removal of Gamete Donor Anonymity: Policy Recommendations for Ontario Based on the Victorian Experience

This paper undertakes a comparative analysis of the gamete-donor anonymity schemes in Ontario, Canada and Victoria, Australia. As of March 1, 2017, Victoria became the first jurisdiction in the world to retrospectively remove gamete-donor anonymity. Conversely, donor anonymity remains protected in Ontario, largely through statutory silence. While many donor conceived individuals are calling for other jurisdictions to follow suit and retrospectively abolish anonymity, an in-depth analysis of Victoria’s policy-making process suggests that Ontario should not take a similar course of action. This conclusion is based on the inherent issues with retrospective legislation, the historical differences between the two jurisdictions in overseeing gamete donation, the Victorian government’s inconsistent reliance on evidence, and the ill- suited reasoning used to justify Victoria’s policy decision. In lieu of enacting retrospective legislation, this paper recommends that Ontario should increase public education and create a voluntary, provincial donor registry. Based on a relational approach, these steps are more conducive to harmonizing the complex, interconnected interests at play and to supporting healthy relationships in whatever form they may take

Circumventing Spatio-Numeric Biases Through Non-Numeric Assessments of Perceived Causal Strength

Knowledge of cause and effect allows individuals to meaningfully interpret the events they perceive in the world, and the understanding of causality is thought to be grounded in the understanding of forces (Wolf, Ritter, & Holmes, 2014). Previous research has linked handedness with both the ability to exert force (e.g., Linkenauger et al., 2005) and causal learning (e.g., Goedert & Czarnowski, 2017). Historically, number lines have been used to assess causality, but because handedness has a strong spatial element, SNARC effects may influence judgments (Fias, 1996). The current experiment replicates previous work by Goedert and Czarnowski (2017) but changes the assessment measure used to capture causal judgments. Right-handed participants underwent a trial-by-trial learning task where they were instructed to discern how effective various plant liquids were on plant blooming. Instead of using a number line, I created a color selector that reduces the impact of spatio-numeric biases by instructing participants to choose a color they feel accurately captures their causal judgment. Bayesian analyses found that individuals were able to use the color selector to appropriately discern between moderately contingent and non-contingent plant liquids. More importantly, no strong evidence for the presence of spatial biases was found

A study of pre-game emotions of high school basketball players

Thesis (Ed. M.)--Boston University, 195

Instance reduction approach to machine learning and multi-database mining

The paper proposes a heuristic instance reduction algorithm as an approach to machine learning and knowledge discovery in centralized and distributed databases. The proposed algorithm is based on an original method for a selection of reference instances and creates a reduced training dataset. The reduced training set consisting of selected instances can be used as an input for the machine learning algorithms used for data mining tasks. The algorithm calculates for each instance in the data set the value of its similarity coefficient. Values of the coefficient are used to group instances into clusters. The number of clusters depends on the value of the so called representation level set by the user. Out of each cluster only a limited number of instances is selected to form a reduced training set. The proposed algorithm uses population learning algorithm for selection of instances. The paper includes a description of the proposed approach and results of the validating experiment

EFSA NDA Panel (EFSA Panel on Dietetic Products, Nutrition and Allergies), 2013 . Scientific opinion on Dietary Reference Values for fluoride

Following a request from the European Commission, the Panel on Dietetic Products, Nutrition and Allergies (NDA) derived Dietary Reference Values (DRVs) for fluoride, which are provided as Adequate Intake (AI) from all sources, including non-dietary sources. Fluoride is not an essential nutrient. Therefore, no Average Requirement for the performance of essential physiological functions can be defined. Nevertheless, the Panel considered that the setting of an AI is appropriate because of the beneficial effects of dietary fluoride on prevention of dental caries. The AI is based on epidemiological studies (performed before the 1970s) showing an inverse relationship between the fluoride concentration of water and caries prevalence. As the basis for defining the AI, estimates of mean fluoride intakes of children via diet and drinking water with fluoride concentrations at which the caries preventive effect approached its maximum whilst the risk of dental fluorosis approached its minimum were chosen. Except for one confirmatory longitudinal study in US children, more recent studies were not taken into account as they did not provide information on total dietary fluoride intake, were potentially confounded by the use of fluoride-containing dental hygiene products, and did not permit a conclusion to be drawn on a dose-response relationship between fluoride intake and caries risk. The AI of fluoride from all sources (including non-dietary sources) is 0.05 mg/kg body weight per day for both children and adults, including pregnant and lactating women. For pregnant and lactating women, the AI is based on the body weight before pregnancy and lactation. Reliable and representative data on the total fluoride intake of the European population are not available