Open problem: Shifting experts on easy data

A number of online algorithms have been developed that have small additional loss (regret) compared to the best “shifting expert”. In this model, there is a set of experts and the comparator is the best partition of the trial sequence into a small number of segments, where the expert of smallest loss is chosen in each segment. The regret is typically defined for worst-case data / loss sequences. There has been a recent surge of interest in online algorithms that combine good worst-case guarantees with much improved performance on easy data. A practically relevant class of easy data is the case when the loss of each expert is iid and the best and second best experts have a gap between their mean loss. In the full information setting, the FlipFlop algorithm by De Rooij et al. (2014) combines the best of the iid optimal Follow-The-Leader (FL) and the worst-case-safe Hedge algorithms, whereas in the bandit information case SAO by Bubeck and Slivkins (2012) competes with the iid optimal UCB and the worst-case-safe EXP3. We ask the same question for the shifting expert problem. First, we ask what are the simple and efficient algorithms for the shifting experts problem when the loss sequence in each segment is iid with respect to a fixed but unknown distribution. Second, we ask how to efficiently unite the performance of such algorithms on easy data with worst-case robustness. A particular intriguing open problem is the case when the comparator shifts within a small subset of experts from a large set under the assumption that the losses in each segment are iid.

An Efficient and Verifiable Solution to the Millionaire Problem

A new solution to the millionaire problem is designed on the base of two new techniques: zero test and batch equation. Zero test is a technique used to test whether one or more ciphertext contains a zero without revealing other information. Batch equation is a technique used to test equality of multiple integers. Combination of these two techniques produces the only known solution to the millionaire problem that is correct, private, publicly verifiable and efficient at the same time.

A systematic review of environmental correlates of obesity-related dietary behaviors in youth

There is increasing interest in the role the environment plays in shaping the dietary behavior of youth, particularly in the context of obesity prevention. An overview of environmental factors associated with obesity-related dietary behaviors among youth is needed to inform the development of interventions. A systematic review of observational studies on environmental correlates of energy, fat, fruit/ vegetable, snack/fast food and soft drink intakes in children (4–12 years) and adolescents (13–18 years) was conducted. The results were summarized using the analysis grid for environments linked to obesity. The 58 papers reviewed mostly focused on sociocultural and economical–environmental factors at the household level. The most consistent associations were found between parental intake and children’s fat, fruit/vegetable intakes, parent and sibling intake with adolescent’s energy and fat intakes and parental education with adolescent’s fruit/ vegetable intake. A less consistent but positive association was found for availability and accessibility on children’s fruit/vegetable intake. Environmental factors are predominantly studied at the household level and focus on sociocultural and economic aspects. Most consistent associations were found for parental influences (parental intake and education).More studies examining environmental factors using longitudinal study designs and validated measures are needed for solid evidence to inform interventions.

Finding order in chaos : a problem based collaborative assignment for 1300 students across 19 disciplines

The use of collaborative assignments for assessment is a risky undertaking for students and course designers. Yet the benefits, in terms of core learning outcomes, competencies, collaborative sense making and student involvement, suggest that the effort is worthwhile. Formal descriptions and rules do little to ameliorate the perception of risk and increased anxiety by students. (Ryan, 2007). BEB100 Introducing Professional Learning is a faculty-wide foundation unit with over 1300 students from 19 disciplines across the Faculty of the Built Environment and Engineering (“BEE”) at the Queensland University of Technology (“QUT”), Brisbane, Australia. Finding order in chaos outlines the approach and justification, assessment criteria, learning resources, teamwork tools, tutorial management, communication strategies, 2007-09 Student Learning Experience Survey results, annual improvements, findings and outcomes.