BACKGROUND The effectiveness of commercial weight-loss programs consisting of very-low-calorie diets (VLCDs) and low-calorie diets (LCDs) is unclear. OBJECTIVE The aim of the study was to quantify weight loss and dropout during a commercial weight-loss program in Sweden (Itrim; cost: $1300/€1000; all participants paid their own fee). DESIGN This observational cohort study linked commercial ...

These days, networked control systems (NCSs) in which data is transmitted via communication have been actively studied for many potential applications. In an NCS, dropout degrades performance depending on network conditions. For NCS with dropout, the authors propose a model-predictive-control-based input optimisation, representing as both Bernoulli model and finite-order Markov chain. Using pro...

The main goal was to test if teacher-student relationships and achievement motivation are predicting dropout intention equally for low and high socio-economic status students. A questionnaire measuring teacher-student relationships and achievement motivation was administered to 2,360 French Canadian secondary students between 12 and 15 years old during the spring of 2005. A hierarchical multipl...

Dropout is a popular stochastic regularization technique for deep neural networks that works by randomly dropping (i.e. zeroing) units from the network during training. This randomization process allows to implicitly train an ensemble of exponentially many networks sharing the same parametrization, which should be averaged at test time to deliver the final prediction. A typical workaround for t...

This paper reports an application to educational intervention of Principal Stratification, a statistical method for estimating the effect of a treatment even when there are different rates of dropout in experimental and control conditions. We consider the potential value for using principal stratification to identify “Tough Love Interventions” – interventions that have a large effect but also i...

Despite all the success that deep neural networks have seen in classifying certain datasets, the challenge of finding optimal solutions that generalize well still remains. In this paper, we propose the Boundary Optimizing Network (BON), a new approach to generalization for deep neural networks when used for supervised learning. Given a classification network, we propose to use a collaborative g...