The effects of weight loss strategies on gastric emptying and appetite control

The gastrointestinal tract plays an important role in the improved appetite control and weight loss in response to bariatric surgery. Other strategies which similarly alter gastrointestinal responses to food intake could contribute to successful weight management. The aim of this review is to discuss the effects of surgical, pharmacological and behavioural weight loss interventions on gastrointestinal targets of appetite control, including gastric emptying. Gastrointestinal peptides are also discussed because of their integrative relationship in appetite control. This review shows that different strategies exert diverse effects and there is no consensus on the optimal strategy for manipulating gastric emptying to improve appetite control. Emerging evidence from surgical procedures (e.g., sleeve gastrectomy and Roux en-Y gastric bypass) suggests a faster emptying rate and earlier delivery of nutrients to the distal small intestine may improve appetite control. Energy restriction slows gastric emptying, while the effect of exercise-induced weight loss on gastric emptying remains to be established. The limited evidence suggests that chronic exercise is associated with faster gastric emptying which we hypothesise will impact on appetite control and energy balance. Understanding how behavioural weight loss interventions (e.g., diet and exercise) alter gastrointestinal targets of appetite control may be important to improve their success in weight management.

Hybrid-Game Strategies for multi-objective design optimization in engineering

A number of Game Strategies (GS) have been developed in past decades. They have been used in the fields of economics, engineering, computer science and biology due to their efficiency in solving design optimization problems. In addition, research in multi-objective (MO) and multidisciplinary design optimization (MDO) has focused on developing robust and efficient optimization methods to produce a set of high quality solutions with low computational cost. In this paper, two optimization techniques are considered; the first optimization method uses multi-fidelity hierarchical Pareto optimality. The second optimization method uses the combination of two Game Strategies; Nash-equilibrium and Pareto optimality. The paper shows how Game Strategies can be hybridised and coupled to Multi-Objective Evolutionary Algorithms (MOEA) to accelerate convergence speed and to produce a set of high quality solutions. Numerical results obtained from both optimization methods are compared in terms of computational expense and model quality. The benefits of using Hybrid-Game Strategies are clearly demonstrated

Optimal strategies and minimax lower bounds for online convex games

A number of learning problems can be cast as an Online Convex Game: on each round, a learner makes a prediction x from a convex set, the environment plays a loss function f, and the learner’s long-term goal is to minimize regret. Algorithms have been proposed by Zinkevich, when f is assumed to be convex, and Hazan et al., when f is assumed to be strongly convex, that have provably low regret. We consider these two settings and analyze such games from a minimax perspective, proving minimax strategies and lower bounds in each case. These results prove that the existing algorithms are essentially optimal.

Adaptive online gradient descent

We study the rates of growth of the regret in online convex optimization. First, we show that a simple extension of the algorithm of Hazan et al eliminates the need for a priori knowledge of the lower bound on the second derivatives of the observed functions. We then provide an algorithm, Adaptive Online Gradient Descent, which interpolates between the results of Zinkevich for linear functions and of Hazan et al for strongly convex functions, achieving intermediate rates between [square root T] and [log T]. Furthermore, we show strong optimality of the algorithm. Finally, we provide an extension of our results to general norms.

Margin-adaptive model selection in statistical learning

A classical condition for fast learning rates is the margin condition, first introduced by Mammen and Tsybakov. We tackle in this paper the problem of adaptivity to this condition in the context of model selection, in a general learning framework. Actually, we consider a weaker version of this condition that allows one to take into account that learning within a small model can be much easier than within a large one. Requiring this “strong margin adaptivity” makes the model selection problem more challenging. We first prove, in a general framework, that some penalization procedures (including local Rademacher complexities) exhibit this adaptivity when the models are nested. Contrary to previous results, this holds with penalties that only depend on the data. Our second main result is that strong margin adaptivity is not always possible when the models are not nested: for every model selection procedure (even a randomized one), there is a problem for which it does not demonstrate strong margin adaptivity.

Optimal allocation strategies for the dark pool problem

We study the problem of allocating stocks to dark pools. We propose and analyze an optimal approach for allocations, if continuous-valued allocations are allowed. We also propose a modification for the case when only integer-valued allocations are possible. We extend the previous work on this problem to adversarial scenarios, while also improving on their results in the iid setup. The resulting algorithms are efficient, and perform well in simulations under stochastic and adversarial inputs.

A comparative analysis of facework strategies of Australians and Sri Lankans working in Australia

This study investigates facework (communicative) strategies of Sri Lankans working in Australia and compares them with strategies used by Australians of European origin working in Australia. The study also explores the values of those Sri Lankans as a reflection of their facework, and how Sri Lankans have adjusted their facework to the Australian culture. The study used a survey questionnaire and interviewed Sri Lankans working in Australia for this investigation. The survey questionnaire was used to understand the facework similarities and difference between the Sri Lankans and Australians as explained in Oetzel and Ting-Toomey’s Face Negotiation Model. The survey revealed that Sri Lankans are higher in interdependent self construal, self face concern and other face concern than the Australians. Nonetheless, Sri Lankans are similar to the Australians in other facework strategies. The interviews clarified that Sri Lankans do not change their values by living in Australia, yet they make some changes to how they do things.