Unsupervised Domain Adaptation with Copula Models

We study the task of unsupervised domain adaptation, where no labeled data from the target domain is provided during training time. To deal with the potential discrepancy between the source and target distributions, both in features and labels, we exploit a copula-based regression framework. The benefits of this approach are two-fold: (a) it allows us to model a broader range of conditional predictive densities beyond the common exponential family, (b) we show how to leverage Sklar's theorem, the essence of the copula formulation relating the joint density to the copula dependency functions, to find effective feature mappings that mitigate the domain mismatch. By transforming the data to a copula domain, we show on a number of benchmark datasets (including human emotion estimation), and using different regression models for prediction, that we can achieve a more robust and accurate estimation of target labels, compared to recently proposed feature transformation (adaptation) methods.Comment: IEEE International Workshop On Machine Learning for Signal Processing 201

Theoretical predictions of melting behaviors of hcp iron up to 4000 GPa

The high-pressure melting diagram of iron is a vital ingredient for the geodynamic modeling of planetary interiors. Nonetheless, available data for molten iron show an alarming discrepancy. Herein, we propose an efficient one-phase approach to capture the solid-liquid transition of iron under extreme conditions. Our basic idea is to extend the statistical moment method to determine the density of iron in the TPa region. On that basis, we adapt the work-heat equivalence principle to appropriately link equation-of-state parameters with melting properties. This strategy allows explaining cutting-edge experimental and ab initio results without massive computational workloads. Our theoretical calculations would be helpful to constrain the chemical composition, internal dynamics, and thermal evolution of the Earth and super-Earths

The Relevance of the Colon to Zinc Nutrition

Globally, zinc deficiency is widespread, despite decades of research highlighting its negative effects on health, and in particular upon child health in low-income countries. Apart from inadequate dietary intake of bioavailable zinc, other significant contributors to zinc deficiency include the excessive intestinal loss of endogenously secreted zinc and impairment in small intestinal absorptive function. Such changes are likely to occur in children suffering from environmental (or tropical) enteropathy (EE)—an almost universal condition among inhabitants of developing countries characterized by morphologic and functional changes in the small intestine. Changes to the proximal gut in environmental enteropathy will likely influence the nature and amount of zinc delivered into the large intestine. Consequently, we reviewed the current literature to determine if colonic absorption of endogenous or exogenous (dietary) zinc could contribute to overall zinc nutriture. Whilst we found evidence that significant zinc absorption occurs in the rodent colon, and is favoured when microbially-fermentable carbohydrates (specifically resistant starch) are consumed, it is unclear whether this process occur in humans and/or to what degree. Constraints in study design in the few available studies may well have masked a possible colonic contribution to zinc nutrition. Furthermore these few available human studies have failed to include the actual target population that would benefit, namely infants affected by EE where zinc delivery to the colon may be increased and who are also at risk of zinc deficiency. In conducting this review we have not been able to confirm a colonic contribution to zinc absorption in humans. However, given the observations in rodents and that feeding resistant starch to children is feasible, definitive studies utilising the dual stable isotope method in children with EE should be undertaken.G.L. Gopalsamy, D.H Alpers, H.J Binder, C.D. Tran, B.S. Ramakrishna, I. Brown, M. Manary, Elissa Mortimer and G.P. Youn

A Robust Adaptive Control using Fuzzy Neural Network for Robot Manipulators with Dead-Zone

In this paper, a robust-adaptive-fuzzy-neural-network controller (RAFNNs) bases on dead zone compensator for industrial robot manipulators (RM) is proposed to dead the unknown model and external disturbance. Here, the unknown dynamics of the robot system is deal by using fuzzy neural network to approximate the unknown dynamics. The online training laws and estimation of the dead-zone are determined by Lyapunov stability theory and the approximation theory. In this proposal, the robust sliding-mode-control (SMC) is constructed to optimize parameter vectors, solve the approximation error and higher order terms. Therefore, the stability, robustness, and desired tracking performance of RAFNNs for RM are guaranteed. The simulations and experiments performed on three-link RM are provided in comparison with neural-network (NNs) and proportional-integral-derivative (PID) to demonstrate the robustness and effectiveness of the RAFNNs

Vectors and malaria transmission in deforested, rural communities in north-central Vietnam

Background: Malaria is still prevalent in rural communities of central Vietnam even though, due to deforestation, the primary vector Anopheles dirus is uncommon. In these situations little is known about the secondary vectors which are responsible for maintaining transmission. Basic information on the identification of the species in these rural communities is required so that transmission parameters, such as ecology, behaviour and vectorial status can be assigned to the appropriate species