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BACKGROUND Sleep difficulty is an unmet public health concern affecting a vast proportion of the world s population Poor sleep duration short or long sleep length and quality affect more than half of older people Sleep difficulty is associated with negative health outcomes such as obesity and reduced longevity We aimed to assess whether poor sleep duration and quality are significant risk factors for obesity in adults aged 15 and over in Australia by examining a nationally representative panel data METHODS We used three waves waves 13 17 and 21 of the nationally representative Household Income and Labour Dynamics in Australia HILDA survey data The study applied generalized estimating equations GEE logistic regression model to assess the relationship between sleep duration and quality with obesity RESULTS The study found that the odds of being obese was significantly higher amongst the study participants with poor sleep duration adjusted odds ratio aOR 1 24 95 confidence interval CI 1 16 1 32 and poor sleep quality aOR 1 29 95 CI 1 02 1 38 compared with their counterparts who had good sleep duration and quality respectively CONCLUSION Having short or long sleep at night and poor sleep quality are associated with an increased risk of obesity Obesity poses a significant threat to the health of Australian adults Enacting policies that raise public awareness of the significance of good sleep hygiene and encouraging healthy sleeping habits should be considered to address the alarming rise in the obesity rate

BACKGROUND Sleep difficulty is an unmet public health concern affecting a vast proportion of the world s population Poor sleep duration short or long sleep length and quality affect more than half of older people Sleep difficulty is associated with negative health outcomes such as obesity and reduced longevity We aimed to assess whether poor sleep duration and quality are significant risk factors for obesity in adults aged 15 and over in Australia by examining a nationally representative panel data METHODS We used three waves waves 13 17 and 21 of the nationally representative Household Income and Labour Dynamics in Australia HILDA survey data The study applied generalized estimating equations GEE logistic regression model to assess the relationship between sleep duration and quality with obesity RESULTS The study found that the odds of being obese was significantly higher amongst the study participants with poor sleep duration adjusted odds ratio aOR 1 24 95 confidence interval CI 1 16 1 32 and poor sleep quality aOR 1 29 95 CI 1 02 1 38 compared with their counterparts who had good sleep duration and quality respectively CONCLUSION Having short or long sleep at night and poor sleep quality are associated with an increased risk of obesity Obesity poses a significant threat to the health of Australian adults Enacting policies that raise public awareness of the significance of good sleep hygiene and encouraging healthy sleeping habits should be considered to address the alarming rise in the obesity rate

BACKGROUND Sleep difficulty is an unmet public health concern affecting a vast proportion of the world s population Poor sleep duration short or long sleep length and quality affect more than half of older people Sleep difficulty is associated with negative health outcomes such as obesity and reduced longevity We aimed to assess whether poor sleep duration and quality are significant risk factors for obesity in adults aged 15 and over in Australia by examining a nationally representative panel data METHODS We used three waves waves 13 17 and 21 of the nationally representative Household Income and Labour Dynamics in Australia HILDA survey data The study applied generalized estimating equations GEE logistic regression model to assess the relationship between sleep duration and quality with obesity RESULTS The study found that the odds of being obese was significantly higher amongst the study participants with poor sleep duration adjusted odds ratio aOR 1 24 95 confidence interval CI 1 16 1 32 and poor sleep quality aOR 1 29 95 CI 1 02 1 38 compared with their counterparts who had good sleep duration and quality respectively CONCLUSION Having short or long sleep at night and poor sleep quality are associated with an increased risk of obesity Obesity poses a significant threat to the health of Australian adults Enacting policies that raise public awareness of the significance of good sleep hygiene and encouraging healthy sleeping habits should be considered to address the alarming rise in the obesity rate

The identification of structured state-space model has been intensively studied for a long time but still has not been adequately addressed. The main challenge is that the involved estimation problem is a non-convex (or bilinear) optimization problem. This paper is devoted to developing an identification method which aims to find the global optimal solution under mild computational burden. Key to the developed identification algorithm is to transform a bilinear estimation to a rank constrained optimization problem and further a difference of convex programming (DCP) problem. The initial condition for the DCP problem is obtained by solving its convex part of the optimization problem which happens to be a nuclear norm regularized optimization problem. Since the nuclear norm regularized optimization is the closest convex form of the low-rank constrained estimation problem, the obtained initial condition is always of high quality which provides the DCP problem a good starting point. The DCP problem is then solved by the sequential convex programming method. Finally, numerical examples are included to show the effectiveness of the developed identification algorithm., Accepted Author Manuscript, Applied Mechanics, Team Michel Verhaegen

The identification of structured state-space model has been intensively studied for a long time but still has not been adequately addressed. The main challenge is that the involved estimation problem is a non-convex (or bilinear) optimization problem. This paper is devoted to developing an identification method which aims to find the global optimal solution under mild computational burden. Key to the developed identification algorithm is to transform a bilinear estimation to a rank constrained optimization problem and further a difference of convex programming (DCP) problem. The initial condition for the DCP problem is obtained by solving its convex part of the optimization problem which happens to be a nuclear norm regularized optimization problem. Since the nuclear norm regularized optimization is the closest convex form of the low-rank constrained estimation problem, the obtained initial condition is always of high quality which provides the DCP problem a good starting point. The DCP problem is then solved by the sequential convex programming method. Finally, numerical examples are included to show the effectiveness of the developed identification algorithm., Accepted Author Manuscript, Applied Mechanics, Team Michel Verhaegen

In this paper we study optimization methods for minimizing large-scale pseudoconvex L∞ problems in multiview geometry. We present a novel algorithm for solving this class of problem based on proximal splitting methods. We provide a brief derivation of the proposed method along with a general convergence analysis. The resulting meta-algorithm requires very little effort in terms of implementation and instead makes use of existing advanced solvers for non-linear optimization. Preliminary experiments on a number of real image datasets indicate that the proposed method experimentally matches or outperforms current state-of-the-art solvers for this class of problems.

In this paper, we tackle the problem of unsupervised domain adaptation for classification. In the unsupervised scenario where no labeled samples from the target domain are provided, a popular approach consists in transforming the data such that the source and target distributions be- come similar. To compare the two distributions, existing approaches make use of the Maximum Mean Discrepancy (MMD). However, this does not exploit the fact that prob- ability distributions lie on a Riemannian manifold. Here, we propose to make better use of the structure of this man- ifold and rely on the distance on the manifold to compare the source and target distributions. In this framework, we introduce a sample selection method and a subspace-based method for unsupervised domain adaptation, and show that both these manifold-based techniques outperform the cor- responding approaches based on the MMD. Furthermore, we show that our subspace-based approach yields state-of- the-art results on a standard object recognition benchmark.