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The Neurobiology of Eating Disorders.
Eating disorders are severe psychiatric illnesses with a typical age of onset in adolescence. Brain research in youth and young adults may help us identify specific neurobiology that contributes to onset and maintenance of those disorders. This article provides a state-of-the-art review of our current understanding of the neurobiology of anorexia nervosa and bulimia nervosa. This includes brain structure and function studies to understand food restriction, binge-eating or purging behaviors, cognitive and emotional factors, as well as interoception. Binge-eating disorder and avoidant restrictive food intake disorder are also discussed, but the literature is still very small
How do neural networks see depth in single images?
Deep neural networks have lead to a breakthrough in depth estimation from
single images. Recent work often focuses on the accuracy of the depth map,
where an evaluation on a publicly available test set such as the KITTI vision
benchmark is often the main result of the article. While such an evaluation
shows how well neural networks can estimate depth, it does not show how they do
this. To the best of our knowledge, no work currently exists that analyzes what
these networks have learned.
In this work we take the MonoDepth network by Godard et al. and investigate
what visual cues it exploits for depth estimation. We find that the network
ignores the apparent size of known obstacles in favor of their vertical
position in the image. Using the vertical position requires the camera pose to
be known; however we find that MonoDepth only partially corrects for changes in
camera pitch and roll and that these influence the estimated depth towards
obstacles. We further show that MonoDepth's use of the vertical image position
allows it to estimate the distance towards arbitrary obstacles, even those not
appearing in the training set, but that it requires a strong edge at the ground
contact point of the object to do so. In future work we will investigate
whether these observations also apply to other neural networks for monocular
depth estimation.Comment: Submitte
The Economics of Local Tourist Systems
In this paper we analyse the Local Tourist System (LTS) as a particular case of Marshallian Industrial District. The LTS allows the identification of more effective policy tools for managing tourism. First, through the concept of LTS, the policy maker can take into account the complexity of tourism, characterised by a strong heterogeneity of goods, services and subjects involved; second, LTS helps promote a stronger co-ordination between the public and the private sector, by identifying a homogeneous territory and recognising its importance in tourists' decisions; third, through the LTS the policymaker can analyze the externalities and promotes the idea of collaborating networks in a context of local development. In the LTS framework, the anticommon problem can be analysed and contrasted. As the tourist has to buy different but intertwined goods which compose the holiday package, the failure in one of the markets can lead to the overall failure of the package. A LTS policy has to: i) co-ordinate the price policy of the different firms supplying âsingle componentsâ of the tourist product; ii) fix the price of the whole product; iii) impute a price to each component. We demonstrate that, through price policy co-ordination and under general conditions, the LTS can increase the size of tourism and the firmsâ profits, thereby reaching a more effective and efficient target in tourism policy. The recent introduction of LTS in the Italian legislation can be seen as a positive attempt of improving co-ordination in a complex sector such as tourism.Local tourist systems, Tourism policy
Temperature in complex networks
Various statistical-mechanics approaches to complex networks have been proposed to describe expected topological properties in terms of ensemble averages. Here we extend this formalism by introducing the fundamental concept of graph temperature, controlling the degree of topological optimization of a network. We recover the temperature-dependent version of various important models as particular cases of our approach, and show examples where, remarkably, the onset of a percolation transition, a scale-free degree distribution, correlations and clustering can be understood as natural properties of an optimized (low-temperature) topology. We then apply our formalism to real weighted networks and we compute their temperature, finding that various techniques used to extract information from complex networks are again particular cases of our approach
Inversion Formulas for the Dunkl Intertwining Operator and Its Dual on Spaces of Functions and Distributions
In this paper we prove inversion formulas for the Dunkl intertwining operator
and for its dual and we deduce the expression of the
representing distributions of the inverse operators and
, and we give some applications.Comment: This is a contribution to the Special Issue on Dunkl Operators and
Related Topics, published in SIGMA (Symmetry, Integrability and Geometry:
Methods and Applications) at http://www.emis.de/journals/SIGMA
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