386,979 research outputs found
Estimating Economic Regional Effects of Euro 2012
In 2007 Poland and Ukraine were awarded by UEFA to co-host the 2012 European Football Championships. This first "mega-event" to take place in the transition countries is commonly intended to yield large and lasting economic bebefits to the host cities. This point of view is rarely shared by economists, who are aware of misuse of economic impact estimates. In this paper, we modify the Keynesian-style multiplier model to investigate the effects of Euro 2012-related spending on local economies. Our goal is two-fold : on the one hand, we can easily investigate the impact on each demand component, on the other hand, we wish to calculate the magnitudes of these multipliers in order to judge the credibility of potential regional welfare benefits. This analysis is strenghtened by taking into account the regional supply constraints. Our study also reviews the existent body of work on mega-sporting events and our results are in line with those researches who argue that the true economic impact of these competitions is overestimated by a large margin. Finally, we stress the organizational and institutional dimension of hosting a "mega-event" by the transition and developing countries that are constantly struggled to tackle the colossal tasks of upgrading stadiums and modernizing airports, rail and road networks and hotels.Transition, sport economics, Economic impact, mega-events.
Probability-Dependent Gradient Decay in Large Margin Softmax
In the past few years, Softmax has become a common component in neural
network frameworks. In this paper, a gradient decay hyperparameter is
introduced in Softmax to control the probability-dependent gradient decay rate
during training. By following the theoretical analysis and empirical results of
a variety of model architectures trained on MNIST, CIFAR-10/100 and SVHN, we
find that the generalization performance depends significantly on the gradient
decay rate as the confidence probability rises, i.e., the gradient decreases
convexly or concavely as the sample probability increases. Moreover,
optimization with the small gradient decay shows a similar curriculum learning
sequence where hard samples are in the spotlight only after easy samples are
convinced sufficiently, and well-separated samples gain a higher gradient to
reduce intra-class distance. Based on the analysis results, we can provide
evidence that the large margin Softmax will affect the local Lipschitz
constraint of the loss function by regulating the probability-dependent
gradient decay rate. This paper provides a new perspective and understanding of
the relationship among concepts of large margin Softmax, local Lipschitz
constraint and curriculum learning by analyzing the gradient decay rate.
Besides, we propose a warm-up strategy to dynamically adjust Softmax loss in
training, where the gradient decay rate increases from over-small to speed up
the convergence rate
Infinite factorization of multiple non-parametric views
Combined analysis of multiple data sources has increasing application interest, in particular for distinguishing shared and source-specific aspects. We extend this rationale of classical canonical correlation analysis into a flexible, generative and non-parametric clustering
setting, by introducing a novel non-parametric hierarchical
mixture model. The lower level of the model describes each source with a flexible non-parametric mixture, and the top level combines these to describe commonalities of the sources. The lower-level clusters arise from hierarchical Dirichlet Processes, inducing an infinite-dimensional contingency table between the views. The commonalities between the sources are modeled by an infinite block
model of the contingency table, interpretable as non-negative factorization of infinite matrices, or as a prior for infinite contingency tables. With Gaussian mixture components plugged in for continuous measurements, the model is applied to two views of genes, mRNA expression and abundance of the produced proteins, to expose groups of genes that are co-regulated in either or both of the views.
Cluster analysis of co-expression is a standard simple way of screening for co-regulation, and the two-view analysis extends the approach to distinguishing between pre- and post-translational regulation
Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood
We consider the problem of discriminative factor analysis for data that are
in general non-Gaussian. A Bayesian model based on the ranks of the data is
proposed. We first introduce a new {\em max-margin} version of the
rank-likelihood. A discriminative factor model is then developed, integrating
the max-margin rank-likelihood and (linear) Bayesian support vector machines,
which are also built on the max-margin principle. The discriminative factor
model is further extended to the {\em nonlinear} case through mixtures of local
linear classifiers, via Dirichlet processes. Fully local conjugacy of the model
yields efficient inference with both Markov Chain Monte Carlo and variational
Bayes approaches. Extensive experiments on benchmark and real data demonstrate
superior performance of the proposed model and its potential for applications
in computational biology.Comment: 14 pages, 7 figures, ICML 201
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