Geographic information is crucial for estimating the future costs of an insurance contract. It helps identify regions exposed to weather-related events and regions exhibiting higher concentrations of socio-demographic risks such as flood or theft. In actuarial science, the current approach of estimating future costs in a territory is through one-hot encoding of zip codes, postal codes or company-defined polygon levels in statistical learning models. This method has two main drawbacks: it does not share information from similar risk territories and does not share information regarding neighboring areas. We propose the Convolutional Regional Autoencoder model, a method for generating geographical risk encodings using convolutional neural networks. This aims to replace the traditional territory variable for estimating future costs of insurance contracts. Experimental results demonstrate that encodings generated by our approach provide more useful features to predict insurance losses from a real dataset.

With super-resolution optical microscopy, it is now possible to observe molecular interactions in living cells. The obtained images have a very high spatial precision but their overall quality can vary a lot depending on the structure of interest and the imaging parameters. Moreover, evaluating this quality is often difficult for non-expert users. In this work, we tackle the problem of learning the quality function of super-resolution images from scores provided by experts. More specifically, we are proposing a system based on a deep neural network that can provide a quantitative quality measure of a STED image of neuronal structures given as input. We conduct a user study in order to evaluate the quality of the predictions of the neural network against those of a human expert. Results show the potential while highlighting some of the limits of the proposed approach.

In order to improve their understanding, cellular mechanisms to the imaging process, or the observability of specific structures. Superresolution we consider a network made of 6 convolutional layers microscopes are highly specialized devices, significantly and 2 fully connected layers. An ELU activation (Exponential more complex to use than conventional optical microscopes, Linear Unit) is used after each convolutional and fully hence reducing their accessibility. Max pooling (kernel 2x2, stride 1) is added overall quality of the obtained images can vary a lot depending after each convolutional unit. Batch normalization is applied on the imaging parameters or the biological structure of to all the layers except the first one.

The 13th International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming (CPAIOR 2016), was held in Banff, Canada, May 29 - June 1, 2016. In order to trigger exchanges between the constraint programming and the operations research community, CPAIOR was co-located with CORS 2016, the Canadian Operational Research society's conference.

In cooperation with the Association for the Advancement of Artificial Intelligence (AAAI), the Twenty-Second International Conference on Case-Based Reasoning (ICCBR), the premier international meeting on research and applications in case-based reasoning (CBR), was held from Monday September 29 to Wednesday October 1, 2014, in Cork, Ireland. ICCBR is the annual meeting of the CBR community and the leading conference on this topic. Started in 1993 as the European Conference on CBR and 1995 as ICCBR, the two conferences alternated biennially until their merger in 2010.

ICCBR is the annual meeting of the CBR community and the leading conference on this topic. Started in 1993 as the European Conference on CBR and 1995 as ICCBR, the two conferences alternated biennially until their merger in 2010. The main conference track featured 19 research paper presentations, 16 posters, and two invited speakers. The papers and posters reflected the state of the art of case-based reasoning, dealing both with open problems at the core of casebased reasoning (especially in similarity assessment, case adaptation, and case-based maintenance), as well as trending applications of CBR. Minor, Goethe University, Germany, and Emmanuel The first invited speaker, Tony Veale from University Nauer, LORIA, France.

We present three new filtering algorithms for the Disjunctive constraint that all have a linear running time complexity in the number of tasks. The first algorithm filters the tasks according to the rules of the time tabling. The second algorithm performs an overload check that could also be used for the Cumulative constraint. The third algorithm enforces the rules of detectable precedences. The two last algorithms use a new data structure that we introduce and that we call the time line. This data structure provides many constant time operations that were previously implemented in logarithmic time by the Theta-tree data structure. Experiments show that these new algorithms are competitive even for a small number of tasks and outperform existing algorithms as the number of tasks increases.