Times-To-Default:Life Cycle, Global and Industry Cycle Impact

This paper studies times-to-default of individual firms across risk classes. Using Standard & Poor’s ratings database we investigate common drivers of default probabilities and address two shortcomings of many papers in the credit literature. First, we identify relevant determinants of default intensities using business cycle and credit market proxies in addition to financial markets indicators, and reveal the time-span of their impacts. We show that misspecifications of financial based factor models are largely corrected by non financial information. Second, we show that past economic conditions are of prime importance in explaining probability changes: current shocks and long term trends jointly determine default probabilities. Finally, we exhibit industry contagion indicators which might be helpful to capture leading and persistency patterns of the default cycle.censored durations; proportional hazard; business cycle; credit cycle; default determinants; default prediction

Note on the method of matched-asymptotic expansions for determining the force acting on a particle

This paper is an addendum to the article by Candelier, Mehaddi & Vauquelin (2013) where the motion of a particle in a stratified fluid is investigated theoretically, at small Reynolds and P\'eclet numbers. We review briefly the method of matched asymptotic expansions which is generally used in order to determine the force acting on a particle embedded in a given flow, in order to account for small, but finite, inertia effects. As part of this method, we present an alternative matching procedure, which is based on a series expansion of the far-field solution of the problem, performed in the sense of generalized functions. The way to perform such a series is presented succinctly and a simple example is provided.Comment: 8 page

The Challenge of Believability in Video Games: Definitions, Agents Models and Imitation Learning

In this paper, we address the problem of creating believable agents (virtual characters) in video games. We consider only one meaning of believability, ``giving the feeling of being controlled by a player'', and outline the problem of its evaluation. We present several models for agents in games which can produce believable behaviours, both from industry and research. For high level of believability, learning and especially imitation learning seems to be the way to go. We make a quick overview of different approaches to make video games' agents learn from players. To conclude we propose a two-step method to develop new models for believable agents. First we must find the criteria for believability for our application and define an evaluation method. Then the model and the learning algorithm can be designed

A lexicographic minimax approach to the vehicle routing problem with route balancing

International audienceVehicle routing problems generally aim at designing routes that minimize transportation costs. However, in practical settings, many companies also pay attention at how the workload is distributed among its drivers. Accordingly, two main approaches for balancing the workload have been proposed in the literature. They are based on minimizing the duration of the longest route, or the difference between the longest and the shortest routes, respectively. Recently, it has been shown on several occasions that both approaches have some flaws. In order to model equity we investigate the lexicographic minimax approach, which is rooted in social choice theory. We define the leximax vehicle routing problem which considers the bi-objective optimization of transportation costs and of workload balancing. This problem is solved by a heuristic based on the multi-directional local search framework. It involves dedicated large neighborhood search operators. Several LNS operators are proposed and compared in experimentations

Learning a Representation of a Believable Virtual Character's Environment with an Imitation Algorithm

In video games, virtual characters' decision systems often use a simplified representation of the world. To increase both their autonomy and believability we want those characters to be able to learn this representation from human players. We propose to use a model called growing neural gas to learn by imitation the topology of the environment. The implementation of the model, the modifications and the parameters we used are detailed. Then, the quality of the learned representations and their evolution during the learning are studied using different measures. Improvements for the growing neural gas to give more information to the character's model are given in the conclusion

Polymer chain generation for coarse-grained models using radical-like polymerization

An innovative method is proposed to generate configurations of coarse grained models for polymer melts. This method, largely inspired by chemical ``radical polymerization'', is divided in three stages: (i) nucleation of radicals (reacting molecules caching monomers); (ii) growth of chains within a solvent of monomers; (iii) termination: annihilation of radicals and removal of residual monomers. The main interest of this method is that relaxation is performed as chains are generated. Pure mono and poly-disperse polymers melts are generated and compared to the configurations generated by the Push Off method from Auhl et al.. A detailed study of the static properties (gyration radius, mean square internal distance, entanglement length) confirms that the radical-like polymerization technics is suitable to generate equilibrated melts. The method is flexible, and can be adapted to generate nano-structured polymers, namely diblock and triblock copolymers.Comment: 9 pages, 12 figure