Abstract
This paper provides a thorough second-best welfare analysis of the standard two-stage model of R&D/product market competition with R&D spillovers. The planner's solution is compared to the standard non-cooperative scenario, the R&D cartel, and the cartelized research joint venture (or joint lab). We introduce the notion of a social joint lab, as a way for the planner to avoid wasteful R&D duplication. With no spillovers, the non-cooperative scenario, the joint lab, and the second-best planner's solutions coincide. However, with spillovers, all three scenarios yield R&D investments that fall short of the socially optimal level. To shed light on the role of the spillover level on these comparisons, we observe that the gaps between the market outcomes and the planners solutions widen as the spillover parameter increases. Finally, we establish that a social planner and a social joint lab solutions may be achieved starting from any of the three scenarios by offering firms respective suitably weighted quadratic R&D subsidization schedules.

Abstract
This work demonstrates a system that provides decision support to players in electricity market negotiations. This contribution is provided by ALBidS (Adaptive Learning strategic Bidding System), a decision support system that includes a large number of distinct market negotiation strategies, and learns which should be used in each context in order to provide the best expected response. The learning process on the best negotiation strategies to use at each moment is developed by means of several integrated reinforcement learning algorithms. ALBidS is integrated with MASCEM (Multi-Agent Simulator of Competitive Electricity Markets), which enables the simulation of realistic market scenarios using real data.

Abstract
Laser Guide Star [LGS] wave-front sensing is a key element of the Laser Tomographic AO system and mainly drives the final performance of any ground based high resolution instrument. In that framework, HARMONI the first light spectro-imager of the ELT [1,2], will use 6 Laser focused around 90km(@Zenith) with a circular geometry in order to sense, reconstruct and correct for the turbulence volume located above the telescope. LGS wave-front sensing suffers from several well-known limitations [3] which are exacerbated by the giant size of the Extremely Large Telescopes. In that context, the presentation is threefold: (1) we will describe, quantify and analyse the various effects (bias and noise) induced by the LGS WFS in the context of ELT. Among other points, we will focus on the spurious low order signal generated by the spatially and temporally variable sodium layer. (2) we will propose a global design trade-off for the LGS WFS and Tomographic reconstruction process in the HARMONI context. We will show that, under strong technical constraints (especially concerning the detectors characteristics), a mix of opto-mechanic and numerical optimisations will allow to get rid of WFS bias induce by spot elongation without degrading the ultimate system performance (3) beyond HARMONI baseline, we will briefly present alternative strategies (from components, concepts and algorithms point of view) that could solve the LGS spot elongation issues at lower costs and better robustness.

Abstract
In IEEE 802.11 based wireless networks interference increases as more access points are added. A metric helping to quantize this interference seems to be of high interest. In this paper we study the relationship between the improved attacking case metric, which captures interference, and throughput for IEEE 802.11 based network using directional antenna. The y(1/3) = a + b (ln x)(3) model was found to best represent the relationship between the interference metric and the network throughput. We use this model to predict the performance of similar networks and decide the best configuration a network operator could use for planning his network.

Abstract
Modern search is heavily powered by knowledge bases, but users still query using keywords or natural language. As search becomes increasingly dependent on the integration of text and knowledge, novel approaches for a unified representation of combined data present the opportunity to unlock new ranking strategies. We have previously proposed the graph-of-entity as a purely graph-based representation and retrieval model, however this model would scale poorly. We tackle the scalability issue by adapting the model so that it can be represented as a hypergraph. This enables a significant reduction of the number of (hyper)edges, in regard to the number of nodes, while nearly capturing the same amount of information. Moreover, such a higher-order data structure, presents the ability to capture richer types of relations, including nary connections such as synonymy, or subsumption. We present the hypergraph-of-entity as the next step in the graph-of-entity model, where we explore a ranking approach based on biased random walks. We evaluate the approaches using a subset of the INEX 2009 Wikipedia Collection. While performance is still below the state of the art, we were, in part, able to achieve a MAP score similar to TF-IDF and greatly improve indexing efficiency over the graph-of-entity.

Abstract
Particle swarm optimization (PSO) is a search algorithm inspired by the collective behavior of flocking birds and fishes. This algorithm is widely adopted for solving optimization problems involving one objective. The evaluation of the PSO progress is usually measured by the fitness of the best particle and the average fitness of the particles. When several objectives are considered, the PSO may incorporate distinct strategies to preserve nondominated solutions along the iterations. The performance of the multiobjective PSO (MOPSO) is usually evaluated by considering the resulting swarm at the end of the algorithm. In this paper, two indices based on the Shannon entropy are presented, to study the swarm dynamic evolution during the MOPSO execution. The results show that both indices are useful for analyzing the diversity and convergence of multiobjective algorithms.