Numerical model for material parameter identification of cells

Bacteria have a complex external layer that render them with an increased stiffness and more resistant to external invasion. The works aims to model the squeezing of a bacteria between two walls, and deduce the composition of bacterial external layer from the observed deformations. A FE based model will be developed for inferring the stiffness of baceria, solving an inverse problem from the applied loading and measured displacements. The results will be applied to laboraotry experiments carried out at Institu of Bioengineering of Catalunya (IBEC)

Patent Races with Dynamic Complementarity

Recent models of multi-stage R&D have shown that a system of weak intellectual property rights may lead to faster innovation by inducing firms to share intermediate technological knowledge. In this article I introduce a distinction between plain and sophisticated technological knowledge, which has not been noticed so far but plays a crucial role in determining how different appropriability rules affect the incentives to innovate. I argue that the positive effect of weak intellectual property regimes on the sharing of intermediate technological knowledge vanishes when technological knowledge is sophisticated, as is likely to be the case in many high tech industries.

Learning-Based Optimization of Cache Content in a Small Cell Base Station

Optimal cache content placement in a wireless small cell base station (sBS) with limited backhaul capacity is studied. The sBS has a large cache memory and provides content-level selective offloading by delivering high data rate contents to users in its coverage area. The goal of the sBS content controller (CC) is to store the most popular contents in the sBS cache memory such that the maximum amount of data can be fetched directly form the sBS, not relying on the limited backhaul resources during peak traffic periods. If the popularity profile is known in advance, the problem reduces to a knapsack problem. However, it is assumed in this work that, the popularity profile of the files is not known by the CC, and it can only observe the instantaneous demand for the cached content. Hence, the cache content placement is optimised based on the demand history. By refreshing the cache content at regular time intervals, the CC tries to learn the popularity profile, while exploiting the limited cache capacity in the best way possible. Three algorithms are studied for this cache content placement problem, leading to different exploitation-exploration trade-offs. We provide extensive numerical simulations in order to study the time-evolution of these algorithms, and the impact of the system parameters, such as the number of files, the number of users, the cache size, and the skewness of the popularity profile, on the performance. It is shown that the proposed algorithms quickly learn the popularity profile for a wide range of system parameters.Comment: Accepted to IEEE ICC 2014, Sydney, Australia. Minor typos corrected. Algorithm MCUCB correcte