How are Italian and Spanish cities tackling climate change? A local comparative study

27 p.

Institutional designs to face the dark side of total allowable catches

Setting total allowable catches (TACs) is an endogenous process in which different agents and institutions, often with conflicting interests and opportunistic behaviour, try to influence policy-makers. Such policy-makers, far from being the benevolent social planners many would wish them to be, may also pursue self-interest when making final decisions. Although restricted knowledge of stock abundance and population dynamics, and weakness in enforcement, have effects, these other factors may explain the reason why TAC management has failed to guarantee sustainable exploitation of fish resources. Rejecting the exogeneity of the TAC and taking advantage of fruitful debate on economic policy (i.e. the rules vs. discretion debate, and that surrounding the independence of central banks), two institutional developments are analysed as potential mechanisms to face up to misconceptions about TACs: long-term harvest control rules, and a central bank of fish.

Fe−TPP Coordination Network with Metalloporphyrinic Neutral Radicals and Face-to-Face and Edge-to-Face π−π Stacking

Artículo científico Inorg. Chem. 2013, 52, 8074−8081

Comparative study of human age estimation based on hand-crafted and deep face features

In the past few years, human facial age estimation has drawn a lot of attention in the computer vision and pattern recognition communities because of its important applications in age-based image retrieval, security control and surveillance, biomet- rics, human-computer interaction (HCI) and social robotics. In connection with these investigations, estimating the age of a person from the numerical analysis of his/her face image is a relatively new topic. Also, in problems such as Image Classification the Deep Neural Networks have given the best results in some areas including age estimation. In this work we use three hand-crafted features as well as five deep features that can be obtained from pre-trained deep convolutional neural networks. We do a comparative study of the obtained age estimation results with these features.