In this manuscript, we propose, analyze, and discuss a possible new principle behind traditional cuisine: the Food-bridging hypothesis and its comparison with the food-pairing hypothesis using the same dataset and graphical models employed in the food-pairing study by Ahn et al. (2011). The Food-bridging hypothesis assumes that if two ingredients do not share a strong molecular or empirical affinity, they may become affine through a chain of pairwise affinities. That is, in a graphical model as employed by Ahn et al., a chain represents a path that joints the two ingredients, the shortest path represents the strongest pairwise chain of affinities between the two ingredients. Food-pairing and Food-bridging are different hypotheses that may describe possible mechanisms behind the recipes of traditional cuisines. Food-pairing intensifies flavor by mixing ingredients in a recipe with similar chemical compounds, and food-bridging smoothes contrast between ingredients. Both food-pairing and food-bridging are observed in traditional cuisines, as shown in this work. We observed four classes of cuisines according to food-pairing and food-bridging: (1) East Asian cuisines, at one extreme, tend to avoid food-pairing as well as food-bridging; and (4) Latin American cuisines, at the other extreme, follow both principles. For the two middle classes: (2) Southeastern Asian cuisines, avoid foodpairing and follow food-bridging; and (3) Western cuisines, follow food-pairing and avoid food-bridging.

On November 20 and 21 2014, Telefonica I+D hosted the Data Transparency Lab ("DTL") Kickoff Workshop on Personal Data Transparency and Online Privacy at its headquarters in Barcelona, Spain. This workshop provided a forum for technologists, researchers, policymakers and industry representatives to share and discuss current and emerging issues around privacy and transparency on the Internet. The objective of this workshop was to kick-start the creation of a community of research, industry, and public interest parties that will work together towards the following objectives: -The development of methodologies and user-friendly tools to promote transparency and empower users to understand online privacy issues and consequences; -The sharing of datasets and research results, and; -The support of research through grants and the provision of infrastructure to deploy tools. With the above activities, the DTL community aims to improve our understanding of technical, ethical, economic and regulatory issues related to the use of personal data by online services. It is hoped that successful execution of such activities will help sustain a fair and transparent exchange of personal data online. This report summarizes the presentations, discussions and questions that resulted from the workshop.