Time with or without death: Researching death in Serbian ethnology during the second half of the 20th century

Topics of ethnological research, as well as scientific discourse in general often represent the mirror of social reality. This paper researches the ways in which dealing with death and current ethnological approaches in Serbian ethnology during the second half of the 20th century, reflect the Zeitgeist. The intensity and the quality of interests for this important anthropological theme varied during the researched period, wherefore it is possible to differentiate two types of works and authors: those who write about funeral rituals, and those who 'read' them. From 1980s until nowadays there are three subgroups of contributions to this theme that reflect critical moments of the contemporary Serbian history. The issues raised in this paper are the following: The way in which state/society regards death the way in which it structures death, the way in which it gives meaning to death, as well as the usage of death for political purpose and the constant effort of civilization to repress it into oblivion

Intelligence sharing and preemptive war in the fight against terrorism

Terrorism is the biggest threat to international security, therefore the United States (US) and the European Union (EU) established different strategies to combat this issue. Given the circumstances aforementioned, this capstone project will analyze how the US use preemptive war and how the EU use intelligence sharing to counter terrorism...El terrorismo es la amenaza más grande a la seguridad internacional, de tal manera los Estados Unidos (EEUU) y la Unión Europea (UE) han establecido estrategias diferentes para combatir este tema. De acuerdo a lo mencionado anteriormente, este trabajo de titulación analizará como EEUU aplica guerra preemptiva y como la UE comparte inteligencia para combatir el terrorismo..

On an unified framework for approachability in games with or without signals

We unify standard frameworks for approachability both in full or partial monitoring by defining a new abstract game, called the "purely informative game", where the outcome at each stage is the maximal information players can obtain, represented as some probability measure. Objectives of players can be rewritten as the convergence (to some given set) of sequences of averages of these probability measures. We obtain new results extending the approachability theory developed by Blackwell moreover this new abstract framework enables us to characterize approachable sets with, as usual, a remarkably simple and clear reformulation for convex sets. Translated into the original games, those results become the first necessary and sufficient condition under which an arbitrary set is approachable and they cover and extend previous known results for convex sets. We also investigate a specific class of games where, thanks to some unusual definition of averages and convexity, we again obtain a complete characterization of approachable sets along with rates of convergence

Learning Multi-item Auctions with (or without) Samples

We provide algorithms that learn simple auctions whose revenue is approximately optimal in multi-item multi-bidder settings, for a wide range of valuations including unit-demand, additive, constrained additive, XOS, and subadditive. We obtain our learning results in two settings. The first is the commonly studied setting where sample access to the bidders' distributions over valuations is given, for both regular distributions and arbitrary distributions with bounded support. Our algorithms require polynomially many samples in the number of items and bidders. The second is a more general max-min learning setting that we introduce, where we are given "approximate distributions," and we seek to compute an auction whose revenue is approximately optimal simultaneously for all "true distributions" that are close to the given ones. These results are more general in that they imply the sample-based results, and are also applicable in settings where we have no sample access to the underlying distributions but have estimated them indirectly via market research or by observation of previously run, potentially non-truthful auctions. Our results hold for valuation distributions satisfying the standard (and necessary) independence-across-items property. They also generalize and improve upon recent works, which have provided algorithms that learn approximately optimal auctions in more restricted settings with additive, subadditive and unit-demand valuations using sample access to distributions. We generalize these results to the complete unit-demand, additive, and XOS setting, to i.i.d. subadditive bidders, and to the max-min setting. Our results are enabled by new uniform convergence bounds for hypotheses classes under product measures. Our bounds result in exponential savings in sample complexity compared to bounds derived by bounding the VC dimension, and are of independent interest.Comment: Appears in FOCS 201