Two algebraic properties of thermal quantum field theories

We establish the Schlieder and the Borchers property for thermal field theories. In addition, we provide some information on the commutation and localization properties of projection operators.Comment: plain tex, 14 page

Canonical Interacting Quantum Fields on Two-Dimensional De Sitter Space

We present the ${\mathscr P}(\varphi)_2$ model on de Sitter space in the canonical formulation. We discuss the role of the Noether theorem and we provide explicit expressions for the energy-stress tensor of the interacting model.Comment: minor correction

Individual attitudes towards trade: Stolper-Samuelson revisited

This paper studies to what extent individuals form their preferences towards trade policies along the lines of the Stolper-Samuelson logic. We employ a novel international survey data set with an extensive coverage of high-, middle-, and low-income countries, address a subtle methodological shortcoming in previous studies and condition on aspects of individualenlightenment. We find statistically significant and economically large Stolper-Samuelson effects. In the United States, being high-skilled increases an individual's probability of favoring free trade by up to twelve percentage points, other things equal. In Ethiopia, the effect amounts to eight percentage points, but in exactly the opposite direction. --Trade policy,Voter preferences,Political economy

Solving Bongard Problems with a Visual Language and Pragmatic Reasoning

More than 50 years ago Bongard introduced 100 visual concept learning problems as a testbed for intelligent vision systems. These problems are now known as Bongard problems. Although they are well known in the cognitive science and AI communities only moderate progress has been made towards building systems that can solve a substantial subset of them. In the system presented here, visual features are extracted through image processing and then translated into a symbolic visual vocabulary. We introduce a formal language that allows representing complex visual concepts based on this vocabulary. Using this language and Bayesian inference, complex visual concepts can be induced from the examples that are provided in each Bongard problem. Contrary to other concept learning problems the examples from which concepts are induced are not random in Bongard problems, instead they are carefully chosen to communicate the concept, hence requiring pragmatic reasoning. Taking pragmatic reasoning into account we find good agreement between the concepts with high posterior probability and the solutions formulated by Bongard himself. While this approach is far from solving all Bongard problems, it solves the biggest fraction yet