International R&D Spillovers and other Unobserved Common Spillovers and Shocks

Studies which are based on Coe and Helpman (1995) and use weighted foreign R&D variables to estimate channel-specific R&D spillovers disregard the interaction between international R&D spillovers and other unobserved common spillovers and shocks. Using a panel of 50 economies from 1970-2011, we find that disregarding this interaction leads to inconsistent estimates whenever knowledge spillovers and other unobserved effects are correlated with foreign and domestic R&D. When this interaction is modeled, estimates are consistent; however, they confound foreign and domestic R&D effects with unobserved effects. Thus, the coefficient of a weighted foreign R&D variable cannot capture genuine channel-specific R&D spillovers.Comment: 28 page

Shallow decision-making analysis in General Video Game Playing

The General Video Game AI competitions have been the testing ground for several techniques for game playing, such as evolutionary computation techniques, tree search algorithms, hyper heuristic based or knowledge based algorithms. So far the metrics used to evaluate the performance of agents have been win ratio, game score and length of games. In this paper we provide a wider set of metrics and a comparison method for evaluating and comparing agents. The metrics and the comparison method give shallow introspection into the agent's decision making process and they can be applied to any agent regardless of its algorithmic nature. In this work, the metrics and the comparison method are used to measure the impact of the terms that compose a tree policy of an MCTS based agent, comparing with several baseline agents. The results clearly show how promising such general approach is and how it can be useful to understand the behaviour of an AI agent, in particular, how the comparison with baseline agents can help understanding the shape of the agent decision landscape. The presented metrics and comparison method represent a step toward to more descriptive ways of logging and analysing agent's behaviours

Circular Wilson loops in defect conformal field theory

We study a D3-D5 system dual to a conformal field theory with a codimension-one defect that separates regions where the ranks of the gauge groups differ by k. With the help of this additional parameter, as observed by Nagasaki, Tanida and Yamaguchi, one can define a double scaling limit in which the quantum corrections are organized in powers of λ/k2, which should allow to extrapolate results between weak and strong coupling regimes. In particular we consider a radius R circular Wilson loop placed at a distance L, whose internal space orientation is given by an angle χ. We compute its vacuum expectation value and show that, in the double scaling limit and for small χ and small L/R, weak coupling results can be extrapolated to the strong coupling limit.Fil: Aguilera Damia, Jeremías. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; ArgentinaFil: Correa, Diego Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; ArgentinaFil: Giraldo Rivera, Victor Ivan. International Centre For Theoretical Sciences; India. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling

Semantic role labeling (SRL) is the task of identifying the predicate-argument structure of a sentence. It is typically regarded as an important step in the standard NLP pipeline. As the semantic representations are closely related to syntactic ones, we exploit syntactic information in our model. We propose a version of graph convolutional networks (GCNs), a recent class of neural networks operating on graphs, suited to model syntactic dependency graphs. GCNs over syntactic dependency trees are used as sentence encoders, producing latent feature representations of words in a sentence. We observe that GCN layers are complementary to LSTM ones: when we stack both GCN and LSTM layers, we obtain a substantial improvement over an already state-of-the-art LSTM SRL model, resulting in the best reported scores on the standard benchmark (CoNLL-2009) both for Chinese and English.Comment: To appear in EMNLP 201