In this task, you are given a paper review. Based on the review, your job is to identify language and generate "en" if the review is in English or generate "es" if the review is in Spanish. Note that URLs in the text have been replaced with [Link].

El artículo presentado, describe someramente los estudios realizados y decisiones adoptadas, que permiten dotar de una infraestructura de alta capacidad a los observatorios ESO Paranal y Cerro Armazones, ubicados en el desierto de atacama.  Se presentan las diversas etapas involucradas en el desafío por dotar de un gran ancho de banda a los mencionados observatorios ubicados en aéreas no pobladas (pleno desierto) para su conexión expedita con diversas redes académicas y la comunidad astronómica europea lo cual constituye un buen desafío desde el punto de vista tecnológico.  Sería importante destacar porqué de la necesidad de contar con grandes anchos de banda, es decir describir en mayor detalle el tipo de tráfico que lo justifica, por ejemplo monitoreo, control y operación remota, etc.  Aún no está en operación el enlace
es

# Summary  This paper presents an empirical study to identify a latent dimension of sentiment in word embeddings.  # Strengths   S1) Tackles a challenging problem of unsupervised sentiment analysis.   S2) Figure 2, in particular, is a nice visualisation.  # Weaknesses   W1) The experiments, in particular, are very thin. I would recommend also measuring F1 performance and expanding the number of techniques compared.   W2) The methodology description needs more organisation and elaboration. The ideas tested are itemised, but insufficiently justified.    W3) The results are quite weak in terms of the reported accuracy and depth of analysis. Perhaps this work needs more development, particularly with validating the central assumption that the Distributional Hypothesis implies that opposite words, although semantically similar, are separated well in the vector space?
en

This is an 18 page paper plus appendix which presents a mathematical derivation for infomax for an actual neural population with noise.  The original Bell & Sejnowski infomax framework only considered the no noise case.  Results are shown for natural image patches and the mnist dataset, which qualitatively resemble results obtained with other methods.  This seems like an interesting and potentially more general approach to unsupervised learning.  However the paper is quite long and it was difficult for me to follow all the twists and turns.  For example the introduction of the hierarchical model was confusing and it took several iterations to understand where this was going.  'Hierarchical' is probably not the right terminology here because it's not like a deep net hierarchy, it's just decomposing the tuning curve function into different parts.  I would recommend that the authors try to condense the paper so that the central message and important steps are conveyed in short order, and then put the more complete mathematical development into a supplementary document.  Also, the authors should look at the work of Karklin & Simoncelli 2011 which is highly related.  They also use an infomax framework for a noisy neural population to derive on and off cells in the retina, and they show the conditions under which orientation selectivity emerges.

en