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].

Input: Consider Input: Summary of the paper  The paper studies the invertiblity of convolutional neural network in the random model. A reconstruction algorithm similar to IHT is proposed for layer-wise inversion of the network.    Clarity:  - The paper is confusing wrt to standard notations in deep learning.  Comments:  The paper makes two simplifications in the analysis of a CNN, that makes it map to a model based compressive sensing framework:  1-  The non linearity (RELU) is dropped. This is a big simplification, for random gaussian weights for instance we know by JL that we can preserve L_2 distance, when RELU is applied the metric changes (see for instance the kernel for n=1 in

Output: en


Input: Consider Input: This paper shows that a deep RL approach augmented with auxiliary tasks improves performance on navigation in complex environments. Specifically, A3C is used for the RL problem, and the agent is simultaneously trained on an unsupervised depth prediction task and a self-supervised loop closure classification task. While the use of auxiliary tasks to improve training of models including RL agents is not new, the main contribution here is the use of tasks that encourage learning an intrinsic representation of space and movement that enables significant improvements on maze navigation tasks.  The paper is well written, experiments are convincing, and the value of the auxiliary tasks for the problem are clear. However, the contribution is relatively incremental given previous work on RL for navigation and on auxiliary tasks. The work could become of greater interest provided broader analysis and insights on either optimal combinations of tasks for visual navigation (e.g. the value of other visual / geometry-based tasks), or on auxiliary tasks with RL in general.  As it is, it is a useful demonstration of the benefit of geometry-based auxiliary tasks for navigation, but of relatively narrow interest.

Output: en


Input: Consider Input: Se ajusta al formato solicitado y se puede leer sin dificultad.  El artículo básicamente es un revisión de las herramientas Xpath, Xquery y SQL/XML en Oracle 10g, ilustrando su uso con varios ejemplos.  No hay un aporte, en ya sea nuevo conocimiento o aplicación de la tecnología para resolver un problema real.  No hay resultados de investigación que mostrar ni discutir.


Output: es