Definition: 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].

Positive Example 1 -
Input: Este artículo no es un artículo de investigación, ya que sólo muestra cómo programar un robot mediante la herramienta de lógica difusa. Este tema ya ha sido propuesto como solución en navegación de robots.
Output: es

Positive Example 2 -
Input: I don't think the work reported in this paper is suitable for the SCCC conference. The author should consider reporting this experience on a conference more focused on practice in computer engineering than in CS reserarch. The material presented in the paper could be of interest for practicioners working with Oracle, namely a particular product.
Output: en

Negative Example 1 -
Input: Este artículo no es un artículo de investigación, ya que sólo muestra cómo programar un robot mediante la herramienta de lógica difusa. Este tema ya ha sido propuesto como solución en navegación de robots.
Output: en

Negative Example 2 -
Input: I don't think the work reported in this paper is suitable for the SCCC conference. The author should consider reporting this experience on a conference more focused on practice in computer engineering than in CS reserarch. The material presented in the paper could be of interest for practicioners working with Oracle, namely a particular product.
Output: es

Now complete the following example -
Input: This paper describes an approach to learning the non-linear activation function in deep neural nets.  This is achieved by representing the activation function in a basis of non-linear functions and learning the coefficients.  Authors use Fourier basis in the paper.  A theoretical analysis of the proposed approach is also presented, using algorithmic stability arguments, to demonstrate good generalization behavior (vanishing generalization error with large data sets) of networks with learnt non-linearities.  The main question I have about this paper is that writing a non-linear activation function as a linear or affine combination of other non-linear basis functions is equivalent to making a larger network whose nodes have the basis functions as non-linearities and whose weights have certain constraints on them.  Thus, the value of the proposed approach of learning non-linearities over optimizing network capacity for a given task (with fixed non-linearities) is not clear to me.  Or could it be argued that the constrained implied by learnt non-linearity approach are somehow good thing to do?  Another question - In the two stage training process for CNNs, when ReLU activation is replaced by NPFC(L,T), is the NPFC(L,T) activation initialized to approximate ReLU, or is it initialized using random coefficients?  Few minor corrections/questions: - Pg 2. “ … the interval [-L+T, L+T] …” should be “ … the interval [-L+T, L-T] … “ ? - Pg 2., Equation for f(x), should it be “ (-L+T) i \pi x / L “ in both sin and cos terms, or without “ x “ ? - Theorem 4.2 “ … some algorithm \eps-uniformly stable …” remove the word “algorithm” - Theorem 4.5.  SGM undefined
Output: 

 en