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].

[Q]: This is an interesting paper about quantized networks that work on temporal difference inputs.  The basic idea is that when a network has only to process differences then this is computational much more efficient specifically with natural video data since large parts of an image would be fairly constant so that the network only has to process the informative sections of the image (video stream). This is of course how the human visual system works, and it is hence of interest even beyond the core machine learning community.   As an aside, there is a strong community interested in event-based vision such as the group of Tobi Delbrück, and it might be interesting to connect to this community. This might even provide a reference for your comments on page 1.  I guess the biggest novel contribution is that a rounding network can be replaced by a sigma-delta network, but that the order of discretization and summation doe make some difference in the actual processing load. I think I followed the steps and  Most of my questions have already been answers in the pre-review period. My only question remaining is on page 3, “It should be noted that when we refer to “temporal differences”, we refer not to the change in the signal over time, but in the change between two inputs presented sequentially. The output of our network only depends on the value and order of inputs, not on the temporal spacing between them.”  This does not make sense to me. As I understand you just take the difference between two frames regardless if you call this temporal or not it is a change in one frame. So this statement rather confuses me and maybe should be dropped unless I do miss something here, in which case some more explanation would be necessary.  Figure 1 should be made bigger.  An improvement of the paper that I could think about is a better discussion of the relevance of the findings. Yes, you do show that your sigma-delta network save some operation compared to threshold, but is this difference essential for a specific task, or does your solution has relevance for neuroscience?
[A]: en


[Q]: Los autores han diseñado una metodología para desarrollar software de apoyo a proyectos de investigación científica. Identifican con bastante precisión las diferencias entre el desarrollo de software de investigación y software con fines comerciales. Asimismo, proponen una metodología siguiendo un enfoque de métodos ágiles de desarrollo de software.  La propuesta se encuentra en una fase de diseño, ha sido elaborada a partir de entrevistas a potenciales usuarios y es susceptible de ser probada en investigaciones futuras.  En mi opinión, en el artículo hace falta un apartado que dé cuenta de la metodología que los autores siguieron para llegar a la propuesta. Se hace referencia a entrevistas a: "participantes en este tipo de proyectos", no obstante, no se informa  cuántas personas fueron consultadas, cómo se seleccionó a los entrevistados, qué instrumentos se aplicaron y cómo se analizaron los datos.
[A]: es


[Q]: This work describes a gated attention-based recurrent neural network method for reading comprehension and question answering. This method employs a self-matching attention technique to counterbalance the limited context knowledge of gated attention-based recurrent neural networks when processing passages. Finally, authors use pointer networks  with signals from the question attention-based vector to predict the beginning and ending of the answer. Experimental results with the SQuAD dataset offer state-of-the-art performance compared with several recent approaches.   The paper is well-written, structured and explained. As far as I know, the mathematics look also good. In my opinion, this is a very interesting work which may be useful for the question answering community.  I was wondering if the authors have plans to release the code of this approach. From that perspective, I miss a bit of information about the technology used for the implementation (theano, CUDA, CuDNN...), which may be useful for readers.  I would appreciate if authors could perform a test of statistical significance of the results. That would highlight even more the quality of your results.  Finally, I know that the space may be a constraint, but an evaluation including some additional dataset would validate more your work.
[A]:
en