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].

Ex Input:
The paper presents a medical test based on images of tissue sections. The approach proposed to cancer cells segmentation (membrane localisation) is based on local pixel features and SVM classification.  The paper describes quite well the practical problem it deals with (cancer cells segmentation on images) and it is generally well written.  - The paper approach described in the paper seems a first try on the problem and follows others in a quite similar (but new) problem. Moreover, the paper concludes that the proposed approach is not good enough.  - The features used for classification lacks of frequency information around each pixel (although it used the first derivative with sobel). Most texture descriptors in the literature use frequency information from gabor wavelets, fourier coefficients, DCT, etc. Why not use this kind of information? Why you have chosen this 7 features and not other ones? The paper lacks of experimental evaluation on different features sets.

Ex Output:
en


Ex Input:
This paper proposes a novel approach ParMAC, a parallel and distributed framework of MAC (the Method of Auxiliary Coordinates) to learn nested and non-convex models which is based on the composition of multiple processing layers (i.e., deep nets). The basic idea of MAC to optimise the nested objective function, which is traditionally learned using methods based on the chain-rule gradients but inconvenient and is hard to parallelise, is to break nested functional relationships judiciously by introducing new variables ( the auxiliary coordinates) as equality constraints, and then to optimise a penalised function using alternating optimisation over the original parameters (W step) and over the coordinates (Z step).  The minimisation (W step) updates the parameters by splitting the nested model into independent submodels and training them using existing algorithms, and the coordination (Z step) ensures that corresponding inputs and outputs of submodels eventually match.  In this paper, the basic assumptions of ParMAC are that with large datasets in distributed systems, it is imperative to minimise data movement over the network because of the communication time generally far exceeds the computation time in modern architectures. Thus, the authors propose the ParMAC to translate the parallelism inherent in MAC into a distributed system by data parallelism and model parallelism. They also analyse its parallel speedup and convergence, and demonstrated it with MPI-based implementation to optimise binary autoencoders. The proposed ParMAC is tested on 3 colour image retrieval datasets.   The organization of the paper is well written, and the presentation is clear. My questions are included in the following: - The MAC framework solves the original problem approximately. If people use the sigmoid function to smooth the stepwise function, the naive optimization methods can be easier applied. What is the difference between these two? Or why do we want to use a new approach to solve it? - The authors do not compare their ParMAC model with other distributed approaches for the same nested function optimization problem.

Ex Output:
en


Ex Input:
El artículo describe una interesante aplicación en el ámbito de la capacitación y que entrega herramientas de gran utilidad para la empresa en el mejoramiento de los procesos del aprendizaje de los trabajadores. El artículo identifica las ventajas que este tipo de plataformas entrega a las empresas que las utilizan.  El artículo no define adecuadamente el propósito del mismo y establece en su inicio que este objetivo es "dar a conocer", lo cual no es un propósito adecuado para estas investigaciones.  El artículo se denomina plataforma para la gestión del conocimiento, sin embargo el artículo no define el marco teórico adecuado a esta temática. (no hay autores relevantes en la gestión del conocimiento y los procesos asociados a esta).  El artículo no justifica adecuadamente en la literatura la relevancia de estas plataformas en los proceso de aprendizaje y experiencias internacionales en el tema  No existe un apartado que presente la metodología de investigación seleccionada por los autores, más bien los autores describen una aplicación y sus componentes. Falta claramente el diseño de la investigación.  Las conclusiones del artículo se relacionan con la importancia de estas plataformas para la empresa, pero no se enfocan en los resultados obtenidos en la empresa que implementa, no hay recomendaciones para futuros estudios y no existe reconocimiento de las limitaciones del mismo

Ex Output:

es