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:
El artículo cumple con el requisito de presentar una experiencia empresarial en aplicaciones de TIC’s con énfasis en la minería, por lo tanto, la evaluación no se hará teniendo en consideración alguna propuesta innovativa en un artículo de carácter científico.  Este artículo describe una experiencia exitosa en el uso de BPMN para modelar el proceso de negocio del área de Ingeniería y Proyectos de una empresa minera peruana. Se usó un pequeño y simple conjunto de elementos de BPMN para modelar el proceso de negocio. Esta experiencia es válida, pero es una solución ad hoc para el proceso de negocio escogido. La solución tendría mayor valoración si se hubiese presentado un modelo pequeño que pudiese ser validado y que pudiese solucionar un conjunto determinado de procesos de negocio asociados a la minería.  Algunas observaciones y/o comentarios menores: - No cumple con el requisito de presentar los artículos como anónimos de autores e institución. - En Resumen, cambiar “control sus procesos” por “control de sus procesos”. - En Introducción, cambiar “beneficios BPMNs” por “beneficios de BPMN”. - En página 3 y otras, utiliza las palabras “figura” y “Figura” en el texto. - En página 5, cambiar “La figura 2 se ha incluido” por “En la Figura 2 se han incluido”. - La referencia a algunas figuras 3 y 4 (mostradas en página 6) solamente se mencionan al final de la página 7. Lo ideal es referenciarla primero y después mostrar la figura. - En página 7, cambiar “el agrupa las tareas” por “agrupa las tareas”. - En Referencia [5] cambiar “BPMNModeling” por “BPMN Modeling”. - La Referencia [7] debe ser completada, la Referencia [12] debe incluir el año.

Ex Output:
es


Ex Input:
Variational auto-encoders, adversarial networks, and kernel scoring rules like MMD have recently gained popularity as methods for learning directed generative models and for other applications like domain adaptation. This paper gives an additional method along the scoring rules direction that uses the matching of central moments to match two probability distributions. The technique is simple, and in the case of domain adaptation, highly effective.  CMD seems like a very nice and straightforward solution to the domain adaptation problem. The method is computationally straightforward to implement, and seems quite stable with respect to the tuning parameters when compared to MMD. I was skeptical reading through this, especially given the fact that you only use K=5 in your experiments, but the results seem quite good. The natural question that I have now is: how will this method do in training generative models? This is beyond the scope of this paper, but it’s the lowest hanging fruit.  Below I give more detailed feedback.  One way to speed up MMD is to use a random Fourier basis as was done in “Fastmmd: Ensemble of circular discrepancy for efficient two-sample test” by Zhao and Meng, 2015. There are also linear time estimators, e.g., in “A Kernel Two-Sample Test“ by Gretton et al., 2012. I don’t think you need to compare against these approaches since you compare to the full MMD, but they should be cited.  The paper “Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy” by Sutherland et al. submitted to ICLR 2017 as well, discusses techniques for optimizing the kernel used in MMD and is worth citing in section 3.  How limiting is the assumption that the distribution has independent marginals?  The sample complexity of MMD depends heavily on the dimensionality of the input space - do you have any intuitions about the sample complexity of CMD? It seems like it's relatively insensitive based on the results in Figure 4, but I would be surprised if this were the case with 10,000 hidden units. I mainly ask this because with generative models, the output space can be quite high-dimensional.  I’m concerned that the central moments won’t be numerically stable at higher orders when backpropagating. This doesn’t seem to be a problem in the experimental results, but perhaps the authors could comment a bit on this? I’m referring to the fact that ck(X) can be very large for k >= 3. Proposition 1 alleviates my concerns that the overall objective is unstable, I’m referring specifically to the individual terms within.  Figure 3 is rather cluttered, and aside from the mouse class it’s not clear to me from the visualization that the CMD regularizer is actually helping. It would be useful to remove some of the classes for the purpose of visualization.  I would like some clarification about the natural geometric interpretations of K=5. Do you mean that the moments up to K=5 have been well-studied? Do you have any references for this? Why does K >= 6 not have a natural geometric interpretation?  Figure 4 should have a legend

Ex Output:
en


Ex Input:
Novedosa propuesta. Bien estructurada. Correctamente escrita. Sólo sugiero: Explicar con más detalle la función trapezoidal de la página 2. Redactar nuevamente  el  párrafo  anterior a la función trapezoidal A(x). Agrandar el esquema de la figura 1. Explicar la arquitectura propuesta mostrada en la figura 2. Revisar referencia de Feng et al. que es la referencia 2 y en un párrafo de la columna 2 página 1 está marcada como referencia 1.  Artículo no está en el formato pedido por Infonor, por favor cambiar a éste formato.

Ex Output:

es