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: 1) Summary  This paper proposes an end-to-end hybrid architecture to predict the local linear trends of time series. A temporal convnet on raw data extracts short-term features. In parallel, long term representations are learned via a LSTM on piecewise linear approximations of the time series. Both representations are combined using a MLP with one hidden layer (in two parts, one for each stream), and the entire architecture is trained end-to-end by minimizing (using Adam) the (l2-regularized) euclidean loss w.r.t. ground truth local trend durations and slopes.   2) Contributions  + Interesting end-to-end architecture decoupling short-term and long-term representation learning in two separate streams in the first part of the architecture. + Comparison to deep and shallow baselines.  3) Suggestions for improvement  Add a LRCN baseline and discussion: The benefits of decoupling short-term and long-term representation learning need to be assessed by comparing to the popular "long-term recurrent convolutional network" (LRCN) of Donahue et al (

Output: en


Input: Consider Input: This paper develops a differentiable interpreter for the Forth programming language. This enables writing a program "sketch" (a program with parts left out), with a hole to be filled in based upon learning from input-output examples. The main technical development is to start with an abstract machine for the Forth language, and then to make all of the operations differentiable. The technique for making operations differentiable is analogous to what is done in models like Neural Turing Machine and Stack RNN. Special syntax is developed for specifying holes, which gives the pattern about what data should be read when filling in the hole, which data should be written, and what the rough structure of the model that fills the hole should be. Motivation for why one should want to do this is that it enables composing program sketches with other differentiable models like standard neural networks, but the experiments focus on sorting and addition tasks with relatively small degrees of freedom for how to fill in the holes.  Experimentally, result show that sorting and addition can be learned given strong sketches.  The aim of this paper is very ambitious: convert a full programming language to be differentiable, and I admire this ambition. The idea is provocative and I think will inspire people in the ICLR community.  The main weakness is that the experiments are somewhat trivial and there are no baselines. I believe that simply enumerating possible values to fill in the holes would work better, and if that is possible, then it's not clear to me what is practically gained from this formulation. (The authors argue that the point is to compose differentiable Forth sketches with neural networks sitting below, but if the holes can be filled by brute force, then could the underlying neural network not be separately trained to maximize the probability assigned to any filling of the hole that produces the correct input-output behavior?)  Related, one thing that is missing, in my opinion, is a more nuanced outlook of where the authors believe this work is going. Based on the small scale of the experiments and from reading other related papers in the area, I sense that it is hard to scale up differentiable forth to large real-world problems. It would be nice to have more discussion about this, and perhaps even an experiment that demonstrates a failure case. Is there a problem that is somewhat more complex than the ones that appear in the paper where the approach does not work? What has been tried to make it work? What are the failure modes? What are the challenges that the authors believe need to be overcome to make this work.  Overall, I think this paper deserves consideration for being provocative. However, I'm hesitant to strongly recommend acceptance because the experiments are weak.

Output: en


Input: Consider Input: Se presentan los siguientes problemas de forma:  - Figura 1: el texto de la figura no es legible y además se encuentra deformado. - Figura 2: no representa la problemática expuesta en el trabajo, además no se encuentra referenciada. - Ortografía: ".. del uso de La tecnologia NFC ..", debe ser: ".. del uso de la tecnología NFC ..". - Faltan puntos a parte. - Ortografía: ".. aplicaciones com relacion al uso .." debe ser: ".. aplicaciones con relación al ..". - Tabla 1: Título incorrecto y tabla cortaba en su final. - Cambio de tamaño de letra después de la tabla 1 - Tabla 2: Texto deformado. - A lo largo del texto no posee referencias a las afirmaciones: "es muy difícil intervenir una comunicación NFC". - Las etapas del proceso de transacción deben estar enumeradas. - Las tablas se encuentran mal numeradas. - Las páginas no se encuentran numeradas. - La descripción de indicadores (sección 7) no aporta al trabajo propuesto.   Problemas de fondo:  - No presenta una solución formal al pago vía tecnología NFC, a pesar que es el punto central del trabajo. - Las interfaces gráficas presentadas no se encuentran definidas en conformidad con las normas gráficas de aplicaciones móviles. - Las conclusiones no arrojan luz sobre la problemáticas planteada.  El trabajo desarrollado no es claro sobre la problemática planteada, debería incluir un análisis más detallado de tecnologías ya implementadas: UberTaxi, etc.


Output: es