In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Example Input: We propose a novel approach to automatically produce multiple colorized versions of a grayscale image. Our method results from the observation that the task of automated colorization is relatively easy given a low-resolution version of the color image. We first train a conditional PixelCNN to generate a low resolution color for a given grayscale image. Then, given the generated low-resolution color image and the original grayscale image as inputs, we train a second CNN to generate a high-resolution colorization of an image. We demonstrate that our approach produces more diverse and plausible colorizations than existing methods, as judged by human raters in a ”Visual Turing Test”.
Example Output: PIXCOLOR: PIXEL RECURSIVE COLORIZATION

Example Input: We develop a new semantics for defeasible infer­ ence based on extended probability measures al­ lowed to take infinitesimal values, on the inter­ pretation of defaults as generalized conditional probability constraints and on a preferred-model implementation of entropy-maximization.
Example Output: Defaults and lnfinitesimals Defeasible Inference by Nonarchimedean Entropy Maximization

Example Input: The role of kernels is central to machine learning. Motivated by the importance of power law distributions in modeling, simulation and learning, in this paper, we propose a powerlaw generalization of the Gaussian kernel. This generalization is based on q-Gaussian distribution, which is a power-law distribution studied in context of nonextensive statistical mechanics. We prove that the proposed kernel is positive definite, and provide some insights regarding the corresponding Reproducing Kernel Hilbert Space (RKHS). We also study practical significance of qGaussian kernels in classification, regression and clustering, and present some simulation results.
Example Output:
On q-Gaussian kernel and its Reproducing Kernel Hilbert Space