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.

Q: Warehouse is one of the important aspects of a company. Therefore, it is necessary to improve Warehouse Management System (WMS) to have a simple function that can determine the layout of the storage goods. In this paper we propose an improved warehouse layout method based on ant colony algorithm and backtracking algorithm. The method works on two steps. First, it generates a solutions parameter tree from backtracking algorithm. Then second, it deducts the solutions parameter by using a combination of ant colony algorithm and backtracking algorithm. This method was tested by measuring the time needed to build the tree and to fill up the space using two scenarios. The method needs 0.294 to 33.15 seconds to construct the tree and 3.23 seconds (best case) to61.41 minutes (worst case) to fill up the warehouse. This method is proved to be an attractive alternative solution for warehouse layout system. Keywords—warehouse layout; block stacking method; ant colony algorithm; backtracking algorithm.

A: Warehouse Layout Method Based on Ant Colony and Backtracking Algorithm
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Q: Dropout and other feature noising schemes control overfitting by artificially cor-<lb>rupting the training data. For generalized linear models, dropout performs a form<lb>of adaptive regularization. Using this viewpoint, we show that the dropout regular-<lb>izer is first-order equivalent to an L2 regularizer applied after scaling the features<lb>by an estimate of the inverse diagonal Fisher information matrix. We also establish<lb>a connection to AdaGrad, an online learning algorithm, and find that a close rel-<lb>ative of AdaGrad operates by repeatedly solving linear dropout-regularized prob-<lb>lems. By casting dropout as regularization, we develop a natural semi-supervised<lb>algorithm that uses unlabeled data to create a better adaptive regularizer. We ap-<lb>ply this idea to document classification tasks, and show that it consistently boosts<lb>the performance of dropout training, improving on state-of-the-art results on the<lb>IMDB reviews dataset.

A: Dropout Training as Adaptive Regularization
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Q: In this paper we propose a special type of aggregation function which generalizes the notion of Ordered Weighted Averaging Function OWA. The resulting functions are called Dynamic Ordered Weighted Averaging Functions — DYOWAs. This generalization will be developed in such way that the weight vectors are variables depending on the input vector. Particularly, this operators generalize the aggregation functions: Minimum, Maximum, Arithmetic Mean, Median etc, which are extensively used in image processing. In this field of research two problems are considered: The determination of methods to reduce images and the ∗Preprint submitted to IEEE Transactions on Fuzzy Systems. †Federal University of Semi-Arid UFERSA, Pau dos Ferros, RN, Brazil, 59.900-000, antonio.diego@ufersa.edu.br ‡DIMAp, valdigleis@ppgsc.ufrn.br §DIMAp, ranyer.lopes@gmail.com ¶DIMAp, bedregal@dimap.ufrn.br ‖Dimap, regivan@dimap.ufrn.br ∗∗DIMAP: Department of Informatics and Applied Mathematics, Federal University of Rio Grande do Norte — UFRN, Natal, RN, Brazil, 59.072-970 1 ar X iv :1 60 1. 03 78 5v 1 [ cs .A I] 1 5 Ja n 20 16 construction of techniques which provide noise reduction. The operators described here are able to be used in both cases. In terms of image reduction we apply the methodology provided in [1]. We use the noise reduction operators obtained here to treat the images obtained in the first part of the paper, thus obtaining images with better quality.

A:
A Method for Image Reduction Based on a Generalization of Ordered Weighted Averaging Functions∗
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