Using deep learning semantic segmentation for land use extraction is the most challenging problem in medium spatial resolution imagery. This because of convolution layer and multiple levels steps baseline network, which can cause a degradation small features. In this paper, algorithm comprises an adjustment network architecture (LoopNet) dataset proposed automatic classification using Landsat 8...

summary by Olivier Dubois and Remi Monasson] The satisfaction of constrained formulae is a key issue in complexity theory. Many computational problems are shown to be NP-complete through a polynomial mapping onto the K-Satissability (SAT) problem. Recently, there has been much interest in a random version of the K-SAT problem deened as follows. Consider N Boolean variables x i , i = 1; : : :; N...

Sparse training is one of the promising techniques to reduce computational cost DNNs while retaining high accuracy. In particular, N:M fine-grained structured sparsity, where only N out consecutive M elements can be nonzero, has attracted attention due its hardware-friendly pattern and capability achieving a sparse ratio. However, potential accelerate DNN not been fully exploited, there lack ef...

Report on a desktop study commissioned by the Food and Agriculture Organization (FAO) This paper was prepared under contract with the Food and Agriculture Organization of the United Nations (FAO). The positions and opinions presented are those of the author alone, and are not intended to represent the views of FAO. 2 CONTENTS List of Tables 4 Abbreviations used in the text 4 Acknowledgements 4 ...

background and objective: magnetic resonance imaging (mri) is the most sensitive technique to detect multiple sclerosis (ms) plaques in central nervous system. in some cases, the patients who were suspected to ms, whereas mri images are normal, but whether patients don’t have ms plaques or mri images are not enough optimized enough in order to show ms plaques? the aim of the current study is ev...

We present Regular-SAT, an extension of Boolean Satisfiability based on a class of many-valued CNF formulas. Regular-SAT shares many properties with Boolean SAT, which allows us to generalize some of the best known SAT results and apply them to Regular-SAT. In addition, Regular-SAT has a number of advantages over Boolean SAT. Most importantly, it produces more compact encodings that capture pro...

Boolean satisfiability [1] (k-SAT) is one of the most studied optimization problems, as an efficient (that is, polynomial-time) solution to k-SAT (for k ≥ 3) implies efficient solutions to a large number of hard optimization problems [2, 3]. Here we propose a mapping of k-SAT into a deterministic continuous-time dynamical system with a unique correspondence between its attractors and the k-SAT ...

In this paper, we propose a new approach, called lemma-reusing, for accelerating SAT based planning and scheduling. Generally, SAT based approaches generate a sequence of SAT problems which become larger and larger. A SAT solver needs to solve the problems until it encounters a satisfiable SAT problem. Many state-of-the-art SAT solvers learn lemmas called conflict clauses to prune redundant sea...

abstract geosystems or geomorphic systems include terrain structure with processes interactions that work as alone or common and create a series of terrain unites. geosystem elements consist of lines, points, surfaces, and volumes. the ways of relationships between the elements form spatial structure identity. the elements prepare spaces for different land uses, when they are located in ratio t...