Titlebook: Hip Dysplasia; Understanding and Tr Paul E. Beaulé Book 2020 Springer Nature Switzerland AG 2020 Adjuvant therapy.Adult hip dysplasia.Carti

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Markus Simon Hanke,Florian Schmaranzer,Till Dominic Lerch,Simon Damian Steppacher,Klaus Arno Siebenris achieved by using hardware event counts as features to describe the behavior of the software program. Then a classifier, such as support vector machine (SVM) or neural network, can be used to detect the anomalous behavior caused by malware attacks. The collected datasets to describe the program b

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Gerd Melkus,Kawan S. Rakhray of and trust in machine learning models, but the construction of more robust models hinges on a rigorous understanding of adversarial robustness as a property of a given model. Point-wise measures for specific threat models are currently the most popular tool for comparing the robustness of classi

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Johnny Huard,Hajime Utsunomiya,Karen K. Briggs,Marc J. Philipponble interest in determining the expressive power mainly of graph neural networks and of graph kernels, to a lesser extent. Most studies have focused on the ability of these approaches to distinguish non-isomorphic graphs or to identify specific graph properties. However, there is often a need for al

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Geoffrey P. Wilkinep text style transfer method on non-parallel datasets. In the first step, the style-relevant words are detected and deleted from the sentences in the source style corpus. In the second step, the remaining style-devoid contents are fed into a Natural Language Generation model to produce sentences in

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Etienne L. Belzile,Antoine Bureau,Maged Shahinoring the inter-image co-attention are two challenges. In this paper, we propose a unified Multiple INducible co-attentions and Edge guidance network (MineNet) for CoSOD. Firstly, a classified inducible co-attention (CICA) is designed to model the classification interactions from a group of images.

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