Titlebook: Highly Selective Neurotoxins; Basic and Clinical A Richard M. Kostrzewa Book 1998 Springer Science+Business Media New York 1998 Neuroscienc

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Joyce E. Royland,J. William Langstone graph and facilitate better data flow between far away nodes. Experimental results on the standard METR-LA and PEMSBAY benchmarks show that the proposed approach yields significant inference and training speedups of up to x5 in the 1-h prediction task and x27 in the 24-h prediction task, while kee

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Cornelis J. Van der Schyf,Etsuko Usuki,Susan M. Pond,Neal Castagnoli Jr.mentation of the SignGuide project, an interactive museum guide system for deaf visitors, which can automatically recognize an exhibit, and create an interactive experience including the provision of content in sign language content using an avatar or video. The proposed system introduces a novel Mu

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Seminar

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Michael R. Pranzatellistration which register a pre-operative CT to an intra-operative echocardiography images. In other words, uncertainty estimation is used to evaluate the registration algorithm performance which integrates intensity-based and feature-based methods. This registration can potentially be used to improve

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Ewa M. Urbanska,Andrzej Dekundy,Zdzislaw Kleinrok,Waldemar A. Turski,Stanislaw J. Czuczwar a photo compared to the tags assigned to a photo during explicit image annotation processes like crowdsourcing. In this context, we explore the descriptive power of hashtags by examining whether other users would use the same, with the owner, hashtags to annotate an image. For this purpose a set of

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Sunita Rajdev,Frank R. Sharpe proposed a scalable NPairLoss-based Deep-ECG (SNL-Deep-ECG) system for ECG verification on a hybrid dataset, mixed with four public ECG datasets. We modify the preprocessing method and trained the deep CNN model with NPairLoss. Compared with Deep-ECG, SNL-Deep-ECG can reduce 90% of the signal coll

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e proposed a scalable NPairLoss-based Deep-ECG (SNL-Deep-ECG) system for ECG verification on a hybrid dataset, mixed with four public ECG datasets. We modify the preprocessing method and trained the deep CNN model with NPairLoss. Compared with Deep-ECG, SNL-Deep-ECG can reduce 90% of the signal coll