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A. Alper Goksoy, Sahil Hassan, Anish Krishnakumar, Radu Marculescu, Ali Akoglu and Umit Y. Ogras
Domain-specific systems on chip (DSSoCs) aim to narrow the gap between general-purpose processors and application-specific designs. CPU clusters enable programmability, whereas hardware accelerators tailored to the target domain minimize task execution t...
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Omar Alaqeeli and Raad Alturki
The glm R package is commonly used for generalized linear modeling. In this paper, we evaluate the ability of the glm package to predict binomial outcomes using logistic regression. We use single-cell RNA-sequencing datasets, after a series of normalizat...
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Alexander Shknevsky, Yuval Shahar and Robert Moskovitch
We propose a new pruning constraint when mining frequent temporal patterns to be used as classification and prediction features, the Semantic Adjacency Criterion [SAC], which filters out temporal patterns that contain potentially semantically contradicto...
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Andreas Nugaard Holm, Dustin Wright and Isabelle Augenstein
Uncertainty approximation in text classification is an important area with applications in domain adaptation and interpretability. One of the most widely used uncertainty approximation methods is Monte Carlo (MC) dropout, which is computationally expensi...
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Qaisar Abbas, Talal Saad Albalawi, Ganeshkumar Perumal and M. Emre Celebi
In recent years, advances in deep learning (DL) techniques for video analysis have developed to solve the problem of real-time processing. Automated face recognition in the runtime environment has become necessary in video surveillance systems for urban ...
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Thomas Albrecht, Ignacio González-Álvarez and Jens Klump
Landscapes evolve due to climatic conditions, tectonic activity, geological features, biological activity, and sedimentary dynamics. Geological processes at depth ultimately control and are linked to the resulting surface features. Large regions in Austr...
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Paulo J. S. Ferreira, João M. P. Cardoso and João Mendes-Moreira
The kNN machine learning method is widely used as a classifier in Human Activity Recognition (HAR) systems. Although the kNN algorithm works similarly both online and in offline mode, the use of all training instances is much more critical online than of...
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