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Yeast drives bourbon and rye fermentation, creating alcohol and key flavor compounds. From sweet fruit notes in bourbon to bold spice in rye, fermentation defines the whiskey’s identity before aging begins.
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In the recent past, research on the utilization of deep learning algorithms for space applications has been widespread. One of the areas where such algorithms are gaining attention is in spacecraft pose estimation, which is a fundamental requirement in many spacecraft rendezvous and navigation operations. Nevertheless, the application of such algorithms in space operations faces unique challenges compared to application in terrestrial operations. In the latter, they are facilitated by powerful computers, servers, and shared resources, such as cloud services. However, these resources are limited in space environment and spacecrafts. Hence, to take advantage of these algorithms, an on-board inferencing that is power- and cost-effective is required. This paper investigates the use of a hybrid Field Programmable Gate Array (FPGA) and Systems-on-Chip (SoC) device for efficient onboard inferencing of the Convolutional Neural Network (CNN) part of such pose estimation methods. In this study, Xilinx's Zynq UltraScale+ MPSoC device is used and proposed as an effective onboard-inferencing solution. The performance of the onboard and computer inferencing is compared, and the effectiveness of the hybrid FPGA-CPU architecture is verified. The FPGA-based inference has comparable accuracy to the PC-based inference with an average RMS error difference of less than 0.55. Two CNN models that are based on encoder-decoder architecture have been investigated in this study and three approaches demonstrated for landmarks localization.
This book constitutes the proceedings of the 21st International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2018, which took place in Thessaloniki, Greece, in April 2018, held as part of the European Joint Conference on Theory and Practice of Software, ETAPS 2018.The 31 papers presented in this volume were carefully reviewed and selected from 103 submissions. The papers are organized in topical sections named: semantics; linearity; concurrency; lambda-calculi and types; category theory and quantum control; quantitative models; logics and equational theories; and graphs and automata.
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Presentations of war and violence in museums generally oscillate between the fascination of terror and its instruments and the didactic urge to explain violence and, by analysing it, make it easier to handle and prevent. The museums concerned also have to face up to these basic issues about the social and institutional handling of war and violence. Does war really belong in museums? And if it does, what objectives and means are involved? Can museums avoid trivializing and aestheticising war, transforming violence, injury, death and trauma into tourist sights? What images of shock or identification does one generate – and what images would be desirable?
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