4 resultados para Deep Survey
em Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco
Resumo:
[EN]A survey of Canadian retail beef was undertaken with emphasis on the trans fatty acid (TFA) and conjugated linoleic acid (CLA) isomers, and compared with current health recommendations. Thirty striploin steaks were collected in the winter and summer from major grocery stores in Calgary (Alberta, Canada). Steak fatty acid compositions (backfat and longissimus lumborum muscle analysed separately) showed minor seasonal differences with lower total saturates (PB0.05) and higher total monounsaturates (PB 0.01) in winter, but no differences in total polyunsaturated fatty acids. The ratio of n-6 and n-3 polyunsaturated fatty acid in longissimus lumborum averaged 5.8. The average TFA content in longissimus lumborum was 0.128 g 100 g_1 serving size, and 10t-18:1 was found to be the predominant isomer (32% of total trans), while vaccenic acid was second most abundant (15% of total trans). The CLA content in longissimus lumborum was similar to that of backfat, ranging from 0.43 to 0.60% of total fatty acids and rumenic acid represented 60% of total isomers. Overall, there is still room for improvement in the saturated, mono- and polyunsaturated fatty acid composition of Canadian beef to meet general dietary guidelines for human consumption and additional targets should include reducing 10t-18:1 while increasing both rumenic and vaccenic acids.
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10 p.
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44 p.
Resumo:
Deep neural networks have recently gained popularity for improv- ing state-of-the-art machine learning algorithms in diverse areas such as speech recognition, computer vision and bioinformatics. Convolutional networks especially have shown prowess in visual recognition tasks such as object recognition and detection in which this work is focused on. Mod- ern award-winning architectures have systematically surpassed previous attempts at tackling computer vision problems and keep winning most current competitions. After a brief study of deep learning architectures and readily available frameworks and libraries, the LeNet handwriting digit recognition network study case is developed, and lastly a deep learn- ing network for playing simple videogames is reviewed.