16,798 research outputs found

    A Birkhoff connection between quantum circuits and linear classical reversible circuits

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    Birkhoff's theorem tells how any doubly stochastic matrix can be decomposed as a weighted sum of permutation matrices. Similar theorems on unitary matrices reveal a connection between quantum circuits and linear classical reversible circuits. It triggers the question whether a quantum computer can be regarded as a superposition of classical reversible computers

    Cut-rose production in response to planting density in two contrasting cultivars

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    Growing in lower planting density, rose plants produce more assimilates, which can be used to produce more and/or heavier flowering shoots. The effect of planting density was investigated during a period including the first five flowering flushes of a young crop. In a heated greenhouse two cut-rose cultivars were grown under bent canopy management. ‘Akito’ on own-roots and ‘Ilios’ on ‘Natal Briar’ rootstock were planted with densities of 8 and 4 plants per m2. Starting at the end of June 2007, flowering shoots were harvested over a time span of eight months. Based on ‘flowering flushes’, times of high harvest rate, the harvesting time span could be divided into five consecutive periods, each including one flush. The cultivars showed contrasting responses to planting density. In the first three periods the response in ‘Ilios’ was extraordinary, because at low density plants did not produce more flowering shoots, as would be expected. However, the response in shoot fresh weight was larger for ‘Ilios’ than for ‘Akito’, 35% compared to 21% over the entire study period. The results imply that there was a genetic difference in the effect of assimilate availability and/or local light environment. During the first three periods, these factors can not have influenced shoot number in ‘Ilios’, while they did in ‘Akito’. It is suggested that decreases of assimilate availability in winter caused the shoot number response to emerge for ‘Ilios’ later on

    Detection of REM Sleep Behaviour Disorder by Automated Polysomnography Analysis

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    Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour Disorder (RBD) is an early predictor of Parkinson's disease. This study proposes a fully-automated framework for RBD detection consisting of automated sleep staging followed by RBD identification. Analysis was assessed using a limited polysomnography montage from 53 participants with RBD and 53 age-matched healthy controls. Sleep stage classification was achieved using a Random Forest (RF) classifier and 156 features extracted from electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) channels. For RBD detection, a RF classifier was trained combining established techniques to quantify muscle atonia with additional features that incorporate sleep architecture and the EMG fractal exponent. Automated multi-state sleep staging achieved a 0.62 Cohen's Kappa score. RBD detection accuracy improved by 10% to 96% (compared to individual established metrics) when using manually annotated sleep staging. Accuracy remained high (92%) when using automated sleep staging. This study outperforms established metrics and demonstrates that incorporating sleep architecture and sleep stage transitions can benefit RBD detection. This study also achieved automated sleep staging with a level of accuracy comparable to manual annotation. This study validates a tractable, fully-automated, and sensitive pipeline for RBD identification that could be translated to wearable take-home technology.Comment: 20 pages, 3 figure
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