As a result of significant advances in deep learning, computer vision technology has been widely adopted in the field of traffic surveillance. Nonetheless, it is difficult to find a universal model that can measure tr...
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As a result of significant advances in deep learning, computer vision technology has been widely adopted in the field of traffic surveillance. Nonetheless, it is difficult to find a universal model that can measure traffic parameters irrespective of ambient conditions such as times of the day, weather, or shadows. These conditions vary recurrently, but the exact points of change are inconsistent and unpredictable. Thus, the application of a multi-regime method would be problematic, even when separate sets of model parameters are prepared in advance. In the present study we devised a robust approach that facilitates multi-regime analysis. This approach employs an online parametric algorithm to determine the change-points for ambient conditions. An autoencoder was used to reduce the dimensions of input images, and reduced feature vectors were used to implement the online change-point algorithm. Seven separate periods were tagged with typical times in a given day. Multi-regime analysis was then performed so that the traffic density could be separately measured for each period. To train and test models for vehicle counting, 1,100 video images were randomly chosen for each period and labeled with traffic counts. The measurement accuracy of multi-regime analysis was much higher than that of an integrated model trained on all data.
This article explores the existence of seasonality in the tails of stock returns. We use a parametric model to describe the returns, and obtain a proxy of the innovation distribution via a pre-processing model. Then, ...
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This article explores the existence of seasonality in the tails of stock returns. We use a parametric model to describe the returns, and obtain a proxy of the innovation distribution via a pre-processing model. Then, we develop a change-point algorithm capturing changes in the tails of the innovations. We confirm the good performance of the procedure through extensive Monte Carlo experiments. An empirical investigation using US stocks data shows that while the lower tail of the innovations is approximately constant over the year, the upper tail is larger in Winter than in Summer, in 9 out of 12 industries.
Conformational dynamics of proteins are important for function. However, obtaining information about specific conformations is difficult for samples displaying heterogeneity. Here, time-resolved fluorescence resonance...
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Conformational dynamics of proteins are important for function. However, obtaining information about specific conformations is difficult for samples displaying heterogeneity. Here, time-resolved fluorescence resonance energy transfer is used to characterize the folding of single cytochrome c molecules. In particular, measurements of the fluorescence lifetimes of individual cytochrome c molecules labeled with a single dye that is quenched by energy transfer to the heme were used to monitor conformational transitions of the protein under partially denaturing conditions. These studies indicate significantly more conformational heterogeneity than has been described previously. Importantly, the use of a purified singly labeled sample made a direct comparison to ensemble data possible. The distribution of lifetimes of single proteins was compared to the distribution of lifetimes determined from analysis of ensemble lifetime fluorescence data. The results show broad agreement between single-molecule and ensemble data, with a similar range of lifetimes. However, the single-molecule data reveal greater conformational heterogeneity.
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