42,418 research outputs found
Use of multiple singular value decompositions to analyze complex intracellular calcium ion signals
We compare calcium ion signaling () between two exposures;
the data are present as movies, or, more prosaically, time series of images.
This paper describes novel uses of singular value decompositions (SVD) and
weighted versions of them (WSVD) to extract the signals from such movies, in a
way that is semi-automatic and tuned closely to the actual data and their many
complexities. These complexities include the following. First, the images
themselves are of no interest: all interest focuses on the behavior of
individual cells across time, and thus, the cells need to be segmented in an
automated manner. Second, the cells themselves have 100 pixels, so that they
form 100 curves measured over time, so that data compression is required to
extract the features of these curves. Third, some of the pixels in some of the
cells are subject to image saturation due to bit depth limits, and this
saturation needs to be accounted for if one is to normalize the images in a
reasonably unbiased manner. Finally, the signals have
oscillations or waves that vary with time and these signals need to be
extracted. Thus, our aim is to show how to use multiple weighted and standard
singular value decompositions to detect, extract and clarify the signals. Our signal extraction methods then lead to simple although
finely focused statistical methods to compare signals
across experimental conditions.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS253 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling
Long-term situation prediction plays a crucial role in the development of
intelligent vehicles. A major challenge still to overcome is the prediction of
complex downtown scenarios with multiple road users, e.g., pedestrians, bikes,
and motor vehicles, interacting with each other. This contribution tackles this
challenge by combining a Bayesian filtering technique for environment
representation, and machine learning as long-term predictor. More specifically,
a dynamic occupancy grid map is utilized as input to a deep convolutional
neural network. This yields the advantage of using spatially distributed
velocity estimates from a single time step for prediction, rather than a raw
data sequence, alleviating common problems dealing with input time series of
multiple sensors. Furthermore, convolutional neural networks have the inherent
characteristic of using context information, enabling the implicit modeling of
road user interaction. Pixel-wise balancing is applied in the loss function
counteracting the extreme imbalance between static and dynamic cells. One of
the major advantages is the unsupervised learning character due to fully
automatic label generation. The presented algorithm is trained and evaluated on
multiple hours of recorded sensor data and compared to Monte-Carlo simulation
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