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Through learning small and large-scale image features, we can capture the local and architectural structure of tumor tissue from histology images. This is done by learning a hierarchy of dictionaries using sparse coding, where each level captures progressively larger scale and more abstract properties. By optimizing the dictionaries further using class labels, discriminating properties of classes...
The analysis of large sums of data can be extremely difficult to perform if the data is not presented graphically. As a result, many graphing techniques have been developed, such as scatter plots, histograms. Generally, the main purpose of graphically displaying data is to do one of two things: First, to find the general average of where most of the data lies. Second, to find the outliers, the data...
Embedded single-chip heterogeneous multiprocessor (SCHM) systems experience frequent system events such as task preemption, power-saving voltage/frequency scaling, or arrival of new events/data from the outside world. Traditionally, the designers model these events by explicitly coupling them to corresponding simulation events within environments such as SystemC. However, this approach places a burden...
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