Her work combines experimental and computational methods with large-scale datasets on political activity in China and other authoritarian regimes to examine these questions.
Trace enables efficient reconstruction of large-scale datasets on multicore clusters where adequate memory is available.
In this paper, we present an approach that is specifically designed to analyze large-scale datasets on published results.
Only through such categorization we can proceed to integrate the two large-scale datasets on RNA localization (our data and Lecuyer, 2007).
Despite the availability of several large-scale datasets on single gene knockouts (KO) or knock-downs (KD) as well as double gene-KOs for all of these organisms, previous studies mainly focused on single organisms like yeast [ 8, 11- 14], worm [ 24] and mouse [ 18, 19].
The identification of all front-line fast-food workers is not directly possible for any available large scale dataset with information on the worker characteristics required for this study, including the CPS and ACS.
Importantly, we reformulate the previously proposed structure-based model to make it computational feasible on a large scale dataset by replacing large number of complicated inequalities with one simple inequality.
However, the enlarging volume of datasets makes regression by ELM on very large scale datasets a challenging task.
Solutions for learning from large scale datasets, including kernel learning algorithms that scale linearly with the volume of the data and experiments carried out on realistically large datasets.
We have demonstrated on a large-scale dataset of mammalian enhancer sequences that pairwise sequence similarity of non-homologous regulatory sequences is able to estimate similar in vivo activity.
However, most of the related studies mainly focus on learning features and representations from very large scale dataset relying on deep network architecture, which is doomed to fail with limited training samples due to its high complexity.
