Abstract: The invention relates to compositions comprising at least one ABA agonist for inhibiting plant or plant part germination/sprouting, and/or promoting fruit ripening and pigmentation; kits comprising such compositions; and methods of using such compositions to inhibiting plant or plant part germination/sprouting and promoting fruit ripening and pigmentation.

Abstract: The invention relates to compositions comprising at least one ABA antagonist for promoting germination/sprouting of plants or plant parts, and/or delaying fruit ripening and pigmentation; kits comprising such compositions; and methods of using such compositions to promote germination/sprouting of plants or plant parts, and/or to delay fruit ripening and pigmentation.

Abstract: A computer-based automated method of performing classification includes learning a deep neural network by maximizing an area under a receiver operating characteristic curve (AUC) or precision-recall curve (AUPRC) score wherein a margin-based surrogate loss function is applied, receiving an input into a deep neural network, and processing the input to the deep neural network to generate a prediction, wherein the prediction comprises a classification of the input. The computer-based automated method may be performed by executing instructions in at least one processor, and wherein said instructions are stored on a non-transitory memory readable by the at least one processor.

Abstract: Systems and methods are disclosed for classifying vehicles by performing scale aware detection; performing detection assisted sampling for convolutional neural network (CNN) training, and performing deep CNN fine-grained image classification to classify the vehicle type.

Abstract: Systems and methods are disclosed for classifying vehicles by performing scale aware detection; performing detection assisted sampling for convolutional neural network (CNN) training, and performing deep CNN fine-grained image classification to classify the vehicle type.

Abstract: Systems and methods are disclosed for training a learning machine by augmenting data from fine-grained image recognition with labeled data annotated by one or more hyper-classes, performing multi-task deep learning; allowing fine-grained classification and hyper-class classification to share and learn the same feature layers; and applying regularization in the multi-task deep learning to exploit one or more relationships between the fine-grained classes and the hyper-classes.

Abstract: Systems and methods are disclosed to find dynamic social networks by applying a dynamic stochastic block model to generate one or more dynamic social networks, wherein the model simultaneously captures communities and their evolutions, and inferring best-fit parameters for the dynamic stochastic model with online learning and offline learning.

Abstract: Systems and methods are disclosed to detect communities of a social network by receiving linked documents from the social network; generating one or more conditional link models and one or more discriminative content models from the linked documents; creating a discriminative model by combining the one or more conditional link models and discriminative content models; and applying the discriminative model to the social networks.

Abstract: Systems and methods are disclosed to find dynamic social networks by applying a dynamic stochastic block model to generate one or more dynamic social networks, wherein the model simultaneously captures communities and their evolutions, and inferring best-fit parameters for the dynamic stochastic model with online learning and offline learning.