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Large amounts of sugar are imported into grape berries from source leaves during ripening, and sucrose transporters play a key role during this process. In this study, a putative grape sucrose transporter gene VvSUC27, primarily expressed in sink tissue, was transformed into a yeast strain to characterize its function as a sucrose transporter. Sucrose was taken up by yeast transformed with VvSUC27 at an optimum pH of 4.0-5.0 and a K m of 8.0-10.5 mM, indicating VvSUC27 is a LAHC (low-affinity/high-capacity) sucrose transporter. The ability of sucrose uptake in transformed yeast was activated by monosaccharides and inhibited by maltose and DEPC.

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Intra-class compactness and inter-class separability are crucial indicators to measure the effectiveness of a model to produce discriminative features, where intra-class compactness indicates how close the features with the same label are to each other and inter-class separability indicates how far away the features with different labels are. In this work, we investigate intra-class compactness and inter-class separability of features learned by convolutional networks and propose a Gaussian-based softmax (G-softmax) function that can effectively improve intraclass compactness and inter-class separability. The proposed function is simple to implement and can easily replace the softmax function. We evaluate the proposed G-softmax function on classification datasets (i.e., CIFAR-10, CIFAR-100, and Tiny ImageNet) and on multi-label classification datasets (i.e., MS COCO and NUS-WIDE). The experimental results show that the proposed G-softmax function improves the state-of-the-art models across all evaluated datasets. In addition, analysis of the intraclass compactness and inter-class separability demonstrates the advantages of the proposed function over the softmax function, which is consistent with the performance improvement. More importantly, we observe that high intra-class compactness and interclass separability are linearly correlated to average precision on MS COCO and NUS-WIDE. This implies that improvement of intra-class compactness and inter-class separability would lead to improvement of average precision.

One of the well-known challenges in computer vision tasks is the visual diversity of images, which could result in an agreement or disagreement between the learned knowledge and the visual content exhibited by the current observation. In this work, we first define such an agreement in a concepts learning process as congruency. Formally, given a particular task and sufficiently large dataset, the congruency issue occurs in the learning process whereby the task-specific semantics in the training data are highly varying. We propose a Direction Concentration Learning (DCL) method to improve congruency in the learning process, where enhancing congruency influences the convergence path to be less circuitous. The experimental results show that the proposed DCL method generalizes to state-of-the-art models and optimizers, as well as improves the performances of saliency prediction task, continual learning task, and classification task. Moreover, it helps mitigate the catastrophic forgetting problem in the continual learning task. The code is publicly available at https://github.com/luoyan407/congruency. She has published more than 50 journal and conference papers in top computer vision, machine learning, and cognitive neuroscience venues, and edited a book with Springer, titled Computational and Cognitive Neuroscience of Vision, that provides a systematic and comprehensive overview of vision from various perspectives, ranging from neuroscience to cognition, and from computational principles to engineering developments. She is a member of the IEEE since 2004.