Research Progress and the Limiting Factors of Direct Seeding Rice in Central China

Symposium paper Part 2: Frontiers of sustainable rice production syste

Learning Segmentation Masks with the Independence Prior

An instance with a bad mask might make a composite image that uses it look fake. This encourages us to learn segmentation by generating realistic composite images. To achieve this, we propose a novel framework that exploits a new proposed prior called the independence prior based on Generative Adversarial Networks (GANs). The generator produces an image with multiple category-specific instance providers, a layout module and a composition module. Firstly, each provider independently outputs a category-specific instance image with a soft mask. Then the provided instances' poses are corrected by the layout module. Lastly, the composition module combines these instances into a final image. Training with adversarial loss and penalty for mask area, each provider learns a mask that is as small as possible but enough to cover a complete category-specific instance. Weakly supervised semantic segmentation methods widely use grouping cues modeling the association between image parts, which are either artificially designed or learned with costly segmentation labels or only modeled on local pairs. Unlike them, our method automatically models the dependence between any parts and learns instance segmentation. We apply our framework in two cases: (1) Foreground segmentation on category-specific images with box-level annotation. (2) Unsupervised learning of instance appearances and masks with only one image of homogeneous object cluster (HOC). We get appealing results in both tasks, which shows the independence prior is useful for instance segmentation and it is possible to unsupervisedly learn instance masks with only one image.Comment: 7+5 pages, 13 figures, Accepted to AAAI 201

Successful treatment of a pure red-cell aplasia patient with γδT cells and clonal TCR gene rearrangement: A case report

Pure red-cell aplasia (PRCA) is a syndrome associated with reduced erythroid precursors. This report presents the case of an elderly PRCA patient with significantly proliferated γδT cells and clonal T-cell receptor (TCR) gene rearrangement. The cause of this patient’s PRCA was confirmed to be an autoimmune disorder rather than malignancy on the basis of flow cytometry, TCR gene rearrangement, and positron emission tomography/computed tomography (PET/CT) findings. Moreover, the γδT cell group identified in this case was captured for the first time under the microscope; this CD4+/CD8− (extremely high CD4/CD8 ratio) population is rare in PRCA patients. Our patient with a monoclonal and polyclonal hybrid of TCR gene rearrangement was sensitive to cyclosporin A (CsA), despite previous reports suggesting that patients with TCR clonal rearrangement may respond poorly to this drug. Overall, this case presents valuable clinical findings for the future diagnosis and management of PRCA caused by autoimmune conditions and further research on γδT cells’ autoimmune pathophysiology and gene rearrangement