Semi-Supervised SAR ATR Framework with Transductive Auxiliary Segmentation

Convolutional neural networks (CNNs) have achieved high performance in synthetic aperture radar (SAR) automatic target recognition (ATR). However, the performance of CNNs depends heavily on a large amount of training data. The insufficiency of labeled training SAR images limits the recognition performance and even invalidates some ATR methods. Furthermore, under few labeled training data, many existing CNNs are even ineffective. To address these challenges, we propose a Semi-supervised SAR ATR Framework with transductive Auxiliary Segmentation (SFAS). The proposed framework focuses on exploiting the transductive generalization on available unlabeled samples with an auxiliary loss serving as a regularizer. Through auxiliary segmentation of unlabeled SAR samples and information residue loss (IRL) in training, the framework can employ the proposed training loop process and gradually exploit the information compilation of recognition and segmentation to construct a helpful inductive bias and achieve high performance. Experiments conducted on the MSTAR dataset have shown the effectiveness of our proposed SFAS for few-shot learning. The recognition performance of 94.18\% can be achieved under 20 training samples in each class with simultaneous accurate segmentation results. Facing variances of EOCs, the recognition ratios are higher than 88.00\% when 10 training samples each class

SAR Target Image Generation Method Using Azimuth-Controllable Generative Adversarial Network

Sufficient synthetic aperture radar (SAR) target images are very important for the development of researches. However, available SAR target images are often limited in practice, which hinders the progress of SAR application. In this paper, we propose an azimuth-controllable generative adversarial network to generate precise SAR target images with an intermediate azimuth between two given SAR images' azimuths. This network mainly contains three parts: generator, discriminator, and predictor. Through the proposed specific network structure, the generator can extract and fuse the optimal target features from two input SAR target images to generate SAR target image. Then a similarity discriminator and an azimuth predictor are designed. The similarity discriminator can differentiate the generated SAR target images from the real SAR images to ensure the accuracy of the generated, while the azimuth predictor measures the difference of azimuth between the generated and the desired to ensure the azimuth controllability of the generated. Therefore, the proposed network can generate precise SAR images, and their azimuths can be controlled well by the inputs of the deep network, which can generate the target images in different azimuths to solve the small sample problem to some degree and benefit the researches of SAR images. Extensive experimental results show the superiority of the proposed method in azimuth controllability and accuracy of SAR target image generation

Generation of mice expressing only the long form of the prolactin receptor reveals that both isoforms of the receptor are required for normal ovarian function

Prolactin (PRL), a pleiotropic hormone essential for maintenance of corpus luteum (CL) function and pregnancy, transduces its signal through two types of receptors, a short form (PRLR-S) and a long form (PRLR-L). Both types of receptors are expressed in the CL, yet their individual roles are not well defined. We have shown previously that female transgenic mice expressing only PRLR-S display total infertility characterized by defective follicular development and early degeneration of CL, suggesting that expression of PRLR-L is a prerequisite for normal follicular development and maintenance of CL. To determine whether PRLR-L alone is the sole receptor required to maintain normal CL formation, differentiation, and progesterone secretion, we generated two transgenic mice which express only PRLR-L, either ubiquitously (Tg-RL) or in a CL-specific manner (CL-RL). To generate CL-specific expression, we used the HSD17B7 promoter. We found both transgenic mice models cycled normally, displayed no apparent defect in follicular development, and had normal ovulation rates. The STAT5 signaling pathway, considered essential for luteinization and progesterone production, was activated by PRL in both transgenic mice models. However, soon after mating, Tg-RL and CL-RL mice showed early regression of CL, lack of progesterone production, and implantation failure that rendered them totally infertile. Embryo transfer studies demonstrated no embryo abnormalities, and supplementation with progesterone rescued implantation failure in these mice. Close observation revealed lack of luteinization and reduced expression of proteins involved in progesterone biosynthesis despite normal levels of LHCGR (LHR), ESR1 (ER-alpha), CEBPB (C/EBP-beta) and CDKN1B (p27), proteins essential for luteinization. However, we found VEGFA, a key regulator of angiogenesis and vascularization, to be dramatically reduced in both Tg-RL and CL-RL mice. We also found collagen IV, a marker for the basal lamina of endothelial cells, aberrantly expressed and a discordant organization of endothelial cells in CL. Although luteinization did not occur in vivo, granulosa cells isolated from these mice luteinized in culture. Taken together, these results suggest that a vascularization defect in the CL may be responsible for lack of luteinization, progesterone production, and infertility in mice expressing only PRLR-L. This investigation therefore demonstrates that in contrast to earlier presumptions that PRLR-L alone is able to support normal CL formation and function, both isoforms of the PRL receptor are required in the CL for normal female fertility.Fil: Le, Jamie A.. University of Illinois; Estados UnidosFil: Wilson, Heather M.. University of Illinois; Estados UnidosFil: Shehu, Aurora. University of Illinois; Estados UnidosFil: Mao, Jifang. University of Illinois; Estados UnidosFil: Devi, Y. Sangeeta. University of Illinois; Estados UnidosFil: Halperin, Julia. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Aguilar, Tetley. University of Illinois; Estados UnidosFil: Seibold, Anita. University of Illinois; Estados UnidosFil: Maizels, Evelyn. University of Illinois; Estados UnidosFil: Gibori, Geula. University of Illinois; Estados Unido