Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization

Unsupervised domain adaptation approaches have recently succeeded in various medical image segmentation tasks. The reported works often tackle the domain shift problem by aligning the domain-invariant features and minimizing the domain-specific discrepancies. That strategy works well when the difference between a specific domain and between different domains is slight. However, the generalization ability of these models on diverse imaging modalities remains a significant challenge. This paper introduces UDA-VAE++, an unsupervised domain adaptation framework for cardiac segmentation with a compact loss function lower bound. To estimate this new lower bound, we develop a novel Structure Mutual Information Estimation (SMIE) block with a global estimator, a local estimator, and a prior information matching estimator to maximize the mutual information between the reconstruction and segmentation tasks. Specifically, we design a novel sequential reparameterization scheme that enables information flow and variance correction from the low-resolution latent space to the high-resolution latent space. Comprehensive experiments on benchmark cardiac segmentation datasets demonstrate that our model outperforms previous state-of-the-art qualitatively and quantitatively. The code is available at https://github.com/LOUEY233/Toward-Mutual-Information}{https://github.com/LOUEY233/Toward-Mutual-InformationComment: CVPR Workshop Paper v3: Fix some description error

PointNorm: Dual Normalization is All You Need for Point Cloud Analysis

Point cloud analysis is challenging due to the irregularity of the point cloud data structure. Existing works typically employ the ad-hoc sampling-grouping operation of PointNet++, followed by sophisticated local and/or global feature extractors for leveraging the 3D geometry of the point cloud. Unfortunately, the sampling-grouping operations do not address the point cloud's irregularity, whereas the intricate local and/or global feature extractors led to poor computational efficiency. In this paper, we introduce a novel DualNorm module after the sampling-grouping operation to effectively and efficiently address the irregularity issue. The DualNorm module consists of Point Normalization, which normalizes the grouped points to the sampled points, and Reverse Point Normalization, which normalizes the sampled points to the grouped points. The proposed framework, PointNorm, utilizes local mean and global standard deviation to benefit from both local and global features while maintaining a faithful inference speed. Experiments show that we achieved excellent accuracy and efficiency on ModelNet40 classification, ScanObjectNN classification, ShapeNetPart Part Segmentation, and S3DIS Semantic Segmentation. Code is available at https://github.com/ShenZheng2000/PointNorm-for-Point-Cloud-Analysis

Low-Light Image and Video Enhancement: A Comprehensive Survey and Beyond

This paper presents a comprehensive survey of low-light image and video enhancement. We begin with the challenging mixed over-/under-exposed images, which are under-performed by existing methods. To this end, we propose two variants of the SICE dataset named SICE_Grad and SICE_Mix. Next, we introduce Night Wenzhou, a large-scale, high-resolution video dataset, to address the issue of the lack of a low-light video dataset that discount the use of low-light image enhancement (LLIE) to videos. Our Night Wenzhou dataset is challenging since it consists of fast-moving aerial scenes and streetscapes with varying illuminations and degradation. We conduct extensive key technique analysis and experimental comparisons for representative LLIE approaches using these newly proposed datasets and the current benchmark datasets. Finally, we address unresolved issues and propose future research topics for the LLIE community. Our datasets are available at https://github.com/ShenZheng2000/LLIE_Survey.Comment: 13 pages, 8 tables, and 13 figure

Plastid structure and carotenogenic gene expression in red- and white-fleshed loquat (Eriobotrya japonica) fruits

Loquat (Eriobotrya japonica Lindl.) can be sorted into red- and white-fleshed cultivars. The flesh of Luoyangqing (LYQ, red-fleshed) appears red-orange because of a high content of carotenoids while the flesh of Baisha (BS, white-fleshed) appears ivory white due to a lack of carotenoid accumulation. The carotenoid content in the peel and flesh of LYQ was approximately 68 μg g−1 and 13 μg g−1 fresh weight (FW), respectively, and for BS 19 μg g−1 and 0.27 μg g−1 FW. The mRNA levels of 15 carotenogenesis-related genes were analysed during fruit development and ripening. After the breaker stage (S4), the mRNA levels of phytoene synthase 1 (PSY1) and chromoplast-specific lycopene β-cyclase (CYCB) were higher in the peel, and CYCB and β-carotene hydroxylase (BCH) mRNAs were higher in the flesh of LYQ, compared with BS. Plastid morphogenesis during fruit ripening was also studied. The ultrastructure of plastids in the peel of BS changed less than in LYQ during fruit development. Two different chromoplast shapes were observed in the cells of LYQ peel and flesh at the fully ripe stage. Carotenoids were incorporated in the globules in chromoplasts of LYQ and BS peel but were in a crystalline form in the chromoplasts of LYQ flesh. However, no chromoplast structure was found in the cells of fully ripe BS fruit flesh. The mRNA level of plastid lipid-associated protein (PAP) in the peel and flesh of LYQ was over five times higher than in BS peel and flesh. In conclusion, the lower carotenoid content in BS fruit was associated with the lower mRNA levels of PSY1, CYCB, and BCH; however, the failure to develop normal chromoplasts in BS flesh is the most convincing explanation for the lack of carotenoid accumulation. The expression of PAP was well correlated with chromoplast numbers and carotenoid accumulation, suggesting its possible role in chromoplast biogenesis or interconversion of loquat fruit

The role of nitric oxide signaling in food intake; insights from the inner mitochondrial membrane peptidase 2 mutant mice

Reactive oxygen species have been implicated in feeding control through involvement in brain lipid sensing, and regulating NPY/AgRP and pro-opiomelanocortin (POMC) neurons, although the underlying mechanisms are unclear. Nitric oxide is a signaling molecule in neurons and it stimulates feeding in many species. Whether reactive oxygen species affect feeding through interaction with nitric oxide is unclear. We previously reported that Immp2l mutation in mice causes excessive mitochondrial superoxide generation, which causes infertility and early signs of aging. In our present study, reduced food intake in mutant mice resulted in significantly reduced body weight and fat composition while energy expenditure remained unchanged. Lysate from mutant brain showed a significant decrease in cGMP levels, suggesting insufficient nitric oxide signaling. Thus, our data suggests that reactive oxygen species may regulate food intake through modulating the bioavailability of nitric oxide

Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization

Unsupervised domain adaptation approaches have recently succeeded in various medical image segmentation tasks. The reported works often tackle the domain shift problem by aligning the domain-invariant features and minimizing the domain-specific discrepancies. That strategy works well when the difference between a specific domain and between different domains is slight. However, the generalization ability of these models on diverse imaging modalities remains a significant challenge. This paper introduces UDA-VAE++, an unsupervised domain adaptation framework for cardiac segmentation with a compact loss function lower bound. To estimate this new lower bound, we develop a novel Structure Mutual Information Estimation (SMIE) block with a global estimator, a local estimator, and a prior information matching estimator to maximize the mutual information between the reconstruction and segmentation tasks. Specifically, we design a novel sequential reparameterization scheme that enables information flow and variance correction from the low-resolution latent space to the high-resolution latent space. Comprehensive experiments on benchmark cardiac segmentation datasets demonstrate that our model outperforms previous state-of-the-art qualitatively and quantitatively

Functional Optimization Reinforcement Learning for Real-Time Bidding

Real-time bidding is the new paradigm of programmatic advertising. An advertiser wants to make the intelligent choice of utilizing a \textbf{Demand-Side Platform} to improve the performance of their ad campaigns. Existing approaches are struggling to provide a satisfactory solution for bidding optimization due to stochastic bidding behavior. In this paper, we proposed a multi-agent reinforcement learning architecture for RTB with functional optimization. We designed four agents bidding environment: three Lagrange-multiplier based functional optimization agents and one baseline agent (without any attribute of functional optimization) First, numerous attributes have been assigned to each agent, including biased or unbiased win probability, Lagrange multiplier, and click-through rate. In order to evaluate the proposed RTB strategy's performance, we demonstrate the results on ten sequential simulated auction campaigns. The results show that agents with functional actions and rewards had the most significant average winning rate and winning surplus, given biased and unbiased winning information respectively. The experimental evaluations show that our approach significantly improve the campaign's efficacy and profitability

Experimental Study on the Behavior of X-Section Pile Subjected to Cyclic Axial Load in Sand

X-section cast-in-place concrete pile is a new type of foundation reinforcement technique featured by the X-shaped cross-section. Compared with a traditional circular pile, an X-section pile with the same cross-sectional area has larger side resistance due to its larger cross-sectional perimeter. The behavior of static loaded X-section pile has been extensively reported, while little attention has been paid to the dynamic characteristics of X-section pile. This paper introduced a large-scale model test for an X-section pile and a circular pile with the same cross-sectional area subjected to cyclic axial load in sand. The experimental results demonstrated that cyclic axial load contributed to the degradation of shaft friction and pile head stiffness. The dynamic responses of X-section pile were determined by loading frequency and loading amplitude. Furthermore, comparative analysis between the X-section pile and the circular pile revealed that the X-section pile can improve the shaft friction and reduce the cumulative settlement under cyclic loading. Static load test was carried out prior to the vibration tests to investigate the ultimate bearing capacity of test piles. This study was expected to provide a reasonable reference for further studies on the dynamic responses of X-section piles in practical engineering

SAPNet: Segmentation-Aware Progressive Network for Perceptual Contrastive Deraining

Deep learning algorithms have recently achieved promising deraining performance-s on both the natural and synthetic rainy datasets. As an essential low-level preprocessing stage, a deraining network should clear the rain streaks and preserve the fine semantic details. However, most existing methods only consider low-level image restoration. That limits their performances at high-level tasks requiring precise semantic information. To address this issue, in this paper, we present a segmentation aware progressive network (SAPNet) based upon contrastive learning for single image deraining. We start our method with a lightweight derain network formed with progressive dilated units (PDU). The PDU can significantly expand the receptive field and characterize multiscale rain streaks without the heavy computation on multiscale images. A fundamental aspect of this work is an unsupervised background segmentation (UBS) network initialized with ImageNet and Gaussian weights. The UBS can faithfully preserve an image s semantic information and improve the generalization ability to unseen photos. Furthermore, we introduce a perceptual contrastive loss (PCL) and a learned perceptual image similarity loss (LPISL) to regulate model learning. By ex-ploiting the rainy image and ground-truth as the negative and the positive sample in the VGG-16 latent space, we bridge the fine semantic details between the derained image and the ground-truth in a fully constrained manner. Comprehensive experiments on synthetic and real-world rainy images show our model surpasses top-performing methods and aids object detection and semantic segmentation with considerable efficacy. A Pytorch Implementation is available at https://github.com/ShenZheng2000/SAPNetfor-image-deraining

Deblur-YOLO: Real-Time Object Detection with Efficient Blind Motion Deblurring

Object detection has been a traditional yet open computer vision research field. In intensive studies, object detection models have achieved promising results regarding recognition accuracy and inference speed. However, previous state-of-the-art algorithms fail to operate at blurry images. In this work, we propose Deblur-YOLO, an efficient, YOLO-based and detection-driven approach robust to motion blur photographs. We introduce a generative adversarial network with a dilated feature pyramid generator, a pair of multi-scale discriminators with spectral normalization, and a detection discriminator. We design a new image quality metric called Smooth Peak Signal-to-Noise Ratio (SPSNR) for measuring the smoothness of the reconstructed image. Empirical studies on benchmark datasets demonstrate Deblur-YOLO\u27s superiority. On COCO 2014, Set 5 and Setl4, Deblur-YOLO achieves leading results for parameters, deblurring time, PSNR, SPSNR and SSIM. We also visually display the excellence of our deblurring performance to competing models