Attention Diversification for Domain Generalization

Convolutional neural networks (CNNs) have demonstrated gratifying results at learning discriminative features. However, when applied to unseen domains, state-of-the-art models are usually prone to errors due to domain shift. After investigating this issue from the perspective of shortcut learning, we find the devils lie in the fact that models trained on different domains merely bias to different domain-specific features yet overlook diverse task-related features. Under this guidance, a novel Attention Diversification framework is proposed, in which Intra-Model and Inter-Model Attention Diversification Regularization are collaborated to reassign appropriate attention to diverse task-related features. Briefly, Intra-Model Attention Diversification Regularization is equipped on the high-level feature maps to achieve in-channel discrimination and cross-channel diversification via forcing different channels to pay their most salient attention to different spatial locations. Besides, Inter-Model Attention Diversification Regularization is proposed to further provide task-related attention diversification and domain-related attention suppression, which is a paradigm of "simulate, divide and assemble": simulate domain shift via exploiting multiple domain-specific models, divide attention maps into task-related and domain-related groups, and assemble them within each group respectively to execute regularization. Extensive experiments and analyses are conducted on various benchmarks to demonstrate that our method achieves state-of-the-art performance over other competing methods. Code is available at https://github.com/hikvision-research/DomainGeneralization.Comment: ECCV 2022. Code available at https://github.com/hikvision-research/DomainGeneralizatio

DNA methylation repatterning accompanying hybridization, whole genome doubling and homoeolog exchange in nascent segmental rice allotetraploids

Allopolyploidization, which entails interspecific hybridization and whole genome duplication (WGD), is associated with emergent genetic and epigenetic instabilities that are thought to contribute to adaptation and evolution. One frequent genomic consequence of nascent allopolyploidization is homoeologous exchange (HE), which arises from compromised meiotic fidelity and generates genetically and phenotypically variable progenies. Here, we used a genetically tractable synthetic rice segmental allotetraploid system to interrogate genome‐wide DNA methylation and gene expression responses and outcomes to the separate and combined effects of hybridization, WGD and HEs. Progenies of the tetraploid rice were genomically diverse due to genome‐wide HEs that affected all chromosomes, yet they exhibited overall methylome stability. Nonetheless, regional variation of cytosine methylation states was widespread in the tetraploids. Transcriptome profiling revealed genome‐wide alteration of gene expression, which at least in part associates with changes in DNA methylation. Intriguingly, changes of DNA methylation and gene expression could be decoupled from hybridity and sustained and amplified by HEs. Our results suggest that HEs, a prominent genetic consequence of nascent allopolyploidy, can exacerbate, diversify and perpetuate the effects of allopolyploidization on epigenetic and gene expression variation, and hence may contribute to allopolyploid evolution

Active contour model based on hybrid signed pressure force function

Abstract To segment noisy and multi‐target images, an active contour model based on a hybrid signed pressure force function that fuses global and local information of the image is proposed. Firstly, a local signed pressure force function is defined using the local area information of the image. Then, it is combined with the existing global grey density function to construct a new signed pressure force function. Finally, the selective binary and Gaussian filtering regularized level set are modified using the newly defined function. Extensive experiments and comparisons on both synthetic and real images demonstrate that the proposed method is robust to noise and can handle noisy and multi‐target images with high accuracy

An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF

Inhomogeneous images cannot be segmented quickly or accurately using local or global image information. To solve this problem, an image segmentation method using a novel active contour model that is based on an improved signed pressure force (SPF) function and a local image fitting (LIF) model is proposed in this paper, which is based on local and global image information. First, a weight function of the global grayscale means of the inside and outside of a contour curve is presented by combining the internal gray mean value with the external gray mean value, based on which a new SPF function is defined. The SPF function can segment blurred images and weak gradient images. Then, the LIF model is introduced by using local image information to segment intensity-inhomogeneous images. Subsequently, a weight function is established based on the local and global image information, and then the weight function is used to adjust the weights between the local information term and the global information term. Thus, a novel active contour model is presented, and an improved SPF- and LIF-based image segmentation (SPFLIF-IS) algorithm is developed based on that model. Experimental results show that the proposed method not only exhibits high robustness to the initial contour and noise but also effectively segments multiobjective images and images with intensity inhomogeneity and can analyze real images well

An Image Segmentation Method Based on Improved Regularized Level Set Model

When the level set algorithm is used to segment an image, the level set function must be initialized periodically to ensure that it remains a signed distance function (SDF). To avoid this defect, an improved regularized level set method-based image segmentation approach is presented. First, a new potential function is defined and introduced to reconstruct a new distance regularization term to solve this issue of periodically initializing the level set function. Second, by combining the distance regularization term with the internal and external energy terms, a new energy functional is developed. Then, the process of the new energy functional evolution is derived by using the calculus of variations and the steepest descent approach, and a partial differential equation is designed. Finally, an improved regularized level set-based image segmentation (IRLS-IS) method is proposed. Numerical experimental results demonstrate that the IRLS-IS method is not only effective and robust to segment noise and intensity-inhomogeneous images but can also analyze complex medical images well

Effect of W Addition on Fe-P-C-B Soft-Magnetic Amorphous Alloy

In this work, the thermal behavior, soft magnetic properties, and structure of Fe86−xP11C2B1Wx (x = 0, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 2, and 4) amorphous alloys were researched by several experimental methods and ab initio molecular dynamics. The addition of W improved the thermal stability of the alloy system when the first onset crystallization temperature (Tx1) increased from 655 K to 711 K, significantly reduced the coercivity Hc, and decreased the saturation magnetization Bs. The Fe85.6P11C2B1W0.4 alloy showed optimal soft magnetic performance, with low Hc of 1.4 A/m and relatively good Bs of 1.52 T. The simulation results suggested that W atoms increased the distance of the neighboring Fe-Fe pair, reduced the coordination number, narrowed the gap between the spin-up and spin-down electrons of each atom, and decreased the average magnetic moment of the Fe atoms. This work demonstrates a micro-alloying strategy to greatly reduce Hc while maintaining high Bs

Slimmable Domain Adaptation

Vanilla unsupervised domain adaptation methods tend to optimize the model with fixed neural architecture, which is not very practical in real-world scenarios since the target data is usually processed by different resource-limited devices. It is therefore of great necessity to facilitate architecture adaptation across various devices. In this paper, we introduce a simple framework, Slimmable Domain Adaptation, to improve cross-domain generalization with a weight-sharing model bank, from which models of different capacities can be sampled to accommodate different accuracy-efficiency trade-offs. The main challenge in this framework lies in simultaneously boosting the adaptation performance of numerous models in the model bank. To tackle this problem, we develop a Stochastic EnsEmble Distillation method to fully exploit the complementary knowledge in the model bank for inter-model interaction. Nevertheless, considering the optimization conflict between inter-model interaction and intra-model adaptation, we augment the existing bi-classifier domain confusion architecture into an Optimization-Separated Tri-Classifier counterpart. After optimizing the model bank, architecture adaptation is leveraged via our proposed Unsupervised Performance Evaluation Metric. Under various resource constraints, our framework surpasses other competing approaches by a very large margin on multiple benchmarks. It is also worth emphasizing that our framework can preserve the performance improvement against the source-only model even when the computing complexity is reduced to $1/64$. Code will be available at https://github.com/hikvision-research/SlimDA.Comment: To appear in CVPR 2022. Code is coming soon: https://github.com/hikvision-research/SlimD

DNA methylation repatterning accompanying hybridization, whole genome doubling and homoeolog exchange in nascent segmental rice allotetraploids

Allopolyploidization, which entails interspecific hybridization and whole genome duplication (WGD), is associated with emergent genetic and epigenetic instabilities that are thought to contribute to adaptation and evolution. One frequent genomic consequence of nascent allopolyploidization is homoeologous exchange (HE), which arises from compromised meiotic fidelity and generates genetically and phenotypically variable progenies. Here, we used a genetically tractable synthetic rice segmental allotetraploid system to interrogate genome‐wide DNA methylation and gene expression responses and outcomes to the separate and combined effects of hybridization, WGD and HEs. Progenies of the tetraploid rice were genomically diverse due to genome‐wide HEs that affected all chromosomes, yet they exhibited overall methylome stability. Nonetheless, regional variation of cytosine methylation states was widespread in the tetraploids. Transcriptome profiling revealed genome‐wide alteration of gene expression, which at least in part associates with changes in DNA methylation. Intriguingly, changes of DNA methylation and gene expression could be decoupled from hybridity and sustained and amplified by HEs. Our results suggest that HEs, a prominent genetic consequence of nascent allopolyploidy, can exacerbate, diversify and perpetuate the effects of allopolyploidization on epigenetic and gene expression variation, and hence may contribute to allopolyploid evolution.This is the peer reviewed version of the following article: Li, Ning, Chunming Xu, Ai Zhang, Ruili Lv, Xinchao Meng, Xiuyun Lin, Lei Gong, Jonathan F. Wendel, and Bao Liu. "DNA methylation repatterning accompanying hybridization, whole genome doubling and homoeolog exchange in nascent segmental rice allotetraploids." New Phytologist (2019), which has been published in final form at doi: 10.1111/nph.15820. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. </p

Significance of serology testing to assist timely diagnosis of SARS-CoV-2 infections: implication from a family cluster

Confirmative diagnosis of SARS-CoV-2 infections has been challenged due to unsatisfactory positive rate of molecular assays. Here we identified a family cluster of SARS-CoV-2 infections, with five of six family members were SARS-CoV-2-specific immunoglobin serology testing positive, while molecular assays only detected two of this five patients even repeated twice. We comprehensively analyzed this familial cluster of cases based on the clinical characteristics, chest CT images, SARS-CoV-2 molecular detection results, and serology testing results. At last, two patients were diagnosed with COVID-19, two were suspected of COVID-19, and two were considered close contacts. Our results emphasized the significance of serology testing to assist timely diagnosis of SARS-CoV-2 infections, especially for COVID-19 close contacts screening

Transcriptome Analysis Reveals That Ascorbic Acid Treatment Enhances the Cold Tolerance of Tea Plants through Cell Wall Remodeling

Cold stress is a major environmental factor that adversely affects the growth and productivity of tea plants. Upon cold stress, tea plants accumulate multiple metabolites, including ascorbic acid. However, the role of ascorbic acid in the cold stress response of tea plants is not well understood. Here, we report that exogenous ascorbic acid treatment improves the cold tolerance of tea plants. We show that ascorbic acid treatment reduces lipid peroxidation and increases the Fv/Fm of tea plants under cold stress. Transcriptome analysis indicates that ascorbic acid treatment down-regulates the expression of ascorbic acid biosynthesis genes and ROS-scavenging-related genes, while modulating the expression of cell wall remodeling-related genes. Our findings suggest that ascorbic acid treatment negatively regulates the ROS-scavenging system to maintain ROS homeostasis in the cold stress response of tea plants and that ascorbic acid’s protective role in minimizing the harmful effects of cold stress on tea plants may occur through cell wall remodeling. Ascorbic acid can be used as a potential agent to increase the cold tolerance of tea plants with no pesticide residual concerns in tea