A Spectral Diffusion Prior for Hyperspectral Image Super-Resolution

Fusion-based hyperspectral image (HSI) super-resolution aims to produce a high-spatial-resolution HSI by fusing a low-spatial-resolution HSI and a high-spatial-resolution multispectral image. Such a HSI super-resolution process can be modeled as an inverse problem, where the prior knowledge is essential for obtaining the desired solution. Motivated by the success of diffusion models, we propose a novel spectral diffusion prior for fusion-based HSI super-resolution. Specifically, we first investigate the spectrum generation problem and design a spectral diffusion model to model the spectral data distribution. Then, in the framework of maximum a posteriori, we keep the transition information between every two neighboring states during the reverse generative process, and thereby embed the knowledge of trained spectral diffusion model into the fusion problem in the form of a regularization term. At last, we treat each generation step of the final optimization problem as its subproblem, and employ the Adam to solve these subproblems in a reverse sequence. Experimental results conducted on both synthetic and real datasets demonstrate the effectiveness of the proposed approach. The code of the proposed approach will be available on https://github.com/liuofficial/SDP

Model Inspired Autoencoder for Unsupervised Hyperspectral Image Super-Resolution

This paper focuses on hyperspectral image (HSI) super-resolution that aims to fuse a low-spatial-resolution HSI and a high-spatial-resolution multispectral image to form a high-spatial-resolution HSI (HR-HSI). Existing deep learning-based approaches are mostly supervised that rely on a large number of labeled training samples, which is unrealistic. The commonly used model-based approaches are unsupervised and flexible but rely on hand-craft priors. Inspired by the specific properties of model, we make the first attempt to design a model inspired deep network for HSI super-resolution in an unsupervised manner. This approach consists of an implicit autoencoder network built on the target HR-HSI that treats each pixel as an individual sample. The nonnegative matrix factorization (NMF) of the target HR-HSI is integrated into the autoencoder network, where the two NMF parts, spectral and spatial matrices, are treated as decoder parameters and hidden outputs respectively. In the encoding stage, we present a pixel-wise fusion model to estimate hidden outputs directly, and then reformulate and unfold the model's algorithm to form the encoder network. With the specific architecture, the proposed network is similar to a manifold prior-based model, and can be trained patch by patch rather than the entire image. Moreover, we propose an additional unsupervised network to estimate the point spread function and spectral response function. Experimental results conducted on both synthetic and real datasets demonstrate the effectiveness of the proposed approach

SwinV2DNet: Pyramid and Self-Supervision Compounded Feature Learning for Remote Sensing Images Change Detection

Among the current mainstream change detection networks, transformer is deficient in the ability to capture accurate low-level details, while convolutional neural network (CNN) is wanting in the capacity to understand global information and establish remote spatial relationships. Meanwhile, both of the widely used early fusion and late fusion frameworks are not able to well learn complete change features. Therefore, based on swin transformer V2 (Swin V2) and VGG16, we propose an end-to-end compounded dense network SwinV2DNet to inherit the advantages of both transformer and CNN and overcome the shortcomings of existing networks in feature learning. Firstly, it captures the change relationship features through the densely connected Swin V2 backbone, and provides the low-level pre-changed and post-changed features through a CNN branch. Based on these three change features, we accomplish accurate change detection results. Secondly, combined with transformer and CNN, we propose mixed feature pyramid (MFP) which provides inter-layer interaction information and intra-layer multi-scale information for complete feature learning. MFP is a plug and play module which is experimentally proven to be also effective in other change detection networks. Further more, we impose a self-supervision strategy to guide a new CNN branch, which solves the untrainable problem of the CNN branch and provides the semantic change information for the features of encoder. The state-of-the-art (SOTA) change detection scores and fine-grained change maps were obtained compared with other advanced methods on four commonly used public remote sensing datasets. The code is available at https://github.com/DalongZ/SwinV2DNet

Explicit Change Relation Learning for Change Detection in VHR Remote Sensing Images

Change detection has always been a concerned task in the interpretation of remote sensing images. It is essentially a unique binary classification task with two inputs, and there is a change relationship between these two inputs. At present, the mining of change relationship features is usually implicit in the network architectures that contain single-branch or two-branch encoders. However, due to the lack of artificial prior design for change relationship features, these networks cannot learn enough change semantic information and lose more accurate change detection performance. So we propose a network architecture NAME for the explicit mining of change relation features. In our opinion, the change features of change detection should be divided into pre-changed image features, post-changed image features and change relation features. In order to fully mine these three kinds of change features, we propose the triple branch network combining the transformer and convolutional neural network (CNN) to extract and fuse these change features from two perspectives of global information and local information, respectively. In addition, we design the continuous change relation (CCR) branch to further obtain the continuous and detail change relation features to improve the change discrimination capability of the model. The experimental results show that our network performs better, in terms of F1, IoU, and OA, than those of the existing advanced networks for change detection on four public very high-resolution (VHR) remote sensing datasets. Our source code is available at https://github.com/DalongZ/NAME

Hole-Like Fermi Surface in the Overdoped Non-Superconducting Bi1.8_{1.8}Pb0.4_{0.4}Sr2_2CuO6+δ_{6+\delta}

In high-temperature cuprate superconductors, the anti-ferromagnetic spin fluctuations are thought to have a very important role in naturally producing an attractive interaction between the electrons in the $d$-wave channel. The connection between superconductivity and spin fluctuations is expected to be especially consequential at the overdoped end point of the superconducting dome. In some materials, that point seems to coincide with a Lifshitz transition, where the Fermi surface changes from the hole-like centered at ($\pi, \pi$) to the electron-like, centered at the $\Gamma$ point causing a loss of large momentum anti-ferromagnetic fluctuations. Here, we study the doping dependence of the electronic structure of Bi$_{1.8}$Pb$_{0.4}$Sr$_2$CuO$_{6+\delta}$ in angle-resolved photoemission and find that the superconductivity vanishes at lower doping than at which the Lifshitz transition occurs. This requires a more detailed re-examination of a spin-fluctuation scenario.Comment: 6 pages, 3 Figures, 1 Tabl

The influence of PC6 on cardiovascular disorders: a review of central neural mechanisms

PC6 is a classic acupuncture point in traditional Chinese medicine. It is considered to be effective when treating cardiovascular disorders. In the present review the authors have focused on the neurophysiological bases of the effects of PC6 stimulation on cardiovascular mechanisms. Experimental studies have shown that the hypothalamic rostral ventrolateral medulla, arcuate nucleus and ventrolateral periaqueductal gray are involved in acupuncture attenuation of sympathoexcitatory cardiovascular reflex responses. This long-loop pathway also appears to contribute to the long-lasting, acupuncture-mediated attenuation of sympathetic premotor outflow and excitatory cardiovascular reflex responses. Acupuncture of PC6 modulates the activity in the cardiovascular system, an effect that may be attributed to attenuation of sympathoexcitatory cardiovascular reflex responses

Taxonomic and phylogenetic characterisations of six species of Pleosporales (in Didymosphaeriaceae, Roussoellaceae and Nigrogranaceae) from China

Pleosporales comprise a diverse group of fungi with a global distribution and significant ecological importance. A survey on Pleosporales (in Didymosphaeriaceae, Roussoellaceae and Nigrogranaceae) in Guizhou Province, China, was conducted. Specimens were identified, based on morphological characteristics and phylogenetic analyses using a dataset composed of ITS, LSU, SSU, tef1 and rpb2 loci. Maximum Likelihood (ML) and Bayesian analyses were performed. As a result, three new species (Neokalmusia karka, Nigrograna schinifolium and N. trachycarpus) have been discovered, along with two new records for China (Roussoella neopustulans and R. doimaesalongensis) and a known species (Roussoella pseudohysterioides). Morphologically similar species and phylogenetically close taxa are compared and discussed. This study provides detailed information and descriptions of all newly-identified taxa

Autophagy in Premature Senescent Cells Is Activated via AMPK Pathway

Autophagy is a highly regulated intracellular process involved in the turnover of most cellular constituents and in the maintenance of cellular homeostasis. In this study, we show that the activity of autophagy increases in H2O2 or RasV12-induced senescent fibroblasts. Inhibiting autophagy promotes cell apoptosis in senescent cells, suggesting that autophagy activation plays a cytoprotective role. Furthermore, our data indicate that the increase of autophagy in senescent cells is linked to the activation of transcription factor FoxO3A, which blocks ATP generation by transcriptionally up-regulating the expression of PDK4, an inhibitor of pyruvate dehydrogenase complex, thus leading to AMPK activation and mTOR inhibition. These findings suggest a novel mechanism by which FoxO3A factors can activate autophagy via metabolic alteration

Macrophage-mediated trogocytosis contributes to destroying human schistosomes in a non-susceptible rodent host, Microtus fortis

Schistosoma parasites, causing schistosomiasis, exhibit typical host specificity in host preference. Many mammals, including humans, are susceptible to infection, while the widely distributed rodent, Microtus fortis, exhibits natural anti-schistosome characteristics. The mechanisms of host susceptibility remain poorly understood. Comparison of schistosome infection in M. fortis with the infection in laboratory mice (highly sensitive to infection) offers a good model system to investigate these mechanisms and to gain an insight into host specificity. In this study, we showed that large numbers of leukocytes attach to the surface of human schistosomes in M. fortis but not in mice. Single-cell RNA-sequencing analyses revealed that macrophages might be involved in the cell adhesion, and we further demonstrated that M. fortis macrophages could be mediated to attach and kill schistosomula with dependence on Complement component 3 (C3) and Complement receptor 3 (CR3). Importantly, we provided direct evidence that M. fortis macrophages could destroy schistosomula by trogocytosis, a previously undescribed mode for killing helminths. This process was regulated by Ca2+/NFAT signaling. These findings not only elucidate a novel anti-schistosome mechanism in M. fortis but also provide a better understanding of host parasite interactions, host specificity and the potential generation of novel strategies for schistosomiasis control

Dual-Comb Ranging

Absolute distance measurement is a fundamental technique in mobile and large-scale dimensional metrology. Dual-comb ranging is emerging as a powerful tool that exploits phase resolution and frequency accuracy for high-precision and fast-rate distance measurement. Using two coherent frequency combs, dual-comb ranging allows time and phase response to be measured rapidly. It breaks through the limitations related to the responsive bandwidth, ambiguity range, and dynamic measurement characteristics of conventional ranging tools. This review introduces dual-comb ranging and summarizes the key techniques for realizing this ranging tool. As optical frequency comb technology progresses, dual-comb ranging shows promise for various professional applications. Keywords: Ranging, Dual-comb interferometer, Phase noise, Timing jitter, Tight-locking, Post-correctio