The value of shikumen buildings as origin of the commercial residential buildings in China: a case study of Meihetang Shikumeng buildings in Hangzhou

Famous for the protection and reuse of “Xintiandi” historical buildings in Shanghai, “Shikumen Building” is China’s earliest commercial residential buildings appeared in the early 20th century. Nowadays, we still could find a similar architectural relics in many coastal cities in China. With a very important historical value, this product is produced and developed in the process western culture and traditional culture collision and fusion. By taking Meihetang Shikumen historic buildings in Hangzhou as example, this paper analyses the historical value of the architectural style, resolves the question how this architectural style from the foreign culture integrate into Chinese cities’ own culture and experience, and explores the initial links between Chinese contemporary urban residential and commercial development with this architectural style.Peer Reviewe

Structured Kernel Estimation for Photon-Limited Deconvolution

Images taken in a low light condition with the presence of camera shake suffer from motion blur and photon shot noise. While state-of-the-art image restoration networks show promising results, they are largely limited to well-illuminated scenes and their performance drops significantly when photon shot noise is strong. In this paper, we propose a new blur estimation technique customized for photon-limited conditions. The proposed method employs a gradient-based backpropagation method to estimate the blur kernel. By modeling the blur kernel using a low-dimensional representation with the key points on the motion trajectory, we significantly reduce the search space and improve the regularity of the kernel estimation problem. When plugged into an iterative framework, our novel low-dimensional representation provides improved kernel estimates and hence significantly better deconvolution performance when compared to end-to-end trained neural networks. The source code and pretrained models are available at \url{https://github.com/sanghviyashiitb/structured-kernel-cvpr23}Comment: main document and supplementary; accepted at CVPR202

Single Frame Atmospheric Turbulence Mitigation: A Benchmark Study and A New Physics-Inspired Transformer Model

Image restoration algorithms for atmospheric turbulence are known to be much more challenging to design than traditional ones such as blur or noise because the distortion caused by the turbulence is an entanglement of spatially varying blur, geometric distortion, and sensor noise. Existing CNN-based restoration methods built upon convolutional kernels with static weights are insufficient to handle the spatially dynamical atmospheric turbulence effect. To address this problem, in this paper, we propose a physics-inspired transformer model for imaging through atmospheric turbulence. The proposed network utilizes the power of transformer blocks to jointly extract a dynamical turbulence distortion map and restore a turbulence-free image. In addition, recognizing the lack of a comprehensive dataset, we collect and present two new real-world turbulence datasets that allow for evaluation with both classical objective metrics (e.g., PSNR and SSIM) and a new task-driven metric using text recognition accuracy. Both real testing sets and all related code will be made publicly available.Comment: This paper is accepted as a poster at ECCV 202

Not Just Learning from Others but Relying on Yourself: A New Perspective on Few-Shot Segmentation in Remote Sensing

Few-shot segmentation (FSS) is proposed to segment unknown class targets with just a few annotated samples. Most current FSS methods follow the paradigm of mining the semantics from the support images to guide the query image segmentation. However, such a pattern of `learning from others' struggles to handle the extreme intra-class variation, preventing FSS from being directly generalized to remote sensing scenes. To bridge the gap of intra-class variance, we develop a Dual-Mining network named DMNet for cross-image mining and self-mining, meaning that it no longer focuses solely on support images but pays more attention to the query image itself. Specifically, we propose a Class-public Region Mining (CPRM) module to effectively suppress irrelevant feature pollution by capturing the common semantics between the support-query image pair. The Class-specific Region Mining (CSRM) module is then proposed to continuously mine the class-specific semantics of the query image itself in a `filtering' and `purifying' manner. In addition, to prevent the co-existence of multiple classes in remote sensing scenes from exacerbating the collapse of FSS generalization, we also propose a new Known-class Meta Suppressor (KMS) module to suppress the activation of known-class objects in the sample. Extensive experiments on the iSAID and LoveDA remote sensing datasets have demonstrated that our method sets the state-of-the-art with a minimum number of model parameters. Significantly, our model with the backbone of Resnet-50 achieves the mIoU of 49.58% and 51.34% on iSAID under 1-shot and 5-shot settings, outperforming the state-of-the-art method by 1.8% and 1.12%, respectively. The code is publicly available at https://github.com/HanboBizl/DMNet.Comment: accepted to IEEE TGR

Structure of photosystem I-LHCI-LHCII from the green alga Chlamydomonas reinhardtii in State 2

Photosystem I (PSI) and II (PSII) balance their light energy distribution absorbed by their light-harvesting complexes (LHCs) through state transition to maintain the maximum photosynthetic performance and to avoid photodamage. In state 2, a part of LHCII moves to PSI, forming a PSI-LHCI-LHCII supercomplex. The green alga Chlamydomonas reinhardtii exhibits state transition to a far larger extent than higher plants. Here we report the cryo-electron microscopy structure of a PSI-LHCI-LHCII supercomplex in state 2 from C. reinhardtii at 3.42 Å resolution. The result reveals that the PSI-LHCI-LHCII of C. reinhardtii binds two LHCII trimers in addition to ten LHCI subunits. The PSI core subunits PsaO and PsaH, which were missed or not well-resolved in previous Cr-PSI-LHCI structures, are observed. The present results reveal the organization and assembly of PSI core subunits, LHCI and LHCII, pigment arrangement, and possible pathways of energy transfer from peripheral antennae to the PSI core

Association between maternal rheumatoid arthritis and small for gestational age neonates: a systematic review and meta-analysis

BackgroundAccording to reports, maternal rheumatoid arthritis (RA) has been suggested as a possible adverse factor for developing small for gestational age (SGA) in offspring. However, some studies have also indicated a need for a more statistically significant association between the two. Understanding the relationship between maternal RA and the risk of SGA is crucial for identifying potential adverse outcomes and implementing appropriate interventions. Therefore, this study aims to elucidate the association between maternal RA and the risk of offspring developing SGA.MethodsThis study was registered on the International Prospective Register of Systematic Reviews (PROSPERO) (ID: CRD42022357590). A systematic literature search was conducted to identify eligible studies up to August 2022. Quality assessment was performed according to the Newcastle-Ottawa scale. The Q test and I2 test tested and estimated heterogeneity among studies. Odds ratios (ORs) with 95% CI were calculated using random or fixed effects models depending on the heterogeneity. Subgroup analyses, sensitivity analyses, and publication bias assessments were also performed.ResultsSeven studies, including 12,323,918 participants, were included in the analysis. The results showed a statistically significant association between maternal RA and SGA (OR = 1.70, 95% CI = 1.29–2.23, p < 0.001). Sensitivity analysis showed stable results. The funnel plot of the symmetric distribution and the results of Begg’s and Egger’s tests showed no publication bias.ConclusionMaternal RA is associated with an increased risk of SGA in offspring. However, more studies are still needed to explore the potential mechanisms underlying maternal RA and SGA association.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier [CRD42022357590]

Toward an intensive understanding of sewer sediment prokaryotic community assembly and function

Prokaryotic communities play important roles in sewer sediment ecosystems, but the community composition, functional potential, and assembly mechanisms of sewer sediment prokaryotic communities are still poorly understood. Here, we studied the sediment prokaryotic communities in different urban functional areas (multifunctional, commercial, and residential areas) through 16S rRNA gene amplicon sequencing. Our results suggested that the compositions of prokaryotic communities varied significantly among functional areas. Desulfomicrobium, Desulfovibrio, and Desulfobacter involved in the sulfur cycle and some hydrolytic fermentation bacteria were enriched in multifunctional area, while Methanospirillum and Methanoregulaceae, which were related to methane metabolism were significantly discriminant taxa in the commercial area. Physicochemical properties were closely related to overall community changes (p < 0.001), especially the nutrient levels of sediments (i.e., total nitrogen and total phosphorus) and sediment pH. Network analysis revealed that the prokaryotic community network of the residential area sediment was more complex than the other functional areas, suggesting higher stability of the prokaryotic community in the residential area. Stochastic processes dominated the construction of the prokaryotic community. These results expand our understanding of the characteristics of prokaryotic communities in sewer sediment, providing a new perspective for studying sewer sediment prokaryotic community structure