267 research outputs found
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
Structure and distinct supramolecular organization of a PSII-ACPII dimer from a cryptophyte alga Chroomonas placoidea
Cryptophyte algae are an evolutionarily distinct and ecologically important group of photosynthetic unicellular eukaryotes. Photosystem II (PSII) of cryptophyte algae associates with alloxanthin chlorophyll a/c-binding proteins (ACPs) to act as the peripheral light-harvesting system, whose supramolecular organization is unknown. Here, we purify the PSII-ACPII supercomplex from a cryptophyte alga Chroomonas placoidea (C. placoidea), and analyze its structure at a resolution of 2.47 & Aring; using cryo-electron microscopy. This structure reveals a dimeric organization of PSII-ACPII containing two PSII core monomers flanked by six symmetrically arranged ACPII subunits. The PSII core is conserved whereas the organization of ACPII subunits exhibits a distinct pattern, different from those observed so far in PSII of other algae and higher plants. Furthermore, we find a Chl a-binding antenna subunit, CCPII-S, which mediates interaction of ACPII with the PSII core. These results provide a structural basis for the assembly of antennas within the supercomplex and possible excitation energy transfer pathways in cryptophyte algal PSII, shedding light on the diversity of supramolecular organization of photosynthetic machinery
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]
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