Semantic-Aware Local-Global Vision Transformer

Vision Transformers have achieved remarkable progresses, among which Swin Transformer has demonstrated the tremendous potential of Transformer for vision tasks. It surmounts the key challenge of high computational complexity by performing local self-attention within shifted windows. In this work we propose the Semantic-Aware Local-Global Vision Transformer (SALG), to further investigate two potential improvements towards Swin Transformer. First, unlike Swin Transformer that performs uniform partition to produce equal size of regular windows for local self-attention, our SALG performs semantic segmentation in an unsupervised way to explore the underlying semantic priors in the image. As a result, each segmented region can correspond to a semantically meaningful part in the image, potentially leading to more effective features within each of segmented regions. Second, instead of only performing local self-attention within local windows as Swin Transformer does, the proposed SALG performs both 1) local intra-region self-attention for learning fine-grained features within each region and 2) global inter-region feature propagation for modeling global dependencies among all regions. Consequently, our model is able to obtain the global view when learning features for each token, which is the essential advantage of Transformer. Owing to the explicit modeling of the semantic priors and the proposed local-global modeling mechanism, our SALG is particularly advantageous for small-scale models when the modeling capacity is not sufficient for other models to learn semantics implicitly. Extensive experiments across various vision tasks demonstrates the merit of our model over other vision Transformers, especially in the small-scale modeling scenarios

Global-Local Stepwise Generative Network for Ultra High-Resolution Image Restoration

While the research on image background restoration from regular size of degraded images has achieved remarkable progress, restoring ultra high-resolution (e.g., 4K) images remains an extremely challenging task due to the explosion of computational complexity and memory usage, as well as the deficiency of annotated data. In this paper we present a novel model for ultra high-resolution image restoration, referred to as the Global-Local Stepwise Generative Network (GLSGN), which employs a stepwise restoring strategy involving four restoring pathways: three local pathways and one global pathway. The local pathways focus on conducting image restoration in a fine-grained manner over local but high-resolution image patches, while the global pathway performs image restoration coarsely on the scale-down but intact image to provide cues for the local pathways in a global view including semantics and noise patterns. To smooth the mutual collaboration between these four pathways, our GLSGN is designed to ensure the inter-pathway consistency in four aspects in terms of low-level content, perceptual attention, restoring intensity and high-level semantics, respectively. As another major contribution of this work, we also introduce the first ultra high-resolution dataset to date for both reflection removal and rain streak removal, comprising 4,670 real-world and synthetic images. Extensive experiments across three typical tasks for image background restoration, including image reflection removal, image rain streak removal and image dehazing, show that our GLSGN consistently outperforms state-of-the-art methods.Comment: submmitted to Transactions on Image Processin

Multi-Faceted Distillation of Base-Novel Commonality for Few-shot Object Detection

Most of existing methods for few-shot object detection follow the fine-tuning paradigm, which potentially assumes that the class-agnostic generalizable knowledge can be learned and transferred implicitly from base classes with abundant samples to novel classes with limited samples via such a two-stage training strategy. However, it is not necessarily true since the object detector can hardly distinguish between class-agnostic knowledge and class-specific knowledge automatically without explicit modeling. In this work we propose to learn three types of class-agnostic commonalities between base and novel classes explicitly: recognition-related semantic commonalities, localization-related semantic commonalities and distribution commonalities. We design a unified distillation framework based on a memory bank, which is able to perform distillation of all three types of commonalities jointly and efficiently. Extensive experiments demonstrate that our method can be readily integrated into most of existing fine-tuning based methods and consistently improve the performance by a large margin

Acute type A dissection without intimal tear in arch: Proximal or extensive repair?

ObjectiveFor acute type A dissection without an intimal tear in the arch, the optimal surgical strategy is unknown. The present study was designed to clarify the issue by comparing the early and late outcomes of proximal (PR) and extensive repair (ER).MethodsFrom January 2002 to June 2010, 331 patients with acute type A dissection were treated surgically at our institute. Of these 331 patients, 197 were identified without an arch tear on the preoperative imaging examination and by intraoperative inspection. Of these 197 patients, 74 underwent proximal repair, including the aortic root, ascending aortic, or hemiarch repair, and 88 underwent extensive repair, including proximal repair, total arch replacement and a stented elephant trunk technique. The perioperative variables and late results were statistically analyzed.ResultsNo significant difference was found in the rates of early mortality and morbidity between the 2 groups, despite the shorter duration of circulatory arrest in the PR group. During long-term follow-up (mean, 55.7 ± 33.1 months; maximum, 129), the overall survival rate in the whole cohort was 100%, 90.8%, and 71.1% at 1, 5, and 8 years, respectively. No difference was found in survival between the 2 groups (P > .05). However, complete thrombosis of the false lumen in the proximal descending aorta was achieved in 100% of the ER group and 24.6% of the PR group (P < .001). For patients with a patent false lumen in the PR group, distal anastomosis leakage and unclosed small intimal tears were identified in 53.3% and 35.6% patients, respectively. The reintervention rate was also lower in the ER group than in the PR group (4.9% vs 15.9%, P < .05) during follow-up. Moreover, the reintervention rate for patients with Marfan syndrome was 9.5% in the ER group and 38.5% in the PR group (P < .05).ConclusionsFor patients with acute type A dissection without an intimal tear in the arch, extensive repair could promote the occlusion of distal false lumen and decrease the reintervention rate without increasing the operative risk

Evaluation of genetic susceptibility of common variants in CACNA1D with schizophrenia in Han Chinese

The heritability of schizophrenia (SCZ) has been estimated to be as high as 80%, suggesting that genetic factors may play an important role in the etiology of SCZ. Cav1.2 encoded by CACNA1C and Cav1.3 encoded by CACNA1D are dominant calcium channel-forming subunits of L-type Voltage-dependent Ca(2+) channels, expressed in many types of neurons. The CACNA1C has been consistently found to be a risk gene for SCZ, but it is unknown for CACNA1D. To investigate the association of CACNA1D with SCZ, we designed a two-stage case-control study, including a testing set with 1117 cases and 1815 controls and a validation set with 1430 cases and 4295 controls in Han Chinese. A total of selected 97 tag single nucleotide polymorphisms (SNPs) in CACNA1D were genotyped, and single-SNP association, imputation analysis and gender-specific association analyses were performed in the two independent datasets. None was found to associate with SCZ. Further genotype and haplotype association analyses indicated a similar pattern in the two-stage study. Our findings suggested CACNA1D might not be a risk gene for SCZ in Han Chinese population, which add to the current state of knowledge regarding the susceptibility of CACNA1D to SCZ

Evaluation of voltage-dependent calcium channel gamma gene families identified several novel potential susceptible genes to schizophrenia

Voltage-gated L-type calcium channels (VLCC) are distributed widely throughout the brain. Among the genes involved in schizophrenia (SCZ), genes encoding VLCC subunits have attracted widespread attention. Among the four subunits comprising the VLCC (α − 1, α −2/δ, β, and γ), the γ subunit that comprises an eight-member protein family is the least well understood. In our study, to further investigate the risk susceptibility by the γ subunit gene family to SCZ, we conducted a large-scale association study in Han Chinese individuals. The SNP rs17645023 located in the intergenic region of CACNG4 and CACNG5 was identified to be significantly associated with SCZ (OR = 0.856, P = 5.43 × 10(−5)). Similar results were obtained in the meta-analysis with the current SCZ PGC data (OR = 0.8853). We also identified a two-SNP haplotype (rs10420331-rs11084307, P = 1.4 × 10(−6)) covering the intronic region of CACNG8 to be significantly associated with SCZ. Epistasis analyses were conducted, and significant statistical interaction (OR = 0.622, P = 2.93 × 10(−6), P(perm) < 0.001) was observed between rs192808 (CACNG6) and rs2048137 (CACNG5). Our results indicate that CACNG4, CACNG5, CACNG6 and CACNG8 may contribute to the risk of SCZ. The statistical epistasis identified between CACNG5 and CACNG6 suggests that there may be an underlying biological interaction between the two genes

Million-scale Object Detection with Large Vision Model

Over the past few years, there has been growing interest in developing a broad, universal, and general-purpose computer vision system. Such a system would have the potential to solve a wide range of vision tasks simultaneously, without being restricted to a specific problem or data domain. This is crucial for practical, real-world computer vision applications. In this study, we focus on the million-scale multi-domain universal object detection problem, which presents several challenges, including cross-dataset category label duplication, label conflicts, and the need to handle hierarchical taxonomies. Furthermore, there is an ongoing challenge in the field to find a resource-efficient way to leverage large pre-trained vision models for million-scale cross-dataset object detection. To address these challenges, we introduce our approach to label handling, hierarchy-aware loss design, and resource-efficient model training using a pre-trained large model. Our method was ranked second in the object detection track of the Robust Vision Challenge 2022 (RVC 2022). We hope that our detailed study will serve as a useful reference and alternative approach for similar problems in the computer vision community. The code is available at https://github.com/linfeng93/Large-UniDet.Comment: This paper is revised by ChatGP

Effect of Asafoetida Extract on Growth and Quality of Pleurotus ferulic

Different concentrations of asafoetida extract were added to the medium of Pleurotus ferulic and the effects of the extract on growth of P. ferulic mycelium and fruiting bodies was observed. As the amount of asafoetida extract additive was increased, the growth of Pleurotus mycelium was faster, the time formation of buds was shorter and that yield of fruiting bodies was stimulated. However, overdosing of asafoetida extract hampered the growth of Pleurotus ferulic. The amino acid composition and volatile components in three kinds of pleurotus’ were contrasted, including wild pleurotus (WP), cultivated pleurotus with asafoetida extract (CPAE) and cultivated pleurotus without asafoetida extract (CP). CPAE with 2.3 g/100 g asafoetida extract addition had the highest content of total amino acids, as well as essential amino acids. WP had a higher content of total amino acids and essential amino acids than CP. In addition, CPAE with 2.3 g/100 g had the highest score of protein content of pleurotus fruiting bodies, while WP had a higher score than CP. In the score of essential amino acid components of pleurotus fruiting bodies, CP had the highest score, while CPAE was higher than WP. Asafoetida extract influenced the volatile components of Pleurotus ferulic greatly, making the volatile components of cultivated pleurotus more similar to those of wild pleurotus (WP)

Electrochemical Conversion of Methane to Ethylene in a Solid Oxide Electrolyer

Conversion of methane to ethylene with high yield remains a fundamental challenge due to the low ethylene selectivity, severe carbon deposition and instability of catalysts. Here we demonstrate a conceptually different process of in situ electrochemical oxidation of methane to ethylene in a solid oxide electrolyzer under ambient pressure at 850 °C. The porous electrode scaffold with an in situ-grown metal/oxide interface enhances coking resistance and catalyst stability at high temperatures. The highest C2 product selectivity of 81.2% together with the highest C2 product concentration of 16.7% in output gas (12.1% ethylene and 4.6% ethane) is achieved while the methane conversion reaches as high as 41% in the initial pass. This strategy provides an optimal performance with no obvious degradation being observed after 100 h of high temperature operation and 10 redox cycles, suggesting a reliable electrochemical process for conversion of methane into valuable chemicals

Highly Efficient Electrochemical Reforming of CH\u3csub\u3e4\u3c/sub\u3e/CO\u3csub\u3e2\u3c/sub\u3e in a Solid Oxide Electrolyser

Reforming CH4 into syngas using CO2 remains a fundamental challenge due to carbon deposition and nanocatalyst instability. We, for the first time, demonstrate highly efficient electrochemical reforming of CH4/CO2 to produce syngas in a solid oxide electrolyser with CO2 electrolysis in the cathode and CH4 oxidation in the anode. In situ exsolution of an anchored metal/oxide interface on perovskite electrode delivers remarkably enhanced coking resistance and catalyst stability. In situ Fourier transform infrared characterizations combined with first principle calculations disclose the interface activation of CO2 at a transition state between a CO2 molecule and a carbonate ion. Carbon removal at the interfaces is highly favorable with electrochemically provided oxygen species, even in the presence of H2 or H2O. This novel strategy provides optimal performance with no obvious degradation after 300 hours of high-temperature operation and 10 redox cycles, suggesting a reliable process for conversion of CH4 into syngas using CO2