Plastic Responses in Growth, Morphology, and Biomass Allocation of Five Subtropical Tree Species to Different Degrees of Shading

We investigated how different degrees of shading affected growth, morphology, and biomass allocation in seedlings from two coniferous and three broadleaved species. The experiment was conducted in a shade house over a 1-year period. Our results showed that under increasing shade, seedlings from most species exhibited lower total biomass, net assimilation rates, relative growth rates, root mass ratios, and root/shoot ratios. In contrast, the slenderness quotients, leaf area ratios, and specific leaf areas increased with increasing shade. For coniferous species, growth traits were relatively more plastic (responsive to shade) than morphology or biomass allocation traits, whereas for broadleaved species, growth and biomass allocation were the most shade-sensitive traits. When comparing coniferous versus broadleaved species, the former had a higher growth plasticity index and lower allocation plasticity than the latter. Root biomass and stem mass ratio were the most and least plastic traits in response to shading. Our results indicate that shade differentially affects coniferous and broadleaved species in terms of their growth, morphology, and biomass allocation. These findings have important implications for the establishment and maintenance of mixed-species stands

Anchor3DLane: Learning to Regress 3D Anchors for Monocular 3D Lane Detection

Monocular 3D lane detection is a challenging task due to its lack of depth information. A popular solution is to first transform the front-viewed (FV) images or features into the bird-eye-view (BEV) space with inverse perspective mapping (IPM) and detect lanes from BEV features. However, the reliance of IPM on flat ground assumption and loss of context information make it inaccurate to restore 3D information from BEV representations. An attempt has been made to get rid of BEV and predict 3D lanes from FV representations directly, while it still underperforms other BEV-based methods given its lack of structured representation for 3D lanes. In this paper, we define 3D lane anchors in the 3D space and propose a BEV-free method named Anchor3DLane to predict 3D lanes directly from FV representations. 3D lane anchors are projected to the FV features to extract their features which contain both good structural and context information to make accurate predictions. In addition, we also develop a global optimization method that makes use of the equal-width property between lanes to reduce the lateral error of predictions. Extensive experiments on three popular 3D lane detection benchmarks show that our Anchor3DLane outperforms previous BEV-based methods and achieves state-of-the-art performances. The code is available at: https://github.com/tusen-ai/Anchor3DLane.Comment: Accepted by CVPR 202

Phenotypic Plasticity of Cunninghamia lanceolata (Lamb.) Hook. Seedlings in Response to Varied Light Quality Treatments

Effects of light quality on phenotypic plasticity in Cunninghamialanceolata (Lamb.) Hook. seedlings during growth and development, and the underlying mechanisms, were investigated. The seedlings showed distinct morphological adjustments when exposed to an equal photosynthetic photon flux density (400 mu mol.m(-2).s(-1)) of different light qualities: monochromatic blue (BL), monochromatic red (RL), monochromatic far-red (FrL), mixed RL and FrL at 1:1 (RFr1:1L), mixed RL and FrL at 1:2 (RFr1:2L), and multi-wavelength white (WL, control). Compared with WL, FrL and BL significantly promoted height increment. However, BL was unfavorable for root growth. The seedling biomass was lower and the root-to-shoot ratio was smaller under BL. RL promoted leaf area enlargement, root growth, axillary bud number, and increased the root-to-shoot ratio, but inhibited stem elongation. Low R/Fr ratios or increased FrL proportion increased seedling stem elongation. The seedling growth under RFr1:1L treatment was poorer than that under other treatments; however, the number of axillary buds was the highest. The plasticity of leaf morphology traits was lower in different treatments, and that of axillary bud traits was crucial in the adaptation of C. lanceolata to light quality. Precise management of light quality and wavelength in controlled environments may maximize the economic efficiency of forest production and enhance its quality

Referring Image Segmentation via Cross-Modal Progressive Comprehension

Referring image segmentation aims at segmenting the foreground masks of the entities that can well match the description given in the natural language expression. Previous approaches tackle this problem using implicit feature interaction and fusion between visual and linguistic modalities, but usually fail to explore informative words of the expression to well align features from the two modalities for accurately identifying the referred entity. In this paper, we propose a Cross-Modal Progressive Comprehension (CMPC) module and a Text-Guided Feature Exchange (TGFE) module to effectively address the challenging task. Concretely, the CMPC module first employs entity and attribute words to perceive all the related entities that might be considered by the expression. Then, the relational words are adopted to highlight the correct entity as well as suppress other irrelevant ones by multimodal graph reasoning. In addition to the CMPC module, we further leverage a simple yet effective TGFE module to integrate the reasoned multimodal features from different levels with the guidance of textual information. In this way, features from multi-levels could communicate with each other and be refined based on the textual context. We conduct extensive experiments on four popular referring segmentation benchmarks and achieve new state-of-the-art performances.Comment: Accepted by CVPR 2020. Code is available at https://github.com/spyflying/CMPC-Refse

Achievements and Challenges in Improving Air Quality in China: Analysis of the Long-Term Trends from 2014 to 2022

Due to the implementation of air pollution control measures in China, air quality has significantly improved, although there are still additional issues to be addressed. This study used the long-term trends of air pollutants to discuss the achievements and challenges in further improving air quality in China. The Kolmogorov-Zurbenko (KZ) filter and multiple-linear regression (MLR) were used to quantify the meteorology-related and emission-related trends of air pollutants from 2014 to 2022 in China. The KZ filter analysis showed that PM2.5 decreased by 7.36 ± 2.92% yr􀀀 1, while daily maximum 8-h ozone (MDA8 O3) showed an increasing trend with 3.71 ± 2.89% yr􀀀 1 in China. The decrease in PM2.5 and increase in MDA8 O3 were primarily attributed to changes in emission, with the relative contribution of 85.8% and 86.0%, respectively. Meteorology variations, including increased ambient temperature, boundary layer height, and reduced relative humidity, also contributed to the reduction of PM2.5 and the enhancement of MDA8 O3. The emission-related trends of PM2.5 and MDA8 O3 exhibited continuous decrease and increase, respectively, from 2014 to 2022, while the variation rates slowed during 2018–2020 compared to that during 2014–2017, highlighting the challenges in further improving air quality, particularly in simultaneously reducing PM2.5 and O3. This study recommends reducing NH3 emissions from the agriculture sector in rural areas and transport emissions in urban areas to further decrease PM2.5 levels. Addressing O3 pollution requires the reduction of O3 precursor gases based on site-specific atmospheric chemistry considerations