Zooplankton community structure in the Yellow Sea and East China Sea in autumn

Study on zooplankton spatial distribution is essential for understanding food web dynamics in marine ecosystems and fishery management. Here we elucidated the composition and distribution of large mesozooplankton on the continental shelf of the Yellow Sea and East China Sea, and explored the zooplankton community structure in these water masses. Sixty vertical hauls (bottom or 200 m in deep water to surface) using a ring net (diameter 0.8 m, 505-μm mesh) were exploited in November 2007. The biogeographic patterns of zooplankton communities were investigated using multivariate analysis methods; copepod biodiversity was analyzed using univariate indices. Copepods and protozoans were dominate in the communities. Based on the species composition, we divided the study areas into six station groups. Significant differences in zooplankton assemblages were detected between the Yellow Sea and East China Sea. Species richness was higher in East China Sea groups than those in Yellow Sea, whereas taxonomic distinctness was higher in Yellow Sea than in East China Sea. There was a clear relationship between the species composition and water mass group.O estudo da distribuição espacial do zooplancton é essencial para o entendimento não só da dinâmica das teias tróficas nos ecossistemas marinhos, mas também para o manejo da pesca. Neste trabalho procuramos elucidar a composição e distribuição do mesozooplancton na plataforma continental do Mar Amarelo e do Mar da China Oriental, e explorar a estruturas das comunidades nessas duas massas de água. Sessenta arrastos verticais (do fundo ou de 200m até a superfície) foram realizados em Novembro de 2007, usando uma rede circular com diâmetro de 0,8m e malhagem de 505μm. Os padrões biogeográficos das comunidades do zooplancton foram investigados, utilizando-se métodos de análise multivariada. A biodiversidade de Copepoda foi analisada através de indices univariados. Copéodes e protozoários foram os organismos dominantes nas amostragens. Baseados na composição de espécies, pudemos dividir a área de estudo em seis grupos de estações. Diferenças significantes nas assembléias de zooplancton foram detectadas entre o Mar Amarelo e o Mar da China Oriental. A riqueza de espécies foi mais elevada nesta última área, enquanto a distinção taxonômica foi mais alta no Mar Amarelo. Houve uma clara relação entre composição de espécies e tipo de massa de água

Comparison of the Methods for Converting Traditional Credit Rating into the Initial Credit Score in Electronic Commerce

When a company undertakes e-commerce transactions for the first time, most major web sites set the initial credit score of the company as zero, which making buyers and sellers can’t judge the partners’ credibility. In recent years, although commercial banks and some specialized credit rating agencies have established more comprehensive and scientific indicators for evaluating the credit of an enterprise, few scholars apply such credit evaluation indicators to the credit management of e-commerce business. Yu Yang and Guangxing Song (2009) put forward a method to convert the traditional credit rating from the credit rating agency—Standard & Poor’s into the initial credit score. Based on this work, the authors convert the traditional credit rating from the credit rating agency—Moody’s into the initial credit score, and compare these two methods, hoping to encourage companies with rating score to participate in e-commerce transactions with true identity. On the background of current internet real-name system implementation, our research is very important for enterprise credit management

Few-Shot Object Detection with Fully Cross-Transformer

Few-shot object detection (FSOD), with the aim to detect novel objects using very few training examples, has recently attracted great research interest in the community. Metric-learning based methods have been demonstrated to be effective for this task using a two-branch based siamese network, and calculate the similarity between image regions and few-shot examples for detection. However, in previous works, the interaction between the two branches is only restricted in the detection head, while leaving the remaining hundreds of layers for separate feature extraction. Inspired by the recent work on vision transformers and vision-language transformers, we propose a novel Fully Cross-Transformer based model (FCT) for FSOD by incorporating cross-transformer into both the feature backbone and detection head. The asymmetric-batched cross-attention is proposed to aggregate the key information from the two branches with different batch sizes. Our model can improve the few-shot similarity learning between the two branches by introducing the multi-level interactions. Comprehensive experiments on both PASCAL VOC and MSCOCO FSOD benchmarks demonstrate the effectiveness of our model.Comment: Accepted by CVPR 202

Multimodal Few-Shot Object Detection with Meta-Learning Based Cross-Modal Prompting

We study multimodal few-shot object detection (FSOD) in this paper, using both few-shot visual examples and class semantic information for detection. Most of previous works focus on either few-shot or zero-shot object detection, ignoring the complementarity of visual and semantic information. We first show that meta-learning and prompt-based learning, the most commonly-used methods for few-shot learning and zero-shot transferring from pre-trained vision-language models to downstream tasks, are conceptually similar. They both reformulate the objective of downstream tasks the same as the pre-training tasks, and mostly without tuning the parameters of pre-trained models. Based on this observation, we propose to combine meta-learning with prompt-based learning for multimodal FSOD without fine-tuning, by learning transferable class-agnostic multimodal FSOD models over many-shot base classes. Specifically, to better exploit the pre-trained vision-language models, the meta-learning based cross-modal prompting is proposed to generate soft prompts and further used to extract the semantic prototype, conditioned on the few-shot visual examples. Then, the extracted semantic prototype and few-shot visual prototype are fused to generate the multimodal prototype for detection. Our models can efficiently fuse the visual and semantic information at both token-level and feature-level. We comprehensively evaluate the proposed multimodal FSOD models on multiple few-shot object detection benchmarks, achieving promising results.Comment: 22 page

Foreword Remote Sensing for Environmental Sustainability in the Asian–Pacific Region

The papers in this special section examine the use of remote sensing technology to promote environmental sustainability in Asia-Pacific regions. Worldwide urbanization and deforestation are the two main interconnected ways that human activities are continually changing and reshaping the earth's surface. How earth observation and remote sensing technologies can contribute to improve the knowledge of the productivity and sustainability of natural and human ecosystems is an important theme in the global change community. In China, for instance, rapid economic growth and urbanization over the past three decades have resulted in dramatic changes in land use and land cover and have led to severe environmental consequences, which have made China's sustainable development a grand challenge. In the meantime, during the past few decades, environmental changes in the Asian–Pacific region have posed significant challenges to the scientific community. Therefore, the global problem of how earth observation and remote sensing technologies may be applied to assessing, monitoring, modeling, and simulating ecosystems, environments, and resources at various spatial and temporal scales translates into peculiar and very urgent questions and applications in this colossal and dynamic geographical region

TempCLR: Temporal Alignment Representation with Contrastive Learning

Video representation learning has been successful in video-text pre-training for zero-shot transfer, where each sentence is trained to be close to the paired video clips in a common feature space. For long videos, given a paragraph of description where the sentences describe different segments of the video, by matching all sentence-clip pairs, the paragraph and the full video are aligned implicitly. However, such unit-level similarity measure may ignore the global temporal context over a long time span, which inevitably limits the generalization ability. In this paper, we propose a contrastive learning framework TempCLR to compare the full video and the paragraph explicitly. As the video/paragraph is formulated as a sequence of clips/sentences, under the constraint of their temporal order, we use dynamic time warping to compute the minimum cumulative cost over sentence-clip pairs as the sequence-level distance. To explore the temporal dynamics, we break the consistency of temporal order by shuffling the video clips or sentences according to the temporal granularity. In this way, we obtain the representations for clips/sentences, which perceive the temporal information and thus facilitate the sequence alignment. In addition to pre-training on the video and paragraph, our approach can also generalize on the matching between different video instances. We evaluate our approach on video retrieval, action step localization, and few-shot action recognition, and achieve consistent performance gain over all three tasks. Detailed ablation studies are provided to justify the approach design

Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates

Landsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonstrates the forest biomass estimation methods and uncertainty analysis. Results indicate that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems. LiDAR can overcome TM’s shortcoming providing better biomass estimation performance but has not been extensively applied in practice due to data availability constraints. The uncertainty analysis indicates that various sources affect the performance of forest biomass/carbon estimation. With that said, the clear dominate sources of uncertainty are the variation of input sample plot data and data saturation problem related to optical sensors. A possible solution to increasing the confidence in forest biomass estimates is to integrate the strengths of multisensor data

An Expanded Three Band Model to Monitor Inland Optically Complex Water Using Geostationary Ocean Color Imager (GOCI)

Due to strict spectral band requirements, the three-band (TB) chlorophyll-a concentration (Cchla) estimation algorithm cannot be applied to GOCI image, which has great potential in frequently monitoring inland complex waters. In this study, the TB algorithm was expanded and applied to GOCI data. The GOCI TB algorithm was subsequently calibrated using an in-situ dataset which contains 281 samples collected from 17 inland lakes in China between 2013 and 2020. MERIS TB and GOCI band ratio (BR) models were selected as comparisons to assess the proposed model. The results showed that the proposed GOCI TB model has similar accuracy with MERIS TB model and overperformed GOCI BR model. The root mean square error (RMSE) of the GOCI TB, MERIS TB, and GOCI BR algorithms are 14.212 μg/L, 12.096 μg/L, and 20.504 μg/L, respectively. The mean absolute percentage error (MAPE) (when Cchla is larger than 10 μg/L) of the three models were 0.377, 0.250, and 0.453, respectively. Similar conclusion could be drawn from a match-up dataset containing 40 samples. Finally, a simulation experiment was carried out to analyze the robustness of the models under various total suspended matter concentration (CTSM) conditions. Both the in-situ validation and simulation experiment indicated that the GOCI TB factor could effectively eliminate the optical influence of CTSM. Furthermore, the broader spectral range requirement of GOCI TB model made it proper for many other multispectral sensors such as Sentinel two Multispectral Instrument (S2 MSI), Moderate Resolution Imaging Spectroradiometer (MODIS) (onboard the Terra/Aqua satellite), and Visible Infrared Imaging Radiometer Suite (VIIRS) (onboard the National Polar-orbiting Partnership satellite). Compared with the GOCI BR algorithm, the GOCI TB algorithm has stronger stability, better accuracy, and greater potential in practice

Hypericin Inhibit Alpha-Coronavirus Replication by Targeting 3CL Protease

The porcine epidemic diarrhea virus (PEDV) is an Alphacoronavirus (α-CoV) that causes high mortality in infected piglets, resulting in serious economic losses in the farming industry. Hypericin is a dianthrone compound that has been shown as an antiviral activity on several viruses. Here, we first evaluated the antiviral effect of hypericin in PEDV and found the viral replication and egression were significantly reduced with hypericin post-treatment. As hypericin has been shown in SARS-CoV-2 that it is bound to viral 3CLpro, we thus established a molecular docking between hypericin and PEDV 3CLpro using different software and found hypericin bound to 3CLpro through two pockets. These binding pockets were further verified by another docking between hypericin and PEDV 3CLpro pocket mutants, and the fluorescence resonance energy transfer (FRET) assay confirmed that hypericin inhibits the PEDV 3CLpro activity. Moreover, the alignments of α-CoV 3CLpro sequences or crystal structure revealed that the pockets mediating hypericin and PEDV 3CLpro binding were highly conserved, especially in transmissible gastroenteritis virus (TGEV). We then validated the anti-TGEV effect of hypericin through viral replication and egression. Overall, our results push forward that hypericin was for the first time shown to have an inhibitory effect on PEDV and TGEV by targeting 3CLpro, and it deserves further attention as not only a pan-anti-α-CoV compound but potentially also as a compound of other coronaviral infections