Adaptive Tag Selection for Image Annotation

Not all tags are relevant to an image, and the number of relevant tags is image-dependent. Although many methods have been proposed for image auto-annotation, the question of how to determine the number of tags to be selected per image remains open. The main challenge is that for a large tag vocabulary, there is often a lack of ground truth data for acquiring optimal cutoff thresholds per tag. In contrast to previous works that pre-specify the number of tags to be selected, we propose in this paper adaptive tag selection. The key insight is to divide the vocabulary into two disjoint subsets, namely a seen set consisting of tags having ground truth available for optimizing their thresholds and a novel set consisting of tags without any ground truth. Such a division allows us to estimate how many tags shall be selected from the novel set according to the tags that have been selected from the seen set. The effectiveness of the proposed method is justified by our participation in the ImageCLEF 2014 image annotation task. On a set of 2,065 test images with ground truth available for 207 tags, the benchmark evaluation shows that compared to the popular top-$k$ strategy which obtains an F-score of 0.122, adaptive tag selection achieves a higher F-score of 0.223. Moreover, by treating the underlying image annotation system as a black box, the new method can be used as an easy plug-in to boost the performance of existing systems

Efficiency Measurement and Spatial Spillover Effect of Green Agricultural Development in China

Green agriculture is mainstream for the sustainable development of agriculture. Based on the Chinese provincial agriculture panel data from 2010 to 2019, we adopted the slack-based measure (SBM) super-efficiency model, sales force automation (SFA) model, and global malmquist–luenberger (GML) production index to measure the efficiency of agricultural green development (AGD). Moreover, Moran’s I and spatial econometric model were applied to analyze factors influencing AGD. The threshold model was used to analyze the relationship between the scale of AGD and gross domestic product (GDP). The results show that 1) Chinese green agricultural development efficiency is on a rising trend, reducing the impact of environmental factors and random interference on the AGD. 2) The analysis of AGD in the spatial effect showed a direct positive effect from agricultural mechanization, science and technology innovation, industrial agglomeration, income level, and environmental rule and a direct negative effect from agricultural yield structure, farmland pollution, and agricultural disasters. Furthermore, industrial structure optimization and environmental rule evoke a demonstration effect, but technical innovation, income level, and agricultural industrial agglomeration triggered a siphonic effect. 3) The threshold model was used to analyze the scale of AGD to realize sustainable development between agriculture and economy

Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study

The flourishing blossom of deep learning has witnessed the rapid development of text recognition in recent years. However, the existing text recognition methods are mainly proposed for English texts. As another widely-spoken language, Chinese text recognition (CTR) in all ways has extensive application markets. Based on our observations, we attribute the scarce attention on CTR to the lack of reasonable dataset construction standards, unified evaluation protocols, and results of the existing baselines. To fill this gap, we manually collect CTR datasets from publicly available competitions, projects, and papers. According to application scenarios, we divide the collected datasets into four categories including scene, web, document, and handwriting datasets. Besides, we standardize the evaluation protocols in CTR. With unified evaluation protocols, we evaluate a series of representative text recognition methods on the collected datasets to provide baselines. The experimental results indicate that the performance of baselines on CTR datasets is not as good as that on English datasets due to the characteristics of Chinese texts that are quite different from the Latin alphabet. Moreover, we observe that by introducing radical-level supervision as an auxiliary task, the performance of baselines can be further boosted. The code and datasets are made publicly available at https://github.com/FudanVI/benchmarking-chinese-text-recognitionComment: Code is available at https://github.com/FudanVI/benchmarking-chinese-text-recognitio

Spatiotemporal Distribution of Eutrophication in Lake Tai as Affected by Wind

One common hypothesis is that wind can affect concentrations of nutrients (i.e., nitrogen and phosphorus) and chlorophyll-a (Chl-a) in shallow lakes. However, the tests of this hypothesis have yet to be conclusive in existing literature. The objective of this study was to use long-term data to examine how wind direction and wind speed affect the spatiotemporal variations of total nitrogen (TN), total phosphorus (TP) and Chl-a in Lake Tai, a typical shallow lake located in east China. The results indicated that the concentrations of nutrients and Chl-a tended to decrease from the northwest to the southeast of Lake Tai, with the highest concentrations in the two leeward bays (namely Meiliang Bay and Zhushan Bay) in the northwestern part of the lake. In addition to possible artificial reasons (e.g., wastewater discharge), the prevalent southeastward winds in warm seasons (i.e., spring and summer) and northwestward winds in cool seasons (i.e., fall and winter) might be the major natural factor for such a northwest-southeast decreasing spatial pattern. For the lake as a whole, the concentrations of TN, TP and Chl-a were highest for a wind speed between 2.1 and 3.2 m·s-1, which can be attributed to the idea that the wind-induced drifting and mixing effects might be dominant in the bays while the wind-induced drifting and resuspension effects could be more important in the other parts of the lake. Given that the water depth of the bays was relatively larger than that of the other parts, the drifting and mixing effects were likely dominant in the bays, as indicated by the negative relationships between the ratios of wind speed to lake depth, which can be a surrogate for the vertical distribution of wind-induced shear stress and the TN, TP and Chl-a concentration. Moreover, the decreasing temporal trend of wind speed in combination with the ongoing anthropogenic activities will likely increase the challenge for dealing with the eutrophication problem of Lake Tai. © 2017 by the authors

Effects of Hydrological and Climatic Variables on Cyanobacterial Blooms in Four Large Shallow Lakes Fed by the Yangtze River

Shallow lakes, one of the most widespread water bodies in the world, are easily shifted to a new trophic state due to external interferences. Shifting hydrologic conditions and climate change can cause cyanobacterial harmful algal blooms (CyanoHABs) in shallow lakes, which pose serious threats to ecological integrity and human health. This study analyzed the effects of hydrologic and meteorological variables on cyanobacterial blooms in Yangtze-connected lakes (Lake Dongting and Poyang) and isolated lakes (Lake Chao and Tai). The results show that (i) chlorophyll-a (Chl-a) concentration tends to decrease exponentially with increasing relative lake level fluctuations (RLLF) and precipitation, but to increase linearly with increasing wind speed and air temperature; (ii) Chl-a concentrations in lakes were significantly higher when RLLF \u3c 100, precipitation \u3c 2.6 mm, wind speed \u3e 2.6 m s−1, or air temperature \u3e 17.8 °C; (iii) the Chl-a concentration of Yangtze-isolated lakes was more significantly affected by water level amplitude, precipitation, wind speed and air temperature than the Yangtze-connected lakes; (iv) the RLLF and the ratio of wind speed to mean water depth could be innovative coupling factors to examine variation characteristics of Chl-a in shallow lakes with greater correlation than single factors

Effect of Non-sodium Salt Substitution on the Properties and Flavor of Coix Starch-Myofibrillar Protein Composite Gel

Objective: To investigate the effect of calcium, magnesium and potassium salts on the properties of coix seed starch-myofibrillar protein (CSS-MP) composite gel with low sodium content. Methods: Based on the results of previous research, the effects of partial substitution of NaCl by CaCl2, MgCl2 or KCl on the gel strength, water holding capacity (WHC), rheological properties, surface hydrophobicity, sulfhydryl group content, gel microstructure and sensory evaluation of CSS-MP composite gel were investigated. Results: The texture and rheological properties of the CaCl2 substitution group were better than those of the control group (3% NaCl), and the WHC and whiteness of the KCl substitution group were not significantly different from those of the control group, but the gel strength was decreased by MgCl2 or KCl substitution. The total sulfhydryl content of the CaCl2 and MgCl2 replacement groups was significantly lower than that of the control group, and the surface hydrophobicity of the CaCl2 replacement group was significantly higher than that of the other three groups. The addition of MgCl2 promoted the formation of hydrogen bonds, enhanced the hydrophobic interaction, while CaCl2 substitution had the opposite effect. In terms of sensory properties, the substitution of 1.0% CaCl2 and 0.5% MgCl2 could attain a saltiness perception similar to 3% NaCl. However, the comprehensive sensory score showed that the substitution of the three chlorine salts caused no significant difference in sensory properties of CSS-MP composite gel. Conclusion: Calcium, magnesium and potassium salts can improve the properties of CSS-MP composite gel in different degrees. The results of this study lay a theoretical foundation for the development of low-sodium composite surimi gel products

N-myristoylation of Antimicrobial Peptide CM4 Enhances Its Anticancer Activity by Interacting With Cell Membrane and Targeting Mitochondria in Breast Cancer Cells

Development of antimicrobial peptides (AMPs) as highly effective and selective anticancer agents would represent great progress in cancer treatment. Here we show that myristoyl-CM4, a new synthetic analog generated by N-myristoylation of AMPs CM4, had anticancer activity against MCF-7, MDA-MB-231, MX-1 breast cancer cells (IC50 of 3–6 μM) and MDA-MB-231 xenograft tumors. The improved activity was attributed to the effect of myristoyl on the cell membrane. Flow cytometry and confocal laser scanning microscopy results showed that N-myristoylation significantly increased the membrane affinity toward breast cancer cells and also effectively mediated cellular entry. Despite increasing cytotoxicity against HEK293 and NIH3T3 cells and erythrocytes associated with its anticancer activity, myristoyl-CM4 maintained a certain selectivity toward breast cancer cells. Accordingly, the membrane affinity toward breast cancer cells was two to threefold higher than that of normal cells. Glycosylation analysis showed that sialic acid-containing oligosaccharides (including O-mucin and gangliosides) were important targets for myristoyl-CM4 binding to breast cancer cells. After internalization, co-localization analysis revealed that myristoyl-CM4 targeted mitochondria and induced mitochondrial dysfunction, including alterations in mitochondrial transmembrane potential, reactive oxygen species (ROS) generation and cytochrome c release. Activation of caspase 9, caspase 3 and cleavage of PARP were observed in MX-1, MCF-7, and MDA-MB-231 cells after myristoyl-CM4 treatment. The current work indicates that increasing hydrophobicity by myristoylation to modulate peptide-membrane interactions and then target mitochondria is a good strategy to develop AMPs as anticancer agents in the future