GAN-based Image Compression with Improved RDO Process

GAN-based image compression schemes have shown remarkable progress lately due to their high perceptual quality at low bit rates. However, there are two main issues, including 1) the reconstructed image perceptual degeneration in color, texture, and structure as well as 2) the inaccurate entropy model. In this paper, we present a novel GAN-based image compression approach with improved rate-distortion optimization (RDO) process. To achieve this, we utilize the DISTS and MS-SSIM metrics to measure perceptual degeneration in color, texture, and structure. Besides, we absorb the discretized gaussian-laplacian-logistic mixture model (GLLMM) for entropy modeling to improve the accuracy in estimating the probability distributions of the latent representation. During the evaluation process, instead of evaluating the perceptual quality of the reconstructed image via IQA metrics, we directly conduct the Mean Opinion Score (MOS) experiment among different codecs, which fully reflects the actual perceptual results of humans. Experimental results demonstrate that the proposed method outperforms the existing GAN-based methods and the state-of-the-art hybrid codec (i.e., VVC)

Molecular Cloning and Functional Characterization of the Dehydrin (IpDHN) Gene From Ipomoea pes-caprae

Dehydrin (DHN) genes can be rapidly induced to offset water deficit stresses in plants. Here, we reported on a dehydrin gene (IpDHN) related to salt tolerance isolated from Ipomoea pes-caprae L. (Convolvulaceae). The IpDHN protein shares a relatively high homology with Arabidopsis dehydrin ERD14 (At1g76180). IpDHN was shown to have a cytoplasmic localization pattern. Quantitative RT-PCR analyses indicated that IpDHN was differentially expressed in most organs of I. pes-caprae plants, and its expression level increased after salt, osmotic stress, oxidative stress, cold stress and ABA treatments. Analysis of the 974-bp promoter of IpDHN identified distinct cis-acting regulatory elements, including an MYB binding site (MBS), ABRE (ABA responding)-elements, Skn-1 motif, and TC-rich repeats. The induced expression of IpDHN in Escherichia coli indicated that IpDHN might be involved in salt, drought, osmotic, and oxidative stresses. We also generated transgenic Arabidopsis lines that over-expressed IpDHN. The transgenic Arabidopsis plants showed a significant enhancement in tolerance to salt/drought stresses, as well as less accumulation of hydrogen peroxide (H2O2) and the superoxide radical (O2−), accompanied by increasing activity of the antioxidant enzyme system in vivo. Under osmotic stresses, the overexpression of IpDHN in Arabidopsis can elevate the expression of ROS-related and stress-responsive genes and can improve the ROS-scavenging ability. Our results indicated that IpDHN is involved in cellular responses to salt and drought through a series of pleiotropic effects that are likely involved in ROS scavenging and therefore influence the physiological processes of microorganisms and plants exposed to many abiotic stresses

Dynamic Compensation Method for Humidity Sensors Based on Temperature and Humidity Decoupling

Currently, integrated humidity sensors with fast-response time are widely needed. The most commonly used polyimide capacitive humidity sensor has a long response time, which is difficult to meet the need for a fast response. Most studies focusing on technology and materials have a high cost and are difficult to ensure compatability with the CMOS process. The dynamic compensation method can shorten the response time by only adding digital circuits or software processing. However, conventional compensation technology is not suitable for humidity sensors due to temperature coupling. This paper proposes a new dynamic compensation method for humidity sensors based on the decoupling of temperature factors by analyzing the coupling relationship between sensor dynamic characteristics and temperature. Simulations and experiments were used to verify the proposed method. The experimental results show that the proposed method reduces the humidity response time of the sensor by 85.6%. The proposed method can effectively shorten the response time of humidity sensors

Steady-state analytical model for vapour-phase VOCs diffusion in layered landfill composite cover systems

An analytical model for one-dimensional vapour-phase volatile organic compounds (VOCs) diffusion through four-layered landfill composite cover system consisting of a protective layer, a drainage layer, a geomembrane (GMB), and a compacted clay liner (CCL) is developed. Effects of degree of water saturation (Sr), adsorption, and degradation on vapour-phase VOCs diffusion in cover system were then analyzed. Vapour-phase benzene concentration profile increases with increase of Sr in the drainage layer and protective layer. When Sr1= Sr2=0.5 (Sr1 and Sr2 are degree of water saturation of protective layer and drainage layer, respectively.), surface flux for the case with the degree of water saturation of CCL layer Sr4=0.3 is 1.3 and 1560 times larger than that with Sr4=0.7 and Sr4=0.9, respectively. Effect of adsorption of VOCs in CCL on performance of cover system is more important than that in drainage layer and protective layer. Surface flux and concentration of benzene tends to be zero when CCL is amended with 0.5% biochar due to increase of retardation factor. The effect of degradation rate on benzene concentration increases with increase of degree of water saturation. The influence of half-life of VOCs in soil layer t1/2 on vapour-phase VOCs concentration can be neglected when Srâ ¤0.3.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Bird-Count: a multi-modality benchmark and system for bird population counting in the wild

The fluctuation of the bird population reflects the change in the ecosystem, which plays a vital role in ecosystem conservation. However, manual counting is still the mainstream method for bird population counting, which is time-consuming and laborious. One major bottleneck in developing efficient, accurate, and intelligent learning algorithms to counting birds is the lack of large-scale datasets. In this paper, the first large-scale bird population counting dataset, named Bird-Count, with multi-modality morphology annotations is proposed. This paper first evaluates various state-of-the-art (SOTA) models for crowd counting on the Bird-Count and gets poor results. The reason is that the forms, appearances, and postures among different birds are more variant than the crowd. To mitigate these challenges, a simple yet effective plug-and-play framework, called Morphology Prior Knowledge Fusion Network (MPKNet), which can be used on-site to help generate a high-precision bird population density map by incorporating morphological prior knowledge, is proposed. Comprehensive evaluations show that the proposed method can reduce the error rate by 6.02% compared with the current SOTA crowd counting algorithms on average. Moreover, with the above technologies, the intelligent bird population monitoring system is deployed in several important wetland national nature reserves for bird protection

Molecular Cloning and Functional Characterization of the Dehydrin (IpDHN) Gene From Ipomoea pes-caprae

Dehydrin (DHN) genes can be rapidly induced to offset water deficit stresses in plants. Here, we reported on a dehydrin gene (IpDHN) related to salt tolerance isolated from Ipomoea pes-caprae L. (Convolvulaceae). The IpDHN protein shares a relatively high homology with Arabidopsis dehydrin ERD14 (At1g76180). IpDHN was shown to have a cytoplasmic localization pattern. Quantitative RT-PCR analyses indicated that IpDHN was differentially expressed in most organs of I. pes-caprae plants, and its expression level increased after salt, osmotic stress, oxidative stress, cold stress and ABA treatments. Analysis of the 974-bp promoter of IpDHN identified distinct cis-acting regulatory elements, including an MYB binding site (MBS), ABRE (ABA responding)-elements, Skn-1 motif, and TC-rich repeats. The induced expression of IpDHN in Escherichia coli indicated that IpDHN might be involved in salt, drought, osmotic, and oxidative stresses. We also generated transgenic Arabidopsis lines that over-expressed IpDHN. The transgenic Arabidopsis plants showed a significant enhancement in tolerance to salt/drought stresses, as well as less accumulation of hydrogen peroxide (H2O2) and the superoxide radical (O-2(-)), accompanied by increasing activity of the antioxidant enzyme system in vivo. Under osmotic stresses, the overexpression of IpDHN in Arabidopsis can elevate the expression of ROS-related and stress-responsive genes and can improve the ROS-scavenging ability. Our results indicated that IpDHN is involved in cellular responses to salt and drought through a series of pleiotropic effects that are likely involved in ROS scavenging and therefore influence the physiological processes of microorganisms and plants exposed to many abiotic stresses

Evaluation of linear, nonlinear and ensemble machine learning models for landslide susceptibility assessment in southwest China

Machine learning models are gradually replacing traditional techniques used for landslide susceptibility assessment. This study aims to comprehensively compare multiple models, including linear, nonlinear, and ensemble models, based on 5281 historical landslides in southwest China, the area most severely affected by the landslide disaster. Linear models represented by logistic regression (LR), nonlinear models represented by support vector machine (SVM), artificial neural network (ANN) and classification 5.0 decision tree (C5.0 DT), and ensemble models represented by random forest (RF) and categorical boosting (Catboost) were selected. The correlation coefficient, variance inflation factor (VIF), and relative important analysis were used to select the dominate landslide conditioning factors. Using multiple statistical indicators (e.g. Area Under the Receiver Operating Characteristic curve (AUC) and Kappa), cross-validation and qualitative methods to evaluate the models’ performance. The findings are: (1) Regarding the model predictive performance, the best predictive performance was demonstrated by the ensemble models Catboost (AUC = 0.823 and Kappa = 0.593) and RF (AUC = 0.821 and Kappa = 0.582), followed by the nonlinear models SVM (AUC = 0.775 and Kappa = 0.520), ANN (AUC = 0.770 and Kappa = 0.486) and C5.0 DT (AUC = 0.751 and Kappa = 0.497), while the linear model LR (AUC = 0.756 and Kappa = 0.456) had a more limited performance. The ensemble model, which uses a tree as its baseline classifier, has a lot of potential for studies into the landslide susceptibility. (2) Regarding the model robustness, the three types of models in nonspatial cross-validation (CV) performed relatively similarly in terms of predictive power, while in spatial cross-validation (SPCV), the linear model LR (median AUC = 0.714) achieved better results than the ensemble and nonlinear models. It implies that when the distribution of landslides is not homogeneous, linear models may be the most robust. It is advisable to consider various evaluation metrics from different perspectives and integrate them with specialist qualitative geomorphological empirical knowledge to determine the best model. (3) The Gini index-based RF model suggests that road density was the dominant factor in the frequency of landslides in the study area