In this paper, we are interested in the human pose estimation problem with a focus on learning reliable high-resolution representations. Most existing methods recover high-resolution representations ...from low-resolution representations produced by a high-to-low resolution network. Instead, our proposed network maintains high-resolution representations through the whole process. We start from a high-resolution subnetwork as the first stage, gradually add high-to-low resolution subnetworks one by one to form more stages, and connect the mutli-resolution subnetworks in parallel. We conduct repeated multi-scale fusions such that each of the high-to-low resolution representations receives information from other parallel representations over and over, leading to rich high-resolution representations. As a result, the predicted keypoint heatmap is potentially more accurate and spatially more precise. We empirically demonstrate the effectiveness of our network through the superior pose estimation results over two benchmark datasets: the COCO keypoint detection dataset and the MPII Human Pose dataset. In addition, we show the superiority of our network in pose tracking on the PoseTrack dataset. The code and models have been publicly available at https://github.com/leoxiaobin/deep-high-resolution-net.pytorch.
Imparting mesopores to organic cages of an intrinsic microporous nature to build up hierarchically porous cage soft materials is a grand challenge and will reshape the property and application scope ...of traditional organic cage molecules. Herein, we discovered how to engineer mesopores into microporous organic cages via their host–guest interactions with long chain ionic surfactants. Equally important, the ionic head of surfactants equips the supramolecularly assembled porous structures with charge‐selective uptake and release function in solution. Interestingly, such hierarchically porous organic cage can serve as a nanoreactor once trapping enzymes within the cavity, which show 5‐fold enhanced activity of enzymatic catalysis when compared with the free enzymes.
Engineering mesopores into microporous organic cages to create hierarchically porous organic cage was explored. The beneficial effect of hierarchical pores on the selective adsorption properties and enhanced catalytic performances was demonstrated.
Being conductive and flexible, 2D transition metal nitrides and carbides (MXenes) can serve in Li–S batteries as sulfur hosts to increase the conductivity and alleviate the volume expansion. However, ...the surface functional groups, such as OH and F, weaken the ability of bare MXenes in the chemisorption of polysulfides. Besides, they create numerous hydrogen bonds which make MXenes liable to restack, resulting in substantial loss of active area and, thus, inaccessibility of ions and electrolyte. Herein, a facile, one‐step strategy is developed for the growth of TiO2 quantum dots (QDs) on ultrathin MXene (Ti3C2Tx) nanosheets by cetyltrimethylammonium bromide‐assisted solvothermal synthesis. These QDs act as spacers to isolate the MXene nanosheets from restacking, and preserve their 2D geometry which guarantees larger electrode–electrolyte contact area and higher sulfur loading. The stronger adsorption energy of polysulfides with TiO2 (than with Ti3C2Tx), as proven by density functional theory calculations, is essential for better on‐site polysulfide retention. The ultrathin nature and protected conductivity ensure rapid ion and electron diffusion, and the excellent flexibility maintains high mechanical integrity. In result, the TiO2 QDs@MXene/S cathode exhibits significantly improved long‐term cyclability and rate capability, disclosing a new opportunity toward fast and stable Li–S batteries.
TiO2 quantum dots (QDs) are grown on ultrathin MXene nanosheets by a facile, one‐step strategy through cetyltrimethylammonium bromide‐assisted solvothermal synthesis, resulting in TiO2 QDs@MXene nanohybrids that serve as a high‐performance sulfur host toward fast and stable Li–S batteries.
Subjective interference is a common difficulty in vocal music teaching, and human ear audition cannot fully objectively analyze the students’ problems in vocal practice due to the influence of ...environment and other factors. This paper takes the convolutional neural network as the vocal music recognition algorithm and the Mel spectrum as the vocal music feature extraction algorithm and constructs the vocal music analysis model based on the optimization and improvement of the two algorithms. Then select the support vector machine, the nearest neighbor node, Wavenet, LSTM, GAN, SAGAN, CLDNN_BILSTM, and other models, as well as this paper’s model, for comparison experiments. Finally, the model was utilized in the vocal education classroom to evaluate the singing practice of four students. It is found that the MSE value of Arousal’s algorithm in this paper is the lowest, and the R
values of 0.51197 and 0.71058 are the highest in the test of the MFCC vocal music feature dataset. Valence’s model in this paper has the MSE value of 0.51996, which is still the lowest, and the R² value of 0.76946, which is still the highest. This paper’s model has the best performance and results. The average rate of professional singers is 61 beats, and the model calculates the average singing rate of the four students as 77, 66, 63, and 61 beats. The first three still have a large gap compared to the standard level, and the student D level is higher. The problem of student practice analysis and vocal feature extraction and recognition in vocal teaching can be solved using new ideas and methods provided in this study.
•Persulfate-based AOPs have extensive application for contaminated soil remediation.•Experimental parameters and soil properties affect contaminant degradation.•Simple persulfate addition has evolved ...to couple with various activation methods.•Soil geochemistry, biology, and contaminant dynamics would change after application.•Mathematical models were developed to understand the oxidation process.
Sulfate radical-based advanced oxidation processes (AOPs) have been applied in soil remediation to degrade traditional pollutants in situ, such as polychlorinated biphenyls (PCBs), diesel, polycyclic aromatic hydrocarbons (PAHs), and total petroleum hydrocarbons (TPH). Emerging pollutants such as pesticides, pharmaceuticals, and phthalates have gradually entered the treatment list, and removal technology has been optimized from simple persulfate addition to coupling with various activation methods, including iron activation, thermal activation, base activation, and electrical activation. Peroxydisulfate (PDS) is widely used as oxidant in soil remediation due to its low cost and long environmental retention time. Experimental parameters and some soil properties significantly impact the contaminant removal rate. Changes in soil geochemistry, biology, and contaminant dynamics occur after application of this technology. The degradation rate of contaminants in soil is generally characterized by a pseudo-first-order reaction kinetic model. Other mathematical models have also been developed to understand the oxidation process. This review provides an overview of persulfate-based AOPs for organic contaminated soil remediation, especially for the deep understanding of the activation mechanisms and influential factors.
Plant invasions can alter the behaviour and performance of native herbivorous insects because the insects are evolutionarily naïve to the novel plants. An ecological trap results when native insects ...prefer invasive plants over their native hosts but suffer reduced fitness on the invaders. Although such traps are predicted to occur frequently, given the prevalence of invasive plants, empirical support for ecological traps and their underlying mechanisms remains sparse.
We examined the potential for the invasive plant Spartina alterniflora to act as an ecological trap for the native moth Laelia coenosa, which previously fed mainly on the indigenous plant Phragmites australis in a Chinese saltmarsh. We surveyed Laelia egg densities on Spartina and Phragmites in the field, and determined adult oviposition preference and offspring development on the two plant species. To investigate the causes of adult preference and offspring performance patterns, we compared resource abundance in the field, plant‐odour attractiveness and leaf nutritional and defensive traits between Spartina and Phragmites.
We found that Laelia egg density and female preference for ovipositing were higher on Spartina than Phragmites. However, performance of offspring was poorer on Spartina than Phragmites. Spartina dominated a larger area and had greater leaf biomass than Phragmites in the field, and volatile odours released by Spartina were more attractive to Laelia females than those released by Phragmites. Although leaf C, C:P ratio and terpenoid content did not differ significantly between the two plant species, Spartina leaves were tougher and more waxy, had lower N and had higher concentrations of alkaloids and phenolics than Phragmites leaves.
Synthesis. Our data suggest that invasive Spartina can create an ecological trap for the native insect Laelia. This trap appears to result from environmental cues (resource availability and leaf odours) that attract the herbivore to the plant, but do not reliably predict the dietary qualities (nutrition and defences) that negatively affect herbivore offspring performance. These findings reveal an important negative effect of plant invasions on resident herbivores and highlight the roles of resource availability and plant traits at different life stages of the insect.
Our results suggest that the decoupling of environmental cues and plant qualities of invasive plants can create an ecological trap for native insects, highlighting the roles of resource availability and plant traits at different life stages of insects.
H. pylori (Hp) infection has been indicated in the pathogenesis of gastric diseases including gastric cancer (GC). This study aimed at exploring the relationships between Hp infection and gastric ...diseases including GC in a large dataset of routine patients undergoing gastroscopy. From November 2007 to December 2017, 70,534 first-time visiting patients aged 18-94 years with gastroscopic biopsies were histologically diagnosed and analyzed. Patients' data were entered twice in an Excel spreadsheet database and analyzed using the SPSS (version 22.0) software package and statistical significance was defined as P<0.05 for all analyses. The first interesting observation was age-related twin-peak prevalence profiles (TPPs) for Hp infection, gastritis, and advanced diseases with different time spans (TS) between the first and second occurring peaks. Hp infection and gastritis had TPPs occurring at earlier ages than TPPs of gastric introepithelial neoplasia (GIN) and GC. More patients were clustered at the second occurring TPPs. The time spans (TS) from the first occurring peak of Hp infection to the first occurring peaks of other gastric diseases varied dramatically with 0-5 years for gastritis; 5-15 years for GINs, and 5-20 years for GC, respectively. The number of males with Hp infection and gastric diseases, excluding non-atrophic gastritis (NAG), was more than that of females (P<0.001). We have first observed age-related twin-peak prevalence profiles for Hp infection, gastritis, GIN, and GC, respectively, among a large population of patients undergoing gastroscopy. The second prevalence peak of GC is at ages of 70-74 years indicating that many GC patients would be missed during screening because the cut-off age for screening is 69 years old in China.
High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection. Existing state-of-the-art frameworks ...first encode the input image as a low-resolution representation through a subnetwork that is formed by connecting high-to-low resolution convolutions in series (e.g., ResNet, VGGNet), and then recover the high-resolution representation from the encoded low-resolution representation. Instead, our proposed network, named as High-Resolution Network (HRNet), maintains high-resolution representations through the whole process. There are two key characteristics: (i) Connect the high-to-low resolution convolution streams in parallel and (ii) repeatedly exchange the information across resolutions. The benefit is that the resulting representation is semantically richer and spatially more precise. We show the superiority of the proposed HRNet in a wide range of applications, including human pose estimation, semantic segmentation, and object detection, suggesting that the HRNet is a stronger backbone for computer vision problems. All the codes are available at https://github.com/HRNet .
The Kullback-Leibler (KL) divergence between two mixture models is a fundamental primitive in many signal processing tasks. Since the KL divergence of mixtures does not admit a closed-form formula, ...it is in practice either estimated using costly Monte-Carlo stochastic integration or approximated. We present a fast and generic method that builds algorithmically closed-form lower and upper bounds on the entropy, the cross-entropy and the KL divergence of univariate mixtures. We illustrate the versatile method by reporting on our experiments for approximating the KL divergence between Gaussian mixture models.
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