Optimization and Innovation of the Ideological and Political Education Paradigm in Adult Colleges

This paper first analyzed the necessity of optimizing and innovating the paradigm of ideological and political in adult colleges, and then elaborated the connotations of the paradigm and the ideological and political education paradigm in adult colleges. Moreover, existing problems of the ideological and political education paradigm in adult colleges were analyzed. On this basis, five paths were proposed to construct the paradigm of Integrate Organism for ideological and political education in adult colleges

The impact of web design on e-branding

This paper examines how properly designed web sites support e-branding as well as convey product information to potential customers as a substitute for buyers\u27 own information gathering activities. Design guidelines to support e-branding are provided. Afterwards, survey results from online consumers are reported followed by future research issues

Grindability and Surface Integrity of Cast Nickel-based Superalloy in Creep Feed Grinding with Brazed CBN Abrasive Wheels

AbstractThe technique of creep feed grinding is most suitable for geometrical shaping, and therefore has been expected to improve effectively material removal rate and surface quality of components with complex profile. This article studies experimentally the effects of process parameters (i.e. wheel speed, workpiece speed and depth of cut) on the grindability and surface integrity of cast nickel-based superalloys, i.e. K424, during creep feed grinding with brazed cubic boron nitride (CBN) abrasive wheels. Some important factors, such as grinding force and temperature, specific grinding energy, size stability, surface topography, microhardness and microstructure alteration of the sub-surface, residual stresses, are investigated in detail. The results show that during creep feed grinding with brazed CBN wheels, low grinding temperature at about 100 °C is obtained though the specific grinding energy of nickel-based superalloys is high up to 200-300 J/mm3. A combination of wheel speed 22.5 m/s, workpiece speed 0.1 m/min, depth of cut 0.2 mm accomplishes the straight grooves with the expected dimensional accuracy. Moreover, the compressive residual stresses are formed in the burn-free and crack-free ground surface

SVDFormer: Complementing Point Cloud via Self-view Augmentation and Self-structure Dual-generator

In this paper, we propose a novel network, SVDFormer, to tackle two specific challenges in point cloud completion: understanding faithful global shapes from incomplete point clouds and generating high-accuracy local structures. Current methods either perceive shape patterns using only 3D coordinates or import extra images with well-calibrated intrinsic parameters to guide the geometry estimation of the missing parts. However, these approaches do not always fully leverage the cross-modal self-structures available for accurate and high-quality point cloud completion. To this end, we first design a Self-view Fusion Network that leverages multiple-view depth image information to observe incomplete self-shape and generate a compact global shape. To reveal highly detailed structures, we then introduce a refinement module, called Self-structure Dual-generator, in which we incorporate learned shape priors and geometric self-similarities for producing new points. By perceiving the incompleteness of each point, the dual-path design disentangles refinement strategies conditioned on the structural type of each point. SVDFormer absorbs the wisdom of self-structures, avoiding any additional paired information such as color images with precisely calibrated camera intrinsic parameters. Comprehensive experiments indicate that our method achieves state-of-the-art performance on widely-used benchmarks. Code will be available at https://github.com/czvvd/SVDFormer.Comment: Accepted by ICCV 202

GeoSegNet: Point Cloud Semantic Segmentation via Geometric Encoder-Decoder Modeling

Semantic segmentation of point clouds, aiming to assign each point a semantic category, is critical to 3D scene understanding.Despite of significant advances in recent years, most of existing methods still suffer from either the object-level misclassification or the boundary-level ambiguity. In this paper, we present a robust semantic segmentation network by deeply exploring the geometry of point clouds, dubbed GeoSegNet. Our GeoSegNet consists of a multi-geometry based encoder and a boundary-guided decoder. In the encoder, we develop a new residual geometry module from multi-geometry perspectives to extract object-level features. In the decoder, we introduce a contrastive boundary learning module to enhance the geometric representation of boundary points. Benefiting from the geometric encoder-decoder modeling, our GeoSegNet can infer the segmentation of objects effectively while making the intersections (boundaries) of two or more objects clear. Experiments show obvious improvements of our method over its competitors in terms of the overall segmentation accuracy and object boundary clearness. Code is available at https://github.com/Chen-yuiyui/GeoSegNet

High-frequency stimulation of nucleus accumbens changes in dopaminergic reward circuit

Deep brain stimulation (DBS) of the nucleus accumbens (NAc) is a potential remedial therapy for drug craving and relapse, but the mechanism is poorly understood. We investigated changes in neurotransmitter levels during high frequency stimulation (HFS) of the unilateral NAc on morphine-induced rats. Sixty adult Wistar rats were randomized into five groups: the control group (administration of saline), the morphine-only group (systematic administration of morphine without electrode implantation), the morphine-sham-stimulation group (systematic administration of morphine with electrode implantation but not given stimulation), the morphine-stimulation group (systematic administration of morphine with electrode implantation and stimulation) and the saline-stimulation group (administration of saline with electrode implantation and stimulation). The stimulation electrode was stereotaxically implanted into the core of unilateral NAc and microdialysis probes were unilaterally lowered into the ipsilateral ventral tegmental area (VTA), NAc, and ventral pallidum (VP). Samples from microdialysis probes in the ipsilateral VTA, NAc, and VP were analyzed for glutamate (Glu) and caminobutyric acid (GABA) by high-performance liquid chromatography (HPLC). The levels of Glu were increased in the ipsilateral NAc and VP of morphine-only group versus control group, whereas Glu levels were not significantly changed in the ipsilateral VTA. Furthermore, the levels of GABA decreased significantly in the ipsilateral NAc, VP, and VTA of morphineonly group when compared with control group. The profiles of increased Glu and reduced GABA in morphine-induced rats suggest that the presence of increased excitatory neurotransmission in these brain regions. The concentrations of the Glu significantly decreased while the levels of GABA increased in ipsilateral VTA, NAc, and VP in the morphine-stimulation group compared with the morphine-only group. No significant changes were seen in the morphine-sham stimulation group compared with the morphine-only group. These findings indicated that unilateral NAc stimulation inhibits the morphineinduced rats associated hyperactivation of excitatory neurotransmission in the mesocorticolimbic reward circuit

Semi-MoreGAN: A New Semi-supervised Generative Adversarial Network for Mixture of Rain Removal

Rain is one of the most common weather which can completely degrade the image quality and interfere with the performance of many computer vision tasks, especially under heavy rain conditions. We observe that: (i) rain is a mixture of rain streaks and rainy haze; (ii) the scene depth determines the intensity of rain streaks and the transformation into the rainy haze; (iii) most existing deraining methods are only trained on synthetic rainy images, and hence generalize poorly to the real-world scenes. Motivated by these observations, we propose a new SEMI-supervised Mixture Of rain REmoval Generative Adversarial Network (Semi-MoreGAN), which consists of four key modules: (I) a novel attentional depth prediction network to provide precise depth estimation; (ii) a context feature prediction network composed of several well-designed detailed residual blocks to produce detailed image context features; (iii) a pyramid depth-guided non-local network to effectively integrate the image context with the depth information, and produce the final rain-free images; and (iv) a comprehensive semi-supervised loss function to make the model not limited to synthetic datasets but generalize smoothly to real-world heavy rainy scenes. Extensive experiments show clear improvements of our approach over twenty representative state-of-the-arts on both synthetic and real-world rainy images.Comment: 18 page

Dynamical Analysis of DTNN with Impulsive Effect

We present dynamical analysis of discrete-time delayed neural networks with impulsive effect. Under impulsive effect, we derive some new criteria for the invariance and attractivity of discretetime neural networks by using decomposition approach and delay difference inequalities. Our results improve or extend the existing ones