Exploring the Digital Creative Product Design of Luoshan Shadow Based on Non-Fungible Tokens

The popularity of Non-fungible Tokens is reshaping the Internet, digital assets and content. Many cultural sectorsworldwide have switched and proposed using NFT technology to enhance the liquidity of financialised art assets. Numerous companies are trying to take advantage of the rapid growth of the NFT markets to increase their competitiveness. Although digital artwork provides numerous technical advantages, few design practices areavailable for Luoshan Shadow’s NFT virtual creative products. This study aimed to investigate the factorsinfluencing the digital design of the Luoshan shadow by NFT technology. We conducted a thematic analysis of the comments and suggestions given by designers and non-geneticists. Data of 185 designers, consumers, andnon-geneticists indicated the main concerning themes; i) copyright and security issues; ii) innovation of art formand content; iii) new presentation, and iv) benefits from Non-fungible Tokens technology. These findings mayprovide experience for Luoshan shadow art to start the layout of digital creative design in the context of themuch-needed NFT market and to organically combine its digital derivatives with physical derivatives to achievejoint development

A study of the Problems of Luoshan Shadow puppet Creative Product Design

With a long history and rich cultural connotation, Luoshan Shadow puppet has become an intangible cultural heritage of China. However, in the midst of modernisation, there are some problems with the design of Luoshan shadow creative products. In this paper, we examined an overview of research into the Luoshan shadow creative products. These products have been criticised for their lack of creativity in all design activities, which has hindered their development. In order to obtain more reliable results, we chose to conduct an extensive questionnaire survey in China. A descriptive analysis of all user data from these questionnaires was carried out. 379 participants' data showed the theme of this study; i. problems of stylistic design; ii. problems of lack of distinctiveness; and iii. problems of overpricing. These findings are beneficial to further research on the innovative design of Luoshan shadow creative products

Abnormal traffic detection system in SDN based on deep learning hybrid models

Software defined network (SDN) provides technical support for network construction in smart cities, However, the openness of SDN is also prone to more network attacks. Traditional abnormal traffic detection methods have complex algorithms and find it difficult to detect abnormalities in the network promptly, which cannot meet the demand for abnormal detection in the SDN environment. Therefore, we propose an abnormal traffic detection system based on deep learning hybrid model. The system adopts a hierarchical detection technique, which first achieves rough detection of abnormal traffic based on port information. Then it uses wavelet transform and deep learning techniques for fine detection of all traffic data flowing through suspicious switches. The experimental results show that the proposed detection method based on port information can quickly complete the approximate localization of the source of abnormal traffic. the accuracy, precision, and recall of the fine detection are significantly improved compared with the traditional method of abnormal traffic detection in SDN

Dnmt3b ablation impairs fracture repair through upregulation of Notch pathway

We previously established that DNA methyltransferase 3b (Dnmt3b) is the sole Dnmt responsive to fracture repair and that Dnmt3b expression is induced in progenitor cells during fracture repair. In the current study, we confirmed that Dnmt3b ablation in mesenchymal progenitor cells (MPCs) resulted in impaired endochondral ossification, delayed fracture repair, and reduced mechanical strength of the newly formed bone in Prx1-Cre;Dnmt3bf/f (Dnmt3bPrx1) mice. Mechanistically, deletion of Dnmt3b in MPCs led to reduced chondrogenic and osteogenic differentiation in vitro. We further identified Rbpjκ as a downstream target of Dnmt3b in MPCs. In fact, we located 2 Dnmt3b binding sites in the murine proximal Rbpjκ promoter and gene body and confirmed Dnmt3b interaction with the 2 binding sites by ChIP assays. Luciferase assays showed functional utilization of the Dnmt3b binding sites in murine C3H10T1/2 cells. Importantly, we showed that the MPC differentiation defect observed in Dnmt3b deficiency cells was due to the upregulation of Rbpjκ, evident by restored MPC differentiation upon Rbpjκ inhibition. Consistent with in vitro findings, Rbpjκ blockage via dual antiplatelet therapy reversed the differentiation defect and accelerated fracture repair in Dnmt3bPrx1 mice. Collectively, our data suggest that Dnmt3b suppresses Notch signaling during MPC differentiation and is necessary for normal fracture repair

Relightable Neural Human Assets from Multi-view Gradient Illuminations

Human modeling and relighting are two fundamental problems in computer vision and graphics, where high-quality datasets can largely facilitate related research. However, most existing human datasets only provide multi-view human images captured under the same illumination. Although valuable for modeling tasks, they are not readily used in relighting problems. To promote research in both fields, in this paper, we present UltraStage, a new 3D human dataset that contains more than 2,000 high-quality human assets captured under both multi-view and multi-illumination settings. Specifically, for each example, we provide 32 surrounding views illuminated with one white light and two gradient illuminations. In addition to regular multi-view images, gradient illuminations help recover detailed surface normal and spatially-varying material maps, enabling various relighting applications. Inspired by recent advances in neural representation, we further interpret each example into a neural human asset which allows novel view synthesis under arbitrary lighting conditions. We show our neural human assets can achieve extremely high capture performance and are capable of representing fine details such as facial wrinkles and cloth folds. We also validate UltraStage in single image relighting tasks, training neural networks with virtual relighted data from neural assets and demonstrating realistic rendering improvements over prior arts. UltraStage will be publicly available to the community to stimulate significant future developments in various human modeling and rendering tasks. The dataset is available at https://miaoing.github.io/RNHA.Comment: Project page: https://miaoing.github.io/RNH