A Study on Brand Public Welfare Marketing Strategies in the Context of New Media: A Case Study of Pereira

Public welfare marketing is an effective method for brands to improve their reputation and credibility. With the development of the Internet and social platforms, public welfare marketing needs new strategies to better play its role. This article summarizes the effective strategies for brand public welfare marketing in the context of new media by analyzing the public welfare marketing projects of the Perleya brand since 2021. Research has found that brand public welfare marketing needs to target the target audience, increase user engagement, and endow the brand with a certain image and spirit that meets consumer expectations through public welfare marketing. At the same time, public welfare marketing should strive to resonate emotionally with consumers, awaken the emotional factors of the audience, and also rely on the power of KOL and authoritative institutions to increase communication efforts and achieve the transfer of trust

Benign Overfitting in Classification: Provably Counter Label Noise with Larger Models

Studies on benign overfitting provide insights for the success of overparameterized deep learning models. In this work, we examine whether overfitting is truly benign in real-world classification tasks. We start with the observation that a ResNet model overfits benignly on Cifar10 but not benignly on ImageNet. To understand why benign overfitting fails in the ImageNet experiment, we theoretically analyze benign overfitting under a more restrictive setup where the number of parameters is not significantly larger than the number of data points. Under this mild overparameterization setup, our analysis identifies a phase change: unlike in the previous heavy overparameterization settings, benign overfitting can now fail in the presence of label noise. Our analysis explains our empirical observations, and is validated by a set of control experiments with ResNets. Our work highlights the importance of understanding implicit bias in underfitting regimes as a future direction.Comment: Published as a conference paper at ICLR 202

The Growth Mindset and Student Social and Emotional Skill Development: An Empirical Analysis Based on the OECD’s SSES

The mindset is a crucial factor influencing the behavior of individuals. This study aims to evaluate the growth mindset of 10- and 15-year-old adolescents and the relationship between their mindsets and social and emotional skills from the viewpoints of students, parents, and teachers, using OECD’s SSES 2019 data from Suzhou City. The research results show that the growth mindset of students is affected by their socioeconomic status; and that the growth mindset of students, parents, and teachers can significantly and positively predict student social and emotional skills

“Reporting or Interpreting?”—A Discoursal Study of Broadcasts on NBA Games in China

From the perspective of empirical discourse analysis, this paper identifies the site broadcasters’ roles and cognitive blending process in NBA (National Basketball Association) broadcasts in China. The authors find that NBA broadcasters chiefly interpret the information they have obtained from sports sites and interviews with the coaches and players, employing various interpreting strategies, such as commentary, amplification, supplementation and restructure. Cognitively, the language that NBA broadcasters applied reveals their cognitive blending process of interpreting techniques, strategies, sports knowledge and attitudes towards the games, of who take up different roles to fulfill different communicating purposes, all of which project various cognitions on NBA games. Despite the fact that one role might make certain linguistic behaviors prevail over the others, especially their interpreting role, NBA site broadcasters coordinate it with other roles properly through which they present different levels of translational and constructional schematicity, thus yielding a coherent and constructional working mode of NBA broadcasting practice in China

Coating titania nanoparticles with epoxy-containing catechol polymers via Cu(0)-living radical polymerization as intelligent enzyme carriers

Immobilization of enzyme could offer the biocatalyst with increased stability and important recoverability, which plays a vital role in the enzyme’s industrial applications. In this study, we present a new strategy to build an intelligent enzyme carrier by coating titania nanoparticles with thermoresponsive epoxy-functionalized polymers. Zero-valent copper-mediated living radical polymerization (Cu(0)-LRP) was utilized herein to copolymerize N-isopropylacrylamide (NIPAM) and glycidyl acrylate (GA) directly from an unprotected dopamine-functionalized initiator to obtain an epoxy-containing polymer with terminal anchor for the “grafting to” or “one-pot” modification of titania nanoparticles. A rhodamine B-labeled laccase has been subsequently used as a model enzyme for successful immobilization to yield an intelligent titania/laccase hybrid bifunctional catalyst. The immobilized laccase has shown excellent thermal stability under ambient or even relatively high temperature above the lower critical solution temperature (LCST) at which temperature the hybrid particles could be facilely recovered for reuse. The enzyme activity could be maintained during the repeated use after recovery and enzymatic degradation of bisphenol A was proven to be efficient. The photocatalytic ability of titania was also investigated by fast degradation of rhodamine B under the excitation of simulated sunlight. Therefore, this study has provided a facile strategy for the immobilization of metal oxide catalysts with enzymes, which constructs a novel bifunctional catalyst that will be promising for the “one-pot” degradation of different organic pollutants

Wired Perspectives: Multi-View Wire Art Embraces Generative AI

Creating multi-view wire art (MVWA), a static 3D sculpture with diverse interpretations from different viewpoints, is a complex task even for skilled artists. In response, we present DreamWire, an AI system enabling everyone to craft MVWA easily. Users express their vision through text prompts or scribbles, freeing them from intricate 3D wire organisation. Our approach synergises 3D B\'ezier curves, Prim's algorithm, and knowledge distillation from diffusion models or their variants (e.g., ControlNet). This blend enables the system to represent 3D wire art, ensuring spatial continuity and overcoming data scarcity. Extensive evaluation and analysis are conducted to shed insight on the inner workings of the proposed system, including the trade-off between connectivity and visual aesthetics.Comment: Project page: https://dreamwireart.github.i

Exploiting the Vulnerability of Flow Table Overflow in Software-Defined Network: Attack Model, Evaluation, and Defense

As the most competitive solution for next-generation network, SDN and its dominant implementation OpenFlow are attracting more and more interests. But besides convenience and flexibility, SDN/OpenFlow also introduces new kinds of limitations and security issues. Of these limitations, the most obvious and maybe the most neglected one is the flow table capacity of SDN/OpenFlow switches. In this paper, we proposed a novel inference attack targeting at SDN/OpenFlow network, which is motivated by the limited flow table capacities of SDN/OpenFlow switches and the following measurable network performance decrease resulting from frequent interactions between data and control plane when the flow table is full. To the best of our knowledge, this is the first proposed inference attack model of this kind for SDN/OpenFlow. We implemented an inference attack framework according to our model and examined its efficiency and accuracy. The evaluation results demonstrate that our framework can infer the network parameters (flow table capacity and usage) with an accuracy of 80% or higher. We also proposed two possible defense strategies for the discovered vulnerability, including routing aggregation algorithm and multilevel flow table architecture. These findings give us a deeper understanding of SDN/OpenFlow limitations and serve as guidelines to future improvements of SDN/OpenFlow

Eight RGS and RGS-like Proteins Orchestrate Growth, Differentiation, and Pathogenicity of Magnaporthe oryzae

A previous study identified MoRgs1 as an RGS protein that negative regulates G-protein signaling to control developmental processes such as conidiation and appressorium formation in Magnaporthe oryzae. Here, we characterized additional seven RGS and RGS-like proteins (MoRgs2 through MoRgs8). We found that MoRgs1 and MoRgs4 positively regulate surface hydrophobicity, conidiation, and mating. Indifference to MoRgs1, MoRgs4 has a role in regulating laccase and peroxidase activities. MoRgs1, MoRgs2, MoRgs3, MoRgs4, MoRgs6, and MoRgs7 are important for germ tube growth and appressorium formation. Interestingly, MoRgs7 and MoRgs8 exhibit a unique domain structure in which the RGS domain is linked to a seven-transmembrane motif, a hallmark of G-protein coupled receptors (GPCRs). We have also shown that MoRgs1 regulates mating through negative regulation of Gα MoMagB and is involved in the maintenance of cell wall integrity. While all proteins appear to be involved in the control of intracellular cAMP levels, only MoRgs1, MoRgs3, MoRgs4, and MoRgs7 are required for full virulence. Taking together, in addition to MoRgs1 functions as a prominent RGS protein in M. oryzae, MoRgs4 and other RGS and RGS-like proteins are also involved in a complex process governing asexual/sexual development, appressorium formation, and pathogenicity