结合Pixel2style2Pixel的年龄转化方法

年龄转化在刑侦、人脸识别等领域有着重要作用。常见的年龄转化方法需要使用成对带有年龄注释的数据集进行训练,并且存在生成图像质量低、年龄语义信息不够解耦等问题。针对上述问题,在Pix2style2Pix的训练框架中,引入年龄识别、contextual损失函数,对整体损失函数做出符合年龄转化的改进,提取年龄信息并保证图像质量。改进编码网络配合损失函数对潜在空间的图像进行编辑,提出一种基于Pixel2style2Pixel的年龄转化方法。通过FFHQ、CelebA数据集,对所提方法进行验证,实验结果表明,在不采用成对年龄注释的训练集下,改进后的损失函数能生成更符合期望年龄的转化图像,人脸相似度距离为0.346、FID为45.69、SSIM为0.593 6、PSNR为19.64 dB,均优于对比方法,证明所提方法能够生成高质量、年龄语义高度解耦的转化结果。

基于Style 3D针织运动装虚拟设计与应用

基于Style 3D虚拟服装试衣软件,通过对虚拟试衣技术现状的分析,以针织运动装作为虚拟试衣对象,从四个部分进行展开,包含人体建模,版片的三维排布与缝合,运动装面料仿真与试穿效果展示,以及驳样改款与模块组合。借助于三维面料扫描技术获取面料材质纹理和相关辅料配件,进一步丰富针织运动装的装饰效果。3D快速驳样改款和虚拟展示能有效地优化设计过程,针织运动装模块组合模拟应用适应快时尚节奏下改款搭配的需求,根据现有版型快速驳样改款亦是对传统设计与展示模式的一种突破。

Impact of parenting styles on preschoolers’behaviors

In this editorial,I comment on the article“Association of preschool children behavior and emotional problems with the parenting behavior of both parents”which was published in the latest issue of“World Journal of Clinical Cases”that demonstrates the prevalence of behavioral disorders in preschool children.Therefore I am focused on parenting which is the most effective factor shown to affect the development and continuity of these behaviors.The management of child behavior problems is crucial.Children in early ages,especially preschoolers who are in the first 5 years of life,are influenced by dramatic changes in various aspects of development,such as social,emotional,and physical.Also,children experience many changes linked to different developmental tasks,such as discovering themselves,getting new friendships,and adapting to a new environment.In this period,parents have a critical role in supporting child development.If parents do not manage and overcome their child’s misbehavior,it could be transformed into psychosocial problems in adulthood.Parenting is the most powerful predictor in the social development of preschool children.Several studies have shown that to reduce the child’s emotional and behavioral problems,a warm relationship between parents and children is needed.In addition,recent studies have demonstrated significant relationships between family regulation factors and parenting,as well as with child behaviors.

基于Style-GAN瑶族景观导识系统设计研究

目的为提升生成对抗网络在非遗景观导识系统中的创新应用,进而强化瑶族地方文化传播属性。方法首先对导识系统各要素和瑶族地方文化特性进行分类和归纳,运用模糊层次分析法(FAHP)对导识系统各设计要素和风格特征进行权重分配;其次,将评分较高的导识系统设计要素和风格特征构建映射矩阵;最后,将选定的风格特征融入风格生成对抗网络(Style-GAN)以辅助瑶族景观导识系统设计。结果本研究集成了多领域跨学科交叉理论模型,将FAHP与Style-GAN相融合,重构地方文化产业的导识交互系统。结论通过以瑶族刺绣为案例,其传统纹样与人工智能技术的有机结合可有效促进当地文化的传承。该研究也为文化导识系统整合设计及相关语义提炼提供了创新视野。

Style 3D软件在汉服虚拟展示设计中的应用

文章探讨了Style3D软件在汉服虚拟展示设计中的应用。结合历史、人物背景与阶级背景,运用Style3D软件构建精确汉服三维模型,设计动态走秀动作,并运用新媒体技术制作推广短视频。此方法突破时间和空间限制,降低成本投入,吸引更多人关注汉服文化。数字文物展示代表博物馆信息化应用,借助Style3D软件创建汉服虚拟模型,实现文物考古研究、保护及展示。此外,为解决汉服定制难题,设计开发汉服虚拟模型,利用软件进行三维虚拟建模,预先展示设计效果,降低成本,满足消费者需求。

基于Style3D技术的甪直水乡妇女服饰虚拟仿真研究

甪直水乡妇女服饰是具有典型特色的中国传统地方服饰,随着现代社会的发展,目前的影响力正逐渐式微。文章通过爬梳文献和实地考察甪直水乡妇女服饰博物馆,对各部件服装的服制特色进行梳理与分析。在此基础上,运用服装CAD软件绘制样板,运用Style 3D软件实现服装的虚拟仿真,包括细节配饰和纹样色彩的还原,为虚拟仿真中遇到的问题提出可行的解决方案。结果表明,文章所提出的数字化虚拟技术是一种有效的传统服饰传承方式,为发展和保护中国传统地方服饰文化拓宽了途径和思路。

Correlation of personality with individual reproductive success in shrub-nesting birds depends on their life history style

Two questions in the research of animal personality—whether there is a correlation between a personality trait and individual reproductive success,and what is the genetic basis underlying a personality trait—remain unresolved.We addressed these two questions in three shrub-nesting birds,the Azure-winged Magpie(Cyanopica cyanus,AM),White-collared Blackbird(Turdus albocinctus,WB),and Brown-cheeked Laughingthrush(Trochalopteron henrici,BL).The personality type of an individual was first identified according to its response to a territorial intruder.Then,we compared the fleeing distance,breeding parameters,and differential expressed genes(DEGs) in the brain transcriptome between bold and shy breeders.In the three species,bold breeders exhibited more aggressiveness towards an intruder of their territory than did shy breeders.The reproductive success of bold breeders was significantly higher than that of shy breeders in AM but not in WB and BL.The three species shared one DEG,crabp1,which was up-regulated in bold relative to in shy individuals.By regulating the expression of corticotropin-releasing hormone,higher crabp1 gene expression can decrease cellular response to retinoic acid.Therefore,bold individuals are insensitive to external stresses and able to exhibit more aggressiveness to intruders than their shier counterparts.Aggressiveness is beneficial to bold individuals in AM but not in WB and BL because the former could evoke neighbors to make the same response of defending against intruders but the latter could not.Although a personality trait may have the same genetic basis across species,its correlation with reproductive success depends largely on the life history style of a species.

基于Style3D的褐地翻鸿金锦棉袍的复原研究

为了更好地保护与传承服饰类文物,促进我国文物复原事业与服饰数字化技术的发展,文章提出了一种基于Style3D的服饰复原方法。在分析“褐地翻鸿金锦棉袍”的形制、纹样、色彩等内容的基础上,对其虚拟复原并进行文物虚拟试穿展示,为服饰文物的有效保护与传承提供参考。

Genetic Analysis of Structural Styles in the Makran Accretionary Wedge–Insight from Physical Simulations

The Makran accretionary wedge has the smallest subduction angle among any accretionary prism in the world. The factors controlling the spacing and morphological development of its deep thrust faults, as well as the formation mechanism of shallow normal faults, remain unclear. Meanwhile, the factors affecting the continuity of plane faults must be comprehensively discussed. Clarifying the development characteristics and deformation mechanisms of the Makran accretionary wedge is crucial to effectively guide the exploration of gas hydrate deposits in the area. This study aims to interpret seismic data to identify typical structures in the Makran accretionary wedge, including deep imbricate thrust faults, shallow and small normal faults, wedge-shaped piggyback basins, mud diapirs with fuzzy and disorderly characteristics of reflection, décollements with a northward tilt of 1° – 2°, and large seamounts. Physical simulation-based experiments are performed to comprehensively analyze the results of the plane, section, and slices of the wedge. Results reveal that the distances between and shapes of thrust faults in the deep parts of the Makran accretionary wedge are controlled by the bottom décollement. The uplift of the thrust fault-related folds and the upwelling of the mud diapirs primarily contribute to the formation of small normal faults in the shallow part of the area. The mud diapirs originate from plastic material at the bottom, while those that have developed in the area near the trench are larger. Seamounts and mud diapirs break the continuity of fault plane distribution.

Relationship between Natural Fracture and Structural Style and its Implication for Tight Gas Enrichment:A Case Study of Deep Ahe Formation in the Dibei–Tuzi Area,Kuqa Depression

The deep Lower Jurassic Ahe Formation(J_(1a))in the Dibei–Tuzi area of the Kuqa Depression has not been extensively explored because of the complex distribution of fractures.A study was conducted to investigate the relationship between the natural fracture distribution and structural style.The J_(1a)fractures in this area were mainly high-angle shear fractures.A backward thrust structure(BTS)is favorable for gas migration and accumulation,probably because natural fractures are more developed in the middle and upper parts of a thick competent layer.The opposing thrust structure(OTS)was strongly compressed,and the natural fractures in the middle and lower parts of the thick competent layer around the fault were more intense.The vertical fracture distribution in the thick competent layers of an imbricate-thrust structure(ITS)differs from that of BTS and OTS.The intensity of the fractures in the ITS anticline is similar to that in the BTS.Fracture density in monoclinic strata in a ITS is controlled by faulting.Overall,the structural style controls the configuration of faults and anticlines,and the stress on the competent layers,which significantly affects deep gas reservoir fractures.The enrichment of deep tight sandstone gas is likely controlled by two closely spaced faults and a fault-related anticline.

Impact of lifestyle interventions on pathogenesis of nonalcoholic fatty liver disease

This editorial builds on the article titled“Establishment and validation of an adherence prediction system for lifestyle interventions in non-alcoholic fatty liver disease”by Zeng et al.We carried out a critical examination of nonalcoholic fatty liver disease(NAFLD)pathogenesis and how lifestyle interventions could facilitate disease resolution,particularly highlighting that non-alcoholic steatohepatitis(NASH)is a severe form of NAFLD.Our discussion details that weight loss is a pivotal factor in disease outcomes:A 3%-5%reduction is enough for resolution in 50%of non-obese individuals,while a 7%-10%reduction achieves similar benefits in obese individuals,as demonstrated by magnetic resonance spectroscopy.Additionally,the editorial underscores that such lifestyle changes are instrumental not only in resolving NAFLD but also in reversing hepatic steatosis and inflammation.These insights,derived from the research,emphasize the critical role of personalized lifestyle modifications in halting the progression of NAFLD to NASH and even reversing fibrosis,thus offering a template for effective patient management.

Robust Information Hiding Based on Neural Style Transfer with Artificial Intelligence

This paper proposes an artificial intelligence-based robust information hiding algorithm to address the issue of confidential information being susceptible to noise attacks during transmission.The algorithm we designed aims to mitigate the impact of various noise attacks on the integrity of secret information during transmission.The method we propose involves encoding secret images into stylized encrypted images and applies adversarial transfer to both the style and content features of the original and embedded data.This process effectively enhances the concealment and imperceptibility of confidential information,thereby improving the security of such information during transmission and reducing security risks.Furthermore,we have designed a specialized attack layer to simulate real-world attacks and common noise scenarios encountered in practical environments.Through adversarial training,the algorithm is strengthened to enhance its resilience against attacks and overall robustness,ensuring better protection against potential threats.Experimental results demonstrate that our proposed algorithm successfully enhances the concealment and unknowability of secret information while maintaining embedding capacity.Additionally,it ensures the quality and fidelity of the stego image.The method we propose not only improves the security and robustness of information hiding technology but also holds practical application value in protecting sensitive data and ensuring the invisibility of confidential information.

基于Style3D的羽绒服虚拟缝制工艺

为了解决传统羽绒服装缝制中,由于工艺繁多耗时且不能快速直观呈现着装效果,而导致羽绒服装生产成本高,用户体验满意度低、较难满足消费者个性化需求的问题,以一款羽绒服外套为研究对象,借助Style3D建模软件完成3D虚拟缝制,并对缝制工艺进行改进与优化,探讨高效可行的羽绒服装虚拟缝制工艺措施。结果表明:采用直接生成里层的方法,可有效减少虚拟缝制时间;选择合适的缝纫线类型,可使服装缝制外观更加整洁;对羽绒服底摆进行单独剪切可实现便利充绒的效果;通过将面料剪切为单层可实现袖口的双层折边效果,增加面料的额外渲染厚度,可减少碰撞检测时间,亦可提高模拟逼真度。研究结果可对类似类型服装的虚拟缝制与高效模拟提供借鉴。

A Harmonic Approach to Handwriting Style Synthesis Using Deep Learning

The challenging task of handwriting style synthesis requires capturing the individuality and diversity of human handwriting.The majority of currently available methods use either a generative adversarial network(GAN)or a recurrent neural network(RNN)to generate new handwriting styles.This is why these techniques frequently fall short of producing diverse and realistic text pictures,particularly for terms that are not commonly used.To resolve that,this research proposes a novel deep learning model that consists of a style encoder and a text generator to synthesize different handwriting styles.This network excels in generating conditional text by extracting style vectors from a series of style images.The model performs admirably on a range of handwriting synthesis tasks,including the production of text that is out-of-vocabulary.It works more effectively than previous approaches by displaying lower values on key Generative Adversarial Network evaluation metrics,such Geometric Score(GS)(3.21×10^(-5))and Fréchet Inception Distance(FID)(8.75),as well as text recognition metrics,like Character Error Rate(CER)and Word Error Rate(WER).A thorough component analysis revealed the steady improvement in image production quality,highlighting the importance of specific handwriting styles.Applicable fields include digital forensics,creative writing,and document security.

Examining Zhang Peiji’s Translation of Beiying:A Study on the Representation of Zhu Ziqing’s Linguistic Style

Based on Yan Fu’s translation norms of“faithfulness,expressiveness,and elegance”and Liu Miqing’s concept of aesthetic representation in translation,the present study employed a combined method of qualitative and quantitative analysis to investigate the linguistic styles employed by Zhu Ziqing in his renowned prose Beiying.Then,using relevant corpora and self-designed Python software,we investigated whether Zhang Peiji,as a translator,has successfully reproduced the simplistic,emotional,and realistic linguistic characteristics in Zhu Ziqing’s prose from the perspectives of“faithfulness,expressiveness,and elegance.”The findings of the research indicate that by employing a dynamic imitative translation approach,Zhang Peiji has successfully enhanced the linguistic aesthetic qualities of the source text,striving to reflect the distinctive linguistic style of Zhu Ziqing.

Linking Perceived Risk of Public Health Emergency to Psychological Distress among Chinese College Students: The Chain Mediation Role of Balanced Time Perspective and Negative Coping Styles

Background:With public health emergencies(PHE)worldwide increasing,the perceived risk of PHE has been one of the critical factors influencing college students’psychological distress.However,the mechanisms by which the perceived risk of PHE affects college students’psychological distress are not clear.The study’s purpose was to investigate the mediation roles of deviation from a balanced time perspective(DBTP)and negative coping styles between the perceived risk of PHE and psychological distress.Methods:A convenience sampling method was used to survey 1054 Chinese college students with self-reporting.Data was collected using the Public Risk Perception Scale(PRPS),the Zimbardo Time Perspective Inventory(ZTPI),the Simplified Coping Style Questionnaire(SCSQ),the PHE Anxiety Scale,and the Chinese version of the Patient Health Questionnaire(PHQ).The associations between the perceived risk of PHE,DBTP,negative coping styles,and psychological distress were clarified using the correlation analysis.Additionally,the mediating roles of DBTP and negative coping styles between the perceived risk of PHE and psychological distress were investigated using a structural equation model.Results:The findings revealed low to moderate correlations between the variables studied.Students’perceived risk of PHE was a positive predictor of their psychological distress(b=0.219,p<0.01).DBTP and negative coping styles played chain mediation roles between them with the effect being 0.009 and a 95%Boot CI of[0.003,0.023].This chain mediation model had an excellent fit index(χ^(2)/df=4.732,CFI=0.973,TLI=0.930,RMSEA=0.048,SRMR=0.047).Conclusion:These findings showed how the perceived risk of PHE affected college students’psychological distress.Specifically,these results suggested that improving students’mental ability to switch effectively among different time perspectives depending on task features and situational considerations and reducing their negative coping styles might be effective ways to promote their mental health.

基于多尺度扩张注意力的Styleformer汽车造型设计

为了提升汽车设计图像的质量,开发了两个专门的数据集并通过多尺度扩张注意力机制优化现有的图像生成模型,以便捕获汽车的主体特征和结构细节.首先,创建了“WhiteCarSet”和“WhiteCarContour”数据集,分别聚焦于汽车的本体特征采集和汽车轮廓的精确收集.其次,针对图像生成模型,我们对Styleformer进行了改进,引入了多尺度扩张注意力操作,以增强模型捕获长距离依赖和理解对象全局结构的能力.在多个数据集上的实验结果显示,改进后的模型FID值相较于传统模型实现了15.57%的提升.在相同的改进模型Styleformer-M而数据集不同的情况下,使用我们开发的数据集,FID值比其它数据集提高了约22.29%.

Style 3D在服装结构与工艺课程教学中的应用

文章基于服装企业数字化转型背景,分析了服装结构与工艺课程传统教学模式的弊端,通过具体案例展示Style 3D在服装结构与工艺课程教学中的实际应用,以期丰富和优化课程教学方法,提高教学效果,培养符合行业所需的服装专业虚拟技术应用人才。

Personality Traits, Thinking Styles, and Emotional Intelligence in Nursing, towards Healthcare Providers’ Characterization and Safer Patient Care

Background: This study explored nursing personality traits (Big Five Inventory BFI), emotional intelligence (EI), and thinking styles (Rational, RS, and Experiential, ES) together with demographic data to see how they could relate and the implication of this on nurses and patient safety. Design: A cross-sectional study. Methods: Nursing sample (n = 435). Participants completed a self-report online survey, which included demographic information, followed by questionnaires to measure personality traits, thinking styles, and emotional intelligence. Results: Spearman’s rank correlation was computed to assess the relationship between EI and Extraversion;there was a moderate positive correlation between the two variables, r = 0.487, p r = 0.731, p r = 0.723, p r = -0.666, p r = 0.467, p Conclusion: Different studies consolidated each other, and all converge and channel into the concept of characterization of healthcare providers for better support to them and safer patient care. EI correlated with all BFI components, and both positively impacted all desirable behaviors. Therefore, it would be valuable if organizations invested in increasing EI in their providers as it might highlight areas for improvement and equip providers with appropriate and advantageous coping strategies.

基于半监督学习的StyleGAN图像生成模型

StyleGAN是一种基于生成对抗网络的图像生成方法,它在图像生成领域占据着重要的地位。但传统的StyleGAN生成图片质量依赖于训练集样本质量,当训练集图片质量较低时,StyleGAN往往不能很好发挥作用。针对此问题,本文提出一种基于半监督的StyleGAN模型(SG-GAN)。对于单个图片的生成,首先根据StyleGAN模型中w向量和图像的一一对应关系,生成训练样本并导入支持向量机(SVM)进行训练。然后,利用SVM和StyleGAN的mapping network在每次生成图像前对w向量进行筛选,挑选合格的w向量生成图像,以提高生成图像质量。对于批量图片的生成,首先经过基因向量生成器生成基因向量并随机组合在一起,采用动态循环回溯算法求得风格向量的所有排列,根据排列结果产生交配后的个体,最后,经过评价函数进行个体的筛选,在模型的多次迭代后,最终找到更加优秀的个体。本文在公开数据集上与几种先进同类方法进行了对比实验,实验结果表明:在lsun猫脸数据集上,模型FID2.74的准确率最高可达74.2%,召回率可达51.2%。经验证,该模型在lsun数据集上的准确率明显优于StyleGAN模型,进一步证实了该模型的有效性。同时,模型在Cat Dataset,CIFAR-100和ImageNet数据集上均达到70%以上的准确率,从而验证了模型具有不错的泛化性。