Aiming at the current process of artistic creation and animation creation, there are a lot of repeated manual operations in the process of conversion from sketch to the stylized image. This paper presented a solution ...Aiming at the current process of artistic creation and animation creation, there are a lot of repeated manual operations in the process of conversion from sketch to the stylized image. This paper presented a solution based on a deep learning framework to realize image generation and style transfer. The method first used the conditional generation to resist the network, optimizes the loss function of the training mapping relationship, and generated the actual image from the input sketch. Then, by defining and optimizing the perceptual loss function of the style transfer model, the style features are extracted from the image, thereby forming the actual The conversion between images and stylized art images. Experiments show that this method can greatly reduce the work of coloring and converting with different artistic effects, and achieve the purpose of transforming simple stick figures into actual object images.展开更多
The complex geometric features of subsurface fractures at different scales makes mesh generation challenging and/or expensive.In this paper,we make use of neural style transfer(NST),a machine learning technique,to gen...The complex geometric features of subsurface fractures at different scales makes mesh generation challenging and/or expensive.In this paper,we make use of neural style transfer(NST),a machine learning technique,to generate mesh from rock fracture images.In this new approach,we use digital rock fractures at multiple scales that represent’content’and define uniformly shaped and sized triangles to represent’style’.The 19-layer convolutional neural network(CNN)learns the content from the rock image,including lower-level features(such as edges and corners)and higher-level features(such as rock,fractures,or other mineral fillings),and learns the style from the triangular grids.By optimizing the cost function to achieve approximation to represent both the content and the style,numerical meshes can be generated and optimized.We utilize the NST to generate meshes for rough fractures with asperities formed in rock,a network of fractures embedded in rock,and a sand aggregate with multiple grains.Based on the examples,we show that this new NST technique can make mesh generation and optimization much more efficient by achieving a good balance between the density of the mesh and the presentation of the geometric features.Finally,we discuss future applications of this approach and perspectives of applying machine learning to bridge the gaps between numerical modeling and experiments.展开更多
The human motion generation model can extract structural features from existing human motion capture data,and the generated data makes animated characters move.The 3D human motion capture sequences contain complex spa...The human motion generation model can extract structural features from existing human motion capture data,and the generated data makes animated characters move.The 3D human motion capture sequences contain complex spatial-temporal structures,and the deep learning model can fully describe the potential semantic structure of human motion.To improve the authenticity of the generated human motion sequences,we propose a multi-task motion generation model that consists of a discriminator and a generator.The discriminator classifies motion sequences into different styles according to their similarity to the mean spatial-temporal templates from motion sequences of 17 crucial human joints in three-freedom degrees.And target motion sequences are created with these styles by the generator.Unlike traditional related works,our model can handle multiple tasks,such as identifying styles and generating data.In addition,by extracting 17 crucial joints from 29 human joints,our model avoids data redundancy and improves the accuracy of model recognition.The experimental results show that the discriminator of the model can effectively recognize diversified movements,and the generated data can correctly fit the actual data.The combination of discriminator and generator solves the problem of low reuse rate of motion data,and the generated motion sequences are more suitable for actual movement.展开更多
目的了解男男性行为者(men who have sex with men,MSM)社交方式现状,分析相关因素。方法用“滚雪球”抽样法,进行四川省绵阳市MSM社交方式等问卷调查和血清学检测。结果有效问卷1062份,社交方式为传统方式9.23%、社交媒体方式90.77%。...目的了解男男性行为者(men who have sex with men,MSM)社交方式现状,分析相关因素。方法用“滚雪球”抽样法,进行四川省绵阳市MSM社交方式等问卷调查和血清学检测。结果有效问卷1062份,社交方式为传统方式9.23%、社交媒体方式90.77%。社交方式为传统方式者艾滋病病毒(human immunodeficiency virus,HIV)阳性率10.20%,高于社交媒体方式的2.49%(校正值χ^(2)=14.684,P<0.001)。多因素分析结果,同伴网络较大(OR=1.914)、领悟社会支持水平越高(OR=2.919)、近1年做过HIV检测(OR=2.515)、寻找性伴途径为互联网(OR=9.111)、近6个月多性伴(OR=3.805)者以社交媒体方式社交的可能更大,HIV检测阳性(OR=0.232)者以传统方式社交的可能更大。结论MSM社交方式以社交媒体为主,以社交媒体社交者更多存在多性伴,以传统方式社交者HIV阳性率更高。展开更多
文摘Aiming at the current process of artistic creation and animation creation, there are a lot of repeated manual operations in the process of conversion from sketch to the stylized image. This paper presented a solution based on a deep learning framework to realize image generation and style transfer. The method first used the conditional generation to resist the network, optimizes the loss function of the training mapping relationship, and generated the actual image from the input sketch. Then, by defining and optimizing the perceptual loss function of the style transfer model, the style features are extracted from the image, thereby forming the actual The conversion between images and stylized art images. Experiments show that this method can greatly reduce the work of coloring and converting with different artistic effects, and achieve the purpose of transforming simple stick figures into actual object images.
基金supported by Laboratory Directed Research and Development(LDRD)funding from Berkeley Laboratoryby the US Department of Energy(DOE),including the Office of Basic Energy Sciences,Chemical Sciences,Geosciences,and Biosciences Division and the Office of Nuclear Energy,Spent Fuel and Waste Disposition Campaign,both under Contract No.DEAC02-05CH11231 with Berkeley Laboratory。
文摘The complex geometric features of subsurface fractures at different scales makes mesh generation challenging and/or expensive.In this paper,we make use of neural style transfer(NST),a machine learning technique,to generate mesh from rock fracture images.In this new approach,we use digital rock fractures at multiple scales that represent’content’and define uniformly shaped and sized triangles to represent’style’.The 19-layer convolutional neural network(CNN)learns the content from the rock image,including lower-level features(such as edges and corners)and higher-level features(such as rock,fractures,or other mineral fillings),and learns the style from the triangular grids.By optimizing the cost function to achieve approximation to represent both the content and the style,numerical meshes can be generated and optimized.We utilize the NST to generate meshes for rough fractures with asperities formed in rock,a network of fractures embedded in rock,and a sand aggregate with multiple grains.Based on the examples,we show that this new NST technique can make mesh generation and optimization much more efficient by achieving a good balance between the density of the mesh and the presentation of the geometric features.Finally,we discuss future applications of this approach and perspectives of applying machine learning to bridge the gaps between numerical modeling and experiments.
文摘The human motion generation model can extract structural features from existing human motion capture data,and the generated data makes animated characters move.The 3D human motion capture sequences contain complex spatial-temporal structures,and the deep learning model can fully describe the potential semantic structure of human motion.To improve the authenticity of the generated human motion sequences,we propose a multi-task motion generation model that consists of a discriminator and a generator.The discriminator classifies motion sequences into different styles according to their similarity to the mean spatial-temporal templates from motion sequences of 17 crucial human joints in three-freedom degrees.And target motion sequences are created with these styles by the generator.Unlike traditional related works,our model can handle multiple tasks,such as identifying styles and generating data.In addition,by extracting 17 crucial joints from 29 human joints,our model avoids data redundancy and improves the accuracy of model recognition.The experimental results show that the discriminator of the model can effectively recognize diversified movements,and the generated data can correctly fit the actual data.The combination of discriminator and generator solves the problem of low reuse rate of motion data,and the generated motion sequences are more suitable for actual movement.
文摘目的了解男男性行为者(men who have sex with men,MSM)社交方式现状,分析相关因素。方法用“滚雪球”抽样法,进行四川省绵阳市MSM社交方式等问卷调查和血清学检测。结果有效问卷1062份,社交方式为传统方式9.23%、社交媒体方式90.77%。社交方式为传统方式者艾滋病病毒(human immunodeficiency virus,HIV)阳性率10.20%,高于社交媒体方式的2.49%(校正值χ^(2)=14.684,P<0.001)。多因素分析结果,同伴网络较大(OR=1.914)、领悟社会支持水平越高(OR=2.919)、近1年做过HIV检测(OR=2.515)、寻找性伴途径为互联网(OR=9.111)、近6个月多性伴(OR=3.805)者以社交媒体方式社交的可能更大,HIV检测阳性(OR=0.232)者以传统方式社交的可能更大。结论MSM社交方式以社交媒体为主,以社交媒体社交者更多存在多性伴,以传统方式社交者HIV阳性率更高。
