Frontiers of Computer Science最新文献_第10页

Skulls, Scalps, and Seminoles 头骨，头皮和塞米诺尔人

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2018-08-13 DOI: 10.5149/NORTHCAROLINA/9781469640471.003.0007

Cameron B. Strang

引用次数: 0

Ethnography and Intelligence in the Time of Conquest 征服时期的民族志与智力

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2018-08-13 DOI: 10.5149/NORTHCAROLINA/9781469640471.003.0005

Cameron B. Strang

引用次数: 0

FusionNet: A Deep Fully Residual Convolutional Neural Network for Image Segmentation in Connectomics 用于连接组学图像分割的深度全残差卷积神经网络

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2016-12-16 DOI: 10.3389/fcomp.2021.613981

Tran Minh Quan, David Grant Colburn Hildebrand, W. Jeong

{"title":"FusionNet: A Deep Fully Residual Convolutional Neural Network for Image Segmentation in Connectomics","authors":"Tran Minh Quan, David Grant Colburn Hildebrand, W. Jeong","doi":"10.3389/fcomp.2021.613981","DOIUrl":"https://doi.org/10.3389/fcomp.2021.613981","url":null,"abstract":"Cellular-resolution connectomics is an ambitious research direction with the goal of generating comprehensive brain connectivity maps using high-throughput, nano-scale electron microscopy. One of the main challenges in connectomics research is developing scalable image analysis algorithms that require minimal user intervention. Deep learning has provided exceptional performance in image classification tasks in computer vision, leading to a recent explosion in popularity. Similarly, its application to connectomic analyses holds great promise. Here, we introduce a deep neural network architecture, FusionNet, with a focus on its application to accomplish automatic segmentation of neuronal structures in connectomics data. FusionNet combines recent advances in machine learning, such as semantic segmentation and residual neural networks, with summation-based skip connections. This results in a much deeper network architecture and improves segmentation accuracy. We demonstrate the performance of the proposed method by comparing it with several other popular electron microscopy segmentation methods. We further illustrate its flexibility through segmentation results for two different tasks: cell membrane segmentation and cell nucleus segmentation.","PeriodicalId":12640,"journal":{"name":"Frontiers of Computer Science","volume":"1 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2016-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73452014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 192

Social influence and spread dynamics in social networks. 社会网络中的社会影响和传播动态。

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2012-01-01 Epub Date: 2012-09-19 DOI: 10.1007/s11704-012-1176-1

Xiaolong Zheng, Yongguang Zhong, Daniel Zeng, Fei-Yue Wang

{"title":"Social influence and spread dynamics in social networks.","authors":"Xiaolong Zheng, Yongguang Zhong, Daniel Zeng, Fei-Yue Wang","doi":"10.1007/s11704-012-1176-1","DOIUrl":"https://doi.org/10.1007/s11704-012-1176-1","url":null,"abstract":"<p><p>Social networks often serve as a critical medium for information dissemination, diffusion of epidemics, and spread of behavior, by shared activities or similarities between individuals. Recently, we have witnessed an explosion of interest in studying social influence and spread dynamics in social networks. To date, relatively little material has been provided on a comprehensive review in this field. This brief survey addresses this issue. We present the current significant empirical studies on real social systems, including network construction methods, measures of network, and newly empirical results. We then provide a concise description of some related social models from both macro- and micro-level perspectives. Due to the difficulties in combining real data and simulation data for verifying and validating real social systems, we further emphasize the current research results of computational experiments. We hope this paper can provide researchers significant insights into better understanding the characteristics of personal influence and spread patterns in large-scale social systems.</p>","PeriodicalId":12640,"journal":{"name":"Frontiers of Computer Science","volume":"6 5","pages":"611-620"},"PeriodicalIF":4.2,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11704-012-1176-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37832850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 31

Graph Separators, with Applications 图形分隔符，与应用程序

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2001-06-30 DOI: 10.1007/b115747

A. Rosenberg, Lenwood S. Heath

引用次数: 70

Parallel evolution of parallel processors 并行处理器的并行进化

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 1994-05-31 DOI: 10.1007/978-1-4615-2856-2

G. Lerman, L. Rudolph

引用次数: 13