Features and performance of a tomographic algorithm, based on a random walk model, for quantification of the optical characteristics of an abnormality embedded within tissue-like turbid media.

1999 ◽  
Author(s):  
Victor Chernomordik ◽  
David Hattery ◽  
Amir H. Gandjbakhche ◽  
Antonio Pifferi ◽  
Paola Taroni ◽  
...  
Keyword(s):  
Random Walk ◽  
Random Walk Model ◽  
Optical Characteristics ◽  
Turbid Media ◽  
And Performance

scholarly journals Zebrafish airinemes optimize their shape between ballistic and diffusive search

2021 ◽  
Author(s):  
Sohyeon Park ◽  
Hyunjoong Kim ◽  
Yi Wang ◽  
Dae Seok Eom ◽  
Jun Allard
Keyword(s):  
Random Walk ◽  
Target Cell ◽  
Live Cell ◽  
Random Walk Model ◽  
Directional Information ◽  
Trade Off ◽  
Persistent Random Walk ◽  
Live Cell Microscopy ◽  
And Performance ◽  
Cell Microscopy

In addition to diffusive signals, cells in tissue also communicate via long, thin cellular protrusions, such as airinemes in zebrafish. Before establishing communication, cellular protrusions must find their target cell. Here we demonstrate that the shape of airinemes in zebrafish are consistent with a finite persistent random walk model. The probability of contacting the target cell is maximized for a balance between ballistic search (straight) and diffusive search (highly curved, random). We find that the curvature of airinemes in zebrafish, extracted from live cell microscopy, is approximately the same value as the optimum in the simple persistent random walk model. We also explore the ability of the target cell to infer direction of the airineme's source, finding that there is a theoretical trade-off between search optimality and directional information. This provides a framework to characterize the shape, and performance objectives, of non-canonical cellular protrusions in general.

A Multi-Label Classification Algorithm Based on Random Walk Model

2010 ◽  
Vol 33 (8) ◽  
pp. 1418-1426 ◽  
Author(s):  
Wei ZHENG ◽  
Chao-Kun WANG ◽  
Zhang LIU ◽  
Jian-Min WANG
Keyword(s):  
Random Walk ◽  
Classification Algorithm ◽  
Random Walk Model

Quartile histogram assessment of glioma malignancy using high b ‐value diffusion MRI with a continuous‐time random‐walk model

2021 ◽  
Vol 34 (4) ◽  
Author(s):  
M. Muge Karaman ◽  
Jiaxuan Zhang ◽  
Karen L. Xie ◽  
Wenzhen Zhu ◽  
Xiaohong Joe Zhou
Keyword(s):  
Random Walk ◽  
Continuous Time ◽  
Diffusion Mri ◽  
B Value ◽  
Continuous Time Random Walk ◽  
Random Walk Model ◽  
High B Value

The prediction model of personality in social networks by using data mining deep learning algorithm and random walk model

2020 ◽  
pp. 002072092093683
Author(s):  
Yu Zhu
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Social Network ◽  
Random Walk ◽  
Prediction Method ◽  
Random Walk Model ◽  
Personality Characteristics ◽  
User Personality ◽  
Network Platform ◽  
Personality Prediction

The objective is to predict and analyze the behaviors of users in the social network platform by using the personality theory and computational technologies, thereby acquiring the personality characteristics of social network users more effectively. First, social network data are analyzed, which finds that the type of text data marks the majority. By using data mining technology, the raw data of numerous social network users can be obtained. Based on the random walk model, the data information of the text status of social network users is analyzed, and a user personality prediction method integrating multi-label learning is proposed. In addition, the online social network platform Weibo is taken as the research object. The blog information of Weibo users is obtained through crawler technology. Then, the users are labeled in accordance with personality characteristics. The Pearson correlation coefficient is used to evaluate the relation between the user personality characteristics and the user behavior characteristics of the Weibo users. The correlation between the network behaviors and personality characteristics of Weibo users is analyzed, and the scientificity of the prediction method is verified by the Big Five Model of Personality. By applying relevant technologies and algorithms of data mining and deep learning, the learning ability of neural networks on data characteristics can be improved. In terms of performance on analyzing text information of social network users, the user personality prediction method of integrated multi-label learning based on the random walk model has a large advantage. For the problem of personality prediction of social network users, through combining data mining technology and deep neural network technology in deep learning, the data processing results of social network user behaviors are more accurate.

Tracking method to random walk model based on orientation code matching

2008 ◽  
Author(s):  
Kazuhiro Kagoike ◽  
Satoru Takahashi ◽  
Hidenori Takauji ◽  
Shun'ichi Kaneko
Keyword(s):  
Random Walk ◽  
Random Walk Model ◽  
Tracking Method ◽  
Model Based
Sign in / Sign up

Export Citation Format

Share Document