CFP: IEEE International Conference on Systems, Man, and Cybernetics Special Session on Machine Learning for Vision and Healthcare

Call for Papers

IEEE International Conference on Systems, Man, and Cybernetics

Special Session on Machine Learning for Vision and Healthcare

 

The emergence of visual data, machine learning algorithms, and advancement in hardware has enabled significant breakthrough in vision and healthcare applications.

 

This special issue focuses on new data, machine learning methods, and applications in computer vision and related healthcare problems. The goal of the special issue is the identification of new and exciting problems and applications that leverage the current data and model advancement. It also aims at the development of new data and machine learning methods that address specific problems in vision and healthcare domains.

 

Manuscripts are solicited to address a wide range of topics in data, models, and applications, including but not limited to the following: data collection, data sharing, crowdsourcing, machine learning, deep neural networks, visualization, visual quality assessment, image and video coding, saliency detection, object detection, object and scene recognition, and understanding of human vision and assistive tools for visual and developmental disorders.

 

Perspective authors should follow the instructions given on the IEEE SMC webpages: http://www.smc2017.org/?q=authors, and submit their manuscripts with the submission system at: https://conf.papercept.net/conferences/scripts/start.pl.

 

Important Dates:

April 7, 2017: Manuscript submission

May 25, 2017: Acceptance notification

July 9, 2017: Camera-ready papers due

August 5, 2017: Deadline for early registration

October 5-8: Conference dates

 

Organizers:

Catherine Qi Zhao (qzhao@cs.umn.edu), University of Minnesota

Weifeng Liu (liuwf@upc.edu.cn), China University of Petroleum

Yicong Zhou (yicongzhou@umac.mo), University of Macau

Sunjun Li (shujun.li@surrey.ac.uk), University of Surrey

 

“p-Laplacian Regularized Sparse Coding for Human Activity Recognition” is formally published.

p-Laplacian Regularized Sparse Coding for Human Activity Recognition

Weifeng Liu 

College of Information and Control Engineering, China University of Petroleum (East China), Qingdao, China 

Zheng-Jun Zha ; Yanjiang Wang ; Ke Lu ; Dacheng Tao

Human activity analysis in videos has increasingly attracted attention in computer vision research with the massive number of videos now accessible online. Although many recognition algorithms have been reported recently, activity representation is challenging. Recently, manifold regularized sparse coding has obtained promising performance in action recognition, because it simultaneously learns the sparse representation and preserves the manifold structure. In this paper, we propose a generalized version of Laplacian regularized sparse coding for human activity recognition called $p$-Laplacian regularized sparse coding (pLSC). The proposed method exploits $p$ -Laplacian regularization to preserve the local geometry. The $p$ -Laplacian is a nonlinear generalization of standard graph Laplacian and has tighter isoperimetric inequality. As a result, pLSC provides superior theoretical evidence than standard Laplacian regularized sparse coding with a proper $p$. We also provide a fast iterative shrinkage-thresholding algorithm for the optimization of pLSC. Finally, we input the sparse codes learned by the pLSC algorithm into support vector machines and conduct extensive experiments on the unstructured social activity attribute dataset and human motion database (HMDB51) for human activity recognition. The experimental results demonstrate that the proposed pLSC algorithm outperforms the manifold regularized sparse coding algorithms including the standard Laplacian regularized sparse coding algorithm with a proper $p$.

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7448894&filter%3DAND%28p_IS_Number%3A7508400%29%26rowsPerPage%3D75

One paper is online on Neurocomputing. Con~ to Hongli！

Hessian Regularization by Patch Alignment Framework

Abstract

In recent years, semi-supervised learning has played a key part in large-scale image management, where usually only a few images are labeled. To address this problem, many representative works have been reported, including transductive SVM, universum SVM, co-training and graph-based methods. The prominent method is the patch alignment framework, which unifies the traditional spectral analysis methods. In this paper, we propose Hessian regression based on the patch alignment framework. In particular, we construct a Hessian using the patch alignment framework and apply it to regression problems. To the best of our knowledge, there is no report on Hessian construction from the patch alignment viewpoint. Compared with the traditional Laplacian regularization, Hessian can better match the data and then leverage the performance. To validate the effectiveness of the proposed method, we conduct human face recognition experiments on a celebrity face dataset. The experimental results demonstrate the superiority of the proposed solution in human face classification.

Keywords

Semi-supervised learning; Hessian; Patch alignment; Least Squares

doi:10.1016/j.neucom.2015.07.152

TIE paper entitled “p-Laplacian Regularized Sparse Coding for Human Activity Recognition” is online.

p-Laplacian Regularized Sparse Coding for Human Activity Recognition

Authors

W. Liu 

Weifeng Liu is with the College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, China (email: liuwf@ upc.edu.cn). 

Z. J. Zha ; Y. Wang ; K. Lu ; D. Tao

Abstract

Human activity analysis in videos has increasingly attracted attention in computer vision research with the massive number of videos now accessible online. Although many recognition algorithms have been reported recently, activity representation is challenging. Recently, manifold regularized sparse coding has obtained promising performance in action recognition, because it simultaneously learns the sparse representation and preserves the manifold structure. In this paper, we propose a generalized version of Laplacian regularized sparse coding for human activity recognition called p-Laplacian regularized sparse coding. The proposed method exploits p-Laplacian regularization to preserve the local geometry. The p-Laplacian is a nonlinear generalization of standard graph Laplacian and has tighter isoperimetric inequality. As a result, p-Laplacian regularized sparse coding provides superior theoretical evidence than standard Laplacian regularized sparse coding with a proper p. We also provide a fast iterative Shrinkage-Thresholding algorithm (FISTA) for the optimization of p-Laplacian regularized sparse coding. Lastly, we input the sparse codes learned by the p- Laplacian regularized sparse coding algorithm into support vector machines and conduct extensive experiments on the unstructured social activity attribute (USAA) dataset and human motion database (HMDB51) for human activity recognition. The experimental results demonstrate that the proposed p-Laplacian regularized sparse coding algorithm outperforms the manifold regularized sparse coding algorithms including the standard Laplacian regularized sparse coding algorithm with a proper p.

Keywords

human activity recognition

manifold

p-Laplacian

sparse coding

DOI:

10.1109/TIE.2016.2552147

CFP: IEEE SMC2016 special session on Cognitive Computing

http://www.smc2016.org/

Special Session Call for Papers

SMC2016 Special Session on Matrix Analysis and Feature Learning for Multimedia Understanding

Introduction/Call for Papers

Matrix analysis and feature learning have important applications in natural sciences, medicine, economics, engineering as well as in industry. The research of matrix analysis and feature learning plays important roles in dimension reduction, spectral analysis, manifold learning, kernel machines, sparse coding, pattern recognition etc. In recent years, the matrix analysis and feature learning present new research opportunities to large scale multimedia data, e.g. large scale face recognition, intelligent visual surveillance, web-scale image retrieval/annotation /classification, massive object recognition etc. As a consequence, matrix analysis and feature learning are never-ending resilience fields and attract growing efforts from different fields.

This special session hunts for original research results for Matrix Analysis and Feature Learning for Multimedia Understanding. The goals of this special session are twofold: 1) developing matrix analysis and feature learning algorithms to target specific applications in multimedia understanding and large-scale visual data analytics and 2) defining novel large-scale multimedia data driven applications, which can be cleared up by conventional matrix analysis and feature learning algorithms.

Indicative Topics/Areas

Manuscripts are solicited to address a wide range of topics in matrix analysis, feature learning, and multimedia understanding, but not limit to the following:

Extension of traditional matrix analysis and feature learning, multiview dimension reduction, spectral analysis, matrix analysis and feature learning for manifold learning, kernel machines and tensor machines, matrix and tensor analysis for biometrics, matrix and tensor analysis for web-scale multimedia information retrieval, video surveillance, sparse analysis, deep learning, etc.

Important Dates

April 15, 2016: Deadline for submission of full-length papers to special sessions.

May 25, 2016: Acceptance/Rejection Notification.

July 9, 2016: Final camera-ready papers due in electronic form.

Submission

Manuscripts for a Special Session should NOT be submitted in duplication to any other regular or special sessions and should be submitted to SMC 2016 main conference online submission system on SMC 2016 conference website.

All submitted papers of Special Sessions have to undergo the same review process (three completed reviews per paper).  The technical reviewers for each Special Session paper will be members of the SMC 2016 Program Committee and qualified peer-reviewers to be nominated by the Special Session organizers.

Organized by IEEE SMC TC on Cognitive Computing

Special Session organizer

Dr. Xinmei Tian

Professor

University of Science and Technology of China, China

Email:

xinmei@ustc.edu.cn

Co-organizer(s):

Dr. Jun Yu

Associate Professor

Hangzhou Dianzi University, China

Email:

yujun@hdu.edu.cn

Dr. Weifeng Liu

Associate Professor

China University of Petroleum, China

Email:

liuwfxy@gmail.com

Dr. Yicong Zhou

Assistant Professor

University of Macau, Macau, China

E-mail: 

yicongzhou@umac.mo

Submission guidlines:

Special Session: Matrix Analysis and Feature Learning for Multimedia Understanding

A link to upload the special session papers can be found in the webpage as below:

http://140.118.207.10/SMC2016SS/

课题组刘红丽学位论文获评校优秀学位论文

课题组刘红丽学位论文获评校优秀学位论文，目前正在公示中http://gs.upc.edu.cn/s/33/t/94/f6/ba/info63162.htm。
刘红丽硕士毕业论文题目“多视角学习在视觉识别中的若干应用研究”，多视角学习算法进行了系列研究，取得了一系列的成果，发表学术论文多篇。

Signal Processing Special Issue: SIGPRO-BDMA is online.

Signal Processing Special Issue: SIGPRO-BDMA is online now.

Big Data Meets Multimedia Analytics

Tat-Seng Chua, Xiangjian He, Weifeng Liu*, Massimo Piccardi, Yonggang Wen, Dacheng Tao

doi:10.1016/j.sigpro.2015.12.004

One paper is online. (HSAE: A Hessian Regularized Sparse Auto-Encoders)

One Neurocomputing paper is online now.

HSAE: A Hessian Regularized Sparse Auto-Encoders

Abstract

Auto-encoders are one kinds of promising non-probabilistic representation learning paradigms that can efficiently learn stable deterministic features. Recently, auto-encoder algorithms are drawing more and more attentions because of its attractive performance in learning insensitive representation with respect to data changes. The most representative auto-encoder algorithms are the regularized auto-encoders including contractive auto-encoder, denoising auto-encoders, and sparse auto-encoders. In this paper, we incorporate both Hessian regularization and sparsity constraints into auto-encoders and then propose a new auto-encoder algorithm called Hessian regularized sparse auto-encoders (HSAE). The advantages of the proposed HSAE lie in two folds: (1) it employs Hessian regularization to well preserve local geometry for data points; (2) it also efficiently extracts the hidden structure in the data by using sparsity constraints. Finally, we stack the single-layer auto-encoders and form a deep architecture of HSAE. To evaluate the effectiveness, we construct extensive experiments on the popular datasets including MNIST and CIFAR-10 dataset and compare the proposed HSAE with the basic auto-encoders, sparse auto-encoders, Laplacian auto-encoders and Hessian auto-encoders. The experimental results demonstrate that HSAE outperforms the related baseline algorithms.

http://www.sciencedirect.com/science/article/pii/S0925231215017683

Two papers are accepted by ICCT2015

Supervised Hessian Eigenmap for Dimensionality Reduction

Abstract: Hessian Eigenmap is one proposed technique for dimensionality reduction. Many methods, such as ISOMAP, LLE, Laplacian Eigenmap, have been proposed under manifold learning for dimensionality reduction. However, all these ideas have not taken the influence of different class into consideration, which limit the effectiveness of manifold learning. To take account for the influence for multiclass and improve the performance of dimensional reduction, we proposed a new method, supervised Hessian LLE(SHLLE). To evaluate the proposed method, extensive experiments were conducted on the artificial dataset and real dataset(COIL-20). Our result demonstrate that the proposed method outperform HLLE method.

Keywords: Manifold Learning; Locally Linear Embedding; Hessian Eigenmap; Supervised Learning

Density Peak based Co-Spectral Clustering

Abstract: Spectral clustering employs spectral-graph structure of a similarity matrix to partition data into disjoint meaningful groups, because of its well-defined mathematical framework, good performance on arbitrary shaped clusters and simplicity, spectral clustering has gained considerable attentions in the recent past. Despite these virtues, spectral clustering suffers from several drawbacks, such as it is sensitive to initial condition, not robust to outliers and unable to determine a reasonable cluster number and so on. In this paper, we present a new approach named density peak spectral clustering (DPSC) which combines spectral clustering with density peak clustering algorithm (DPCA) into a unified framework to solve these problems. Since multi-view data is common in clustering problem, to further bootstrap the clustering performance by using complementary information from different view, then we propose co-trained density peak spectral clustering (Co-DPSC) which is an extension of DPSC to multi-views based on the co-training idea. Experimental comparisons with a number of baselines on a toy and three real-world datasets show the effectiveness of our proposed DPSC and Co-DPSC algorithm.

Keywords: Spectral clustering; Density peak clustering; Multi-view; Co-training

课题组获“麦芒杯”第一届全国研究生移动终端应用设计创新大赛三等奖。

2015-08-10，课题组研究生张连波获"麦芒杯"第一届全国研究生移动终端应用设计创新大赛三等奖。

全国研究生移动终端应用设计创新大赛（英文名称：China Graduate Contest on Application, Design and Innovation of Mobile-Terminal）（以下简称"大赛"）是"全国研究生创新实践系列活动"赛事之一。大赛由教育部学位与研究生教育发展中心和中国科协青少年科技中心共同主办，由全国工程专业学位研究生教育指导委员会联合主办。

    第一届大赛由北京邮电大学承办，由中国通信学会、移动智能终端技术创新与产业联盟和中国移动互联网产业联盟共同协办，由华为终端（东莞）有限公司赞助冠名"麦芒杯"，即本届大赛命名为"'麦芒杯'第一届全国研究生移动终端应用设计创新大赛"。

    第一届大赛由中国信息通信研究院泰尔终端实验室、北京邮电大学计算机学院和北京邮电大学软件学院提供技术支持。

http://www.mtdasai.com.cn/MT/index2.jsp

Two papers are accepted by ICIMCS2015.

Sparse canonical correlation analysis for recognition 

 

ABSTRACT

Canonical correlation analysis (CCA) is one promising feature extraction and subspace learning method for multivariate vectors by exploiting the correlation between two multidimensional variables in a linear way. Hence CCA has been widely employed in many applications such as statistics, economics and signal processing. However, the traditional CCA may be difficult to interpret especially when the original variables are expected to involve only a few components. In this paper, we propose sparse canonical correlation analysis (SCCA) to overcome the above problem. SCCA can find a reasonable trade-off between statistical fidelity and interpretability. Furthermore, we use a generalized power method to optimize the proposed SCCA algorithm. And finally we conduct extensive experiments for recognition on several popular databases including UCI datasets and USAA dataset. Experimental results demonstrate that the proposed SCCA algorithm outperforms the traditional CCA algorithm.

Sparse Principle Motion Component for One-shot Gesture Recognition

 

ABSTRACT

With the rapid development of computer vision technology, gesture recognition has attracted much attention in recent years. However, the traditional gesture recognition methods waste a lot of time in the process of building a model with a large number of examples. To tackle the above problems, in this paper we propose sparse PCA based principle motion component (SPMC) method for one-shot gesture recognition, which can properly enhance recognition accuracy only with few training examples and unspecialized sensors. To evaluate the SPMC method, we conduct one-shot gesture recognition experiments on ChaLearn Gesture Dataset. Experimental results show that the proposed approach can improve the accuracy of gesture recognition.

Two paper are accepted by Neurocomputing Journal.

Hessian Regularization by Patch Alignment Framework

Abstract

In recent years, semi-supervised learning has played a key part in large-scale image management, where usually only a few images are labeled. To address this problem, many representative works have been reported, including transductive SVM, universum SVM, co-training and graph-based methods. The prominent method is the patch alignment framework, which unifies the traditional spectral analysis methods. In this paper, we propose Hessian regression based on the patch alignment framework. In particular, we construct a Hessian using the patch alignment framework and apply it to regression problems. To the best of our knowledge, there is no report on Hessian construction from the patch alignment viewpoint. Compared with the traditional Laplacian regularization, Hessian can better match the data and then leverage the performance. To validate the effectiveness of the proposed method, we conduct human face recognition experiments on a celebrity face dataset. The experimental results demonstrate the superiority of the proposed solution in human face classification.

Keywords—semi-supervised learning; Hessian; patch alignment; Least Squares

HSAE: A Hessian Regularized Sparse Auto-Encoders

Abstract

Auto-encoders are one kinds of promising non-probabilistic representation learning paradigms that can efficiently learn stable deterministic features. Recently, auto-encoder algorithms are drawing more and more attentions because of its attractive performance in learning insensitive representation with respect to data changes. The most representative auto-encoder algorithms are the regularized auto-encoders including contractive auto-encoder, denoising auto-encoders, and sparse auto-encoders. In this paper, we incorporate both Hessian regularization and sparsity constraints into auto-encoders and then propose a new auto-encoder algorithm called Hessian regularized sparse auto-encoders (HSAE). The advantages of the proposed HSAE lie in two folds: (1) it employs Hessian regularization to well preserve local geometry for data points; (2) it also efficiently extracts the hidden structure in the data by using sparsity constraints. Finally, we stack the single-layer auto-encoders and form a deep architecture of HSAE. To evaluate the effectiveness, we construct extensive experiments on the popular datasets including MNIST and CIFAR-10 dataset and compare the proposed HSAE with the basic auto-encoders, sparse auto-encoders, Laplacian auto-encoders and Hessian auto-encoders. The experimental results demonstrate that HSAE outperforms the related baseline algorithms.

Keywords: Hessian regularization; Sparse Representation; Auto-Encoder; Manifold

zz 学习Linux（创建、删除文件和文件夹命令）

原文http://www.cnblogs.com/zf2011/archive/2011/05/17/2049155.html

创建文件夹【mkdir】

  一、mkdir命令使用权限

    所有用户都可以在终端使用 mkdir 命令在拥有权限的文件夹创建文件夹或目录。

    二、mkdir命令使用格式

    格式：mkdir [选项] DirName

    三、mkdir命令功能

    通过 mkdir 命令可以实现在指定位置创建以 DirName(指定的文件名)命名的文件夹或目录。要创建文件夹或目录的用户必须对所创建的文件夹的父文件夹具有写权限(了解Linux文件-文件夹权限请点击这里)。并且，所创建的文件夹(目录)不能与其父目录(即父文件夹)中的文件名重名，即同一个目录下不能有同名的(区分大小写)。

    四、mkdir命令选项说明

    命令中的［选项］一般有以下两种：

    -m    用于对新建目录设置存取权限，也可以用 chmod 命令进行设置。

    -p     需要时创建上层文件夹(或目录)，如果文件夹(或目录)已经存在，则不视为错误。

    五、mkdir命令使用举例

    例一：在桌面下面创建以“demo”命名的文件夹。

    使用以下命令即可。

    mkdir 桌面/demo

　 例二：在桌面下面创建以“demo”命名的文件夹，并且给文件夹赋权限，权限为123。

    mkdir  123  桌面/demo

删除文件夹【rm】

  一、rm命令使用权限

    所有用户都可以在终端使用 rm命令删除目录。

    二、rm命令使用格式

    格式：rm [选项] DirName

    三、rm命令功能

    删除档案及目录。

    四、rm命令选项说明

    命令中的［选项］一般有以下几种：

    -i 删除前逐一询问确认。

　 -f 即使原档案属性设为唯读，亦直接删除，无需逐一确认。

　 -r 将目录及以下之档案亦逐一删除。

    五、rm命令使用举例

    例一：删除所有C语言程序文档；删除前逐一询问确认。

    rm -i *.c

　 例二：将 Finished 子目录及子目录中所有档案删除。

    rm -r Finished

　 注：在linux没有回收站，在试用rm命令的时候，一定要小心些，删除之后就无法再恢复了。

创建文件【vi】

　 一、进入vi的命令

　    vi filename :打开或新建文件，并将光标置于第一行首

　    vi +n filename ：打开文件，并将光标置于第n行首

　   vi + filename ：打开文件，并将光标置于最后一行首

　   vi +/pattern filename：打开文件，并将光标置于第一个与pattern匹配的串处

　   vi -r filename ：在上次正用vi编辑时发生系统崩溃，恢复filename

　   vi filename....filename ：打开多个文件，依次进行编辑

　 二、移动光标类命令

　　h ：光标左移一个字符

　　l ：光标右移一个字符

　　space：光标右移一个字符

　　Backspace：光标左移一个字符

　　k或Ctrl+p：光标上移一行

　　j或Ctrl+n ：光标下移一行

　　Enter ：光标下移一行

　　w或W ：光标右移一个字至字首

　　b或B ：光标左移一个字至字首

　　e或E ：光标右移一个字至字尾

　　) ：光标移至句尾

　　( ：光标移至句首

　　}：光标移至段落开头

　　{：光标移至段落结尾

　　nG：光标移至第n行首

　　n+：光标下移n行

　　n-：光标上移n行

　　n$：光标移至第n行尾

　　H ：光标移至屏幕顶行

　　M ：光标移至屏幕中间行

　　L ：光标移至屏幕最后行

　　0：（注意是数字零）光标移至当前行首

　　$：光标移至当前行尾

　三、屏幕翻滚类命令

　　Ctrl+u：向文件首翻半屏

　　Ctrl+d：向文件尾翻半屏

　　Ctrl+f：向文件尾翻一屏

　　Ctrl＋b；向文件首翻一屏

　　nz：将第n行滚至屏幕顶部，不指定n时将当前行滚至屏幕顶部。

　四、插入文本类命令

　　i ：在光标前

　　I ：在当前行首

　　a：光标后

　　A：在当前行尾

　　o：在当前行之下新开一行

　　O：在当前行之上新开一行

　　r：替换当前字符

　　R：替换当前字符及其后的字符，直至按ESC键

　　s：从当前光标位置处开始，以输入的文本替代指定数目的字符

　　S：删除指定数目的行，并以所输入文本代替之

　　ncw或nCW：修改指定数目的字

　　nCC：修改指定数目的行

　五、删除命令

　　ndw或ndW：删除光标处开始及其后的n-1个字

　　do：删至行首

　　d$：删至行尾

　　ndd：删除当前行及其后n-1行

　　x或X：删除一个字符，x删除光标后的，而X删除光标前的

　　Ctrl+u：删除输入方式下所输入的文本

　六、搜索及替换命令

　　/pattern：从光标开始处向文件尾搜索pattern

　　?pattern：从光标开始处向文件首搜索pattern

　　n：在同一方向重复上一次搜索命令

　　N：在反方向上重复上一次搜索命令

　　：s/p1/p2/g：将当前行中所有p1均用p2替代

　　：n1,n2s/p1/p2/g：将第n1至n2行中所有p1均用p2替代

　　：g/p1/s//p2/g：将文件中所有p1均用p2替换

　七、vi命令使用举例

　　例一：创建文件a.txt。

　　vi a.txt

　　Hello everyone!

　　:wq //在退出时，直接输入:wq会发现退不出去，退出方法是：编辑完成后按ESC，然后输入:q就是退出;还有:wq是保存后退出，加感叹号是表示强制

　　

　　

　　

修改档案时间【touch】

　  一、touch命令使用权限

    所有用户都可以在终端使用 touch命令。

    二、touch命令使用格式

    格式：touch [-acfm][-d <日期时间>][-r <参考文件或目录>][-t <日期时间>][--help] [--version][文件或目录...] 或 touch [-acfm][--help][--version][日期时间][文件或目录...]

    （touch [-acfm][-r reference-file] [--file=reference-file][-t MMDDhhmm[[CC]YY][.ss]][-d time] [--date=time][--time={atime,access,use,mtime,modify}][--no-create][--help] [--version]file1 [file2 ...]）

    三、touch命令功能

　 touch fileA，如果fileA存在，使用touch命令可更改这个文件或目录的日期时间，包括存取时间和更改时间；如果fileA不存在，touch命令会在当前目录下新建一个空白文件fileA。

　 注：使用touch指令可更改文件或目录的日期和时间，包括存取时间和更改时间。文件的时间属性包括文件的最后访问时间，最后修改时间以及最后在磁盘上修改的时间，命令stat显示结果显示了三个时间属性。

    四、touch命令选项说明

    a 改变档案的读取时间记录。

　 m 改变档案的修改时间记录。

　 c 假如目的档案不存在，不会建立新的档案。与 --no-create 的效果一样。

　 f 不使用，是为了与其他 unix 系统的相容性而保留。

　 r 使用参考档的时间记录，与 --file 的效果一样。

　 d 设定时间与日期，可以使用各种不同的格式。

　 t 设定档案的时间记录，格式与 date 指令相同。[[CC]YY]MMDDhhmm[.SS]，CC为年数中的前两位，即”世纪数”;YY为年数的后两位，即某世纪中的年数.如果不给出CC的值，则linux中touch命令参数将把年数CCYY限定在1969--2068之内.MM为月数，DD为天将把年数CCYY限定在1969--2068之内.MM为月数，DD为天数，hh 为小时数(几点)，mm为分钟数，SS为秒数.此处秒的设定范围是0--61，这样可以处理闰秒.这些数字组成的时间是环境变量TZ指定的时区中的一个时间.由于系统的限制，早于1970年1月1日的时间是错误的.

　 --no-create 不会建立新档案。

　 --help 列出指令格式。

　 --version 列出版本讯息。

    五、touch命令使用举例

    例一：更新file1.txt的存取和修改时间。

　 touch file1.txt

　 例二：如果file1.txt不存在，不创建文件

　 touch -c file1.txt

　 例三：更新file1.txt的时间戳和ref+file相同

　 touch -r ref+file file1.txt

　 例四：设置文件的时间戳为2011年5月18日9点45分10秒

　 ls -l file1.txt

　 touch -t 1105190945.10 file1.txt

在新建文件时用touch命令可以建立一个空文件，而vi命令则可以直接编辑文件的内容并保存

zz Mac: Terminal 下不同类型的文件显示不同的颜色

原文: http://www.macfans.com.cn/thread-34827-1-1.html

Terminal 下不同类型的文件显示不同的颜色
Terminal 默认的 shell 是 bash (提示符是 $)
在 ~ 先建立一个文件  ~/.bash_profile 
加入下面的两行：
export CLICOLOR=1
export LSCOLORS=gxfxaxdxcxegedabagacad

存盘， 退出Terminal, 重起Terminal.
更改LSCOLORS可以有不同的颜色效果。 

-----------------------------------------------------
如果你用的是 csh/tcsh (提示符是 %), 建立  ~/.cshrc 加入
setenv CLICOLOR 1
setenv LSCOLORS gxfxaxdxcxegedabagacad

存盘， 执行 source ~/.cshrc
------------------------------------------------------

LSCOLORS 的含义：

    LSCOLORS The value of this variable describes what color to use
    for which attribute when colors are enabled with
    CLICOLOR. This string is a concatenation of pairs of the
    format fb, where f is the foreground color and b is the
    background color.

    The color designators are as follows:

    a black
    b red
    c green
    d brown
    e blue
    f magenta
    g cyan
    h light grey
    A bold black, usually shows up as dark grey
    B bold red
    C bold green
    D bold brown, usually shows up as yellow
    E bold blue
    F bold magenta
    G bold cyan
    H bold light grey; looks like bright white
    x default foreground or background

    Note that the above are standard ANSI colors. The actual
    display may differ depending on the color capabilities of
    the terminal in use.

    The order of the attributes are as follows:

    1. directory
    2. symbolic link
    3. socket
    4. pipe
    5. executable
    6. block special
    7. character special
    8. executable with setuid bit set
    9. executable with setgid bit set
    10. directory writable to others, with sticky bit
    11. directory writable to others, without sticky
    bit

    The default is “exfxcxdxbxegedabagacad”, i.e. blue fore-
    ground and default background for regular directories,
    black foreground and red background for setuid executa-
    bles, etc.