• metacharacters :
• .  ^  $  *  +  ?  {  [  ]  \  |  (  )
  

• [abc] = [a-c] : will match any of the characters "a", "b" or "c"
• metacharacters are not active inside classes.
• [abc$] : will match any of the characters of "a", "b", "c" or "$"
• "$" is usually a metacharacter, but inside a character class it's stripped of its special nature.
• not within the range-- complementing
• [^5] : will match any character except "5"
• .
• match anything except a newline character
  
• "\" is used to escape all the metacharactes so you can still match them in patterns
• \[ or \\ : match "[" or "\"
• \d = [0-9]
  
• match any decimal digit
• \D = [^0-9]
• match any non-digit character
• \s = [\t\n\r\f\v]
• match nay whitespace character
• \S = [^\t\n\r\f\v]
• match any non-whitespace character
• \w = [a-zA-Z0-9_]
• match any alphanumeric character
• \W = [^a-zA-Z0-9_]
• match any non-alphanumeric character
• 
  

Archiving with Tar

Tar files place several files or the contents of a directory or directories in one file. This is a good way to create backups and archives. Usually, tar files end with the .tar extension.

To create a tar file, type:

tar -cvf filename.tar files/directories

In this example, filename.tar represents the file you are creating and files/directories represents the files or directories you want to put in the new file.

tar -xvf foo.tar

This command does not remove the .tar file, but it places copies of the .tar contents in the current working directory.

The tar command does not compress files automatically. You can compress tar files with:

tar -czvf foo.tar

Compressed tar files are conventionally given the extension .tgz and are compressed with gzip.

To expand a compressed tar file type:

create:

extracting:

testing/viewing:

tar xvf **.tar.tar

$ du -hs /path/to/directory

Vi: Search and Replace

Change to normal mode with <ESC>.

Search (Wraped around at end of file):

  Search STRING forward :   / STRING.
  Search STRING backward:   ? STRING.

  Repeat search:   n
  Repeat search in opposite direction:  N  (SHIFT-n)

 
 First occurrence on current line:      :s/OLD/NEW
  
 Globally (all) on current line:        :s/OLD/NEW/g 

 Between two lines #,#:                 :#,#s/OLD/NEW/g
  
 Every occurrence in file:              :%s/OLD/NEW/g 

check all process of certain user :


$ top -u johndoe

$ top -u 500

$ top -U johndoe








kill all process of certain user:
kill -9 `ps -u <username> -o "pid="` 

sudo apt-get install linux-headers-$(uname -r)

Information Analysis and Retrieval

Scientific Literature Mining (LitMiner)

How can scientific researchers hope to know about all of the latest advances and new discoveries in their field, given that more than 40,000 scholarly articles are published in the scientific literature every month? How can they be sure of finding all of the relevant knowledge “hidden” in journal articles?

Even with the advent of massive numerical and structural databases, the scientific literature still holds the newest information and the intelligence surrounding the data. The problem is that researchers cannot hope to read all the articles relevant to their field of study if they are also to conduct research.

In response to this pressing challenge, the NRC Institute for Information Technology (NRC-IIT), in collaboration with the NRC Institute for Biological Sciences (NRC-IBS), the Canada Institute for Scientific and Technical Information (CISTI), the Samuel Lunenfeld Institute and Blueprint International, is developing a unified collection of text and language processing tools to solve the real information needs of genomic and proteomic scientists.

In the short-term, the goal is to save researchers time by letting computers assume some of the tasks. In the longer term, the goal is to support hypothesis formation in ways that are not possible with the current organization of the literature.

The LitMiner project is currently in its first stage, which is to integrate several existing text tools into a proof-of-concept prototype. More elaborate scenarios of use will be possible from that prototype, which will ultimately lead to more useful systems.

The research is being conducted in both in text processing and bio-informatics. Most of the tools being combined in LitMiner are machine learning, information retrieval or text mining algorithms, either new or based on novel modifications of existing algorithms.

While the application to the scientific literature is driving the further development of these algorithms, the research is important in its own right.