How a Computer Program Became Classical Music's Hot, New Composer

University of California, Santa Cruz professor David Cope has developed Emily Howell, a music-composing program that generates its own compositions by following musical rules that Cope has taught it. The program is only fed music composed by an earlier program of Cope's, Experiments in Musical Intelligence. Critics say that Emily's music, while impressive, lacks the ability to trigger emotional reactions in listeners. A 2008 University of Essex study determined that the human brain has a stronger emotional reaction to music played by humans than by machines, even when the listener does not know the source of a performance. Cope says that Emily and other programs capable of artistic creation offer an opportunity for collaboration with human artists, rather than replacing them. "Computers are there [for us] to extend ourselves through them," he says. "It seems so utterly natural to me. It's not like I taught a rock to compose music."
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Stanford brain tutorial

here you can find a tutorial about brain, it looks good for fast learning . It's Stanford university present :)
Good Luck

motif finding softwares

Motif-finding softwares:

1-Gimsan cornell university Gibbs
2-scope used algorithms:BEAM ,PRISM, SPACER Dartmouth College
3-PLACE Affrit Japan
4-IBM Computational Biology Center watson.ibm
5-Elm eu.org
6-improbizer uc santa curze
7-FIRE princeton university
8-ModuleMaster tuebingen university
9-MotifVoterNational University of Singapore
10-WebMotifs MIT university
11-MiniMotifFinderuniversity of Connecticut
12-The MEME SUITSan diego supercomputer center
13-blockMaker Fred Hutchinson Cancer Research Center
14-Gibbs motif sampledeveloped by Eric C. Rouchka and others
15-phylogibbsuniversity of Basel
16- Pscan University of Milan
17-Multi Finder Harvard university
18- amadeous algorithm:Allegro TelAviv university
19-YMF find short motifs, algorithm:A Statistical Method for Finding Transcription Factor Binding Sites, university of Washington
20-SOMBRERO algorithm:a neural network algorithm called the "Self-Organizing Map",Article, National University of Ireland Galway
21-motif tool manager university of Memphis
22-GLAM2 GLAM2 will analyze your sequences for gapped motifs, San Diego supercomputer center
23- MEME MEME will analyze your sequences for similarities among them and produce a description (motif) for each pattern it discovers.
24-GLAM2SCAN submit a GLAM2 motif to GLAM2SCAN to be used in searching a sequence database. San Diego supercomputer center
25-http://meme.sdsc.edu/meme4_4_0/cgi-bin/gomo.cgi submit motifs to GOMO. GOMO will use the DNA binding motifs to find putative target genes and analyze their associated GO terms. A list of significant GO terms that can be linked to the given motifs will be produced. San Diego supercomputer center
26-FIMO submit motifs to FIMO to be used in searching a sequence database. San Diego supercomputer center
27-MCAST ,motif cluster alignment and search tool ,San Diego supercomputer center
28-tomtom motif comparison tool,TOMTOM will rank the motifs in the target database by the q-value of the similarity score ,San Diego supercomputer center
29-MAST motif alignment and search tool, ,San Diego supercomputer center

The MEME Suite(software that is mentioned from 24 to 29) allows you to:

* discover motifs using MEME or GLAM2 on groups of related DNA or protein sequences,
* search sequence databases using motifs,
* compare a motif to all motifs in a database of motifs, and
* associate motifs with Gene Ontology terms via their putative target genes

30-motif cluster allows you to analyze motifs in a set of protein sequences, relating them to the alignment, phylogeny, and (where available) the 3D structures. It is useful for detecting remote homologies between protein families, for understanding which proteins are most likely to share functions, and for identifying residue changes that might be important for the evolution of new enzyme activities. It's article university of Colorado
31-NestedMICA works by optimizing a probabilistic model which treats the input data as a mixture of interesting motifs and background sequence. NestedMICA uses a new and robust inference technique called nested sampling ,Sanger institute
32-mochiview Johnson lab
33-Consensus Use a greedy algorithm to iteratively build up motifs by adding more and more pattern instances.Washington university in St.Louis
34-phyloGibbs PhyloGibbs, our recent Gibbs-sampling motif-finder, takes phylogeny into account in detecting binding sites for transcription factors in DNA and assigns posterior probabilities to its predictions obtained by sampling the entire configuration space. university of Basel
35-Phylogibbs Onlineuniversity of Basel

We have limitations?

Hello guys
last week I saw an article with this title "Limitations of human brain mean we may never understand the secrets of universe, says Britain's top scientist", I was surprised how this can be possible, we have limitations but our equipments do not have limitations and also we are able to add extra memory, processor's and so on to our brains who said we can't.
I hope this guy with this nice smile have answers for these issues.

Do you agree with me?

Without education we are in a horrible and deadly danger of taking educated people seriously.

G.K.Chesterson

Researchers predict human visual attention using computer intelligence for the first time

Queen Mary, University of London scientists have developed a computer-based model to better understand change blindness--the inability for people to see obvious changes to scenes around them. "The biologically inspired mathematics we have developed and tested can have future uses in letting computer vision systems such as robots detect interesting elements in their visual environment," says Queen Mary professor Peter McOwan. As part of the study, participants were asked to spot the differences between pre-change and post-change versions of a series of pictures. To eliminate bias, the researchers developed an algorithm that enabled the computer determine how to change the images that study participants were asked to view. In addition to being able to predict change blindness, the research showed that the addition or subtraction of an object from the scene is detected more often than changes in the color of the object. The researchers say the model will be useful in designing displays for road signs, emergency services, security, and surveillance to draw attention to a change or part of the display that requires immediate attention.
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