The Nuit Blanche Mailbag

MIA2012 starts today. The Call for papers: Advances in signal and image processing for physico-chemical analysis has been extended. Following up on the X-Prize Tricorder Challenge,  I already have gotten a person interested. Also Phil Schniter sent me this:

Dear Igor,

I'm writing to let you know about two faculty positions in _Machine Learning_ that we recently announced at the Ohio State University. Both faculty will be jointly appointed across the departments of ECE and Biomedical Informatics (BMI). We seek candidates who will make solid theoretical contributions, and who are also excited by the real-world datasets that BMI has to offer.

More details about these positions can be found at: https://ece.osu.edu/about/employment

Perhaps you can mention this on your blog.

Thanks and best regards,

Phil 

Thanks  Phil  for the heads-up. Dick Gordon also mentioned one of his paper in Biotechnology Focus back in 2000:

Dear Igor,

Wrote:

Blyden, E.R. & R. Gordon (2000). Genomics, pharmacology and 3D imaging: self-knowledge in the post-genomic era. Biotechnology Focus 3(6), 14, 16.

a while ago. Must be 15 diseases diagnosable via ultrasound, so that’s where I’d start.

Fifteen diseases detected through ultrasound ?! I did not realize they was even one. Thanks Dick. Finally,  Jim Fowler sent the following:

Hi Igor,

I would like to let you know of a journal article that we will have appearing soon in Foundations and Trends in Signal Processing. The details are:

J. E. Fowler, S. Mun, and E. W. Tramel, “Block-Based Compressed Sensing of Images and Video,” Foundations and Trends in Signal Processing, to appear.

Abstract:

A number of techniques for the compressed sensing of imagery are surveyed. Various imaging media are considered, including still images, motion video, as well as multiview image sets and multiview video. A particular emphasis is placed on block-based compressed sensing due to its advantages in terms of both lightweight reconstruction complexity as well as a reduced memory burden for the random-projection measurement operator. For multiple-image scenarios, including video and multiview imagery, motion and disparity compensation is employed to exploit frame-to-frame redundancies due to object motion and parallax, resulting in residual frames which are more compressible and thus more easily reconstructed from compressed-sensing measurements. Extensive experimental comparisons evaluate various prominent reconstruction algorithms for still-image, motion-video, and multiview scenarios in terms of both reconstruction quality as well as computational complexity.

PDF at: http://www.ece.msstate.edu/~fowler/Publications/FMT2012.html
Best Regards,
-Jim 

Thanks Jim.

One of you asked for permission to make this blog more accessible in the Middle Empire, it is a great initiative.For those of you who want to catch, you may want to download The Nuit Blanche Chronicles featuring a pdf of all the entries of most of 2011.

Finally, in the Matrix Factorization Jungle Group on LinkedIn, Danny Bickson asked the following about a GraphLab workshop:

GraphLab workshop?

Hi all,

We are thinking about arranging a graphlab workshop in the bay area around April. We thought about having demos and tutorials about graphlab v2 with some contributed talks from industry about future challenges in large scale machine learning. A preliminary list of companies who already confirmed their participation: Intel, Cloudera, WallMart Labs, Technicolor Labs, LinkedIn, Pandora Internet Radio, Oracle Labs and multiple startups. If you are interested in participating and or giving a talk please contact me.

We are also checking the possibility of planning a similar workshop at the east coast. Here we are less sure about the demand. Please contact me if you are interested!

Thanks a lot!

As a reminder, there are currently 154 members in that group and 1253 members in the compressive sensing group.

Image Credit: NASA/JPL/Space Science Institute
Full-Res: W00071706.jpg
W00071706.jpg was taken on January 13, 2012 and received on Earth January 15, 2012. The camera was pointing toward SATURN at approximately 2,801,959 kilometers away, and the image was taken using the CB2 and CL2 filters.




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