Monday, January 02, 2012

Could SOPA shut down Nuit Blanche ? and responding to OSTP's RFI

I wanted to write on this but Dave Bacon wrote a good summary on this. The short of it is  "an accusation of alleged infringement" is good enough to shut down a service like this blog, since at some point in time, it won't matter if publishing a copyrighted abstract or using figures from journals are really fair use. SOPA as currently written is badly written and will eventually get abused because of its uncertain language.

On a different level, Dave Bacon also mentions an Request For Interest for the Office of Science and Technology Policy (OSTP) which ends tonight (January 2) on Public Access to Peer-Reviewed Scholarly Publications Resulting From Federally Funded Research. The RFI is here.

Who should respond ?

".. The Task Force is now seeking additional insight from ``non-Federal stakeholders, including the public, universities, nonprofit and for- profit publishers, libraries, federally funded and non-federally funded research scientists, and other organizations and institutions with a stake in long-term preservation and access to the results of federally funded research,'' as described in Section 103(b)(6) of the ACRA. Specifically, OSTP seeks further public comment on the questions listed below, on behalf of the Task Force:.."
Essentially most of the readership of this blog. Here are the questions:

"...Specifically, OSTP seeks further public comment on the questions listed below, on behalf of the Task Force:


  1. Are there steps that agencies could take to grow existing and new markets related to the access and analysis of peer-reviewed publications that result from federally funded scientific research? How can policies for archiving publications and making them publically accessible be used to grow the economy and improve the productivity of the scientific enterprise? What are the relative costs and benefits of such policies? What type of access to these publications is required to maximize U.S. economic growth and improve the productivity of the American scientific enterprise? 
  2. What specific steps can be taken to protect the intellectual property interests of publishers, scientists, Federal agencies, and other stakeholders involved with the publication and dissemination of peer-reviewed scholarly publications resulting from federally funded scientific research? Conversely, are there policies that should not be adopted with respect to public access to peer-reviewed scholarly publications so as not to undermine any intellectual property rights of publishers, scientists, Federal agencies, and other stakeholders? 
  3. What are the pros and cons of centralized and decentralized approaches to managing public access to peer-reviewed scholarly publications that result from federally funded research in terms of interoperability, search, development of analytic tools, and other scientific and commercial opportunities? Are there reasons why a Federal agency (or agencies) should maintain custody of all published content, and are there ways that the government can ensure long-term stewardship if content is distributed across multiple private sources? 
  4. Are there models or new ideas for public-private partnerships that take advantage of existing publisher archives and encourage innovation in accessibility and interoperability, while ensuring long- term stewardship of the results of federally funded research? 
  5. What steps can be taken by Federal agencies, publishers, and/or scholarly and professional societies to encourage interoperable search, discovery, and analysis capacity across disciplines and archives? What are the minimum core metadata for scholarly publications that must be made available to the public to allow such capabilities? How should Federal agencies make certain that such minimum core metadata associated with peer-reviewed publications resulting from federally funded scientific research are publicly available to ensure that these publications can be easily found and linked to Federal science funding? 
  6. How can Federal agencies that fund science maximize the benefit of public access policies to U.S. taxpayers, and their investment in the peer-reviewed literature, while minimizing burden and costs for stakeholders, including awardee institutions, scientists, publishers, Federal agencies, and libraries? 
  7. Besides scholarly journal articles, should other types of peer- reviewed publications resulting from federally funded research, such as book chapters and conference proceedings, be covered by these public access policies? 
  8. What is the appropriate embargo period after publication before the public is granted free access to the full content of peer-reviewed scholarly publications resulting from federally funded research? Please describe the empirical basis for the recommended embargo period. Analyses that weigh public and private benefits and account for external market factors, such as competition, price changes, library budgets, and other factors, will be particularly useful. Are there evidence-based arguments that can be made that the delay period should be different for specific disciplines or types of publications? Please identify any other items the Task Force might consider for Federal policies related to public access to peer-reviewed scholarly publications resulting from federally supported research. 


Response to this RFI is voluntary. Responders are free to address any or all the above items, as well as provide additional information that they think is relevant to developing policies consistent with increased public access to peer-reviewed scholarly publications resulting from federally funded research. Please note that the U.S. Government will not pay for response preparation or for the use of any information contained in the response..."


Also

"...How To Submit a Response All comments must be submitted electronically to: publicaccess@ostp.gov. Responses to this RFI will be accepted through January 2, 2012. You will receive an electronic confirmation acknowledging receipt of your response,..."

If you submit something it will eventually be made public. Today is the last day,  the response date has been extended till January 12, 2012.





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Discussions

There are  now 1221 members on the LinkedIn compressive sensing group and 141 on the LinkedIn Matrix Factorization group asking questions and/or giving answers. Shouldn't you be one of them ?

Also on Quora, here is a list of questions of interest:

Saturday, December 31, 2011

Thank You and Happy New Year

In particular, let me thank the following people who made these  "nuit blanches" in 2011 more entertaining:

Yves Wiaux, Stephen Becker, Jong Chul Ye , Sergey Ten, Sylvain Gigan, Tom Kaye, Art Anderson, Justin Ziniel, Jerome Gilles, Son Hua, Bo Zhang, Mateusz Malinowski, Patrick Gill, Henrik Ohlsson, Jason T. Parker, Volkan Cevher, Philip Schniter, Jason Laska, Mark Davenport, Florent Krzakala, Lenka Zdeborova , Marc Mézard, François Sausset,Yifan Sun , Thong Do, Gael Varoquaux, Matthieu Puigt, Ming Yan, Laurent Duval. Peter Gerstoft, Kiryung Lee, Yoram Bresler, Aswin C. Sankaranarayanan, Daniel Reetz, Dror Baron, Olivier Grisel, Hervé Jégou, Danny Bickson, Joel Tropp, Piotr Indyk, Gongguo Tang, Silvio Ventres, Marco Duarte, Vicente Malave, Zeno Ganter, Tianyi David Zhou, Ulugbeck Kamilov, Mark Tygert, Jared Tanner, Bob Sturm, Henry Pfister, Andreas Tillman, Mike Wakin, Mark Davenport, Genevera Allen, Xiaobo Qu, Jake Hofman, Rebecca Willett, Martin Jaggi, Nematollah K. Batmanghelich, Ori Katz, Zai Yang, Ivan Papusha, Hyrum Anderson, Greg Charvat, David San Segundo, Tibault Reveyrand, Laurent Jacques, Eric Tramel, Daniel Reetz, Alain K., Dick Gordon, Cable Kurwitz, Emily Allstot, Remi Gribonval, Max Little, Angshul Majumdar, Dirk Lorenz, Julien Mairal, Jort Gemmeke, Krzysztof, Ilan, Andrew McGregor, Piotr Indyk, Laurent Daudet, Namrata Vaswani, Jim Fowler, Rishi Gupta, Thomas, Ramesh Raskar, Zhilin Zhang, Petros Boufounos, Thomas Arildsen, R. H. Sri Hari, Ravi Kiran B., Pierre Vandergheynst, Emmanuel Candes, Gabriel Peyre, Gilles Chardon, Sebastien Popoff, Geoffroy Lerosey, Borhan Sannadaji, Steven Vandeput,  Maria Jafari, Stefano Marchesini, Alessandro Foi, Kun Qiu, Vladimir Kofman, Ori Shental, Emil Sidky, Varun AV, Tim Lin , Mohammadreza (Reza) Mahmudimanesh and Alejandro Weinstein



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Inception and Philosophy





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We're the 1%

Thank you Stan.


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Friday, December 30, 2011

Entangled-photon compressive ghost imaging


Here the last paper of the year: Entangled-photon compressive ghost imaging by Petros Zerom, Kam Wai Clifford Chan, John C. Howell, and Robert W. Boyd. The abstract reads:
We have experimentally demonstrated high-resolution compressive ghost imaging at the single-photon level using entangled photons produced by a spontaneous parametric down-conversion source and using single-pixel detectors. For a given mean-squared error, the number of photons needed to reconstruct a two-dimensional image is found to be much smaller than that in quantum ghost imaging experiments employing a raster scan. This procedure not only shortens the data acquisition time, but also suggests a more economical use of photons for low-light-level and quantum image formation.

Since it is behind a paywall, the following presentations might provide some additional insight:









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It's not a spinning disk either ...

Remember when a year and a half ago, I mentioned that the 'thing' that enables one to render compressive measurement universal and timeless should not be called a light modulating device or a rotating collection of masks, but rather a Randomized Carousel.



Mad Men ´The Carousel´ from Emilio on Vimeo.



Well, it looks like we have an instance of one: Spinning disk for compressive imaging by H. Shen, L. Gan, N. Newman, Y. Dong, C. Li, Y. Huang, and Y. C. Shen. The abstract reads:

We report the first, to the best of our knowledge, experimental implementation of a spinning-disk configuration for high-speed compressive image acquisition. A single rotating mask (i.e., the spinning disk) with random binary patterns was utilized to spatially modulate a collimated terahertz (THz) or IR beam. After propagating through the sample, the THz or IR beam was measured using a single detector, and THz and IR images were subsequently reconstructed using compressive sensing. We demonstrate that a 32-by-32 pixel image could be obtained from 160 to 240 measurements in both the IR and THz ranges. This spinning-disk configuration allows the use of an electric motor to rotate the spinning disk, thus enabling the experiment to be performed automatically and continuously. This, together with its compact design and computational efficiency, makes it promising for real-time imaging applications


While the paper is behind a paywall, here are the videos showing some results of reconstruction using:

TV-min nonlinear algorithm
   
 TV-min
   
 MMSE linear estimation
 


This will be added to compressive sensing hardware shortly.

Credit: AMC, Mad Men.
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Thursday, December 29, 2011

Sparse- and Low-Rank Approximation Wiki

Stephen Becker let me know of this new wiki he set up: the Sparse- and low-rank Approximation Wiki. From the introduction:
"...Welcome to the sparse- and low-rank approximation wiki. This wiki has information on solvers and problems that arise in these fields (and subfields, such as compressed sensing). Everyone is welcome and encouraged to edit this wiki; it runs on the same software as wikipedia. Please use common-sense and standard etiquette when editing. The first step to editing is to create an account. For an idea of where to start editing, see pages that are wanted. If you have an implementation of an algorithm, please link to it! There are many excellent lists of related material on the web. This wiki is not trying to duplicate this information, but rather provide a less comprehensive but more detailed listing of available methods. The goal is that this website becomes a useful tool for practitioners searching for the ideal algorithm, and for researchers wanting to develop new methods..."
I have started adding a few resources, don't hesitate to participate. Thanks Stephen for the initiative.



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Wednesday, December 28, 2011

Compressive MUSIC, Forward/Backward Compressive Subspace Fitting Implementations

Jong Chul Ye alerted me to this new resource:
".....Hi Igor,

....  I would like to bring your attention to our website with a compressive sensing joint sparse recovery software packages for multiple measurement vector (MMV) problems.
The current version of package includes three algorithms to address MMV problems:
  1. Compressive MUSIC (CS-MUSIC)
  2. Forward Compressive Subspace Fitting (forward CSF)
  3. Backward Compressive Subspace Fitting (backward CSF)
More specifically, Compressive MUSIC identifies the parts of support using CS, after which the remaining supports are estimated using a novel generalized MUSIC criterion. Using a large system MMV model, we showed that our compressive MUSIC requires a smaller number of sensor elements for accurate support recovery than the existing CS methods and that it can approach the optimal L0-bound with finite number of snapshots. The theoretical analysis of Compressive MUSIC will appear in 2012 January Issue of IEEE Trans. on Information Theory.
While these type of hybrid algorithms are optimal for critically sampled cases, they have limitations in exploiting the redundant sampling to improve noise robustness. To address this issue,   we recently introduced a novel subspace fitting criterion that extends the generalized MUSIC criterion so that it exhibits near-optimal behaviors for various sampling conditions. In addition, the subspace fitting criterion leads to two alternative forms of compressive subspace fitting (CSF) algorithms with forward and backward support selection (forward CSF, and backward CSF), which significantly improve the noise robustness.
Related manuscripts can be downloaded from the following links:
[1] J. M. Kim, O. K. Lee, and J. C. Ye. Compressive MUSIC: A Missing Link between Compressive Sensing and Array Signal Processing. IEEE Trans. Inf. Theory, Jan. 2012 [preprint is here]
[2] J. M. Kim, O. K. Lee, and J. C. Ye, 2011. Noise Robust Joint Sparse Recovery using Compressive Subspace Fitting.  arXiv:1112.3446v1 [cs.IT]
If you have any question, please feel free to contact me.
Happy New Year !
-Jong..."

Thanks  Jong . This webpage will be both featured in the Big Picture in Compressive Sensing and in the Matrix Factorization Jungle Page..The earlier mention "Compressive MUSIC with optimized partial support for joint sparse recovery by Jong Min Kim, Ok Kyun Lee, Jong Chul Ye [no code]" will now be replaced with a link to the page where the code resides.


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