CS: Far-Field Microscopy of Sparse Subwavelength Objects, Group Lasso estimation of high-dimensional covariance matrices, Postdoc and a CfP: special issue of Elsevier Signal Processing on Latent Variable Analysis and Signal Separation

The NAE Frontiers of Engineering site has a small introduction on  Compressive Sensing written by Emmanuel Candes.

Geoffroy Lerosey let me know of the following on arxiv (my keywords did not catch it)

Far-Field Microscopy of Sparse Subwavelength Objects by Alexander Szameit, Yoav Shechtman, H. Dana, S. Steiner, S. Gazit, T. Cohen-Hyams, E. Bullkich, O. Cohen, Yonina C. Eldar, S. Shoham, E. B. Kley, M. Segev. The abstract reads:

We present the experimental reconstruction of sub-wavelength features from the far-field of sparse optical objects. We show that it is sufficient to know that the object is sparse, and only that, and recover 100 nm features with the resolution of 30 nm, for an illuminating wavelength of \lambda=532 nm. Our technique works in real-time, requires no scanning, and can be implemented in all existing microscopes - optical and non-optical.

So shall we consider all microscope and camera to be de-facto compressive sensing devices :-) ? Also on arxiv:

Group Lasso estimation of high-dimensional covariance matrices by Jérémie Bigot, Rolando Biscay, Jean-Michel Loubes, Lilian Muniz Alvarez. The abstract reads:

In this paper, we consider the Group Lasso estimator of the covariance matrix of a stochastic process corrupted by an additive noise. We propose to estimate the covariance matrix in a high-dimensional setting under the assumption that the process has a sparse representation in a large dictionary of basis functions. Using a matrix regression model, we propose a new methodology for high-dimensional covariance matrix estimation based on empirical contrast regularization by a group Lasso penalty. Using such a penalty, the method selects a sparse set of basis functions in the dictionary used to approximate the process, leading to an approximation of the covariance matrix into a low dimensional space. Consistency of the estimator is studied in Frobenius and operator norms and an application to sparse PCA is proposed.

 Mark Plumbley just sent me the following job position (it has been added to the jobs page):

Postdoctoral Research Assistant: Sparse Representations for Audio Signals

Centre for Digital Music

 Queen Mary University of London

Applications are invited for a Postdoctoral Research Assistant to work on the EPSRC project "Machine Listening using Sparse Representations". The aim of this multi-person project is to investigate the automatic analysis and understanding of real-world sounds, using sparse representations and related methods.

The purpose of this particular post is to explore and develop underlying theory and efficient algorithms for sparse representations of audio, such as: efficient sparse recovery algorithms; sparse dictionary learning; compressed sensing of audio; application of sparse representations to the analysis of real-world sounds; and (optionally) to investigate parallels between sparse representations and biological processing of audio signals.

The project is based in the Centre for Digital Music (C4DM) at Queen Mary, University of London. C4DM is a world-leading multidisciplinary research group in the field of Digital Music & Audio Technology, and is part of the School of Electronic Engineering and Computer Science (EECS). Details about the School can be found at www.eecs.qmul.ac.uk, and about the Centre for Digital Music at www.elec.qmul.ac.uk/digitalmusic.

The post is full time for 24 months starting from 1st December 2010 or as soon as possible thereafter. Starting salary will be in the range £30,229 - £33,659 per annum inclusive of London Allowance. Benefits include 30 days annual leave, final salary pension scheme and interest-free season ticket loan.

Candidates must be able to demonstrate their eligibility to work in the UK in accordance with the Immigration, Asylum and Nationality Act 2006. Where required this may include entry clearance or continued leave to remain under the Points Based Immigration Scheme.

Informal enquiries should be addressed to the Prof Mark Plumbley at mark.plumbley@elec.qmul.ac.uk.

Details about the School can be found at www.eecs.qmul.ac.uk. Further details and an application form can be found at: www.hr.qmul.ac.uk/vacancies (http://webapps.qmul.ac.uk/hr/vacancies/jobs.php?id=1999).

To apply for this position, please email the following documents to Ms Julie Macdonald at applications@eecs.qmul.ac.uk: Completed application form quoting reference number 10371/CE; a CV listing all publications; a pdf of a representative publication and a research statement describing your previous research experience, outlining the relevance to this project. Postal applications should be sent to Ms Julie Macdonald, School of EECS, Queen Mary University of London, Mile End Road, London, E1 4NS.

The closing date for applications is 12 noon on Wednesday 27th October 2010. Applications received after this time may not be considered.

*Please note that applications will be rejected if they do not include a completed QMUL application form.*

Interviews are expected to be held on 11 November 2010.

Valuing Diversity & Committed to Equality

Finally, Remi Gribonval sent me this Call for Papers for a special issue of Elsevier Signal Processing on Latent Variable Analysis and Signal Separation

After the LVA/ICA 2010 conference in St-Malo, which featured several sessions on sparsity and dictionary learning, we are preparing an upcoming special issue of Elsevier Signal Processing on Latent Variable Analysis and Signal Separation.

Contributions are welcome both from attendees of the above conference and from authors who did not attend the conference but are active in areas of research such as low-rank matrix or tensor decompositions, dictionary design and learning. A broader list of examples of topics relevant to the special issue include:
- Non-negative matrix factorization
- Joint tensor factorization
- Latent variables
- Source separation
- Nonlinear ICA
- Noisy ICA
- BSS/ICA applications: image analysis, speech and audio data, encoding of natural scenes and sound, telecommunications, data mining, medical data processing, genomic data analysis, finance,...
- Unsolved and emerging problems: causality detection, feature selection, data mining,...

SUBMISSION INSTRUCTIONS:
Manuscript submissions shall be made through the Elsevier Editorial System (EES) at
http://ees.elsevier.com/sigpro/
Once logged in, click on “Submit New Manuscript” then select “Special Issue: LVA” in the “Choose Article Type” dropdown menu.

IMPORTANT DATES:
January 15, 2011: Manuscript submission deadline
May 15, 2011: Notification to authors
September 15, 2011: Final manuscript submission
December 15, 2011: Publication

GUEST EDITORS:
Vincent Vigneron, University of Evry – Val d’Essonne, France
Remi Gribonval, INRIA, France
Emmanuel Vincent, INRIA, France
Vicente Zarzoso, University of Nice – Sophia Antipolis, France
Terrence J. Sejnowski, Salk Institute, USA

The detailed call for papers is at http://www.irisa.fr/metiss/members/evincent/news_item.2010-10-07.3737705098