Adventures in Signal Processing and Open Science

Open review in the wild

Few journals and conferences so far seem to use open review. We mostly see open review practised as post-publication commenting on for example pubpeer.com where it so far seems to be mainly about spotting errors in already published papers.

I would personally like to see more open review employed by journals and conferences in the publishing of scientific papers to increase transparency in the process.
Today I have found such an example thanks to Igor Carron’s post The papers for ICLR 2015 are now open for discussion! The machine learning conference International Conference on Learning Representations uses an open review model where reviews are published, anyone can comment on the papers, and anyone can ask to become a designated reviewer: http://www.iclr.cc/doku.php?id=pubmodel.

Even though independent sites exist for post-publication commenting and review, I think it is especially exciting to see it being actively encouraged and fully integrated into the paper submission and acceptance process by the conference organisers. In addition to providing transparency in the process, I hope it also stimulates more discussion when the it is actively encouraged as we see here.

Publishing mathematics in ebooks – part 1

This is the first part of what I hope will be a series of posts on my explorations of how to author maths-heavy writing in ebook format.

I have for quite some time now been annoyed with PDFs on mobile phones and tablets. Although there are some fine PDF viewers avaible, it usually still takes a lot of annoying scrolling to read a scientific paper on my phone or tablet. On the other hand, I have recently read a few novels as ebooks on my phone and my tablet and this has been an entirely different, enjoyable experience. The main difference is that the text in ebooks is re-flowable so as to make it easily adaptable to the screen size and preferred font size. This makes ebooks seem like a promising choice as an alternative to PDF for distributing scientific papers in more screen-friendly format. There is just one hurdle: mathematicsRead the rest of this entry »

Should we pay reviewers for their work?

I have previously discussed paying reviewers for their work. Although that was in the slightly different context of attracting reviewers for open post-publication peer review, a new open access journal is now introducing this idea in their workflow: http://collabra.org/.

They do this by assigning reviewers and editors points for each paper they handle. A part of the APC of accepted papers goes into a pool and the accumulated points are then used as a basis of distribution to determine how large a bite of the cake each individual is payed. Editors and reviewers may then choose to keep the money, give the money back to the journal’s APC waiver pool, or donate it to their own university’s open access payments.

The journal has taken steps to ensure that this does not lead to inflation in the number of accepted papers just to earn points; editors and reviewers are assigned points for handling papers regardless of whether they are eventually accepted. Another IMO appealing feature of the journal is that reviews can be open if both authors and reviewers agree to this.

I am looking forward to seeing how this goes…

Workshop on Compressed Sensing in Wireless Communication

Qi Zhang, Jacek Pierzchlewski, and I (Thomas Arildsen) are organising a workshop on Compressed Sensing in Wireless Communication on May 22, 2015. The workshop is part of the conference European Wireless 2015 in Budapest, Hungary. Please see the workshop webpage for details on submission etc.

Teaching with the IPython Notebook

I have been teaching introductory Python for modelling and simulation and for scientific computing for a couple of years now. I am still somewhat new to Python myself, having “converted” from Matlab a couple of years ago. I find the open approach of using free and open source software instead of expensive proprietary software very motivating and I was easily talked into using it by my colleagues and quickly decided to base my teaching on it as well.
Read the rest of this entry »

Magni: A Python Package for Compressive Sampling and Reconstruction of Atomic Force Microscopy Images

Our new software metapaper Magni: A Python Package for Compressive Sampling and Reconstruction of Atomic Force Microscopy Images has just been published in Journal of Open Research Software. The paper describes our new software package Magni:

Magni is an open source Python package that embraces compressed sensing and Atomic Force Microscopy (AFM) imaging techniques. It provides AFM-specific functionality for undersampling and reconstructing images from AFM equipment and thereby accelerating the acquisition of AFM images. Magni also provides researchers in compressed sensing with a selection of algorithms for reconstructing undersampled general images, and offers a consistent and rigorous way to efficiently evaluate the researchers own developed reconstruction algorithms in terms of phase transitions. The package also serves as a convenient platform for researchers in compressed sensing aiming at obtaining a high degree of reproducibility of their research.

The software itself is on GitHub as well as on Aalborg University’s repository: DOI 10.5278/VBN/MISC/Magni

Go ahead and check it out if you are into compressed sensing or atomic force microscopy. Pull requests welcome if you have ideas.

Live-tweeting iTWIST 2014 workshop

As an experiment I am live-tweeting the workshop iTWIST in Namur, Belgium. Look for the tag #itwist14.
See also http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003789 for inspiration (by @collabchem and @eperlste)

Episciences.org progress

Lately, I have been following the Episciences project as you may have noticed in my previous post. It seems there has been some more progress recently: I have just noticed that another “epi-committee” has been added to the site (I understand these epi-committees as a sort of editorial boards responsible for a given subject area). In addition to the existing math committee, the new committee is Episciences IAM (Informatics and Applied Mathematics). This sounds a bit closer to my area. I wonder if they consider signal processing to be in their area?
The page so far says that the committee is being formed and as such does not list any members yet. It will be interesting to see what this turns into.

Episciences.org update

I mentioned the Episciences project the other day in Scientific journals as an overlay. In the meantime I have tried to contact the people behind this project and The Open Journal, apparently without any luck.

I went and checked the Episciences website yesterday and it actually seems that they are moving forward. They changed the page design completely and there is now a button in the upper right corner to create an account and log in. I took the liberty of doing so to have a look around. I was able to create an account, but is just about it so far. The site still seems quite “beta” – I was not able to save changes to my profile and I cannot yet find anywhere to submit papers. It is nice to see some progress on the platform and I will be keeping an eager eye on it to find out when they will go operational.

Compressed Sensing – and more – in Python

Compressed Sensing – and more – in Python

The availability of compressed sensing reconstruction algorithms for Python has so far been quite scarce. A new software package improves on this situation. The package PyUnLocBox from the LTS2 lab at EPFL is a convex optimisation toolbox using proximal splitting methods. It can, among other things, be used to solve the regularised version of the LASSO/BPDN optimisation problem used for reconstruction in compressed sensing:

\underset{x}{\mathrm{argmin}} \| Ax - y \|_2 + \tau \| x \|_1

See http://pyunlocbox.readthedocs.org/en/latest/tutorials/compressed_sensing_1.html

Heard through Pierre Vandergheynst.

I have yet to find out if it also solves the constrained version. Update: Pierre Vandergheynst informed me that the package does not yet solve the constrained version of the above optimisation problem, but it is coming:

\underset{x}{\mathrm{argmin}} \quad \| x \|_1 \\ \text{s.t.} \quad \| Ax - y \|_2 < \epsilon

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