Monday, September 15, 2014

Accelerating Random Forests in Scikit-Learn


Accelerating Random Forests in Scikit-Learn by Gilles Louppe



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CSjob: Associate professor in signal processing with special emphasis on compressive sensing, Aalborg, Denmark


Found on the interweb:

Associate professor in signal processing with special emphasis on compressive sensing (42102)

At the Faculty of Engineering and Science, Department of Electronic Systems, Signal and Informations Processing section (SIP), a position as associate professor in signal processing with special emphasis on compressive sensing is open for appointment from 1 October 2014 or soon hereafter. The position is for a period of four years. The Department of Electronic Systems is one of the largest departments at Aalborg University with a total of more than 300 employees. The department is internationally recognized in particular for its contributions within Information and Communication Technology (ICT). The research and teaching of the Department of Electronic Systems focus on electronic engineering and the activity areas are organized in five sections (http://www. es.aau.dk/research/sections/) The department also hosts some larger research centers (http://www. es.aau.dk/research/centres/) (with great international scope). The department focuses on maintaining a close interplay with the university’s surroundings - locally, nationally and internationally – as well as producing unique basic research and educating talented and creative engineers. The department collaborates with leading ICT researchers all over the world.   

Job description

4 years position as associate professor in signal processing with special emphasis on compressive sensing

For a project supported by the Danish Research Councils and Aalborg University we need an associate professor for compressive sensing applied to Atomic Force Microscopy. Significant teaching (up to around 50% average over the four years) must be expected. This can be at bachelor, master and PhD level. Documented pedagogical skills at university level is mandatory.

Competences:
  - Must have master and PhD degrees in signal processing or similar.
  - Experience in developing high quality computational software for open source projects    (also willing to make software publicly available as demanded by the research project).
  - Must be able to document research experience in compressive sensing – including non-ideal effects.
  - Experience in Python programming for computational science.
  - Documented pedagogical competences at university level (preferably in mathematical modeling, Python programming, compressive sensing, scientific computing and related areas).
  - Experience in developing new courses – preferably both at bachelor, master and PhD level.
  - Knowledge in Atomic Force Microscopy (AFM) is an advantage.

You may obtain further professional information from Professor, Dr.Techn. Torben Larsen, phone: +45 20206856 or e-mail: tl@es.aau.dk.

Qualification requirements:

The level of qualification for Associate Professors shall correspond to the level, which can be achieved on the basis of the appointment as Assistant Professor, but may be achievable in other ways. The appointment presupposes that the applicant can demonstrate original scientific production at an international level as well as documented teaching qualifications.

Appointment to the position requires that both research and teaching qualifications are at the requested level. The two qualifications will be given equal and principal priority in the overall assessment.

The application must contain the following:
• A motivated text wherein the reasons for applying, qualifications in relation to the position, and intentions and visions for the position are stated.
• A current curriculum vitae.
• Copies of relevant diplomas (Master of Science and PhD).
• Scientific qualifications. A complete list of publications must be attached with an indication of the works the applicant wishes to be considered. You may attach up to 10 publications.
• Teaching qualifications described in the teaching portfolio.  If this is not enclosed the applicant must include an explanation for its absence.
• Dissemination qualifications, including participation on committees or boards, participation in organisations and the like.
• Additional qualifications in relation to the position.
• References/recommendations.
• Personal data.
The applications are only to be submitted online by using the "Apply online" button below.

An assessment committee will assess all candidates.
For further information concerning the application procedure please contact Mads Brask Andersen by mail man@adm.aau.dk or phone (+45) 9940 9680.

Information regarding guidelines, ministerial circular in force, teaching portfolio and procedures can be seen here.

Agreement

Employment is in accordance with the Ministerial Order on the Appointment of Academic Staff at Universities (the Appointment Order) and the Ministry of Finance’s current Job Structure for Academic Staff at Universities. Employment and salary are in accordance with the collective agreement for state-employed academics or the collective agreement for academics under the Danish Society of Engineers’ (IDA) and the Danish Association of Chartered Surveyors’ (DDL) negotiation areas.   

Vacancy number

42102

Deadline

15/09/2014
 
 
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Thesis: Randomized Algorithms For Large-Scale Strongly Overdetermined Linear Regression Problems - Xiangrui Meng

Here is another thesis of interest: Randomized Algorithms For Large-Scale Strongly Overdetermined  Linear Regression Problems by Xiangrui Meng
In the era of big data, distributed systems built on top of clusters of commodity hardware provide cheap and reliable storage and scalable data processing. With cheap storage, instead of storing only currently relevant data, most people choose to store data as much as possible, expecting that its value can be extracted later. In this way, exabytes (1018) of data are being created on a daily basis. However, extracting value from big data requires scalable implementation of advanced analytical algorithms beyond simple data processing, e.g., regression analysis and optimization. Many traditional methods are designed to minimize floating-point operations, which is the dominant cost of in-memory computation on a single machine. In a distributed environment, load balancing and communication including disk and network I/O can easily dominate computation. These factors greatly increase the complexity and challenge the way of thinking in the design of distributed algorithms. Randomized methods for big data analysis have received a great deal of attention in recent years because they are generally faster and simpler to implement than traditional methods, it is easier to distribute the work, and they sometimes have theoretically provable performance. In this work, we are most interested in random projection and random sampling algorithms for `2 regression and its robust alternative, `1 regression, with strongly rectangular data. Random projection and random
sampling are used to create preconditioned systems that are easier to solve or sub-sampled problems that provide relative-error approximations. Our main result shows that in near input-sparsity time and only a few passes through the data we can obtain a good approximate solution, with high probability. Our theory holds for general p ∈ [1, 2], and thus we formulate our results in `p.
In the first chapter, we introduce `p regression problems and `p-norm conditioning, as well as traditional solvers for `p regression problems and how they are affected by the condition number.
The second chapter describes the solution framework, where we discuss how ellipsoidal rounding and subspace embedding are connected to `p regression and develop faster rounding and embedding algorithms via random projection and random sampling. Chapter 3 describes a parallel solver named LSRN for strongly over- or under-determined linear least squares (`2 regression), and Chapter 4 establishes the theory for `p subspace embedding and its application to `p regression.





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Friday, September 12, 2014

Job : Signal processing and signal fusion methods for irregularly sampled signals obtained with crowd-sensing. Application to air quality monitoring.

Matthieu Puigt just sent me the following after we talked at today's meeting:

As you offered during our chat, here is the advertisement I would appreciate you to share on Nuit Blanche. I thank you so much for your help.

Best regards,

Matthieu--


A few months ago, we had an opening for a Ph.D. student position (see below for the description of the subject). The thesis was about to start in October but the selected candidate just left us and, as the Ph.D. thesis is granted, we are urgently looking for a new candidate (the beginning of the thesis should be slightly delayed to November). More details about the subject can be found at: http://www-lisic.univ-littoral.fr/~puigt/jobs_opening.html

If you are interested in, please send us as soon as possible an e-mail with a resume, a cover letter, your grades and ranks obtained during your graduate studies, and two recommendation letters (or the name of two referents).

Otherwise, we thank you in advance for forwarding this message to potentially interested candidates.

Please find below the initial call:

Signal processing and signal fusion methods for irregularly sampled signals obtained with crowd-sensing. Application to air quality monitoring.

Descrition:

This PhD thesis will focus on semi-blind sensor calibration and on data assimilation, from data irregularly sampled in time and space. The proposed approaches will be tested in the framework of air quality monitoring by citizen sensing (crowd-sensing).

Crowd-sensing refers to the use of an important and diffuse group of people, connected via their smartphones which are sensing information about their environment. The sensed data are transmitted to a server which merges them. Indeed, smartphones may be viewed as acquisition devices since they contain a GPS, a compass, accelerometers, camera(s), microphones, wireless connections (WIFI, Bluetooth, etc) and can be connected to external devices. By encouraging people to measure the levels of air pollutants through an ad-hoc acquisition device, and by using the GPS geolocation data, we will get a database to be processed, e.g., using advanced matrix factorization approaches and data assimilation methods.

In particular, the work performed during this Ph.D. thesis will consist of:
  • proposing semi-blind calibration approaches for an heterogeneous sensor network, where the measure uncertainties due to the accuracy of the sensors will be considered,
  • developing fusion methods for irregularly sampled data, using semi-physical assimilation or matrix factorization.
This Ph.D. thesis will be done in the LISIC lab (http://www-lisic.univ-littoral.fr/), in Calais, France, in collaboration with the Spirals project-team (Inria Lille - Nord Europe) which will provide its APISENSE crowd-sensing software platform (http://www.apisense.fr/).

From the application point of view, the proposed methods will be tested in the framework of a collaboration with the following French associations:
  1. ATMO Nord-Pas de Calais (http://www.atmo-npdc.fr/home.htm), which is in charge to monitor the air quality in the Nord-Pas de Calais country (Northern French country),
  2. Bâtisseurs d'Economie Solidaire (http://www.ecozone-littoral.fr/), which will deploy the air sensing devices.

Eligibility:

The successful candidate should have an M.Sc. in computer science, electrical engineering, applied mathematics, or a related field. She / he should get a significant programming experience in Matlab and/or C/C++. An experience in matrix factorization, sparse approximation, or compressed sensing would be a plus.

The potential candidate is invited to contact the supervisors before May 1st 2014. She or he should provide a CV, a cover letter, the marks and ranks obtained during the graduate studies (even partially if the M.Sc is not yet defended), and two recommendation letters (or at least the names of two referents).

Keywords:

semi-blind sensor calibration, data assimilation, matrix factorization, sparsity, non-negativity, crowd-sensing, air quality monitoring.

Related work which was previously investigated in the research group:

[1] G. Roussel, L. Bourgois, M. Benjelloun, G. Delmaire, "Estimation of a semi-physical GLBE model using dual EnKF learning algorithm coupled with a sensor network design strategy: application to air field monitoring," Elsevier, Information Fusion 14 (2013), pp. 335-348, http://dx.doi.org/10.1016/j.inffus.2013.03.001

[2] M. Plouvin, A. Limem, M. Puigt, G. Delmaire, G. Roussel, D. Courcot, "Enhanced NMF initialization using a physical model for pollution source apportionment," in Proceedings of the 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2014), Bruges, Belgium, April 23-25, 2014.

[3] A. Limem, G. Delmaire, M. Puigt, G. Roussel, D. Courcot, "Non-negative matrix factorization using weighted Beta divergence and equality constraints for industrial source apportionment," in Proceedings of the 23rd IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2013), Southampton, UK, September 22-25, 2013.

[4] A. Limem, G. Delmaire, G. Roussel, D. Courcot, "Kullback-Leibler NMF Under Linear Equality Constraints. Application to Pollution Source Apportionment," in Proceedings of the International Conference on Information Science Signal Processing and their Applications (ISSPA 2012), Montreal, Canada, July 3-5, 2012.

Contact:

Gilles ROUSSEL (gilles.roussel [at] lisic.univ-littoral.fr)
Matthieu PUIGT (matthieu.puigt [at] lisic.univ-littoral.fr)
Gilles DELMAIRE (gilles.delmaire [at] lisic.univ-littoral.fr)


--
Matthieu PUIGT, Ph.D.

(1) Laboratoire d’Informatique Signal et Image de la Côte d’Opale (LISIC)
Université du Littoral Côte d'Opale (ULCO)
Maison de la Recherche Blaise Pascal
50 rue Ferdinand Buisson, B.P. 719
62228 Calais Cedex, France
E-mail: matthieu.puigt@lisic.univ-littoral.fr
Webpage: http://www-lisic.univ-littoral.fr/~puigt/

(2) IUT du Littoral Côte d'Opale
Département Génie Industriel et Maintenance
Avenue Descartes B.P. 40099
62698 Longuenesse Cedex, France
E-mail: matthieu.puigt@univ-littoral.fr
 
 
 
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CSMeeting : Acquisition/Echantillonnage comprimé : quelles réalisations/applications pratiques ?

Today, I'll be at this meeting entitled "Compressive Sensing, What Practical Applications ?" organized here in Paris.
 
« Acquisition/Echantillonnage comprimé : quelles réalisations/applications pratiques ? »
Journée thématique conjointe GDR/ISIS et GDR/SoC-SiP

Organisateurs :

Patricia Desgreys (LTCI/IMT/Télécom ParisTech, patricia.desgreys@telecom-paristech.fr) et Laurent Daudet (Institut Langevin / Université Paris Diderot, laurent.daudet@espci.fr )

Objectifs :

Cette journée thématique organisée conjointement par les GdR ISIS et SoC-SiP est consacrée aux applications concrètes des travaux dans le domaine de l’acquisition comprimée (ou Compressed Sensing, CS) depuis que le concept a été défini par E. Candes et D. Donoho en 2004. Cette théorie montre que la plupart des signaux (à représentation parcimonieuse dans une base élémentaire) peuvent être échantillonnés linéairement avec un taux d’échantillonnage très faible (proportionnel à leur parcimonie). Ainsi le CS permet d’acquérir une information avec un nombre réduit d’observations, potentiellement bien moindre que selon les critères de Shannon-Nyquist.
Depuis ces publications fondatrices, l’acquisition comprimée a été très étudiée, particulièrement pour le choix des bases de projection et l’optimisation des algorithmes de reconstruction. Concrètement le CS peut être utilisé pour réduire la complexité et la consommation dans un grand nombre de domaine d’applications : radio-intelligente, acquisition d’ondes acoustiques ou optiques, acquisition de signaux médicaux. Plus récemment, des réalisations physiques illustrant les principes de l’acquisition comprimée ont été publiées pour la conversion analogique à information (A2I), l’acquisition de signaux radio, ou l’imagerie.
Nous proposons, lors de cette journée, de faire un bilan des applications concrètes visées par le CS, des premières réalisations et de leurs performances.
La matinée sera consacrée à deux exposés tutoriels, de 1h chacun (45’ + 15’ de questions) :
  • C. Studer (Cornell University) and Qiuting Huang (ETH Zurich): Analog-to-Information Converters: From Applications to Circuits
  • Laurent Jacques (UC Louvain) : Optique et acquisition comprimée : déflectométrie schlieren compressive et reconstruction de cartes d'indice de réfraction
L’après-midi sera consacré à des exposés courts (20’ + 10’ de questions).
Toute personne intéressée par cette journée est priée de s’inscrire sur le site http://www.gdr-isis.fr/.


Programme

Programme de la journée :  (susceptible de modifications)

9:30 - 10:00 accueil café

10:00 - 11:00  Christoph Studer (Cornell University) : Analog-to-Information Converters: From Applications to Circuits
11:00 - 12:00  Laurent Jacques (UC Louvain) : Optique et acquisition comprimée : déflectométrie schlieren compressive et reconstruction de cartes d'indice de réfraction

12:00 - 13:30  pause déjeuner

13:30 - 14:00  Claire Boyer : An analysis of blocks sampling strategies in compressed sensing
14:00 - 14:30  Patrice Simard : Représentation parcimonieuse et codage compressif; application à la restauration d'un signal créneau
14:30 - 15:00  Guillaume Neveu : Utilisation de la technique d'échantillonnage compressé pour la caractérisation de dispositifs RF non linéaires

15:00 - 15:30  pause café

15:30 - 16:00  Nicolas Chauffert : Variable density sampling in MRI, from Compressed Sensing to admissible trajectories
16:00 - 16:30  Laurent Daudet : Compressive Imaging Using a Multiply Scattering Medium
 
 
 
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Thursday, September 11, 2014

A Detailed Overview of Nanopore Sequencing

Because the sensor technology is so important [0], I recently mentioned why long read technology was such a breakthrough (it's an information theoretic issue [5]). For the past two years, we have featured, here on Nuit Blanche, at least three makers of sequencing technology that will change the course of how we do many things. Those outfits are:
Nick Loman is one of the few people who has had access to Oxford Nanopore Technologies' long read technology through their early access program. To get a sense of how the technology will develop, he just organized a Hangout yesterday with Clive Brown of Oxford Nanopore Technologies. You really need to watch this video of the hangout to get a sense of the algorithms being envisionned and those already implemented for alignement purposes. In a future entry, I will talk specifically about Clive's presentation as I think there is potentially some additional information to be gained from their raw data in light of recent advances in compressive sensing and attendant algorithm development, stay tuned. Without further ado, here is the program of the video: 
  • Clive Brown, Nanopore sequencing 
  • Nick Loman, Early data from nanopore sequencing: bioinformatics opportunities and challenges 
  • Matt Loose, Streaming data solutions for nanopore 
  • Josh Quick, Nanopore sequencing in outbreaks 
  • Torsten Seemann, Awesome pipelines for microbial genomics 

and the video, enjoy !

 
Thank you Nick !
 
 
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The Long Distance Blogger




In May, Nuit Blanche reached 1 million visits and its 3,000th post, then it was 3 million page views in June and now we just passed our 2,000th post on Compressive Sensing in September. All this, ten years after the first set of communications on the subject.

You want to show your appreciation ? Don't hesitate to provide a recommendation on LinkedIn under my profile like the few of you who already have.


Also don't hesitate to join/comment on the Google+ Community (1086), the CompressiveSensing subreddit (465), the LinkedIn Compressive Sensing group (3039) or the Advanced Matrix Factorization Group (951) 

Credit: ESA, #Rosetta comet #67P Image: ESA/Rosetta/Philae/CIVA pic.twitter.com/JJwJEWEEDd
 
 
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Testable uniqueness conditions for empirical assessment of undersampling levels in total variation-regularized x-ray CT

This is the 2000th blog entry with a Compressive Sensing tag. Woohoo !


In the Map Makers, one can see that the sharp phase transitions can be used in many ways. One of the ways is to figure ut if a specifc mesurement essemble can be used efficiently. Another is to figure out if a specific technology can be improved. That discussion was somehow started for CT by Xiaochuan Pan, Emil Sidky and Michael Vannier in Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction? and later [2]. A while back, I used a rule of thumb using sparsity-only to look into the issue of CT with an assumption on the measurement matrix [1]. Today, we have a very well designed and deep study of the subject with the intent described by the authors that "the goal was to simply document phase transition behavior for CT measurements.". This is awesome ! Without further ado: Testable uniqueness conditions for empirical assessment of undersampling levels in total variation-regularized x-ray CT by Jakob S. Jørgensen, Christian Kruschel, Dirk A. Lorenz

We study recoverability in fan-beam computed tomography (CT) with sparsity and total variation priors: how many underdetermined linear measurements suffice for recovering images of given sparsity? Results from compressed sensing (CS) establish such conditions for, e.g., random measurements, but not for CT. Recoverability is typically tested by checking whether a computed solution recovers the original. This approach cannot guarantee solution uniqueness and the recoverability decision therefore depends on the optimization algorithm. We propose new computational methods to test recoverability by verifying solution uniqueness conditions. Using both reconstruction and uniqueness testing we empirically study the number of CT measurements sufficient for recovery on new classes of sparse test images. We demonstrate an average-case relation between sparsity and sufficient sampling and observe a sharp phase transition as known from CS, but never established for CT. In addition to assessing recoverability more reliably, we show that uniqueness tests are often the faster option.
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Wednesday, September 10, 2014

Randomized Nonlinear Component Analysis - implementation -




Classical methods such as Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) are ubiquitous in statistics. However, these techniques are only able to reveal linear relationships in data. Although nonlinear variants of PCA and CCA have been proposed, these are computationally prohibitive in the large scale.
In a separate strand of recent research, randomized methods have been proposed to construct features that help reveal nonlinear patterns in data. For basic tasks such as regression or classification, random features exhibit little or no loss in performance, while achieving drastic savings in computational requirements.
In this paper we leverage randomness to design scalable new variants of nonlinear PCA and CCA; our ideas extend to key multivariate analysis tools such as spectral clustering or LDA. We demonstrate our algorithms through experiments on real-world data, on which we compare against the state-of-the-art. A simple R implementation of the presented algorithms is provided.
The implementation is here.

Let me note, something we pointed out earlier on Nuit Blanche:

It is of special interest that randomized algorithms are in many cases more robust than their deterministic analogues (Mahoney, 2011) because of the implicit regularization induced by randomness.
Indeed the seminal paper by Mike Mahoney was very clear on the advantages of randomization. Re-reading the introduction makes it plainly clear and is the basis for RandNLA (Randomized Numerical Linear Algebra)




(Submitted on 29 Apr 2011 (v1), last revised 15 Nov 2011 (this version, v3))

Randomized algorithms for very large matrix problems have received a great deal of attention in recent years. Much of this work was motivated by problems in large-scale data analysis, and this work was performed by individuals from many different research communities. This monograph will provide a detailed overview of recent work on the theory of randomized matrix algorithms as well as the application of those ideas to the solution of practical problems in large-scale data analysis. An emphasis will be placed on a few simple core ideas that underlie not only recent theoretical advances but also the usefulness of these tools in large-scale data applications. Crucial in this context is the connection with the concept of statistical leverage. This concept has long been used in statistical regression diagnostics to identify outliers; and it has recently proved crucial in the development of improved worst-case matrix algorithms that are also amenable to high-quality numerical implementation and that are useful to domain scientists. Randomized methods solve problems such as the linear least-squares problem and the low-rank matrix approximation problem by constructing and operating on a randomized sketch of the input matrix. Depending on the specifics of the situation, when compared with the best previously-existing deterministic algorithms, the resulting randomized algorithms have worst-case running time that is asymptotically faster; their numerical implementations are faster in terms of clock-time; or they can be implemented in parallel computing environments where existing numerical algorithms fail to run at all. Numerous examples illustrating these observations will be described in detail.



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Tuesday, September 09, 2014

Sparse recovery by means of nonnegative least squares - implementation -

Sparse recovery by means of nonnegative least squares by Simon Foucart, David Koslicki
This short note demonstrates that sparse recovery can be achieved by an `1-minimization ersatz easily implemented using a conventional nonnegative least squares algorithm. A connection with orthogonal matching pursuit is also highlighted. The preliminary results call for more investigations on the potential of the method and on its relations to classical sparse recovery algorithms.
The implementation is on Simon Foucart's publication page.


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