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Deadline Extension: BBCI Workshop 2009 on Advances in Neurotechnology (Berlin, July 8-10)

Due to several requests and the fact that June 1st is a national holiday in some countries, we have extended the poster submission deadline as well as the early registration deadline.

Call for Participation
Call for Poster Submissions

BBCI Workshop 2009
July 8-10, Berlin, Germany

http://bbci.agilemeetings.com

IMPORTANT DATES

* Submission Date: 2009-06-09 **extended**
* Notification of Acceptance: 2009-06-15 **extended**
* Workshop Date: 2009-07-08 – 2009-07-10

ORGANIZATION

* Bernstein Focus: Neurotechnology (BFNT-B) in cooperation with the
* Bernstein Center for Computation Neuroscience Berlin (BCCN-B) and the
* Berlin Institute of Technology (TUB)

Klaus-Robert Mueller, TUB and BFNT-B
Benjamin Blankertz, TUB and BFNT-B
John-Dylan Haynes, BCCN-B
Michael Tangermann, TUB
Steven Lemm, Fraunhofer FIRST
Matthias L. Jugel, BFNT-B

Andrea Gerdes, TUB/Workshop Secretary

DESCRIPTION

Different approaches to Brain-Computer Interfaces have been developed, each one with specific solutions that range from understanding and explaining cognitive functions to communicating with real and virtual environments by thought alone.

The Berlin BCI Workshop presents an overview, in-depth tutorials and discussions on the latest research at all levels of interaction. The research presented will cover invasive recording, with its high temporal and spatial resolution, semi-invasive ECoG, non-invasive EEG, with high temporal and low spatial resolution, non-invasive NIRS and fMRI measurement, with partially high spatial and low temporal resolution and potential combinations of the different methods.

The workshop programme includes one day full of tutorials on invasive BCI, electro-physiology and non-invasive BCI. The two other workshop days cover all aspects of invasive and non-invasive EEG, NIRS and fMRI, plus informative results of the “TOBI: Tools for Brain-Computer Interaction” project, aspects of Brain@Work (neurotechnology-based man-machine interaction for industrial applications) and our newly founded Bernstein Focus: Neurotechnology (Noninvasive Neurotechnologies for Man-Machine Interactions). The poster session following the tutorials will cross over into the BBCI barbecue, smoothing discussions with drinks and food.

SUBMISSION

Please send your posters (in PDF) or abstracts (max. 2 pages, PDF or plain text) to the poster chair Michael Tangermann , no later than 2009-06-09.

Maximum final poster size is A0 (width x height: 841mm × 1189mm).

CONFIRMED SPEAKERS

– Miguel Nicolelis, Duke University
– Gerwin Schalk, Wadsworth Center
– Theresa Vaughan, Wadsworth Center
– Niels Birbaumer, Eberhard-Karls-University Tuebingen
– Nikos K. Logothetis, MPI for Biological Cybernetics
– Gert Pfurtscheller, University of Graz
– Eberhard E. Fetz, University of Washington
– Christa Neuper, University of Graz
– Lars Kai Hansen, Technical University of Denmark
– Jack Gallant, University of California – Berkeley
– Rainer Goebel, Maastricht University
– Alexander Gail, Georg-August-Universitaet Goettingen
– Gert Pfurtscheller, University of Graz
– Theresa Vaughan, Wadsworth Center
– Eilon Vaadia, Hebrew University of Jerusalem
– Ruediger Rupp, Stiftung Orthopaedische Universitaetsklinik, Heidelberg
– Gabriel Curio, Charité Berlin
– John-Dylan Haynes, Bernstein Center for Computational Neuroscience
– Klaus-Robert Mueller, Berlin Institute of Technology
– Benjamin Blankertz, Berlin Institute of Technology
– Michael Tangermann, Berlin Institute of Technology

VENUE

Bernstein Center for Computational Neuroscience Berlin, located at the beautiful campus Mitte of the Charité – Universitätsmedizin Berlin.

http://www.bccn-berlin.de/

WORKSHOP FEES

Business: 300 EUR
Standard: 250 EUR

* Early Registration Discount (before 2009-06-18)

– Academic : 200 EUR
– Bernstein: 100 EUR
– Students : 50 EUR

FUNDING

The workshop is supported by the Bernstein Focus: Neurotechnology Berlin and an application for PASCAL funding is pending.

Postdoctoral Position, Kings College London

Area: interface between bio-informatics, applied mathematics, and statistical mechanics

Subject keywords:
data integration, machine learning, Bayesian analysis, gene arrays

Duration: 3 years

Starting date: Sept 2009

Closing date for applications: June 12th 2009

The aim of the project is to perform a Bayesian classification and regression analysis by integrating data representing tumour image traits (MRI scans, mammograms, FRET/FLIM, and PET images) with gene expression profiles, in order to predict clinical outcomes and treatment response for breast cancer patients. We also expect to study the gene regulation networks involved in the disease progression, possibly using additional public data sets.

The candidate will assess existing statistical and machine learning methods to extract and integrate information from gene array, image traits and biophysical experiments. He/she must be able to develop and implement new or improved algorithms to make the best use of available data. The candidate will also perform mathematical investigations of gene regulatory networks and their disruptions caused by cancer.

Ideally, the candidate for this position would have a very solid mathematical background, preferably in statistical mechanics and/or statistical (machine) learning theory, and have experience in and affinity for dealing with biological data. The candidate must have scientific programming experience, and be comfortable with communicating with scientists from different backgrounds (mathematicians, biologists, physicists, and physicians).

Application forms:
www.mth.kcl.ac.uk/~tcoolen/postdoc1/

Further information:

Dr E Blanc
MRC Centre for Developmental Neurobiology
and KCL Centre for Bioinformatics (KCBI)
eric.blanc (at) kcl.ac.uk

Prof ACC Coolen
Department of Mathematics
ton.coolen (at) kcl.ac.uk

Bats recognize the individual voices of other bats

Bats can use the characteristics of other bats’ voices to recognize each other, according to a study by researchers from the University of Tuebingen, Germany and the University of Applied Sciences in Konstanz, Germany. The study, published June 5 in the open-access journal PLoS Computational Biology, explains how bats use echolocation for more than just spatial knowledge.

The researchers first tested the ability of four greater mouse-eared bats to distinguish between the echolocation calls of other bats. After observing that the bats learned to discriminate the voices of other bats, they then programmed a computer model that reproduces the recognition behaviour of the bats. Analysis of the model suggests that the spectral energy distribution in the signals contains individual-specific information that allows one bat to recognize another.

Animals must recognize each other in order to engage in social behaviour. Vocal communication signals are helpful for recognizing individuals, especially in nocturnal organisms such as bats. Little is known about how bats perform strenuous social tasks, such as remaining in a group when flying at high speeds in darkness, or avoiding interference between echolocation calls. The finding that bats can recognize other bats within their own species based on their echolocation calls may therefore have some significant implications.

The full article is available at the following link
http://dx.plos.org/10.1371/journal.pcbi.1000400

###

FINANCIAL DISCLOSURE: This work was funded by SFB 550, by the Graduiertenkolleg Neurobiologie. It was supported in part by the IST Program of the European Community, under the PASCAL network of excellence, IST-2002-506778. This work was also supported by the human resources and mobility activity Marie Curie host fellowships for early stage research training under contract MEST-CT-2004-504321 PERACT by the European Union. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

COMPETING INTERESTS: The authors have declared that no competing interests exist.

CITATION: Yovel Y, Melcon ML, Franz MO, Denzinger A, Schnitzler H-U (2009) The Voice of Bats: How Greater Mouse-eared Bats Recognize Individuals Based on Their Echolocation Calls. PLoS Comput Biol 5(6): e1000400. doi:10.1371/journal.pcbi.1000400

CONTACT:

Dr. Yossi Yovel
Weizmann institute of science (current affiliation)
phone: 97289346304
cell: 972506463642
email: yossiyovel (at) hotmail.com

The original press release can be found here.

SOKD-2009: Service-oriented knowledge discovery workshop at ECML/PKDD-2009

SoKD-09: Third Generation Data Mining: Towards Service-oriented Knowledge Discovery

2nd SoKD Workshop at ECML PKDD 2009
7 September 2009, Bled, Slovenia

Paper submission deadline is June 10.

See the Call for papers and other details at http://zulu.ijs.si/WEB/SoKD09/

You are invited to submit papers related to the topic of this workshop, which include:

– third generation data mining systems
– service-oriented knowledge discovery
– data mining services
– data mining ontologies
– ontologies as background knowledge used for learning
– information fusion
– knowledge mining, mining of patterns and models
– data mining workflows
– heterogenous data sources
– … and related topics

Proceedings of SoKD-08 can be found at
http://www.ecmlpkdd2008.org/workshop-papers-sokd

Attend KDD-09 (early reg deadline May 31) – The Data Mining and Knowledge Discovery conf., Paris

KDD-2009: The Fifteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’09)

Paris, France
June 28 – July 1, 2009.
http://www.kdd.org/kdd2009/

Register by May 31 to get the early registration rates!
http://www.kdd.org/kdd2009/registration.html

As the premier international conference on Data Mining and Knowledge Discovery, KDD 2009 provides a forum for academic researchers and industry and government innovators to share their results and experiences. Researchers and practitioners will gather to present academic and industrial papers, panels, implemented software demos, posters, workshops, tutorials, and insights from the popular KDD Cup competition.

New this year: a social networking platform where attendees can learn about the proceedings, collaborate on research papers, discuss individual sessions, receive real-time updates on the conference, and help organize social events in Paris using the site.

CONFERENCE VENUE
—————-

For the first time KDD will leave America and come to Europe; KDD 2009 will take place in beautiful downtown Paris, at the Marriott Paris Rive Gauche Hotel, 17 Boulevard St Jacques, 75014 Paris, France.

On Monday, June 29 the Conference reception will be held at the Hotel de Ville of Paris, in the main reception room, the Salle des Fetes, where Paris usually welcomes Heads of States and VIPs.

Please go to http://www.kdd.org/kdd2009/registration.html to register online for the conference. The early conference registration deadline is May 31 Find the hotel reservation code on
http://www.kdd.org/kdd2009/travel.html to enjoy the group rate for the hotel.

This year the organizers are introducing a new option besides full participation: “Workshops, Tutorials,and Evenings” (or, “Nights and Weekends”). This enables you to participate all day Sunday, for the workshops and/or tutorials, as well as the evenings (5pm+ Sunday-Tuesday),
which feature invited industry talks, receptions at the beautiful Paris Town Hall and Marriott Hotel, and technical poster sessions. (The option omits, however, the full technical program during the 3 days, Mon-Wed.)

If you have any registration questions direct them to:

Mandy Mann (mandy.mann at regmaster.com) or +1 407 971 4451.

For other questions, the KDD organizer contact information is at:
http://www.kdd.org/kdd2009/

CONFERENCE HIGHLIGHTS
——————–

a) INVITED SPEAKERS:

This year, KDD features five distinguished invited speakers:
– David J. Hand, “Mismatched Models, Wrong Results, and Dreadful Decisions: On choosing appropriate data mining tools”
– Heikki Mannila, “Randomization Methods in Data Mining”
– Stanley Wasserman, ““Network Science: An Introduction to Recent Statistical Approaches”
– Ravi Kumar, “Mining Web Logs: Applications and Challenges”
– Ashok N. Srivastava, “Data Mining at NASA: from Theory to Applications”

b) TUTORIALS: (all tutorials are free with conference registration)

9 diverse half-day tutorials will be presented on Sunday, June 28th.
http://www.kdd.org/kdd2009/tutorials.html

c) WORKSHOPS (all workshops are free with conference registration)

11 workshops, including 4 challenge workshops, will be held Sunday,
June 28th . http://www.kdd.org/kdd2009/workshops.html

The detailed Conference program will soon be available on the KDD-09 web
site.

d) KDD CUP

Based on challenge data provided by Orange Labs, this year’s competition focuses on predicting customer scores from large marketing databases from the French Telecom company, Orange.
10,000 Euros of prizes and travel grants — generously donated by Orange — will be distributed among the cup winners.
See progress on: http://www.kddcup-orange.com/

ENJOY PARIS
———–

While in Paris, enjoy the city! Register on http://www.kdd.org/kdd2009/travel.html#deals for special deals available to KDD participants on Saturday 27 June, Thursday 2 July, and Friday 3 July.

— KDD organizers http://www.sigkdd.org/kdd2009/organizers.html

Machine Learning Summer School 2009

University of Cambridge, UK
29 August – 10 September 2009
http://mlg.eng.cam.ac.uk/mlss09/

We invite you to apply to attend the 13th Machine Learning Summer School, which will be held at the University of Cambridge. The school will offer lectures and practicals given by leading researchers in the field on a wide range of topics in machine learning. We hope to attract international students, young researchers and industry practitioners with a keen interest in machine learning and a strong mathematical background.

APPLICATION DEADLINE: 1 June 2009

APPLICATION WEBSITE: http://mlg.eng.cam.ac.uk/mlss09/application.htm

We can offer a limited number of travel grants, and encourage students to apply even if they may not be able to meet the full costs of travel and attendance.

Confirmed Lecturers:
Christopher Bishop
Andrew Blake
David Blei
Philip Dawid
Zoubin Ghahramani
Simon Godsill
Geoffrey Hinton
Thomas Hofmann
Michael Jordan
Michael Littman
David MacKay
Thomas P. Minka
Iain Murray
Peter Orbanz
Carl Edward Rasmussen
Bernhard Schölkopf
John Shawe-Taylor
Yee Whye Teh
Josh Tenenbaum
Lieven Vandenberghe

This year’s MLSS is organised by the University of Cambridge, with Microsoft Research and PASCAL.

We look forward to receiving your applications.

Sincerely,

The Organisers
Zoubin Ghahramani, Carl Edward Rasmussen, Christopher M. Bishop, A. Philip Dawid, David J.C. Mackay, Peter Orbanz, Joaquin Quiñonero Candela

PASCAL Visual Object Classes Recognition Challenge 2009

We are running the PASCAL Visual Object Classes Recognition Challenge again this year. As in 2008 there are 20 object classes. Participants can recognize any or all of the classes, and there are classification, detection and pixel-wise segmentation competitions. (New for 2009, segmentation has been promoted from a “taster” to a full competition.)
There is also a “taster” competition on person layout (detecting head, hands, feet).

The development kit (Matlab code for evaluation, and baseline algorithms) and training data is now available at:

http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2009/index.html

where further details are given. The timetable of the challenge is:

* 15 May 2009: Development kit (training and validation data plus
evaluation software) made available.

* 15 June 2009: Test set made available.

* 7 September 2009. Deadline for submission of results.

* 3 October 2009: Workshop in association with ICCV 2009, Kyoto, Japan.

Mark Everingham
Luc Van Gool
Chris Williams
John Winn
Andrew Zisserman

Funded PhD position in machine learning – University of Saint-Etienne (France)

The Machine Learning research group of the University of Saint-Etienne (France) invites applications for a fully funded 3-years PhD position at the Hubert-Curien lab.

Topic of the studentship:
Machine Learning for Image Recognition
(starting date is 1 October 2009)

This project mainly concerns the design of new methods for learning similarities between images that are represented in the form of strings, trees or graphs. The selected candidate will work in the context of the SATTIC (http://labh-curien.univ-st-etienne.fr/wiki-sattic) project, financed by the French National Research Agency. He/She will join the machine learning group composed of about 20 researchers working on the crossroads of machine learning, data mining and information retrieval.

Candidates must have demonstrable interest and expertise in machine learning, statistical theory and have strong programming skills. A background in image processing is encouraged but not required. Applicant should get a Master degree in Computer Science in 2009.

Expressions of interest with a short CV should be sent to: marc.sebban (at) univ-st-etienne.fr by 1 June 2009 at the latest.

Some links:
-Some facts about Saint-Etienne can be found here: http://en.wikipedia.org/wiki/Saint-Étienne.
-Saint Etienne is a medium size city with a quite low cost of living (compared to same size cities in France).
-Paris can be reached from Saint Etienne in less than 3 hours via a direct train. The closest airport is Lyon Saint Exupéry (http://www.lyon.aeroport.fr).
-The city is surrounded by the “regional parc of pilat” in which almost any outdoor activity can be practiced (http://www.parc-naturel-pilat.fr/eng/).
-From a cultural point of view, the art museum in Saint-Etienne holds the second national contemporary art collection, and classic concerts, dance shows, and lyric operas are performed at the Saint-Etienne “Massenet Opera” (http://www.decouvrez-le-votre.com/).

Fourth Summerschool on Advanced Statistics and Data Mining

The Polytechnical Univ. of Madrid organizes a summerschool on “Advanced Statistics and Data Mining” in Madrid between July 6th and July 17th. The summerschool comprises 18 courses divided in 2 weeks.
Attendees may register in each course independently. Registration will be considered upon strict arrival order.

For more information, please, visit
http://www.dia.fi.upm.es/index.php?page=presentation&hl=es_ES or
http://biocomp.cnb.csic.es/~coss/Docencia/ADAM/ADAM.htm.

List of courses and brief description
(Full description at http://biocomp.cnb.csic.es/~coss/Docencia/ADAM/ADAM.htm)

Week 1 (July 6th – July 10th, 2009)

Course 1: Bayesian networks (15 h), Practical sessions: Hugin, Elvira, Weka, LibB
Bayesian networks basics. Inference in Bayesian networks.
Learning Bayesian networks from data

Course 2: Multivariate data analysis (15 h), Practical sessions: MATLAB
Introduction. Data Examination. Principal component analysis (PCA).
Factor Analysis. Multidimensional Scaling (MDS). Correspondence analysis.
Multivariate Analysis of Variance (MANOVA). Canonical correlation.

Course 3: Dimensionality reduction (15 h), Practical sessions: MATLAB
Introduction. Matrix factorization methods. Clustering methods. Projection methods. Applications

Course 4: Supervised pattern recognition (Classification) (15 h), Practical sessions: Weka
Introduction. Assessing the Performance of Supervised Classification Algorithms.
Classification techniques. Combining Classifiers.
Comparing Supervised Classification Algorithms

Course 5: Introduction to MATLAB (15 h)
Overview of the Matlab suite. Data structures and files. Programming in Matlab.
Visualization tools. Some applications in pattern recognition.

Course 6: Datamining, a practical perspective (15h), Practical sessions: MATLAB, R, Weka
Introduction to Data Mining and Knowledge Discovery. Prediction in data mining.
Classification. Association studies. Data mining in free-form texts: text mining.

Course 7: Time series analysis (15 h), Practical sessions: MATLAB
Introduction. Probability models to time series. Regression and Fourier analysis.
Forecasting and Data mining.

Course 8: Neural networks (15 h), Practical sessions: MATLAB
Introduction to the biological models. Nomenclature. Perceptron networks.
The Hebb rule. Foundations of multivariate optimization. Numerical optimization.
Rule of Widrow-Hoff. Backpropagation algorithm.
Practical data modelling with neural networks

Course 9: Introduction to SPSS (15 h)
Introduction. Describing data. Statistical inference. Time series. Sampling.
Classification and regression

Week 2 (July 13th – July 17th, 2009)

Course 10: Regression (15 h), Practical sessions: SPSS
Introduction. Simple Linear Regression Model. Measures of model adequacy.
Multiple Linear Regression. Regression Diagnostics and model violations.
Polynomial regression. Variable selection. Indicator variables as regressors.
Logistic regression. Nonlinear Regression.

Course 11: Practical Statistical Questions (15 h), Practical sessions: study of cases (without computer)
I would like to know the intuitive definition and use of …: The basics.
How do I collect the data? Experimental design.
Now I have data, how do I extract information? Parameter estimation
Can I see any interesting association between two variables, two populations, …?
How can I know if what I see is “true”? Hypothesis testing
How many samples do I need for my test?: Sample size
Can I deduce a model for my data? Other questions?

Course 12: Missing data and outliers (15 h), Practical sessions: R
Missing Data: Typology of missing data; Simple missing-data methods;
Imputation Methods; Diagnostics and Overimputing. Outliers and robust statistics:
Typology of outliers; Influence measures; Robust methods

Course 13: Hidden Markov Models (15 h), Practical sessions:HTK
Introduction. Discrete Hidden Markov Models. Basic algorithms for Hidden Markov Models.
Semicontinuous Hidden Markov Models. Continuous Hidden Markov Models.
Unit selection and clustering. Speaker and Environment Adaptation for HMMs.
Other applications of HMMs

Course 14: Statistical inference (15 h), Practical sessions: SPSS
Introduction. Some basic statistical test. Multiple testing. Introduction to bootstrapping

Course 15: Features Subset Selection (15 h), Practical sessions: MATLAB, R, Weka
Filter approaches. Wrapper methods. Embedded methods.

Course 16: Introduction to R (15 h)
An introductory R session. Data in R. Importing/Exporting data. Programming in R.
R Graphics. Statistical Functions in R

Course 17: Unsupervised pattern recognition (clustering) (15 h), Practical sessions: MATLAB
Introduction. Prototype-based clustering. Density-based clustering.
Graph-based clustering. Cluster evaluation. Miscellanea

Course 18: Evolutionary computation (15 h), Practical sessions: MATLAB
Genetic algorithms. Genetic programming. Robust and self-adapting intelligent systems.
Introduction to Estimation of Distribution Algorithms.
Improvements, extensions and applications of EDAs. Current research in EDAs.

PhD Studentship in Machine Learning

Applications are invited for a PhD position in the Computational Learning and Computational Linguistics group of the Artificial Intelligence Laboratory, in the Computer Science Department of the University of Geneva, available immediately. The successful candidates will pursue research in connection with a project on machine learning funded by the Swiss National Science Foundation, where they will investigate theoretical and practical issues in statistical modelling of structured objects, such as natural language semantic structures. Further information on related research activities of the CLCL group can be found on the home page of James Henderson.

Candidates should have a solid background in both computer science and mathematics, particularly in statistical learning and/or optimization theory. They should have excellent programming skills as well as communication skills in English (and ideally in French). Preference will be given to candidates with a strong interest and/or experience in computational linguistics or natural language processing. A strong academic record, excellent analytical skills, and a clear aptitude for autonomous, creative research will be priority selection criteria.

The position is available immediately. Starting salary will be around 4400 CHF/month (gross) at the PhD level (1 CHF = 0.66 EUR). Applications will be accepted until the position is filled.

Applicants should send their curriculum vitae, academic transcript, a statement of purpose, and names and addresses (with e-mail and telephone number) of at least 2 references to the address below (preferably by e-mail):

James Henderson
CUI – University of Geneva
Battelle bâtiment A
7 route de Drize
CH-1227 Carouge, Switzerland
E-mail: James.Henderson (at) unige.ch

Informal inquiries should be directed to James Henderson at James.Henderson (at) unige.ch.