PhD vacancies at KU Leuven

Machine Understanding for interactive StorytElling

Description: The Department of Computer Science at KU Leuven has an open PhD position in the frame of the European MUSE (Machine Understanding for interactive StorytElling) project. MUSE will introduce a new way of exploring and understanding information by “bringing text to life” through 3D interactive storytelling. Taking as input natural language text like children’s stories or medical patient education materials, MUSE will process the natural language, translate it into formal knowledge that represents the actions, actors, plots and surrounding world, and then render these as virtual 3D worlds in which the user can explore the text through interaction, re-enactment and guided game play.

The PhD student will study advanced natural language processing techniques that enable the translation of natural language text to the necessary knowledge representation based on probabilistic models of translation, latent class paraphrasing models, and automatic methods for acquiring world knowledge from large corpora. He or she will focus on a specific aspect of natural language understanding and will work in a team of senior researchers in the frame of the MUSE project. The student will have contacts with outstanding European groups in the domains of machine reading of text, knowledge representation, cognitive understanding, and virtual storytelling.

The ideal candidate will recently have completed or will soon complete a master in computer science or a similar discipline. He or she has a large interest in natural language processing, statistical and probabilistic modeling, and machine learning. Excellent (honors-level) results in prior studies are required. The candidate is fluent in spoken and written English.
The interested candidate is asked to submit his or her CV and motivation letter to Marie-Francine Moens (Sien.Moens@cs.kuleuven.be) before June 21, 2012.

Latest application date: 2012-20-06.

Start date of the project: 2012-09-01.

Source of funding: EU FP7-296703 Future and Emerging Technologies call.

Duration of the project: 3 years +1 year extra funding available.

Mining of User Generated Content

The Department of Computer Science at KU Leuven has an open PhD position in the frame of cross-media processing of user generated content. User generated content, e.g. available through social networking sites on the Web, offers a wealth of information. The aim of the IWT-SBO project PARIS (Personalized AdveRtisements buIlt fom web Sources), by which the PhD is sponsored, is to study adequate natural language, image and video understanding techniques that mine user generated content. This is challenging as user generated content is often present in language that is not well-formed or in visual material that is not professionally captured. An important application of the PARIS technologies will be personalized advertising, where relevant advertisements are searched and generated that fit the world of living recognized in the user generated content. This requires efficient and scalable methods of “machine understanding” of content that can be applied in an online setting.

The offered PhD position regards the mining and linking of user generated content focusing on the joint processing of text and visual data (collaboration with a computer vision group). The focus is on developing information extraction methods that learn with a minimum of human supervision and that combine uncertain evidences from different sources making use of advanced probabilistic inference methods. An additional focus is to make the methods scalable and efficient for real-time use.

The ideal candidate will recently have completed or will soon complete a master in computer science or a similar discipline. He or she has a large interest in multimedia processing, statistical and probabilistic modeling, and machine learning. Excellent (honors-level) results in prior studies are required. The candidate is fluent in spoken and written English.
The interested candidate is asked to submit his or her CV and motivation letter to Marie-Francine Moens (Sien.Moens@cs.kuleuven.be) before June 21, 2012.

Key words: information retrieval, information extraction, data mining, machine learning, reasoning about uncertainty.


Latest application date: 2012-20-06.

Start date of the project: 2012-09-01.

Source of funding: IWT (SBO-110067).

Duration of the project: 4 years.

PhD Studentship in Statistical Machine Learning and Computational Systems Biology (Helsinki, Finland)

PhD studentship in developing novel probabilistic modelling and statistical inference methodology and applying these methods to problems in computational systems biology

Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki

—————————————————————

The Helsinki Institute for Information Technology (HIIT) and the Department of Computer Science at the University of Helsinki are looking for a skilled
DOCTORAL STUDENT.

The Department of Computer Science is the leading unit for computer science research and education in Finland. The focus areas of research and teaching at the department are (1) algorithms and machine learning, (2) networking and services, and (3) software systems. Three Finnish Academy-funded centres of excellence operate at the department, and it works in close collaboration with the Helsinki Institute of Information Technology. The department is one of ten national centres of excellence in university education. The department employs some 170 persons, and its total budget is 11 Million Euros. The department has an outstanding research infrastructure, including a 1920-core computing cluster

The doctoral student will develop novel probabilistic modelling and statistical inference methodology incorporating structured prior information from mechanistic models and apply these methods to problems in computational systems biology. The aim of the project is to develop hierarchical Gaussian process models for modelling gene expression and regulation in complex experiments, such as with evolutionarily related specimen. The work will take place in the group of Dr Antti Honkela but it will involve collaboration with experimental biologists. The project will build upon recent experience in application of Gaussian process models on modelling gene regulation by Dr Honkela and collaborators (Honkela et al., PNAS 2010; Titsias et al., BMC Systems Biology 2012).

A successful applicant must have a MSc degree in computer science, electrical engineering, mathematics, physics, or a related field. A strong mathematical background and an interest in Bayesian modelling and/or machine learning are necessary. An interest in computational biology is essential but no prior experience is necessary.

The application deadline is 21 June 2012.
For more details and application instructions, see
http://www.helsinki.fi/recruitment/index.html?id=56832

Harvest Project: Call for Participation

Under the auspices of the PASCAL Harvest Programme we will be running a twelve week project from 9th July to 28th September hosted by the Institute Jozef Stefan, Ljubljana, Slovenia. Ljubljana is situated close to the Julian Alps and a short distance from the Mediterranean.

The project La Vie (Learning Adapted Video Information Enhancer) involves machine learning to develop a recommender system for users of http://videolectures.net/.

La Vie will develop a proof-of-concept system that will provide users with advice on suitable videos for their needs. The key components that the project will bring to videolectures.net are:
1. topic extraction and modeling based on text extracted from associated slides and audio transcriptions. This will ensure that the devised user models can capture semantic level interests of the users.
2. inclusion of the information currently being logged about individual users in the recommender system running live on the videolectures site.
3. visualization of the developed recommender system. This will provide a topic landscape that will enable users to see the available videos emphasizing those likely to be of interest to the user.

Detailed description of the project is available at http://pascallin2.ecs.soton.ac.uk/PublicDocuments/LaVieProposal.pdf

The project was conceived by the following team: Rayid Ghani, Marko Grobelnik, Colin de la Higuera, Mitja Jermol, Alfons Juan, Matjaz Rihtar and John Shawe-Taylor and will be managed by Matjaz Rihtar.

The Harvest Project will cover the travel expenses, accommodation and subsistence for participants in the project not based at the Jozef Stefan Institute. We are looking to recruit programmers, machine learners, software engineers and welcome applicants at all levels.

We invite expressions of interest in participating in this project. Please indicate interest by replying to this email before 11th of June and include a short CV and the name of one referee.

Best regards,
Matjaz Rihtar & Mitja Jermol

Second Call for Papers: IEEE Transaction on Pattern Analysis and Machine Intelligence Special Issue on Bayesian Nonparametrics

Second Call for Papers
* Due Date for White Papers: June 30 *

IEEE Transaction on Pattern Analysis and Machine Intelligence Special Issue on Bayesian Nonparametrics

Topic description:
Bayesian nonparametric models are probabilistic models defined over infinite dimensional parameter spaces. Examples include Gaussian processes, used in regression and classification, where the parameter space consists of the set of smooth functions, and Dirichlet process mixture models for density estimation problems, where the parameter space is dense in the space of densities. Bayesian nonparametrics present a flexible framework for modeling complex data and a viable alternative to model selection, and have gained increasing attention in machine learning, statistics, and related fields in recent years.

We invite paper submissions for a special issue on Bayesian nonparametrics to be published in IEEE Transactions in Pattern Analysis and Machine Intelligence. Original research papers as well as overview and survey papers are welcome, on topics including, but not limited to:

• Statistical and learning theory for Bayesian nonparametric methods; • Novel Bayesian nonparametric models and stochastic processes; • Novel methodologies for learning and inference, including Monte Carlo, variational, message-passing, online, and large scale algorithms.
• Applications, to signal processing, image processing, speech, language processing and others.

Priority will be given to papers with high novelty and originality for research papers, and to papers with high potential impact for survey/overview papers.

Paper submission and review:
We invite interested authors to submit 2-page white papers outlining their submission by June 30, 2012, by email to
npbayes2012pami@gmail.com
Feedback on the white paper will be provided, and suitable submissions invited to submit full papers online, by August 31, 2012, through the TPAMI site at,
https://mc.manuscriptcentral.com/tpami-cs
selecting the choice that indicates this special issue. Peer reviewing will follow the standard IEEE review process. Full length manuscripts are expected at this second stage, following the TPAMI guidelines in http://www.computer.org/portal/web/peerreviewjournals/author

Time line:
Submission of 2-page white papers: June 30, 2012.
Feedback and invitations to submit full papers: July 15, 2012.
Submission of full papers: August 31, 2012.
First reviews: November 15, 2012.
Revisions due: January 15, 2013.
Decisions announced: February 28, 2013.
Final manuscripts due: March 31, 2013.

Guest editors:
• Ryan P. Adams, Harvard University, rpa@seas.harvard.edu • Emily Fox, University of Pennsylvania, ebfox@wharton.upenn.edu • Erik Sudderth, Brown University, sudderth@cs.brown.edu • Yee Whye Teh, University College London, ywteh@gatsby.ucl.ac.uk

Working with Watson: Postdoc positions available at TrentoRise (Italy)

Dear candidate,
have you been fascinated by the IBM Watson system’s achievements?
Have you ever thought that you could contribute to it?
If yes, there might be two great opportunities for this.

TWO Post-doctoral positions for 1 year (with the possibility of extension) are available
at the new IBM Language and Knowledge Center for Advanced Studies of TrentoRise – Italy
(TrentoRise is a joint research institution including the University of Trento, FBK and other important
research institutions of Trento)

Position 1: Information Extraction and Question Answering (Ref.Code IBM_PostDoc2012_IEQA)

This grant aims at developing a framework for Information Extraction and Retrieval based on deep
linguistic analysis. The main idea of the framework is to provide a flexible pipeline of NLP and IR
components, which can be used to model state-of-the-art systems, e.g., in the field of Question Answering.

Candidate Profile: the candidate must hold a PhD in Computer Science, e.g., Computational Linguistics,
Natural Language Processing, Information Retrieval or equivalent, and should be familiar with or
willing to learn the following topics:

– UIMA (Unstructured Information Management Architecture);
– Named Entity Recognition & Normalization / Concept Segmentation and Labeling
– Relation Extraction;
– Question interpretation, answer classification and extraction
– Dependency-based parsing, practice and theory
– Semantic role labeling
– Question analysis
– Search engine design
– Text Categorization/Filtering
– Document/passage ranking and re-ranking using different sources
– Indexing, search and retrieval for unstructured, semi-structured and structured data
– Retrieval models for Question Answering
– Relational models for sentence, paragraph and document representation

Programming skills are important whereas knowledge of the Italian language is not required.

Scientific tutors: Bernardo Magnini (FBK, Trento – http://hlt.fbk.eu/en/home)
Alessandro Moschitti (University of Trento – http://disi.unitn.it/moschitti/
– http://projects.disi.unitn.it/iKernels/)

Position 2: Machine Learning for NLP (Ref.Code IBM_PostDoc2012_LSM)

This grant aims at modeling and implementing a machine learning framework, which can be applied for
fast system prototyping. Kernel methods are seen as a viable approach to automatic feature engineering,
which is a severe bottleneck for the design of real-world applications. The other interesting problem that
will be studied concerns domain adaptation. Although the framework is supposed to be general, its
primary application domain will be natural language processing.

Candidate Profile: the candidate must hold a PhD in Computer Science, e.g., Machine Learning,
Computational Linguistics, Natural Language Processing, data mining or equivalent and should be
familiar with most of the following topics:

– Discriminative models, including Support Vector Machines and other Max-Margin approaches,
for tagging, extraction, (shallow) parsing and so on.
– Advanced data representation through Kernel Methods and Kernel Machines
– Online learning and Active Learning
– Supervised, Semi-Supervised, Unsupervised Learning, Domain Adaptation, Multi-task Learning in NLP
– Sequence labeling in NLP
– Relational Learning
– Graphical models, e.g., Conditional Random Fields and LDA, are a plus

Scientific tutor: Alessandro Moschitti (University of Trento – http://disi.unitn.it/moschitti/
– http://projects.disi.unitn.it/iKernels/)

Prof. Moschitti has been collaborating with the IBM Watson team since 2009. He has received two awards
from IBM. The postdocs (if successful in their research) will have the possibility to collaborate with
the IBM Watson team and carrying out working stage at IBM Watson of NY.

You can directly apply to

http://www.trentorise.eu/call-for-participation/bando-di-selezione-call-positions

If you want to know more, please send an email to
moschitti@disi.unitn.it
subject: IBM-POSTDOCS2012
salary net per month: 2k euros

The deadline for applications is June 30, 2012
(please note that the deadline indicated in the website above is going to be updated).

Your curriculum must show an appropriate record of publications in the areas of ML, NLP/CL or IR.

Best

Alessandro

CFP: BBCI Workshop 2012 on Advances in Neurotechnology September 17-19, Berlin, Germany

Call for Participation
Call for Poster Submissions

BBCI Workshop 2012 on Advances in Neurotechnology September 17-19, Berlin, Germany

Official: http://bbci12.ml.tu-berlin.de/

************************************************************************

Important Dates
===============

Submission Date: 2012-07-30
Notification of Acceptance: 2012-08-13
Workshop Date: 2012-09-17 – 2012-09-19

Organization
============

Bernstein Focus: Neurotechnology (BFNT-B) Humboldt University Berlin (HU) Berlin Institute of Technology (TUB) Charité – University Medicine Berlin Bernstein Center Computational Neuroscience Berlin (BCCN-B)

– Benjamin Blankertz (chair), Berlin Institute of Technology
– Ulrich Egert, University of Freiburg
– Siamac Fazli (poster co-chair), Berlin Institute of Technology
– Dario Farina, University of Göttingen
– Stefan Haufe (poster co-chair), Berlin Institute of Technology
– Klaus-Robert Müller (chair), Berlin Institute of Technology
– Susanne Schreiber (co-chair), Humboldt University Berlin
– Michael Tangermann (poster chair), Berlin Institute of Technology
– Jochen Triesch, University of Frankfurt

Matthias L. Jugel, BFNT-B/Industry
Imke Weitkamp, BFNT-B/Coordination

Description
===========

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

The Advances in Neurotechnology Workshop presents an overview, in-depth tutorials and discussions on the latest research at all levels of Neurotechnology and BCI research. What is presented will cover invasive recording, semi-invasive ECoG, non-invasive EEG, non-invasive NIRS and fMRI measurement and potential combinations of the different methods. Additional focus will be devoted to advances in data analysis.

The poster session following the tutorials will cross over into the BBCI barbecue, smoothing discussions with drinks and food.

Following the workshop there will be a summerschool on selected topics in BCI and neurotechnology from September 20th to 28th.
It has educational tutorials in the morning session (two tutorials of 2h each, held by internationally renowned researchers), and practical hand-ons sessions in the afternoon. The practical sessions are partly multi-track and will allow researchers in BCI/neurotechnology to complement their expertise in the interdisciplinary field.

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 2012-07-30.

Poster size should be a maximum of A0 (width x height: 841mm × 1189mm)

Confirmed Speakers
==================

– Felix Biessmann, Berlin Institute of Technology
– Benjamin Blankertz, Berlin Institute of Technology
– Mark Cohen, UCLA
– Tom Eichele, Haukeland University Hospital, Bergen, Norway
– Dario Farina, BFNT Göttingen
– Rainer Goebel, University Maastricht
– John-Dylan Haynes, Bernstein Center for Computational Neuroscience
– Bo Hong, Tsinghua University, Beijing, China
– Yukiyasu Kamitani, ATR Computational Neuroscience Laboratories, Kyoto
– Motoaki Kawanabe, ATR Computational Neuroscience Laboratories, Kyoto
– Christof Koch, California Institute of Technology
– Andrea Kübler, Universität Würzburg
– Seong-Whan Lee, Dept. of Brain and Cognitive Engineering, Korea
– Donatella Mattia, Fondazione Santa Lucia, IRCCS, Italy
– José del R. Millán, Ecole Polytechnique Fédérale de Lausanne (EPFL)
– Klaus-Robert Müller, Berlin Institute of Technology
– Gernot Müller-Putz, TU Graz
– Gerwin Schalk, Wadsworth Center

Venue
=====

Audimax der Humboldt-Universität zu Berlin, Unter den Linden 6 / Dorotheenstr. 17-19, Berlin, Germany

Workshop Fees
=============

Business: 300 EUR
Standard: 250 EUR

* Early Registration Discount (until 2012-08-19)

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

Funding
=======

The workshop is supported by the Bernstein Focus: Neurotechnology Berlin.

ECML PKDD 2012 Discovery Challenge on Large Scale Hierarchical Text Classification

ECML/PKDD 2012 Discovery Challenge: Third Challenge on Large Scale Hierarchical Text Classification

Web site: http://lshtc.iit.demokritos.gr/
Email: lshtc_info@iit.demokritos.gr

This year’s discovery challenge hosts the third edition of the successful PASCAL challenges on large scale hierarchical text classification. The challenge comprises three tracks and it is based on two large datasets created from the ODP web directory (DMOZ) and Wikipedia. The datasets are multi-class, multi-label and hierarchical. The number of categories ranges between 13,000 and 325,000 roughly and the number of documents between 380,000 and 2,400,000.

The tracks of the challenge are organized as follows:

1. Standard large-scale hierarchical classification
a) On collection of medium size from Wikipedia
b) On a large collection from Wikipedia
2. Multi-task learning, based on both DMOZ and Wikipedia category systems
3. Refinement-learning
a) Semi-Supervised approach
b) Unsupervised approach

In order to register for the challenge and gain access to the datasets you
must have an account at the challenge Web site.

*** Please note that participants can now upload intermediary results on the web site and track the progress of their work!

Important dates:

– March 30, start of the challenge
– April 20, opening of the evaluation
– June 29, closing of evaluation
– July 20, paper submission deadline
– August 3, paper notifications

Organizers
– Ion Androutsopoulos, AUEB, Athens, Greece
– Thierry Artieres, LIP6, Paris, France
– Patrick Gallinari, LIP6, Paris, France
– Eric Gaussier, LIG, Grenoble, France
– Aris Kosmopoulos, NCSR “Demokritos” & AUEB, Athens, Greece
– George Paliouras, NCSR “Demokritos”, Athens, Greece
– Ioannis Partalas, LIG, Grenoble, France

PhD position in machine learning, data analysis and visualization

Aalto University School of Science invites applications for a

doctoral student / research assistant

position for a fixed term (4 years: initial contract for 1 year, extension to the remaining 3 years decided at the end of the year).
Start date: 1 September 2012 (negotiable)
Application deadline: 22 June 2012

Visualization and exploratory analysis are crucial in analysis of new phenomena where strong hypotheses are not yet available. You will develop advanced machine learning methods for exploratory analysis, including methods for nonlinear dimensionality reduction, visualization, and exploratory data analysis with multiple data sources. The methods will involve novel probabilistic models for the structure and dependencies between multiple data sets. The methods will be used in bioinformatics, neuroinformatics, analysis of structured data like graphs, proactive information retrieval, and other domains. We have developed novel frameworks for visualization from an information retrieval perspective, and for multitask learning in asymmetric scenarios; your work will extend these research lines.

Research Site:

The position is at the Department of Information and Computer Science, Aalto University School of Science. The focus of the department’s research and teaching activity is on advanced computational methods for modelling, analysing, and solving complex tasks in technology and science. The research aims at the development of fundamental computer science methods for the analysis of large and high-dimensional data sets, and for the modelling and design of complex software, networking and other computational systems. The department hosts three national Centres of Excellence: the Centre of Excellence in Computational Inference Research (COIN) and parts of the Centres of Excellence in Algorithmic Data Analysis Research and Molecular Systems Immunology and Physiology Research.The department also contributes to the Helsinki Institute for Information Technology HIIT and the Helsinki node of the EIT ICT Labs. The department is a member of Informatics Europe, the association of European computer science departments. The department was ranked among the top two departments of Aalto University in the Research Assessment Exercise 2009.

The position is located in the Statistical Machine Learning and Bioinformatics research group at the Department. The group is part of Helsinki Institute for Information Technology HIIT in Aalto University. The group is a member of the new Finnish Centre of Excellence in Computational Inference Research (COIN), and is also a member in the EU PASCAL2 network of excellence. The work will be supervised by academy research fellow Jaakko Peltonen. The work involves collaboration with Finnish researchers including members of the Statistical Machine Learning and Bioinformatics research group at Aalto University led by Prof. Samuel Kaski, and international researchers from the UK, Belgium, and the USA.

The research site is located on the Aalto University campus in Otaniemi, a short bus ride away from the centre of Finland’s capital Helsinki. Helsinki and the capital area are a great place to live, with numerous local attractions and events, scenic landscapes of small forests, islands and urban areas, a high standard of living, and excellent travel connections. Helsinki is the World Design Capital in 2012.

Department website: http://ics.aalto.fi/en/ HIIT website: http://www.hiit.fi/ Research group website: http://research.ics.aalto.fi/mi/ Principal investigator website: http://users.ics.aalto.fi/jtpelto/

Required and Desired Qualities of the Applicant:

A successful applicant must have a MSc degree in computer science, electrical engineering, mathematics, physics, or a related field. It is also possible to start as a research assistant working on one’s Master’s thesis.

– A strong mathematical background and an interest in probabilistic
modeling and/or machine learning are necessary.
– An interest in some of the following topics is essential:
exploratory data analysis, dimensionality reduction, manifold
learning, visualization, and multi-task learning. Experience in
these topics is an advantage.
– A strong study record and strong track record in research are
advantages.
– Good programming skills in languages such as C/C++/Matlab/R/Python
and good written and spoken communication skills are desired.

Contract Details:

The salary will be determined based on the Aalto University salary system (2200-3200 euro per month before tax for a doctoral student depending on qualifications and performance). Funding is expected to be available for 4 years: the initial appointment will be for one year with extension to the remaining 3 years decided at the end of the year.

Application Procedure:

The application deadline is 22 June 2012. The application materials must include a curriculum vitae, a copy of study records, contact details of at least two references, and any other materials deemed relevant. Applications must be submitted to the Registry of Aalto University, no later than the deadline. Applications must be submitted preferably by email to kirjaamo@aalto.fi or alternatively by physical mail to The Registry of Aalto University, Aalto University, P.O.Box 11000, FI-00076 Aalto, Finland (street address Otakaari 1, Espoo). Application materials will not be returned.

Candidates may be asked for an interview at Aalto University or via phone or Skype.

For additional information, please contact academy research fellow Jaakko Peltonen or HR Coordinator Stefan Ehrstedt. E-mail:
firstname.lastname@aalto.fi

See this ad online: http://users.ics.aalto.fi/jtpelto/openposition.shtml

PAutomaC Last Call for Participation

=================================================================
Probabilistic Automata learning Competition http://ai.cs.umbc.edu/icgi2012/challenge/Pautomac/
=================================================================
— Please, accept our apologies in case of multiple receptions —

This last call advertises that the competition data sets are available on its website since May 20th.

PAutomaC is a competition about learning probabilistic finite state models (PFA, HMM, WA, …). These machines are well-known models for characterizing the behaviour of systems or processes. Easy to interpret, their original design is usually unknown in many application fields.That is why learning approaches has been used, for instance:
– To modelize DNA or protein sequences in bioinformatics.
– To find patterns underlying different sounds for speech processing.
– To develop morphological or phonological rules for natural language processing.
– To modelize unknown mechanical processes in Physics
– To discover the exact environment of robots.
– To detect Anomaly for detecting intrusions in computer security.
– To do behavioural modelling of users in applications ranging from web systems to the automotive sector.
– To discover the structure of music styles for music classification and generation.

In all such cases, an automaton model is learned from observations of the system, i.e., a finite set of strings. As the data gathered from observations is usually unlabelled, the standard method of dealing with this situation is to assume a probabilistic automaton model, i.e., a distribution over strings.
This is what the competition is about.

*Schedule:*

– Before May 20th: training phase
– May 20th: Competition data sets are available
– June 30th: Competition is over
– July 20th: Short papers are submitted
– September 5-8th: a special session takes place duting ICGI’12 in Washington, DC, USA (http://www.coral-lab.org/icgi2012/ )

*Prizes and publications:*

Our sponsor CASL (http://http://www.casl.umd.edu/) will award a prize of
$500 to the winner of the competition.
Participants are encouraged to submit an extended abstract and to present their innovations at the PAutomaC special session that will be organised during ICGI 2012. The European network of Excellence PASCAL 2 will help best participants to travel to the conference.
The winner is expected to submit a paper to the Machine Learning Journal special issue that will follow the ICGI 2012 conference.

*Scientific committee:*

– Peter Adriaans, Universiteit van Amsterdam, The Netherlands
– Dana Angluin, Yale University, USA
– Alexander Clark, Royal Holloway University of London, UK
– Pierre Dupont, Université catholique de Louvain, Belgium.
– Ricard Gavalda, Universitat Politècnica de Catalunya, Spain
– Colin de la Higuera, Université de Nantes, France
– Jean-Christophe Janodet, Université d’Evry, France
– Tim Oates, University of Maryland Baltimore County, USA
– Jose Oncina, Universitat de Alicante, Spain
– Menno van Zaanen, Tilburg University, The Netherlands

All informations about the competition can be found on its website:
http://ai.cs.umbc.edu/icgi2012/challenge/Pautomac/
Contact email: pautomac@gmail.com

BMVC 2012 Call for *Demos and Videos*

BMVC 2012 offers the opportunity to showcase your research to the computer vision community. The following two types of contributions are both encouraged:

(1) Live demonstrations showing the effectiveness of computer vision methods. These are not limited to methods described in papers that will appear at BMVC 2012. Prospective demo participants should submit the application form (http://bmvc2012.surrey.ac.uk/demo_application_bmvc12.pdf) and a 200 word abstract via email to the Demo and Video Chair. Commercial products should be presented as part of the exhibits rather than demonstrations. Accepted demonstrations will be held concurrent with the poster sessions.

(2) Any precompiled videos showing the results of computer vision related research. Videos should not exceed three minutes in length or 100MB in size. Prospective video participants should submit an FTP/HTTP link to the video and a 200 word abstract via email. Videos advertising commercial products are not appropriate. Accepted videos will be shown throughout the conference.

Please be advised that at least one of the authors of each demo/video must be registered for the conference. The conference also reserves the right to select demos and videos based on the degree of appropriateness for BMVC.

6 August 2012 Demo and video submission due
13 August 2012 Demo and video notification

Looking forward to your submissions,

Fei Yan, BMVC 2012 Demo and Video Chair
f.yan@surrey.ac.uk
Centre for Vision, Speech and Signal Processing, University of Surrey Guildford, United Kingdom
GU2 7XH