Journal of Interesting Negative Results (JINR)

Dear colleagues,

This year’s main Computational Linguistics conference ACL 2010 features a special category of negative-result papers, so there must be a need. Now as the deadline season for this year’s conferences is winding down, we invite you to revisit your less-than-successful experiences (even if we hope you had none). Consider sharing your interesting negative results with the community. We invite, nay, we welcome all submissions which meet the journal’s standards. Visit its Web site for more.

For the disinclined to surf, here are the essential points on the home page of the Journal of Interesting Negative Results in Natural Language Processing and Machine Learning:

“The journal’s scope encompasses all areas of Natural Language Processing and Machine Learning. Papers published in JINR will meet the highest quality standards, as measured by the originality and significance of the contribution. They will describe research with theoretical and practical significance. All theories and ideas will have to be clearly stated and justified by a deep literature review.

Because of the nature of the journal, there should be good justification for trying out the ideas presented. The experiments reported should be shown in a manner that allows their reproduction. The negative results should be explained and justified, along with the reasons why the idea did not lead to the predicted results. The lessons learned should be clearly stated.”

All the best,

Vivi Nastase and Stan Szpakowicz

INFORMS Data Mining Contest 2010

Call for participation: INFORMS Data Mining Contest 2010

The INFORMS Data Mining Section (in conjunction with Sinapse) is pleased to announce its third annual Data Mining Contest: http://kaggle.com/informs2010.

This contest requires participants to develop a model that predicts stock price movements at five minute intervals.

Competitors will be provided with intraday trading data showing stock price movements at five minute intervals, sectoral data, economic data, experts’ predictions and indexes.

We have provided a training database to allow participants to build their predictive models. Participants will be evaluated according to the arithmetic mean of the AUC on the test database.

Being able to better predict short-term stock price movements would be a boon for high-frequency traders, so the methods developed in this contest could have a big impact on the finance industry.

The submission deadline is October 10th 2010. The winners of this contest will be honoured at a session during INFORMS Annual Meeting in Austin-Texas (November 7-10).

Visit the INFORMS Data Mining Contest web page for more details: http://kaggle.com/informs2010

Call For Participation: IEEE International Conference on Development and Learning 2010

Call For Participation

IEEE INTERNATIONAL CONFERENCE ON DEVELOPMENT AND LEARNING 2010
University of Michigan, Ann Arbor USA

18-21 August 2010, Ann Arbor, USA

http://www.icdl-2010.org

This is an invitation to attend the
Ninth IEEE International Conference on Development and Learning 2010.

The conference will be composed of a single track with 4 keynote addresses, 24 oral paper presentations, 30 full paper posters, and poster highlights. ICDL-2010 will be held at the Rackham Building, University of Michigan, Ann Arbor, USA, 18-21 August 2010.

Registration is required to attend. See below for:
A. General Information
B. Topical Scope
C. Registration Information
D. Student Travel Scholarships for Full-Time Students
E. Keynote Addresses
F. Detailed Congress Schedules/Programs
G. Location of ICDL 2010
H. List of Co-Sponsors of ICDL 2010
We look forward to seeing you in August.

General Co-Chairs
Professor Benjamin Kuipers, University of Michigan
Professor Thomas Shultz, McGill University

Program Co-Chairs
Professor Alexander Stoytchev, Iowa State University
Professor Chen Yu, Indiana University

http://www.eecs.umich.edu/icdl-2010/committee.html

A. General Information:
http://www.icdl-2010.org

B. Topical Scope:

The goal of the Ninth IEEE International Conference on Development and Learning is to bring together leading researchers in robotics, machine learning, neuroscience, and developmental psychology, in order to gain new insights about learning and development in natural organisms and robots. The scope of developmental processes to be considered is broad, including cognitive, social, emotional, and many other skills exhibited by humans and other animals.

C. Registration Information:
http://eecs.umich.edu/icdl-2010/registration.htm

D. Financial Assistantship for full-Time Students:
http://eecs.umich.edu/icdl-2010/grants.html

Thanks to a generous gift from Microsoft Research, we can offer up to eight (8) student travel scholarships of $200 each, for graduate students attending ICDL-2010. (If additional funds become available, we may make more and/or larger awards.) The awards will be made, and funds distributed, at the conference.

To apply, please send a hardcopy letter to Prof. Benjamin Kuipers, University of Michigan, Computer Science & Engineering Division, 2260 Hayward Street, Ann Arbor, Michigan 48109.

The letter should be on your university letterhead. Please provide:
– your name
– mailing address
– email address
– whether you are the author of a paper or poster to be presented
– your estimated travel expenses for attending ICDL
– a signed endorsement by your advisor
The letter must be received by July 20, 2010.

E. Keynote Addresses:
http://www.icdl-2010.org

F. Detailed Conference Schedules/Programs:
http://eecs.umich.edu/icdl-2010/program.html

G. Location of ICDL:
http://eecs.umich.edu/icdl-2010/venue.html

H. List of Co-Sponsors of ICDL 2010:
http://www.eecs.umich.edu/icdl-2010/home.html

Book Announcement: Algorithms for Reinforcement Learning by Csaba Szepesvári

Algorithms for Reinforcement Learning by Csaba Szepesvári, University of Alberta
ISBN: 1608454924 / 9781608454921
Publication Date: July 2010
List Price: $35.00 / £19.99

Abstract:
Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner’s predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms’ merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. Table of

Contents: Markov Decision Processes / Value Prediction Problems / Control / For Further Exploration

Download the book here: http://www.morganclaypool.com/doi/abs/10.2200/S00268ED1V01Y201005AIM009.

This title is available online free of charge to members of institutions that that have licensed the Synthesis Digital Library of Engineering and Computer Science. Use of this book as a course text is encouraged; and the text may be downloaded without restriction at licensing institutions, or after a one-time fee of $30.00 at non-licensing schools. To find out whether your institution is a subscriber, visit , or follow the link above and attempt to download the PDF. Additional information about Synthesis can be found through the following links, or by contacting me directly.

Available titles and subject areas: http://www.morganclaypool.com/page/ForthcomingSynthesisLectures
Information for librarians, including pricing and license: http://www.morganclaypool.com/page/librarian_info
A review of Synthesis in ISTL: http://www.istl.org/09-winter/electronic.html

This book can also be purchased in paperback format directly from the Morgan & Claypool Bookstore for $35.00 USD, or from Amazon and other booksellers worldwide.

For more information and to request desk copies, please email andrea(at)morganclaypool.com.

Postdoc and PhD position at the IDIAP research institute

The Idiap Research Institute (www.idiap.ch), associated with EPFL ( Swiss Federal
Institute of Technology, Lausanne) seeks qualified candidates for:

* A Post-doc position on Head pose tracking and visual attention modeling
and multi-person interaction analysis.

* A Ph.D Student on 3D face tracking and gaze modeling

For further details about positions and Idiap, see below.
Interested candidates should send a letter of motivation, a detailed CV, and the names
of three referees to

Jean-Marc Odobez (odobez(at)idiap.ch, tel : +41 (0)27 721 77 26)

————-
* A Post-doc position on Head pose tracking and visual attention modeling
and multi-person interaction analysis.

The research will be conducted in the context of the TA2 project
(http://www.ta2-project.eu) funded by the European Commission. The overall goal
of the project is to develoop tools to favor remote communication and engagement
between groups of people separated in space and time.

More specifically, the postdoc will be involved in the research and development of
algorithms for the analysis of the gaze and visual attention of people engaged in
family video conference scenarios. This research will rely on Idiap’s previous
expertise on the design of multimodal person attention modeling in small group
meetings.

The postdoctoral researcher should have a strong background in computer vision.
Experience in one or several of the following areas is required: visual tracking; head
modeling (detection, recognition), facial feature analysis; human or object detection;
machine learning.
The applicant should also have strong programming skills, with experience in
C/C++ development. The position is initially for one year.

————-
* A Ph.D Student on 3D face tracking and gaze modeling

The research will be conducted in the project TRACOME (“Robust face tracking,
feature extraction and multimodal fusion for audio-visual
speech recognition and visual attention modeling in complex
environment”) funded by the Swiss Science Foundation. The PhD
candidate will work on the design of new methods for robust face tracking under
natural head movements using 3D deformable models, the tracking of gaze directions,
and its application to attention modeling in human-human or robot-to-group of human
interaction scenarios. The work will be done in collaboration with the LTS 5 laboratory
of EPFL, which will handle the audio-visual speech recognition task of the project.

The successful Ph.D student, who will be enrolled in the EPFL doctoral program, should
have a master in computer science or related field, with a good background in
mathematics. Experience or background in statistical learning theory, image
processing or computer vision is a plus. The applicant should be familiar with C/C++
programming and the Linux environment. Appointment for the PhD student position is
for a maximum of 4 years. Starting date: immediately.

————
About Idiap:

Idiap is an independent, non-profit research institute recognized and supported by the
Swiss Government, and affiliated with the Ecole Polytechnique Fédérale de Lausanne
(EPFL). It is located in the town of Martigny in Valais, a scenic region in the south
of Switzerland, surrounded by the highest mountains of Europe, and offering
exciting recreational activities, including hiking, climbing and skiing, as well as varied
cultural activities. It is within close proximity to Geneva and Lausanne. Although Idiap
is located in the French part of Switzerland, English is the working language. Free
French lessons are provided. Idiap offers competitive salaries and
conditions at all levels in a young, dynamic, and multicultural environment. Idiap
is an equal opportunity employer and is actively
involved in the “Advancement of Women in Science” European
initiative. The Institute seeks to maintain a principle of open competition (on the
basis of merit) to appoint the best candidate, provides equal opportunity for all
candidates, and equally encourage both genders to apply.

MLG-2010: Call for Participation

Call for Participation
Eighth Workshop on Mining and Learning with Graphs 2010 (MLG-2010)
http://www.cs.umd.edu/mlg2010
Washington, DC, July, 24-25
(co-located with KDD 2010 )

This year’s workshop on Mining and Learning with Graphs will be held in
conjunction with the 16th ACM SIGKDD Conference on Knowledge Discovery
and Data Mining that will take place in July 25-28, 2010 in Washington,
DC.

The importance of being able to effectively mine and learn from data
that is best represented as a graph is growing, as more and more
structured and semi-structured data is becoming available. Examples
include the WWW, social networks, biological networks, communication
networks, food webs, and many others. Traditionally, a number of
subareas have worked with mining and learning from graph structured
data, including communities in graph mining, learning from structured
data, statistical relational learning, inductive logic programming, and,
moving beyond sub-disciplines in computer science, social network
analysis, and, more broadly network science. The objective of this
workshop is to bring together researchers from a variety of these areas,
and discuss commonality and differences in challenges faced, survey some
of the different approaches, and provide a forum to present and learn
about some of the most cutting edge research in this area. As an
outcome, we expect participants to walk away with a better sense of the
variety of different tools available for graph mining and learning, and
an appreciation for some of the interesting emerging applications for
mining and learning from graphs.

Registration

You can register via http://www.sigkdd.org/kdd2010/registration.shtml

Program

Each day will consist of keynote speakers, short presentations
showcasing accepted papers, discussions at end of sessions, and a poster
session to promote dialog. A tentative schedule is available online
http://www.cs.umd.edu/mlg2010/schedule.html
featuring the following invited talks and accepted papers:

Invited Talks

* Stephen Fienberg, CMU
* Thomas Gärtner, University of Bonn and Fraunhofer IAIS
* Aristides Gionis, Yahoo! Research
* Jennifer Neville, Purdue University
* Padhraic Smyth, UCI
* Chris Volinsky, AT&T Labs
* Eric Xing, CMU

Accepted Papers

* Time-Based Sampling of Social Network Activity Graphs
Nesreen Ahmed, Fredrick Berchmans, Jennifer Neville and Ramana
Kompella

* Structure, Tie Persistence and Event Detection in Large Phone
and SMS Networks
Leman Akoglu and Bhavana Dalvi

* SVM Optimization for Lattice Kernels
Cyril Allauzen, Corinna Cortes and Mehryar Mohri

* A Compact Representation of Graph Databases
Sandra Álvarez, Nieves R. Brisaboa, Susana Ladra and Óscar
Pedreira

* Binary Bit String Representation for Networks based on
Exchangeable Graph Modeling
Hossein Azari, Edoardo Airoldi and Vahid Tarokh

* A Community-Based Model of Online Social Networks
Leendert Botha and Steve Kroon

* Enhancing Link-Based Similarity Through the Use of Non-Numerical
Labels and Prior Information
Christian Desrosiers and George Karypis

* Network Community Discovery: Solving Modularity Clustering via
Normalized Cut
Chris Ding and Linbin Yu

* Analyzing Graph Databases by Aggregate Queries
Anton Dries and Siegfried Nijssen

* Multi-Network Fusion for Collective Inference
Hoda Eldardiry and Jennifer Neville

* Bayesian Block Modelling for Weighted Networks
Ian Gallagher

* An efficient block model for clustering sparse graphs
Adam Gyenge, Janne Sinkkonen and Andras A. Benczur

* Centrality Metric for Dynamic Networks
Kristina Lerman, Rumi Ghosh and Jeon Hyung Kang

* Design Patterns for Efficient Graph Algorithms in MapReduce
Jimmy Lin and Michael Schatz

* Prioritizing Candidate Genes by Network Analysis of Differential
Expression using Machine Learning Approaches
Daniela Nitsch

* Document Classification Utilising Ontologies and Relations
between Documents
Katariina Nyberg, Tapani Raiko, Teemu Tiinanen and Eero Hyvönen

* Graph Visualization With Latent Variable Models
Juuso Parkkinen, Kristian Nybo, Jaakko Peltonen and Samuel Kaski

* Relational motif discovery via graph spectral ranking
Alberto Pinto

* Symmetrizations for Clustering Directed Graphs
Venu Satuluri and Srinivasan Parthasarathy

* Pruthak- mining and analyzing graph substructures
Swapnil Shrivastava, Kriti Kulshrestha, Pratibha Singh and
Supriya N Pal

* Structural Correlation Pattern Mining for Large Graphs
Arlei Silva, Wagner Meira Jr. and Mohammed J. Zaki

* Meaningful Selection of Temporal Resolution for Dynamic Networks
Rajmonda Sulo, Tanya Berger-Wolf and Robert Grossman

* Community Evolution Detection in Dynamic Heterogeneous
Information Networks
Yizhou Sun, Jie Tang, Jiawei Han, Manish Gupta and Bo Zhao

* Network Quantification Despite Biased Labels
Lei Tang, Huiji Gao and Huan Liu

* Frequent Subgraph Discovery in Dynamic Networks
Bianca Wackersreuther, Peter Wackersreuther, Annahita Oswald,
Christian Böhm and Karsten Borgwardt

* Querying Graphs with Uncertain Predicates
Hao Zhou, Anna Shaverdian, H. V. Jagadish and George Michailidis

* Frequent Subgraph Mining on a Single Large Graph Using Sampling
Techniques
Ruoyu Zou and Lawrence Holder

We look forward to seeing you in Washington!

Lise Getoor, Sofus Macskassy, and Ulf Brefeld

Postdoc in Cancer Systems Biology/Bioinformatics/Machine Learning

Postdoc in Cancer Systems Biology/Bioinformatics/Machine Learning

A 3-year postdoctoral position is available in the laboratory of Dr V Anne Smith to
perform systems biology research aiming to improve predictive patient selection for
ovarian cancer.
The project is part of a collaborative effort with researchers at the Edinburgh Cancer
Research Centre under the auspices of Centre for Research in Informatics and Systems
Pathology (CRISP).
You will work with gene and protein data collected by our Edinburgh colleagues from
xenograft and clinical samples; you will apply Bayesian network inference algorithms to
identify biomarker pathways predicting patient response to therapy.

You should have a PhD in Systems or Computational Biology, a Computational
Science, or related field, strong computer programming skills, and a desire to perform
clinically relevant systems biology research.

Informal enquiries to Dr V Anne Smith, email:
anne.smith(at)st-andrews.ac.uk and for further information about the lab please visit
http://biology.st-andrews.ac.uk/vannesmithlab/.

Application deadline is 2 Aug 2010. Please make formal applications through
University of St Andrews HR via:
https://www.vacancies.st-andrews.ac.uk/

Postdoc/PhD student for computational genetics (1,0 fte)

Advert available at http://www.snn.ru.nl/nijmegen/index.php?option=com_content&view=article&id=54&It

PhD Position in Biomedical Data Analysis at the University of Basel, Switzerland

PhD Position in Biomedical Data Analysis at the University of Basel, Switzerland

Applications are invited for a PhD position in the Biomedical Data Analysis Group at
the Department of Computer Science of the University of Basel.
The open position is part of the “SINERGIA”-research project “Emotional Memory in
Health and Disease” funded by the Swiss National Science Foundation.
This interdisciplinary research effort aims at identifying the molecular genetic basis of
emotional memory by combining experimental, clinical, and computational approaches.
The position involves development of computational methods for genome-wide
association studies, statistical analysis of gene interactions, computational modeling of
disease-related gene regulatory pathways, as well as data integration and human-mouse
comparative data analysis.

Successful applicants have a profound knowledge in mathematical modeling and in
algorithmics. Furthermore, the project requires substantial programming skills and a
genuine interest in computational biology. Candidates are expected to engage in
interdisciplinary research groups and to foster collaborations with clinicians and
biologists.
A prerequisite is a Masters degree in Computer Science, Bioinformatics, Mathematics or
Physics.

For further inquiries, please contact
Volker Roth, Email: volker.roth(at)unibas.ch
Phone: +41-61-2670549

Successful candidates will be awarded a fellowship with a competitive salary (~43000
CHF/year).
Applications with a full CV, list of publications, short statement of research interests
and names of at least one referee should be submitted (in electronic form) to
volker.roth(at)unibas.ch

Register for Pattern Recognition School Sept 2010

6th International Summer School and Workshop on Pattern Recognition (5-10 September, 2010)

Registration is now Open – take advantage of early registration fee

The International Summer School on Pattern Recognition is the premier event on research training in the area of pattern recognition and machine learning. The school is fully residential and its registration includes all attendance, and living costs. The vision of the summer school is to empower its participants with the state-of-the-art techniques in pattern recognition and machine learning – to provide a deep understanding of how techniques work, their strengths and limitations, and the future of things to come in this field.

If you have used or will use pattern recognition technology, this is a must attend event. You can:

– Learn from leading experts in pattern recognition and machine learning on a range of topics including statistical pattern recognition, Bayesian approaches, structural approaches, neural networks and support vector machines, data mining, classification, evolutionary computation, markov models, feature selection and reduction, and many more.

– Demonstrate your research till date and win best research prizes sponsored by Microsoft. The event is supported by Microsoft Research, Springer, and Mathworks.

– Meet some of the leading exponents in pattern recognition and machine learning area, network with your peers from around the world using similar tools to solve complex problems

– Enjoy a unique learning experience within a fully residential summer school, admired by previous participants as one of the best summer schools available

www.patternrecognitionschool.com

Email for enquiries: m.singh(at)lboro.ac.uk, or enquiries(at)patternrecognitionschool.com