SIMBAD 2013 (Similarity-Based Pattern Analysis and Recognition), York, UK

SECOND CALL FOR PAPERS

SIMBAD 2013

2nd International Workshop on Similarity-Based Pattern Analysis and Recognition

July 3-5, 2013
York, UK

http://www.dais.unive.it/~simbad

MOTIVATIONS AND OBJECTIVES

Traditional pattern recognition techniques are intimately linked to the notion of “feature space.” Adopting this view, each object is described in terms of a vector of numerical attributes and is therefore mapped to a point in a Euclidean (geometric) vector space so that the distances between the points reflect the observed (dis)similarities between the respective objects. This kind of representation is attractive because geometric spaces offer powerful analytical as well as computational tools that are simply not available in other representations. However, the geometric approach suffers from a major intrinsic limitation, which concerns the representational power of vectorial, feature-based descriptions. In fact, there are numerous application domains where either it is not possible to find satisfactory features or they are inefficient for learning purposes.

In the last few years, interest around purely similarity-based techniques has grown considerably. For example, within the supervised learning paradigm the well-established kernel-based methods shift the focus from the choice of an appropriate set of features to the choice of a suitable kernel, which is related to object similarities.
However, this shift of focus is only partial, as the classical interpretation of the notion of a kernel is that it provides an implicit transformation of the feature space rather than a purely similarity-based representation. Similarly, in the unsupervised domain, there has been an increasing interest around pairwise or even multiway algorithms, such as spectral and graph-theoretic clustering methods, which avoid the use of features altogether.

By departing from vector-space representations one is confronted with the challenging problem of dealing with (dis)similarities that do not necessarily possess the Euclidean behavior or not even obey the requirements of a metric. The lack of such properties undermines the very foundations of traditional pattern recognition theories and algorithms, and poses totally new theoretical/computational questions and challenges.

The aim of this workshop, which follows the one held in Venice in 2011 (http://www.dais.unive.it/~simbad/2011/), is to consolidate research efforts in this area, and to provide an informal discussion forum for researchers and practitioners interested in this important yet diverse subject. We aim at covering a wide range of problems and perspectives, from supervised to unsupervised learning, from generative to discriminative models, and from theoretical issues to real-world applications.

Original, unpublished papers dealing with these issues are solicited.
Topics of interest include (but are not limited to):

– Embedding and embeddability
– Graph spectra and spectral geometry
– Indefinite and structural kernels
– Game-theoretic models of pattern recognition
– Characterization of non-(geo)metric behavior
– Foundational issues
– Measures of (geo)metric violations
– Learning and combining similarities
– Multiple-instance learning
– Applications

PAPER SUBMISSION

All papers (not exceeding 16 pages) must be submitted electronically at the conference website (http://www.dais.unive.it/~simbad/2013/).
All submissions will be subject to a rigorous peer-review process.
Accepted papers will appear in the workshop proceedings, which will be published in Springer’s Lecture Notes in Computer Science (LNCS) series.

In addition to regular, original contributions, we also solicit papers (in any LaTeX format, no page restriction) that have been recently published elsewhere. These papers will undergo the same review process as regular ones: if accepted, they will be presented at the workshop but will not be published in the workshop proceedings.

Submission implies the willingness of at least one of the authors to register and present the paper, if accepted.

INVITED SPEAKERS

Avrim Blum, Carnegie Mellon University, USA Nello Cristianini, University of Bristol, UK Frank Nielsen, Sony Computer Science Laboratories Inc, Japan

IMPORTANT DATES

Paper submission: February 1, 2013
Notifications: March 15, 2013
Camera-ready due: April 25, 2013
Workshop: July 3-5, 2013

ORGANIZATION

Program Chairs
Edwin Hancock, University of York, UK
Marcello Pelillo, University of Venice, Italy

Steering Committee
Joachim Buhmann, ETH Zurich, Switzerland
Robert Duin, Delft University of Technology, The Netherlands
Mario Figueiredo, Technical University of Lisbon, Portugal
Edwin Hancock, University of York, UK
Vittorio Murino, Italian Institute of Technology, Italy
Marcello Pelillo (chair), University of Venice, Italy

Program Committee
Maria-Florina Balcan, Georgia Institute of Technology, USA
Manuele Bicego, University of Verona, Italy
Avrim Blum, Carnegie Mellon University, USA
Joachim Buhmann, ETH Zurich, Switzerland
Terry Caelli, NICTA, Australia
Tiberio Caetano, NICTA, Australia
Umberto Castellani, University of Verona, Italy
Luca Cazzanti, University of Washington, Seattle, USA
Nello Cristianini, University of Bristol, UK
Robert Duin, Delft University of Technology, The Netherlands
Aykut Erdem, Hacettepe University, Ankara, Turkey
Francisco Escolano, University of Alicante, Spain
Mario Figueiredo, Technical University of Lisbon, Portugal
Ana Fred, Technical University of Lisbon, Portugal
Mehmet Gonen, Aalto University School of Science, Finland
Marco Gori, University of Siena, Italy
Bernard Haasdonk, Universitaet Stuttgart, Germany
Edwin Hancock, University of York, UK
Robert Krauthgamer, Weizmann Institute of Science, Israel
Marco Loog, Delft University of Technology, The Netherlands
Marina Meila, University of Washington, Seattle, USA
Vittorio Murino, Italian Institute of Technology, Italy
Marcello Pelillo, University of Venice, Italy
Massimiliano Pontil, University College London, UK
Antonio Robles-Kelly, NICTA, Australia
Fabio Roli, University of Cagliari, Italy
Samuel Rota Bulo’, University of Venice, Italy
Volker Roth, University of Basel, Switzerland
John Shawe-Taylor, University College London, UK
Andrea Torsello, University of Venice, Italy
Richard Wilson, University of York, UK
Lior Wolf, Tel Aviv University, Israel

University of Oxford faculty position in Machine Learning

UNIVERSITY LECTURER IN COMPUTER SCIENCE (MACHINE LEARNING) University of Oxford Department of Computer Science in association with Linacre College

The Department of Computer Science proposes to appoint a University Lecturer (US tenure-track assistant/associate professor equivalent) in Computer Science with effect from 1 May 2013 or a mutually agreed date prior to 1 October 2013. The successful candidate will be offered a Non-Tutorial Fellowship at Linacre College under arrangements described in the further particulars. The salary will be on a scale currently up to £57,581 per annum.

Applicants should hold a relevant PhD and have a demonstrated track record of research excellence in any area of theoretical or applied machine learning. Candidates are expected to have a strong track record of publication in leading machine learning venues (e.g. NIPS, ICML, JMLR etc.), or conferences and journals of relevant applied fields. Full details of the qualifications required and the duties of the post can be found in the further particulars.

The closing date for applications is Friday, 25th January 2013.

Queries about the post should be addressed in the first instance to Elizabeth Walsh at Elizabeth.walsh@cs.ox.ac.uk or telephone: +44 (0)
1865 283503.

Applications are particularly welcome from women and black and minority ethnic candidates, who are under-represented in academic posts in Oxford. The University is an Equal Opportunities Employer.

Further particulars for this position can be found at:
http://www.cs.ox.ac.uk/files/5254/ML_UL_Fin.pdf

i-like Opening workshop – 31 January 2013, 2:10pm to 5:30pm

Please go to http://www.i-like.org.uk/launch-day-31st-january-2013.html for details of this event.

The day is principally aimed at phd and post doc level, although others are more than welcome to attend as well.

Postdoc positions a KU Leuven, ESAT-SCD

The research group KU Leuven ESAT-SCD is currently offering 2 Postdoc positions (1-year, extendable) within the framework of the ERC Advanced Grant A-DATADRIVE-B http://www.esat.kuleuven.be/sista/ADB .

The research positions relate to the following possible topics:
-1- Prior knowledge incorporation
-2- Kernels and tensors
-3- Modelling structured dynamical systems
-4- Sparsity
-5- Optimization algorithms
-6- Core models and mathematical foundations
-7- Next generation software tool

The research group ESAT-SCD http://www.esat.kuleuven.be/scd/ at the university KU Leuven Belgium provides an excellent research environment being active in the broad area of mathematical engineering, including systems and control theory, neural networks and machine learning, nonlinear systems and complex networks, optimization, signal processing, bioinformatics and biomedicine.

The research will be conducted under the supervision of Prof. Johan Suykens. Interested candidates having a solid mathematical background and PhD degree can apply for these positions by sending their CV and motivation letter to johan.suykens@esat.kuleuven.be. For further information on these positions you may contact johan.suykens@esat.kuleuven.be.

AVSS-2013 Call for Workshop and Contest Proposals

AVSS-2013 Call for Workshop and Contest Proposals

10-th IEEE International Conference on
Advanced Visual and Signal-Based Surveillance

August 27-30, 2013 Krakow, Poland
http://www.avss2013.org

Workshop and contest proposals are being solicited for AVSS-2013. Workshops will take place on Aug. 27, 2013 prior to the start of the technical program of the conference.

The topic of the proposal should be related to the field of interest of AVSS community, namely surveillance based on various modalities including, but not limited to, visible light, infrared, mm-wave, audio, radio, etc., and in particular:

– Sensor-Centric Processing
– Data Management & Human-Computer Interaction
– Security and Privacy
– Processing, Detection, Tracking & Recognition
– Analytics, Situation Awareness & Decision Making
– Surveillance Systems and Applications

For a more detailed list of areas, please visit http://www.avss2013.org/call-for-papers

We particularly encourage workshops in the form of a contest targeting a task relevant to the conference areas of interest above. Papers published as part of the workshops will be included in the Conference proceedings and referenced by IEEE.

Details on workshop proposal submission procedure can be found at http://www.avss2013.org/call-for-workshops

Important Dates:

Workshop proposals due: February 18, 2013 Notification of workshop proposal acceptance: March 3, 2013 Workshop paper submission: Defined by workshop organizers Notification of workshop paper acceptance: Defined by workshop organizers Workshop camera-ready paper due: June 17, 2013

Workshops Chair
Jean-Marc Odobez,
Idiap research institute, Switzerland
odobez[at]idiap.ch

4 year phd position on stochastic optimal control theory for neural networks

In the context of the Marie Curie NETT project

http://www.neural-engineering.eu/

we have two 4 year PhD positions available in my research group in Nijmegen, the Netherlands.

See http://www.snn.ru.nl/nijmegen/index.php?option=com_content&view=article&id=54&Itemid=81

The aim of the work package is to build neural architectures for stochastic optimal control and learning. The research is motivated by the recent work on path integral control methods. For this class of control methods, the optimal control can be computed using sampling.
This approach has shown to be very effective for robotics and learning. The current project will address the question of how such control computations can be implemented in neural networks.

The project requires advanced expertise on neural networks, control theory and machine learning. The candidates are not required to have good knowledge of these fields at the start of the project, but are expected to learn these topics.
Candidates should have a completed academic degree in theoretical physics, mathematics or engineering. As part of our commitment to promoting diversity, we particularly encourage female candidates to apply. To comply with the mobility rules of the Marie Curie Actions, applicants must not have resided, worked or studied in the Netherlands for more than 12 months in the three years prior to September 2012.

For an overview of the research in my group see: www.snn.ru.nl/~bertk and www.snn.ru.nl

Two PhD positions in Computer Vision at the University of Edinburgh

University of Edinburgh
School of Informatics
Two PhD positions in Computer Vision

Applications are invited for two PhD students to work on a project funded by a European Research Council Starting Grant. The two main topics of interest are:

* continuously learn new object classes helped by the knowledge of classes learned before
* learning object classes from consumer videos

Applicants must have:

* Master degree in Computer Science or Mathematics
* Excellent programming skills (the project is in Matlab and C++)
* Solid mathematics foundations (especially algebra and statistics)
* Highly motivated
* Fluent in English, both written and spoken
* UK or EU nationality is mandatory
* Experience in computer vision and/or machine learning is a plus

The School of Informatics at Edinburgh is one of the top-ranked departments of Computer Science in Europe and offers an exciting research environment. Edinburgh is a beautiful historic city with a high quality of life.

Starting date: January 2013 or later

The PhD work will be carried out under the supervision of Dr. Vittorio Ferrari. For an overview of current research activities, please visit

http://groups.inf.ed.ac.uk/calvin/

For pre-screening, please send applications to the email address below, including:
* complete CV
* title and abstract of master thesis
* complete grades for all exams passed during both the bachelor and master
(to obtain this position you need high grades, especially in mathematics and programming disciplines)
* the name and email address of one reference (preferably your master thesis supervisor)
* if you already have research experience, please include a publication list

email: vferrari@staffmail.ed.ac.uk

Research Fellow

Research Fellows

Looking for researcher of exceptional talent to conduct 2 year research fellowship to work closely with Professor Philip Torr (http://cms.brookes.ac.uk/staff/PhilipTorr/), at Oxford Brookes vision research group http://cms.brookes.ac.uk/research/visiongroup to work on various projects connected with our ongoing work on scene understanding (see here for recent publications http://cms.brookes.ac.uk/staff/PhilipTorr/papers.htm).

There will be a large amount of freedom for the researcher and it is expected that applicants will have a top flight record, as evinced by publications in top venues such as ICCV, NIPS, ECCV, SIGGRAPH, CVPR, PAMI, JMLR, IJCV etc. The candidate will benefit from close mentoring to encourage them to develop into a top flight researcher.

The computer vision group at Oxford Brookes has a strong international reputation, having taken scientific best paper awards at all the conferences mentioned previously, it is an exceptionally stimulating environment, with strong connections to the top industrial research labs such as Sony, Microsoft and Google, as well as very close links with the Visual Geometry Group at Oxford University with whom we share reading groups and seminars.
It is soon to move to brand new facilities near the heart of Oxford, a very academically vibrant city.
The applications are competitive across disciplines, but those with a very good CV will take precedence.

For further information contact philiptorr@brookes.ac.uk or apply at http://www.brookes.ac.uk/services/hr/research_recruitment/index.html

The closing date for these vacancies is 11 January 2013.

CFP: Mathematics in Computer Science, Special Issue on Mathematics, Data and Knowledge

There is a growing interest in applying mathematical theories and methods (from topology, computational geometry, differential equations, fluid dynamics, quantum statistics, etc.) to describe and to analyze scientific regularities of diverse, massive, complex, nonlinear, and fast changing data accumulated continuously around the world and in discovering and revealing the valid, insightful, and valuable knowledge that data imply. With increasingly solid mathematical foundations, various methods and techniques have been studied and developed for data mining, modeling, and processing, and knowledge representation, organization, and verification; different systems and mechanisms have been designed to perform data-intensive tasks in many application fields for classification, predication, recommendation, ranking, filtering, etc. This special issue of Mathematics in Computer Science invites submissions of original research articles on the exploration of new mathematical theories and methodologies for data modeling and analysis, and knowledge discovery and management, on the study of existing mathematical models of big data and complex knowledge, and on the development of novel solutions and strategies to enhance the performance of existing systems and mechanisms for data and knowledge processing.

Specific topics include, but are not limited to:
• Mathematical foundations and theories for data-intensive and knowledge-based systems
• Mathematical, statistical, and dynamic analysis of data and knowledge models
• Mathematical methods for big data storage, transferring, and processing
• Mathematical methods for complex knowledge representation, organization, visualization, and management
• Mathematical methods for data mining, pattern recognition, artificial intelligence, and knowledge discovery
• Algebraic, geometric, analytic, discrete, probabilistic, fuzzy, rough set, and cognitive modeling of recommendation systems, ranking systems, rating systems, expert systems, etc.
• Mathematical theories for the development of evolutionary computing, neural networks, and genetic algorithms
Important Dates
• Deadline for paper submission: March 31, 2013
• Notification of acceptance: August 15, 2013
• Final paper submission: October 1, 2013
• Publication of special issue: December 2013

Submission Guidelines

Authors are encouraged to prepare submissions by using LaTeX with the class file mathincl.cls. Papers should be sent as PDF files to special.issue.mdk@gmail.com. All submitted papers will be refereed according to the usual MCS refereeing process. More information can be found at: http://mine.kaust.edu.sa/Pages/CFP-MCS-SI.aspx.

Guest Editors

Xiaoyu Chen, School of Computer Science and Engineering, Beihang University, China
Dongming Wang, Laboratoire d’Informatique de Paris 6, CNRS-UPMC, France
Xiangliang Zhang, King Abdullah University of Science and Technology, Saudi Arabia

Full Professor in Advanced Analytics

School of Business and Economics

We are seeking qualified applicants for teaching and research in the area of Advanced Analytics. The starting date is as soon as possible.

Within the “Excellence Initiative” – the German federal and state governments’ framework to promote cutting-edge research in and to enhance the quality of the country’s universities – the School of Business and Economics at RWTH Aachen University is currently in the process of establishing four Research Areas. All four of them will be brought together under one roof, that of the Interdisciplinary Management Factory (IMF), which will serve to substantially enhance the School’s research profile. Each Research Area will address global challenges, which can only be tackled via interdisciplinary and integrated research – a form of research that one of Europe’s leading universities of technology is optimally equipped to carry out.

The successful candidate will play a crucial role in establishing the Research Laboratory of the Operations Research and Management Research Area (ORM). The actors in this Research Area are economists, mathematicians, and computer scientists, whose research interests embrace operations research, operations management, discrete optimization, and efficient algorithms. The successful candidate will be expected to contribute to this pool of expertise with know-how from fields like prescriptive analytics, big data, or optimization under uncertainty/robust optimization. Apart from having conducted top-level research, reflected in publications in high-ranking journals, applicants should also demonstrate a successful professional orientation. Experience in procuring third-party funding is also very desirable. The teaching load will encompass two hours per semester. The professorship is limited to a period of four years.

A Ph.D. degree is required; additionally, Habilitation (post-doctoral lecturing qualification), an exemplary record of research achievement as an assistant / an associate / a junior professor or university researcher and/or an outstanding career outside academia are highly desirable. Ability in and commitment to teaching are essential. German is not necessary to begin. Applications from early-stage researchers are particularly welcome. Should more detailed information about this position be required, please contact Prof. Marco Lübbecke (luebbecke@or.rwth-aachen.de), who heads the ORM Research Area.

The application should include supporting documents regarding success in teaching.

Please send a cover letter stating research aims and a CV to: An den Dekan der Fakultät für Wirtschaftswissenschaften der RWTH Aachen, Prof. Dr. Oliver Lorz, D-52056 Aachen, Germany. The deadline for applications is December 12, 2012.

This position is also available as part-time employment per request.
RWTH Aachen University is certified as a family-friendly university and offers a dual career program for partner hiring. We particularly welcome and encourage applications from women, disabled people and ethnic minority groups, recognizing they are underrepresented across RWTH Aachen University. The principles of fair and open competition apply and appointments will be made on merit.