TPAMI Special Issue : Deadline April 1st 2013

CALL FOR PAPERS – TPAMI Special Issue on “Higher Order Graphical Models in Computer Vision: Modelling, Inference & Learning”

This special issue invites paper submissions addressing the issues of modelling, inference, and learning in models with higher-order terms and global constraints. We also welcome survey and overview papers in these general areas.

The call for papers is available at:
(pdf) http://www.computer.org/cms/Computer.org/transactions/cfps/cfp_tpamisi_hogmcvmil.pdf
(html) http://www.di.ens.fr/~alahari/tpami-cfp.html.

The timeline is as follows:

Submission Deadline: April 1, 2013
Reviews: October 1, 2013
Revisions of Submissions: January 1, 2014
Final Decisions/Manuscript: April 1, 2014
Estimated Online Publication: Fall 2014

Regards,
Karteek Alahari

[on behalf of the guest editors:
Karteek Alahari (INRIA-WILLOW / ENS)
Dhruv Batra (TTI-Chicago / Virginia Tech)
Srikumar Ramalingam (MERL)
Nikos Paragios (Ecole Centrale de Paris / Ecole des Ponts-ParisTech / INRIA-Saclay)
Rich Zemel (University of Toronto)]

KTP Associate – Applied Machine Learning – Newcastle University

Salary: up to £27,900 per annum
Closing date: 22 March 2013
Reference: D1181R

You will work on the application of cutting edge machine learning and computational intelligence methods to real world industrial problems. In particular, we are interested in the combined use of support vector machines and unsupervised clustering methods to analyse high dimensional heterogeneous industrial data. The aim of the analysis is to improve the precision of the manufacturing process of printed circuit boards. The only company in the world that offers a fully automated solution to this problem is the XACT PCB Ltd based near Newcastle in the North East of England. The size of the market of printed circuit boards is in the range of billions of dollars. You can have a real impact on how printed circuit boards are made by joining this project.
The project is a collaboration between the XACT PCB Ltd and Newcastle University. You will work most of the time at the offices of the company and every week you will spend at least a half day at the School of Computing Science of Newcastle University where you will also have an office place. The School of Computing Science recently expanded its academic staff with interests in applied machine learning and computational intelligence. Ongoing research includes the analysis of human behavioural data recorded by movement sensors, development of intelligent and adaptive living environments, data mining of bioinformatics databases, analysis of neuroinformatics imaging data, optimisation of gene regulation for synthetic biology, and experimental validation of cyber-physical systems. You will work under the supervision of Dr Peter Andras (peter.andras@ncl.ac.uk).

We are looking for a self-motivated person with a PhD on a topic related to machine learning or computational intelligence (the actual PhD area can be computer science, mathematics, physics, statistics, engineering or any other related field). You should have exceptionally strong skills in developing and coding machine learning algorithms (preferably in C# or other similar languages, including Java, C++, Matlab, R). You should have a clear desire to move towards industry and to make a real world impact through top quality research.

You must have a First Class honours degree or a Distinction level MSc degree in Computer Science, Mathematics, Physics or related fields and preferably a PhD in one of these fields. You will have the experience in software development, large-scale data analytics, development and application of machine learning methods, together with a positive attitude and good interpersonal, communication and team working skills.

The position comes with benefits including a £4,000 individual training budget and management training.

The post is fixed term for a duration of 2 Years.

To apply go to www.ncl.ac.uk/vacancies/ and search for the vacancy with reference D1181R.

Please note this position is subject to the confirmation of funding. Applicants are expected to be contacted by April 2013.

XACT is the world’s leading provider of integrated registration solutions to many of the world’s highest technology PCB plants.

For further details about the XACT PCB Ltd please see www.xactpcb.com
Newcastle University is one of the top UK universities, member of the select Russell Group formed by the 24 leading UK universities. The School of Computing Science is one of the top research departments in the UK in the area of computer science with a research budget of over £4 million per year. The School is a partner in the Newcastle Culture Lab which is a leading UK hub for innovative cultural and social applications of digital technology.

For further details about Newcastle University please visit our information page at http://www.ncl.ac.uk/about/

For further details on the School of Computing Science please see http://www.ncl.ac.uk/computing/

For further details about Knowledge Transfer Partnerships please visit our Research and Enterprise Service webpage at: http://www.ncl.ac.uk/res/knowledge/ktp/index.htm

ECML/PKDD 2013: second call for tutorials

The ECML-PKDD 2013 Organizing Committee invites proposals for half-day tutorials to be held on the first and last days of the conference, which will take place in Prague, Czech Republic from September 23th to 27th, 2013.

Tutorials are intended to provide a comprehensive introduction to core techniques and areas of interest for the machine learning and the data mining community. We are interested in tutorials on established or emerging research topics in the areas above but we also welcome tutorials from related research fields or applications. The ideal tutorial should attract a wide audience. It should be broad enough to provide a basic introduction to the chosen research area, but it should also cover the most important topics in depth. Each tutorial should be well-focused so that its content can be covered in a half day slot (3.5h including a 30-minute break). Proposals that exclusively focus on the presenter’s own work or commercial presentations are strongly
discouraged.

Tutorial slides will be made available online at the main ECML-PKDD server, although authors can provide them in additional websites as well.

Tutorial speakers will be offered the possibility of a reduced or waived registration fee, depending on the number of speakers for the tutorial.

Submission details
• A title and abstract of the tutorial.
• A brief description of the tutorial content and its relevance to the ECML PKDD community (no more than 2 pages).
• A brief outline of the tutorial structure showing that the tutorial’s core content can be covered in a 3.5 hours slot (including a coffee break).
• The names, postal addresses, phone numbers, and email addresses of the tutorial instructors, including one-paragraph statement of their research interests and areas of expertise.
• A list of previous venues and approximate audience sizes, if the same or a similar tutorial has been given elsewhere; otherwise an estimate of the audience size.
• A description of special requirements for technical equipment (e.g., internet access).

Proposals should be submitted by electronic mail, in plain ASCII text no later than March 8, 2013 to: tutorials@ecmlpkdd2013.org

The subject line should be: “ECML PKDD 2013: TUTORIAL PROPOSAL“.

The proponents will receive an email confirmation that their proposal has been received. If no confirmation arrives within 24 hours after sending the proposal, the proponents should contact the tutorial co-chairs again.

Proposal review
The proposal will be reviewed by the tutorial co-chairs, who may use the help of external reviewers, expert on the submission topics.
The features that will be evaluated are:
i. the interestingness for the ECML PKDD areas, which should result in a large audience;
ii. the clarity of the tutorial, which should emerge from its description;
iii. good organization as appearing from the outline;
iv. the adequacy of the speakers, i.e., her/his background/experience in teaching the target topics; and
v. the ability to explain the topics to a large audience with heterogeneous background.
Tutorial speaker responsibilities

Accepted tutorial speakers will be notified by March 29, 2013, and must then provide the revised abstracts (ASCII format) of their tutorials by April 5, 2013 for advertisement purposes in the conference website. Tutorial speakers must provide their tutorial materials (pdf format), at least containing copies of the course slides as well as a bibliography for the material covered in the tutorial, by September 1, 2013.

Important dates
• Tutorial proposal deadline: Friday, March 8, 2013
• Tutorial acceptance notification: Friday, March 29, 2013
• Tutorial abstract due: Friday, April 5, 2013
• Tutorial slides due: Sunday, September 1, 2013

Contacts
In case you have any question, please do not hesitate to contact us. We are looking forward to your proposals,
Sofus Macskassy (University Southern California)
Kurt Driessens (University of Maastricht)
(The ECML/PKDD 2013 Tutorial co-chairs)
tutorials@ecmlpkdd2013.org

POSTDOC IN MACHINE LEARNING AND PROBABILISTIC MODELING, DL 28 FEB 2013 3:00 PM EET

The Finnish Center of Excellence in Computational Inference Research (COIN), a joint research center of Aalto University and University of Helsinki, is now seeking postdoctoral researchers to join us in developing and applying new methods of machine learning and probabilistic modeling, together with professors Samuel Kaski and Petri Myllymäki, with opportunities for collaboration with other professors at COIN. There are opportunities for both methods development and theoretical work, and interdisciplinary applications.

Deadline 28 February 2013 at 3:00 p.m. EET. Please see more details of the application procedure at http://research.ics.aalto.fi/coin/vacancies.shtml

The Finnish Center of Excellence in Computational Inference Research
(COIN) works on fundamental questions of inference and in applications in Intelligent Information Access, Computational Molecular Biology and Medicine, Computational History, Computational Climate, Computational Neuroscience and other directions yet to be determined.

More information:
http://research.ics.aalto.fi/coin/index.shtml (COIN) http://www.aalto.fi/en (Aalto University) http://www.helsinki.fi/research (University of Helsinki)

Funded PhD position in Sequential Learning with Similarities, SequeL team, INRIA, Lille, France

Funded PhD position in Sequential Learning with Similarities, SequeL team, INRIA, Lille, France

Description: The goal of this PhD position is to design and analyze efficient algorithms for decision making under uncertainty. Specifically, this position will focus on cases when the possible actions are somehow related. This extra information can be provided in a form of weighted graph or a similarity metric. The aim is to provide provably optimal algorithms that can be applied in large-scale scenarios, such as movie recommendation or social networks. The purpose is to minimize feedback that we need to give the algorithm in order to make it “intelligent”. In other words, we want the decision-making algorithms that converge fast to nearly-optimal solutions with minimal feedback (samples). For this purpose, we often need to learn about the environment (explore) at the same time as choosing the currently most promising option (exploit). This requires careful allocation of resources, which could be financial costs, CPU time, or a human effort.
Keywords: machine learning, graph-based learning, exploration-exploitation tradeoff, bandit algorithms, learning with similarities, minimal feedback
Objectives: The multi-armed bandit problem is a simple model to study the trade-off between exploration and exploitation. While simple enough, it displays most of the issues that arise in a variety of decision-making problems under uncertainty. The following problems are the most pertinent to the proposed research program:
• Contextual bandits: This setting is an extension of the multi-armed bandit problem where the best decision depends on the information (context) that is repeatedly given to the decision maker [S09]. Selecting relevant web advertisement or the news feed recommendations are the most common applications of this setting [LCLS10].
• Bandits on Graphs: In many problems, the similarities between the decisions are provided in the form of a graph that relates the pairs of the decisions (nodes), potentially with a weight. This can refer to connections in the social networks [CKLB12] or more general combinatorial problems [BL12, YM11]. Another use is in the recommender systems where we want to discover user preferences (assumed to be smooth on a given graph) with minimal number of queries.
• Stochastic optimization: The optimization of a noisy objective function is a very general problem whose difficulty strictly depends on the properties of the function itself (e.g., linear [AHR08], Lipschitz [BMSS08], submodular [HK12]) and the space the function is defined on (e.g., finite support, continuous). What other interesting settings are (functions and spaces) and what set of assumptions is really needed to successfully optimize the function are issues currently under investigation.
Job Description: The PhD candidate will focus on one or more issues related to the problem of learning and decision-making under uncertainty with some similarity information. The PhD candidate will first acquire expertise in different topics of machine learning such as online learning, multi-armed bandit, statistical learning theory, and graph-based learning. The candidate is then expected to contribute to the advancement of the literature on this problem along many different lines: methodological (e.g., definition of general abstract models for a wide range of decision-making problems), theoretical (e.g., near optimality performance guarantees), and algorithmic (e.g., development of novel algorithms for specific decision-making problems). The candidate will work with Michal Valko (http://researchers.lille.inria.fr/~valko/), Remi Munos, and other members of the lab. The applicant will also have the opportunity to collaborate with researchers in several countries in Europe and USA.

Requirements: The successful candidate will have a MSc or equivalent degree in computer science with a strong background in theory or in mathematics. Programming skills will be considered a plus. The working language in the lab is English, a good written and oral communication skill are required.
• Application closing date: Spring 2013
• Duration: 3 years (a full time position)
• Starting date: October 1st, 2013
• Supervisors: Michal Valko and Remi Munos
• Place: SequeL, INRIA Lille – Nord Europe
About INRIA: SequeL (https://sequel.lille.inria.fr/) is one of the most dynamic labs at INRIA (http://www.inria.fr/), with over 25 researchers and PhD students working on both fundamental and practical aspects of sequential learning problems: from statistical learning, through reinforcement learning, to games. Lille is the capital of the north of France, a metropolis with 1 million inhabitants, with excellent train connection to Brussels (30 min), Paris (1h) and London (1h30). Established in 1967, Inria is the only public research body fully dedicated to computational sciences. Combining computer sciences with mathematics, Inria’s 3,400 researchers strive to invent the digital technologies of the future. Educated at leading international universities, they creatively integrate basic research with applied research and dedicate themselves to solving real problems, collaborating with the main players in public and private research in France and abroad and transferring the fruits of their work to innovative companies. The researchers at Inria published over 4000 articles a year. They are behind over 270 active patents and 105 start-ups. The 171 project teams are distributed in eight research centers located throughout France.

Benefits:
• Duration: 36 months – starting date of the contract : October 2013, 15th
• Salary: 1957,54 € the first two years and 2058,84 € the third year
• Salary after taxes: around 1597,11€ the 1st two years and 1679,76 € the 3rd year (benefits included).
• Possibility of French courses
• Help for housing
• Participation for public transport
• Scientific Resident card and help for husband/wife visa
References:
• [AHR08] Jacob Abernethy, Elad Hazan, and Alexander Rakhlin. Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization. In Proceedings of the 21st Annual Conference on Learning Theory (COLT’08), 2008.
• [BL12] N. Cesa-Bianchi and G. Lugosi Combinatorial bandits Journal of Computer and Systems Sciences, 78:1404-1422, 2012.
• [BMSS08] S. Bubeck and R. Munos and G. Stoltz and Cs. Szepesvari. Online Optimization of {X}-armed Bandits. In Proceedings of the Advances in Neural Information Processing Systems (NIPS’08), 2008.
• [BMS09] S. Bubeck and R. Munos and G. Stoltz. Pure Exploration in Multi-Armed Bandits Problems. Proceedings of the 20th International Conference on Algorithmic Learning Theory (ALT’09), 2009.
• [CKLB12] Stephane Caron, Branislav Kveton, Marc Lelarge, and Smriti Bhagat. Leveraging Side Observations in Stochastic Bandits. Uncertainty in Artificial Intelligence, 2012.
• [HK12] Elad Hazan, Satyen Kale. Online Submodular Minimization. Journal of Machine learning Research (JMLR), 13(Oct):2903−2922, 2012.
• [LCLS10] Lihong Li, Wei Chu, John Langford, Robert E Schapire. A Contextual-Bandit Approach to Personalized News Article Recommendation WWW 10, Volume: 173 (2010)
• [S09] Contextual Bandits with Similarity Information Aleksandrs Slivkins Proceedings of the 24th annual Conference On Learning Theory, Issue: June (2009)
• [YM11] J-Y Yu and S. Mannor. Unimodal Bandits. International Conference on Machine Learning (ICML), 2011
This call is posted at http://researchers.lille.inria.fr/~valko/hp/call-phd-2013.php.
For further information please send an email to michal.valko-at-inria.fr as soon as possible.
—
Michal Valko (equipe SequeL)

CENTRE DE RECHERCHE LILLE – NORD EUROPE
Parc scientifique de la Haute Borne, 40 Avenue Halley, Bât A – Park Plaza
59650 Villeneuve d’Ascq, Office #5 Tel : +33 (0)3 59 57 7801 www.inria.fr
Suivez‐nous sur Twitter twitter.com/inria et YouTube youtube.com/inriachannel

POST-DOC POSITION Telecom ParisTech – Safety Line

POSTDOCTORAL POSITION
possibly leading to a PERMANENT RESEARCH POSITION

Statistician, specialized in risk management

Context: Safety Line offers a permanent contract starting by a one-year post-doc
position co-supervised by the lab of Information and Communication Theory
(LTCI) of Telecom Paristech.

Safety Line is a French innovative company specialising in digital technology for risk
management. It develops statistical algorithms for data mining of data recorded in
flight by airlines (of the magnitude of hundreds of parameters per seconds per flight),
in order to contribute to solving complex problems such as the detection of “weak
signals” in anticipation of disasters. More precisely, from an operational point of view
it is crucial to be capable of identifying “risky sequence”, that is to say a combination
of actions and of phenomena that could lead to damages. In order to enhance the
performance of the analysis, supervised learning-based algorithms which are already
explored should be complemented by unsupervised methods. However, the data are
not well separable, therefore the goal of the research is to develop a general-purpose
detection framework to sort out “meaningful” types of sequences. By “meaningful
type of sequence” we mean a class of time-series which show comparable variations,
in order to point out non-consistent pilot actions for instance.

Activities & Responsibilities: The research topics include but are not limited to:

– Developing statistical and computational methodologies for the analysis of highdimensional
multivariate time-series (flight data or biomedial data), especially for
the risky sequence recognition problems;
– Integrating these methods in a software dedicated to the Risk Management.

From the scientific point of view, the performance of purely unsupervised
methodologies should be compared with semi-supervised learning techniques
integrating risk management expertise.
Deliverables are mainly routines and research reports that detail the performance
comparison between the various techniques used. A particular attention should be
given to the computing performance, in the perspective of an operational
implementation of the algorithms.
The computer developments are performed in C++ or Python, in order to facilitate the
possible embedding of algorithms in the C++ programs that are already in use.
Databases are of type SQL and NoSQL (MongoDB). An access to the computational
resources of Telecom’s lab is granted in order to take advantage of high-end
capabilities. Furthermore, Safety Line is developing its own high-performance
computation capability in collaboration with INRIA and GENCI.

Environment:
The post-doc shall be supervised by Prof. Stéphan Clémençon (Telecom ParisTech)
and Sébastien Travadel (Safety Line).

The LTCI groups all the research activities of Telecom ParisTech (Paris Institute of
Technology), which represents today approximately 160 permanent researchers and
teachers (among which 28 researchers of the National Center for Scientific Research
(CNRS), 270 Ph.D. students as well as 60 post-docs. The Statistics & Applications
Group of the LTCI enjoys a high national and international recognition with editorial
board members in high quality journals (Bernoulli, ESAIM P&S, the Journal of the
Royal Statistical Society, DSP journal) as well as regular participation as program
comity members in the major international conferences(IEEE ICASSP, International
Conference on Machine Learning).

Website:
http://www.ltci.telecom-°©‐paristech.fr/
Safety Line offers an exciting scientific environment to create cutting-edge solutions
for hazard prevention. The team brings together experts in risk management and
researchers in the fields of statistics & human performance. Strong collaborative links
with internationally recognized labs ensure a high-level research program. Indeed,
the company is actively interacting with high-profile scientists in mathematic fields
(statistical & optimization), high performance computation and clinical research on
human performance. The research and development team is a dynamic team of
young professionals dedicated to safety and sustainable development, which reflects
the core values of the firm.
While initially focused on aviation, the activity of the company is developing rapidly
and is now opening on new types of industrial risks. Website: www.safety-line.fr
The successful candidate will work in our Paris-based office (15, rue Jean-Baptiste
Berlier, 75013 Paris). The daily hours are 9 to 5 pm, 5 days a week. 5 weeks of
holidays granted. During the first year (post-doc), working time will be shared as
follows: 4 days/week in Safety Line’s premises, 1 day/week in LTCI’s facilities (37/39,
rue Dareau, 75014 Paris).
The team is multinational and the communication can be in English or in French.

Position Qualifications: Ph.D. in statistics or other relevant, closely related
quantitative field (statistical physics for instance), strong quantitative research
background, statistical and programming proficiency. We are seeking an individual
with a strong background in practical statistical machine learning and computation.

Salary range: 37 to 40 k€ per year + bonus.

Application deadline: 31/03/2013

Benefits: The position offers health coverage, unemployment benefits, pension
contribution and maternity leave.

Application: Applications should include a resumé, brief statements of research
interests, publication records and a link towards the Ph.D. thesis. Applications and
letters should be sent via electronic mail to:
clemenco@telecom-paristech.fr & sebastien.travadel@safety-line.fr

Postdoctoral Research Fellow (three-year fixed term) on “Statistical Anomaly Detection”, CVSSP, University of Surrey, U.K.

Research Fellow
Statistical anomaly detection

Centre for Vision Speech and Signal Processing (CVSSP)
University of Surrey, United Kingdom
Salary: £29,541-£31,331 per annum
(Subject to qualifications and experience)

Applications are invited for a three-year postdoctoral research fellow position available at CVSSP, starting on Monday, April 1, 2013, to work on a project entitled “Signal Processing Solutions for a Networked Battlespace”, funded by the Engineering and Physical Sciences Research Council (EPSRC) and Defence Science and Technology Laboratory (Dstl), as part of the Ministry of Defence (MoD) University Defence Research Centre (UDRC) Scheme in signal processing. This project will be undertaken by a unique consortium of academic experts from Loughborough, Surrey, Strathclyde and Cardiff (LSSC) Universities together with six industrial project partners QinetiQ, Selex-Galileo, Thales, Texas Instruments, PrismTech and Steepest Ascent. The overall aim of the project is to provide fundamental signal processing solutions to enable intelligent and robust processing of the very large amount of multi-sensor data acquired from various networked communications and weapons platforms, in order to retain military advantage and mitigate smart adversaries who present multiple threats within an anarchic and extended operating area (battlespace). The research fellow will be expected to work in close collaboration with our academic and industrial partners together with members of the lead consortium based at Edinburgh and Heriott Watt Universities.

The prospective research fellow will be expected to develop algorithms and systems for automated statistical anomaly detection and classification in high dimensions for the networked battlespace. In particular, he/she will develop algorithms for automatic detection of anomalies from multidimensional, undersampled, non-complete datasets and unreliable sources, and solutions to anomaly detection with the presence of uncertainties and in complex networks (graphs), e.g., using domain knowledge.

Successful applicants will join the CVSSP, a leading research group in sensory (visual and auditory) data analysis and interpretation, and will work closely with Dr Wenwu Wang, Prof Josef Kittler and Dr Philip Jackson. CVSSP is one of the largest UK research groups in machine vision and audition with more than 120 researchers, with core expertise in Signal Processing, Image and Video Processing, Pattern Recognition, Computer Vision, Machine Learning and Artificial Intelligence, Computer Graphics and Human Computer Interaction. CVSSP forms part of the Department of Electronic Engineering, which received one of the highest ratings (joint second position across the UK) in the last research quality assessment, i.e. 2008 RAE, with 70% of its research classified as either 4* (“world-leading”) or 3* (“internationally excellent”).

Applicants should have a PhD degree or equivalent in electrical and electronic engineering, computer science, mathematical science, statistics, physics, or related disciplines. Applicants should be able to demonstrate excellent mathematical, analytical and computer programming skills. Advantages will be given to the applicants who have experience in anomaly detection, statistics, machine learning, signal processing, and/or pattern recognition.
For informal inquiries about the position, please contact Dr Wenwu Wang (w.wang@surrey.ac.uk) or Prof Josef Kittler (j.kittler@surrey.ac.uk).
For an application pack and to apply on-line please go to our website: http://www.surrey.ac.uk/vacancies. If you are unable to apply on-line please contact Mr Peter Li, HR Assistant on Tel: +44 (0) 1483 683419 or email: k.li@surrey.ac.uk
The closing date for applications is Thursday February 28th, 2013.

For further information about the University of Surrey, please visit www.surrey.ac.uk.

We acknowledge, understand and embrace cultural diversity.

2 Postdocs for 3 years in video search: recognition and explanation

*2 POSTDOCS FOR 3 YEARS IN VIDEO SEARCH* Faculty of Science – Informatics Institute

The Informatics Institute at the University of Amsterdam invites applications for 2 Postdocs in Video Search starting Spring 2013.

*/Project Description/*
The positions are part of a 5-year Personal VIDI Grant funded by the Dutch Organization for Scientific Research and headed by Dr. Cees Snoek.
The successful candidates will participate in a frontier research project on video recognition and explanation, and will work in a stimulating environment of a leading and highly active research team including one faculty member and six PhD students. The team has repeatedly won the major visual search competitions, including NIST TRECVID, PASCAL Visual Object Challenge, ImageCLEF, and the ImageNet Large Scale Visual Recognition Challenge.

*/Requirements/*
o PhD in Computer Science, emphasizing computer vision, and/or machine learning.
o Strong publication record in top-tier international conferences and journals.
o Solid knowledge of programming in C/C++ for large-scale processing.
o Motivated and capable to coordinate and supervise PhD research and teaching.

*/Specific to Position nr. 1 (Video recognition)/* o Research record in spatiotemporal descriptors, object localization, action recognition and/or data-intensive machine learning.

*/Specific to Position nr. 2 (Video explanation)/* o Research record in attribute categorization, event recognition, natural language processing, and/or data-intensive machine learning.

Conditions of employment, things we have to offer and application procedure, see: http://bit.ly/11xNutU

Call for participation: PASCAL2 IASD challenge

=================================================================
Interactive Annotation of Sequential Data (IASD)
PASCAL2 challenge
http://translectures.eu/iasd =================================================================
— Please, accept our apologies in case of multiple receptions —

Dear colleagues,

We are pleased to announce the launch of the Interactive Annotation of Sequential Data (IASD) PASCAL2 challenge. The aim of the IASD challenge is to explore innovative, cost-effective solutions for
generating accurate transcriptions for video lectures from
VideoLectures.NET, and, at a more general level, speech-like
sequential data. The focus is not on developing advanced speech recognition techniques, so much as on the study of techniques for interacting intelligently with users. These techniques should seek to optimise the trade-off between user effort and accuracy, in such a way that the winning approaches are those reaching the top-3 accuracy with minimum feedback.

Important Dates:

Feb 12, 2013 – First challenge phase starts.
Mar 12, 2013 – First phase ends: selection of the 3 best systems.
Mar 13, 2013 – Second phase starts (for the 3 best systems only).
Mar 25, 2013 – Second phase ends: winners annouced and ranked.
Apr 2, 2013 – Reports deadline for the winners.

Presentation and awards:

The winners will be invited to present their systems at the joint EUCOGIII/PASCAL meeting in Palma de Mallorca on April 11, 2013, with travel costs covered by the meeting organisation. Winners attending the meeting will be awarded with the following net prizes (after
taxes):

1st prize: €1000
2nd prize: €600
3rd prize: €300

You will find a detailed description of the challenge, data and evaluation methodology at:

http://translectures.eu/iasd

Challenge Organizers:

Nicolas Serrano, Jesus Andres, Alfons Juan (UPV) John Shawe-Taylor, Davor Orlic (K4A) Mitja Jermol (JSI)

Challenge sponsors:

PASCAL2 Network (http://www.pascal-network.org) EUCOGIII Network (http://www.eucognition.org) transLectures project (http://translectures.eu) Universitat Politecnica de Valencia (http://www.upv.es/index-en.html) Knowledge for all (http://www.k4all.org) Jožef Stefan Institute (http://www.ijs.si/ijsw/JSI)

Call for Papers: IEEE TNNLS Special Issue on “Learning in Non-(geo)metric Spaces”

CALL FOR PAPERS

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

Special Issue on
Learning in Non-(geo)metric Spaces

Traditional machine learning and 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. 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 “(geo)metric” (i.e., geometric and/or metric) properties undermines the very foundations of traditional machine learning theories and algorithms, and poses totally new theoretical/computational questions and challenges that the research community is currently trying to address. The goal of the special issue is to consolidate research efforts in this area by soliciting and publishing high-quality papers which, together, will present a clear picture of the state of the art.

SCOPE OF THE SPECIAL ISSUE
We will encourage submissions of papers addressing theoretical, algorithmic, and practical issues related to the two fundamental questions that arise when abandoning the realm of vectorial, feature-based representations, namely:

– how can one obtain suitable similarity information from data representations that are more powerful than, or simply different from, the vectorial?
– how can one use similarity information in order to perform learning and classification tasks?

Accordingly, 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 and learning
– Characterization of non-(geo)metric behavior
– Foundational issues
– Measures of (geo)metric violations
– Learning and combining similarities
– Multiple-instance learning
– Applications

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.

IMPORTANT DATES
October 1, 2013 – Deadline for manuscript submission April 1, 2014 – Notification to authors July 1, 2014 – Deadline for submission of revised manuscripts October 1, 2014 – Final decision

GUEST EDITORS
Marcello Pelillo, Ca Foscari University, Venice, Italy (pelillo@dsi.unive.it) Edwin Hancock, University of York, UK (edwin.hancock@york.ac.uk) Xuelong Li, Chinese Academy of Sciences, China (xuelong_li@ieee.org) Vittorio Murino, Istituto Italiano di Tecnologia & University of Verona, Italy (vittorio.murino@iit.it)

SUBMISSION INSTRUCTIONS
1. Read the information for authors at: http://cis.ieee.org/publications.html
2. Submit the manuscript by October 1, 2013 at the TNNLS webpage
(http://mc.manuscriptcentral.com/tnnls) and follow the submission procedure. Please, clearly indicate on the first page of the manuscript and in the cover letter that the manuscript has been submitted to the special issue on “Learning in non-(geo)metric spaces.” Send also an e-mail to M. Pelillo (pelillo@dsi.unive.it) with subject “TNNLS special issue submission” to notify the editors of your submission.