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Multi-timescale Modeling of Activity-Dependent Metabolic Coupling in the Neuron-Glia-Vasculature Ensemble
by Jolivet, Renaud March 3, 2015

March 3, 2015

Dear colleagues, You might be interested by our recent paper: "Multi-timescale Modeling of Activity-Dependent Metabolic Coupling in the Neuron-Glia-Vasculature Ensemble”, recently published in PLOS Computational Biology (http://dx.doi.org/10.1371/journal.pcbi.1004036) From the abstract: Glucose is the main energy substrate in the adult brain under normal conditions. Accumulating evidence, however, indicates that lactate produced in astrocytes (a type of glial cell) can also fuel neuronal activity. The quantitative aspects of this so-called astrocyte-neuron lactate shuttle (ANLS) are still debated. To address this question, we developed a detailed biophysical model of the brain’s metabolic interactions. Our model integrates three modeling approaches, the Buxton-Wang model of vascular dynamics, the Hodgkin-Huxley formulation of neuronal membrane excitability and a biophysical model of metabolic pathways. This approach provides a template for large-scale simulations of the neuron-glia-vasculature (NGV) ensemble, and for the first time integrates the respective timescales at which energy metabolism and neuronal excitability occur. The model is constrained by relative neuronal and astrocytic oxygen and glucose utilization, by the concentration of metabolites at rest and by the temporal dynamics of NADH upon activation. These constraints produced four observations. First, a transfer of lactate from astrocytes to neurons emerged in response to activity. Second, constrained by activity-dependent NADH transients, neuronal oxidative metabolism increased first upon activation with a subsequent delayed astrocytic glycolysis increase. Third, the model correctly predicted the dynamics of extracellular lactate and oxygen as observed in vivo in rats. Fourth, the model correctly predicted the temporal dynamics of tissue lactate, of tissue glucose and oxygen consumption, and of the BOLD signal as reported in human studies. These findings not only support the ANLS hypothesis but also provide a quantitative mathematical description of the metabolic activation in neurons and glial cells, as well as of the macroscopic measurements obtained during brain imaging. Best regards, Renaud -- Renaud Jolivet, PhD Neuroscience, Physiology & Pharmacology University College London r.jolivet(a)ucl.ac.uk<mailto:r.jolivet@ucl.ac.uk> https://sites.google.com/site/renaudjolivet/ +44 20 7679 3243

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INNS (International Neural Network Society) BigData 2015 San Francisco, Aug. 8-10, 2015 - $2000 Best Paper Award - Papers submission deadline is March 22nd.
by Asim Roy March 2, 2015

March 2, 2015

[Apologies for cross-postings] ########################################### The inaugural INNS Big Data conference 2015 August 8-10, 2015, San Francisco, USA CALL FOR PAPERS ########################################### Homepage: http://www.innsbigdata.org ########################################### The aim of the INNS BigData conference is to promote new advances and research directions in efficient and innovative algorithmic approaches to analyzing big data (e.g. deep networks, nature-inspired and brain-inspired algorithms), implementations on different computing platforms (e.g. neuromorphic, GPUs, clouds, clusters) and applications of Big Data Analytics to solve real-world problems (e.g. weather prediction, transportation, energy management). Please refer to our website for a more detailed list of topics. Being INNS' inaugural conference on the theme of big data, we are especially motivated to synthesize ideas, promote activities and generate broad interest in areas where neural networks have many unique advantages. We also have Twitter, Facebook and Google+ pages! Elsevier Best Paper Award ########################################################### * Recognizes the best paper presented at the INNS Big Data conference. Both application and theoretical papers will be considered. * It will be awarded by the Big Data Analytics Section of the International Neural Network Society and is sponsored by Elsevier. * The Award consists of a plaque and a $2000 honorarium ########################################################### Dr. Fen Zhao Talk ########################################################### * Dr. Fen Zhao is a Staff Associate at the Office of the Assistant Director (OAD) for Computer & Information Science & Engineering (CISE) at the National Science Foundation. * She will give a talk on national big data R&D initiative and on building public-private partnerships around CISE's Big Data, next generation internet, and cybersecurity R&D portfolios. ########################################################### Plenary Speakers ########################################################### * Bin Yu, Chancellor's Professor, University of California , Berkeley * Raghu Ramakrishnan, Head of Cloud and Information Services Lab (CISL) and big data team, Microsoft * Brenda Dietrich, IBM Fellow and VP, Leads the Emerging Technologies Team for IBM Watson, IBM * Juergen Schmidhuber, Scientific Director, IDSIA ########################################################### Tutorials ########################################################### * Deep Learning - Profs. Juergen Schmidhuber (University of Lugano, and the Swiss AI Lab IDSIA) and Dong Yu (Microsoft Research) * Introduction to How Brain Deals with Big Data - Juyang Weng (Michigan State University) * Platforms and Algorithms for Big Data Analytics - Prof. Chandan K. Reddy (Wayne State University) * Big Data Analytics, Machine Learning Cognitive Algorithms and the Mind - Prof. Leonid I. Perlovsky (Northeastern University) * Spiking Neural Networks and Neuromorphic Spatio-Temporal Data Machines - Prof. Nikola Kasabov (Auckland University of Technology) * Online Learning for Big Data Analytics - Prof. Irwin King (Chinese University of Hong Kong) ########################################################### Workshops ########################################################### * Deep Learning - Profs. Juergen Schmidhuber and Dong Yu * Neuromorphic Spatio-Temporal Big Data Machines - Prof. Nikola Kasabov * Neural networks and wearable devices - Prof. Danilo Mandic * Big Data and Power Systems - Profs. Dejan Sobajic and Kumar Venayagamoorthy * Crowd Behaviour and Big Data - Profs. Chrisina Jayne and Mehmed Kantardzic ########################################################### Important Dates ########################################################### * Paper Submission March 22, 2015 * Paper Decision Notification May 22, 2015 * Camera Ready Submission of papers June 8, 2015 ########################################################### Paper Submission and Publication ########################################################### * Prospective authors should submit a full-length draft manuscript (8 pages), including figures, tables and references using the Elsevier Standard template without page numbers. * The full papers should be submitted to the INNS-BigData'2015 EasyChair online submission website: https://easychair.org/conferences/?conf=innsbigdata2015 * Conference proceedings will be published by Elsevier as a separate issue of Procedia Computer Science, which is an open access publication and is part of Science Direct electronic service. ########################################################### Topics and Areas include, but not limited to: * Autonomous, online, incremental learning - theory, algorithms and applications in big data * High dimensional data, feature selection, feature transformation - theory, algorithms and applications for big data * Scalable algorithms for big data * Learning algorithms for high-velocity streaming data * Kernel methods and statistical learning theory * Big data streams analytics * Deep neural network learning * Machine vision and big data * Brain-machine interfaces and big data * Cognitive modeling and big data * Embodied robotics and big data * Fuzzy systems and big data * Evolutionary systems and big data * Evolving systems for big data analytics * Neuromorphic hardware for scalable machine learning * Parallel and distributed computing for big data analytics (cloud, map-reduce, etc.) * Big data and collective intelligence/collaborative learning * Big data and hybrid systems * Big data and self-aware systems * Big Data and infrastructure * Big data analytics and healthcare/medical applications * Big data analytics and energy systems/smart grids * Big data analytics and transportation systems * Big data analytics in large sensor networks * Big data and machine learning in computational biology, bioinformatics * Recommendation systems/collaborative filtering for big data * Big data visualization * Online multimedia/ stream/ text analytics * Link and graph mining * Big data and cloud computing, large scale stream processing on the cloud Big Data Analytics Section @ INNS ########################################################### Considering the growing interest to process and analyse big data, the International Neural Network Society (INNS) has a new Section on Big Data Analytics (BDA) to help the neural network field position itself as a leading technology contributor to big data analytics. By actively promoting the use of neural networks for big data analytics, the INNS-BDA section is dedicated towards bringing the neural network field to greater heights. Anyone who is interested to know more is encouraged to visit the homepage of the INNS-BDA Section at http://www.inns.org/big-data-section. ACCOMMODATION RESERVATION ########################################################### * A dedicated website is now available for attendees to book their hotel rooms online. * Reservations can be made starting 31-Oct-2014 at this web address: https://resweb.passkey.com/Resweb.do?mode=welcome_ei_new&eventID=12490679 ########################################################### Co-Sponsors * International Neural Network Society (INNS) * Elsevier Advisory Board * Walter Freeman, University of California, Berkeley, USA * Ali Minai, University of Cincinnati, USA * Danil Prokhorov, Toyota Tech Center * Bernard Widrow, Stanford University, USA * Bart Kosko, University of Southern California, USA General Chairs * Plamen Angelov, Lancaster University, UK * Asim Roy, Arizona State University, Tempe, USA PC Chairs * Adel M. Alimi, University of Sfax, Sfax, Tunisia * Theodore Trafalis, University of Oklahoma, USA * Kumar Venayagamoorthy, Clemson University, USA Plenary Chairs * Nikola Kasabov, Auckland University of Technology, New Zealand * Irwin King, Chinese University of Hongkong, China Special Sessions Chairs * Bonny Banerjee, University of Memphis, USA * Alessandro Ghio, University of Genoa, Italy Tutorials/Workshops Chair * Marley Vellasco, PUC-Rio, Rio de Janeiro, Brazil * Trevor Martin, Univ. of Bristol, UK. Poster Session Chairs * Yi Lu Murphy, University of Michigan-Dearborn, USA * Bernardete Ribeiro, University of Coimbra, Portugal Sponsors/Exhibit Chairs * James Dankert, BAE Systems, USA * Rosemary Paradis, Lockheed Martin, USA Publication Chairs * Danilo Mandic, Imperial College, London, UK * John Weng, Michigan State University, East Lansing, USA * Mariette Awad, American University of Beirut, Lebanon * Amir Hussain, University of Stirling, Scotland, UK

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Community Session on reproducibility in the neurosciences - 11th Göttingen meeting of the NWG
by Andrea Huber Brösamle March 2, 2015

March 2, 2015

----------------------------------------------------------- Community Session Reproducibility in the neurosciences Novel tools and links to the Human Brain Project (HBP) platforms and the International Neuroinformatics Coordinating Facility (INCF) ----------------------------------------------------------- We invite you to attend a Community Session on Reproducibility in the neurosciences taking place during the 11th Göttingen Meeting of the German Neuroscience Society. Date and Time: March 19th, 2015, 14:30-16:30h Location: University of Göttingen, Main Lecture Building, Room 1.141 Snacks and beverages will be served Recently, experiments gathering neuronal data in behaving animals as well as the corresponding data analysis workflows have dramatically gained in complexity. At present, this creates a lack of reproducibility impairing the reliability and efficiency of research. In this community session, the status and approaches to address this challenge will be reviewed and discussed. *Speakers:* Sonja Grün (Jülich Researach Centre & RWTH Aachen) Thomas Wachtler (G-Node, LMU Munich) Jeff Muller (EPFL, Lausanne) We are looking forward to seeing you in Göttingen. Community Session organizer: Sonja Grün (Jülich Researach Centre & RWTH Aachen)

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3rd CFP: EXTENDED DEADLINE Special Session on Quantifying Embodiment, ECAL'15
by Keyan Ghazi-Zahedi March 2, 2015

March 2, 2015

3rd CALL FOR PAPERS *** EXTENDED DEADLINE *** 13th European Conference on Artificial Life (ECAL 2015) Special Session on Quantifying Embodiment Session paper submission deadline: Monday, 16 March 2015 Notification of acceptance of papers: Friday, 24 April 2015 Paper Camera-Ready-Copy deadline: Monday, 18 May 2015 Conference convenes: 20-24 July 2015, York, United Kingdom Conference website: http://ecal2015.alife.org <http://ecal2015.alife.org/> Special Session website: http://www.cs.york.ac.uk/nature/ecal2015/embodiment.html <http://www.cs.york.ac.uk/nature/ecal2015/embodiment.html> There is a growing acknowledgement in the artificial life and artificial intelligence community that embodiment is a key for the understanding of cognitive systems. Despite the consensus about the importance of embodiment, there is no clear definition of what the agent’s embodiment actually is. We believe that a quantitative approach can help to answer this open question. This is inspired by Einstein’s answer to the question ”What is time?”, to which he replied ”Time is what clocks measure”. Although this seems to be a trivial answer, it reveals that what we mean by time depends on the way we measure it. We are interested in quantifications of embodiment from various view points and different fields. A non-exclusive list of examples is: • Quantifications of the embodiment’s effect on sensing • Quantifications of the embodiment’s effect on cognitive processes • Quantifications of the embodiment’s effect on acting • Quantifications of brain-body-environment separation We are open to quantifications from any formal approach. Examples are information theory and dynamical systems, but others are highly welcome too. The ideal outcome of the special session would be a good overview of different concepts and quantifications of embodiment and an understanding how they overlap and differ, thereby leading to a better understanding and a clearer picture of what embodiment actually is. Papers are submitted via the ECAL paper submission form and published in the conference proceedings. You need to select “Quantifying Embodiment” when you are submitting your paper. Special Session Chairs: Keyan Ghazi-Zahedi, Christoph Salge, Georg Martius, Daniel Polani

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PhD and Postdoc positions (eye-movements, stereo) at QMUL
by Miles Hansard March 2, 2015

March 2, 2015

PhD Studentship: Computational Analysis of Eye-Movements. School of EECS, Queen Mary, University of London. Applications are invited for a fully-funded PhD studentship, to conduct research into human eye-movements. The project will develop new computational models of visual behaviour, based on recent ideas from computer vision, spatial statistics and time-series analysis. The models will be tested in psychophysical experiments, and implemented in an open-source tool-kit. Supervised by Miles Hansard (computer vision, http://www.eecs.qmul.ac.uk/~milesh/) and Isabelle Mareschal (experimental psychology). All nationalities are eligible to apply, for details see: http://www.jobs.ac.uk/job/AKO352/phd-studentship-in-eye-movement-analysis/ -------------------------------------------------------------------------- Research Assistant: Geometric Evaluation of Stereoscopic Video. School of EECS, Queen Mary, University of London. This new project will develop a new approach to the evaluation of 3D content, based on computational models of human binocular vision. Disparity statistics will be computed from stereoscopic video, by a prototype system, for use in depth-grading and other post-production tasks. To work with Miles Hansard (http://www.eecs.qmul.ac.uk/~milesh/) in conjunction with a high-profile graphics and visual effects company. All nationalities are eligible to apply, for details see: http://www.jobs.ac.uk/job/ATT437/research-assistant/ -------------------------------------------------------------------------- -- http://www.eecs.qmul.ac.uk/~milesh/

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INCF Nodes Workshop in Warsaw, April 16-17
by Daniel Wójcik March 2, 2015

March 2, 2015

Dear Colleagues, Each Spring, the International Neuroinformatics Coordinating Facility (INCF; http://www.incf.org) holds the INCF Nodes Workshop. This is the main event bringing representatives from each Node country together to present their activities, begin and continue collaborations, and inspire the future directions of INCF: http://incf.org/activities/workshops/node-workshops/node-workshops The next workshop will be organized by the Polish Node of INCF, and will spotlight Polish neuroinformatics research in areas from molecular neuroscience to brain-computer interfaces and supercomputing. It will be held at the Nencki Institute of Experimental Biology of the Polish Academy of Sciences (http://en.nencki.gov.pl/) in Warsaw, 16-17 April 2015, and will be the first Nodes Workshop open to the wider scientific community. Registration is free. Deadline for registration: * March 16* Workshop details: http://bit.ly/incf2015spring Looking forward to welcoming you in Warsaw, Daniel Wojcik On behalf of the Polish Node of INCF -- Daniel K. Wojcik, PhD, DSc Laboratory of Neuroinformatics, Head PhD Studies, Head Nencki Institute of Experimental Biology 3 Pasteur St, 02-093 Warsaw, Poland tel: (+48 22) 5892348 fax: (+48 22) 8225342 skype: danek8317 http://dwojcik.pl/ http://neuroinflab.wordpress.com/ http://orcid.org/0000-0003-0812-9872

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Systems and Computational Neuroscience Down Under (SCiNDU)
by Geoffrey Goodhill March 1, 2015

March 1, 2015

SCiNDU: SYSTEMS AND COMPUTATIONAL NEUROSCIENCE DOWN UNDER qbi.uq.edu.au/scindu<http://qbi.uq.edu.au/scindu> December 15-17th, 2015, Queensland Brain Institute, The University of Queensland, Brisbane, Australia This conference will bring together international leaders in understanding the computational principles underlying how neural circuits decode sensory information, make decisions, and learn from experience. Abstract submissions for poster presentation are welcome. Deadline for abstracts and early registration is Oct 16th 2015. Some abstracts will be selected for short talks. Confirmed invited speakers include: Ehsan Arabzadeh (ANU) Mark Bear (MIT) Michael Breakspear (QIMR) Allen Cheung (QBI) Yang Dan (UC Berkeley) Peter Dayan (UCL) Geoffrey Goodhill (QBI) Zach Mainen (Champalimaud) Jason Mattingley (QBI) Linda Richards (QBI) Peter Robinson (Sydney) Marcello Rosa (Monash) Pankaj Sah (QBI) Mandyam Srinivasan (QBI) Greg Stuart (ANU) Stephen Williams (QBI) Li Zhaoping (UCL) On December 15th the conference will be preceded by tutorials including: Mark Bear: Experience-dependent synaptic plasticity Peter Dayan: Neural reinforcement learning Jason Mattingley: Brain stimulation, attention and plasticity Li Zhaoping: Vision, efficient coding and salience We gratefully acknowledge financial support from the Queensland Brain Institute and the Australian Research Council Centre of Excellence for Integrative Brain Function. Professor Geoffrey J Goodhill Queensland Brain Institute and School of Mathematics & Physics University of Queensland St Lucia, QLD 4072, Australia Email: g.goodhill(a)uq.edu.au<mailto:g.goodhill@uq.edu.au> http://www.qbi.uq.edu.au/professor-geoffrey-goodhill

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Reminder: MBL Methods in Computational Neuroscience Course 2015: applications due March 5
by Mark Goldman Feb. 28, 2015

Feb. 28, 2015

Applications are open for the Methods in Computational Neuroscience course at the Marine Biology Laboratory in Woods Hole, MA. The course will run from July 29 to August 26, 2014, and the online application form can be found at: http://ws2.mbl.edu/studentapp/studentapp.asp?CourseID=MCN. The course application deadline is March 5. The course covers a range of topics in computational neuroscience including neuronal biophysics, neural coding & information processing, circuit dynamics, learning & memory, motor control, and cognitive processing & disease. In addition, numerous tutorials and problem sets will cover a broad range of computational and mathematical modeling methods. The course strongly emphasizes the collaboration between theory and experiment in solving neuroscience problems, and lectures will be given by a mixture of theorists and experimentalists. The final weeks of the course are primarily reserved for development and work on projects that students design in collaboration with the resident faculty. Further information can be found on the MCN website: http://www.mbl.edu/mcn/ 2015 Course Directors: Michale Fee, MIT Mark Goldman, UC Davis 2015 Confirmed Faculty: Larry Abbott, Columbia University Steve Baccus, Stanford University Jeff Beck, Duke University William Bialek, Princeton University Dmitri Chklovskii, HHMI Janelia Farm Peter Dayan, University College London Bard Ermentrout, University of Pittsburgh Adrienne Fairhall, University of Washington Ila Fiete, UT Austin Loren Frank, UCSF Michael Frank, Brown University Surya Ganguli, Stanford University John Huguenard, Stanford University David Kleinfeld, UC San Diego Nancy Kopell, Boston University John Lisman, Brandeis University Eve Marder, Brandeis University Bartlett Mel, University of Southern California Jonathan Pillow, Princeton University Maneesh Sahani, University College London Terry Sejnowski, Salk Institute Michael Shadlen, Columbia University Josh Shaevitz, Princeton University Sara Solla, Northwestern University Haim Sompolinsky, Hebrew University David Tank, Princeton University Josh Tenenbaum, MIT Srini Turaga, Janelia Farm Xiao-Jing Wang, NYU Daniel Wolpert, Cambridge University Ryohei Yasuda, Duke University

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a neural architecture for autonomous spatial and object attention, invariant object category learning and recognition, and Where’s Waldo search with freely moving eyes
by Stephen Grossberg Feb. 27, 2015

Feb. 27, 2015

The following series of articles progressively develop a neural architecture for autonomous spatial and object attention, invariant object category learning and recognition, and Where’s Waldo search with freely moving eyes: ARTSCAN View-invariant object category learning and recognition with freely moving eyes in a 2D scene Fazl, A., Grossberg, S., and Mingolla, E. (2009). View-invariant object category learning, recognition, and search: How spatial and object attention are coordinated using surface-based attentional shrouds. Cognitive Psychology, 58, 1-48. http://cns.bu.edu/~steve/FazGroMin2008.pdf pARTSCAN View-, position-, and size-invariant object category learning and recognition with freely moving eyes in a 2D scene Cao, Y., Grossberg, S., and Markowitz, J. (2011). How does the brain rapidly learn and reorganize view- and positionally-invariant object representations in inferior temporal cortex? Neural Networks, 24, 1050-1061. http://cns.bu.edu/~steve/NN2853.pdf dARTSCAN ARTSCAN with transient and sustained distributed attention and prefrontal priming in a 2D scene Foley, N.C., Grossberg, S. and Mingolla, E. (2012). Neural dynamics of object-based multifocal visual spatial attention and priming: Object cueing, useful-field-of-view, and crowding. Cognitive Psychology, 65, 77-117. http://cns.bu.edu/~steve/FolGroMin2012.pdf ARTSCAN SEARCH Solution of Where’s Waldo Problem: finding a valued object in a 2D scene Chang, H.-C., Grossberg, S., and Cao, Y. (2014) Where's Waldo? How perceptual cognitive, and emotional brain processes cooperate during learning to categorize and find desired objects in a cluttered scene. Frontiers in Integrative Neuroscience, doi: 10.3389/fnint.2014.0043. http://journal.frontiersin.org/Journal/10.3389/fnint.2014.00043/full 3D ARTSCAN ARTSCAN of a 3D scene whose 3D perceptual representations remain stable as the eyes move Grossberg, S., Srinivasan, K., and Yazdanbakhsh, A. (2014). Binocular fusion and invariant category learning due to predictive remapping during scanning of a depthful scene with eye movements. Frontiers in Psychology: Perception Science, doi: 10.3389/fpsyg.2014.01457. http://journal.frontiersin.org/Journal/10.3389/fpsyg.2014.01457/full Stephen Grossberg Wang Professor of Cognitive and Neural Systems Professor of Mathematics, Psychology, and Biomedical Engineering Director, Center for Adaptive Systems http://www.cns.bu.edu/about/cas.html http://cns.bu.edu/~steve steve(a)bu.edu

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ECMLPKDD 2015 : Call for Papers, Tutorials and Workshops
by ECMLPKDD 2015 Feb. 27, 2015

Feb. 27, 2015

The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD) will take place in Porto, Portugal, from September 7th to 11th, 2015 (http://www.ecmlpkdd2015.org) This event is the leading European scientific event on machine learning and data mining and builds upon a very successful series of 25 ECML and 18 PKDD conferences, which have been jointly organized for the past 14 years. ECMLPKDD 2015 will host three tracks, tutorials and a set of workshops. Therefore, we invite all researchers and practitioners from different communities to submit papers and/or present tutorial and workshop proposals. ************************* CALL FOR PAPERS ************************* JOURNAL TRACK ********************* Articles for this track are submitted all year long directly to either Machine Learning or Data Mining and Knowledge Discovery, and are reviewed like regular journal articles. Accepted articles appear in full in the journal and the authors are given a presentation slot at the conference. Articles deemed insufficiently mature for journal publication may be accepted for inclusion in the proceedings. Submissions to the journal track will be managed by the Guest Editorial Board. Paper Submission: Cut-off dates for the bi-weekly batches are 18 Jan, 1 Feb, 15 Fev, 1 Mar, 15 Mar, 29 Mar, 12 Apr, 26 Apr of 2015 Web Page: http://www.ecmlpkdd2015.org/submission/journal-track RESEARCH PROCEEDINGS TRACK ******************************************* The research proceedings track, which is organized in the traditional way. Accepted papers will be published in the Lecture Notes in Artificial Intelligence (LNCS/LNAI) of Springer, after reviewing by the programme committee. Abstract Submission Deadline: March 26, 2015 Paper Submission Deadline: April 2, 2015 Paper Acceptance Notification: June 1, 2015 Paper Camera Ready Submission: June 15, 2015 Web Page: http://www.ecmlpkdd2015.org/submission/research-proceedings-track INDUSTRIAL, GOVERNMENTAL & NON-GOVERNMENTAL PROCEEDINGS TRACK **************************************************************************** ************************** The NEW industrial, governmental & non-governmental (NGO) proceedings track is independent and distinct from the Research Track. Submissions to this track should solve real-world problems and focus on engineering systems, applications, and challenges. Accepted papers will be published in the Lecture Notes in Artificial Intelligence (LNCS/LNAI) of Springer, after reviewing by the programme committee. Abstract Submission Deadline: March 26, 2015 Paper Submission Deadline: April 2, 2015 Paper Acceptance Notification: June 1, 2015 Paper Camera Ready Submission: June 15, 2015 Web Page: http://www.ecmlpkdd2015.org/submission/industrial-proceedings-track ***************************************************************** CALL FOR TUTORIAL AND WORKSHOP PROPOSALS ***************************************************************** TUTORIALS ************** The tutorials are intended to provide a comprehensive introduction to established or emerging research topics of interest for the machine learning and the data mining community. These topics include 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. We welcome half day workshop proposals. Proposal Deadline: March 2, 2015 Proposal Acceptance Notification: March 23, 2015 Web Page: http://www.ecmlpkdd2015.org/submission/call-for-tutorials WORKSHOPS **************** The workshops will be on relevant and current topics in Machine Learning and Data Mining. The scope of the proposal should be consistent with the conference themes as described in the ECMLPKDD 2015 Call for Papers (http://www.ecmlpkdd2015.org/submission) Interdisciplinary workshops that bring together researchers and practitioners from different communities are especially welcome. We encourage workshops that bridge the gap between theoretical advances and important and/or innovative applications of machine learning and data mining. We welcome both full and half day workshop proposals. Proposal Deadline: March 2, 2015 Proposal Acceptance Notification: March 23, 2015 Workshop Websites and Call for Papers Online: March 27, 2015 Workshop Proceedings (Camera-ready): August 3, 2015 Web Page: http://www.ecmlpkdd2015.org/submission/call-for-workshop-proposals Hope to see you all soon in Porto, Portugal!!! The publicity chairs of the ECMLPKDD 2015, Carlos Abreu Ferreira Ricardo Campos --- Este e-mail foi verificado em termos de vírus pelo software antivírus Avast. http://www.avast.com

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