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Today's Topics:
1. Call for papers: International Workshop on Hardware
Accelerated Data Mining (Arindam Pal)
2. PhD Position: Maintenance Optimization for Rail
Infrastructure Systems (Hamish Waterer)
3. CFP: Elsevier Ad Hoc Networks Journal [IF=1.94] -- SI on
"Smart Wireless Access Networks and Systems For Smart Cities"
(Valeria Loscri)
4. PhD position in Operational Research applied to logistics, Le
Havre, France (Eric Sanlaville)
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Message: 1
Date: Tue, 30 Jun 2015 07:31:13 +0530
From: Arindam Pal <arindamp@gmail.com>
To: DMANET <DMANET@zpr.uni-koeln.de>
Subject: [DMANET] Call for papers: International Workshop on Hardware
Accelerated Data Mining
Message-ID:
<CAK4LEKnRKEp=b4KT=i3m5mB=27J_LaTKkXug4===Ax1P1ML5Eg@mail.gmail.com>
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Dear All,
We are organizing the International Workshop on Hardware Accelerated Data
Mining, in conjunction with ICDM 2015. IEEE International Conference on
Data Mining (ICDM) is one of the world's premier research conference in
data mining. You can find more details about the workshop here.
http://volga.usc.edu/hadm/
I am including the CFP.
Regards,
Dr. Arindam Pal
Research Scientist
Innovation Labs Kolkata
TCS Research
http://www.cse.iitd.ac.in/~arindamp/
==================================================
Call for papers:
International Workshop on Hardware Accelerated Data Mining (HADM'15) to be
held with IEEE International Conference on Data Mining
14 November 2015, Atlantic City, New Jersey, USA.
Website: http://www.usc.edu/hadm
==================================================
Data mining is expected to work on increasingly complex workloads (e.g.,
Petabytes of networked-data under real-time constraints) using emerging
hardware accelerators (e.g., commodity and specialized Multi-core, GPUs,
FPGAs, and ASICs) and corresponding programming models (e.g., MapReduce,
GraphLab, CUDA, OpenCL, and OpenACC). The use of hardware accelerators for
mining high-rate data streams is becoming common mainly due to the rapidly
increasing amount of data available for real-time analytics. The idea of
using special-purpose hardware to accelerate computation has a long
tradition in data processing but has thus far not made its way into
mainstream data mining. Many essential issues in this area have yet to be
explored. For instance, large-scale graph computations are commonplace in
many fields. However, this graph data is sparse and highly non-uniform.
Graph structure mining algorithms exhibit weak spatial locality when
processing graphs with power law distributions and such algorithms are
data-intensive and cache-hostile.
The aim of this workshop is to provide a venue for designers,
practitioners, researchers, developers, and industrial/governmental
partners to come together, present and discuss leading research results,
use cases, innovative ideas, challenges, and opportunities that arise from
accelerating mining of big data using new hardware, and identify future
directions and challenges in this area.
Topics of Interest
Topics of interests include but are not limited to:
Algorithms, models, and theory of hardware accelerated data mining
Hardware accelerated data mining systems and platforms
Scalable algorithms & architectures for Machine learning over
structured, semi-structured, spatio-temporal, graph, streaming, data
Domain-Specific Languages for hardware synthesis of data mining
applications
Novel data mining algorithms optimized for massively parallel
architectures
Hardware acceleration of data mining in applications from different
domains, including social science, bioinformatics, and smart grids
Key dates:
Due date for full workshop papers: July 20, 2015 Notification of workshop
papers acceptance to authors: September 1, 2015 Camera-ready deadline for
accepted papers: September 10, 2015 Workshop date: November 14, 2015
Papers should be at most 10 pages in the IEEE 2-column format (for IEEE
Computer Society conference proceedings).
Workshop Organization
Co-chairs
Charalampos Chelmis, University of Southern California, USA
Anand Panangadan, University of Southern California, USA
Program Committee
Jaume Bacardit, Newcastle University, United Kingdom
Zachary Baker, Los Alamos National Laboratory, USA
Rajesh Bordawekar, Thomas J. Watson Research Center, USA
Sutanay Choudhury, Pacific Northwest National Laboratory, USA
Eric Chung, Microsoft Research, USA
Hadi Esmaeilzadeh, Georgia Institute of Technology, USA
Joo-Young Kim, Microsoft Research, USA
Ioannis Koltsidas, IBM Zurich Research Laboratory, Switzerland
Walid Najjar, University of California, Riverside, USA
Arindam Pal, Innovation Labs Kolkata, TCS Research, India
Ippokratis Pandis, Cloudera, USA
Edward Yi-Hua Yang, Google, Inc., USA
Yinglong Xia, IBM Thomas J. Watson Research Center, USA
------------------------------
Message: 2
Date: Tue, 30 Jun 2015 15:39:49 +1000
From: Hamish Waterer <hamish.waterer@newcastle.edu.au>
To: <dmanet@zpr.uni-koeln.de>
Subject: [DMANET] PhD Position: Maintenance Optimization for Rail
Infrastructure Systems
Message-ID:
<CADq54rw9HHOkQ0YW1xt=Dc8NVHwYXn3zSwZDDX6TkgfzD+acsg@mail.gmail.com>
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The School of Mathematical and Physical Sciences at the University of
Newcastle is offering an Australian Postgraduate Award (Industry)
scholarship worth AU$25,849 per annum. The scholarship will be funded
under an Australia Research Council (ARC) Linkage Grant, and is
collaborative with Martin Savelsbergh and Natashia Boland from Georgia
Institute of Technology's ISyE School, and with Aurizon, Australia's
largest rail freight operator.
Coal and iron ore export supply chains critically depend on the
transport capacity provided by Australia's rail infrastructure.
Increasingly so, because coal and iron ore export supply chains are
literally getting longer: mineral bodies closer to ports have been
exploited first, and as these become exhausted, supply chains are
stretching further to reach harder-to-get-at resources. Combined with
increasing export volumes, this makes intelligent, efficient, and
effective management of this critical piece of infrastructure vitally
important. Maintenance plays a crucial role in the management of rail
infrastructure as it ensures that infrastructure components, e.g.,
track, signals, and rail crossings, are in a condition that allows
safe, reliable, and efficient transport. This project will investigate
the key planning activities in preventive maintenance of rail
infrastructure systems, and seek to develop effective optimization
algorithms for their solution.
This PhD will contribute to a larger team activity, addressing the
whole system; the successful candidate will join a team of several
other researchers working on complementary aspects of the system.
Expressions of interest are preferred before August 1, 2015, however
later interest will be considered until the position is filled. To
express your interest in a scholarship please send your CV, together
with an academic transcript showing details of all courses you have
taken, the grades you were awarded, an interpretation of those grades,
and the names and contact details of at least two persons who can
provide confidential references, to the contact person at the address
shown below. If your transcript is not in English, please provide an
English translation.
This project will be supervised by Dr Thomas Kalinowski.
Main contact:
Dr Thomas Kalinowski
School of Mathematical and Physical Sciences
The University of Newcastle
T: +61 2 4921 6558
E: Thomas.Kalinowski@newcastle.edu.au
------------------------------
Message: 3
Date: Tue, 30 Jun 2015 10:47:50 +0200
From: Valeria Loscri <valeria.loscri@inria.fr>
To: dmanet@zpr.uni-koeln.de
Subject: [DMANET] CFP: Elsevier Ad Hoc Networks Journal [IF=1.94] --
SI on "Smart Wireless Access Networks and Systems For Smart Cities"
Message-ID: <254D2382-9081-4343-A319-390EDAF323F0@inria.fr>
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Message: 4
Date: Tue, 30 Jun 2015 11:28:48 +0200
From: Eric Sanlaville <eric.sanlaville@univ-lehavre.fr>
To: dmanet@zpr.uni-koeln.de
Subject: [DMANET] PhD position in Operational Research applied to
logistics, Le Havre, France
Message-ID: <55926150.8050504@univ-lehavre.fr>
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NOTE : second call for PhD candidates, be careful to the short delay.
The LITIS laboratory of le Havre university offers a financed PhD
position in logistics starting in october 2015. The candidate should
possess (or terminate) a master in computer sciences, with skills in OR
and/or AI.
See below the description of the subject.
Application files should be sent by e-mail to the three supervisors,
Stefan Balev, Antoine Dutot, an Eric Sanlaville (see email addresses in
this mail header). They should contain: CV, marks from last two years,
motivation letter, possibly recommendation letters. The file can be
entirely in English.
The application files should be sent before Sunday, july 5th.
*Operational management of flows along a logistics corridor: application
to the Seine valley**
Supervisors:*Stefan Balev, Antoine Dutot, Eric Sanlaville (LITIS, Le
Havre University)*
Funding:*3 year grant, Le Havre University *
Keywords:*Decision making support, Artificial intelligence, Operational
Research, Logistics, Optimization under uncertainties
*Description:*Given data on transportation infrastructure of a
multi-modal logistic corridor (different supply chains, storage and
transport capacities, etc.) the goal is to check the feasibility of a
transportation plan for a given period of time, taking into account the
uncertainties of the travel time and the hazardsinherent to these
infrastructures (breakdowns, accidents and litigations). In case of
infeasibility, alternative transportation plans willbe computed, either
with better feasibility guarantee or with better cost.
This goal requires the use of a detailed multi-scale model of the
logistic corridor containing the different infrastructures and their
capacities, as well as data on the flows, their sources and
destinations. It also requires to work on planification tools at the
tactical level. The main challenge consists in proposing a tool of
operational management from collected data, able to modify the original
plan to adapt it to new conditions. This tool shall be based on robust
optimization, the study of dynamic systems and the techniques form
collective intelligence (specially the multi agent systems) developed
within the RI2C team of LITIS.
*Environment:*The student will benefit from the knowledge gained during
previous research projects on flow management and optimization in
logistics systems, and in particular two theses in progress: one on
spatial agent-based modeling of logistics stakeholders and another one
on flow optimization in extended seaport. The first thesis uses the
agent-based modeling tool Gama and the second one uses combinatorial
optimization tools such as CPLEX.
The thesis is part of the multi-disciplinary and multi-institutional
project CLASSE (Logistic corridors: application to the Seine valley and
its environment) funded by Haute Normandie region, France and Europe.
The RI2C (Interaction networks and swarm intelligence) team of LITIS
focuses its fundamental researcheson morpho-dynamics of interaction
networks using analysis and optimization methods from artificial
intelligence and operations research domains. An important feature of
complex systems is the large share of uncertainty in the models,
resulting in the need of robust solutions or policies to control these
systems. The main applications of this research are in domains such as
territorial intelligence, industrial risk management, ad-hoc
communication networks and logistics, in particular the analysis of
logistic networks along a logistic corridor such as the Seine axis.
*Skills and profile:*The candidate must have a degree (M. Sc., M. Eng.
or equivalent) in computer science with decision making support related
specialization. In addition to programming skills acquired in such a
curriculum, expertise on concepts and techniques is required in one of
the following domains: artificial intelligence and particularly
multi-agent systems or operations research (combinatorial optimization,
robust optimization). Knowledge in both areas (AI and OR) will be
privileged. Previous experience in GIS would be beneficial.
--
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Eric Sanlaville
professeur, université du Havre
coordinateur de SFLOG, la Structure Fédérative en LOGistique de l'université du Havre
(https://sflog.univ-lehavre.fr)
laboratoire LITIS-EA 41-08
équipe RI2C : Réseaux d'Interaction et Intelligence Collective.
======================================
Eric Sanlaville - LITIS
UNIVERSITE LE HAVRE - UFR SCIENCES ET TECHNIQUES
25 RUE PHILIPPE LEBON
CS 80540
76058 LE HAVRE CEDEX
======================================
Tel : +33 232 744 548
Fax : +33 232 744 314
http://litis.univ-lehavre.fr/~sanlaville
------------------------------
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