Edinburgh Data-Intensive Research - News

Congratulations: Gary McGilvary wins competition at CERN

Malcolm.Atkinson — Thu, 29 Aug 2013 15:08:20 +0000

Congratulations to Gary, who as his hard-won internship in CERN approaches its end, has won a prize in the lightening presentations given by the 20 interns at CERN yesterday. His talk "The Implementation of OpenStack Cinder and Integration with NetApp and Ceph" was one of the three talks awarded a prize:
http://openlab.web.cern.ch/news/openlab-summer-students-present-their-pr...

We're all looking forward to Gary's return next week, when he will tell us what that means; rumour has it that he's enjoyed his internship.
Malcolm

Topic of this submission:

News

Research topics:

Data-intensive computing

Professor Mark Parsons

Malcolm.Atkinson — Fri, 14 Jun 2013 15:19:10 +0000

Congratulations to Mark Parsons, http://www.epcc.ed.ac.uk/about/staff/dr-mark-parsons, on his promotion to a professorship.

Topic of this submission:

News

Applications invited for OSDC PIRE workshop

Malcolm.Atkinson — Mon, 03 Jun 2013 07:37:32 +0000

The second OSDC PIRE workshop will be held in the Informatics Forum from 17 to 21 June 2013. Applications are invited from researchers in the SICSA member institutions www.sicsa.ac.uk/home as a result of sponsorship from SICSA and CISA www.cisa.inf.ed.ac.uk. The first ten suitable applicants to register at osdc-pire-edinburgh2013.eventbrite.com will be accepted. Details of the programme of the event, discussing how to exploit Big Data using globally shared cloud facilities, can be found on page http://research.nesc.ac.uk/node/917. Ideal applicants will be part-way through a PhD and will either be trying to develop methods of using our growing wealth of data, or will be researching into tools or technology than enables such researchers to more easily and more effectively mine the gold from the data bonanza.

Topic of this submission:

News

Projects:

NRP

Attachment	Size
Latest version of provisional programme	1.36 MB

Gary McGilvary selected as an CERN openlab summer student 2013

Malcolm.Atkinson — Wed, 08 May 2013 13:57:27 +0000

Gary McGilvary has succeeded against considerable competition in obtaining a position on the CERN openlab Summer Student Programme.

CERN openlab is a unique public-private partnership between CERN and leading ICT companies. Its mission is to accelerate the development of cutting-edge solutions to be used by the worldwide LHC community. The openlab Summer Student Programme gives students the chance to work with some of the latest hardware and software technologies and see how advanced IT solutions are used in high energy physics.

Currently, CERN is establishing a large scale private cloud based on OpenStack as part of the expansion of the computing infrastructure for the Large Hadron Collider experiment. Depending on the application running on the cloud, some virtual machines require large disk capacities or high reliability/performance volumes.

Gary will be working on the configuration, deployment and testing of OpenStack Cinder which provides extendible storage for virtual machines, managing the storage quotas and migrating storage from one hypervisor to another.

I am sure you will all join me in congratulating him. We look forward to many exciting tales and discoveries on his return.

Topic of this submission:

News

The first VERCE project newsletter!

Michelle.Galea — Thu, 20 Dec 2012 16:54:56 +0000

Issue 1 of the VERCE newsletter...read on...

Topic of this submission:

News

Research topics:

Data-intensive computing

Projects:

VERCE

Attachment	Size
VERCE-Autumn-Issue1.pdf	5.43 MB

Congratulations to Fan Zhu

Malcolm.Atkinson — Fri, 19 Oct 2012 16:28:41 +0000

I am delighted to report that Fan Zhu's external examiners were pleased with his thesis and his spirited viva defense. They have recommended a pass with a few minor corrections. A big thank you to Professors Gary Green and Richard Baldock.

Title:
Brain Perfusion Imaging - Performance and Accuracy

Abstract:
Brain perfusion weighted images acquired using dynamic contrast studies have an important clinical role in acute stroke diagnosis and treatment decisions.
The purpose of my PhD research is to develop novel methodologies for improving the efficiency and quality of brain perfusion-imaging analysis so that clinical decisions can be made more accurately and in shorter time.
This thesis consists of three parts:

1. My research investigates the possibilities that parallel computing brings to make perfusion-imaging analysis faster in order to deliver results that are used in stroke diagnosis earlier. Brain perfusion analysis using local Arterial Input Functions (AIF) technique takes a long time to execute due to its heavy computational load. As time is vitally important in the case of acute stroke, reducing analysis time and therefore diagnosis time can reduce the number of brain cells damaged and improve the chances for patient recovery. We present the implementation of a deconvolution algorithm for brain perfusion quantification on GPGPU (General Purpose computing on Graphics Processing Units) using the CUDA programming model. Our method aims to accelerate the process without any quality loss.

2. Specific features of perfusion source images are also used to reduce noise impact, which consequently improves the accuracy of hemodynamic maps. The majority of existing approaches for denoising CT images are optimized for 3D (spatial) information, including spatial decimation (spatially weighted mean filters) and techniques based on wavelet and curvelet transforms. However, perfusion imaging data is 4D as it also contains temporal information. Our approach using Gaussian process regression (GPR) makes use of the temporal information in the perfusion source imges to reduce the noise level. Over the entire image, our noise reduction method based on Gaussian process regression gains a 99% contrast-to-noise ratio improvement over the raw image and also improves the quality of hemodynamic maps, allowing a better identification of edges and detailed information. At the level of individual voxels, GPR provides a stable baseline, helps identify key parameters from tissue time-concentration curves and reduces the oscillations in the curves. Furthermore, the results shows that GPR is superior to the alternative techniques compared in this study.

3. My research also explores automatic segmentation of perfusion images into potentially healthy areas and lesion areas which can be used as additional information that assists in clinical diagnosis. Since perfusion source images contain more information than hemodynamic maps, good utilisation of source images leads to better understanding than the hemodynamic maps alone. Correlation coefficient tests are used to measure the similarities between the expected tissue time-concentration curves (from (reference tissue)) and the measured time-concentration curves (from target tissue). This information is then used to distinguish tissues at risk and dead tissues from healthy tissues. A correlation coefficient based signal analysis method that directly spots suspected lesion areas from perfusion source images is presented. Our method delivers a clear automatic segmentation of healthy tissue, tissue at risk and dead tissue. From our segmentation maps, it is easier to identify lesion boundaries than using traditional hemodynamic maps.

Topic of this submission:

News

Research topics:

Biological/Medical Image Processing

Projects:

SINAPSE

2nd UK NI Node Congress: 26-28 March 2012

David.Rodriguez — Tue, 27 Mar 2012 10:55:25 +0000

The UK Node is holding its biennial conference at the Informatics Forum, University of Edinburgh, from 26th to 28th March 2012.
Visit http://www.neuroinformatics.org.uk/events-and-projects/22-2012congress for more information.

Topic of this submission:

News

Vacancy at CNRS-INSU (Paris): Scientific Software Research Engineer - VERCE project (Applications by February 10th, 2012)

Paul.Martin — Tue, 24 Jan 2012 11:47:54 +0000

The Centre National de la Recherche Scientifique – Institut National des Sciences de l’Univers (CNRS-INSU) is looking for a new R&D Scientific Software Research Engineer to assist in the VERCE project (http://www.verce.eu/). Details attached, as well as available from the VERCE website.

Topic of this submission:

News

Research topics:

Efficient distributed systems

Effective algorithms

Data-intensive computing

Collaborative environments

Attachment	Size
SoftwareEngineer.pdf	315.98 KB

EDIM1 related: "Astronomers reach new frontiers of dark matter"

Paul.Martin — Thu, 12 Jan 2012 10:49:50 +0000

From http://www.cfht.hawaii.edu/en/news/CFHTLens/:

"For the first time, astronomers have mapped dark matter on the largest scale ever observed. The results, presented by Dr Catherine Heymans of the University of Edinburgh, Scotland, and Associate Professor Ludovic Van Waerbeke of the University of British Columbia, Vancouver, Canada, are being presented today to the American Astronomical Society meeting in Austin, Texas. Their findings reveal a Universe comprised of an intricate cosmic web of dark matter and galaxies that spans more than one billion light years."

These results were collected with the assistance of an algorithm written by Dr. Thomas Kitching, which was tested on the Edinburgh Data Intensive Machine EDIM1.

From the press release:

'An international team of researchers lead by Van Waerbeke and Heymans achieved their results by analysing images of about 10 million galaxies in four different regions of the sky. They studied the distortion of the light emitted from these galaxies, which is bent as it passes massive clumps of dark matter during its journey to Earth.
Their project, known as the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS), uses data from the Canada-France-Hawaii Telescope Legacy Survey. This accumulated images over five years using the wide field imaging camera MegaCam, a 1 degree by 1 degree field-of-view 340 Megapixel camera on the CFHT in Hawaii.
Galaxies included in the survey are typically six billion light years away. The light captured by the telescope images used in the study was emitted when the Universe was six billion years old - approximately half the age it is today.

The team's result has been suspected for a long time from studies based on computer simulations, but was difficult to verify owing to the invisible nature of dark matter. This is the first direct glimpse at dark matter on large scales showing the cosmic web in all directions.

Professor Ludovic Van Waerbeke, from the University of British Columbia, said: "It is fascinating to be able to 'see' the dark matter using space-time distortion. It gives us privileged access to this mysterious mass in the Universe which cannot be observed otherwise. Knowing how dark matter is distributed is the very first step towards understanding its nature and how it fits within our current knowledge of physics."

Dr Catherine Heymans, a Lecturer in the University of Edinburgh's School of Physics and Astronomy, said: "By analysing light from the distant Universe, we can learn about what it has travelled through on its journey to reach us. We hope that by mapping more dark matter than has been studied before, we are a step closer to understanding this material and its relationship with the galaxies in our Universe."

For Dr Christian Veillet, CFHT Executive Director, this dark matter study illustrates the strong legacy value of the CFTHLS: it is now enabling exciting results obtained by teams from many nations which use the CFHTLS images retrieved from the Canadian Astronomy Data Centre where they are archived and publicly available.

Professor Lance Miller, from Oxford University said: "This result has been achieved through advances in our analysis techniques which we are now applying to data from the Very Large Telescope's (VLT) Survey Telescope in Chile."

Professor Koen Kuijken, from Leiden University, said: "Over the next three years we will image more than 10 times the area mapped by CFHTLenS, bringing us ever closer to our goal of understanding the mysterious dark side of the Universe."'

This research was supported by the European Research Council, Natural Sciences and Engineering Research Council of Canada, the Canadian Institute for Advanced Research and the Canadian Astronomy Data Centre.

Topic of this submission:

News

Research topics:

Effective algorithms

Data-intensive computing

AWS Research Grant Award

Anonymous — Thu, 24 Nov 2011 14:54:19 +0000

Gary McGilvary has secured 10,000 USD from the Amazon Web Services Research grant programme to aid in his research of determining the optimal cloud configuration parameters for a computational job running on Amazon's Elastic Compute Cloud (EC2). This includes the number of instances, their type and size as well as the number of cores and processes per instance and the minimum allocations of memory, storage and network performance. More information can be found here

Topic of this submission:

News