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Durham University

Advanced Research Computing

Advanced Research Computing

Advanced Research Computing (ARC) is a dedicated computing support unit within the Research Division of the University. We support academic researchers in all faculties across the University, where there is a requirement for the use of computers as part of their research. We provide facilities and expertise, connect people across different disciplines, and build on the services provided by Computing and Information Services. Our range of activity extends from simple coding assistance through to supporting computationally intensive research requiring High Performance Computing.

Northern Intensive Computing Environment

The N8 Centre of Excellence in Computationally Intensive Research, N8 CIR, has been awarded £3.1m from the Engineering and Physical Sciences Resources Council to establish a new Tier 2 computing facility in the north of England. This investment will be matched by £5.3m from the eight universities in the N8 Research Partnership which will fund operational costs and dedicated research software engineering support.

The new facility, known as the Northern Intensive Computing Environment or NICE, will be housed at Durham University and co-located with the existing STFC DiRAC Memory Intensive National Supercomputing Facility. NICE will be based on the same technology that is used in current world-leading supercomputers and will extend the capability of accelerated computing. The technology has been chosen to combine experimental, modelling and machine learning approaches and to bring these specialist communities together to address new research challenges.

Click here for more information on NICE19

News & Events

Data Science Seminar: GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

27th March 2020, 16:00 to 17:00

Anomaly detection is a classical problem in computer vision, namely the determination of the normal from the abnormal when datasets are highly biased towards one class (normal) due to the insufficient sample size of the other class (abnormal). While this can be addressed as a supervised learning problem, a significantly more challenging problem is that of detecting the unknown/unseen anomaly case that takes us instead into the space of a one-class, semi-supervised learning paradigm. We introduce such a novel anomaly detection model, by using a conditional generative adversarial network that jointly learns the generation of high-dimensional image space and the inference of latent space. Employing encoder-decoder-encoder sub-networks in the generator network enables the model to map the input image to a lower dimension vector, which is then used to reconstruct the generated output image. The use of the additional encoder network maps this generated image to its latent representation. Minimizing the distance between these images and the latent vectors during training aids in learning the data distribution for the normal samples. As a result, a larger distance metric from this learned data distribution at inference time is indicative of an outlier from that distribution - an anomaly. Experimentation over several benchmark datasets, from varying domains, shows the model efficacy and superiority over previous state-of-the-art approaches.