Memorability concept has been mostly investigated on the images using Machine Learning [1]. However, there is no measure available in the literature that can assess the difficulty of remembering a given password (word) or number.

Computer assisted semen analysis (CASA) systems has improved since the first were introduced to the marked, but they are still not accurate and need improvement. Automatic classification of sperm morphology would be of significant assistance in semen analysis.

Large computing systems of the future are likely to be heterogeneous, that is, containing at least two types of processor architectures.

MongoDB is a NoSQL database for Big Data applications, also used to provide the input data for Machine Learning and Artificial Intelligence systems.

What we want to find out is whether machine learning can be trained to create an appropriate CAD model from only the Lidar point cloud, or a Lidar point cloud with some images, if it has been trained with CAD reconstructions that have been created by humans using all the methods mentioned above.

Computer simulation has become an indispensable tool in Science. Often, the intricate mechanisms and details found in the research subject will require very high resolutions of the computer simulation, which has to be executed on large-scale parallel computers.

Cardiovascular diseases are burdening the healthcare systems and the costs are expected to rise in the years to come. Acute stroke alone is estimated to cost the European countries an overwhelming 40 billion annually.

Cancer is characterised by profound changes in the surrounding vasculature because the cancerous tissue is more energy demanding than normal tissue.

In recent years, due to the existence of fast and reliable core networks and the increasing prevalence of cloud infrastructures, more services tend to move from the end users to the cloud servers. Cloud gaming is one of these services which is rapidly growing.

The electrical activity in the heart can be modeled in terms of differential equations.
A collection of basic and optimized solvers for these equations coupled with cardiac cell models has been developed, based on the FEniCS project together with the package dolfin-adjoint.

Goal

The aim of the project is to obtain a deeper understanding of GANs and develop new models and estimation techniques with improved properties.

We hypothesize that these gradients are important for healthy metabolism. Conditions that disturb the CSF circulation likely disturb the natural concentration gradients between the various compartments associated with the brain.

For network optimisation and analysis, network traces are important to capture and store. Such traces can take a lot of storage space, even if only packet headers are captured and saved.

In recent years, due to the existence of fast and reliable core networks and the increasing prevalence of cloud infrastructures, more services tend to move from the end users to the cloud servers. Cloud gaming is one of these services which is rapidly growing.

Algorithmic differentiation (AD) allows one to automatically compute derivatives of arbitrary functions by decomposing the function into a sequence of elementary operations and applying the chain rule.

The current state-of the-art of machine learning adopts the use of batch learning where the data is available offline, mostly divided into test and training sets.

Machine learning is a popular topic in data science, and there exists many frameworks that can be used for training and inference of these neural networks. However, many of these frameworks are still only optimised for one machine and only the CPU architecture.

The massive increase in the incidence of diabetes is now a major global healthcare challenge, and the treatment of diabetes is one of the most complicated therapies to manage, because of the difficulty in predicting blood glucose (BG) levels of diabetic patients.

We are interested in a variety of topics in the applications of intelligent methods (e.g., machine learning and search algorithms) for engineering complex software systems. Example of these topics include:

Hydrocephalus (Norwegian: vannhode) is a condition where the ventricles (cavities filled with cerebrospinal fluid (CSF) - essentially water) within the brain become enlarged.

Cardiovascular diseases are a major cause of death in industrialized societies. In particular, the development of aneurysms, which represent a weakness along the vascular system, may rupture and lead to strokes.

In a 2017 paper and three previous Master's theses, we introduced and studied the notion of data delivery deadlines in Less-than-best-effort (LBE) congestion control. The goal of LBE is to try to be as non-intrusive as possible to other network traffic.

The goal of repo2docker is to automate existing best practices for specifying and installing environments by building a docker image using environment specifications, such as conda environments or pip requirements files.

As Bushido, or "the way of the samurai", is providing the guiding principles and philosophy of the samurai - in this master project, we looking for students that are interested in the fundamental part of systems and architectures that can address the challenges

IPython Parallel provides an interactive parallel programming environment using the Jupyter Protocol for remote, interactive computing, but it can scale only to a small number (100s) of processes.

Colon cancer is the third most common cause of cancer mortality for both men and women, and it is a condition where early detection is of clear value for the ultimate survival.

In this project the goal is to explore existing state of the art methods from machine learning and statistics such as Recurrent neural networks, ARMA and Long short term memory networks.

In this project, a simple environment for the emergence of general intelligence features will be investigated, where agents can evolve through environmental rewards, and learn throughout their lifetime. The chosen control system for agents is spiking neural networks.

Uncertainty is inherent in large-scale systems such as Cyber-physical systems, IoTs, and any other smart systems. In the last few years, explicitly considering uncertainty during the software/system development lifecycle has been recognized by the research community and also industry.

Vast amounts of data is being collected to assess the reliability and performance of mobile broadband network and needs to be processed in order to understand the potential causes of failures [1, 2]. We started collecting this data in 2013 and it amounts to over 13 TB in volume.

Performance of many algorithms in machine learning crucially depends on one or more tuning parameters. At the same time, the issue of parameter selection and tuning still remains a big challenge for most real-life applications.

Myocardial ischemia due to coronary artery occlusion promotes cardiac tissue remodeling that increases patient risk to lethal arrhythmias and sudden cardiac death (SCD). However, determining individual patient risk to SCD remains difficult and often involves invasive techniques.

The classical finite element method (FEM) uses a single mesh to discretise the computational domain. While this approach works well for static domains, it requires mesh deformation or remeshing techniques if the computational domain changes during the simulation.