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Detection of tissue and cell oxygenation is of high importance in fundamental biological and in many medical applications, particularly for monitoring dysfunction in the early stages of cancer. Measurements of the luminescence lifetimes of molecular probe ...
The task of optimally designing and scheduling energy systems with a high share of renewable energies is complex and computationally demanding. A widespread method for tackling this task is to apply mixed integer linear programming (MILP). Even though the ...
A recent series of theoretical works showed that the dynamics of neural networks with a certain initialisation are well-captured by kernel methods. Concurrent empirical work demonstrated that kernel methods can come close to the performance of neural netwo ...
The main objective of this PhD thesis is to link the atomic scale structure to the catalytic properties of self-assembled nanostructures. The growth and characterization of these nanostructures was carried out in an ultra-high vacuum (UHV) chamber initiall ...
In this dissertation, we propose gradient-based methods for characterizing model behaviour for the purposes of knowledge transfer and post-hoc model interpretation. Broadly, gradients capture the variation of some output feature of the model upon unit vari ...
Understanding aerosol–cloud–climate interactions in the Arctic is key to predicting the climate in this rapidly changing region. Whilst many studies have focused on submicrometer aerosol (diameter less than 1 µm), relatively little is known about the super ...
We study the spatial structure of soil moisture fields within savanna ecosystems, whose persistence is vital because it is the driver of the entire ecological structure and function. These include changes in the physical and biogeochemical conditions of th ...
One of the primary driving factors in building energy performance is occupant behavioral dynamics. As a result, the layout of building occupant workstations is likely to influence energy consumption. In this paper, we introduce methods for relating lightin ...
Anomaly detection is the process of identifying unexpected data samples in datasets. Automated anomaly detection is either performed using supervised machine learning models, which require a labelled dataset for their calibration, or unsupervised models, w ...
Our work addresses long-term motion context issues for predicting future frames. To predict the future precisely, it is required to capture which long-term motion context (e.g., walking or running) the input motion (e.g., leg movement) belongs to. The bott ...
