Identification of neutral tumor evolution across cancer types

A.S. is supported by The Chris Rokos Fellowship in Evolution and Cancer. B.W. is supported by the Geoffrey W. Lewis Post-Doctoral Training fellowship. This work was supported by the Wellcome Trust (105104/Z/14/Z). C.P.B. acknowledges funding from the Wellcome Trust through a Research Career Development Fellowship (097319/Z/11/Z). This work was supported by a Cancer Research UK Career Development Award to T.A.G. M.J.W. is supported by a UK Medical Research Council student fellowship

Closing the sea surface mixed layer temperature budget from in situ observations alone: Operation Advection during BoBBLE

Sea surface temperature (SST) is a fundamental driver of tropical weather systems such as monsoon rainfall and tropical cyclones. However, understanding of the factors that control SST variability is lacking, especially during the monsoons when in situ observations are sparse. Here we use a ground-breaking observational approach to determine the controls on the SST variability in the southern Bay of Bengal. We achieve this through the first full closure of the ocean mixed layer energy budget derived entirely from in situ observations during the Bay of Bengal Boundary Layer Experiment (BoBBLE). Locally measured horizontal advection and entrainment contribute more significantly than expected to SST evolution and thus oceanic variability during the observation period. These processes are poorly resolved by state-of-the-art climate models, which may contribute to poor representation of monsoon rainfall variability. The novel techniques presented here provide a blueprint for future observational experiments to quantify the mixed layer heat budget on longer time scales and to evaluate these processes in models

Near-Field Analysis of Terahertz Pulse Generation From Photo-Excited Charge Density Gradients

Excitation of photo-current transients at semiconductor surfaces by subpicosecond optical pulses gives rise to emission of electromagnetic pulses of terahertz (THz) frequency radiation. To correlate the THz emission with the photo-excited charge density distribution and the photo-current direction, we mapped near-field and far-field distributions of the generated THz waves from GaAs and Fe-doped InGaAs surfaces. The experimental results show that the charge dynamics in the plane of the surface can radiate substantially stronger THz pulses than the charge dynamics in the direction normal to the surface, which is generally regarded as the dominant origin of the emission

Monolithically integrated optical phase lock loop with 1 THz tuneability

We have demonstrated a monolithically integrated optical phase lock loop based on a foundry fabricated photonic integrated circuit. The InP chip contains PIN photodiodes integrated with a 1.5 μm DBR laser which can be phase stabilised with respect to an external optical reference tone at all wavelengths throughout its 1 THz (8 nm) tuning range. The frequency offset between the two lasers can be set to any value between 4 GHz and 12 GHz. The phase noise of the heterodyne signal is also reported. Such an OPLL, together with an optical frequency comb and broad-bandwidth photodetector, could be used for high purity, tuneable mm-wave / THz signal generation

Denoising diffusion models for out-of-distribution detection

Out-of-distribution detection is crucial to the safe deployment of machine learning systems. Currently, unsupervised out-of-distribution detection is dominated by generative-based approaches that make use of estimates of the likelihood or other measurements from a generative model. Reconstruction-based methods offer an alternative approach, in which a measure of reconstruction error is used to determine if a sample is out-of-distribution. However, reconstruction-based approaches are less favoured, as they require careful tuning of the model's information bottleneck-such as the size of the latent dimension - to produce good results. In this work, we exploit the view of denoising diffusion probabilistic models (DDPM) as denoising autoencoders where the bottleneck is controlled externally, by means of the amount of noise applied. We propose to use DDPMs to reconstruct an input that has been noised to a range of noise levels, and use the resulting multi-dimensional reconstruction error to classify out-of-distribution inputs. We validate our approach both on standard computer-vision datasets and on higher dimension medical datasets. Our approach outperforms not only reconstruction-based methods, but also state-of-the-art generative-based approaches. Code is available at https://github.com/marksgraham/ddpm-ood

Cosmology with redshift surveys of radio sources

We use the K-z relation for radio galaxies to illustrate why it has proved difficult to obtain definitive cosmological results from studies based entirely on catalogues of the brightest radio sources, e.g. 3C. To improve on this situation we have been undertaking redshift surveys of complete samples drawn from the fainter 6C and 7C radio catalogues. We describe these surveys, and illustrate the new studies they are allowing. We also discuss our `filtered' 6C redshift surveys: these have led to the discovery of a radio galaxy at z=4.4, and are sensitive to similar objects at higher redshift provided the space density of these objects is not declining too rapidly with z. There is currently no direct evidence for a sharp decline in the space density of radio galaxies for z > 4, a result only barely consistent with the observed decline of flat-spectrum radio quasars.Comment: 8 pages Latex, To appear in the "Cosmology with the New Radio Surveys" Conference - Tenerife 13-15 January 199

Test-time unsupervised domain adaptation

Convolutional neural networks trained on publicly available medical imaging datasets (source domain) rarely generalise to different scanners or acquisition protocols (target domain). This motivates the active field of domain adaptation. While some approaches to the problem require labelled data from the target domain, others adopt an unsupervised approach to domain adaptation (UDA). Evaluating UDA methods consists of measuring the model’s ability to generalise to unseen data in the target domain. In this work, we argue that this is not as useful as adapting to the test set directly. We therefore propose an evaluation framework where we perform test-time UDA on each subject separately. We show that models adapted to a specific target subject from the target domain outperform a domain adaptation method which has seen more data of the target domain but not this specific target subject. This result supports the thesis that unsupervised domain adaptation should be used at test-time, even if only using a single target-domain subject

Hierarchical Brain Parcellation with Uncertainty

Many atlases used for brain parcellation are hierarchically organised, progressively dividing the brain into smaller sub-regions. However, state-of-the-art parcellation methods tend to ignore this structure and treat labels as if they are ‘flat’. We introduce a hierarchically-aware brain parcellation method that works by predicting the decisions at each branch in the label tree. We further show how this method can be used to model uncertainty separately for every branch in this label tree. Our method exceeds the performance of flat uncertainty methods, whilst also providing decomposed uncertainty estimates that enable us to obtain self-consistent parcellations and uncertainty maps at any level of the label hierarchy. We demonstrate a simple way these decision-specific uncertainty maps may be used to provided uncertainty-thresholded tissue maps at any level of the label tree