While the accuracy of modern deep learning models has significantly improved in recent years, the ability of these models to generate uncertainty estimates has not progressed to the same degree. Uncertainty methods are designed to provide an estimate of class probabilities when predicting class assignment. While there are a number of proposed methods for estimating uncertainty, they all suffer from a lack of calibration: predicted probabilities can be off from empirical ones by a few percent or more. By restricting the scope of our predictions to only the probability of Top-1 error, we can decrease the calibration error of existing methods to less than one percent. As a result, the scores of the methods also improve significantly over benchmarks.
Mathematical models for the tumour control probability (TCP) are used to estimate the expected success of radiation treatment protocols of cancer. There are several TCP models in the literature, from the simplest (Poissonian TCP) to the well-advanced stochastic birth–death processes. Simple and complex models often make the same predictions. Hence, here, we present a systematic study where we compare six of these TCP models: the Poisson TCP, the Zaider–Minerbo TCP, a Monte Carlo TCP and their corresponding cell cycle (two-compartment) models. Several clinical non-uniform treatment protocols for prostate cancer are employed to evaluate these models. These include fractionated external beam radiotherapies, and high and low dose rate brachytherapies. We find that in realistic treatment scenarios, all one-compartment models and all two-compartment models give basically the same results. A difference occurs between one-compartment and two-compartment models due to reduced radiosensitivity of quiescent cells.We find that care must be taken for the right choice of parameters, such as the radiosensitivities α and β and the hazard function h. Typically, different hazard functions are used for fractionated treatment (fractionated survival fraction) and for brachytherapies (Lea–Catcheside protraction factor). We were able to combine these two approaches into one 'effective' hazard function. Based on our results, we can recommend the use of the Poissonian TCP for everyday treatment planning. More complicated models should only be used when absolutely necessary.
Radio interferometry calibration and Radio Frequency Interference (RFI) removal are usually done separately. Here we show that jointly modelling the antenna gains and RFI has significant benefits when the RFI follows precise trajectories, such as for satellites. One surprising benefit is improved calibration solutions, by leveraging the RFI signal itself. We present tabascal (TrAjectory BAsed RFI Subtraction and CALibration), a new algorithm that jointly models the RFI and calibration parameters in visibilities. We test tabascal on simulated MeerKAT calibration observations contaminated by satellite-based RFI. We obtain gain estimates that are both unbiased and up to an order of magnitude better constrained compared to uncontaminated data. When combined with an ad hoc RFI subtraction scheme, tabascal solutions can be further applied to an adjacent target observation: 5 minutes of calibration data results in an image with about a third the noise achieved when using flagging alone. The recovered flux distribution of RFI subtracted data was on par with uncontaminated data. In contrast, RFI flagging alone resulted in a higher detection threshold and consistent underestimation of source fluxes. For a mean RFI amplitude of 17 Jy, using RFI subtraction leads to less than 1% loss of data compared to 75% data loss from an ideal $3σ$ flagging algorithm, a very significant increase in data available for science analysis. Although we have examined the case of satellite RFI, tabascal should work for any RFI moving on parameterizable trajectories, relative to the phase centre, such as planes and/or objects fixed to the ground.
Multiple studies have suggested the spread of COVID-19 is affected by factors such as climate, BCG vaccinations, pollution and blood type. We perform a joint study of these factors using the death growth rates of 40 regions worldwide with both machine learning and Bayesian methods. We find weak, non-significant ( 3$\sigma$) is the rate of positive COVID-19 tests, with higher positive rates correlating with higher daily growth of deaths.
Radio astronomy is a vital tool for astronomers to study the Universe and has seen a wave of renewed interest and advancement over recent years. Next-generation radio telescope arrays like the SKA, ALMA and VLA are developed to be significantly more sensitive compared to older telescopes, which as a result also make them more susceptible to radio frequency interference (RFI). This highlights the need for effective RFI mitigation techniques in radio astronomy. We present a machine learning-based RFI mitigation approach that aims to separate RFI-corrupted spectrogram observations into signal of interest and RFI components in an unsupervised manner using a modified generative adversarial network (GAN) framework. We show that this unsupervised source separation approach is able to achieve performance comparable to a fully supervised approach.
M ultiple studies have suggested the spread of COVID-19 is affected by factors such as climate, BCG vaccinations, pollution and blood type. We perform a joint study of these factors using the death growth rates of 40 regions worldwide with both machine learning and Bayesian methods. We find weak, non-significant (< 3 σ ) evidence for temperature and relative humidity as factors in the spread of COVID-19 but little or no evidence for BCG vaccination prevalence or PM 2.5 pollution. The only variable detected at a statistically significant level (>3 σ ) is the rate of positive COVID-19 tests, with higher positive rates correlating with higher daily growth of deaths.
In the first TABASCAL paper we showed how to calibrate in the presence of Radio Frequency Interference (RFI) sources by simultaneously isolating the trajectories and signals of the RFI sources. Here we show that we can accurately remove RFI from simulated MeerKAT radio interferometry target data, for a single frequency channel, corrupted by up to 9 simultaneous satellites with average RFI amplitudes varying from weak to very strong (1 - 1000 Jy). Additionally, TABASCAL also manages to leverage the RFI signal-to-noise to phase calibrate the recovered astronomical signal. TABASCAL effectively performs a suitably phased up fringe filter for each RFI source which allows essentially perfect removal of RFI across all strengths. As a result, TABASCAL reaches image noises equivalent to the uncorrupted, no-RFI, case. Consequently, point-source science with TABASCAL almost matches the no-RFI case with near perfect completeness for all RFI amplitudes. In contrast the completeness of AOFlagger and idealised 3$\sigma$ flagging drops below 40% for strong RFI amplitudes where recovered flux errors are $\sim$10x-100x worse than those from TABASCAL. Finally we highlight that TABASCAL works for both static and varying astronomical sources.
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