However, since the energy upgrade, CEBAF has suffered from significant FE induced radiation. With RF on, dose Jefferson Lab's Continuous Electron Beam Accelerator rates observed at 30 cm from the beamline are as high Facility (CEBAF) [1] relies on two superconducting as 10 rem/h and 100 rem/h for neutron and gamma radiation, radio-frequency linear accelerators (SRF linacs) to deliver respectively. This level of radiation causes significant high-energy electron beams to nuclear physics experiments damage to beamline components, including vacuum in the four experimental halls [2]. An integral valves, magnets, and cables of beam position monitors part of these linacs are cryomodules which contain and ion pumps. Replacing these components can use multiple SRF cavities. These SRF cavities provide the significant resources. Worse, portions of both linacs are main accelerating gradients to the electron beam, and considered "Radiation Areas" for days or even weeks into currently produce the 12 GeV beam necessary for scientific scheduled downtime, limiting maintenance activities to discovery.

We introduce the Lossy Implicit Network Activation Coding (LINAC) defence, an input transformation which successfully hinders several common adversarial attacks on CIFAR-$10$ classifiers for perturbations up to $\epsilon = 8/255$ in $L_\infty$ norm and $\epsilon = 0.5$ in $L_2$ norm. Implicit neural representations are used to approximately encode pixel colour intensities in $2\text{D}$ images such that classifiers trained on transformed data appear to have robustness to small perturbations without adversarial training or large drops in performance. The seed of the random number generator used to initialise and train the implicit neural representation turns out to be necessary information for stronger generic attacks, suggesting its role as a private key. We devise a Parametric Bypass Approximation (PBA) attack strategy for key-based defences, which successfully invalidates an existing method in this category. Interestingly, our LINAC defence also hinders some transfer and adaptive attacks, including our novel PBA strategy. Our results emphasise the importance of a broad range of customised attacks despite apparent robustness according to standard evaluations. LINAC source code and parameters of defended classifier evaluated throughout this submission are available: https://github.com/deepmind/linac

Patient scheduling is a difficult task as it involves dealing with stochastic factors such as an unknown arrival flow of patients. Scheduling radiotherapy treatments for cancer patients faces a similar problem. Curative patients need to start their treatment within the recommended deadlines, i.e., 14 or 28 days after their admission while reserving treatment capacity for palliative patients who require urgent treatments within 1 to 3 days after their admission. Most cancer centers solve the problem by reserving a fixed number of treatment slots for emergency patients. However, this flat-reservation approach is not ideal and can cause overdue treatments for emergency patients on some days while not fully exploiting treatment capacity on some other days, which also leads to delaying treatment for curative patients. This problem is especially severe in large and crowded hospitals. In this paper, we propose a prediction-based approach for online dynamic radiotherapy scheduling. An offline problem where all future patient arrivals are known in advance is solved to optimality using Integer Programming. A regression model is then trained to recognize the links between patients' arrival patterns and their ideal waiting time. The trained regression model is then embedded in a prediction-based approach that schedules a patient based on their characteristics and the present state of the calendar. The numerical results show that our prediction-based approach efficiently prevents overdue treatments for emergency patients while maintaining a good waiting time compared to other scheduling approaches based on a flat-reservation policy.

Will the critical shortages of therapy radiographers mean that we are about to be replaced by AI, robots and machine learning systems and that will essentially solve the training, retention and employment problems in our profession by stealth? The Society of Radiographers have just announced that the new apprentice programs are now "GO" and where a more vocational training environment in combination with a prospective employer and a degree course will allow employers to "attract and select individuals they believe have the potential to become radiographers". It has also been announced this month that the University of Portsmouth is to close its degree course in radiotherapy and oncology in 2020 for which the timing is particularly ironic and may well impact on recruitment in the South further exacerbating the current problem. I looked at these issues in my January blog and reported on some items in the media relating to this. The College of Radiographers published some of their latest feedback and information on Radiographer Apprenticeships in my February blog and now having read some of the latest books on the impact of Artificial Intelligence on us and especially the workplace, I thought it would be interesting this month to see how this might impact on our profession.