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This paper presents a study of early epidemiological assessment of COVID-19 transmission dynamics in Indonesia. The aim is to quantify heterogeneity in the numbers of secondary infections. To this end, we estimate the basic reproduction number R 0 and the overdispersion parameter K at two regions in Indonesia: Jakarta–Depok and Batam. The method to estimate R 0 is based on a sequential Bayesian method, while the parameter K is estimated by fitting the secondary case data with a negative binomial distribution. Based on the first 1288 confirmed cases collected from both regions, we find a high degree of individual-level variation in the transmission. The basic reproduction number R 0 is estimated at 6.79 and 2.47, while the overdispersion parameter K of a negative-binomial distribution is estimated at 0.06 and 0.2 for Jakarta–Depok and Batam, respectively. This suggests that superspreading events played a key role in the early stage of the outbreak, i.e., a small number of infected individuals are responsible for large numbers of COVID-19 transmission. This finding can be used to determine effective public measures, such as rapid isolation and identification, which are critical since delay of diagnosis is the most common cause of superspreading events.

A prevalent class of challenges in modern physics are inverse problems, where physical quantities must be extracted from experimental measurements. End-to-end machine learning approaches to inverse problems typically require constructing sophisticated estimators to achieve the desired accuracy, largely because they need to learn the complex underlying physical model. Here, we discuss an alternative paradigm: by making the physical model auto-differentiable we can construct a neural surrogate to represent the unknown physical quantity sought, while avoiding having to relearn the known physics entirely. We dub this process surrogate training embedded in physics (STEP) and illustrate that it generalizes well and is robust against overfitting and significant noise in the data. We demonstrate how STEP can be applied to perform dynamic kernel deconvolution to analyse resonant inelastic X-ray scattering spectra and show that surprisingly simple estimator architectures suffice to extract the relevant physical information.

Diagnosing plasma conditions can give great advantages in optimizing plasma wakefield accelerator experiments. One possible method is that of photon acceleration. By propagating a laser probe pulse through a plasma wakefield and extracting the imposed frequency modulation, one can obtain an image of the density modulation of the wakefield. In order to diagnose the wakefield parameters at a chosen point in the plasma, the probe pulse crosses the plasma at oblique angles relative to the wakefield. In this paper, mathematical expressions relating the frequency modulation of the laser pulse and the wakefield density profile of the plasma for oblique crossing angles are derived. Multidimensional particle-in-cell simulation results presented in this paper confirm that the frequency modulation profiles and the density modulation profiles agree to within 10%. Limitations to the accuracy of the measurement are discussed in this paper. This technique opens new possibilities to quantitatively diagnose the plasma wakefield density at known positions within the plasma column.

One obstacle in plasma accelerator development is the limitation of techniques to diagnose and measure plasma wakefield parameters. In this paper, we present a novel concept for the density measurement of a plasma wakefield using photon acceleration, supported by extensive particle in cell simulations of a laser pulse that copropagates with a wakefield. The technique can provide the perturbed electron density profile in the laser’s reference frame, averaged over the propagation length, to be accurate within 10%. We discuss the limitations that affect the measurement: small frequency changes, photon trapping, laser displacement, stimulated Raman scattering, and laser beam divergence. By considering these processes, one can determine the optimal parameters of the laser pulse and its propagation length. This new technique allows a characterization of the density perturbation within a plasma wakefield accelerator.

Background and purpose The lack of equitable access to radiotherapy (RA) linear accelerators (LINACs) is a substantial barrier to cancer care in low‐ and middle‐income countries (LMICs). These nations are expected to bear up to 75% of cancer‐related deaths globally by 2030. State‐of‐the‐art LINACs in LMICs experience major issues in terms of robustness, with mechanical and electrical breakdowns resulting in downtimes ranging from days to months. While existing research has identified the higher failure frequency and downtimes between LMICs (Nigeria, Botswana) compared to high‐income countries (HICs, the UK), there has been a need for additional data and study particularly relating to multileaf collimators (MLCs). Materials and methods This study presents for the first time the analysis of data gathered through a dedicated survey and workshop including participants from 14 Indonesian hospitals, representing a total of 19 LINACs. We show the pathways to failure of radiotherapy LINACs and frequency of breakdowns with a focus on the MLC subsystem. Results This dataset shows that LINACs throughout Indonesia are out of operation for seven times longer than HICs, and the mean time between failures of a LINAC in Indonesia is 341.58 h or about 14 days. Furthermore, of the LINACs with an MLC fitted, 59.02−1.61+1.98 $59.02_{ - 1.61}^{ + 1.98}$ % of all mechanical faults are due to the MLC, and 57.14−1.27+0.78 $57.14_{ - 1.27}^{ + 0.78}$% of cases requiring a replacement component are related to the MLC. Conclusion These results highlight the pressing need to improve robustness of RT technology for use in LMICs, highlighting the MLC as a particularly problematic component. This work motivates a reassessment of the current generation of RT LINACs and demonstrates the need for dedicated efforts toward a future where cancer treatment technology is robust for use in all environments where it is needed.

Four novel methods to diagnose plasma wakefield accelerators using optical probes are presented in this thesis. The first method involves sending an optical probe pulse to cross the wakefield at an oblique angle of incidence. The wakefield then imprints a phase modulation onto the probe which is read using a spectral interferometry technique. At first, the method was developed analytically and verified against multi-dimensional Particle-in-Cell (PIC) simulations. These allowed the relation between the phase modulation of the probe and the electron density in the wakefield to be extracted. An experiment employing this technique was also performed and modulations with similar wavelength to those expected in the wakefield accelerator were detected. The second method, which I have named three-dimensional (3D) spectrometer for brevity, is based on the concept of compressed sensing. It involves the retrieval of multiple two-dimensional (2D) spectral profiles just from a single 2D image captured by a planar detector. Numerical tests show that it can retrieve up to ten sets of 2D spectral profile just from a single image. The retrieved signal is also robust for further post-processing. The third method is based on a numerical technique to retrieve information from shadowgraphy or proton radiography. The information is the self-generated magnetic and/or electric fields for proton radiography and the variation of refractive index for shadowgraphy. The technique is adapted from a computational graphics algorithm. Numerical simulations show that the retrieved information is accurate with an error of 10% using the method, even if caustics appear. The algorithm is also applied to retrieve the modulation of the refractive index from a real experimental result of plasma wakefield using shadowgraphy. The fourth and the final method is a novel optimisation algorithm and software based on neuromorphic computing to optimise systems via simulations. The software was employed to optimise the performance of a laser-driven plasma wakefield system by testing it using PIC simulations on a computer cluster. It is demonstrated that by running it and letting it learn laser-driven plasma wakefield parameters for a number of days, the software can find optimal parameters in a laser-plasma system without explicitly being taught.

This paper presents the forward and backward derivatives of partial eigendecomposition, i.e. where it only obtains some of the eigenpairs, of a real symmetric matrix for degenerate cases. The numerical calculation of forward and backward derivatives can be implemented even if the degeneracy never disappears and only some eigenpairs are available.

The beauty of physics is that there is usually a conserved quantity in an always-changing system, known as the constant of motion. Finding the constant of motion is important in understanding the dynamics of the system, but typically requires mathematical proficiency and manual analytical work. In this paper, we present a neural network that can simultaneously learn the dynamics of the system and the constants of motion from data. By exploiting the discovered constants of motion, it can produce better predictions on dynamics and can work on a wider range of systems than Hamiltonian-based neural networks. In addition, the training progresses of our method can be used as an indication of the number of constants of motion in a system which could be useful in studying a novel physical system.

Conservation of energy is at the core of many physical phenomena and dynamical systems. There have been a significant number of works in the past few years aimed at predicting the trajectory of motion of dynamical systems using neural networks while adhering to the law of conservation of energy. Most of these works are inspired by classical mechanics such as Hamiltonian and Lagrangian mechanics as well as Neural Ordinary Differential Equations. While these works have been shown to work well in specific domains respectively, there is a lack of a unifying method that is more generally applicable without requiring significant changes to the neural network architectures. In this work, we aim to address this issue by providing a simple method that could be applied to not just energy-conserving systems, but also dissipative systems, by including a different inductive bias in different cases in the form of a regularisation term in the loss function. The proposed method does not require changing the neural network architecture and could form the basis to validate a novel idea, therefore showing promises to accelerate research in this direction.

Sequential models, such as Recurrent Neural Networks and Neural Ordinary Differential Equations, have long suffered from slow training due to their inherent sequential nature. For many years this bottleneck has persisted, as many thought sequential models could not be parallelized. We challenge this long-held belief with our parallel algorithm that accelerates GPU evaluation of sequential models by up to 3 orders of magnitude faster without compromising output accuracy. The algorithm does not need any special structure in the sequential models' architecture, making it applicable to a wide range of architectures. Using our method, training sequential models can be more than 10 times faster than the common sequential method without any meaningful difference in the training results. Leveraging this accelerated training, we discovered the efficacy of the Gated Recurrent Unit in a long time series classification problem with 17k time samples. By overcoming the training bottleneck, our work serves as the first step to unlock the potential of non-linear sequential models for long sequence problems.