Neural Network Methods for Radiation Detectors and Imaging

Recent advances in image data processing through machine learning and especially deep neural networks (DNNs) allow for new optimization and performance-enhancement schemes for radiation detectors and imaging hardware through data-endowed artificial intelligence. We give an overview of data generation at photon sources, deep learning-based methods for image processing tasks, and hardware solutions for deep learning acceleration. Most existing deep learning approaches are trained offline, typically using large amounts of computational resources. However, once trained, DNNs can achieve fast inference speeds and can be deployed to edge devices. A new trend is edge computing with less energy consumption (hundreds of watts or less) and real-time analysis potential. While popularly used for edge computing, electronic-based hardware accelerators ranging from general purpose processors such as central processing units (CPUs) to application-specific integrated circuits (ASICs) are constantly reaching performance limits in latency, energy consumption, and other physical constraints. These limits give rise to next-generation analog neuromorhpic hardware platforms, such as optical neural networks (ONNs), for high parallel, low latency, and low energy computing to boost deep learning acceleration

Unusual breathing behavior of optically excited barium titanate nanocrystals

Coherent X-ray diffraction patterns were recorded by using an X-ray free-electron laser to illuminate barium titanate nanocrystals as a function of time delay after laser excitation. Rather than seeing any significant thermal expansion effects, the diffraction peaks were found to move perpendicular to the momentum transfer direction. This suggests a laser driven rotation of the crystal lattice, which is delayed by the aggregated state of the crystals. Internal deformations associated with crystal contacts were also observed

Elucidation of Relaxation Dynamics Beyond Equilibrium Through AI-informed X-ray Photon Correlation Spectroscopy

Understanding and interpreting dynamics of functional materials \textit{in situ} is a grand challenge in physics and materials science due to the difficulty of experimentally probing materials at varied length and time scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited for characterizing materials dynamics over wide-ranging time scales, however spatial and temporal heterogeneity in material behavior can make interpretation of experimental XPCS data difficult. In this work we have developed an unsupervised deep learning (DL) framework for automated classification and interpretation of relaxation dynamics from experimental data without requiring any prior physical knowledge of the system behavior. We demonstrate how this method can be used to rapidly explore large datasets to identify samples of interest, and we apply this approach to directly correlate bulk properties of a model system to microscopic dynamics. Importantly, this DL framework is material and process agnostic, marking a concrete step towards autonomous materials discovery

Myocardial infarction risk is increased by periodontal pathobionts : a cross-sectional study

Acknowledgements The authors thank the laboratory and administrative staff at the Institute of Dentistry and the Institute of Medical Sciences of the University of Aberdeen for their help. We are grateful to Dr Pirkko Pussinen (Institute of Dentistry, University of Turku, Finland) for sharing the P. gingivalis-antibody positive serum samples and a detailed ELISA protocol. We would like to thank all Consultant Cardiologists at Aberdeen Royal Infirmary for identifying suitable patients during the acute on-call: Dr Andrew Hannah, Dr Andrew Stewart, Dr Adelle Dawson, Dr Deepak Garg, Dr Paul Broadhurst, Dr Nicola Ryan. Lastly, we are grateful to all the participating patients. Funding “Periodontal health in patients acutely admitted for myocardial infarction study” was funded by the Elphinstone Award to Dr Hijazi. Dr Dawson is supported by the British Heart Foundation (FS/RTF/20/30009, NH/19/1/34595, PG/18/35/33786, CS/17/4/32960, PG/15/88/31780, ), Chest Heart and Stroke Scotland (19/53), Tenovus Scotland (G.18.01), Friends of Anchor and Grampian NHS-Endowments.Peer reviewedPublisher PD

AI-NERD: Elucidation of relaxation dynamics beyond equilibrium through AI-informed X-ray photon correlation spectroscopy

Understanding and interpreting dynamics of functional materials in situ is a grand challenge in physics and materials science due to the difficulty of experimentally probing materials at varied length and time scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited for characterizing materials dynamics over wide-ranging time scales. However, spatial and temporal heterogeneity in material behavior can make interpretation of experimental XPCS data difficult. In this work, we have developed an unsupervised deep learning (DL) framework for automated classification of relaxation dynamics from experimental data without requiring any prior physical knowledge of the system. We demonstrate how this method can be used to accelerate exploration of large datasets to identify samples of interest, and we apply this approach to directly correlate microscopic dynamics with macroscopic properties of a model system. Importantly, this DL framework is material and process agnostic, marking a concrete step towards autonomous materials discovery

Spontaneous supercrystal formation during a strain-engineered metal-insulator transition

Mott metal-insulator transitions possess electronic, magnetic, and structural degrees of freedom promising next generation energy-efficient electronics. We report a previously unknown, hierarchically ordered state during a Mott transition and demonstrate correlated switching of functional electronic properties. We elucidate in-situ formation of an intrinsic supercrystal in a Ca2RuO4 thin film. Machine learning-assisted X-ray nanodiffraction together with electron microscopy reveal multi-scale periodic domain formation at and below the film transition temperature (TFilm ~ 200-250 K) and a separate anisotropic spatial structure at and above TFilm. Local resistivity measurements imply an intrinsic coupling of the supercrystal orientation to the material's anisotropic conductivity. Our findings add an additional degree of complexity to the physical understanding of Mott transitions, opening opportunities for designing materials with tunable electronic properties

Habituation based synaptic plasticity and organismic learning in a quantum perovskite

A central characteristic of living beings is the ability to learn from and respond to their environment leading to habit formation and decision making. This behavior, known as habituation, is universal among all forms of life with a central nervous system, and is also observed in single-cell organisms that do not possess a brain. Here, we report the discovery of habituation-based plasticity utilizing a perovskite quantum system by dynamical modulation of electron localization. Microscopic mechanisms and pathways that enable this organismic collective charge-lattice interaction are elucidated by first-principles theory, synchrotron investigations, ab initio molecular dynamics simulations, and in situ environmental breathing studies. We implement a learning algorithm inspired by the conductance relaxation behavior of perovskites that naturally incorporates habituation, and demonstrate learning to forget: A key feature of animal and human brains. Incorporating this elementary skill in learning boosts the capability of neural computing in a sequential, dynamic environment.United States. Army Research Office (Grant W911NF-16-1-0289)United States. Air Force Office of Scientific Research (Grant FA9550-16-1-0159)United States. Army Research Office (Grant W911NF-16-1-0042

Neural network methods for radiation detectors and imaging

Recent advances in image data proccesing through deep learning allow for new optimization and performance-enhancement schemes for radiation detectors and imaging hardware. This enables radiation experiments, which includes photon sciences in synchrotron and X-ray free electron lasers as a subclass, through data-endowed artificial intelligence. We give an overview of data generation at photon sources, deep learning-based methods for image processing tasks, and hardware solutions for deep learning acceleration. Most existing deep learning approaches are trained offline, typically using large amounts of computational resources. However, once trained, DNNs can achieve fast inference speeds and can be deployed to edge devices. A new trend is edge computing with less energy consumption (hundreds of watts or less) and real-time analysis potential. While popularly used for edge computing, electronic-based hardware accelerators ranging from general purpose processors such as central processing units (CPUs) to application-specific integrated circuits (ASICs) are constantly reaching performance limits in latency, energy consumption, and other physical constraints. These limits give rise to next-generation analog neuromorhpic hardware platforms, such as optical neural networks (ONNs), for high parallel, low latency, and low energy computing to boost deep learning acceleration (LA-UR-23-32395)

Non-invasive quantification of cerebral oxygenation in ischaemic stroke using MRI

Measurements of oxygen availability can be used to distinguish regions of the brain that are at risk of permanent damage during and after ischaemic stroke. This has the potential to inform management decisions, or monitor progress after treatment. Differences in the oxygenation of blood (quantified as oxygen extraction fraction [OEF]) cause changes in the rate of reversible transverse relaxation, as measured using asymmetric spin echo (ASE) magnetic resonance imaging (MRI). This relationship is described by the quantitative blood oxygen level dependent (qBOLD) signal model. A streamlined version of the qBOLD model has recently been used to measure OEF in healthy subjects, and to detect changes in related parameters in ischaemic stroke. In this thesis, a Bayesian framework for inferring on a multiple-compartment qBOLD model is developed, and applied to simulated and in vivo data to estimate OEF and other parameters. This model is then used to test potential improvements to the already established streamlined qBOLD framework. The possibility of acquiring data without a fluid-attenuated inversion recovery (FLAIR) sequence is investigated, and the complications that arise when cerebrospinal fluid contributes to the qBOLD signal are described. Then, modifications to the model that account for the effect of diffusion, and of differences in blood vessel distributions, are tested using Monte Carlo simulations, and validated in healthy subjects. These are tested alongside changes to other acquisition parameters that could lead to more efficient data collection. Finally, the qBOLD model is used to infer OEF in ischaemic stroke patients. It is shown that oxygenation differs between pathological regions, which suggests that this method could be usefully applied to stroke assessment in the clinic.</p