Cunningham Collection Finding Aid: Container List

This document provides background information to the Finding Aid to the Cunningham Collection. Dr Kenneth Stewart Cunningham (1890 – 1976) was a leading Australian educationalist and educational researcher who was instrumental in the creation and development of the Australian Council for Educational Research (ACER). After his death in 1976, Dr Cunningham’s daughter, Lesley Cunningham, became the custodian of her father’s personal papers. Much of this material was donated to the Australian Council for Educational Research (ACER) by Lesley Cunningham a few years before her death

Non-reversible Gaussian processes for identifying latent dynamical structure in neural data

A common goal in the analysis of neural data is to compress large population recordings into sets of interpretable, low-dimensional latent trajectories. This problem can be approached using Gaussian process (GP)-based methods which provide uncertainty quantification and principled model selection. However, standard GP priors do not distinguish between underlying dynamical processes and other forms of temporal autocorrelation. Here, we propose a new family of “dynamical” priors over trajectories, in the form of GP covariance functions that express a property shared by most dynamical systems: temporal non-reversibility. Non-reversibility is a universal signature of autonomous dynamical systems whose state trajectories follow consistent flow fields, such that any observed trajectory could not occur in reverse. Our new multi-output GP kernels can be used as drop-in replacements for standard kernels in multivariate regression, but also in latent variable models such as Gaussian process factor analysis (GPFA). We therefore introduce GPFADS (Gaussian Process Factor Analysis with Dynamical Structure), which models single-trial neural population activity using low-dimensional, non-reversible latent processes. Unlike previously proposed non-reversible multi-output kernels, ours admits a Kronecker factorization enabling fast and memory-efficient learning and inference. We apply GPFADS to synthetic data and show that it correctly recovers ground truth phase portraits. GPFADS also provides a probabilistic generalization of jPCA, a method originally developed for identifying latent rotational dynamics in neural data. When applied to monkey M1 neural recordings, GPFADS discovers latent trajectories with strong dynamical structure in the form of rotations

The I-mode confinement regime at ASDEX Upgrade: global propert ies and characterization of strongly intermittent density fluctuations

Properties of the I­mode confinement regime on the ASDEX Upgrade tokamak are summarized. A weak dependence of the power threshold for the L­I transition on the toroidal magnetic field strength is found. During improved confinement, the edge radial electric field well deepens. Stability calculations show that the I­mode pedestal is peeling­ballooning stable. Turbulence investigations reveal strongly intermittent density fluctuations linked to the weakly coherent mode in the confined plasma, which become stronger as the confinement quality increases. Across all investigated structure sizes ( ≈ ⊥ k 5 – 12 cm − 1 , with ⊥ k the perpendicular wavenumber of turbulent density fluctuations), the intermittent turbulence bursts are observed. Comparison with bolometry data shows that they move poloidally toward the X­point and finally end up in the divertor. This might be indicative that they play a role in inhibiting the density profile growth, such that no pedestal is formed in the edge density profile.European Union (EUROfusion 633053)European Union (EUROfusion AWP15­ENR­09/IPP­02