A hybrid systems model for supervisory cognitive state identification and estimation in neural prosthetics

This paper presents a method to identify a class of hybrid system models that arise in cognitive neural prosthetic medical devices that aim to help the severely handicapped. In such systems a “supervisory decoder” is required to classify the activity of multi-unit extracellular neural recordings into a discrete set of modes that model the evolution of the brain’s planning process. We introduce a Gibbs sampling method to identify the key parameters of a GLHMM, a hybrid dynamical system that combines a set of generalized linear models (GLM) for dynamics of neuronal signals with a hidden Markov model (HMM) that describes the discrete transitions between the brain’s cognitive or planning states. Multiple neural signals of mixed type, including local field potentials and spike arrival times, are integrated into the model using the GLM framework. The identified model can then be used as the basis for the supervisory decoding (or estimation) of the current cognitive or planning state. The identification algorithm is applied to extracellular neural recordings obtained from set of electrodes acutely implanted in the posterior parietal cortex of a rhesus monkey. The results demonstrate the ability to accurately decode changes in behavioral or cognitive state during reaching tasks, even when the model parameters are identified from small data sets. The GLHMM models and the associated identification methods are generally applicable beyond the neural application domain

Equal or Just? Intergenerational Allocations within Family Farm Businesses

A multi-disciplinary literature review was conducted in order to integrate multiple perspectives pertaining to family farm business transfer. Factors affecting perceptions of equality in family farm transfers were identified. Preliminary survey results analyze perceptions of equality within farm families and how these perceptions affect family farm transfer planning and implementation.family farm succession, intergenerational transfer, Farm Management, Q10, Q12,

MODISTools - downloading and processing MODIS remotely sensed data in R

Remotely sensed data – available at medium to high resolution across global spatial and temporal scales – are a valuable resource for ecologists. In particular, products from NASA's MODerate-resolution Imaging Spectroradiometer (MODIS), providing twice-daily global coverage, have been widely used for ecological applications. We present MODISTools, an R package designed to improve the accessing, downloading, and processing of remotely sensed MODIS data. MODISTools automates the process of data downloading and processing from any number of locations, time periods, and MODIS products. This automation reduces the risk of human error, and the researcher effort required compared to manual per-location downloads. The package will be particularly useful for ecological studies that include multiple sites, such as meta-analyses, observation networks, and globally distributed experiments. We give examples of the simple, reproducible workflow that MODISTools provides and of the checks that are carried out in the process. The end product is in a format that is amenable to statistical modeling. We analyzed the relationship between species richness across multiple higher taxa observed at 526 sites in temperate forests and vegetation indices, measures of aboveground net primary productivity. We downloaded MODIS derived vegetation index time series for each location where the species richness had been sampled, and summarized the data into three measures: maximum time-series value, temporal mean, and temporal variability. On average, species richness covaried positively with our vegetation index measures. Different higher taxa show different positive relationships with vegetation indices. Models had high R2 values, suggesting higher taxon identity and a gradient of vegetation index together explain most of the variation in species richness in our data. MODISTools can be used on Windows, Mac, and Linux platforms, and is available from CRAN and GitHub (https://github.com/seantuck12/MODISTools)

Gene-history correlation and population structure

Correlation of gene histories in the human genome determines the patterns of genetic variation (haplotype structure) and is crucial to understanding genetic factors in common diseases. We derive closed analytical expressions for the correlation of gene histories in established demographic models for genetic evolution and show how to extend the analysis to more realistic (but more complicated) models of demographic structure. We identify two contributions to the correlation of gene histories in divergent populations: linkage disequilibrium, and differences in the demographic history of individuals in the sample. These two factors contribute to correlations at different length scales: the former at small, and the latter at large scales. We show that recent mixing events in divergent populations limit the range of correlations and compare our findings to empirical results on the correlation of gene histories in the human genome.Comment: Revised and extended version: 26 pages, 5 figures, 1 tabl

Impact of g-factors and valleys on spin qubits in a silicon double quantum dot

We define single electron spin qubits in a silicon MOS double quantum dot system. By mapping the qubit resonance frequency as a function of gate-induced electric field, the spectrum reveals an anticrossing that is consistent with an inter-valley spin-orbit coupling. We fit the data from which we extract an inter-valley coupling strength of 43 MHz. In addition, we observe a narrow resonance near the primary qubit resonance when we operate the device in the (1,1) charge configuration. The experimental data is consistent with a simulation involving two weakly exchanged-coupled spins with a g-factor difference of 1 MHz, of the same order as the Rabi frequency. We conclude that the narrow resonance is the result of driven transitions between the T- and T+ triplet states, using an ESR signal of frequency located halfway between the resonance frequencies of the two individual spins. The findings presented here offer an alternative method of implementing two-qubit gates, of relevance to the operation of larger scale spin qubit systems

Radial and circumferential flow surveys at the inlet and exit of the Space Shuttle Main Engine High Pressure Fuel Turbine Model

The main objective of this test was to obtain detailed radial and circumferential flow surveys at the inlet and exit of the SSME High Pressure Fuel Turbine model using three-hole cobra probes, hot-film probes, and a laser velocimeter. The test was designed to meet several objectives. First, the techniques for making laser velocimeter, hot-film probe, and cobra probe measurements in turbine flows were developed and demonstrated. The ability to use the cobra probes to obtain static pressure and, therefore, velocity had to be verified; insertion techniques had to be established for the fragile hot-film probes; and a seeding method had to be established for the laser velocimetry. Once the measurement techniques were established, turbine inlet and exit velocity profiles, temperature profiles, pressure profiles, turbulence intensities, and boundary layer thicknesses were measured at the turbine design point. The blockage effect due to the model inlet and exit total pressure and total temperature rakes on the turbine performance was also studied. A small range of off-design points were run to obtain the profiles and to verify the rake blockage effects off-design. Finally, a range of different Reynolds numbers were run to study the effect of Reynolds number on the various measurements

A hybrid systems model for supervisory cognitive state identification and estimation in neural prosthetics

This paper presents a method to identify a class of hybrid system models that arise in cognitive neural prosthetic medical devices that aim to help the severely handicapped. In such systems a “supervisory decoder” is required to classify the activity of multi-unit extracellular neural recordings into a discrete set of modes that model the evolution of the brain’s planning process. We introduce a Gibbs sampling method to identify the key parameters of a GLHMM, a hybrid dynamical system that combines a set of generalized linear models (GLM) for dynamics of neuronal signals with a hidden Markov model (HMM) that describes the discrete transitions between the brain’s cognitive or planning states. Multiple neural signals of mixed type, including local field potentials and spike arrival times, are integrated into the model using the GLM framework. The identified model can then be used as the basis for the supervisory decoding (or estimation) of the current cognitive or planning state. The identification algorithm is applied to extracellular neural recordings obtained from set of electrodes acutely implanted in the posterior parietal cortex of a rhesus monkey. The results demonstrate the ability to accurately decode changes in behavioral or cognitive state during reaching tasks, even when the model parameters are identified from small data sets. The GLHMM models and the associated identification methods are generally applicable beyond the neural application domain

Are patients willing participants in the new wave of community-based medical education in regional and rural Australia?

Community-based medical education is escalating to meet the increased demand for quality clinical education in expanded settings and patient participation is vital to the sustainability of this endeavour. This study aimed to investigate patients’ views on being used as an educational resource in medical student teaching, and whether they are being under- or over-used