title: Local anisotropy, higher order statistics, and turbulence spectra

authors: Matthaeus, William H.

subjects: interplanetary medium

abstract: Correlation anisotropy emerges dynamically in magnetohydrodynamics (MHD), producing stronger gradients across the large-scale mean magnetic field than along it. This occurs both globally and locally, and has significant implications in space and astrophysical plasmas, including particle scattering and transport, and theories of turbulence. Properties of local correlation anisotropy are further documented here by showing through numerical experiments that the effect is intensified in more localized estimates of the mean field. The mathematical formulation of this property shows that local anisotropy mixes second-order with higher order correlations. Sensitivity of local statistical estimates to higher order correlations can be understood in connection with the stochastic coordinate system inherent in such formulations. We demonstrate this in specific cases, and illustrate the connection to higher order statistics by showing the sensitivity of local anisotropy to phase randomization, after which the global measure of anisotropy is recovered at all scales of averaging. This establishes that anisotropy of the local structure function is not a measure of anisotropy of the energy spectrum. Evidently, the local enhancement of correlation anisotropy is of substantial fundamental interest and must be understood in terms of higher order correlations, specifically fourth-order and above.

text: Local anisotropy, higher order statistics, and turbulence spectra Correlation anisotropy emerges dynamically in magnetohydrodynamics (MHD), producing stronger gradients across the large-scale mean magnetic field than along it. This occurs both globally and locally, and has significant implications in space and astrophysical plasmas, including particle scattering and transport, and theories of turbulence. Properties of local correlation anisotropy are further documented here by showing through numerical experiments that the effect is intensified in more localized estimates of the mean field. The mathematical formulation of this property shows that local anisotropy mixes second-order with higher order correlations. Sensitivity of local statistical estimates to higher order correlations can be understood in connection with the stochastic coordinate system inherent in such formulations. We demonstrate this in specific cases, and illustrate the connection to higher order statistics by showing the sensitivity of local anisotropy to phase randomization, after which the global measure of anisotropy is recovered at all scales of averaging. This establishes that anisotropy of the local structure function is not a measure of anisotropy of the energy spectrum. Evidently, the local enhancement of correlation anisotropy is of substantial fundamental interest and must be understood in terms of higher order correlations, specifically fourth-order and above.

year: 2012

title: Furthering deep learning in near-infrared spectroscopy for fruit quality assessment

authors: Wohlers, Mark W.

abstract: Near-infrared (NIR) spectroscopy is widely used to assess fruit quality in the horticulture industry. It enables non-destructive estimation of key fruit quality measures from spectra, including dry matter content (associated with taste) and soluble solids content (associated with ripeness). Traditionally, partial least squares regression (PLSR) has been the dominant modelling method. However, more recently, deep learning (DL) has shown promise due to its ability to learn features automatically and model non-linear patterns. However, there are several challenges DL faces when applied to NIR. Labelled datasets are complicated, expensive, and time-consuming to obtain at the size required to fit these models. Deciding on the appropriate architecture and hyperparameters can also be challenging when validation data is sparse. Additionally, a problem of great practical importance in NIR spectroscopy is the difficulty of generalising across different devices of the same model or under different conditions, such as temperature. 

This thesis addresses these challenges through three complementary methods. The first uses a data augmentation technique that samples from a multivariate normal distribution with a covariance matrix designed to simulate spectral differences observed across devices. The experiments investigate whether the augmentation improves generalisability and training with small sample sizes.

The second method is a metric based on model stability to diffeomorphic transformations relative to uncorrelated perturbations of similar magnitude. The experiment evaluates the appropriateness of this method for model selection tasks and compares its performance with standard validation methods.

The third method adapts the Barlow Twins contrastive learning method to enable semi-supervised learning in the NIR setting. The Barlow Twins loss function allows unlabelled data to compensate when labelled data is scarce. This method also improves generalisability by encouraging multiple measurements on the same fruit to be similar in the encoded latent space. 

Evaluation of these methods is conducted on two datasets: a new dataset containing 5418 kiwifruit sampled across five devices and three seasons, and a previously published dataset of 4675 mangoes measured across four seasons. 

The results show that the methods improve predictive performance, especially for small labelled datasets and calibration transfer problems. This allows for the easier application of deep learning to NIR spectroscopy by reducing the requirements for labelled data, improving model generalisability across devices, and enabling model selection under data constraints.

text: Furthering deep learning in near-infrared spectroscopy for fruit quality assessment Near-infrared (NIR) spectroscopy is widely used to assess fruit quality in the horticulture industry. It enables non-destructive estimation of key fruit quality measures from spectra, including dry matter content (associated with taste) and soluble solids content (associated with ripeness). Traditionally, partial least squares regression (PLSR) has been the dominant modelling method. However, more recently, deep learning (DL) has shown promise due to its ability to learn features automatically and model non-linear patterns. However, there are several challenges DL faces when applied to NIR. Labelled datasets are complicated, expensive, and time-consuming to obtain at the size required to fit these models. Deciding on the appropriate architecture and hyperparameters can also be challenging when validation data is sparse. Additionally, a problem of great practical importance in NIR spectroscopy is the difficulty of generalising across different devices of the same model or under different conditions, such as temperature. 

This thesis addresses these challenges through three complementary methods. The first uses a data augmentation technique that samples from a multivariate normal distribution with a covariance matrix designed to simulate spectral differences observed across devices. The experiments investigate whether the augmentation improves generalisability and training with small sample sizes.

The second method is a metric based on model stability to diffeomorphic transformations relative to uncorrelated perturbations of similar magnitude. The experiment evaluates the appropriateness of this method for model selection tasks and compares its performance with standard validation methods.

The third method adapts the Barlow Twins contrastive learning method to enable semi-supervised learning in the NIR setting. The Barlow Twins loss function allows unlabelled data to compensate when labelled data is scarce. This method also improves generalisability by encouraging multiple measurements on the same fruit to be similar in the encoded latent space. 

Evaluation of these methods is conducted on two datasets: a new dataset containing 5418 kiwifruit sampled across five devices and three seasons, and a previously published dataset of 4675 mangoes measured across four seasons. 

The results show that the methods improve predictive performance, especially for small labelled datasets and calibration transfer problems. This allows for the easier application of deep learning to NIR spectroscopy by reducing the requirements for labelled data, improving model generalisability across devices, and enabling model selection under data constraints.

year: 2026

title: A community psychology-based evaluation of the you good? You Good! program delivered by Jade speaks up educational trust in Aotearoa. Empowering tamariki emotional literacy, supporting mental health and encouraging well-being.

authors: Webbon, Abigail

abstract: This evaluation was completed for Jade Speaks Up (JSU) Educational Trust to provide feedback from school teachers and whānau who were involved in the You Good? You Good! (YG?YG!) program, delivered by JSU Educational Trust to schools across Aotearoa. The evaluation goal was to collate the narratives of participating school staff, whānau, and JSU Educational Trust trainers, contrasting experiences and perspectives to determine program effectiveness.
This program is for schools, management, and teachers who work with tamariki, to help teachers and students understand anxiety and develop skills and strategies to have self-agency. JSU Educational Trust developed YG?YG! in 2021, and in 2022 the YG?YG! pilot program was implemented.
This evaluation had three research objectives: 1) Identify and collate the experiences of school staff and other key stakeholders when implementing the YG?YG!, 2) Identify the strengths and barriers to inclusion for tamariki in schools where the program was delivered, and 3) Draw on the stakeholder narratives to assess the program's ability to achieve its goal: enable tamariki to be more agentic in responding to early signs of anxiety in themselves and others.
Information for this evaluation was collected from four sources: 1) Focus groups with JSU trainers, 2) Interviews with school staff, 3) Online surveys with school staff, 4) Archival supervision, and 5) Literature review. Data collected was interpreted through the use of thematic analysis, informed by Community Psychology principles, and the “Six C” framework, guided by He Awa Whiria.
Thematic analysis was completed by coding themes and categories from data sources. The findings from this empirical research engagement informed the analysis and discussion on program effectiveness. 
The findings and analysis found that forming a program design and approach informed with respect for the community’s needs and world-views can enable recognition, awareness and inclusion. A way for the program to begin to do so is to collaborate with communities.
Including researchers from a local community can enable empowerment, build social power, and create responses that promote the needs of the involved communities. The program can champion the field and those they work with by being accountable and enabling transparency.
The program can take an inclusive approach to sustaining its relationships, content relevance, and development for tamariki emotional well-being. The program can work on cultivating and empowering participants to be active partners, enabling the program to be shaped with equity in mind.
Key to program efficacy is acknowledging change and encompassing adaptation. The way to do this is to consider local health issues driven by who experience them, elevating the voice of the community.
Change and need cannot be determined without considering the voice of involved communities. By taking participants feedback, the program can create change for tamariki mental health, whilst acknowledging system and structural barriers, striving for community competence.
Importance lies within the work the program does to enable and support those affected by external factors. The program can acknowledge these challenges, and provide hope that community competence can be achieved.
To conclude, recommendations are presented for JSU Educational Trust to consider.

text: A community psychology-based evaluation of the you good? You Good! program delivered by Jade speaks up educational trust in Aotearoa. Empowering tamariki emotional literacy, supporting mental health and encouraging well-being. This evaluation was completed for Jade Speaks Up (JSU) Educational Trust to provide feedback from school teachers and whānau who were involved in the You Good? You Good! (YG?YG!) program, delivered by JSU Educational Trust to schools across Aotearoa. The evaluation goal was to collate the narratives of participating school staff, whānau, and JSU Educational Trust trainers, contrasting experiences and perspectives to determine program effectiveness.
This program is for schools, management, and teachers who work with tamariki, to help teachers and students understand anxiety and develop skills and strategies to have self-agency. JSU Educational Trust developed YG?YG! in 2021, and in 2022 the YG?YG! pilot program was implemented.
This evaluation had three research objectives: 1) Identify and collate the experiences of school staff and other key stakeholders when implementing the YG?YG!, 2) Identify the strengths and barriers to inclusion for tamariki in schools where the program was delivered, and 3) Draw on the stakeholder narratives to assess the program's ability to achieve its goal: enable tamariki to be more agentic in responding to early signs of anxiety in themselves and others.
Information for this evaluation was collected from four sources: 1) Focus groups with JSU trainers, 2) Interviews with school staff, 3) Online surveys with school staff, 4) Archival supervision, and 5) Literature review. Data collected was interpreted through the use of thematic analysis, informed by Community Psychology principles, and the “Six C” framework, guided by He Awa Whiria.
Thematic analysis was completed by coding themes and categories from data sources. The findings from this empirical research engagement informed the analysis and discussion on program effectiveness. 
The findings and analysis found that forming a program design and approach informed with respect for the community’s needs and world-views can enable recognition, awareness and inclusion. A way for the program to begin to do so is to collaborate with communities.
Including researchers from a local community can enable empowerment, build social power, and create responses that promote the needs of the involved communities. The program can champion the field and those they work with by being accountable and enabling transparency.
The program can take an inclusive approach to sustaining its relationships, content relevance, and development for tamariki emotional well-being. The program can work on cultivating and empowering participants to be active partners, enabling the program to be shaped with equity in mind.
Key to program efficacy is acknowledging change and encompassing adaptation. The way to do this is to consider local health issues driven by who experience them, elevating the voice of the community.
Change and need cannot be determined without considering the voice of involved communities. By taking participants feedback, the program can create change for tamariki mental health, whilst acknowledging system and structural barriers, striving for community competence.
Importance lies within the work the program does to enable and support those affected by external factors. The program can acknowledge these challenges, and provide hope that community competence can be achieved.
To conclude, recommendations are presented for JSU Educational Trust to consider.

year: 2024-11-14