Vascular Dynamics Aid a Coupled Neurovascular Network Learn Sparse Independent Features: A Computational Model

Cerebral vascular dynamics are generally thought to be controlled by neural activity in a unidirectional fashion. However, both computational modeling and experimental evidence point to the feedback effects of vascular dynamics on neural activity. Vascular feedback in the form of glucose and oxygen controls neuronal ATP, either directly or via the agency of astrocytes, which in turn modulates neural firing. Recently, a detailed model of the neuron-astrocyte-vessel system has shown how vasomotion can modulate neural firing. Similarly, arguing from known cerebrovascular physiology, an approach known as “hemoneural hypothesis” postulates functional modulation of neural activity by vascular feedback. To instantiate this perspective, we present a computational model in which a network of “vascular units” supplies energy to a neural network. The complex dynamics of the vascular network, modeled by a network of oscillators, turns neurons ON and OFF randomly. The informational consequence of such dynamics is explored in the context of an auto-encoder network. In the proposed model, each vascular unit supplies energy to a subset of hidden neurons of an autoencoder network, which constitutes its “projective field.” Neurons that receive adequate energy in a given trial have reduced threshold, and thus are prone to fire. Dynamics of the vascular network are governed by changes in the reconstruction error of the auto-encoder network, interpreted as the neuronal demand. Vascular feedback causes random inactivation of a subset of hidden neurons in every trial. We observe that, under conditions of desynchronized vascular dynamics, the output reconstruction error is low and the feature vectors learnt are sparse and independent. Our earlier modeling study highlighted the link between desynchronized vascular dynamics and efficient energy delivery in skeletal muscle. We now show that desynchronized vascular dynamics leads to efficient training in an auto-encoder neural network

The Paradox of Noise: An Empirical Study of Noise-Infusion Mechanisms to Improve Generalization, Stability, and Privacy in Federated Learning

In a data-centric era, concerns regarding privacy and ethical data handling grow as machine learning relies more on personal information. This empirical study investigates the privacy, generalization, and stability of deep learning models in the presence of additive noise in federated learning frameworks. Our main objective is to provide strategies to measure the generalization, stability, and privacy-preserving capabilities of these models and further improve them. To this end, five noise infusion mechanisms at varying noise levels within centralized and federated learning settings are explored. As model complexity is a key component of the generalization and stability of deep learning models during training and evaluation, a comparative analysis of three Convolutional Neural Network (CNN) architectures is provided. The paper introduces Signal-to-Noise Ratio (SNR) as a quantitative measure of the trade-off between privacy and training accuracy of noise-infused models, aiming to find the noise level that yields optimal privacy and accuracy. Moreover, the Price of Stability and Price of Anarchy are defined in the context of privacy-preserving deep learning, contributing to the systematic investigation of the noise infusion strategies to enhance privacy without compromising performance. Our research sheds light on the delicate balance between these critical factors, fostering a deeper understanding of the implications of noise-based regularization in machine learning. By leveraging noise as a tool for regularization and privacy enhancement, we aim to contribute to the development of robust, privacy-aware algorithms, ensuring that AI-driven solutions prioritize both utility and privacy

Towards reliable parameter extraction in MEMS final module testing using Bayesian inference

In micro-electro-mechanical systems (MEMS) testing high overall precision and reliability are essential. Due to the additional requirement of runtime efficiency, machine learning methods have been investigated in recent years. However, these methods are often associated with inherent challenges concerning uncertainty quantification and guarantees of reliability. The goal of this paper is therefore to present a new machine learning approach in MEMS testing based on Bayesian inference to determine whether the estimation is trustworthy. The overall predictive performance as well as the uncertainty quantification are evaluated with four methods: Bayesian neural network, mixture density network, probabilistic Bayesian neural network and BayesFlow. They are investigated under the variation in training set size, different additive noise levels, and an out-of-distribution condition, namely the variation in the damping factor of the MEMS device. Furthermore, epistemic and aleatoric uncertainties are evaluated and discussed to encourage thorough inspection of models before deployment striving for reliable and efficient parameter estimation during final module testing of MEMS devices. BayesFlow consistently outperformed the other methods in terms of the predictive performance. As the probabilistic Bayesian neural network enables the distinction between epistemic and aleatoric uncertainty, their share of the total uncertainty has been intensively studied

Online Non-linear Prediction of Financial Time Series Patterns

We consider a mechanistic non-linear machine learning approach to learning signals in financial time series data. A modularised and decoupled algorithm framework is established and is proven on daily sampled closing time-series data for JSE equity markets. The input patterns are based on input data vectors of data windows preprocessed into a sequence of daily, weekly and monthly or quarterly sampled feature measurement changes (log feature fluctuations). The data processing is split into a batch processed step where features are learnt using a Stacked AutoEncoder (SAE) via unsupervised learning, and then both batch and online supervised learning are carried out on Feedforward Neural Networks (FNNs) using these features. The FNN output is a point prediction of measured time-series feature fluctuations (log differenced data) in the future (ex-post). Weight initializations for these networks are implemented with restricted Boltzmann machine pretraining, and variance based initializations. The validity of the FNN backtest results are shown under a rigorous assessment of backtest overfitting using both Combinatorially Symmetrical Cross Validation and Probabilistic and Deflated Sharpe Ratios. Results are further used to develop a view on the phenomenology of financial markets and the value of complex historical data under unstable dynamics

Leveraging Supervoxels for Medical Image Volume Segmentation With Limited Supervision

The majority of existing methods for machine learning-based medical image segmentation are supervised models that require large amounts of fully annotated images. These types of datasets are typically not available in the medical domain and are difficult and expensive to generate. A wide-spread use of machine learning based models for medical image segmentation therefore requires the development of data-efficient algorithms that only require limited supervision. To address these challenges, this thesis presents new machine learning methodology for unsupervised lung tumor segmentation and few-shot learning based organ segmentation. When working in the limited supervision paradigm, exploiting the available information in the data is key. The methodology developed in this thesis leverages automatically generated supervoxels in various ways to exploit the structural information in the images. The work on unsupervised tumor segmentation explores the opportunity of performing clustering on a population-level in order to provide the algorithm with as much information as possible. To facilitate this population-level across-patient clustering, supervoxel representations are exploited to reduce the number of samples, and thereby the computational cost. In the work on few-shot learning-based organ segmentation, supervoxels are used to generate pseudo-labels for self-supervised training. Further, to obtain a model that is robust to the typically large and inhomogeneous background class, a novel anomaly detection-inspired classifier is proposed to ease the modelling of the background. To encourage the resulting segmentation maps to respect edges defined in the input space, a supervoxel-informed feature refinement module is proposed to refine the embedded feature vectors during inference. Finally, to improve trustworthiness, an architecture-agnostic mechanism to estimate model uncertainty in few-shot segmentation is developed. Results demonstrate that supervoxels are versatile tools for leveraging structural information in medical data when training segmentation models with limited supervision

Therapist´s facilitative interpersonal skills and persuasiveness in psychotherapy

Tese apresentada para cumprimento dos requisitos necessários à obtenção do grau de Doutor em Psicologia na área de especialização Psicologia Clínica apresentada no ISPA - Instituto Universitário no ano de 2021.A evidência empírica demonstra uma associação robusta entre as competências interpessoais do psicoterapeuta e os seus resultados clínicos. No entanto, existe um foco predominante no estudo de algumas competências interpessoais (por exemplo, empatia) em detrimento de outras. Especificamente, existe uma falta de contributos teóricos e científicos focados na persuasão do psicoterapeuta, uma competência interpessoal que engloba os comportamentos verbais e não-verbais do terapeuta que influenciam as expectativas e credibilidade do cliente quanto à intervenção psicológica. Os estudos aqui apresentados visam aumentar a base de conhecimento teórico e empírico para as competências interpessoais do terapeuta no geral, e para a persuasão terapêutica em particular. No primeiro estudo, revemos os principais contributos teóricos e literatura empírica sobre a persuasão do psicoterapeuta. Com base na investigação disponível, apresentamos um consenso sobre os principais comportamentos verbais e não-verbais do terapeuta que parecem influenciar as expectativas e credibilidade do cliente em relação à psicoterapia. Este estudo sugere que o fornecer de racionais clínicos dentro de sessão, tanto para a origem dos problemas do cliente como para a sua solução, é a tarefa persuasiva mais relevante no qual os terapeutas poderão ser treinados de modo a aumentar a eficácia clínica. Concluímos com implicações para o treino e investigação de persuasão psicoterapêutica. Destacamos a necessidade de desenvolver diretrizes de prática deliberada para persuasão clínica, e a análise de processo das competências interpessoais do terapeuta dentro de sessão. No segundo estudo, propomos diretrizes com suporte empírico para o treino de psicoterapeutas focado no fornecer de racionais clínicos convincentes. Apresentamos critérios para o treino sistemático desta competência, bem como um exemplo de implementação dessas diretrizes. Concluímos com implicações sobre como os métodos de prática deliberada poderão contribuir para o treino tradicional de psicoterapeutas. No último estudo, investigamos as competências interpessoais e persuasão terapêutica numa amostra de 18 psicoterapeutas de três modalidades clínicas e 54 sessões gravadas em vídeo. Os resultados indicam que as competências interpessoais do terapeuta são um preditor positivo significativo do envolvimento emocional e cognitivo do cliente (“experienciação”) dentro de sessão. Foi também encontrado que fornecer racionais clínicos foi um preditor negativo significativo da experienciação do cliente. Nenhuma diferença foi encontrada para as competências interpessoais do terapeuta entre diferentes modalidades, mas diferenças foram encontradas para a experienciação do cliente e o fornecer de racionais clínicos. Os contributos decorrentes destes estudos fornecem implicações para o treino de psicoterapeutas e investigação empírica futura, sugerindo próximos passos que poderão, em última instância, contribuir para o aumento da eficácia clínica de psicoterapeutas.There is robust evidence that psychotherapist’s facilitative interpersonal skills are a significant predictor of client outcomes. However, there has been a prevalent focus in the study of some interpersonal skills (e.g., therapist’s accurate empathy) to the detriment of others. Specifically, therapist’s persuasiveness, an interpersonal skill encompassing the verbal and nonverbal therapist behaviors that influence client’s treatment expectations and credibility, has lagged in theoretical, training, and research contributions. The studies presented aim at increasing the theoretical and empirical knowledge base for therapist’s interpersonal skill in general, and therapeutic persuasiveness in particular. In the first study, we reviewed the theoretical and empirical literature on psychotherapist’s persuasiveness. Based on the available research, we present a consensus on the main verbal and nonverbal therapist behaviors that might influence therapy client’s treatment expectations and credibility. Our review found that the delivery of cogent treatment rationales, both for the origin of client’s distress and tasks to alleviate said distress, is arguably the most supported persuasiveness-related task therapists can train to increase treatment outcomes. We conclude with therapy training and research implications, namely, that deliberate practice training guidelines are a necessary next step in the development of therapist’s persuasiveness, and that process analysis on therapist’s in-session interpersonal skills is warranted. The remaining studies presented here address these two issues. In the second study, we propose empirically supported guidelines for therapist training in providing cogent treatment rationales. We provide step-by-step description and criteria for systematic training, as well as a case example implementing these guidelines. We conclude with implications for how deliberate practice methods augment traditional therapist training. In the last study, we investigated therapist’s in-session interpersonal skills and persuasiveness for a sample of 18 therapist and 54 videorecorded sessions from three treatment modalities. Results indicate that therapist’s interpersonal skills are a significant positive predictor of client’s emotional and cognitive engagement (“experiencing”) in session. We also found that providing cogent treatment rationales was a significant negative predictor of client experiencing. No differences were found for therapist’s interpersonal skills across modalities, but differences were found for client experiencing and provision of treatment rationales. The novel contributions stemming from these studies provide implications for future therapist training and empirical research, thereby suggesting next steps that may ultimately aid in increasing psychotherapy training effects and client outcomes

From the Suffering (Black) Jesus to the Sacrilegious Yeezus: Representations of Christ in African-American Art and Religious Thought

Broadly definable as an interdisciplinary study of religion, music, literature, and history, this thesis analyzes the music of Kanye West and its evolution from the tradition of African-American art and religious thought. Tracing the roots of West’s rap music lyrically and thematically to its foundations in the slave songs, the blues, the literature/art of the Harlem Renaissance era, and the gangsta rap of Tupac Shakur, I explore how the catalog of his albums show a (post)modern evolution of African-American religious thought that first began in the slaves’ paradoxical re-appropriation of the hegemonic religion of their masters. Furthermore, I illustrate how West’s music evinces an evolution of the slaves’ divided religious identity and contributes to the subversions against the hegemony and oppression of white (supremacist) Christianity by African Americans throughout history. In demonstrating how the frameworks of both race and religion interact and collide in the historical battles over the metaphysical significations and color of Christ, I emphasize how each representation of Christ—whether the massa Jesus of the slave songs, the black lynched Jesus of the Harlem Renaissance, the Black Jesuz of Tupac Shakur’s gangsta rap, or the sacrilegious Yeezus/Jesus of Kanye West—reflects the contextual realities of African Americans

Dropout causes of students funded by the National Student Financial Aid Scheme in South African universities

The dropout of students funded by the National Student Financial Aid Scheme (NSFAS) is a perennial problem in many higher education institutions (HEIs) in South Africa. Despite this, little research has been conducted to investigate this phenomenon, and this study sought to address this gap by investigating the dropout of NSFAS-funded students from HEIs in Northern Gauteng. The study adopted a qualitative methodology and a phenomenological design to explore the lived experiences of students who dropped out of HEIs. Thirty-one NSFAS-funded students, three senior management officials from three HEIs and one NSFAS senior official were purposively selected to form part of the study. Semi-structured interviews, document analysis and observations were utilised as reseach instruments and interpretative phenomenological analysis (IPA) was employed to analyse data. The findings of the study established that a lack of support for students, and personal, socioeconomic, institutional and health factors contributed to the dropout of students from HEIs. It was further established that the majority of students who dropped out did so because of the inefficient operations of NSFAS and the new student-centred model. The study also found that insufficient funding, late allocation of funds, stringent NSFAS requirements, lack of communication, late payment or nonpayment of allowances contributed to students’ dropout. To address these shortfalls, the study recommends that the student-centred model should be overhauled and replaced with an integrated system including departments such as DOH, SARS, DSD and DOL to identify students who are eligible for funding and assist in the efficient administration of NSFAS. It is further recommended that funding administered by both the national and provincial government departments be centralized and administred by the NSFAS to circumvent double dipping. Finally, it is recommended that students who fall within the R0 – R350,000 per annum household income category including SASSA beneficiaries be flagged by the system to automatically qualify for funding.Educational Management and LeadershipD. Ed. (Education Management