Dissociation, victimisation, and their associations with voice hearing in young adults experiencing first-episode psychosis

Background: It has been proposed that voice hearing, even in the context of psychosis, is associated with high levels of dissociation - especially amongst individuals with a history of childhood abuse. This thesis studies these relationships using more rigorous research methods than have been applied in much existing observational work, and contributes original evidence for understanding the incidence of, and associations between, voice hearing, dissociation, and life adversity (particularly childhood sexual abuse: CSA) in a first-episode psychosis sample. Study 1 and 2: Evaluates current knowledge on associations between (1) voice hearing and dissociation, and (2) voice hearing and CSA using systematic, critical literature review. Both studies found strong associations between key variables, although methodological limitations in the literature preclude assumptions of causal relationships. Study 3: Employs self-report measures and a retrospective case-control design to assess voice hearing, dissociation, psychological distress, and adversity exposure within a pseudo-random sample of voice hearers (n=31) and non-voice hearing controls (n=31). CSA and dissociation were significantly higher amongst case participants. Dissociation retained a significant association with voice hearing when controlling for pre-illness adversity exposures and psychological distress. Study 4: Employs self-report measures and a cross-sectional between-groups design to assess dissociation, distress, and voice phenomenology within a pseudo-random sample of voice hearers with (n=23) and without (n=23) self-reported CSA exposure. CSA severity was associated with higher dissociation. Both groups reported similar voice characteristics, although CSA survivors perceived voices as more omnipotent. Emotional responses to voices showed strongest associations with psychological distress when controlling for dissociation and adversity exposure. Summary: Considerable heterogeneity was apparent for all measures between and within groups of voice hearers and non-voice hearers, and voice hearers with and without CSA exposure. Associations between voice hearing and dissociation remain significant when controlling for adversity exposure and the type of stress, anxiety, and depression that occurs in the more general context of psychosis. However, while dissociation increases the likelihood of voice hearing per se, psychological distress has stronger associations for experiencing voices as negative. The datasets are interpreted within the context of wider clinical/conceptual debates around the role of dissociation, distress, and adverse life events in psychosis, and are used to generate recommendations for both therapeutic intervention and future research

Federated Domain Generalization: A Survey

Machine learning typically relies on the assumption that training and testing distributions are identical and that data is centrally stored for training and testing. However, in real-world scenarios, distributions may differ significantly and data is often distributed across different devices, organizations, or edge nodes. Consequently, it is imperative to develop models that can effectively generalize to unseen distributions where data is distributed across different domains. In response to this challenge, there has been a surge of interest in federated domain generalization (FDG) in recent years. FDG combines the strengths of federated learning (FL) and domain generalization (DG) techniques to enable multiple source domains to collaboratively learn a model capable of directly generalizing to unseen domains while preserving data privacy. However, generalizing the federated model under domain shifts is a technically challenging problem that has received scant attention in the research area so far. This paper presents the first survey of recent advances in this area. Initially, we discuss the development process from traditional machine learning to domain adaptation and domain generalization, leading to FDG as well as provide the corresponding formal definition. Then, we categorize recent methodologies into four classes: federated domain alignment, data manipulation, learning strategies, and aggregation optimization, and present suitable algorithms in detail for each category. Next, we introduce commonly used datasets, applications, evaluations, and benchmarks. Finally, we conclude this survey by providing some potential research topics for the future