1,984 research outputs found

    Personality Dysfunction Manifest in Words : Understanding Personality Pathology Using Computational Language Analysis

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    Personality disorders (PDs) are some of the most prevalent and high-risk mental health conditions, and yet remain poorly understood. Today, the development of new technologies means that there are advanced tools that can be used to improve our understanding and treatment of PD. One promising tool – indeed, the focus of this thesis – is computational language analysis. By looking at patterns in how people with personality pathology use words, it is possible to gain access into their constellation of thinking, feelings, and behaviours. To date, however, there has been little research at the intersection of verbal behaviour and personality pathology. Accordingly, the central goal of this thesis is to demonstrate how PD can be better understood through the analysis of natural language. This thesis presents three research articles, comprising four empirical studies, that each leverage computational language analysis to better understand personality pathology. Each paper focuses on a distinct core feature of PD, while incorporating language analysis methods: Paper 1 (Study 1) focuses on interpersonal dysfunction; Paper 2 (Studies 2 and 3) focuses on emotion dysregulation; and Paper 3 (Study 4) focuses on behavioural dysregulation (i.e., engagement in suicidality and deliberate self-harm). Findings from this research have generated better understanding of fundamental features of PD, including insight into characterising dimensions of social dysfunction (Paper 1), maladaptive emotion processes that may contribute to emotion dysregulation (Paper 2), and psychosocial dynamics relating to suicidality and deliberate self-harm (Paper 3) in PD. Such theoretical knowledge subsequently has important implications for clinical practice, particularly regarding the potential to inform psychological therapy. More broadly, this research highlights how language can provide implicit and unobtrusive insight into the personality and psychological processes that underlie personality pathology at a large-scale, using an individualised, naturalistic approach

    Explainable Artificial Intelligence Methods in FinTech Applications

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    The increasing amount of available data and access to high-performance computing allows companies to use complex Machine Learning (ML) models for their decision-making process, so-called ”black-box” models. These ”black-box” models typically show higher predictive accuracy than linear models on complex data sets. However, this improved predictive accuracy can only be achieved by deteriorating the explanatory power. ”Open the black box” and make the model predictions explainable is summarised under the research area of Explainable Artificial Intelligence (XAI). Using black-box models also raises practical and ethical issues, especially in critical industries such as finance. For this reason, the explainability of models is increasingly becoming a focus for regulators. Applying XAI methods to ML models makes their predictions explainable and hence, enables the application of ML models in the financial industries. The application of ML models increases predictive accuracy and supports the different stakeholders in the financial industries in their decision-making processes. This thesis consists of five chapters: a general introduction, a chapter on conclusions and future research, and three separate chapters covering the underlying papers. Chapter 1 proposes an XAI method that can be used in credit risk management, in particular, in measuring the risks associated with borrowing through peer-to-peer lending platforms. The model applies correlation networks to Shapley values and thus the model predictions are grouped according to the similarity of the underlying explanations. Chapter 2 develops an alternative XAI method based on the Lorenz Zonoid approach. The new method is statistically normalised and can therefore be used as a standard for the application of Artificial Intelligence (AI) in credit risk management. The novel ”Shapley-Lorenz”-approach can facilitate the validation of model results and supports the decision whether a model is sufficiently explained. In Chapter 3, an XAI method is applied to assess the impact of financial and non-financial factors on a firm’s ex-ante cost of capital, a measure that reflects investors’ perceptions of a firm’s risk appetite. A combination of two explanatory tools: the Shapley values and the Lorenz model selection approach, enabled the identification of the most important features and the reduction of the independent features. This allowed a substantial simplification of the model without a statistically significant decrease in predictive accuracy.The increasing amount of available data and access to high-performance computing allows companies to use complex Machine Learning (ML) models for their decision-making process, so-called ”black-box” models. These ”black-box” models typically show higher predictive accuracy than linear models on complex data sets. However, this improved predictive accuracy can only be achieved by deteriorating the explanatory power. ”Open the black box” and make the model predictions explainable is summarised under the research area of Explainable Artificial Intelligence (XAI). Using black-box models also raises practical and ethical issues, especially in critical industries such as finance. For this reason, the explainability of models is increasingly becoming a focus for regulators. Applying XAI methods to ML models makes their predictions explainable and hence, enables the application of ML models in the financial industries. The application of ML models increases predictive accuracy and supports the different stakeholders in the financial industries in their decision-making processes. This thesis consists of five chapters: a general introduction, a chapter on conclusions and future research, and three separate chapters covering the underlying papers. Chapter 1 proposes an XAI method that can be used in credit risk management, in particular, in measuring the risks associated with borrowing through peer-to-peer lending platforms. The model applies correlation networks to Shapley values and thus the model predictions are grouped according to the similarity of the underlying explanations. Chapter 2 develops an alternative XAI method based on the Lorenz Zonoid approach. The new method is statistically normalised and can therefore be used as a standard for the application of Artificial Intelligence (AI) in credit risk management. The novel ”Shapley-Lorenz”-approach can facilitate the validation of model results and supports the decision whether a model is sufficiently explained. In Chapter 3, an XAI method is applied to assess the impact of financial and non-financial factors on a firm’s ex-ante cost of capital, a measure that reflects investors’ perceptions of a firm’s risk appetite. A combination of two explanatory tools: the Shapley values and the Lorenz model selection approach, enabled the identification of the most important features and the reduction of the independent features. This allowed a substantial simplification of the model without a statistically significant decrease in predictive accuracy

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Neuroimaging investigations of cortical specialisation for different types of semantic knowledge

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    Embodied theories proposed that semantic knowledge is grounded in motor and perceptual experiences. This leads to two questions: (1) whether the neural underpinnings of perception are also necessary for semantic cognition; (2) how do biases towards different sensorimotor experiences cause brain regions to specialise for particular types of semantic information. This thesis tackles these questions in a series of neuroimaging and behavioural investigations. Regarding question 1, strong embodiment theory holds that semantic representation is reenactment of corresponding experiences, and brain regions for perception are necessary for comprehending modality-specific concepts. However, the weak embodiment view argues that reenactment may not be necessary, and areas near to perceiving regions may be sufficient to support semantic representation. In the particular case of motion concepts, lateral occipital temporal cortex (LOTC) has been long identified as an important area, but the roles of its different subregions are still uncertain. Chapter 3 examined how different parts of LOTC reacted to written descriptions of motion and static events, using multiple analysis methods. A series of anterior to posterior sub-regions were analyzed through univariate, multivariate pattern analysis (MVPA), and psychophysical interaction (PPI) analyses. MVPA revealed strongest decoding effects for motion vs. static events in the posterior parts of LOTC, including both visual motion area (V5) and posterior middle temporal gyrus (pMTG). In contrast, only the middle portion of LOTC showed increased activation for motion sentences in univariate analyses. PPI analyses showed increased functional connectivity between posterior LOTC and the multiple demand network for motion events. These findings suggest that posterior LOTC, which overlapped with the motion perception V5 region, is selectively involved in comprehending motion events, while the anterior part of LOTC contributes to general semantic processing. Regarding question 2, the hub-and-spoke theory suggests that anterior temporal lobe (ATL) acts as a hub, using inputs from modality-specific regions to construct multimodal concepts. However, some researchers propose temporal parietal cortex (TPC) as an additional hub, specialised in processing and integrating interaction and contextual information (e.g., for actions and locations). These hypotheses are summarized as the "dual-hub theory" and different aspects of this theory were investigated in in Chapters 4 and 5. Chapter 4 focuses on taxonomic and thematic relations. Taxonomic relations (or categorical relations) occur when two concepts belong to the same category (e.g., ‘dog’ and ‘wolf’ are both canines). In contrast, thematic relations (or associative relations) refer to situations that two concepts co-occur in events or scenes (e.g., ‘dog’ and ‘bone’), focusing on the interaction or association between concepts. Some studies have indicated ATL specialization for taxonomic relations and TPC specialization for thematic relations, but others have reported inconsistent or even converse results. Thus Chapter 4 first conducted an activation likelihood estimation (ALE) meta-analysis of neuroimaging studies contrasting taxonomic and thematic relations. This found that thematic relations reliably engage action and location processing regions (left pMTG and SMG), while taxonomic relations only showed consistent effects in the right occipital lobe. A primed semantic judgement task was then used to test the dual-hub theory’s prediction that taxonomic relations are heavily reliant on colour and shape knowledge, while thematic relations rely on action and location knowledge. This behavioural experiment revealed that action or location priming facilitated thematic relation processing, but colour and shape did not lead to priming effects for taxonomic relations. This indicates that thematic relations rely more on action and location knowledge, which may explain why the preferentially engage TPC, whereas taxonomic relations are not specifically linked to shape and colour features. This may explain why they did not preferentially engage left ATL. Chapter 5 concentrates on event and object concepts. Previous studies suggest ATL specialization for coding similarity of objects’ semantics, and angular gyrus (AG) specialization for sentence and event structure representation. In addition, in neuroimaging studies, event semantics are usually investigated using complex temporally extended stimuli, unlike than the single-concept stimuli used to investigate object semantics. Thus chapter 5 used representational similarity analysis (RSA), univariate analysis, and PPI analysis to explore neural activation patterns for event and object concepts presented as static images. Bilateral AGs encoded semantic similarity for event concepts, with the left AG also coding object similarity. Bilateral ATLs encoded semantic similarity for object concepts but also for events. Left ATL exhibited stronger coding for events than objects. PPI analysis revealed stronger connections between left ATL and right pMTG, and between right AG and bilateral inferior temporal gyrus (ITG) and middle occipital gyrus, for event concepts compared to object concepts. Consistent with the meta-analysis in chapter 4, the results in chapter 5 support the idea of partial specialization in AG for event semantics but do not support ATL specialization for object semantics. In fact, both the meta-analysis and chapter 5 findings suggest greater ATL involvement in coding objects' associations compared to their similarity. To conclude, the thesis provides support for the idea that perceptual brain regions are engaged in conceptual processing, in the case of motion concepts. It also provides evidence for a specialised role for TPC regions in processing thematic relations (pMTG) and event concepts (AG). There was mixed evidence for specialisation within the ATLs and this remains an important target for future research

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    UMSL Bulletin 2022-2023

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    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

    Linking Datasets on Organizations Using Half A Billion Open Collaborated Records

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    Scholars studying organizations often work with multiple datasets lacking shared unique identifiers or covariates. In such situations, researchers may turn to approximate string matching methods to combine datasets. String matching, although useful, faces fundamental challenges. Even when two strings appear similar to humans, fuzzy matching often does not work because it fails to adapt to the informativeness of the character combinations presented. Worse, many entities have multiple names that are dissimilar (e.g., "Fannie Mae" and "Federal National Mortgage Association"), a case where string matching has little hope of succeeding. This paper introduces data from a prominent employment-related networking site (LinkedIn) as a tool to address these problems. We propose interconnected approaches to leveraging the massive amount of information from LinkedIn regarding organizational name-to-name links. The first approach builds a machine learning model for predicting matches from character strings, treating the trillions of user-contributed organizational name pairs as a training corpus: this approach constructs a string matching metric that explicitly maximizes match probabilities. A second approach identifies relationships between organization names using network representations of the LinkedIn data. A third approach combines the first and second. We document substantial improvements over fuzzy matching in applications, making all methods accessible in open-source software ("LinkOrgs")

    Estimating Policy Effects in a Social Network with Independent Set Sampling

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    Evaluating the impact of policy interventions on respondents who are embedded in a social network is often challenging due to the presence of network interference within the treatment groups, as well as between treatment and non-treatment groups throughout the network. In this paper, we propose a modeling strategy that combines existing work on stochastic actor-oriented models (SAOM) with a novel network sampling method based on the identification of independent sets. By assigning respondents from an independent set to the treatment, we are able to block any spillover of the treatment and network influence, thereby allowing us to isolate the direct effect of the treatment from the indirect network-induced effects, in the immediate term. As a result, our method allows for the estimation of both the direct as well as the net effect of a chosen policy intervention, in the presence of network effects in the population. We perform a comparative simulation analysis to show that our proposed sampling technique leads to distinct direct and net effects of the policy, as well as significant network effects driven by policy-linked homophily. This study highlights the importance of network sampling techniques in improving policy evaluation studies and has the potential to help researchers and policymakers with better planning, designing, and anticipating policy responses in a networked society

    Learning and Control of Dynamical Systems

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    Despite the remarkable success of machine learning in various domains in recent years, our understanding of its fundamental limitations remains incomplete. This knowledge gap poses a grand challenge when deploying machine learning methods in critical decision-making tasks, where incorrect decisions can have catastrophic consequences. To effectively utilize these learning-based methods in such contexts, it is crucial to explicitly characterize their performance. Over the years, significant research efforts have been dedicated to learning and control of dynamical systems where the underlying dynamics are unknown or only partially known a priori, and must be inferred from collected data. However, much of these classical results have focused on asymptotic guarantees, providing limited insights into the amount of data required to achieve desired control performance while satisfying operational constraints such as safety and stability, especially in the presence of statistical noise. In this thesis, we study the statistical complexity of learning and control of unknown dynamical systems. By utilizing recent advances in statistical learning theory, high-dimensional statistics, and control theoretic tools, we aim to establish a fundamental understanding of the number of samples required to achieve desired (i) accuracy in learning the unknown dynamics, (ii) performance in the control of the underlying system, and (iii) satisfaction of the operational constraints such as safety and stability. We provide finite-sample guarantees for these objectives and propose efficient learning and control algorithms that achieve the desired performance at these statistical limits in various dynamical systems. Our investigation covers a broad range of dynamical systems, starting from fully observable linear dynamical systems to partially observable linear dynamical systems, and ultimately, nonlinear systems. We deploy our learning and control algorithms in various adaptive control tasks in real-world control systems and demonstrate their strong empirical performance along with their learning, robustness, and stability guarantees. In particular, we implement one of our proposed methods, Fourier Adaptive Learning and Control (FALCON), on an experimental aerodynamic testbed under extreme turbulent flow dynamics in a wind tunnel. The results show that FALCON achieves state-of-the-art stabilization performance and consistently outperforms conventional and other learning-based methods by at least 37%, despite using 8 times less data. The superior performance of FALCON arises from its physically and theoretically accurate modeling of the underlying nonlinear turbulent dynamics, which yields rigorous finite-sample learning and performance guarantees. These findings underscore the importance of characterizing the statistical complexity of learning and control of unknown dynamical systems.</p
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