2,037 research outputs found

    Utilization of Linguistic Markers in Differentiation of Internalizing Disorders, Suicidality, and Identity Distress

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    The adolescent period of development is associated with a significant increase in the occurrence of mental illness. In addition, death by suicide is one of the leading causes of death amongst adolescents. Identity formation is a key developmental task of adolescence, and successful navigation of this process is associated with greater well-being and resilience, while difficulties are associated with risk for mental health disorders and suicidality. Adolescents today spend enormous amounts of time on digital devices, which have become a new instrument by which they explore and confirm their identities and experiences. The study of natural language use is related to wide range of psychological phenomena, including psychopathology, and offers a tool by which we can begin to ask and answer these questions utilizing new tools that allow us to passively collect adolescents’ language use directly from their digital devices. The current study leverages a unique clinical sample of adolescents who have been followed over six months to explore the relationship between both between and within participant measures of psychopathology, suicidal thought and behaviors, and putative linguistic markers of adolescent identity formation derived from online communications in order to further understand the association between these variables using ecologically valid measures in a community sample of adolescents experiencing significant mental health challenges. The aims of the study were to (1) assess whether there are differences in how adolescents with psychopathology, suicidal ideation, and previous suicide attempts use language, (2) language differences associated with mental illness symptomology, (3) and language differences in hypothesized identity domains associated with mental illness symptomology communicated through social communication apps via text. Participants completed baseline measures of depression, suicidality, and anxiety symptoms. Participants downloaded the EARS tool onto their digital devices that passively collected text data sent through social communication applications. The results of this study indicated that there are natural language use differences between adolescents with psychopathology and those who experience suicidality, depression, and anxiety symptoms

    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

    Hidden in the Cloud : Advanced Cryptographic Techniques for Untrusted Cloud Environments

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    In the contemporary digital age, the ability to search and perform operations on encrypted data has become increasingly important. This significance is primarily due to the exponential growth of data, often referred to as the "new oil," and the corresponding rise in data privacy concerns. As more and more data is stored in the cloud, the need for robust security measures to protect this data from unauthorized access and misuse has become paramount. One of the key challenges in this context is the ability to perform meaningful operations on the data while it remains encrypted. Traditional encryption techniques, while providing a high level of security, render the data unusable for any practical purpose other than storage. This is where advanced cryptographic protocols like Symmetric Searchable Encryption (SSE), Functional Encryption (FE), Homomorphic Encryption (HE), and Hybrid Homomorphic Encryption (HHE) come into play. These protocols not only ensure the confidentiality of data but also allow computations on encrypted data, thereby offering a higher level of security and privacy. The ability to search and perform operations on encrypted data has several practical implications. For instance, it enables efficient Boolean queries on encrypted databases, which is crucial for many "big data" applications. It also allows for the execution of phrase searches, which are important for many machine learning applications, such as intelligent medical data analytics. Moreover, these capabilities are particularly relevant in the context of sensitive data, such as health records or financial information, where the privacy and security of user data are of utmost importance. Furthermore, these capabilities can help build trust in digital systems. Trust is a critical factor in the adoption and use of digital services. By ensuring the confidentiality, integrity, and availability of data, these protocols can help build user trust in cloud services. This trust, in turn, can drive the wider adoption of digital services, leading to a more inclusive digital society. However, it is important to note that while these capabilities offer significant advantages, they also present certain challenges. For instance, the computational overhead of these protocols can be substantial, making them less suitable for scenarios where efficiency is a critical requirement. Moreover, these protocols often require sophisticated key management mechanisms, which can be challenging to implement in practice. Therefore, there is a need for ongoing research to address these challenges and make these protocols more efficient and practical for real-world applications. The research publications included in this thesis offer a deep dive into the intricacies and advancements in the realm of cryptographic protocols, particularly in the context of the challenges and needs highlighted above. Publication I presents a novel approach to hybrid encryption, combining the strengths of ABE and SSE. This fusion aims to overcome the inherent limitations of both techniques, offering a more secure and efficient solution for key sharing and access control in cloud-based systems. Publication II further expands on SSE, showcasing a dynamic scheme that emphasizes forward and backward privacy, crucial for ensuring data integrity and confidentiality. Publication III and Publication IV delve into the potential of MIFE, demonstrating its applicability in real-world scenarios, such as designing encrypted private databases and additive reputation systems. These publications highlight the transformative potential of MIFE in bridging the gap between theoretical cryptographic concepts and practical applications. Lastly, Publication V underscores the significance of HE and HHE as a foundational element for secure protocols, emphasizing its potential in devices with limited computational capabilities. In essence, these publications not only validate the importance of searching and performing operations on encrypted data but also provide innovative solutions to the challenges mentioned. They collectively underscore the transformative potential of advanced cryptographic protocols in enhancing data security and privacy, paving the way for a more secure digital future

    An examination of the verbal behaviour of intergroup discrimination

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    This thesis examined relationships between psychological flexibility, psychological inflexibility, prejudicial attitudes, and dehumanization across three cross-sectional studies with an additional proposed experimental study. Psychological flexibility refers to mindful attention to the present moment, willing acceptance of private experiences, and engaging in behaviours congruent with one’s freely chosen values. Inflexibility, on the other hand, indicates a tendency to suppress unwanted thoughts and emotions, entanglement with one’s thoughts, and rigid behavioural patterns. Study 1 found limited correlations between inflexibility and sexism, racism, homonegativity, and dehumanization. Study 2 demonstrated more consistent positive associations between inflexibility and prejudice. And Study 3 controlled for right-wing authoritarianism and social dominance orientation, finding inflexibility predicted hostile sexism and racism beyond these factors. While showing some relationships, particularly with sexism and racism, psychological inflexibility did not consistently correlate with varied prejudices across studies. The proposed randomized controlled trial aims to evaluate an Acceptance and Commitment Therapy intervention to reduce sexism through enhanced psychological flexibility. Overall, findings provide mixed support for the utility of flexibility-based skills in addressing complex societal prejudices. Research should continue examining flexibility integrated with socio-cultural approaches to promote equity

    Backpropagation Beyond the Gradient

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    Automatic differentiation is a key enabler of deep learning: previously, practitioners were limited to models for which they could manually compute derivatives. Now, they can create sophisticated models with almost no restrictions and train them using first-order, i. e. gradient, information. Popular libraries like PyTorch and TensorFlow compute this gradient efficiently, automatically, and conveniently with a single line of code. Under the hood, reverse-mode automatic differentiation, or gradient backpropagation, powers the gradient computation in these libraries. Their entire design centers around gradient backpropagation. These frameworks are specialized around one specific task—computing the average gradient in a mini-batch. This specialization often complicates the extraction of other information like higher-order statistical moments of the gradient, or higher-order derivatives like the Hessian. It limits practitioners and researchers to methods that rely on the gradient. Arguably, this hampers the field from exploring the potential of higher-order information and there is evidence that focusing solely on the gradient has not lead to significant recent advances in deep learning optimization. To advance algorithmic research and inspire novel ideas, information beyond the batch-averaged gradient must be made available at the same level of computational efficiency, automation, and convenience. This thesis presents approaches to simplify experimentation with rich information beyond the gradient by making it more readily accessible. We present an implementation of these ideas as an extension to the backpropagation procedure in PyTorch. Using this newly accessible information, we demonstrate possible use cases by (i) showing how it can inform our understanding of neural network training by building a diagnostic tool, and (ii) enabling novel methods to efficiently compute and approximate curvature information. First, we extend gradient backpropagation for sequential feedforward models to Hessian backpropagation which enables computing approximate per-layer curvature. This perspective unifies recently proposed block- diagonal curvature approximations. Like gradient backpropagation, the computation of these second-order derivatives is modular, and therefore simple to automate and extend to new operations. Based on the insight that rich information beyond the gradient can be computed efficiently and at the same time, we extend the backpropagation in PyTorch with the BackPACK library. It provides efficient and convenient access to statistical moments of the gradient and approximate curvature information, often at a small overhead compared to computing just the gradient. Next, we showcase the utility of such information to better understand neural network training. We build the Cockpit library that visualizes what is happening inside the model during training through various instruments that rely on BackPACK’s statistics. We show how Cockpit provides a meaningful statistical summary report to the deep learning engineer to identify bugs in their machine learning pipeline, guide hyperparameter tuning, and study deep learning phenomena. Finally, we use BackPACK’s extended automatic differentiation functionality to develop ViViT, an approach to efficiently compute curvature information, in particular curvature noise. It uses the low-rank structure of the generalized Gauss-Newton approximation to the Hessian and addresses shortcomings in existing curvature approximations. Through monitoring curvature noise, we demonstrate how ViViT’s information helps in understanding challenges to make second-order optimization methods work in practice. This work develops new tools to experiment more easily with higher-order information in complex deep learning models. These tools have impacted works on Bayesian applications with Laplace approximations, out-of-distribution generalization, differential privacy, and the design of automatic differentia- tion systems. They constitute one important step towards developing and establishing more efficient deep learning algorithms

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Genomic architecture of selection for adaptation to challenging environments in aquaculture

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    Aquaculture, including freshwater and marine farming, has been important for global fish production during the past few decades. However, climate change presents a major risk threatening both quality and quantity of aquaculture production. The environmental stressors in aquaculture resulting from climate change, are temperature rise, salinity changes, sea level rise, acidification and changes of other chemical properties and changes of oxygen levels. Although a reasonable genetic gain can be achieved by selective breeding, this genetic response may not be enough to adapt fish species to the effects of climate change. Marker assisted selection focusing on specific genes or alleles that allow fish to cope with these changes would allow more rapid adaptation of fish to these new environments. In this thesis, I focused on three essential environmental stressors - dissolved oxygen, salinity and temperature as primarily determined in aquaculture production. The main objective is to provide insight in the genomic architecture underlying the mechanism of adaptation to challenging environments of aquaculture species under farming conditions. First, I determined candidate QTL associated with phenotypic variation during adaptation to hypoxia or normoxia. I identified overrepresented pathways that could explain the genetic regulation of hypoxia on growth. To identify fish with better hypoxia tolerance and growth under a hypoxic environment, I quantified the genetic correlations between an indicator trait for hypoxia tolerance (critical swimming performance) and growth. Moreover, the genomic architecture associated with swimming performance was demonstrated, while the effect of significant QTLs on growth was estimated. Beyond applying genome-wide association studies, I used selection signatures to identify QTLs and genes contributing to salinity tolerance. In addition, I also compared the genome of the saline-tolerant and highly productive tilapia “Sukamandi”, that was developed by the aquaculture research institute in Indonesia, to that of blue tilapia and Nile tilapia, to identify the QTLs contributing to salinity tolerance. Finally, I investigated QTLs associated with growth-related traits and organ weights at two distinct commercial Mediterranean product sites differing in temperature (farms in Spain and Greece). Overall, this thesis considerably adds to insight into how fish adapt to challenging environments, which will aid marker-assisted selection for improved resilience of aquaculture species under climate change

    Causal Inference with Differentially Private (Clustered) Outcomes

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    Estimating causal effects from randomized experiments is only feasible if participants agree to reveal their potentially sensitive responses. Of the many ways of ensuring privacy, label differential privacy is a widely used measure of an algorithm's privacy guarantee, which might encourage participants to share responses without running the risk of de-anonymization. Many differentially private mechanisms inject noise into the original data-set to achieve this privacy guarantee, which increases the variance of most statistical estimators and makes the precise measurement of causal effects difficult: there exists a fundamental privacy-variance trade-off to performing causal analyses from differentially private data. With the aim of achieving lower variance for stronger privacy guarantees, we suggest a new differential privacy mechanism, "Cluster-DP", which leverages any given cluster structure of the data while still allowing for the estimation of causal effects. We show that, depending on an intuitive measure of cluster quality, we can improve the variance loss while maintaining our privacy guarantees. We compare its performance, theoretically and empirically, to that of its unclustered version and a more extreme uniform-prior version which does not use any of the original response distribution, both of which are special cases of the "Cluster-DP" algorithm.Comment: 41 pages, 10 figure

    Exploring the Relationship between Nurse Supervisor’s Servant Leadership Behavior and Nursing Employee’s Self-Assessment of Engagement and Burnout in Nigeria

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    Burnout is a problem among workers in Nigeria, especially among nurses (Ozumba, & Alabere, 2019). This study examined whether there was a significant relationship between the employee perception of the servant leadership behaviors of the nurse supervisor and the employee’s self-rating of burnout: exhaustion and disengagement, and servant leadership behaviors of the nurse supervisor, and engagement: vigor, dedication, and absorption. Exhaustion refers to an intensive physical, affective, and cognitive strain while disengagement refers to the distancing of oneself from one’s work, and experiencing negative attitudes toward the work object, work content, or one’s work in general (Demerouti et al., 2001). Vigor is characterized by high levels of energy and mental resilience while working, the willingness to invest effort in one’s work, and persistence even in the face of difficulties. Dedication refers to being strongly involved in one\u27s work and experiencing a sense of significance, enthusiasm, inspiration, pride, and challenge. Absorption is characterized by being fully concentrated and happily engrossed in one’s work, whereby time passes quickly, and one has difficulties with detaching oneself from work (Schaufeli & Bakker, 2003). The study also examined if employees at an institution that explicitly endorses the principles of servant leadership behaviors of the supervisor would score higher in vigor, dedication, and absorption and score lower on exhaustion and disengagement. The study took place at three university teaching hospitals in Nigeria: Lagos University Teaching Hospital (172 participants), University of Nigeria Teaching Hospital Enugu (172 participants), and University of Port-Harcourt Teaching Hospital (154 participants). There were 498 participants in the study. Most of the study participants were female (463, 93.0%), while the rest were male (35, 7.0%). This reflected the national average concerning gender of the nursing population in Nigeria. The study utilized already validated psychometric instruments: Linden’s Servant Leadership Scale 7, to measure the servant leadership behaviors of the supervisor. The Utrecht Work Engagement Scale was used to measure employee work engagement, and the Oldenburg Burnout Inventory was used to measure the burnout of the employees. This study found a small significant negative correlation between the employee perception of the servant leadership scale and employee burnout: exhaustion and disengagement. It also found a small positive significant relationship between employee perception of the servant leadership behaviors of the supervisor and employee engagement: vigor, dedication, and absorption among the study participants. However, more servant leadership behaviors did not result in less burnout or more work engagement
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