764 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    La traduzione specializzata all’opera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.

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    Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The “Language Toolkit – Le lingue straniere al servizio dell’internazionalizzazione dell’impresa” project, promoted by the Department of Interpreting and Translation (ForlĂŹ Campus) in collaboration with the Romagna Chamber of Commerce (ForlĂŹ-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices

    ENGINEERING HIGH-RESOLUTION EXPERIMENTAL AND COMPUTATIONAL PIPELINES TO CHARACTERIZE HUMAN GASTROINTESTINAL TISSUES IN HEALTH AND DISEASE

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    In recent decades, new high-resolution technologies have transformed how scientists study complex cellular processes and the mechanisms responsible for maintaining homeostasis and the emergence and progression of gastrointestinal (GI) disease. These advances have paved the way for the use of primary human cells in experimental models which together can mimic specific aspects of the GI tract such as compartmentalized stem-cell zones, gradients of growth factors, and shear stress from fluid flow. The work presented in this dissertation has focused on integrating high-resolution bioinformatics with novel experimental models of the GI epithelium systems to describe the complexity of human pathophysiology of the human small intestines, colon, and stomach in homeostasis and disease. Here, I used three novel microphysiological systems and developed four computational pipelines to describe comprehensive gene expression patterns of the GI epithelium in various states of health and disease. First, I used single cell RNAseq (scRNAseq) to establish the transcriptomic landscape of the entire epithelium of the small intestine and colon from three human donors, describing cell-type specific gene expression patterns in high resolution. Second, I used single cell and bulk RNAseq to model intestinal absorption of fatty acids and show that fatty acid oxidation is a critical regulator of the flux of long- and medium-chain fatty acids across the epithelium. Third, I use bulk RNAseq and a machine learning model to describe how inflammatory cytokines can regulate proliferation of intestinal stem cells in an experimental model of inflammatory hypoxia. Finally, I developed a high throughput platform that can associate phenotype to gene expression in clonal organoids, providing unprecedented resolution into the relationship between comprehensive gene expression patterns and their accompanying phenotypic effects. Through these studies, I have demonstrated how the integration of computational and experimental approaches can measurably advance our understanding of human GI physiology.Doctor of Philosoph

    Computational Approaches to Drug Profiling and Drug-Protein Interactions

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    Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a long period of stagnation in drug approvals. Due to the extreme costs associated with introducing a drug to the market, locating and understanding the reasons for clinical failure is key to future productivity. As part of this PhD, three main contributions were made in this respect. First, the web platform, LigNFam enables users to interactively explore similarity relationships between ‘drug like’ molecules and the proteins they bind. Secondly, two deep-learning-based binding site comparison tools were developed, competing with the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold relationships and has already been used in multiple projects, including integration into a virtual screening pipeline to increase the tractability of ultra-large screening experiments. Together, and with existing tools, the contributions made will aid in the understanding of drug-protein relationships, particularly in the fields of off-target prediction and drug repurposing, helping to design better drugs faster

    Next Generation Business Ecosystems: Engineering Decentralized Markets, Self-Sovereign Identities and Tokenization

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    Digital transformation research increasingly shifts from studying information systems within organizations towards adopting an ecosystem perspective, where multiple actors co-create value. While digital platforms have become a ubiquitous phenomenon in consumer-facing industries, organizations remain cautious about fully embracing the ecosystem concept and sharing data with external partners. Concerns about the market power of platform orchestrators and ongoing discussions on privacy, individual empowerment, and digital sovereignty further complicate the widespread adoption of business ecosystems, particularly in the European Union. In this context, technological innovations in Web3, including blockchain and other distributed ledger technologies, have emerged as potential catalysts for disrupting centralized gatekeepers and enabling a strategic shift towards user-centric, privacy-oriented next-generation business ecosystems. However, existing research efforts focus on decentralizing interactions through distributed network topologies and open protocols lack theoretical convergence, resulting in a fragmented and complex landscape that inadequately addresses the challenges organizations face when transitioning to an ecosystem strategy that harnesses the potential of disintermediation. To address these gaps and successfully engineer next-generation business ecosystems, a comprehensive approach is needed that encompasses the technical design, economic models, and socio-technical dynamics. This dissertation aims to contribute to this endeavor by exploring the implications of Web3 technologies on digital innovation and transformation paths. Drawing on a combination of qualitative and quantitative research, it makes three overarching contributions: First, a conceptual perspective on \u27tokenization\u27 in markets clarifies its ambiguity and provides a unified understanding of the role in ecosystems. This perspective includes frameworks on: (a) technological; (b) economic; and (c) governance aspects of tokenization. Second, a design perspective on \u27decentralized marketplaces\u27 highlights the need for an integrated understanding of micro-structures, business structures, and IT infrastructures in blockchain-enabled marketplaces. This perspective includes: (a) an explorative literature review on design factors; (b) case studies and insights from practitioners to develop requirements and design principles; and (c) a design science project with an interface design prototype of blockchain-enabled marketplaces. Third, an economic perspective on \u27self-sovereign identities\u27 (SSI) as micro-structural elements of decentralized markets. This perspective includes: (a) value creation mechanisms and business aspects of strategic alliances governing SSI ecosystems; (b) business model characteristics adopted by organizations leveraging SSI; and (c) business model archetypes and a framework for SSI ecosystem engineering efforts. The dissertation concludes by discussing limitations as well as outlining potential avenues for future research. These include, amongst others, exploring the challenges of ecosystem bootstrapping in the absence of intermediaries, examining the make-or-join decision in ecosystem emergence, addressing the multidimensional complexity of Web3-enabled ecosystems, investigating incentive mechanisms for inter-organizational collaboration, understanding the role of trust in decentralized environments, and exploring varying degrees of decentralization with potential transition pathways

    Outcome Measurement in Functional Neurological Symptom Disorder

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    Outcome measurement in Functional Neurological Symptom Disorders (FNSDs) is particularly complex. Pressing questions include what kind of measure is more accurate or meaningful, or how to achieve standardisation in a clinically heterogenous group where subjective and objective observations of the same construct may deviate. This project aimed to build on the limited knowledge of measuring outcomes in FNSDs and attempts to address one of its inherent complexities; where clinical aspects of the disorder confound the usual prioritisation of "objective" over "subjective" (or patient-rated) measures. This PhD comprised a literature review and three research studies, each using different measures to assess the current status and (potential) outcomes in FNSD patients. A narrative description of systematically identified literature on stress, distress, and arousal measures in FNSD presents an overarching profile of the relationships between subjective and objective study measures. Eighteen studies (12 functional seizures, six other FNSD) capturing 396 FNSD patients were included. Eleven reported no correlation between subjective and objective measures. Only four studies reported significant correlations (r's=-0.74-0.59, p's <0.05). The small number of studies and diverse methodologies limit the conclusions of this review. However, the review's findings underscore the importance of validating outcome measures in patients with FNSD, carefully selecting the most appropriate measures for the research objectives, and possibly combining different measures optimally to triangulate a patient's current state, level of functioning or disability. Study One used factor analysis and Rasch modelling to investigate the psychometric properties of a novel FNSD-specific resource-based measure developed as an outcome measure for psychological therapies (The sElf-efficacy, assertiveness, Social support, self-awareness and helpful thinking (EASE) questionnaire). A 4-factor model identified self-efficacy (SE), self-awareness/assertiveness (SA), social support (SS) and interpersonal illness burden (IIB) as relevant domains. Each latent scale fits the Rasch model after refinement of the category responses and removing two items. With further improvement, the EASE-F has the potential to reliably measure self-reported SE, SA, SS, and IIB constructs which were found to be meaningful to patients with FNSD. This can identify patients with strengths and deficits in these constructs, allowing therapists to individualise interventions. Recommendations for refinement of future instrument versions, using the measure in clinical practice, and research in FNSDs are discussed. Study Two sought to understand the urgent and emergency care (UEC) service usage patterns among FNSD patients. Retrospective FNSD patient data from 2013 to 2016 UEC records (including NHS 111 calls, ambulance services, A&E visits, and acute admissions) were used to compare FNSD UEC usage rates with those of the general population and to model rates before and after psychotherapy. FNSD patients displayed 23 to 60 times higher UEC usage than the general population. Emergency service usage rates showed a significant reduction in level (rate level change = -0.90--0.70, p's <0.05) immediately after psychotherapy. While this study was uncontrolled, and a causal relationship between psychotherapy and reduced UEC service use cannot be proven by its design, the decrease in pre-treatment service usage among FNSD patients mirrors treatment-related improvements in health status and functioning previously documented using self-reported outcome measures. Further research is warranted to elucidate features of emergency care service use by patients with FNSD, assess interventions' cost-effectiveness, and help to optimise limited health care resource allocation. Study Three utilised a delay discounting and emotional bias task to assess if these measures could indicate the health state of FNSD patients and to compare findings in patients with those in healthy controls. This online-based study collected data on cognitive-affective functioning, decision-making and, indirectly, emotion regulation, alongside self-reported health data and indicators of mood while completing the tasks. Delay discounting (DD) was steeper in patients with FNSD, indicating a preference for less subjectively valuable immediate rewards. Patients displayed priming and interference effects for angry and happy facial expressions, which differed from the interference effects observed in healthy controls [F(1,76) = 3.5, p = 0.037, η2p = 0.084]. Modest associations (r's =0.26-0.33, p's <0.05) were found between the DD estimates and self-reported generalised anxiety, but not current feelings of anxiety in FNSD. There were no correlations with indices for negative affective priming or interference. These measures did not show predictive ability for self-reported difficulty regulating emotions, anxiety, depression or coping in FNSD. However, the fact that the DD task and self-reported constructs failed to correlate does not invalidate this objective test. The findings underscore the importance of using a combined approach to outcome measurement. This project highlights the importance of a more comprehensive understanding of outcomes and measures that capture clinically valid and meaningful health information. Given that subjective and objective measures capture different aspects of health state or function, a combination of measurement approaches will likely produce the most comprehensive understanding of patients' current state or treatment outcome. Because of the attentional, emotional, and perceptual alterations implicated in FNSD and the variable external representations of these, the difference between objective and subjective measures represents an interesting observation in its own right. The size of the discrepancy between subjective and objective measures may provide additional valuable insights into the underlying pathology. Nonetheless, there is still a need for standardisation and consistency in FNSD outcome measurement and reporting. Several important factors, such as the timeframe of measures, the influence of confounding factors, and the variety of presentation of any aspect of the disorder (e.g., physiological, cognitive, social, or behavioural presentations of arousal/stress), will need to be considered when designing and interpreting measurements for research or clinical analysis of the patient group

    Generalising weighted model counting

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    Given a formula in propositional or (finite-domain) first-order logic and some non-negative weights, weighted model counting (WMC) is a function problem that asks to compute the sum of the weights of the models of the formula. Originally used as a flexible way of performing probabilistic inference on graphical models, WMC has found many applications across artificial intelligence (AI), machine learning, and other domains. Areas of AI that rely on WMC include explainable AI, neural-symbolic AI, probabilistic programming, and statistical relational AI. WMC also has applications in bioinformatics, data mining, natural language processing, prognostics, and robotics. In this work, we are interested in revisiting the foundations of WMC and considering generalisations of some of the key definitions in the interest of conceptual clarity and practical efficiency. We begin by developing a measure-theoretic perspective on WMC, which suggests a new and more general way of defining the weights of an instance. This new representation can be as succinct as standard WMC but can also expand as needed to represent less-structured probability distributions. We demonstrate the performance benefits of the new format by developing a novel WMC encoding for Bayesian networks. We then show how existing WMC encodings for Bayesian networks can be transformed into this more general format and what conditions ensure that the transformation is correct (i.e., preserves the answer). Combining the strengths of the more flexible representation with the tricks used in existing encodings yields further efficiency improvements in Bayesian network probabilistic inference. Next, we turn our attention to the first-order setting. Here, we argue that the capabilities of practical model counting algorithms are severely limited by their inability to perform arbitrary recursive computations. To enable arbitrary recursion, we relax the restrictions that typically accompany domain recursion and generalise circuits (used to express a solution to a model counting problem) to graphs that are allowed to have cycles. These improvements enable us to find efficient solutions to counting fundamental structures such as injections and bijections that were previously unsolvable by any available algorithm. The second strand of this work is concerned with synthetic data generation. Testing algorithms across a wide range of problem instances is crucial to ensure the validity of any claim about one algorithm’s superiority over another. However, benchmarks are often limited and fail to reveal differences among the algorithms. First, we show how random instances of probabilistic logic programs (that typically use WMC algorithms for inference) can be generated using constraint programming. We also introduce a new constraint to control the independence structure of the underlying probability distribution and provide a combinatorial argument for the correctness of the constraint model. This model allows us to, for the first time, experimentally investigate inference algorithms on more than just a handful of instances. Second, we introduce a random model for WMC instances with a parameter that influences primal treewidth—the parameter most commonly used to characterise the difficulty of an instance. We show that the easy-hard-easy pattern with respect to clause density is different for algorithms based on dynamic programming and algebraic decision diagrams than for all other solvers. We also demonstrate that all WMC algorithms scale exponentially with respect to primal treewidth, although at differing rates

    Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology

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    The great behavioral heterogeneity observed between individuals with the same psychiatric disorder and even within one individual over time complicates both clinical practice and biomedical research. However, modern technologies are an exciting opportunity to improve behavioral characterization. Existing psychiatry methods that are qualitative or unscalable, such as patient surveys or clinical interviews, can now be collected at a greater capacity and analyzed to produce new quantitative measures. Furthermore, recent capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometer, open avenues of novel questioning that were previously entirely unrealistic. Their temporally dense nature enables a cohesive study of real-time neural and behavioral signals. To develop comprehensive neurobiological models of psychiatric disease, it will be critical to first develop strong methods for behavioral quantification. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge -- one that will necessitate new data processing tools, new machine learning techniques, and ultimately a shift in how interdisciplinary work is conducted. In my thesis, I detail research projects that take different perspectives on digital psychiatry, subsequently tying ideas together with a concluding discussion on the future of the field. I also provide software infrastructure where relevant, with extensive documentation. Major contributions include scientific arguments and proof of concept results for daily free-form audio journals as an underappreciated psychiatry research datatype, as well as novel stability theorems and pilot empirical success for a proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop
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