345 research outputs found

    In the name of status:Adolescent harmful social behavior as strategic self-regulation

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    Adolescent harmful social behavior is behavior that benefits the person that exhibits it but could harm (the interest of) another. The traditional perspective on adolescent harmful social behavior is that it is what happens when something goes wrong in the developmental process, classifying such behaviors as a self-regulation failure. Yet, theories drawing from evolution theory underscore the adaptiveness of harmful social behavior and argue that such behavior is enacted as a means to gain important resources for survival and reproduction; gaining a position of power This dissertation aims to examine whether adolescent harmful social behavior can indeed be strategic self-regulation, and formulated two questions: Can adolescent harmful social behavior be seen as strategic attempts to obtain social status? And how can we incorporate this status-pursuit perspective more into current interventions that aim to reduce harmful social behavior? To answer these questions, I conducted a meta-review, a meta-analysis, two experimental studies, and an individual participant data meta-analysis (IPDMA). Meta-review findings of this dissertation underscore that when engaging in particular behavior leads to the acquisition of important peer-status-related goals, harmful social behavior may also develop from adequate self-regulation. Empirical findings indicate that the prospect of status affordances can motivate adolescents to engage in harmful social behavior and that descriptive and injunctive peer norms can convey such status prospects effectively. IPDMA findings illustrate that we can reach more adolescent cooperation and collectivism than we are currently promoting via interventions. In this dissertation, I argue we can do this in two ways. One, teach adolescents how they can achieve status by behaving prosocially. And two, change peer norms that reward harmful social behavior with popularity

    A comparative analysis of good enterprise data management practices:insights from literature and artificial intelligence perspectives for business efficiency and effectiveness

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    Abstract. This thesis presents a comparative analysis of enterprise data management practices based on literature and artificial intelligence (AI) perspectives, focusing on their impact on data quality, business efficiency, and effectiveness. It employs a systematic research methodology comprising of a literature review, an AI-based examination of current practices using ChatGPT, and a comparative analysis of findings. The study highlights the importance of robust data governance, high data quality, data integration, and security, alongside the transformative potential of AI. The limitations revolve around the primarily qualitative nature of the study and potential restrictions in the generalizability of the findings. However, the thesis offers valuable insights and recommendations for enterprises to optimize their data management strategies, underscoring the enhancement potential of AI in traditional practices. The research contributes to scientific discourse in information systems, data science, and business management

    Identification and interpretation of pathogenic variants following Next Generation Sequencing (NGS) analysis in human Mendelian disorders

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    Durante il programma di dottorato, l'attenzione è stata rivolta al supporto del laboratorio di diagnostica nell'implementazione della convalida o nella scoperta di varianti insolite. Questo è di massima importanza per comprendere i meccanismi eziopatogenetici molecolari, ma anche per offrire la migliore consulenza alle famiglie. Di conseguenza, sono stati portati a termine diversi progetti come segue: I) un caso enigmatico di una femmina con un disturbo granulomatoso cronico legato all'X (CGD) con una presunta variante di splicing: (NM_000397:ex9:c.1151+2T>C) nel gene CYBB. II) una nuova presunta variante di splicing emizigote nel gene MAGT1 (NM_032121:c.627+2T>C) situato sul cromosoma X. III) Analisi delle variazioni del numero di copie (CNV) per aumentare il tasso di diagnosi di un pannello NGS per gli errori congeniti dell'immunità, poiché è ben noto che le CNV (inserzioni o eliminazioni di dimensioni comprese tra 2 e 50 megabasi) rappresentano circa il 12% delle anomalie genetiche. Identificare questa ampia variazione è ancora problematico, specialmente con le piattaforme Ion Torrent che utilizziamo per la diagnostica, pertanto abbiamo eseguito un'approfondita analisi in silico utilizzando diversi nuovi software. IV) Otto famiglie con una storia personale o familiare di cancro sono state testate per un pannello di geni multipli osono state sottoposte al sequenziamento completo dell’esoma. Sono state trovate otto varianti patogeniche e verificate tramite sequenziamento di Sanger o MLPA e PCR in Real-time. L'uso di NGS e la rilevazione di CNV hanno migliorato la diagnosi nei pazienti affetti da cancro. Alcune delle famiglie iraniane che soddisfacevano i criteri di Amsterdam sono state incluse in programmi di sorveglianza indipendentemente dal loro stato di portatori di mutazioni prima dei test genetici, mentre dopo la rivelazione del portatore solo i portatori sono stati inclusi, migliorando la conformità e riducendo i costi di gestione.During the PhD program the focus was to support diagnostic lab implementing validation or discover of unusual variants. This is of utmost importance to understand molecular etiopathogenic mechanisms, but also in order to offer the best counselling to families. Thus, different projects were accomplished as follows: I) a puzzling patient of a female with X-linked chronic granulomatous disorder (CGD) with a putative splicing variant: (NM_000397:ex9:c.1151+2T>C) in the CYBB gene. II) a novel hemizygous putative splicing mutation in the MAGT1 gene (NM_032121:c.627+2T>C) located on the X-chromosome. III) Analysis of Copy Number Variations (CNVs) to increase the diagnostic rate of a NGS panel for Inborn errors of immunity, as is well known that CNVs (indels between 2 and 50 megabases), account for roughly 12% of genetic abnormalities. Identifying this large variation is still problematic, especially with the Ion Torrent platforms we use for diagnostic, thus we performed an extensive in silico analysis using multiple new softwares. IV) Eight families possessing a familial or personal history of cancer underwent multigene panel testing or whole exome sequencing. Eight pathogenic variants were found and verified through Sanger sequencing or MLPA and real-time PCR. The use of NGS and CNV detection improved the diagnostic yields in cancer patients. Some of Iranian families who met Amsterdam criteria were enrolled in surveillance programs irrespective of their mutation carrier status before genetic testing, while after carrier detection disclosures only carriers were enrolled improving compliance and decreasing the managing cost

    The LOFAR Two-Metre Sky Survey (LoTSS):VI. Optical identifications for the second data release

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    The second data release of the LOFAR Two-Metre Sky Survey (LoTSS) covers 27% of the northern sky, with a total area of 5,700\sim 5,700 deg2^2. The high angular resolution of LOFAR with Dutch baselines (6 arcsec) allows us to carry out optical identifications of a large fraction of the detected radio sources without further radio followup; however, the process is made more challenging by the many extended radio sources found in LOFAR images as a result of its excellent sensitivity to extended structure. In this paper we present source associations and identifications for sources in the second data release based on optical and near-infrared data, using a combination of a likelihood-ratio cross-match method developed for our first data release, our citizen science project Radio Galaxy Zoo: LOFAR, and new approaches to algorithmic optical identification, together with extensive visual inspection by astronomers. We also present spectroscopic or photometric redshifts for a large fraction of the optical identifications. In total 4,116,934 radio sources lie in the area with good optical data, of which 85% have an optical or infrared identification and 58% have a good redshift estimate. We demonstrate the quality of the dataset by comparing it with earlier optically identified radio surveys. This is by far the largest ever optically identified radio catalogue, and will permit robust statistical studies of star-forming and radio-loud active galaxies

    Coping with low data availability for social media crisis message categorisation

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    During crisis situations, social media allows people to quickly share information, including messages requesting help. This can be valuable to emergency responders, who need to categorise and prioritise these messages based on the type of assistance being requested. However, the high volume of messages makes it difficult to filter and prioritise them without the use of computational techniques. Fully supervised filtering techniques for crisis message categorisation typically require a large amount of annotated training data, but this can be difficult to obtain during an ongoing crisis and is expensive in terms of time and labour to create. This thesis focuses on addressing the challenge of low data availability when categorising crisis messages for emergency response. It first presents domain adaptation as a solution for this problem, which involves learning a categorisation model from annotated data from past crisis events (source domain) and adapting it to categorise messages from an ongoing crisis event (target domain). In many-to-many adaptation, where the model is trained on multiple past events and adapted to multiple ongoing events, a multi-task learning approach is proposed using pre-trained language models. This approach outperforms baselines and an ensemble approach further improves performance..

    Data ethics : building trust : how digital technologies can serve humanity

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    Data is the magic word of the 21st century. As oil in the 20th century and electricity in the 19th century: For citizens, data means support in daily life in almost all activities, from watch to laptop, from kitchen to car, from mobile phone to politics. For business and politics, data means power, dominance, winning the race. Data can be used for good and bad, for services and hacking, for medicine and arms race. How can we build trust in this complex and ambiguous data world? How can digital technologies serve humanity? The 45 articles in this book represent a broad range of ethical reflections and recommendations in eight sections: a) Values, Trust and Law, b) AI, Robots and Humans, c) Health and Neuroscience, d) Religions for Digital Justice, e) Farming, Business, Finance, f) Security, War, Peace, g) Data Governance, Geopolitics, h) Media, Education, Communication. The authors and institutions come from all continents. The book serves as reading material for teachers, students, policy makers, politicians, business, hospitals, NGOs and religious organisations alike. It is an invitation for dialogue, debate and building trust! The book is a continuation of the volume “Cyber Ethics 4.0” published in 2018 by the same editors

    Data-Driven Exploration of Coarse-Grained Equations: Harnessing Machine Learning

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    In scientific research, understanding and modeling physical systems often involves working with complex equations called Partial Differential Equations (PDEs). These equations are essential for describing the relationships between variables and their derivatives, allowing us to analyze a wide range of phenomena, from fluid dynamics to quantum mechanics. Traditionally, the discovery of PDEs relied on mathematical derivations and expert knowledge. However, the advent of data-driven approaches and machine learning (ML) techniques has transformed this process. By harnessing ML techniques and data analysis methods, data-driven approaches have revolutionized the task of uncovering complex equations that describe physical systems. The primary goal in this thesis is to develop methodologies that can automatically extract simplified equations by training models using available data. ML algorithms have the ability to learn underlying patterns and relationships within the data, making it possible to extract simplified equations that capture the essential behavior of the system. This study considers three distinct learning categories: black-box, gray-box, and white-box learning. The initial phase of the research focuses on black-box learning, where no prior information about the equations is available. Three different neural network architectures are explored: multi-layer perceptron (MLP), convolutional neural network (CNN), and a hybrid architecture combining CNN and long short-term memory (CNN-LSTM). These neural networks are applied to uncover the non-linear equations of motion associated with phase-field models, which include both non-conserved and conserved order parameters. The second architecture explored in this study addresses explicit equation discovery in gray-box learning scenarios, where a portion of the equation is unknown. The framework employs eXtended Physics-Informed Neural Networks (X-PINNs) and incorporates domain decomposition in space to uncover a segment of the widely-known Allen-Cahn equation. Specifically, the Laplacian part of the equation is assumed to be known, while the objective is to discover the non-linear component of the equation. Moreover, symbolic regression techniques are applied to deduce the precise mathematical expression for the unknown segment of the equation. Furthermore, the final part of the thesis focuses on white-box learning, aiming to uncover equations that offer a detailed understanding of the studied system. Specifically, a coarse parametric ordinary differential equation (ODE) is introduced to accurately capture the spreading radius behavior of Calcium-magnesium-aluminosilicate (CMAS) droplets. Through the utilization of the Physics-Informed Neural Network (PINN) framework, the parameters of this ODE are determined, facilitating precise estimation. The architecture is employed to discover the unknown parameters of the equation, assuming that all terms of the ODE are known. This approach significantly improves our comprehension of the spreading dynamics associated with CMAS droplets

    Each book its own Babel:Conceptual unity and disunity in early modern natural philosophy

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    Natural philosophy changed quickly during the early modern period (1600-1800). Aristotelian philosophy was combated by Cartesian mechanicism, which was soon itself ousted by the Newtonian school. The development of new ideas within a scientific discipline is partially an issue of doing empirical research, in order to exclude positions and progress the field. However, it is also an issue of developing new concepts and a fitting language, in order to be able to express all these new positions being investigated. This second development however also implies that the differences between thinkers might grow too large - the languages in which they express their philosophy can become too different for them to have a meaningful discussion. In this dissertation I investigate, using algorithms that extract the meaning of words from texts, a few hundred texts from these three different school. I do this in order to see how they differ from each other conceptually, how the meaning of words can travel through lines of influence from author to author and how guarding the boundaries of a school and guarding the language they use, relate

    Parallel and Flow-Based High Quality Hypergraph Partitioning

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    Balanced hypergraph partitioning is a classic NP-hard optimization problem that is a fundamental tool in such diverse disciplines as VLSI circuit design, route planning, sharding distributed databases, optimizing communication volume in parallel computing, and accelerating the simulation of quantum circuits. Given a hypergraph and an integer kk, the task is to divide the vertices into kk disjoint blocks with bounded size, while minimizing an objective function on the hyperedges that span multiple blocks. In this dissertation we consider the most commonly used objective, the connectivity metric, where we aim to minimize the number of different blocks connected by each hyperedge. The most successful heuristic for balanced partitioning is the multilevel approach, which consists of three phases. In the coarsening phase, vertex clusters are contracted to obtain a sequence of structurally similar but successively smaller hypergraphs. Once sufficiently small, an initial partition is computed. Lastly, the contractions are successively undone in reverse order, and an iterative improvement algorithm is employed to refine the projected partition on each level. An important aspect in designing practical heuristics for optimization problems is the trade-off between solution quality and running time. The appropriate trade-off depends on the specific application, the size of the data sets, and the computational resources available to solve the problem. Existing algorithms are either slow, sequential and offer high solution quality, or are simple, fast, easy to parallelize, and offer low quality. While this trade-off cannot be avoided entirely, our goal is to close the gaps as much as possible. We achieve this by improving the state of the art in all non-trivial areas of the trade-off landscape with only a few techniques, but employed in two different ways. Furthermore, most research on parallelization has focused on distributed memory, which neglects the greater flexibility of shared-memory algorithms and the wide availability of commodity multi-core machines. In this thesis, we therefore design and revisit fundamental techniques for each phase of the multilevel approach, and develop highly efficient shared-memory parallel implementations thereof. We consider two iterative improvement algorithms, one based on the Fiduccia-Mattheyses (FM) heuristic, and one based on label propagation. For these, we propose a variety of techniques to improve the accuracy of gains when moving vertices in parallel, as well as low-level algorithmic improvements. For coarsening, we present a parallel variant of greedy agglomerative clustering with a novel method to resolve cluster join conflicts on-the-fly. Combined with a preprocessing phase for coarsening based on community detection, a portfolio of from-scratch partitioning algorithms, as well as recursive partitioning with work-stealing, we obtain our first parallel multilevel framework. It is the fastest partitioner known, and achieves medium-high quality, beating all parallel partitioners, and is close to the highest quality sequential partitioner. Our second contribution is a parallelization of an n-level approach, where only one vertex is contracted and uncontracted on each level. This extreme approach aims at high solution quality via very fine-grained, localized refinement, but seems inherently sequential. We devise an asynchronous n-level coarsening scheme based on a hierarchical decomposition of the contractions, as well as a batch-synchronous uncoarsening, and later fully asynchronous uncoarsening. In addition, we adapt our refinement algorithms, and also use the preprocessing and portfolio. This scheme is highly scalable, and achieves the same quality as the highest quality sequential partitioner (which is based on the same components), but is of course slower than our first framework due to fine-grained uncoarsening. The last ingredient for high quality is an iterative improvement algorithm based on maximum flows. In the sequential setting, we first improve an existing idea by solving incremental maximum flow problems, which leads to smaller cuts and is faster due to engineering efforts. Subsequently, we parallelize the maximum flow algorithm and schedule refinements in parallel. Beyond the strive for highest quality, we present a deterministically parallel partitioning framework. We develop deterministic versions of the preprocessing, coarsening, and label propagation refinement. Experimentally, we demonstrate that the penalties for determinism in terms of partition quality and running time are very small. All of our claims are validated through extensive experiments, comparing our algorithms with state-of-the-art solvers on large and diverse benchmark sets. To foster further research, we make our contributions available in our open-source framework Mt-KaHyPar. While it seems inevitable, that with ever increasing problem sizes, we must transition to distributed memory algorithms, the study of shared-memory techniques is not in vain. With the multilevel approach, even the inherently slow techniques have a role to play in fast systems, as they can be employed to boost quality on coarse levels at little expense. Similarly, techniques for shared-memory parallelism are important, both as soon as a coarse graph fits into memory, and as local building blocks in the distributed algorithm

    Development and implementation of in silico molecule fragmentation algorithms for the cheminformatics analysis of natural product spaces

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    Computational methodologies extracting specific substructures like functional groups or molecular scaffolds from input molecules can be grouped under the term “in silico molecule fragmentation”. They can be used to investigate what specifically characterises a heterogeneous compound class, like pharmaceuticals or Natural Products (NP) and in which aspects they are similar or dissimilar. The aim is to determine what specifically characterises NP structures to transfer patterns favourable for bioactivity to drug development. As part of this thesis, the first algorithmic approach to in silico deglycosylation, the removal of glycosidic moieties for the study of aglycones, was developed with the Sugar Removal Utility (SRU) (Publication A). The SRU has also proven useful for investigating NP glycoside space. It was applied to one of the largest open NP databases, COCONUT (COlleCtion of Open Natural prodUcTs), for this purpose (Publication B). A contribution was made to the Chemistry Development Kit (CDK) by developing the open Scaffold Generator Java library (Publication C). Scaffold Generator can extract different scaffold types and dissect them into smaller parent scaffolds following the scaffold tree or scaffold network approach. Publication D describes the OngLai algorithm, the first automated method to identify homologous series in input datasets, group the member structures of each group, and extract their common core. To support the development of new fragmentation algorithms, the open Java rich client graphical user interface application MORTAR (MOlecule fRagmenTAtion fRamework) was developed as part of this thesis (Publication E). MORTAR allows users to quickly execute the steps of importing a structural dataset, applying a fragmentation algorithm, and visually inspecting the results in different ways. All software developed as part of this thesis is freely and openly available (see https://github.com/JonasSchaub)
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