3,976 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Graduate Catalog of Studies, 2023-2024

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    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

    Graduate Catalog of Studies, 2023-2024

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    EPSILOD: efficient parallel skeleton for generic iterative stencil computations in distributed GPUs

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    Producción CientíficaIterative stencil computations are widely used in numerical simulations. They present a high degree of parallelism, high locality and mostly-coalesced memory access patterns. Therefore, GPUs are good candidates to speed up their computa- tion. However, the development of stencil programs that can work with huge grids in distributed systems with multiple GPUs is not straightforward, since it requires solv- ing problems related to the partition of the grid across nodes and devices, and the synchronization and data movement across remote GPUs. In this work, we present EPSILOD, a high-productivity parallel programming skeleton for iterative stencil computations on distributed multi-GPUs, of the same or different vendors that sup- ports any type of n-dimensional geometric stencils of any order. It uses an abstract specification of the stencil pattern (neighbors and weights) to internally derive the data partition, synchronizations and communications. Computation is split to better overlap with communications. This paper describes the underlying architecture of EPSILOD, its main components, and presents an experimental evaluation to show the benefits of our approach, including a comparison with another state-of-the-art solution. The experimental results show that EPSILOD is faster and shows good strong and weak scalability for platforms with both homogeneous and heterogene- ous types of GPUJunta de Castilla y León, Ministerio de Economía, Industria y Competitividad, y Fondo Europeo de Desarrollo Regional (FEDER): Proyecto PCAS (TIN2017-88614-R) y Proyecto PROPHET-2 (VA226P20).Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación y “European Union NextGenerationEU/PRTR” : (MCIN/ AEI/10.13039/501100011033) - grant TED2021-130367B-I00CTE-POWER and Minotauro and the technical support provided by Barcelona Supercomputing Center (RES-IM-2021-2-0005, RES-IM-2021-3-0024, RES- IM-2022-1-0014).Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL

    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

    Low- and high-resource opinion summarization

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    Customer reviews play a vital role in the online purchasing decisions we make. The reviews express user opinions that are useful for setting realistic expectations and uncovering important details about products. However, some products receive hundreds or even thousands of reviews, making them time-consuming to read. Moreover, many reviews contain uninformative content, such as irrelevant personal experiences. Automatic summarization offers an alternative – short text summaries capturing the essential information expressed in reviews. Automatically produced summaries can reflect overall or particular opinions and be tailored to user preferences. Besides being presented on major e-commerce platforms, home assistants can also vocalize them. This approach can improve user satisfaction by assisting in making faster and better decisions. Modern summarization approaches are based on neural networks, often requiring thousands of annotated samples for training. However, human-written summaries for products are expensive to produce because annotators need to read many reviews. This has led to annotated data scarcity where only a few datasets are available. Data scarcity is the central theme of our works, and we propose a number of approaches to alleviate the problem. The thesis consists of two parts where we discuss low- and high-resource data settings. In the first part, we propose self-supervised learning methods applied to customer reviews and few-shot methods for learning from small annotated datasets. Customer reviews without summaries are available in large quantities, contain a breadth of in-domain specifics, and provide a powerful training signal. We show that reviews can be used for learning summarizers via a self-supervised objective. Further, we address two main challenges associated with learning from small annotated datasets. First, large models rapidly overfit on small datasets leading to poor generalization. Second, it is not possible to learn a wide range of in-domain specifics (e.g., product aspects and usage) from a handful of gold samples. This leads to subtle semantic mistakes in generated summaries, such as ‘great dead on arrival battery.’ We address the first challenge by explicitly modeling summary properties (e.g., content coverage and sentiment alignment). Furthermore, we leverage small modules – adapters – that are more robust to overfitting. As we show, despite their size, these modules can be used to store in-domain knowledge to reduce semantic mistakes. Lastly, we propose a simple method for learning personalized summarizers based on aspects, such as ‘price,’ ‘battery life,’ and ‘resolution.’ This task is harder to learn, and we present a few-shot method for training a query-based summarizer on small annotated datasets. In the second part, we focus on the high-resource setting and present a large dataset with summaries collected from various online resources. The dataset has more than 33,000 humanwritten summaries, where each is linked up to thousands of reviews. This, however, makes it challenging to apply an ‘expensive’ deep encoder due to memory and computational costs. To address this problem, we propose selecting small subsets of informative reviews. Only these subsets are encoded by the deep encoder and subsequently summarized. We show that the selector and summarizer can be trained end-to-end via amortized inference and policy gradient methods

    The Diffusion of Dynamic Capability in Organizations in Digitalizing Operating Environments

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    Digitalisaation myötä erilaiset teknologiat yleistyvät muuttaen organisaatioita, toimialoja ja liiketoimintaympäristöjä. Organisaatioissa tarvitaan uusia kyvykkyyksiä ja osaamista, kun niin arvontuotto ja toimintamallit kuin yhteistyön tekeminen ja päivittäiset toiminnot muuttuvat. Usein dynaamiset kyvykkyydet nähdään ensi sijassa johdon kykynä havaita organisaatioon vaikuttavia mahdollisuuksia ja uhkia, tarttua niihin ja muuttaa organisaatiota tarvittavalla tavalla. Tarve monipuolisemmalle ymmärrykselle dynaamisista kyvykkyyksistä digitalisaation kontekstissa on tunnistettu huomioiden myös muun henkilöstön tärkeä rooli organisaation muutoskyvykkyyden luomisessa. Tämän väitöskirjan tavoitteena on tuottaa uutta tietämystä siitä, kuinka dynaaminen kyvykkyys kehittyy ja levittäytyy organisaatioissa yli erilaisten työroolien. Tutkimusongelmana on, kuinka dynaaminen kyvykkyys leviää organisaatioissa, jotka toimivat digitalisoituvissa toimintaympäristöissä. Tutkimusongelmaa tarkasteltiin tulkitsevan laadullisen monitapaustutkimuksen menetelmällä kolmen case-organisaation kanssa. Case-organisaatiot edustavat tutkimuskentästä teknologian käyttäjäorganisaation, teknologian kehittäjäorganisaation sekä teknologian ja prosessien integraattoriorganisaation näkökulmia. Pääasiallinen aineiston keruumenetelmä oli laadulliset teemahaastattelut. Yhteensä tutkimuksessa toteutettiin 59 yksilöhaastattelua 36 haastateltavan kanssa. Lisäksi tutkimuksen aikana toteutettiin useita keskusteluita organisaatioiden yhteyshenkilöiden kanssa. Aineisto kerättiin ja analysoitiin vuosina 2018–2022 induktiivisesti ja abduktiivisesti laadullisella sisällönanalyysilla tulkitsevan kenttätutkimuksen ja grounded theory -lähestymistavan oppeja hyödyntäen. Tutkimuksen luotettavuuden arviointiin käytettiin laadullisen, tulkitsevan ja tapaustutkimuksen kriteereitä. Tutkimuksen keskeisenä tuloksena tuotettiin malli siitä, kuinka nykypäivän digitalisoituvissa toimintaympäristöissä dynaaminen kyvykkyys näyttäytyy monitasoisena ilmiönä siten, että operatiivinen dynaaminen kyvykkyys ja johdon dynaaminen kyvykkyys ovat erillisiä toisistaan. Johdon tason ja operatiivisen tason dynaamiset kyvykkyydet ilmenevät eri tavoin eri työrooleissa vaikuttaen näin organisaation kehitykseen vastavuoroisten johdon ja henkilöstön toimien kautta. Väitöskirjassa tuotetaan seuraavat suositukset johdolle siitä, kuinka monitasoisen dynaamisen kyvykkyyden leviämistä organisaatioissa voitaisiin tukea: (1) jatkuva ja aito sidosryhmien osallistuminen, (2) muutoksen tavoitteiden, vaikutusten, saavuttamiskeinojen ja hyötyjen selkeyden varmistaminen, (3) henkilökohtaisen työssä kehittymisen resurssien turvaaminen, (4) taustalla vaikuttavien yhteistyötä haittaavien jännitteiden käsitteleminen ja (5) ihmistenvälistä dynaamista kyvykkyyttä tukevien käytäntöjen hyödyntäminen. Teorian näkökulmasta tulokset tarjoavat lisäymmärrystä dynaamisten kyvykkyyksien vuorovaikutteisesta luonteesta johdon ja muun henkilöstön välillä. Käytännön näkökulmasta tulokset auttavat johtoa organisaation ja sen kyvykkyyksien kehittämisessä. Kiihtyvän digitalisaation ja jatkuvan muutosvaatimuksen myötä vaikuttaa ratkaisevalta, että organisaatiot kykenevät täydellä potentiaalillaan hyödyntämään kykynsä havaita mahdollisuuksia ja uhkia, tarttua niihin sekä muuntautua tarvittavalla tavalla. Tässä väitöskirjassa esitetyt tulokset tukevat osaltaan näitä pyrkimyksiä. Jatkotutkimuksena suositellaan monimenetelmällisiä lähestymistapoja, operatiivisen dynaamisen kyvykkyyden olemukseen tarkempaa pureutumista, organisaatioiden kontekstuaalisten tekijöiden kattavampaa sisällyttämistä, pitkittäisiä johdon ja henkilöstön näkökulmia huomioivia tarkasteluita sekä tutkimusta siitä, kuinka esitettyjä johdon suosituksia voidaan hyödyntää organisaatioissa käytännössä.Digitalization introduces new technologies changing organizations, industries, and operating environments. New capabilities and expertise are required, as organizations need to rethink their value offerings, operating models, and ways of collaborating and conducting day-to-day tasks. While dynamic capabilities are often viewed as managerial capacities of sensing, seizing and transforming, recently the focus on employees in creating organizational capacity for change has increased. Likewise, the need for a more nuanced understanding of the development of dynamic capabilities in digitalization has been noted. The aim of this dissertation is to better understand, how dynamic capability develops and spreads in organizations across different work roles. The research problem is how dynamic capability diffuses in organizations in digitalizing operating environments. The research problem was studied by an interpretive qualitative multiple-case study with three case organizations representing the perspectives of a technology user, technology creator, and technology and process integrator. The main data collection method was semi-structured, theme-based interviews. In total 59 individual interviews with 36 informants were conducted, and additionally several discussions were held with company representatives. The data were collected and analysed over the period of 2018–2022 by inductive and abductive approaches, qualitative thematic analysis, and drawing from the guidelines of interpretive field research and grounded theory methodology. The reliability and validity were evaluated by utilizing the criteria of qualitative, interpretive, and case-study research. As findings, a model of how dynamic capability in today’s digitalizing operating environments appears as a multilevel phenomenon comprising of operative dynamic capability and managerial dynamic capability is presented. The managerial- and operative level dynamic capabilities manifest differently in different work roles and contribute to organizational development through reciprocal actions of the management and employees. Additionally, the following managerial propositions are given on how the diffusion of dynamic capability could be supported in organizations: (1) exercising continuous and genuine stakeholder participation, (2) ensuring clear goals, implications, way to, and benefits of change, (3) securing resources for individual development at work, (4) addressing underlying tensions hindering collaboration, and (5) deploying organizational practices enabling interpersonal dynamic capability. As theoretical contributions, the findings provide new understanding on dynamic capabilities as reciprocal phenomena between the management and employees. As practical implications, the findings help management in their organizational and capability development efforts. As digitalization accelerates pace invoking requirements of continuous adaptation, it seems vital for organizations to utilize their full potential of sensing, seizing, and renewing capacities. The findings presented in this dissertation aim to support these endeavours. As future research, mixed methods approaches, closer investigations on the essence of operative dynamic capability, more comprehensive considerations on organizational contextual factors, further longitudinal study incorporating both employee and managerial views, and examinations on utilizing the presented propositions in practice in organizations are suggested

    Knowledge Hiding and Knowledge Manipulation; An Investigation from a Contexual, Relational and Dyadic Perspective

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    Given that knowledge gives firms a competitive advantage, interest in knowledge management is expanding (Bibi et al., 2021; Jasimuddin, 2006; Wang & Noe, 2010). To gain knowledge, organizations must ensure that knowledge is shared amongst their employees (Hinds et al., 2001; Wang & Noe, 2010). Although knowledge sharing has been the subject of much research (Wang & Noe, 2010), we still have more to learn about other knowledge-management behaviours, such as knowledge hiding and knowledge manipulation (Rhee & Choi, 2017). In this dissertation, I will investigate the antecedents of knowledge hiding and knowledge manipulation in three studies from a contextual, relational, and dyadic perspective. In study 1, I explore the contextual factors of the work environment and how they impact knowledge hiding and knowledge manipulation. In study 2, I explore the relational factors by investigating the mechanism that impacts work engagement, knowledge hiding and knowledge manipulation through team member exchange. In study 3, I explore knowledge hiding and knowledge manipulation from a dyadic perspective in a purely theoretical piece. In addition to theoretical contribution by extending the literature on knowledge hiding and knowledge manipulation, this research offers important implications for managers and employees on how contextual, relational, and dyadic factors can be modified to decrease knowledge hiding and knowledge manipulation

    Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data

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    Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería, Ciencia de Datos y BioinformáticaClave Programa: DBICódigo Línea: 111The root causes of disease are still poorly understood. The success of current therapies is limited because persistent diseases are frequently treated based on their symptoms rather than the underlying cause of the disease. Therefore, biomedical research is experiencing a technology-driven shift to data-driven holistic approaches to better characterize the molecular mechanisms causing disease. Using omics data as an input, emerging disciplines like network biology attempt to model the relationships between biomolecules. To this effect, gene co- expression networks arise as a promising tool for deciphering the relationships between genes in large transcriptomic datasets. However, because of their low specificity and high false positive rate, they demonstrate a limited capacity to retrieve the disrupted mechanisms that lead to disease onset, progression, and maintenance. Within the context of statistical modeling, we dove deeper into the reconstruction of gene co-expression networks with the specific goal of discovering disease-specific features directly from expression data. Using ensemble techniques, which combine the results of various metrics, we were able to more precisely capture biologically significant relationships between genes. We were able to find de novo potential disease-specific features with the help of prior biological knowledge and the development of new network inference techniques. Through our different approaches, we analyzed large gene sets across multiple samples and used gene expression as a surrogate marker for the inherent biological processes, reconstructing robust gene co-expression networks that are simple to explore. By mining disease-specific gene co-expression networks we come up with a useful framework for identifying new omics-phenotype associations from conditional expression datasets.In this sense, understanding diseases from the perspective of biological network perturbations will improve personalized medicine, impacting rational biomarker discovery, patient stratification and drug design, and ultimately leading to more targeted therapies.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e Informátic
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