9,334 research outputs found

    UMSL Bulletin 2023-2024

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

    Splenic nerve bundle stimulation in acute and chronic inflammation

    Get PDF
    Splenic neurovascular bundle stimulation holds potential to treat acute and chronic inflammatory conditions. In the first part of the thesis, the available literature on the interactions between the immune system and nervous system in the intestine is summarized. Then, it is shown that a specialized T-cell, that can produce the neurotransmitter acetylcholine, resides in the gut an plays a dual role in the development of experimental colitis in mice. Furthermore, electrical splenic neurovascular bundle stimulation ameliorated the outcomes of colitis in mice and reversed transcriptomic changes in the gut that were induced by colitis. The second part of the thesis focused on the translation of splenic neurovascular bundle stimulation to the human situation. It is shown that there are significant changes between murine and human innervation of the spleen. Using computed tomography (CT) images the course and the characteristics of the splenic artery were described. These data were used to develop a cuff electrode that could be used for electrical stimulation of the splenic neurovascular bundle in humans. Finally, it was demonstrated that splenic neurovascular bundle stimulation in humans was safe and feasible in a pilot study with patients that underwent esophagectomy

    Communicating a Pandemic

    Get PDF
    This edited volume compares experiences of how the Covid-19 pandemic was communicated in the Nordic countries – Denmark, Finland, Iceland, Norway, and Sweden. The Nordic countries are often discussed in terms of similarities concerning an extensive welfare system, economic policies, media systems, and high levels of trust in societal actors. However, in the wake of a global pandemic, the countries’ coping strategies varied, creating certain question marks on the existence of a “Nordic model”. The chapters give a broad overview of crisis communication in the Nordic countries during the first year of the Covid-19 pandemic by combining organisational and societal theoretical perspectives and encompassing crisis response from governments, public health authorities, lobbyists, corporations, news media, and citizens. The results show several similarities, such as political and governmental responses highlighting solidarity and the need for exceptional measures, as expressed in press conferences, social media posts, information campaigns, and speeches. The media coverage relied on experts and was mainly informative, with few critical investigations during the initial phases. Moreover, surveys and interviews show the importance of news media for citizens’ coping strategies, but also that citizens mostly trusted both politicians and health authorities during the crisis. This book is of interest to all who are looking to understand societal crisis management on a comprehensive level. The volume contains chapters from leading experts from all the Nordic countries and is edited by a team with complementary expertise on crisis communication, political communication, and journalism, consisting of Bengt Johansson, Øyvind Ihlen, Jenny Lindholm, and Mark Blach-Ørsten. Publishe

    Modular lifelong machine learning

    Get PDF
    Deep learning has drastically improved the state-of-the-art in many important fields, including computer vision and natural language processing (LeCun et al., 2015). However, it is expensive to train a deep neural network on a machine learning problem. The overall training cost further increases when one wants to solve additional problems. Lifelong machine learning (LML) develops algorithms that aim to efficiently learn to solve a sequence of problems, which become available one at a time. New problems are solved with less resources by transferring previously learned knowledge. At the same time, an LML algorithm needs to retain good performance on all encountered problems, thus avoiding catastrophic forgetting. Current approaches do not possess all the desired properties of an LML algorithm. First, they primarily focus on preventing catastrophic forgetting (Diaz-Rodriguez et al., 2018; Delange et al., 2021). As a result, they neglect some knowledge transfer properties. Furthermore, they assume that all problems in a sequence share the same input space. Finally, scaling these methods to a large sequence of problems remains a challenge. Modular approaches to deep learning decompose a deep neural network into sub-networks, referred to as modules. Each module can then be trained to perform an atomic transformation, specialised in processing a distinct subset of inputs. This modular approach to storing knowledge makes it easy to only reuse the subset of modules which are useful for the task at hand. This thesis introduces a line of research which demonstrates the merits of a modular approach to lifelong machine learning, and its ability to address the aforementioned shortcomings of other methods. Compared to previous work, we show that a modular approach can be used to achieve more LML properties than previously demonstrated. Furthermore, we develop tools which allow modular LML algorithms to scale in order to retain said properties on longer sequences of problems. First, we introduce HOUDINI, a neurosymbolic framework for modular LML. HOUDINI represents modular deep neural networks as functional programs and accumulates a library of pre-trained modules over a sequence of problems. Given a new problem, we use program synthesis to select a suitable neural architecture, as well as a high-performing combination of pre-trained and new modules. We show that our approach has most of the properties desired from an LML algorithm. Notably, it can perform forward transfer, avoid negative transfer and prevent catastrophic forgetting, even across problems with disparate input domains and problems which require different neural architectures. Second, we produce a modular LML algorithm which retains the properties of HOUDINI but can also scale to longer sequences of problems. To this end, we fix the choice of a neural architecture and introduce a probabilistic search framework, PICLE, for searching through different module combinations. To apply PICLE, we introduce two probabilistic models over neural modules which allows us to efficiently identify promising module combinations. Third, we phrase the search over module combinations in modular LML as black-box optimisation, which allows one to make use of methods from the setting of hyperparameter optimisation (HPO). We then develop a new HPO method which marries a multi-fidelity approach with model-based optimisation. We demonstrate that this leads to improvement in anytime performance in the HPO setting and discuss how this can in turn be used to augment modular LML methods. Overall, this thesis identifies a number of important LML properties, which have not all been attained in past methods, and presents an LML algorithm which can achieve all of them, apart from backward transfer

    Developing International Mindedness through the Arts in the International Baccalaureate (IB) Diploma Programme (DP): An International Survey Design Conducted across all Continents

    Get PDF
    One distinct purpose of international education is to develop greater international understanding and intercultural competences. For the International Baccalaureate, this translates into students developing international mindedness throughout its programmes and courses. However, international mindedness is not measured and the impact of the programmes on the development of international mindedness remains mainly anecdotal. Furthermore, in the Diploma Programme, the choice of Arts courses is optional and the value of an Arts education, or specifically the value of taking a Diploma Programme Arts course in developing international mindedness, is equally unclear. This study investigated the development of international mindedness in students who opted for a Diploma Programme Arts course versus those who did not. The study followed a repeated measures, comparative and mixed-methods research design using a survey tool for data collection. The survey consisted of a quantitative section based on existing surveys and a qualitative section with six open-ended questions. The quantitative data showed an increase in intercultural knowledge and behaviours, while no change in attitudes, and a decrease in values was identified for both student groups, Diploma Programme Arts and Non-Arts-students. Furthermore, there was an increase in intercultural communication skills particularly in Diploma Programme Arts-students. Qualitative data analysis revealed a spectrum of categories of responses. The qualitative data also identified themes in addition to those identified in International Baccalaureate documentation and literature. Recommendations include for the International Baccalaureate Organization to integrate some of the emerging themes in their documentations, for example themes relating to adaptability and interconnectedness, which may also provide an interesting focus for curriculum design. Furthermore, curriculum and programme design should place a greater focus on the development of attitudes and values in the Diploma Programme and a reconsideration of the optionality of the Arts in this context

    Complex refractive index from scattering measurements for acoustically levitated single particles

    Get PDF
    A method for deriving the complex refractive index of a mm-sized single particle in a specific wavelength using laboratory measurements is presented. Laboratory measurements were done using the 4π scatterometer, which measures Mueller matrix elements of a particle suspended in air using acoustic levitation as a function of scattering angle. To obtain the complex refractive index of the particle, measurements were compared to simulations from a newly developed SIRIS4 Fixed Orientation (SIRIS4 FO) geometric optics simulation. The 4π scatterometer is a unique instrument which measures Mueller matrix elements from a particle using linear polarizers and a detector rotating about the particle on a rotational stage. The scatterometer uses an acoustic levitator as a sample holder which provides nondestructive measurements and full orientation control of the sample. To compare the measurement results to simulations, SIRIS4 single-particle geometric optics code was modified to handle particles in a fixed orientation. The original code is able to calculate the Mueller matrix elements for a given 3D model, but averages the results over the orientation of the particle. The modified SIRIS4 FO calculates the Mueller matrix elements over the full solid angle as functions of the two scattering angles, which give the direction of observation of the scattered wave compared to the direction of the incident wave. A 3D model of the shape of the measured particle was constructed using X-ray microtomography, and was translated to SIRIS4 FO. The complex refractive index was obtained with a nonlinear least squares analysis by minimizing the sum of squared residuals between the measurements and simulations with varying refractive index values. Finally, confidence regions were constrained for the results, by estimating the computed residuals between simulations and measurements as the random errors in the nonlinear model

    Marine Data Fusion for Analyzing Spatio-Temporal Ocean Region Connectivity

    Get PDF
    This thesis develops methods to automate and objectify the connectivity analysis between ocean regions. Existing methods for connectivity analysis often rely on manual integration of expert knowledge, which renders the processing of large amounts of data tedious. This thesis presents a new framework for Data Fusion that provides several approaches for automation and objectification of the entire analysis process. It identifies different complexities of connectivity analysis and shows how the Data Fusion framework can be applied and adapted to them. The framework is used in this thesis to analyze geo-referenced trajectories of fish larvae in the western Mediterranean Sea, to trace the spreading pathways of newly formed water in the subpolar North Atlantic based on their hydrographic properties, and to gauge their temporal change. These examples introduce a new, and highly relevant field of application for the established Data Science methods that were used and innovatively combined in the framework. New directions for further development of these methods are opened up which go beyond optimization of existing methods. The Marine Science, more precisely Physical Oceanography, benefits from the new possibilities to analyze large amounts of data quickly and objectively for its exact research questions. This thesis is a foray into the new field of Marine Data Science. It practically and theoretically explores the possibilities of combining Data Science and the Marine Sciences advantageously for both sides. The example of automating and objectifying connectivity analysis between marine regions in this thesis shows the added value of combining Data Science and Marine Science. This thesis also presents initial insights and ideas on how researchers from both disciplines can position themselves to thrive as Marine Data Scientists and simultaneously advance our understanding of the ocean

    Tradition and Innovation in Construction Project Management

    Get PDF
    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings
    corecore