3,158 research outputs found

    Graduate Catalog of Studies, 2023-2024

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    Proceedings of the 10th International congress on architectural technology (ICAT 2024): architectural technology transformation.

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    The profession of architectural technology is influential in the transformation of the built environment regionally, nationally, and internationally. The congress provides a platform for industry, educators, researchers, and the next generation of built environment students and professionals to showcase where their influence is transforming the built environment through novel ideas, businesses, leadership, innovation, digital transformation, research and development, and sustainable forward-thinking technological and construction assembly design

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Redefining Disproportionate Arrest Rates: An Exploratory Quasi-Experiment that Reassesses the Role of Skin Tone

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    The New York Times reported that Black Lives Matter was the third most-read subject of 2020. These articles brought to the forefront the question of disparity in arrest rates for darker-skinned people. Questioning arrest disparity is understandable because virtually everything known about disproportionate arrest rates has been a guess, and virtually all prior research on disproportionate arrest rates is questionable because of improper benchmarking (the denominator effect). Current research has highlighted the need to switch from demographic data to skin tone data and start over on disproportionate arrest rate research; therefore, this study explored the relationship between skin tone and disproportionate arrest rates. This study also sought to determine which of the three theories surrounding disproportionate arrests is most predictive of disproportionate rates. The current theories are that disproportionate arrests increase as skin tone gets darker (stereotype threat theory), disproportionate rates are different for Black and Brown people (self-categorization theory), or disproportionate rates apply equally across all darker skin colors (social dominance theory). This study used a quantitative exploratory quasi-experimental design using linear spline regression to analyze arrest rates in Alachua County, Florida, before and after the county’s mandate to reduce arrests as much as possible during the COVID-19 pandemic to protect the prison population. The study was exploratory as no previous study has used skin tone analysis to examine arrest disparity. The findings of this study redefines the understanding of the existence and nature of disparities in arrest rates and offer a solid foundation for additional studies about the relationship between disproportionate arrest rates and skin color

    Novel 129Xe Magnetic Resonance Imaging and Spectroscopy Measurements of Pulmonary Gas-Exchange

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    Gas-exchange is the primary function of the lungs and involves removing carbon dioxide from the body and exchanging it within the alveoli for inhaled oxygen. Several different pulmonary, cardiac and cardiovascular abnormalities have negative effects on pulmonary gas-exchange. Unfortunately, clinical tests do not always pinpoint the problem; sensitive and specific measurements are needed to probe the individual components participating in gas-exchange for a better understanding of pathophysiology, disease progression and response to therapy. In vivo Xenon-129 gas-exchange magnetic resonance imaging (129Xe gas-exchange MRI) has the potential to overcome these challenges. When participants inhale hyperpolarized 129Xe gas, it has different MR spectral properties as a gas, as it diffuses through the alveolar membrane and as it binds to red-blood-cells. 129Xe MR spectroscopy and imaging provides a way to tease out the different anatomic components of gas-exchange simultaneously and provides spatial information about where abnormalities may occur. In this thesis, I developed and applied 129Xe MR spectroscopy and imaging to measure gas-exchange in the lungs alongside other clinical and imaging measurements. I measured 129Xe gas-exchange in asymptomatic congenital heart disease and in prospective, controlled studies of long-COVID. I also developed mathematical tools to model 129Xe MR signals during acquisition and reconstruction. The insights gained from my work underscore the potential for 129Xe gas-exchange MRI biomarkers towards a better understanding of cardiopulmonary disease. My work also provides a way to generate a deeper imaging and physiologic understanding of gas-exchange in vivo in healthy participants and patients with chronic lung and heart disease

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    Leveraging Self-Supervised Vision Transformers for Neural Transfer Function Design

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    In volume rendering, transfer functions are used to classify structures of interest, and to assign optical properties such as color and opacity. They are commonly defined as 1D or 2D functions that map simple features to these optical properties. As the process of designing a transfer function is typically tedious and unintuitive, several approaches have been proposed for their interactive specification. In this paper, we present a novel method to define transfer functions for volume rendering by leveraging the feature extraction capabilities of self-supervised pre-trained vision transformers. To design a transfer function, users simply select the structures of interest in a slice viewer, and our method automatically selects similar structures based on the high-level features extracted by the neural network. Contrary to previous learning-based transfer function approaches, our method does not require training of models and allows for quick inference, enabling an interactive exploration of the volume data. Our approach reduces the amount of necessary annotations by interactively informing the user about the current classification, so they can focus on annotating the structures of interest that still require annotation. In practice, this allows users to design transfer functions within seconds, instead of minutes. We compare our method to existing learning-based approaches in terms of annotation and compute time, as well as with respect to segmentation accuracy. Our accompanying video showcases the interactivity and effectiveness of our method

    Bildebehandling og Autonomi

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    Abstrakt. Denne bachelor oppgaven omhandler utvikling og implementering av programvare for en undervanns robot (ROV), som tar i bruk blide behandling og data syn for å utføre autonome oppgaver. Dette gjøres som en en del av et tverrfaglig prosjekt i student organisasjonen UiS Subsea. ROV-en er bygd med mål om å delta i MATE ROV World Championship, som holdes i Colorado, USA, den 20-24. juni 2023. Denne bachelor oppgaven beskriver hvordan kamera-strømmer mottas på topside systemet, fra ROV-en, hvordan bilde behandling blir brukt til å løse autonome oppgaver, hvordan programmet implementeres i et brukergrensesnitt (GUI) og hvordan kjøre kommandoer sendes ned til ROV-en. Et modulært system har blitt laget, som mottar flere kamera strømmer, utfører autonome oppgaver og sender styre kommandoer. Programmet er blitt implementert inn i en GUI og testet på land. Tydelige definerte bildebehandlings oppgaver er gitt ut av MATE. Alle oppgavene er løst, utenom 3D modellering. Programmet er testet på land og oppfører seg som ønsket. Derimot, er ikke programmene testet i vann. Grunnen til dette er at ROV-en ikke var ferdigstilt i tide for vår gruppe å teste programmene våre. Testing og forbedring av programmet vill fortsette i forberedelser til MATE. Grunnet leveringsfristen for bachelor oppgaven, blir ikke dette inkludert i denne rapporten. Se vår GitHub repository for koden: https://github.com/UiS-Subsea/Bachelor_Bildebehandling Se GUI gruppens GitHub repository for implementeringen av vår kode inn i deres kode: https://github.com/UiS-Subsea/Bachelor_GUIAbstract. This bachelor’s thesis is about creating and implementing a software program on an underwater robot (ROV), that utilizes image processing and computer vision to perform autonomous tasks. This is done as part of an interdisciplinary project in the student organization UiS Subsea. The ROV is built with the purpose of competing in the MATE ROV World Championship, which is held in Colorado, USA, on the 20-24th of June 2023. This thesis describes how camera feeds are received on the topside system, from the ROV, how image processing is utilized to solve autonomous tasks, the implementation of the program into a graphical user interface (GUI), and the sending of driving commands down to the ROV. Additionally, an attempt was made to use computer vision to create a 3D model of a coral head. A modular program has been made, receiving multiple camera feeds, performing autonomous tasks, and sending driving commands. This program was implemented into a GUI, and tested on land. Clearly defined image-processing tasks were given by MATE. All of them were solved except for 3D modeling. The program was tested on land and behaved as intended. However, the programs were not tested in water. This was due to the ROV not being finished in time for our group to test our programs. Testing and improving the program will continue in preparation for MATE. Due to the submission date for the bachelor’s thesis, this will not be included in this paper. See our GitHub repository for the code: https://github.com/UiS-Subsea/Bachelor_Bildebehandling See the GUI group’s repository for our code implemented into their code: https://github.com/UiS-Subsea/Bachelor_GU

    Modern meat: the next generation of meat from cells

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    Modern Meat is the first textbook on cultivated meat, with contributions from over 100 experts within the cultivated meat community. The Sections of Modern Meat comprise 5 broad categories of cultivated meat: Context, Impact, Science, Society, and World. The 19 chapters of Modern Meat, spread across these 5 sections, provide detailed entries on cultivated meat. They extensively tour a range of topics including the impact of cultivated meat on humans and animals, the bioprocess of cultivated meat production, how cultivated meat may become a food option in Space and on Mars, and how cultivated meat may impact the economy, culture, and tradition of Asia

    Enabling Neural Radiance Fields (NeRF) for Large-scale Aerial Images -- A Multi-tiling Approach and the Geometry Assessment of NeRF

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    Neural Radiance Fields (NeRF) offer the potential to benefit 3D reconstruction tasks, including aerial photogrammetry. However, the scalability and accuracy of the inferred geometry are not well-documented for large-scale aerial assets,since such datasets usually result in very high memory consumption and slow convergence.. In this paper, we aim to scale the NeRF on large-scael aerial datasets and provide a thorough geometry assessment of NeRF. Specifically, we introduce a location-specific sampling technique as well as a multi-camera tiling (MCT) strategy to reduce memory consumption during image loading for RAM, representation training for GPU memory, and increase the convergence rate within tiles. MCT decomposes a large-frame image into multiple tiled images with different camera models, allowing these small-frame images to be fed into the training process as needed for specific locations without a loss of accuracy. We implement our method on a representative approach, Mip-NeRF, and compare its geometry performance with threephotgrammetric MVS pipelines on two typical aerial datasets against LiDAR reference data. Both qualitative and quantitative results suggest that the proposed NeRF approach produces better completeness and object details than traditional approaches, although as of now, it still falls short in terms of accuracy.Comment: 9 Figur
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