10,610 research outputs found
Machine Learning Approaches for the Prioritisation of Cardiovascular Disease Genes Following Genome- wide Association Study
Genome-wide association studies (GWAS) have revealed thousands of genetic loci, establishing itself as a valuable method for unravelling the complex biology of many diseases. As GWAS has grown in size and improved in study design to detect effects, identifying real causal signals, disentangling from other highly correlated markers associated by linkage disequilibrium (LD) remains challenging. This has severely limited GWAS findings and brought the method’s value into question. Although thousands of disease susceptibility loci have been reported, causal variants and genes at these loci remain elusive. Post-GWAS analysis aims to dissect the heterogeneity of variant and gene signals. In recent years, machine learning (ML) models have been developed for post-GWAS prioritisation. ML models have ranged from using logistic regression to more complex ensemble models such as random forests and gradient boosting, as well as deep learning models (i.e., neural networks). When combined with functional validation, these methods have shown important translational insights, providing a strong evidence-based approach to direct post-GWAS research. However, ML approaches are in their infancy across biological applications, and as they continue to evolve an evaluation of their robustness for GWAS prioritisation is needed. Here, I investigate the landscape of ML across: selected models, input features, bias risk, and output model performance, with a focus on building a prioritisation framework that is applied to blood pressure GWAS results and tested on re-application to blood lipid traits
Machine learning and mixed reality for smart aviation: applications and challenges
The aviation industry is a dynamic and ever-evolving sector. As technology advances and becomes more sophisticated, the aviation industry must keep up with the changing trends. While some airlines have made investments in machine learning and mixed reality technologies, the vast majority of regional airlines continue to rely on inefficient strategies and lack digital applications. This paper investigates the state-of-the-art applications that integrate machine learning and mixed reality into the aviation industry. Smart aerospace engineering design, manufacturing, testing, and services are being explored to increase operator productivity. Autonomous systems, self-service systems, and data visualization systems are being researched to enhance passenger experience. This paper investigate safety, environmental, technological, cost, security, capacity, and regulatory challenges of smart aviation, as well as potential solutions to ensure future quality, reliability, and efficiency
Southern Adventist University Undergraduate Catalog 2022-2023
Southern Adventist University\u27s undergraduate catalog for the academic year 2022-2023.https://knowledge.e.southern.edu/undergrad_catalog/1121/thumbnail.jp
Screen Correspondence: Mapping Interchangeable Elements between UIs
Understanding user interface (UI) functionality is a useful yet challenging
task for both machines and people. In this paper, we investigate a machine
learning approach for screen correspondence, which allows reasoning about UIs
by mapping their elements onto previously encountered examples with known
functionality and properties. We describe and implement a model that
incorporates element semantics, appearance, and text to support correspondence
computation without requiring any labeled examples. Through a comprehensive
performance evaluation, we show that our approach improves upon baselines by
incorporating multi-modal properties of UIs. Finally, we show three example
applications where screen correspondence facilitates better UI understanding
for humans and machines: (i) instructional overlay generation, (ii) semantic UI
element search, and (iii) automated interface testing
Deep Multimodality Image-Guided System for Assisting Neurosurgery
Intrakranielle Hirntumoren gehören zu den zehn häufigsten bösartigen Krebsarten und sind für eine erhebliche Morbidität und Mortalität verantwortlich. Die größte histologische Kategorie der primären Hirntumoren sind die Gliome, die ein äußerst heterogenes Erschei-nungsbild aufweisen und radiologisch schwer von anderen Hirnläsionen zu unterscheiden sind. Die Neurochirurgie ist meist die Standardbehandlung für neu diagnostizierte Gliom-Patienten und kann von einer Strahlentherapie und einer adjuvanten Temozolomid-Chemotherapie gefolgt werden.
Die Hirntumorchirurgie steht jedoch vor großen Herausforderungen, wenn es darum geht, eine maximale Tumorentfernung zu erreichen und gleichzeitig postoperative neurologische Defizite zu vermeiden. Zwei dieser neurochirurgischen Herausforderungen werden im Folgenden vorgestellt. Erstens ist die manuelle Abgrenzung des Glioms einschließlich seiner Unterregionen aufgrund seines infiltrativen Charakters und des Vorhandenseins einer heterogenen Kontrastverstärkung schwierig. Zweitens verformt das Gehirn seine Form ̶ die so genannte "Hirnverschiebung" ̶ als Reaktion auf chirurgische Manipulationen, Schwellungen durch osmotische Medikamente und Anästhesie, was den Nutzen präopera-tiver Bilddaten für die Steuerung des Eingriffs einschränkt.
Bildgesteuerte Systeme bieten Ärzten einen unschätzbaren Einblick in anatomische oder pathologische Ziele auf der Grundlage moderner Bildgebungsmodalitäten wie Magnetreso-nanztomographie (MRT) und Ultraschall (US). Bei den bildgesteuerten Instrumenten handelt es sich hauptsächlich um computergestützte Systeme, die mit Hilfe von Computer-Vision-Methoden die Durchführung perioperativer chirurgischer Eingriffe erleichtern. Die Chirurgen müssen jedoch immer noch den Operationsplan aus präoperativen Bildern gedanklich mit Echtzeitinformationen zusammenführen, während sie die chirurgischen Instrumente im Körper manipulieren und die Zielerreichung überwachen. Daher war die Notwendigkeit einer Bildführung während neurochirurgischer Eingriffe schon immer ein wichtiges Anliegen der Ärzte.
Ziel dieser Forschungsarbeit ist die Entwicklung eines neuartigen Systems für die peri-operative bildgeführte Neurochirurgie (IGN), nämlich DeepIGN, mit dem die erwarteten Ergebnisse der Hirntumorchirurgie erzielt werden können, wodurch die Gesamtüberle-bensrate maximiert und die postoperative neurologische Morbidität minimiert wird. Im Rahmen dieser Arbeit werden zunächst neuartige Methoden für die Kernbestandteile des DeepIGN-Systems der Hirntumor-Segmentierung im MRT und der multimodalen präope-rativen MRT zur intraoperativen US-Bildregistrierung (iUS) unter Verwendung der jüngs-ten Entwicklungen im Deep Learning vorgeschlagen. Anschließend wird die Ergebnisvor-hersage der verwendeten Deep-Learning-Netze weiter interpretiert und untersucht, indem für den Menschen verständliche, erklärbare Karten erstellt werden. Schließlich wurden Open-Source-Pakete entwickelt und in weithin anerkannte Software integriert, die für die Integration von Informationen aus Tracking-Systemen, die Bildvisualisierung und -fusion sowie die Anzeige von Echtzeit-Updates der Instrumente in Bezug auf den Patientenbe-reich zuständig ist.
Die Komponenten von DeepIGN wurden im Labor validiert und in einem simulierten Operationssaal evaluiert. Für das Segmentierungsmodul erreichte DeepSeg, ein generisches entkoppeltes Deep-Learning-Framework für die automatische Abgrenzung von Gliomen in der MRT des Gehirns, eine Genauigkeit von 0,84 in Bezug auf den Würfelkoeffizienten für das Bruttotumorvolumen. Leistungsverbesserungen wurden bei der Anwendung fort-schrittlicher Deep-Learning-Ansätze wie 3D-Faltungen über alle Schichten, regionenbasier-tes Training, fliegende Datenerweiterungstechniken und Ensemble-Methoden beobachtet.
Um Hirnverschiebungen zu kompensieren, wird ein automatisierter, schneller und genauer deformierbarer Ansatz, iRegNet, für die Registrierung präoperativer MRT zu iUS-Volumen als Teil des multimodalen Registrierungsmoduls vorgeschlagen. Es wurden umfangreiche Experimente mit zwei Multi-Location-Datenbanken durchgeführt: BITE und RESECT. Zwei erfahrene Neurochirurgen führten eine zusätzliche qualitative Validierung dieser Studie durch, indem sie MRT-iUS-Paare vor und nach der deformierbaren Registrierung überlagerten. Die experimentellen Ergebnisse zeigen, dass das vorgeschlagene iRegNet schnell ist und die besten Genauigkeiten erreicht. Darüber hinaus kann das vorgeschlagene iRegNet selbst bei nicht trainierten Bildern konkurrenzfähige Ergebnisse liefern, was seine Allgemeingültigkeit unter Beweis stellt und daher für die intraoperative neurochirurgische Führung von Nutzen sein kann.
Für das Modul "Erklärbarkeit" wird das NeuroXAI-Framework vorgeschlagen, um das Vertrauen medizinischer Experten in die Anwendung von KI-Techniken und tiefen neuro-nalen Netzen zu erhöhen. Die NeuroXAI umfasst sieben Erklärungsmethoden, die Visuali-sierungskarten bereitstellen, um tiefe Lernmodelle transparent zu machen. Die experimen-tellen Ergebnisse zeigen, dass der vorgeschlagene XAI-Rahmen eine gute Leistung bei der Extraktion lokaler und globaler Kontexte sowie bei der Erstellung erklärbarer Salienzkar-ten erzielt, um die Vorhersage des tiefen Netzwerks zu verstehen. Darüber hinaus werden Visualisierungskarten erstellt, um den Informationsfluss in den internen Schichten des Encoder-Decoder-Netzwerks zu erkennen und den Beitrag der MRI-Modalitäten zur end-gültigen Vorhersage zu verstehen. Der Erklärungsprozess könnte medizinischen Fachleu-ten zusätzliche Informationen über die Ergebnisse der Tumorsegmentierung liefern und somit helfen zu verstehen, wie das Deep-Learning-Modell MRT-Daten erfolgreich verar-beiten kann.
Außerdem wurde ein interaktives neurochirurgisches Display für die Eingriffsführung entwickelt, das die verfügbare kommerzielle Hardware wie iUS-Navigationsgeräte und Instrumentenverfolgungssysteme unterstützt. Das klinische Umfeld und die technischen Anforderungen des integrierten multimodalen DeepIGN-Systems wurden mit der Fähigkeit zur Integration von (1) präoperativen MRT-Daten und zugehörigen 3D-Volumenrekonstruktionen, (2) Echtzeit-iUS-Daten und (3) positioneller Instrumentenver-folgung geschaffen. Die Genauigkeit dieses Systems wurde anhand eines benutzerdefi-nierten Agar-Phantom-Modells getestet, und sein Einsatz in einem vorklinischen Operati-onssaal wurde simuliert. Die Ergebnisse der klinischen Simulation bestätigten, dass die Montage des Systems einfach ist, in einer klinisch akzeptablen Zeit von 15 Minuten durchgeführt werden kann und mit einer klinisch akzeptablen Genauigkeit erfolgt.
In dieser Arbeit wurde ein multimodales IGN-System entwickelt, das die jüngsten Fort-schritte im Bereich des Deep Learning nutzt, um Neurochirurgen präzise zu führen und prä- und intraoperative Patientenbilddaten sowie interventionelle Geräte in das chirurgi-sche Verfahren einzubeziehen. DeepIGN wurde als Open-Source-Forschungssoftware entwickelt, um die Forschung auf diesem Gebiet zu beschleunigen, die gemeinsame Nut-zung durch mehrere Forschungsgruppen zu erleichtern und eine kontinuierliche Weiter-entwicklung durch die Gemeinschaft zu ermöglichen. Die experimentellen Ergebnisse sind sehr vielversprechend für die Anwendung von Deep-Learning-Modellen zur Unterstützung interventioneller Verfahren - ein entscheidender Schritt zur Verbesserung der chirurgi-schen Behandlung von Hirntumoren und der entsprechenden langfristigen postoperativen Ergebnisse
Harmonious management for sustainable development
The article shows that the concepts of harmonious and evolutionary management have significant heuristic potential in the development of institutional engineering, design and management of complex self-organizing systems, as well as contribute to sustainable economic development. The purpose of the article is to identify the mechanisms of conservatism and liberalism synthesis through the tools of harmonious and evolutionary management for productive sustainable economic development. Methodology: An interdisciplinary and systemic methodology is used, the logical and semiotic nature of economic genes and the genotype of the economic system determining characteristics, its orientation and content of the stability of its development are revealed. Results and novelty: basic genotypic concepts and their frames are identified, which determine the motives of economic growth and strategic directed development, mechanisms for implementing the synthesis of economic conservatism and liberalism, the principles of superposition, complementarity, interaction of individual and collective, parts and the whole, management of the economic genotype of the system with its structure − genotypic concepts forming the processes of harmonization, and its sustainable development
Modeling and design of heterogeneous hierarchical bioinspired spider web structures using generative deep learning and additive manufacturing
Spider webs are incredible biological structures, comprising thin but strong
silk filament and arranged into complex hierarchical architectures with
striking mechanical properties (e.g., lightweight but high strength, achieving
diverse mechanical responses). While simple 2D orb webs can easily be mimicked,
the modeling and synthesis of 3D-based web structures remain challenging,
partly due to the rich set of design features. Here we provide a detailed
analysis of the heterogenous graph structures of spider webs, and use deep
learning as a way to model and then synthesize artificial, bio-inspired 3D web
structures. The generative AI models are conditioned based on key geometric
parameters (including average edge length, number of nodes, average node
degree, and others). To identify graph construction principles, we use
inductive representation sampling of large experimentally determined spider web
graphs, to yield a dataset that is used to train three conditional generative
models: 1) An analog diffusion model inspired by nonequilibrium thermodynamics,
with sparse neighbor representation, 2) a discrete diffusion model with full
neighbor representation, and 3) an autoregressive transformer architecture with
full neighbor representation. All three models are scalable, produce complex,
de novo bio-inspired spider web mimics, and successfully construct graphs that
meet the design objectives. We further propose algorithm that assembles web
samples produced by the generative models into larger-scale structures based on
a series of geometric design targets, including helical and parametric shapes,
mimicking, and extending natural design principles towards integration with
diverging engineering objectives. Several webs are manufactured using 3D
printing and tested to assess mechanical properties
Bi-directional Training for Composed Image Retrieval via Text Prompt Learning
Composed image retrieval searches for a target image based on a multi-modal
user query comprised of a reference image and modification text describing the
desired changes. Existing approaches to solving this challenging task learn a
mapping from the (reference image, modification text)-pair to an image
embedding that is then matched against a large image corpus. One area that has
not yet been explored is the reverse direction, which asks the question, what
reference image when modified as describe by the text would produce the given
target image? In this work we propose a bi-directional training scheme that
leverages such reversed queries and can be applied to existing composed image
retrieval architectures. To encode the bi-directional query we prepend a
learnable token to the modification text that designates the direction of the
query and then finetune the parameters of the text embedding module. We make no
other changes to the network architecture. Experiments on two standard datasets
show that our novel approach achieves improved performance over a baseline
BLIP-based model that itself already achieves state-of-the-art performance.Comment: 12 pages, 5 figure
Recommended from our members
Sonic heritage: listening to the past
History is so often told through objects, images and photographs, but the potential of sounds to reveal place and space is often neglected. Our research project ‘Sonic Palimpsest’1 explores the potential of sound to evoke impressions and new understandings of the past, to embrace the sonic as a tool to understand what was, in a way that can complement and add to our predominant visual understandings. Our work includes the expansion of the Oral History archives held at Chatham Dockyard to include women’s voices and experiences, and the creation of sonic works to engage the public with their heritage. Our research highlights the social and cultural value of oral history and field recordings in the transmission of knowledge to both researchers and the public. Together these recordings document how buildings and spaces within the dockyard were used and experienced by those who worked there. We can begin to understand the social and cultural roles of these buildings within the community, both past and present
An IoT architecture for decision support system in precision livestock
Sustainable animal production is a primary goal of technological development in
the livestock industry. However, it is crucial to master the livestock environment due
to the susceptibility of animals to variables such as temperature and humidity, which
can cause illness, production losses, and discomfort. Thus, livestock production systems
require monitoring, reasoning, and mitigating unwanted conditions with automated actions.
The principal contribution of this study is the introduction of a self-adaptive architecture
named e-Livestock to handle animal production decisions. Two case studies were conducted
involving a system derived from the e-Livestock architecture, encompassing a Compost
Barn production system - an environment and technology where bovine milk production
occurs. The outcomes demonstrate the effectiveness of e-Livestock in three key aspects: (i)
abstraction of disruptive technologies based on the Internet of Things (IoT) and Artificial
Intelligence and their incorporation into a single architecture specific to the livestock
domain, (ii) support for the reuse and derivation of an adaptive self-architecture to
support the engineering of a decision support system for the livestock subdomain, and (iii)
support for empirical studies in a real smart farm to facilitate future technology transfer
to the industry. Therefore, our research’s main contribution is developing an architecture
combining machine learning techniques and ontology to support more complex decisions
when considering a large volume of data generated on farms. The results revealed that the
e-Livestock architecture could support monitoring, reasoning, forecasting, and automated
actions in a milk production/Compost Barn environment.Na indústria pecuária, a produção animal sustentável é o principal objetivo do
desenvolvimento tecnológico. Porém, é fundamental manter boas condições no ambiente
devido à suscetibilidade dos animais a variáveis como temperatura e umidade, que podem
causar doenças, perdas de produção e desconforto. Assim, os sistemas de produção pecuária
requerem monitoramento, controle e mitigação das condições indesejadas através de ações
automatizadas. A principal contribuição deste estudo é a introdução de uma arquitetura
auto-adaptativa denominada e-Livestock para apoiar as decisões relacionadas à produção
animal. Foram conduzidos dois estudos de caso, envolvendo a arquitetura e-Livestock,
que foi utilizada no sistema de produção Compost Barn - ambiente e tecnologia onde
ocorre a produção de gado leiteiro. Os resultados demonstraram a utilidade do e-Livestock
para avaliar três aspectos principais: (i) abstração de tecnologias disruptivas baseadas em
Internet das Coisas (IoT) e Inteligência Artificial, e sua incorporação em uma arquitetura
única, específica para o domínio da pecuária, (ii) suporte para a reutilização e derivação
de uma arquitetura auto-adaptativa para apoiar o desenvolvimento de uma aplicação de
apoio à decisão para o subdomínio da pecuária e (iii) suporte para estudos empíricos em
uma fazenda inteligente real para facilitar a transferência de tecnologia para a indústria.
Portanto, a principal contribuição dessa pesquisa é o desenvolvimento de uma arquitetura
combinando técnicas de machine learning e ontologia para apoiar decisões mais complexas
ao considerar um grande volume de dados gerados nas fazendas. Os resultados revelaram
que a arquitetura e-Livestock pode apoiar monitoramento, controle, previsão e ações
automatizadas em um ambiente de produção de leite/Compost Barn.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superio
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