336 research outputs found

    Artificial Intelligence in the Differential Diagnosis of Cardiomyopathy Phenotypes

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    Artificial intelligence (AI) is rapidly being applied to the medical field, especially in the cardiovascular domain. AI approaches have demonstrated their applicability in the detection, diagnosis, and management of several cardiovascular diseases, enhancing disease stratification and typing. Cardiomyopathies are a leading cause of heart failure and life-threatening ventricular arrhythmias. Identifying the etiologies is fundamental for the management and diagnostic pathway of these heart muscle diseases, requiring the integration of various data, including personal and family history, clinical examination, electrocardiography, and laboratory investigations, as well as multimodality imaging, making the clinical diagnosis challenging. In this scenario, AI has demonstrated its capability to capture subtle connections from a multitude of multiparametric datasets, enabling the discovery of hidden relationships in data and handling more complex tasks than traditional methods. This review aims to present a comprehensive overview of the main concepts related to AI and its subset. Additionally, we review the existing literature on AI-based models in the differential diagnosis of cardiomyopathy phenotypes, and we finally examine the advantages and limitations of these AI approaches

    A Template Is All You Meme

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    Memes are a modern form of communication and meme templates possess a base semantics that is customizable by whomever posts it on social media. Machine learning systems struggle with memes, which is likely due to such systems having insufficient context to understand memes, as there is more to memes than the obvious image and text. Here, to aid understanding of memes, we release a knowledge base of memes and information found on www.knowyourmeme.com, which we call the Know Your Meme Knowledge Base (KYMKB), composed of more than 54,000 images. The KYMKB includes popular meme templates, examples of each template, and detailed information about the template. We hypothesize that meme templates can be used to inject models with the context missing from previous approaches. To test our hypothesis, we create a non-parametric majority-based classifier, which we call Template-Label Counter (TLC). We find TLC more effective than or competitive with fine-tuned baselines. To demonstrate the power of meme templates and the value of both our knowledge base and method, we conduct thorough classification experiments and exploratory data analysis in the context of five meme analysis tasks.Comment: 9 pages, 11 supplemental pages, 6 Tables, 10 Figure

    Taylor University Catalog 2023-2024

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    The 2023-2024 academic catalog of Taylor University in Upland, Indiana.https://pillars.taylor.edu/catalogs/1128/thumbnail.jp

    High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysis

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    We propose a novel method for Zero-Shot Anomaly Localization that leverages a bidirectional mapping derived from the 1-dimensional Wasserstein Distance. The proposed approach allows pinpointing the anomalous regions in a texture with increased precision by aggregating the contribution of a pixel to the errors of all nearby patches. We validate our solution on several datasets and obtain more than a 40% reduction in error over the previous state of the art on the MVTec AD dataset in a zero-shot setting

    A Behavioural Decision-Making Framework For Agent-Based Models

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    In the last decades, computer simulation has become one of the mainstream modelling techniques in many scientific fields. Social simulation with Agent-based Modelling (ABM) allows users to capture higher-level system properties that emerge from the interactions of lower-level subsystems. ABM is itself an area of application of Distributed Artificial Intelligence and Multiagent Systems (MAS). Despite that, researchers using ABM for social science studies do not fully benefit from the development in the field of MAS. It is mainly because the MAS architectures and frameworks are built upon cognitive and computer science foundations and principles, creating a gap in concepts and methodology between the two fields. Building agent frameworks based on behaviour theory is a promising direction to minimise this gap. It can provide a standard practice in interdisciplinary teams and facilitate better usage of MAS technological advancement in social research. From our survey, Triandis' Theory of Interpersonal Behaviour (TIB) was chosen due to its broad set of determinants and inclusion of an additive value function to calculate utility values of different outcomes. As TIB's determinants can be organised in a tree-like structure, we utilise layered architectures to formalise the agent's components. The additive function of TIB is then used to combine the utilities of different level determinants. The framework is then applied to create models for different case studies from various domains to test its ability to explain the importance of multiple behavioural aspects and environmental properties. The first case study simulates the mobility demand for Swiss households. We propose an experimental method to test and investigate the impact of core determinants in the TIB on the usage of different transportation modes. The second case study presents a novel solution to simulate trust and reputation by applying subjective logic as a metric to measure an agent's belief about the consequence(s) of action, which can be updated through feedback. The third case study investigates the possibility of simulating bounded rationality effects in an agent's decision-making scheme by limiting its capability of perceiving information. In the final study, a model is created to simulate migrants' choice of activities in centres by applying our framework in conjunction with Maslow's hierarchy of needs. The experiment can then be used to test the impact of different combinations of core determinants on the migrants' activities. Overall, the design of different components in our framework enables adaptations for various contexts, including transportation modal choice, buying a vehicle or daily activities. Most of the work can be done by changing the first-level determinants in the TIB's model based on the phenomena simulated and the available data. Several environmental properties can also be considered by extending the core components or employing other theoretical assumptions and concepts from the social study. The framework can then serve the purpose of theoretical exposition and allow the users to assess the causal link between the TIB's determinants and behaviour output. This thesis also highlights the importance of data collection and experimental design to capture better and understand different aspects of human decision-making

    Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

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    The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient’s autonomy.N/

    Learning to Taste: A Multimodal Wine Dataset

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    We present WineSensed, a large multimodal wine dataset for studying the relations between visual perception, language, and flavor. The dataset encompasses 897k images of wine labels and 824k reviews of wines curated from the Vivino platform. It has over 350k unique vintages, annotated with year, region, rating, alcohol percentage, price, and grape composition. We obtained fine-grained flavor annotations on a subset by conducting a wine-tasting experiment with 256 participants who were asked to rank wines based on their similarity in flavor, resulting in more than 5k pairwise flavor distances. We propose a low-dimensional concept embedding algorithm that combines human experience with automatic machine similarity kernels. We demonstrate that this shared concept embedding space improves upon separate embedding spaces for coarse flavor classification (alcohol percentage, country, grape, price, rating) and aligns with the intricate human perception of flavor.Comment: Accepted to NeurIPS 2023. See project page: https://thoranna.github.io/learning_to_taste

    NEGOTIATING THE SACRED: UNDERSTANDING IMPACTS TO IKS AND ITEK FROM USE OF REMOTE SENSING AND GIS TECHNOLOGIES WITHIN TRIBAL LANDSCAPES

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    How we see the world and ourselves in relation to it is largely achieved by the lens we are looking through and associated experiences within this relationship. This is additionally true when considering the acknowledged fact that Indigenous Knowledges are derived from natural and cultural sources and these assist in constituting the cultural identities of those Peoples associated with these sources. Presently there is a hunger for access and use of Indigenous Knowledges (IK) as never before seen in public ways, through a national Call for collaborative means to apply these knowledges to such as the issues we globally face as a result of Climate Change. What are Indigenous Knowledges? How are they created? Who holds these and can utilize them in public ways? These questions are an embedded aspect of this Call that requires attention. Further, what impacts exist that benefit, but also challenge, the endeavor to utilize Indigenous Knowledges outside local areas where they are derived? What of these sacred ways of knowing are being negotiated to attain their use? Five areas of concern were identified in response to these questions through application of An Indigenous Research Way (AIRW), a novel continuous improvement model for implementing Indigenous Research Methodologies and Methods, within research design and practice. Synthesizing these concerns into three themes, Education, Technology, and Tribal Leader Decision-Making, awareness was revealed of these as first level and gateway impacts. Indigenous ways of knowing, being, and doing operationalizes Indigenous worldviews about relationality and this as central to how Indigenous Knowledges Systems (IKS) are created and in turn create Indigenous Traditional Ecological Knowledges (ITEK). Understanding how we “see” ourselves in relation to this process is imperative. A burgeoning method for seeing landscapes, and they as sources of IK, is through use of remote sensing and Geographical Information Systems (GIS). This Phase I study, through a Kin-based Case Study and mixed-methods approach, sought to understand impacts to IKS and ITEK from use of these technologies within tribal landscapes through review and assessment of 73 ESRI tribal GIS public StoryMap projects, led by tribal practitioners, accomplished in 2017 - 2021. Assessment provides there exists an assumption that identifying as being Indigenous includes being a holder of cultural knowledges and that these are utilized at will and regularly. The data troubles this assumption with respect to tribal individuals trained as practitioners of these technologies and their use of ITEK then provided through public digital media. Impacts to IKS and ITEK reveal enhancements and also replacement of the “seeing” accomplished by Indigenous People through technological means and the public perceptions of their cultural lifeways and persona of being Holders of Indigenous Knowledges. These impacts are broad in their implications as they attend to not only understandings of past and present access to ITEK but also future applications that brings the conversation into the realms of understanding being Indigenous off-earth

    Neil Postman\u27s Loving Resistance Fighter: A Philosophy of Communication in the Age of Technopoly

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    This project walks the work of Neil Postman (1931-2003) into the philosophy of communication. Traditional conceptions of Neil Postman’s body of work position his ideas within the traditions of media ecology, general semantics, or, more broadly, as a form of media studies and criticism. In addition, others label Postman’s work, especially in Technopoly (1992), as pessimistic, deterministic, and/or imbibed with Luddite tendencies. This project articulates a different view and contends that Postman’s scholarship, in particular his articulation of the loving resistance fighter in the final chapter of Technopoly, is committed to resisting the nefarious forces embedded in both technology and modernity. It shows that Postman’s loving resistance fighter provides meaningful communicative practices that prevent one from falling into existential despair or acquiescing to the demands of technopoly. The loving resistance fighter’s emphasis on creating social and psychic distance from technology allows one to view technology with unclouded judgment and to see how technology becomes intertwined with the goods of modernity (progress, efficiency, and individual autonomy). Therefore, this project shows that the loving resistance fighter offers hope and the narrative ground to refuse both technology and modernity
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