Centrum Wiskunde & Informatica

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    26838 research outputs found

    Opportunities beyond reality: Exploring the new creative frontier

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    We explored the opportunities that are being created at the intersection of art and technology. The discussion featured artists and innovators who are actively pushing beyond traditional boundaries, discussing how emerging technologies can be leveraged to create new artistic formats and leverage on existing experiences. This session was designed to inspire, providing insights into the real-world applications and business models that are unlocking new possibilities in the sectors of art and culture

    Women in XR webinar: Celebrating women in Extended Reality

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    Extended Reality (XR) is transforming the way we learn, work, and interact, but women remain underrepresented in the field. In this special webinar, we bring together leading female experts from EU-funded XR projects to share their journeys, challenges, and insights

    Waarom is computertaal in 1-en en 0-en?

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    We klikken, swipen en streamen onszelf een ongeluk. Onze computers maken overuren – en doen dat blijkbaar op 1-en en 0-en. Maar hoe werkt dat eigenlijk? We duiken in deze computermaterie met wiskundedocent Ruben Ras en onderzoeker bij het Centrum Wiskunde en Informatica, Steven Pemberton

    The SIGCHI Bulletin: A retrospective

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    I first got involved with SIGCHI in 1991, when I went to CHI '91 in New Orleans, since I had had a demo accepted. It was a great conference, and I got to hang out with some quite well-known HCI people in jazz bars in the evening as well. It was my first ever visit to the USA, which added to the excitement: only a few months earlier I had been for the first time to the (then still) USSR for the very first East-West HCI conference, as relations thawed under Gorbachev, and consequently I was on the streets of Moscow during the coup against Gorbachev. It was an exciting year for me in many ways

    ChatGPT is je beste vriend

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    Why we do need explainable AI for healthcare

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    The recent uptake in certified Artificial Intelligence (AI) tools for healthcare applications has renewed the debate around their adoption. Explainable AI, the sub-discipline promising to render AI devices more transparent and trustworthy, has also come under scrutiny as part of this discussion. Some experts in the medical AI space debate the reliability of Explainable AI techniques, expressing concerns on their use and inclusion in guidelines and standards. Revisiting such criticisms, this article offers a balanced perspective on the utility of Explainable AI, focusing on the specificity of clinical applications of AI and placing them in the context of healthcare interventions. Against its detractors and despite valid concerns, we argue that the Explainable AI research program is still central to human-machine interaction and ultimately a useful tool against loss of control, a danger that cannot be prevented by rigorous clinical validation alone.http://creativecommons.org/licenses/by/4.0

    Counterfactual explanations for linear optimization

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    In recent years, the concept of counterfactual explanations (CE) has become increasingly important in understanding the inner workings of complex AI systems. In this paper, we introduce the idea of CEs in the context of linear optimization and propose, explain, and analyze three different classes of CEs: relative, weak, and strong. We discuss in which situation each type of CE is needed and examine the structure of the optimization problems that arise from considering them. By detecting and leveraging the underlying convex structure of the relative CE problem, we demonstrate that computing the relative CEs takes the same order of time as solving the original problems. We also address the computational challenges associated with weak and strong CE problems. To illustrate our findings, we present a case study with data sourced from the World Food Programme in which we calculate each type of CE. Finally, we conduct comprehensive numerical experiments using the NETLIB library to demonstrate that relative CE problems can be solved as quickly as solving the original linear optimization problem

    Comparative analysis of acoustic reconstruction methods for chromophore and oxygenation quantification in limited-view 3D quantitative photoacoustic tomography

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    Three-dimensional quantitative photoacoustic tomography is challenged by uncertainties in light fluence distribution and artifacts from limited view problem. This study evaluates advanced acoustic reconstruction algorithms using model-based inversion schemes to improve chromophore and oxygenation quantification

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