908 research outputs found

    Graduate Catalog of Studies, 2023-2024

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    Graduate Catalog of Studies, 2023-2024

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    AI: Limits and Prospects of Artificial Intelligence

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    The emergence of artificial intelligence has triggered enthusiasm and promise of boundless opportunities as much as uncertainty about its limits. The contributions to this volume explore the limits of AI, describe the necessary conditions for its functionality, reveal its attendant technical and social problems, and present some existing and potential solutions. At the same time, the contributors highlight the societal and attending economic hopes and fears, utopias and dystopias that are associated with the current and future development of artificial intelligence

    2023-2024 Graduate School Catalog

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    You and your peers represent more than 67 countries and your shared scholarship spans 140 programs - from business administration and biomedical engineering to history, horticulture, musical performance, marine science, and more. Your ideas and interests will inform public health, create opportunities for art and innovation, contribute to the greater good, and positively impact economic development in Maine and beyond

    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

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Architecture and Advanced Electronics Pathways Toward Highly Adaptive Energy- Efficient Computing

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    With the explosion of the number of compute nodes, the bottleneck of future computing systems lies in the network architecture connecting the nodes. Addressing the bottleneck requires replacing current backplane-based network topologies. We propose to revolutionize computing electronics by realizing embedded optical waveguides for onboard networking and wireless chip-to-chip links at 200-GHz carrier frequency connecting neighboring boards in a rack. The control of novel rate-adaptive optical and mm-wave transceivers needs tight interlinking with the system software for runtime resource management

    AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges

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    Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes, particularly in cloud infrastructures, to provide actionable insights with the primary goal of maximizing availability. There are a wide variety of problems to address, and multiple use-cases, where AI capabilities can be leveraged to enhance operational efficiency. Here we provide a review of the AIOps vision, trends challenges and opportunities, specifically focusing on the underlying AI techniques. We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful. We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions. We discuss the problem formulation for each task, and then present a taxonomy of techniques to solve these problems. We also identify relatively under explored topics, especially those that could significantly benefit from advances in AI literature. We also provide insights into the trends in this field, and what are the key investment opportunities

    GAMMA: Eine Software für den automatischen Aufbau von Makromolekülen

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    Die Vielfalt möglicher makromolekularer Strukturen gepaart mit den explorativen Fähigkeiten computergestützter Generierung chemischer (Makro-)Molekülstrukturen bietet großes Forschungspotential. Die Eigenschaften solcher Strukturen, darunter häufig Hohlräume, in die kleine Moleküle passen, führen z. B. zu Anwendungen in Wirt-Gast-Strukturen. Das Hauptthema der vorliegenden Arbeit war die Entwicklung eines neuen Computerprogrammes, das, ausgehend von (kleinen) molekularen Bausteinen, makromolekulare Strukturen erstellt. Nach Vorgabe eines Templatgraphen werden molekulare Bausteine so miteinander verknüpft, dass ein Makromolekül entsteht. Dieses kann anschließend um polare funktionelle Gruppen erweitert werden, die das elektrische Feld des Makromoleküls gezielt verändern, um beispielsweise eine katalytische Wirkung zu entfalten
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