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and simple embedding alignment to develop architectures that can process and reason across modalities (vision, language, audio, sensor data) from the ground up. How do we build a truly unified
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a breakthrough concept to upgrade existing fiber optic networks to acoustic sensor arrays, becoming a key component for managing smart cities. Except for a few applications, DAS data are typically
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algorithms for dynamic structured data, with a particular focus on time sequences of graphs, graph signals, and time sequences on groups and manifolds. Special emphasis will be placed on non-parametric
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complicates both learning and inference processes. Another challenge is that dynamic structured data are generated by a variety of sensors and infrastructures that continuously produce, disseminate, and store
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, sensor failures, or the aggregation of datasets from multiple sources. There is a rich literature on how to impute missing values, for example, considering the EM algorithm [Dempster et al., 1977], low
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and applications compiling algorithms for use on early-generation hardware. We also encourage applicants interested in other quantum technologies such as quantum sensors and simulators, and their
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of the following topics: physical layer design for ultra energy-efficient wireless spike-based sensor node communication digital baseband design for energy-efficient terabit/sec wireless communications using
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detection, to cite a few. As telecom fibers are ubiquitous in urban environments, DAS appears as a breakthrough concept to upgrade existing fiber optic networks to acoustic sensor arrays, and a key component