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motivated the development of Federated Learning (FL) [1,2], a framework for on-device collaborative training of machine learning models. FL algorithms like FedAvg [3] allow clients to train a common global
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training of an artificial intelligence algorithm capable of automatically segmenting the bony structures of both healthy and fractured tibial plateaus. This will serve three main purposes: 1) Enable
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algorithms will be developed to extract discriminative and predictive features from a multimodal dataset consisting of digital histopathological images, lung CT images, clinical, genomics, and multiproteomics
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, transport, or defense. On the technical side, we aim at combining statistical latent variable models with deep learning algorithms to justify existing results and allow a better understanding of their
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the technical side, we aim at combining statistical latent variable models with deep learning algorithms to justify existing results and allow a better understanding of their performances and their limitations
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research work will be to devise efficient algorithms for source separation in DAS measurements. Issues such as large data volumes that can exceed 1 To per day and per fiber, instrument noise, complex nature
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analyzed. The tensor model structure estimated by suitable optimization algorithms, such as that recently developed in [GOU20], will be considered as a starting point. • Exploiting data multimodality and
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(morphological patterns), based on the experts’ knowledge. Then, tools like Procrustes analysis, linear dimensionality reduction (PCA) and standard clustering algorithms are employed. A first objective of our
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to support research. Implement algorithms, data structures, and other computational techniques to solve complex problems. Collaborate with cross-functional teams to integrate software components into larger
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the use of synthetic data in precision medicine research and applications through development of AI algorithms, tools and other processes to allow for the enrichment of clinical data sets Providing training