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coupled NEMS network, consisting of 2 or more double-drum resonators. This is beyond current state of art and relies on deep understand of more degrees of nonlinear complexity of the coupled resonator
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Systems Symposium (RTSS), 2019, pp. 299–311 [4] A. Mifdaoui and T. Leydier, “Beyond the Accuracy-Complexity Trade-offs of Compositional Analyses using Network Calculus for Complex Networks,” Dec. 2017, p
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treatment and catalysis. While at bulk freezing and melting are already highly complex mechanisms of phase transitions, at the nanoscale, traditional thermodynamic considerations are challenged by
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to address the deployment limitations of multi-purpose robots in shared environments. Rather than a single monolithic controller, the robot is treated as a network of collaborating semi-autonomous agents (a
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organelles, and macro-complex structures in their cellular context (in situ structural biology). The laboratory Cell and Plant Physiology (LPCV), which aims to understand the adaptive response of microalgae
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optimize hardware neural networks made of approximately one hundred magnetic tunnel junctions, with radio-frequency inputs, in order to classify RF signals directly in the physical domain. Chains of magnetic
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between nonlinear matter waves and quantum information processing. The recent observation of dense collisional soliton complexes in two-component BECs [4] and advances in optimal control of nonlinear
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remains largely unexplored and represents a fascinating frontier for molecular computing. The development of new methods will be necessary to enable the manipulation of data within complex molecular
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the simulation of turbulent flows using a tensor network representation of the Navier–Stokes equations. Unlike recent approaches based on tensor networks, which simulate fluid flows in physical space using finite
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the context of a PEPR project, in collaboration with eight CNRS and CEA laboratories working on the future of electrical grids. More specifically, it is linked to WP5, dedicated to cybersecurity and network