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  1. Free, publicly-accessible full text available December 10, 2024
  2. Free, publicly-accessible full text available November 8, 2024
  3. Interfacing electronics with optical fiber networks is key to the long-distance transfer of classical and quantum information. Piezo-optomechanical transducers enable such interfaces by using gigahertz-frequency acoustic vibrations as mediators for converting microwave photons to optical photons via the combination of optomechanical and piezoelectric interactions. However, despite successful demonstrations, efficient quantum transduction remains out of reach due to the challenges associated with hybrid material integration and increased loss from piezoelectric materials when operating in the quantum regime. Here, we demonstrate an alternative approach in which we actuate 5-GHz phonons in a conventional silicon-on-insulator platform. In our experiment, microwave photons resonantly drive a phononic crystal oscillator via the electrostatic force realized in a charge-biased narrow-gap capacitor. The mechanical vibrations are subsequently transferred via a phonon waveguide to an optomechanical cavity, where they transform into optical photons in the sideband of a pump laser field. Operating at room temperature and atmospheric pressure, we measure a microwave-to-optical photon conversion efficiency of 1.72±0.14×10−7in a 3.3 MHz bandwidth. Our results mark a stepping stone towards quantum transduction with integrated devices made from crystalline silicon, which promise efficient high-bandwidth operation and integration with superconducting qubits. Additionally, the lack of need for piezoelectricity or other intrinsic nonlinearities makes our approach applicable to a wide range of materials for potential applications beyond quantum technologies.

     
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  4. Cell spreading and migration play central roles in many physiological and pathophysiological processes. We have previously shown that MFN2 regulates the migration of human neutrophil-like cells via suppressing Rac activation. Here, we show that in mouse embryonic fibroblasts, MFN2 suppresses RhoA activation and supports cell polarization. After initial spreading, the wild-type cells polarize and migrate, whereas theMfn2-/-cells maintain a circular shape. Increased cytosolic Ca2+resulting from the loss of Mfn2 is directly responsible for this phenotype, which can be rescued by expressing an artificial tether to bring mitochondria and endoplasmic reticulum to close vicinity. Elevated cytosolic Ca2+activates Ca2+/calmodulin-dependent protein kinase II, RhoA, and myosin light-chain kinase, causing an overactivation of nonmuscle myosin II, leading to a formation of a prominent F-actin ring at the cell periphery and increased cell contractility. The peripheral actin band alters cell physics and is dependent on substrate rigidity. Our results provide a novel molecular basis to understand how MFN2 regulates distinct signaling pathways in different cells and tissue environments, which is instrumental in understanding and treating MFN2-related diseases.

     
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    Free, publicly-accessible full text available September 19, 2024
  5. Adrian Weller (Ed.)
    A wide range of machine learning applications such as privacy-preserving learning, algorithmic fairness, and domain adaptation/generalization among others, involve learning invariant representations of the data that aim to achieve two competing goals: (a) maximize information or accuracy with respect to a target response, and (b) maximize invariance or independence with respect to a set of protected features (e.g. for fairness, privacy, etc). Despite their wide applicability, theoretical understanding of the optimal tradeoffs — with respect to accuracy, and invariance — achievable by invariant representations is still severely lacking. In this paper, we provide an information theoretic analysis of such tradeoffs under both classification and regression settings. More precisely, we provide a geometric characterization of the accuracy and invariance achievable by any representation of the data; we term this feasible region the information plane. We provide an inner bound for this feasible region for the classification case, and an exact characterization for the regression case, which allows us to either bound or exactly characterize the Pareto optimal frontier between accuracy and invariance. Although our contributions are mainly theoretical, a key practical application of our results is in certifying the potential sub-optimality of any given representation learning algorithm for either classification or regression tasks. Our results shed new light on the fundamental interplay between accuracy and invariance, and may be useful in guiding the design of future representation learning algorithms. 
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  6. Abstract

    Optical computing with integrated photonics brings a pivotal paradigm shift to data-intensive computing technologies. However, the scaling of on-chip photonic architectures using spatially distributed schemes faces the challenge imposed by the fundamental limit of integration density. Synthetic dimensions of light offer the opportunity to extend the length of operand vectors within a single photonic component. Here, we show that large-scale, complex-valued matrix-vector multiplications on synthetic frequency lattices can be performed using an ultra-efficient, silicon-based nanophotonic cavity acousto-optic modulator. By harnessing the resonantly enhanced strong electro-optomechanical coupling, we achieve, in a single such modulator, the full-range phase-coherent frequency conversions across the entire synthetic lattice, which constitute a fully connected linear computing layer. Our demonstrations open up the route toward the experimental realizations of frequency-domain integrated optical computing systems simultaneously featuring very large-scale data processing and small device footprints.

     
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