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1. Neural network–enabled, all-electronic control of non-Newtonian fluid flow

   https://doi.org/10.1063/5.0226525  

   Bao, Huilu; Zhang, Xin; Zhang, Xiaoyu; Fan, Xiao; Boley, J William; Ping, Jinglei (October 2024, Applied Physics Letters)

   Real-time, all-electronic control of non-Newtonian fluid flow through a microscale channel is crucial for various applications in manufacturing and healthcare. However, existing methods lack the sensitivity required for accurate measurement and the real-time responsiveness necessary for effective adjustment. Here, we demonstrate an all-electronic system that enables closed-loop, real-time, high-sensitivity control of various waveforms of non-Newtonian fluid flow (0.76 μl min−1) through a micro-sized outlet. Our approach combines a contactless, cuff-like flow sensor with a neural-network control program. This system offers a simple, miniaturized, versatile, yet high-performance solution for non-Newtonian fluid flow control, easily integrated into existing setups. 

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2. Augmenting Simulation Data with Sensor Effects for Improved Domain Transfer

   Berlier, Adam J; Bhatt, Anjali; Matuszek, Cynthia (October 2022, Tenth International Workshop on Assistive Computer Vision and Robotics (ACVR) at ECCV)

   Simulation provides vast benefits for the field of robotics and Human-Robot Interaction (HRI). This study investigates how sensor effects seen in the real domain can be modeled in simulation and what role they play in effective Sim2Real domain transfer for learned perception models. The study considers introducing naive noise approaches such as additive Gaussian and salt and pepper noise as well as data-driven sensor effects models into simulation for representing Microsoft Kinect sensor capabilities and phenomena seen on real world systems. This study quantifies the benefit of multiple approaches to modeling sensor effects in simulation for Sim2Real domain transfer by their object classification improvements in the real domain. User studies are conducted to address hypotheses by training grounded language models in each of the sensor effects modeling cases and evaluated on the robot's interaction capabilities in the real domain. In addition to grounded language performance metrics, user study evaluation includes surveys on the human participant's assessment of the robot's capabilities in the real domain. Results from this pilot study show benefits to modeling sensor noise in simulation for Sim2Real domain transfer. This study also begins to explore the effects that such models have on human-robot interactions. 

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3. Distributed quantum sensing with a mode-entangled network of spin-squeezed atomic states

   https://doi.org/10.48550/arXiv.2205.06382  

   Malia, B.K.; Wu, Y.; Martínez-Rincón, J.; Kasevich, M.A. (July 2022, ArXivorg)

   Quantum sensors are used for precision timekeeping, field sensing, and quantum communication. Comparisons among a distributed network of these sensors are capable of, for example, synchronizing clocks at different locations. The performance of a sensor network is limited by technical challenges as well as the inherent noise associated with the quantum states used to realize the network. For networks with only local entanglement at each node, the noise performance of the network improves at best with square root of the number of nodes. Here, we demonstrate that nonlocal entanglement between network nodes offers better scaling with network size. A shared quantum nondemolition measurement entangles a clock network with up to four nodes. This network provides up to 4.5 dB better precision than one without nonlocal entanglement, and 11.6 dB improvement as compared to a network of sensors operating at the quantum projection noise limit. We demonstrate the generality of the approach with atomic clock and atomic interferometer protocols, in scientific and technologically relevant configurations optimized for intrinsically differential comparisons of sensor outputs. 

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4. Optomechanical Accelerometers for Geodesy

   https://doi.org/10.3390/rs14174389  

   Hines, Adam; Nelson, Andrea; Zhang, Yanqi; Valdes, Guillermo; Sanjuan, Jose; Stoddart, Jeremiah; Guzmán, Felipe (September 2022, Remote Sensing)

   We present a novel optomechanical inertial sensor for low-frequency applications and corresponding acceleration measurements. This sensor has a resonant frequency of 4.715 (1) Hz, a mechanical quality factor of 4.76(3) × 105, a test mass of 2.6 g, and a projected noise floor of approximately 5 × 10−11 ms−2/Hz at 1 Hz. Such performance, together with its small size, low weight, reduced power consumption, and low susceptibility to environmental variables such as magnetic field or drag conditions makes it an attractive technology for future space geodesy missions. In this paper, we present an experimental demonstration of low-frequency ground seismic noise detection by direct comparison with a commercial seismometer, and data analysis algorithms for the identification, characterization, and correction of several noise sources. 

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5. Waveform accuracy and systematic uncertainties in current gravitational wave observations

   https://doi.org/10.1103/PhysRevD.108.044018  

   Owen, Caroline B.; Haster, Carl-Johan; Perkins, Scott; Cornish, Neil J.; Yunes, Nicolás (August 2023, Physical Review D)

   The post-Newtonian formalism plays an integral role in the models used to extract information from gravitational wave data, but models that incorporate this formalism are inherently approximations. Disagreement between an approximate model and nature will produce mismodeling biases in the parameters inferred from data, introducing systematic error. We here carry out a proof-of- principle study of such systematic error by considering signals produced by quasi-circular, inspiraling black hole binaries through an injection and recovery campaign. In particular, we study how un- known, but calibrated, higher-order post-Newtonian corrections to the gravitational wave phase impact systematic error in recovered parameters. As a first study, we produce injected data of non-spinning binaries as detected by a current, second-generation network of ground-based observatories and recover them with models of varying PN order in the phase. We find that the truncation of higher order (>3.5) post-Newtonian corrections to the phase can produce significant systematic error even at signal-to-noise ratios of current detector networks. We propose a method to mitigate systematic error by marginalizing over our ignorance in the waveform through the inclusion of higher-order post-Newtonian coefficients as new model parameters. We show that this method can reduce systematic error greatly at the cost of increasing statistical error. 

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