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  1. Unlike traditional structural materials, soft solids can often sustain very large deformation before failure, and many exhibit nonlinear viscoelastic behavior. Modeling nonlinear viscoelasticity is a challenging problem for a number of reasons. In particular, a large number of material parameters are needed to capture material response and validation of models can be hindered by limited amounts of experimental data available. We have developed a Gaussian Process (GP) approach to determine the material parameters of a constitutive model describing the mechanical behavior of a soft, viscoelastic PVA hydrogel. A large number of stress histories generated by the constitutive model constitute themore »training sets. The low-rank representations of stress histories by Singular Value Decomposition (SVD) are taken to be random variables which can be modeled via Gaussian Processes with respect to the material parameters of the constitutive model. We obtain optimal material parameters by minimizing an objective function over the input set. We find that there are many good sets of parameters. Further the process reveals relationships between the model parameters. Results so far show that GP has great potential in fitting constitutive models.« less
    Free, publicly-accessible full text available December 14, 2022
  2. Free, publicly-accessible full text available December 14, 2022
  3. A 6D human pose estimation method is studied to assist autonomous UAV control in human environments. As autonomous robots/UAVs become increasingly prevalent in the future workspace, autonomous robots must detect/estimate human movement and predict their trajectory to plan a safe motion path. Our method utilize a deep Convolutional Neural Network to calculate a 3D torso bounding box to determine the location and orientation of human objects. The training uses a loss function that includes both 3D angle and translation errors. The trained model delivers <10-degree angular error and outperforms a reference method based on RSN.
    Free, publicly-accessible full text available November 11, 2022
  4. In this paper, we propose a novel method for matrix completion under general non- uniform missing structures. By controlling an upper bound of a novel balancing error, we construct weights that can actively adjust for the non-uniformity in the empirical risk without explicitly modeling the observation probabilities, and can be computed efficiently via convex optimization. The recovered matrix based on the proposed weighted empirical risk enjoys appealing theoretical guarantees. In particular, the proposed method achieves stronger guarantee than existing work in terms of the scaling with respect to the observation probabilities, under asymptotically heterogeneous missing settings (where entry-wise observation probabilitiesmore »can be of different orders). These settings can be regarded as a better theoretical model of missing patterns with highly varying probabilities. We also provide a new minimax lower bound under a class of heterogeneous settings. Numerical experiments are also provided to demonstrate the effectiveness of the proposed method.« less
    Free, publicly-accessible full text available November 27, 2022
  5. Smectite clays are implicated in solute trapping in natural and engineered processes. Here we evaluated 23Na solid-state nuclear magnetic resonance (NMR) and thermogravimetric analysis-coupled mass spectrometry (TGA–MS) for quantitative analysis of a hydrated natural Na-montmorillonite equilibrated at different relative humidity (RH). Using X-ray diffraction, we determined predominantly large-sized (∼1.55 nm) interlayers at 93% and 75% RH, 2:1 ratio of medium-sized (∼1.23 nm) to large-sized interlayers at 55% RH, and 2:1 ratio of small-sized (<0.96 nm) to medium-sized interlayers at 11% RH. Informed by simulated NMR of differently hydrated model Na-MONT systems, the experimental 23Na NMR data revealed only fully hydratedmore »Na+ populations at 93% RH, a 2:1 ratio of partially hydrated (outer-sphere) to mineral-bound (inner-sphere) Na+ populations at 55% RH and, remarkably, a near-equal proportion of these latter two Na+ populations at 11% RH. Between 93% and 11% RH, the TGA–MS data captured a 57% increase in tightly bound waters (water loss at 100–300 °C) but only a 22% decrease in freely exchangeable waters (water loss below 40 °C). The addition of exogenous NaCl altered the aforementioned hydration behaviors, particularly at low RH. Our findings of persisting hydrated environments despite interlayer collapse implied water populations incongruent with predictions from smectite interlayer nanopore size distributions.« less
    Free, publicly-accessible full text available November 26, 2022
  6. Free, publicly-accessible full text available October 1, 2022
  7. Free, publicly-accessible full text available September 16, 2022
  8. Abstract The Higgs mechanism, i.e., spontaneous symmetry breaking of the quantum vacuum, is a cross-disciplinary principle, universal for understanding dark energy, antimatter and quantum materials, from superconductivity to magnetism. Unlike one-band superconductors (SCs), a conceptually distinct Higgs amplitude mode can arise in multi-band, unconventional superconductors  via strong interband Coulomb interaction, but is yet to be accessed. Here we discover such hybrid Higgs mode and demonstrate its quantum control by light in iron-based high-temperature SCs. Using terahertz (THz) two-pulse coherent spectroscopy, we observe a tunable amplitude mode coherent oscillation of the complex order parameter from coupled lower and upper bands. The nonlinear dependence ofmore »the hybrid Higgs mode on the THz driving fields is distinct from any known SC results: we observe a large reversible modulation of resonance strength, yet with a persisting mode frequency. Together with quantum kinetic modeling of a hybrid Higgs mechanism, distinct from charge-density fluctuations and without invoking phonons or disorder, our result provides compelling evidence for a light-controlled coupling between the electron and hole amplitude modes assisted by strong interband quantum entanglement. Such light-control of Higgs hybridization can be extended to probe many-body entanglement and hidden symmetries in other complex systems.« less
    Free, publicly-accessible full text available December 1, 2022
  9. A new framework for advanced machine learning-based analysis of hyperspectral datasets HSKL was built using the well-known package scikit-learn. In this paper, we describe HSKL’s structure and basic usage. We also showcase the diversity of models supported by the package by applying 17 classification algorithms and measure their baseline performance in segmenting objects with highly similar spectral properties.
    Free, publicly-accessible full text available July 19, 2022