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This work-in-progress poster reports on the development process of a virtual environment to support embodied cognition about the scale of scientific entities from subatomic particles to galaxies. Research shows that learners struggle to comprehend the sizes of entities beyond human scale. In order to determine specific entities to use in the virtual environment, a document analysis of US K-undergraduate science education standards was undertaken. Entities, categories of entities, and ranges of sizes were identified. 

Abstract We propose an integrated photonics device for mapping qubits encoded in the polarization of a photon onto the spin state of a solid-state defect coupled to a photonic crystal cavity: a “polarization-encoded photon-to-spin interface” (PEPSI). We perform a theoretical analysis of the state fidelity’s dependence on the device’s polarization extinction ratio and atom–cavity cooperativity. Furthermore, we explore the rate-fidelity trade-off through analytical and numerical models. In simulation, we show that our design enables efficient, high fidelity photon-to-spin mapping. 

Models recently used in the literature proving residual networks (ResNets) are better than linear predictors are actually different from standard ResNets that have been widely used in computer vision. In addition to the assumptions such as scalar-valued output or single residual block, the models fundamentally considered in the literature have no nonlinearities at the final residual representation that feeds into the final affine layer. To codify such a difference in nonlinearities and reveal a linear estimation property, we define ResNEsts, i.e., Residual Nonlinear Estimators, by simply dropping nonlinearities at the last residual representation from standard ResNets. We show that wide ResNEsts with bottleneck blocks can always guarantee a very desirable training property that standard ResNets aim to achieve, i.e., adding more blocks does not decrease performance given the same set of basis elements. To prove that, we first recognize ResNEsts are basis function models that are limited by a coupling problem in basis learning and linear prediction. Then, to decouple prediction weights from basis learning, we construct a special architecture termed augmented ResNEst (A-ResNEst) that always guarantees no worse performance with the addition of a block. As a result, such an A-ResNEst establishes empirical risk lower bounds for a ResNEst using corresponding bases. Our results demonstrate ResNEsts indeed have a problem of diminishing feature reuse; however, it can be avoided by sufficiently expanding or widening the input space, leading to the above-mentioned desirable property. Inspired by the densely connected networks (DenseNets) that have been shown to outperform ResNets, we also propose a corresponding new model called Densely connected Nonlinear Estimator (DenseNEst). We show that any DenseNEst can be represented as a wide ResNEst with bottleneck blocks. Unlike ResNEsts, DenseNEsts exhibit the desirable property without any special = architectural re-design. 

Sea salt aerosols contribute significantly to the mass loading of ambient aerosol, which may serve as cloud condensation nuclei and can contribute to light scattering in the atmosphere. Two major chemical components commonly found in sea salts are ammonium sulfate (AS) and sodium chloride (NaCl). It has been shown that alkylamines, derivatives of ammonia, can react with ammonium salts in the particle-phase to displace ammonia and likely change the particle properties. This study investigated the effects of atmospheric alkylamines on the composition and properties of sea salt aerosols using a chemical system of methylamine (MA, as a proxy of alkylamines), AS and NaCl (as a proxy of sea salt aerosol). The concentrations of ammonia and MA in aqueous/gas phases at the thermodynamic equilibrium were determined using the Extended Aerosols and Inorganics Model (E-AIM) under varying initial inputs, along with the deliquescence relative humidity (DRH) and the corresponding particle water content. Our findings indicated a notable negative relationship between MA concentration and the DRH for both AS and NaCl while the effect of MA on NaCl is smaller than that on AS. The salt of MA in the particle phase may absorb water vapor and may lead to the displacement reaction between AS and NaCl due to the low solubility of sodium sulfate. The acidity in the particle phase also played a significant role in affecting the DRH of sea salt aerosols. Since both sea salt aerosol and alkylamines are emitted into the atmosphere from the ocean in large quantities, our study suggested the potential impact of alkylamines on the environment and the climate via the modification of sea salt aerosol properties.