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The crisis induced by the Coronavirus pandemic severely impacted educational institutes. Even with vaccination efforts underway in 2021, it was not clear that sufficient confidence will be achieved for schools to reopen soon. This paper considers the impact of testing rates in addition to vaccination rates in order to reduce infections and hospitalizations and evaluates strategies that allow educational institute in urban settings to remain open. These strategies are also applicable to big campus style businesses and would help planning to keep the businesses open and help the economy. Our analysis is based on a graph model where nodes represent population groups and edges represent population exchanges due to commuting populations. The commuting population is associated with edges and is associated with one of the end nodes of the edge during part of the time period and with the other node during the remainder of the time period. The progression of the disease at each node is determined via compartment models, that include vaccination rates and testing to place infected people in quarantine along with consideration of asymptomatic and symptomatic populations. Applying this to a university population in Chicago with a substantial commuter population, chosen to be 80% of themore »school’s population as an illustration, provides an analysis which specifies benefits of testing and vaccination strategies over a time period of 150 days.« less

While high latitude amplification is seen in modern observations, paleoclimate records, and climate modeling, better constraints on the magnitude and pattern of amplification would provide insights into the mechanisms that drive it, which remain actively debated. Here we present multi-proxy multi-site paleotemperature records over the last 10 million years from the Western Pacific Warm Pool (WPWP) – the warmest endmember of the global ocean that is uniquely important in the global radiative feedback change. These sea surface temperature records, based on lipid biomarkers and seawater Mg/Ca-adjusted foraminiferal Mg/Ca, unequivocally show warmer WPWP in the past, and a secular cooling over the last 10 million years. Compiling these data with existing records reveals a persistent, nearly stationary, extratropical response pattern in the Pacific in which high latitude (~50°N) temperatures increase by ~2.4° for each degree of WPWP warming. This relative warming pattern is also evident in model outputs of millennium-long climate simulations with quadrupling atmospheric CO₂, therefore providing a strong constraint on the future equilibrium response of the Earth System.

The lack of a bulk second-order nonlinearity (χ⁽²⁾) in silicon nitride (Si₃N₄) keeps this low-loss, CMOS-compatible platform from key active functions such as Pockels electro-optic (EO) modulation and efficient second harmonic generation (SHG). We demonstrate a successful induction ofχ⁽²⁾in Si₃N₄through electrical poling with an externally-applied field to align the Si-N bonds. This alignment breaks the centrosymmetry of Si₃N₄, and enables the bulkχ⁽²⁾. The sample is heated to over 500°C to facilitate the poling. The comparison between the EO responses of poled and non-poled Si₃N₄, measured using a Si₃N₄micro-ring modulator, shows at least a 25X enhancement in ther₃₃EO component. The maximumχ⁽²⁾we obtain through poling is 0.30pm/V. We observe a remarkable improvement in the speed of the measured EO responses from 3 GHz to 15 GHz (3 dB bandwidth) after the poling, which confirms theχ⁽²⁾nature of the EO response induced by poling. This work paves the way for high-speed active functions on the Si₃N₄platform.

Machine learning approaches have recently been applied to the study of various problems in physics. Most of these studies are focused on interpreting the data generated by conventional numerical methods or the data on an existing experimental database. An interesting question is whether it is possible to use a machine learning approach, in particular a neural network, for solving the many-body problem. In this paper, we present a neural network solver for the single impurity Anderson model, the paradigm of an interacting quantum problem in small clusters. We demonstrate that the neural-network-based solver provides quantitative accurate results for the spectral function as compared to the exact diagonalization method. This opens the possibility of utilizing the neural network approach as an impurity solver for other many-body numerical approaches, such as the dynamical mean field theory.

Changing the frequency of light outside the laser cavity is essential for an integrated photonics platform, especially when the optical frequency of the on-chip light source is fixed or challenging to be tuned precisely. Previous on-chip frequency conversion demonstrations of multiple GHz have limitations of tuning the shifted frequency continuously. To achieve continuous on-chip optical frequency conversion, we electrically tune a lithium niobate ring resonator to induce adiabatic frequency conversion. In this work, frequency shifts of up to 14.3 GHz are achieved by adjusting the voltage of an RF control. With this technique, we can dynamically control light in a cavity within its photon lifetime by tuning the refractive index of the ring resonator electrically.