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We present a photonically driven on-chip millimeter wave (mmWave) source enabled by the heterogeneous integration of a high-speed InGaAs/InP photodiode and silicon nitride (Si₃N₄) microcavity solitons. The chip delivers mmWaves with −18dBm of electrical power at a frequency of 98 GHz with kHz-class linewidth and low phase noise and marks a significant advancement in on-chip photonic mmWave source performance. This breakthrough not only demonstrates capabilities of heterogeneous photonic integration but also offers a compact and scalable solution for future low-noise mmWave applications in communications and sensing technologies. 

We demonstrate InGaAs/InAlGaAs/InP waveguide photodiodes on Si₃N₄with up to 81 GHz 3-dB bandwidth, 0.76 A/W responsivity, and -1.8 dBm and -9 dBm output RF power at 50 GHz and 100 GHz, respectively. 

Abstract ObjectivesRespiratory syncytial virus (RSV) is a significant cause of pediatric hospitalizations. This article aims to utilize multisource data and leverage the tensor methods to uncover distinct RSV geographic clusters and develop an accurate RSV prediction model for future seasons. Materials and MethodsThis study utilizes 5-year RSV data from sources, including medical claims, CDC surveillance data, and Google search trends. We conduct spatiotemporal tensor analysis and prediction for pediatric RSV in the United States by designing (i) a nonnegative tensor factorization model for pediatric RSV diseases and location clustering; (ii) and a recurrent neural network tensor regression model for county-level trend prediction using the disease and location features. ResultsWe identify a clustering hierarchy of pediatric diseases: Three common geographic clusters of RSV outbreaks were identified from independent sources, showing an annual RSV trend shifting across different US regions, from the South and Southeast regions to the Central and Northeast regions and then to the West and Northwest regions, while precipitation and temperature were found as correlative factors with the coefficient of determination R2≈0.5, respectively. Our regression model accurately predicted the 2022-2023 RSV season at the county level, achieving R2≈0.3 mean absolute error MAE < 0.4 and a Pearson correlation greater than 0.75, which significantly outperforms the baselines with P-values <.05. ConclusionOur proposed framework provides a thorough analysis of RSV disease in the United States, which enables healthcare providers to better prepare for potential outbreaks, anticipate increased demand for services and supplies, and save more lives with timely interventions. 

Abstract In this work, we aim to accurately predict the number of hospitalizations during the COVID-19 pandemic by developing a spatiotemporal prediction model. We propose HOIST, an Ising dynamics-based deep learning model for spatiotemporal COVID-19 hospitalization prediction. By drawing the analogy between locations and lattice sites in statistical mechanics, we use the Ising dynamics to guide the model to extract and utilize spatial relationships across locations and model the complex influence of granular information from real-world clinical evidence. By leveraging rich linked databases, including insurance claims, census information, and hospital resource usage data across the U.S., we evaluate the HOIST model on the large-scale spatiotemporal COVID-19 hospitalization prediction task for 2299 counties in the U.S. In the 4-week hospitalization prediction task, HOIST achieves 368.7 mean absolute error, 0.6$${R}^{2}$$ $R^2$and 0.89 concordance correlation coefficient score on average. Our detailed number needed to treat (NNT) and cost analysis suggest that future COVID-19 vaccination efforts may be most impactful in rural areas. This model may serve as a resource for future county and state-level vaccination efforts. 

We demonstrate InGaAs/InP balanced photodiodes onSi₃N₄waveguides with record-high 3-dB bandwidth of 30 GHz, 0.72 A/W responsivity, and high common mode rejection ratio (CMRR) of 26 dB at 30 GHz. 

We report InGaAs/InP based p-i-n photodiodes with an external quantum efficiency (EQE) above 98% from 1510 nm to 1575 nm. For surface normal photodiodes with a diameter of 80 µm, the measured 3-dB bandwidth is 3 GHz. The saturation current is 30.5 mA, with an RF output power of 9.3 dBm at a bias of −17 V at 3 GHz. 

Abstract Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.