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We report what we believe to be a novel and unique approach for achieving high-performance and broadband THz phase shifting based on spatially-resolved photoconductivity modulation (SRPM). By changing the illumination area on a hybrid Au-Ge mesa-array (AGMA) structure in front of an indium tin oxide (ITO) layer for local photoconductivity modulation, the phase difference between the incident- and reflected-waves can be tuned nearly continuously with extremely low reflection loss. For a prototype demonstration, a photonically-driven THz phase shifting device based on the WR-5.1 (140-220 GHz) waveguide configuration was designed, modeled and simulated. To achieve phase tuning in the range of 0° to -180° at 180 GHz (band center frequency), a mesa-array consisting of 12 × 6 unit cells (each 105 μm × 105 μm) was designed, and a distancedof 250 μm between the AGMA and ITO was used. The SRPM is accomplished using computer-generated light patterns from a closely-coupled micro-LED array for through-ITO illumination, without the need for any biasing circuitry. Full wave simulation results have shown that pseudo-continuous and broadband phase shifting can be achieved in the entire WR-5.1 band, and a shifting range of 0° to -180° at 180 GHz can be realized as designed. In addition, by using light patterns of different combinations of vertical strips, a fine phase tuning step as small as ∼0.05° can be demonstrated. For all phase tuning states, the simulated reflection loss is generally less than 1 dB with low loss variation. The proposed technology for high-performance THz phase modulation is promising and powerful, while offering far more design flexibility and frequency scalability than the current state-of-the-art since it requires no biasing wires thus eliminating parasitic-related performance degradation. Therefore, this technology is suitable for the development of large-scale THz phased-arrays, reconfigurable reflectarrays, and tunable metasurfaces for dynamic beam steering/forming required in next generation (6G or beyond) wireless communications. 

Extensive literature has been proposed for the analysis of correlated survival data. Subjects within a cluster share some common characteristics, e.g., genetic and environmental factors, so their time-to-event outcomes are correlated. The frailty model under proportional hazards assumption has been widely applied for the analysis of clustered survival outcomes. However, the prediction performance of this method can be less satisfactory when the risk factors have complicated effects, e.g., nonlinear and interactive. To deal with these issues, we propose a neural network frailty Cox model that replaces the linear risk function with the output of a feed-forward neural network. The estimation is based on quasi-likelihood using Laplace approximation. A simulation study suggests that the proposed method has the best performance compared with existing methods. The method is applied to the clustered time-to-failure prediction within the kidney transplantation facility using the national kidney transplant registry data from the U.S. Organ Procurement and Transplantation Network. All computer programs are available at https://github.com/rivenzhou/deep_learning_clustered. 

Deep learning has enabled breakthroughs in automated diagnosis from medical imaging, with many successful applications in ophthalmology. However, standard medical image classi cation approaches only assess disease presence at the time of acquisition, neglecting the common clinical setting of longitudinal imaging. For slow, progressive eye diseases like age-related macular degeneration (AMD) and primary open-angle glaucoma (POAG), patients undergo repeated imaging over time to track disease progression and forecasting the future risk of developing a disease is critical to properly plan treatment. Our proposed Longitudinal Transformer for Survival Analysis (LTSA) enables dynamic disease prognosis from longitudinal medical imaging, modeling the time to disease from sequences of fundus photography images captured over long, irregular time periods. Using longitudinal imaging data from the Age-Related Eye Disease Study (AREDS) and Ocular Hypertension Treatment Study (OHTS), LTSA signi cantly outperformed a single-image baseline in 19/20 head-to- head comparisons on late AMD prognosis and 18/20 comparisons on POAG prognosis. A temporal attention analysis also suggested that, while the most recent image is typically the most in uential, prior imaging still provides additional prognostic value. 

Heterozygous mutations in two genes encoding key regulators of development improve kernel row number in inbred and hybrid maize, revealing their potential for yield improvement.