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Abstract In this study, we present a method based on Monte Carlo Dropout (MCD) as Bayesian neural network (BNN) approximation for confidence-aware severity classification of lung diseases in COVID-19 patients using chest X-rays (CXRs). Trained and tested on 1208 CXRs from Hospital 1 in the USA, the model categorizes severity into four levels (i.e., normal, mild, moderate, and severe) based on lung consolidation and opacity. Severity labels, determined by the median consensus of five radiologists, serve as the reference standard. The model’s performance is internally validated against evaluations from an additional radiologist and two residents that were excluded from the median. The performance of the model is further evaluated on additional internal and external datasets comprising 2200 CXRs from the same hospital and 1300 CXRs from Hospital 2 in South Korea. The model achieves an average area under the curve (AUC) of 0.94 ± 0.01 across all classes in the primary dataset, surpassing human readers in each severity class and achieves a higher Kendall correlation coefficient (KCC) of 0.80 ± 0.03. The performance of the model is consistent across varied datasets, highlighting its generalization. A key aspect of the model is its predictive uncertainty (PU), which is inversely related to the level of agreement among radiologists, particularly in mild and moderate cases. The study concludes that the model outperforms human readers in severity assessment and maintains consistent accuracy across diverse datasets. Its ability to provide confidence measures in predictions is pivotal for potential clinical use, underscoring the BNN’s role in enhancing diagnostic precision in lung disease analysis through CXR. 

It has been recently shown that an additional therapeutic gain may be achieved if a radiotherapy plan is altered over the treatment course using a new treatment paradigm referred to in the literature as spatiotemporal fractionation. Because of the nonconvex and large-scale nature of the corresponding treatment plan optimization problem, the extent of the potential therapeutic gain that may be achieved from spatiotemporal fractionation has been investigated using stylized cancer cases to circumvent the arising computational challenges. This research aims at developing scalable optimization methods to obtain high-quality spatiotemporally fractionated plans with optimality bounds for clinical cancer cases. In particular, the treatment-planning problem is formulated as a quadratically constrained quadratic program and is solved to local optimality using a constraint-generation approach, in which each subproblem is solved using sequential linear/quadratic programming methods. To obtain optimality bounds, cutting-plane and column-generation methods are combined to solve the Lagrangian relaxation of the formulation. The performance of the developed methods are tested on deidentified clinical liver and prostate cancer cases. Results show that the proposed method is capable of achieving local-optimal spatiotemporally fractionated plans with an optimality gap of around 10%–12% for cancer cases tested in this study. Summary of Contribution: The design of spatiotemporally fractionated radiotherapy plans for clinical cancer cases gives rise to a class of nonconvex and large-scale quadratically constrained quadratic programming (QCQP) problems, the solution of which requires the development of efficient models and solution methods. To address the computational challenges posed by the large-scale and nonconvex nature of the problem, we employ large-scale optimization techniques to develop scalable solution methods that find local-optimal solutions along with optimality bounds. We test the performance of the proposed methods on deidentified clinical cancer cases. The proposed methods in this study can, in principle, be applied to solve other QCQP formulations, which commonly arise in several application domains, including graph theory, power systems, and signal processing. 

Abstract Phase-change materials (PCMs) offer a compelling platform for active metaoptics, owing to their large index contrast and fast yet stable phase transition attributes. Despite recent advances in phase-change metasurfaces, a fully integrable solution that combines pronounced tuning measures, i.e., efficiency, dynamic range, speed, and power consumption, is still elusive. Here, we demonstrate an in situ electrically driven tunable metasurface by harnessing the full potential of a PCM alloy, Ge₂Sb₂Te₅(GST), to realize non-volatile, reversible, multilevel, fast, and remarkable optical modulation in the near-infrared spectral range. Such a reprogrammable platform presents a record eleven-fold change in the reflectance (absolute reflectance contrast reaching 80%), unprecedented quasi-continuous spectral tuning over 250 nm, and switching speed that can potentially reach a few kHz. Our scalable heterostructure architecture capitalizes on the integration of a robust resistive microheater decoupled from an optically smart metasurface enabling good modal overlap with an ultrathin layer of the largest index contrast PCM to sustain high scattering efficiency even after several reversible phase transitions. We further experimentally demonstrate an electrically reconfigurable phase-change gradient metasurface capable of steering an incident light beam into different diffraction orders. This work represents a critical advance towards the development of fully integrable dynamic metasurfaces and their potential for beamforming applications. 

Abstract The phase matching between the propagating fundamental and nonlinearly generated waves plays an important role in the efficiency of the nonlinear frequency conversion in macroscopic crystals. However, in nanoscale samples, such as nanoplasmonic structures, the phase-matching condition is often ignored due to the sub-wavelength nature of the materials. Here, we first show that the phase matching of the lattice plasmon modes at the fundamental and second-harmonic frequencies in a plasmonic nanoantenna array can effectively enhance the surface-enhanced second-harmonic generation. Additionally, a significant enhancement of the second-harmonic generation is demonstrated using stationary band-edge lattice plasmon modes with zero phase. 

Thermophotovoltaics (TPVs) are a potential technology for waste-heat recovery applications and utilize IR sensitive photovoltaic diodes to convert long wavelength photons (>800nm) into electrical energy. The most common conversion regions utilize Gallium Antimonide (GaSb) as the standard semiconductor system for TPV diodes due to its high internal quantum efficiencies (close to 90%) for infrared radiation (~1700nm). However, parasitic losses prevent high conversion efficiencies from being achieved in the final device. One possible avenue to improve the conversion efficiency of these devices is to incorporate metallic photonic crystals (MPhCs) onto the front surface of the diode. In this work, we study the effect of MPhCs on GaSb TPV diodes. Simulations are presented which characterize a specific MPhC design for use with GaSb. E-field intensity vs. wavelength and depth are investigated as well as the effect of the thickness of the PhC on the interaction time between the e-field and semiconductor. It is shown that the thickness of MPhC has little effect on width of the enhancement band, and the depth the ideal p-i-n junction is between 0.6􀈝m and 2.1um. Additionally, simulated results demonstrate an increase of E-field/semiconductor interaction time of approximately 40% and 46% for a MPhC thickness of 350nm and 450nm respectively.