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Abstract Photonic device development (PDD) has achieved remarkable success in designing and implementing new devices for controlling light across various wavelengths, scales, and applications, including telecommunications, imaging, sensing, and quantum information processing. PDD is an iterative, five-step process that consists of: (i) deriving device behavior from design parameters, (ii) simulating device performance, (iii) finding the optimal candidate designs from simulations, (iv) fabricating the optimal device, and (v) measuring device performance. Classically, all these steps involve Bayesian optimization, material science, control theory, and direct physics-driven numerical methods. However, many of these techniques are computationally intractable, monetarily costly, or difficult to implement at scale. In addition, PDD suffers from large optimization landscapes, uncertainties in structural or optical characterization, and difficulties in implementing robust fabrication processes. However, the advent of machine learning over the past decade has provided novel, data-driven strategies for tackling these challenges, including surrogate estimators for speeding up computations, generative modeling for noisy measurement modeling and data augmentation, reinforcement learning for fabrication, and active learning for experimental physical discovery. In this review, we present a comprehensive perspective on these methods to enable machine-learning-assisted PDD (ML-PDD) for efficient design optimization with powerful generative models, fast simulation and characterization modeling under noisy measurements, and reinforcement learning for fabrication. This review will provide researchers from diverse backgrounds with valuable insights into this emerging topic, fostering interdisciplinary efforts to accelerate the development of complex photonic devices and systems. 

Avoidance of novel stimuli (neophobia) affects how wild animals interact with their environment and may partly determine whether animals persist in human-altered landscapes. The neuroendocrine mediators of neophobia are poorly understood, although past work demonstrated that experimentally reducing circulating corticosterone in wild-caught house sparrows (Passer domesticus) decreased neophobia toward novel objects placed near the food dish. In this experiment, we directly tested the role of one of the two types of corticosterone receptors, the glucocorticoid receptor (GR), in mediating neophobia in house sparrows by administering a GR antagonist (RU486, n = 10) or a vehicle control (peanut oil, n = 10) over 5 consecutive days and measuring responses to novel objects both pre- and post-treatment. We also measured baseline and stress-induced corticosterone in all sparrows on the final day of behavior trials. To better understand the effects of RU486 on corticosterone over time, in a separate group of sparrows (n = 12) we administered RU486 or vehicle over 5 days and took multiple blood samples to assess baseline and stress-induced corticosterone. Overall, we did not detect an effect of subcutaneous RU486 injections on neophobia behavior. However, we did find that RU486 injections significantly decreased stress-induced corticosterone levels starting 1 day post-injection and baseline corticosterone levels starting 6 days post-injection, compared to vehicle-injected controls. Our results suggest that GR is not involved in mediating neophobia behavior in house sparrows. 

Ultrafast UV photophysics of adenine and its derivatives are interrogated by XUV-TRPES with a gas-dynamic flat liquid jet.