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The ability to assess sleep at home, capture sleep stages, and detect the occurrence of apnea (without on-body sensors) simply by analyzing the radio waves bouncing off people's bodies while they sleep is quite powerful. Such a capability would allow for longitudinal data collection in patients' homes, informing our understanding of sleep and its interaction with various diseases and their therapeutic responses, both in clinical trials and routine care. In this article, we develop an advanced machine learning algorithm for passively monitoring sleep and nocturnal breathing from radio waves reflected off people while asleep. Validation results in comparison with the gold standard (i.e., polysomnography) (n=849) demonstrate that the model captures the sleep hypnogram (with an accuracy of 81% for 30-second epochs categorized into Wake, Light Sleep, Deep Sleep, or REM), detects sleep apnea (AUROC = 0.88), and measures the patient's Apnea-Hypopnea Index (ICC=0.95; 95% CI = [0.93, 0.97]). Notably, the model exhibits equitable performance across race, sex, and age. Moreover, the model uncovers informative interactions between sleep stages and a range of diseases including neurological, psychiatric, cardiovascular, and immunological disorders. These findings not only hold promise for clinical practice and interventional trials but also underscore the significance of sleep as a fundamental component in understanding and managing various diseases. 

Nonlinear monotone transformations are used extensively in normalizing flows to construct invertible triangular mappings from simple distributions to complex ones. In existing literature, monotonicity is usually enforced by restricting function classes or model parameters and the inverse transformation is often approximated by root-finding algorithms as a closed-form inverse is unavailable. In this paper, we introduce a new integral-based approach termed: Atomic Unrestricted Time Machine (AUTM), equipped with unrestricted integrands and easy-to-compute explicit inverse. AUTM offers a versatile and efficient way to the design of normalizing flows with explicit inverse and unrestricted function classes or parameters. Theoretically, we present a constructive proof that AUTM is universal: all monotonic normalizing flows can be viewed as limits of AUTM flows. We provide a concrete example to show how to approximate any given monotonic normalizing flow using AUTM flows with guaranteed convergence. Our result implies that AUTM can be used to transform an existing flow into a new one equipped with explicit inverse and unrestricted parameters. The performance of the new approach is evaluated on high dimensional density estimation, variational inference and image generation. 

Abstract On 11 September 2021, two small thunderstorms developed over the Telescope Array Surface Detector (TASD) that produced an unprecedented number of six downward terrestrial gamma ray flashes (TGFs) within one‐hour timeframe. The TGFs occurred during the initial stage of negative cloud‐to‐ground flashes whose return strokes had increasingly large peak currents up to 223 kA, 147 GeV energy deposit in up to 25 1.2 km‐spaced surface detectors, and intermittent bursts of gamma‐rays with total durations up to 717 s. The analyses are based on observations recorded by the TASD network, complemented by data from a 3D lightning mapping array, broadband VHF interferometer, fast electric field change sensor, high‐speed video camera, and the National Lightning Detection Network. The TGFs of the final two flashes had gamma fluences of and 8, logarithmically bridging the gap between previous TASD and satellite‐based detections. The observations further emphasize the similarity between upward and downward TGF varieties, suggesting a common mechanism for their production. 

Abstract Optical emissions associated with Terrestrial Gamma ray Flashes (TGFs) have recently become important subjects in space‐based and ground‐based observations as they can help us understand how TGFs are produced during thunderstorms. In this paper, we present the first time‐resolved leader spectra of the optical component associated with a downward TGF. The TGF was observed by the Telescope Array Surface Detector (TASD) simultaneously with other lightning detectors, including a Lightning Mapping Array (LMA), an INTerFerometer (INTF), a Fast Antenna (FA), and a spectroscopic system. The spectroscopic system recorded leader spectra at 29,900 frames per second (33.44 s time resolution), covering a spectral range from 400 to 900 nm, with 2.1 nm per pixel. The recordings of the leader spectra began 11.7 ms before the kA return stroke and at a height of 2.37 km above the ground. These spectra reveal that optical emissions of singly ionized nitrogen and oxygen occur between 167 s before and 267 s after the TGF detection, while optical emissions of neutrals (H I, 656 nm; N I, 744 nm, and O I, 777 nm) occur right at the moment of the detection. The time‐dependent spectra reveal differences in the optical emissions of lightning leaders with and without downward TGFs.