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The state-of-the-art in biometric recognition algorithms and operational systems has advanced quickly in recent years providing high accuracy and robustness in more challenging collection environments and consumer applications. However, the technology still suffers greatly when applied to non-conventional settings such as those seen when performing identification at extreme distances or from elevated cameras on buildings or mounted to UAVs. This paper summarizes an extension to the largest dataset currently focused on addressing these operational challenges, and describes its composition as well as methodologies of collection, curation, and annotation., Comment: 9 pages, 11 figures, 2 tables

We present Mantis Shrimp, a multi-survey deep learning model for photometric redshift estimation that fuses ultra-violet (GALEX), optical (PanSTARRS), and infrared (UnWISE) imagery. Machine learning is now an established approach for photometric redshift estimation, with generally acknowledged higher performance in areas with a high density of spectroscopically identified galaxies over template-based methods. Multiple works have shown that image-based convolutional neural networks can outperform tabular-based color/magnitude models. In comparison to tabular models, image models have additional design complexities: it is largely unknown how to fuse inputs from different instruments which have different resolutions or noise properties. The Mantis Shrimp model estimates the conditional density estimate of redshift using cutout images. The density estimates are well calibrated and the point estimates perform well in the distribution of available spectroscopically confirmed galaxies with (bias = 1e-2), scatter (NMAD = 2.44e-2) and catastrophic outlier rate ($\eta$=17.53$\%$). We find that early fusion approaches (e.g., resampling and stacking images from different instruments) match the performance of late fusion approaches (e.g., concatenating latent space representations), so that the design choice ultimately is left to the user. Finally, we study how the models learn to use information across bands, finding evidence that our models successfully incorporates information from all surveys. The applicability of our model to the analysis of large populations of galaxies is limited by the speed of downloading cutouts from external servers; however, our model could be useful in smaller studies such as generating priors over redshift for stellar population synthesis.

We employ dynamical mean-field theory (DMFT) combined with density functional theory (DFT) and the non-equilibrium Green's function technique to investigate the steady-state transport properties of an Fe/MgO/Fe magnetic tunnel junction (MTJ), focusing on the impact of dynamical electron correlations on the Fe $3d$ orbitals. By applying the rigid shift approximation, we extend the calculations from zero- to finite-bias in a simple and computationally efficient manner, obtaining the bias-dependent electronic structure and current-versus-voltage characteristic curve in both the parallel and antiparallel configurations. In particular, we find that dynamical electron correlation manifests as a reduction in the spin splitting of the Fe $3d_{z^2}$ state compared to DFT predictions and introduces a finite relaxation time. The impact of these effects on the transport properties, however, varies significantly between magnetic configurations. In the parallel configuration, the characteristic curves obtained with DFT and DMFT are similar, as the transport is mostly due to the coherent transmission of spin-up electrons through the MgO barrier. Conversely, in the antiparallel configuration, correlation effects become more significant, with DMFT predicting a sharp current increase due to bias-driven inelastic electron-electron scattering. As a consequence, DMFT gives a lower bias threshold for the suppression of the tunneling magnetoresistance ratio compared to DFT, matching experimental data more closely.

The Suborbital Imaging Spectrograph for Transition region Irradiance from Nearby Exoplanet host stars (SISTINE) is a rocket-borne ultraviolet (UV) imaging spectrograph designed to probe the radiation environment of nearby stars. SISTINE operates over a bandpass of 98 -- 127 and 130 -- 158 nm, capturing a broad suite of emission lines tracing the full 10$^4$ -- 10$^5$ K formation temperature range critical for reconstructing the full UV radiation field incident on planets orbiting solar-type stars. SISTINE serves as a platform for key technology developments for future ultraviolet observatories. SISTINE operates at moderate resolving power ($R\sim$1500) while providing spectral imaging over an angular extent of $\sim$6', with $\sim$2" resolution at the slit center. The instrument is composed of an f/14 Cassegrain telescope that feeds a 2.1x magnifying spectrograph, utilizing a blazed holographically ruled diffraction grating and a powered fold mirror. Spectra are captured on a large format microchannel plate (MCP) detector consisting of two 113 x 42 mm segments each read out by a cross delay-line anode. Several novel technologies are employed in SISTINE to advance their technical maturity in support of future NASA UV/optical astronomy missions. These include enhanced aluminum lithium fluoride coatings (eLiF), atomic layer deposition (ALD) protective optical coatings, and ALD processed large format MCPs. SISTINE was launched a total of three times with two of the three launches successfully observing targets Procyon A and $\alpha$ Centauri A and B.

The moist shallow water equations offer a promising route for advancing understanding of the coupling of physical parametrisations and dynamics in numerical atmospheric models, an issue known as 'physics-dynamics coupling'. Without moist physics, the traditional shallow water equations are a simplified form of the atmospheric equations of motion and so are computationally cheap, but retain many relevant dynamical features of the atmosphere. Introducing physics into the shallow water model in the form of moisture provides a tool to experiment with numerical techniques for physics-dynamics coupling in a simple dynamical model. In this paper, we compare some of the different moist shallow water models by writing them in a general formulation. The general formulation encompasses three existing forms of the moist shallow water equations and also a fourth, previously unexplored formulation. The equations are coupled to a three-state moist physics scheme that interacts with the resolved flow through source terms and produces two-way physics-dynamics feedback. We present a new compatible finite element discretisation of the equations and apply it to the different formulations of the moist shallow water equations in three test cases. The results show that the models capture generation of cloud and rain and physics-dynamics interactions, and demonstrate some differences between moist shallow water formulations and the implications of these different modelling choices.

The considerable body of data available for evaluating biometric recognition systems in Research and Development (R\&D) environments has contributed to the increasingly common problem of target performance mismatch. Biometric algorithms are frequently tested against data that may not reflect the real world applications they target. From a Testing and Evaluation (T\&E) standpoint, this domain mismatch causes difficulty assessing when improvements in State-of-the-Art (SOTA) research actually translate to improved applied outcomes. This problem can be addressed with thoughtful preparation of data and experimental methods to reflect specific use-cases and scenarios. To that end, this paper evaluates research solutions for identifying individuals at ranges and altitudes, which could support various application areas such as counterterrorism, protection of critical infrastructure facilities, military force protection, and border security. We address challenges including image quality issues and reliance on face recognition as the sole biometric modality. By fusing face and body features, we propose developing robust biometric systems for effective long-range identification from both the ground and steep pitch angles. Preliminary results show promising progress in whole-body recognition. This paper presents these early findings and discusses potential future directions for advancing long-range biometric identification systems based on mission-driven metrics.

This paper examines covariate effects on fused whole body biometrics performance in the IARPA BRIAR dataset, specifically focusing on UAV platforms, elevated positions, and distances up to 1000 meters. The dataset includes outdoor videos compared with indoor images and controlled gait recordings. Normalized raw fusion scores relate directly to predicted false accept rates (FAR), offering an intuitive means for interpreting model results. A linear model is developed to predict biometric algorithm scores, analyzing their performance to identify the most influential covariates on accuracy at altitude and range. Weather factors like temperature, wind speed, solar loading, and turbulence are also investigated in this analysis. The study found that resolution and camera distance best predicted accuracy and findings can guide future research and development efforts in long-range/elevated/UAV biometrics and support the creation of more reliable and robust systems for national security and other critical domains.

We present new near-ultraviolet (NUV, $\lambda$ = 2479 $-$ 3306 $\r{A}$) transmission spectroscopy of KELT-9b, the hottest known exoplanet, obtained with the Colorado Ultraviolet Transit Experiment ($CUTE$) CubeSat. Two transits were observed on September 28th and September 29th 2022, referred to as Visits 1 and 2 respectively. Using a combined transit and systematics model for each visit, the best-fit broadband NUV light curves are R$_{\text{p}}$/R$_{\star}$ $=$ 0.136$_{0.0146}^{0.0125}$ for Visit 1 and R$_{\text{p}}$/R$_{\star}$ $=$ 0.111$_{0.0190}^{0.0162}$ for Visit 2, appearing an average of 1.54$\times$ larger in the NUV than at optical wavelengths. While the systematics between the two visits vary considerably, the two broadband NUV light curves are consistent with each other. A transmission spectrum with 25 $\r{A}$ bins suggests a general trend of excess absorption in the NUV, consistent with expectations for ultra-hot Jupiters. Although we see an extended atmosphere in the NUV, the reduced data lack the sensitivity to probe individual spectral lines.

The Solar eruptioN Integral Field Spectrograph (SNIFS) is a solar-gazing spectrograph scheduled to fly in the summer of 2025 on a NASA sounding rocket. Its goal is to view the solar chromosphere and transition region at a high cadence (1s) both spatially (0.5") and spectrally (33 m{\AA}) viewing wavelengths around Lyman Alpha (1216 {\AA}), Si iii (1206 {\AA}) and O v (1218 {\AA}) to observe spicules, nanoflares, and possibly a solar flare. This time cadence will provide yet-unobserved detail about fast-changing features of the Sun. The instrument is comprised of a Gregorian-style reflecting telescope combined with a spectrograph via a specialized mirrorlet array that focuses the light from each spatial location in the image so that it may be spectrally dispersed without overlap from neighboring locations. This paper discusses the driving science, detailed instrument and subsystem design, and pre-integration testing of the SNIFS instrument., Comment: 22 pages (not including references), 7 figures, submitting to Solar Physics

The recent emergence of van der Waals (vdW) ferromagnets has opened new opportunities for designing spintronic devices. We theoretically investigate the coherent spin-dependent transport properties of the vdW ferromagnet Fe$_4$GeTe$_2$, by using density functional theory combined with the non-equilibrium Green's functions method. We find that the conductance in the direction perpendicular to the layers is half-metallic, namely it is entirely spin-polarized, as a result of the material's electronic structure. This characteristic persists from bulk to single layer, even under significant bias voltages, and it is little affected by spin-orbit coupling and electron correlation. Motivated by this observation, we then investigate the tunnel magnetoresistance (TMR) effect in an magnetic tunnel junction, which comprises two Fe$_4$GeTe$_2$ layers separated by the vdW gap acting as insulating barrier. We predict a TMR ratio of almost 500\%, which can be further boosted by increasing the number of Fe$_4$GeTe$_2$ layers in the junction., Comment: 16 pages, 15 figures and 4 tables

The availability of large, public, multi-modal astronomical datasets presents an opportunity to execute novel research that straddles the line between science of AI and science of astronomy. Photometric redshift estimation is a well-established subfield of astronomy. Prior works show that computer vision models typically outperform catalog-based models, but these models face additional complexities when incorporating images from more than one instrument or sensor. In this report, we detail our progress creating Mantis Shrimp, a multi-survey computer vision model for photometric redshift estimation that fuses ultra-violet (GALEX), optical (PanSTARRS), and infrared (UnWISE) imagery. We use deep learning interpretability diagnostics to measure how the model leverages information from the different inputs. We reason about the behavior of the CNNs from the interpretability metrics, specifically framing the result in terms of physically-grounded knowledge of galaxy properties., Comment: 4 pages, 1 figure, 1 table. Submitted to AI4Differential Equations in Science Workshop at ICLR24. Public repository unavailable while under institutional review

The Far- and Lyman-Ultraviolet Imaging Demonstrator (FLUID) is a rocket-borne arcsecond-level ultraviolet (UV) imaging instrument covering four bands between 92 -- 193 nm. FLUID will observe nearby galaxies to find and characterize the most massive stars that are the primary drivers of the chemical and dynamical evolution of galaxies, and the co-evolution of the surrounding galactic environment. The FLUID short wave channel is designed to suppress efficiency at Lyman-$\alpha$ (121.6 nm), while enhancing the reflectivity of shorter wavelengths. Utilizing this technology, FLUID will take the first ever images of local galaxies isolated in the Lyman ultraviolet (90 -- 120 nm). As a pathfinder instrument, FLUID will employ and increase the TRL of band-selecting UV coatings, and solar-blind UV detector technologies including microchannel plate and solid state detectors; technologies prioritized in the 2022 NASA Astrophysical Biennial Technology Report. These technologies enable high throughput and high sensitivity observations in the four co-aligned UV imaging bands that make up the FLUID instrument. We present the design of FLUID, status on the technology development, and results from initial assembly and calibration of the FLUID instrument., Comment: Accepted to JATIS, March 18, 2024