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The flow within adhering droplets subjected to external shear flows has a significant influence on the stability and eventual detachment of the droplets from the surface. Most commonly, the velocity field inside adhering droplets is measured by means of particle image velocimetry (PIV), which requires a correction step for distortion caused by refraction of light at the gas-liquid interface. Current methods for distortion correction based on ray tracing are limited to low external flow velocities. However, the ray-tracing method can be extended to arbitrarily deformed droplet shapes if the instantaneous three-dimensional droplet interface is availble. In the present work, a previously introduced method for the image-based reconstruction of gas-liquid interfaces by means of deep learning is adapted to determine the instantaneous interface of adhering droplets in external shear flows. In this regard, a purposefully developed optical measurement technique based on the shadowgraphy method is employed that encodes additional three-dimensional (3D) information of the interface in the images via glare points from lateral light sources. On the basis of the images recorded in the experiments, the volumetric shape of the droplet is reconstructed by a neural network that was trained on the spatio-temporal dynamics of the gas-liquid interface from a synthetic dataset obtained by numerical simulation. The results for experiments with adhering droplets at different velocities of external flow demonstrate that the combination of the learned droplet geometry with the depth encoding through the glare points facilitates a robust and flexible reconstruction. The proposed method reconstructs the instantaneous three-dimensional interface of adhering droplets at both high resolution and spatial accuracy and thereby enables the distortion correction of PIV measurements at high external flow velocities.

Two-phase flow phenomena play a key role in many engineering applications, including hydrogen fuel cells, spray cooling techniques and combustion. Specialized techniques like shadowgraphy and particle image velocimetry can reveal gas-liquid interface evolution and internal velocity fields; however, they are largely limited to planar measurements, while flow dynamics are inherently three-dimensional (3D). Deep learning techniques based on convolutional neural networks provide a powerful approach for volumetric reconstruction based on the experimental data by leveraging spatial structure of images and extracting context-rich features. Building on this foundation, Physics-informed neural networks (PINNs) offer a complementary and promising alternative integrating prior knowledge in the form of governing equations into the networks training process. This integration enables accurate predictions even with limited data. By combining the strengths of both approaches, we propose a novel convolutional feature-enhanced PINNs framework, designed for the spatio-temporal reconstruction of two-phase flows from color-coded shadowgraphy images. The proposed approach is first validated on synthetic data generated through direct numerical simulation, demonstrating high spatial accuracy in reconstructing the three-dimensional gas-liquid interface, along with the inferred velocity and pressure fields. Subsequently, we apply this method to interface reconstruction for an impinging droplet using planar experimental data, highlighting the practical applicability and significant potential of the proposed approach to real-world fluid dynamics analysis.

The Legacy Survey of Space and Time (LSST) at Vera C. Rubin Observatory is planned to begin in the Fall of 2025. The LSST survey cadence has been designed via a community-driven process regulated by the Survey Cadence Optimization Committee (SCOC), which recommended up to 3% of the observing time to carry out Target of Opportunity (ToO) observations. Experts from the scientific community, Rubin Observatory personnel, and members of the SCOC were brought together to deliver a recommendation for the implementation of the ToO program during a workshop held in March 2024. Four main science cases were identified: gravitational wave multi-messenger astronomy, high energy neutrinos, Galactic supernovae, and small potentially hazardous asteroids possible impactors. Additional science cases were identified and briefly addressed in the documents, including lensed or poorly localized gamma-ray bursts and twilight discoveries. Trigger prioritization, automated response, and detailed strategies were discussed for each science case. This document represents the outcome of the Rubin ToO 2024 workshop, with additional contributions from members of the Rubin Science Collaborations. The implementation of the selection criteria and strategies presented in this document has been endorsed in the SCOC Phase 3 Recommendations document (PSTN-056). Although the ToO program is still to be finalized, this document serves as a baseline plan for ToO observations with the Rubin Observatory.

We introduce a novel method that utilizes the longitude-velocity (l-v) envelope to constrain the Milky Way (MW) bar potential. Previous work (Habing 2016) used the l-v diagram to explain the distribution of the observed high-velocity stars. We successfully reproduce their results, but find that their method is limited to only one single type of periodic orbits. In contrast, we propose that the l-v envelope provides much more comprehensive constraints. We compare the properties of test particles in the Portai et al. (2017) MW potential model (P17) with the observed SiO maser stars from the Bulge Asymmetries and Dynamical Evolution (BAaDE) survey. We find that the l-v envelope generated by the bar potential demonstrates reasonable agreement with the observational data, albeit with slight discrepancies near the Galactic center. The inconsistencies suggest that the P17 potential yields a lower central rotation curve, a slightly larger quadrupole strength, or a possibly underestimated pattern speed. We also adopt an updated version of the P17 potential with a modified central mass component (CMC) proposed by Hunter et al. (2024) (H24). The fitting of the l-v envelope suggests that the H24 potential does not completely address the existing challenges and may hint at a possible underestimation of the central bar mass. Our study demonstrates that the l-v envelope can be used as a valuable tool for constraining the Galactic potential and provides insights into the Milky Way bar potential., Comment: 20 pages, 17 figures, ApJ accepted

The flow convergence method includes calculation of the proximal isovelocity surface area (PISA) and is widely used to classify mitral regurgitation (MR) with echocardiography. It constitutes a primary decision factor for determination of treatment and should therefore be a robust quantification method. However, it is known for its tendency to underestimate MR and its dependence on user expertise. The present work systematically compares different pulsatile flow profiles arising from different regurgitation orifices using transesophageal echocardiographic (TEE) probe and particle image velocimetry (PIV) as a reference in an in-vitro environment. It is found that the inter-observer variability using echocardiography is small compared to the systematic underestimation of the regurgitation volume for large orifice areas (up to 52%) where a violation of the flow convergence method assumptions occurs. From a flow perspective, a starting vortex was found as a dominant flow pattern in the regurgant jet for all orifice shapes and sizes. A series of simplified computational fluid dynamics (CFD) simulations indicate that selecting a suboptimal aliasing velocity during echocardiography measurements might be a primary source of potential underestimation in MR characterization via the PISA-based method, reaching up to 40%. In this study, it has been noted in clinical observations that physicians often select an aliasing velocity higher than necessary for optimal estimation in diagnostic procedures.

We present the results from an extensive follow-up campaign of the Tidal Disruption Event (TDE) ASASSN-15oi spanning $\delta t \sim 10 - 3000$ d, offering an unprecedented window into the multiwavelength properties of a TDE during its first $\approx 8$ years of evolution. ASASSN-15oi is one of the few TDEs with strong detections at X-ray, optical/UV, and radio wavelengths and featured two delayed radio flares at $\delta t \sim 180$ d and $\delta t \sim 1400$ d. Our observations at $> 1400$ d reveal an absence of thermal X-rays, a late-time variability in the non-thermal X-ray emission, and sharp declines in the non-thermal X-ray and radio emission at $\delta t \sim 2800$ d and $\sim 3000$ d, respectively. The UV emission shows no significant evolution at $>400$ d and remains above the pre-TDE level. We show that a cooling envelope model can explain the thermal emission consistently across all epochs. We also find that a scenario involving episodic ejection of material due to stream-stream collisions is conducive to explaining the first radio flare. Given the peculiar spectral and temporal evolution of the late-time emission, however, constraining the origins of the second radio flare and the non-thermal X-rays remains challenging. Our study underscores the critical role of long-term, multiwavelength follow-up., Comment: 36 pages, 13 Figures, 8 Tables. Submitted to ApJ

Oblivious transfer between two untrusting parties is an important primitive in cryptography. There are different variants of oblivious transfer. In Rabin oblivious transfer, the sender Alice holds a bit, and the receiver Bob either obtains the bit, or obtains no information with probability $p_?$. Alice should not know whether or not Bob obtained the bit. We examine a quantum Rabin oblivious transfer (OT) protocol that uses two pure states. Investigating different cheating scenarios for the sender and for the receiver, we determine optimal cheating probabilities in each case. Comparing the quantum Rabin oblivious transfer protocol to classical Rabin oblivious transfer protocols, we show that the quantum protocol outperforms classical protocols, which do not use a third party, for some values of $p_?$. We find that quantum Rabin OT protocols that use mixed states can outperform quantum Rabin OT protocols that use pure states for some values of $p_?$., Comment: 12 pages, 3 figures; v2: added Appendix C and other minor additions

We present a method to estimate distances to Asymptotic Giant Branch (AGB) stars in the Galaxy, using spectral energy distributions (SEDs) in the near- and mid-infrared. By assuming that a given set of source properties (initial mass, stellar temperature, composition, and evolutionary stage) will provide a typical SED shape and brightness, sources are color-matched to a distance-calibrated template and thereafter scaled to extract the distance. The method is tested by comparing the distances obtained to those estimated from Very Long Baseline Interferometry or Gaia parallax measurements, yielding a strong correlation in both cases. Additional templates are formed by constructing a source sample likely to be close to the Galactic center, and thus with a common, typical distance for calibration of the templates. These first results provide statistical distance estimates to a set of almost 15,000 Milky Way AGB stars belonging to the Bulge Asymmetries and Dynamical Evolution (BAaDE) survey, with typical distance errors of $\pm 35$%. With these statistical distances a map of the intermediate-age population of stars traced by AGBs is formed, and a clear bar structure can be discerned, consistent with the previously reported inclination angle of 30$^\circ$ to the GC-Sun direction vector. These results motivate deeper studies of the AGB population to tease out the intermediate-age stellar distribution throughout the Galaxy, as well as determining statistical properties of the AGB population luminosity and mass-loss rate distributions., Comment: Accepted to APJ

Intensive care unit (ICU) patients exhibit erratic blood glucose (BG) fluctuations, including hypoglycemic and hyperglycemic episodes, and require exogenous insulin delivery to keep their BG in healthy ranges. Glycemic control via glycemic management (GM) is associated with reduced mortality and morbidity in the ICU, but GM increases the cognitive load on clinicians. The availability of robust, accurate, and actionable clinical decision support (CDS) tools reduces this burden and assists in the decision-making process to improve health outcomes. Clinicians currently follow GM protocol flow charts for patient intravenous insulin delivery rate computations. We present a mechanistic model-based control algorithm that predicts the optimal intravenous insulin rate to keep BG within a target range; the goal is to develop this approach for eventual use within CDS systems. In this control framework, we employed a stochastic model representing BG dynamics in the ICU setting and used the linear quadratic Gaussian control methodology to develop a controller. We designed two experiments, one using virtual (simulated) patients and one using a real-world retrospective dataset. Using these, we evaluate the safety and efficacy of this model-based glycemic control methodology. The presented controller avoids hypoglycemia and hyperglycemia in virtual patients, maintaining BG levels in the target range more consistently than two existing GM protocols. Moreover, this methodology could theoretically prevent a large proportion of hypoglycemic and hyperglycemic events recorded in a real-world retrospective dataset., Comment: 26 pages, 4 figures, 5 tables

The forecasting of entity trajectories at future points in time is a critical capability gap in applications across both Commercial and Defense sectors. Transformers, and specifically Generative Pre-trained Transformer (GPT) networks have recently revolutionized several fields of Artificial Intelligence, most notably Natural Language Processing (NLP) with the advent of Large Language Models (LLM) like OpenAI's ChatGPT. In this research paper, we introduce TrackGPT, a GPT-based model for entity trajectory forecasting that has shown utility across both maritime and air domains, and we expect to perform well in others. TrackGPT stands as a pioneering GPT model capable of producing accurate predictions across diverse entity time series datasets, demonstrating proficiency in generating both long-term forecasts with sustained accuracy and short-term forecasts with high precision. We present benchmarks against state-of-the-art deep learning techniques, showing that TrackGPT's forecasting capability excels in terms of accuracy, reliability, and modularity. Importantly, TrackGPT achieves these results while remaining domain-agnostic and requiring minimal data features (only location and time) compared to models achieving similar performance. In conclusion, our findings underscore the immense potential of applying GPT architectures to the task of entity trajectory forecasting, exemplified by the innovative TrackGPT model., Comment: 16 pages, 8 figures

Ultrafast optical excitations allow creating new metastable and hidden states in quantum materials. However, the fundamental material properties required to support new emergent order are largely unknown. Here we show for two colossal magnetoresistive (CMR) manganites that electronic phase separation (EPS) strongly favors non-thermal behavior and exploit this to stabilize an optically-induced conducting state. Our results shed light on the role of EPS in optical control of CMR manganites and provide guidance for the design of materials that can exhibit non-equilibrium states of matter., Comment: Main Text: 6 pages, 4 figures Supplemental: 5 pages, 6 figures

The present work introduces a deep learning approach for the three-dimensional reconstruction of the spatio-temporal dynamics of the gas-liquid interface in two-phase flows on the basis of monocular images obtained via optical measurement techniques. The dynamics of liquid droplets impacting onto structured solid substrates are captured through high-speed imaging in an extended shadowgraphy setup with additional reflective glare points from lateral light sources that encode further three-dimensional information of the gas-liquid interface in the images. A neural network is learned for the physically correct reconstruction of the droplet dynamics on a labelled dataset generated by synthetic image rendering on the basis of gas-liquid interface shapes obtained from direct numerical simulation. The employment of synthetic image rendering allows for the efficient generation of training data and circumvents the introduction of errors resulting from the inherent discrepancy of the droplet shapes between experiment and simulation. The accurate reconstruction of the gas-liquid interface during droplet impingement on the basis of images obtained in the experiment demonstrates the practicality of the presented approach based on neural networks and synthetic training data generation. The introduction of glare points from lateral light sources in the experiments is shown to improve the reconstruction accuracy, which indicates that the neural network learns to leverage the additional three-dimensional information encoded in the images for a more accurate depth estimation. Furthermore, the physically reasonable reconstruction of unknown gas-liquid interface shapes indicates that the neural network learned a versatile model of the involved two-phase flow phenomena during droplet impingement.

We present radio observations of 23 optically discovered tidal disruption events (TDEs) on timescales of 500-3200 days post discovery. We detect nine new TDEs that did not have detectable radio emission at earlier times, indicating a late-time brightening after several hundred (and up to 2300) days; an additional seven TDEs exhibit radio emission whose origin is ambiguous or may be attributed to the host galaxy or an active galactic nucleus. We also report a new rising component in one TDE previously detected in the radio at 10^3 days. While the radio emission in some of the detected TDEs peaked on a timescale 2-4 yr, over half of the sample still show rising emission. The range of luminosities for the sample is 10^37-10^39 erg/s, about 2 orders of magnitude below the radio luminosity of the relativistic TDE Sw J1644+57. Our data set indicates 40% of all optical TDEs are detected in radio hundreds to thousands of days after discovery, and that this is probably more common than early radio emission peaking at 10^2 days. Using an equipartition analysis, we find evidence for a delayed launch of the radio-emitting outflows, with delay timescales of 500-2000 days, inferred velocities of 0.02-0.15c, and kinetic energies of 10^47-10^49 erg. We rule out off axis relativistic jets as a viable explanation for this population, and conclude delayed outflows are a more likely explanation, possibly from delayed disk formation. We conclude late radio emission marks a fairly ubiquitous but heretofore overlooked phase of TDE evolution., Comment: 30 pages. Published in ApJ

We present the results from a multi-year radio campaign of the superluminous supernova (SLSN) 2017ens, which yielded the earliest radio detection of a SLSN to date at the age of $\sim$3.3 years after explosion. SN2017ens was not detected at radio frequencies in the first $\sim$300\,d of evolution but reached $L_{\nu}\approx 10^{28}\,\rm{erg\,s^{-1}\,cm^{-2}}$ at $\nu\sim 6$ GHz, $\sim1250$ days post-explosion. Interpreting the radio observations in the context of synchrotron radiation from the supernova shock interaction with the circumstellar medium (CSM), we infer an effective mass-loss rate of $\approx 10^{-4}\,\rm{M_{\odot}yr^{-1}}$ at $r\sim 10^{17}$ cm from the explosion's site, for a wind speed of $v_w=50-60\,\rm{km\,s^{-1}}$ measured from optical spectra. These findings are consistent with the spectroscopic metamorphosis of SN2017ens from hydrogen-poor to hydrogen-rich $\sim190$ d after explosion reported by Chen et al., 2018. SN2017ens is thus an addition to the sample of hydrogen-poor massive progenitors that explode shortly after having lost their hydrogen envelope. The inferred circumstellar densities, implying a CSM mass up to $\sim0.5\,\rm{M_{\odot}}$, and low velocity of the ejection point at binary interactions (in the form of common envelope evolution and subsequent envelope ejection) playing a role in shaping the evolution of the stellar progenitors of SLSNe in the $\lesssim 500$ yr preceding core collapse., Comment: 9 pages, 4 figures, 1 table

The purpose of the present work is to examine two possibilities; firstly, predicting equivalent sand-grain roughness size $k_s$ based on the roughness height probability density function and power spectrum leveraging machine learning as a regression tool, and secondly, extracting information about relevance of different roughness scales to skin-friction drag by interpreting the output of the trained data-driven model. The model is an ensemble neural network consisting of 50 deep neural networks. The data for the training of the model is obtained from direct numerical simulations (DNSs) of turbulent flow in plane channels over 85 irregular multi-scale roughness samples at friction Reynolds number Re$_\tau=800$. The 85 roughness samples are selected from a repository of 4200 samples, covering a wide parameter space, through an active learning (AL) framework. The selection is made in several iterations, based on the informativeness of samples in the repository, quantified by the variance of ENN predictions. This AL framework aims to maximize the generalizability of the predictions with a certain amount of data. This is examined using three different testing data sets with different types of roughness, including 21 surfaces from the literature. The model yields an overall mean error of 5\% to 10\% on different testing data sets. Subsequently, a data interpretation technique, known as layer-wise relevance propagation, is applied to measure the contributions of different roughness wave-lengths to the predicted $k_s$. High-pass filtering is then applied to the roughness PS to exclude the wave-numbers identified as drag-irrelevant. The filtered rough surfaces are investigated using DNS, and it is demonstrated that, despite significant impact of filtering on the roughness topographical appearance and statistics, the skin-friction coefficient of the original roughness is successfully preserved., Comment: 24 pages, 11 figures, submitted to JFM

Transfer of recent advances in deep reinforcement learning to real-world applications is hindered by high data demands and thus low efficiency and scalability. Through independent improvements of components such as replay buffers or more stable learning algorithms, and through massively distributed systems, training time could be reduced from several days to several hours for standard benchmark tasks. However, while rewards in simulated environments are well-defined and easy to compute, reward evaluation becomes the bottleneck in many real-world environments, e.g., in molecular optimization tasks, where computationally demanding simulations or even experiments are required to evaluate states and to quantify rewards. Therefore, training might become prohibitively expensive without an extensive amount of computational resources and time. We propose to alleviate this problem by replacing costly ground-truth rewards with rewards modeled by neural networks, counteracting non-stationarity of state and reward distributions during training with an active learning component. We demonstrate that using our proposed ACRL method (Actively learning Costly rewards for Reinforcement Learning), it is possible to train agents in complex real-world environments orders of magnitudes faster. By enabling the application of reinforcement learning methods to new domains, we show that we can find interesting and non-trivial solutions to real-world optimization problems in chemistry, materials science and engineering.

We present the Young Supernova Experiment Data Release 1 (YSE DR1), comprised of processed multi-color Pan-STARRS1 (PS1) griz and Zwicky Transient Facility (ZTF) gr photometry of 1975 transients with host-galaxy associations, redshifts, spectroscopic/photometric classifications, and additional data products from 2019 November 24 to 2021 December 20. YSE DR1 spans discoveries and observations from young and fast-rising supernovae (SNe) to transients that persist for over a year, with a redshift distribution reaching z~0.5. We present relative SN rates from YSE's magnitude- and volume-limited surveys, which are consistent with previously published values within estimated uncertainties for untargeted surveys. We combine YSE and ZTF data, and create multi-survey SN simulations to train the ParSNIP and SuperRAENN photometric classification algorithms; when validating our ParSNIP classifier on 472 spectroscopically classified YSE DR1 SNe, we achieve 82% accuracy across three SN classes (SNe Ia, II, Ib/Ic) and 90% accuracy across two SN classes (SNe Ia, core-collapse SNe). Our classifier performs particularly well on SNe Ia, with high (>90%) individual completeness and purity, which will help build an anchor photometric SNe Ia sample for cosmology. We then use our photometric classifier to characterize our photometric sample of 1483 SNe, labeling 1048 (~71%) SNe Ia, 339 (~23%) SNe II, and 96 (~6%) SNe Ib/Ic. YSE DR1 provides a training ground for building discovery, anomaly detection, and classification algorithms, performing cosmological analyses, understanding the nature of red and rare transients, exploring tidal disruption events and nuclear variability, and preparing for the forthcoming Vera C. Rubin Observatory Legacy Survey of Space and Time., Comment: Accepted to ApJS; 64 pages; 35 figures; 10 tables

Oblivious transfer (OT) is an important cryptographic primitive. Any multi-party computation can be realised with OT as building block. XOR oblivious transfer (XOT) is a variant where the sender Alice has two bits, and a receiver Bob obtains either the first bit, the second bit, or their XOR. Bob should not learn anything more than this, and Alice should not learn what Bob has learnt. Perfect quantum OT with information-theoretic security is known to be impossible. We determine the smallest possible cheating probabilities for unrestricted dishonest parties in non-interactive quantum XOT protocols using symmetric pure states, and present an optimal protocol, which outperforms classical protocols. We also "reverse" this protocol, so that Bob becomes sender of a quantum state and Alice the receiver who measures it, while still implementing oblivious transfer from Alice to Bob. Cheating probabilities for both parties stay the same as for the unreversed protocol. We optically implemented both the unreversed and the reversed protocols, and cheating strategies, noting that the reversed protocol is easier to implement., Comment: 23 pages, 6 figures; v2: added Subsection IV.A and other minor additions