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We present the discovery of a second radio flare from the tidal disruption event (TDE) AT2020vwl via long-term monitoring radio observations. Late-time radio flares from TDEs are being discovered more commonly, with many TDEs showing radio emission 1000s of days after the stellar disruption, but the mechanism that powers these late-time flares is uncertain. Here we present radio spectral observations of the first and second radio flares observed from the TDE AT2020vwl. Through detailed radio spectral monitoring, we find evidence for two distinct outflow ejection episodes, or a period of renewed energy injection into the pre-existing outflow. We deduce that the second radio flare is powered by an outflow that is initially slower than the first flare, but carries more energy and accelerates over time. Through modelling the long-term optical and UV emission from the TDE as arising from an accretion disc, we infer that the second radio outflow launch or energy injection episode occurred approximately at the time of peak accretion rate. The fast decay of the second flare precludes environmental changes as an explanation, while the velocity of the outflow is at all times too low to be explained by an off-axis relativistic jet. Future observations that search for any link between the accretion disc properties and late time radio flares from TDEs will aid in understanding what powers the radio outflows in TDEs, and confirm if multiple outflow ejections or energy injection episodes are common., Comment: 19 pages, 7 figures, submitted to ApJ. Comments welcome

Computational design of protein-binding proteins is a fundamental capability with broad utility in biomedical research and biotechnology. Recent methods have made strides against some target proteins, but on-demand creation of high-affinity binders without multiple rounds of experimental testing remains an unsolved challenge. This technical report introduces AlphaProteo, a family of machine learning models for protein design, and details its performance on the de novo binder design problem. With AlphaProteo, we achieve 3- to 300-fold better binding affinities and higher experimental success rates than the best existing methods on seven target proteins. Our results suggest that AlphaProteo can generate binders "ready-to-use" for many research applications using only one round of medium-throughput screening and no further optimization., Comment: 45 pages, 17 figures

Quasi-periodic eruptions (QPEs) are an extreme X-ray variability phenomenon associated with low-mass supermassive black holes. First discovered in the nucleus of the galaxy GSN 069, they have been so far securely detected in five other galaxies, including RX J1301.9+2747. When detected, the out-of-QPE emission (quiescence) is consistent with the high-energy tail of thermal emission from an accretion disk. We present the X-ray and radio properties of RX J1301.9+2747, both in quiescence and during QPEs. We analyse X-ray data taken during five XMM-Newton observations between 2000 and 2022. The last three observations were taken in coordination with radio observations with the Karl G. Jansky Very Large Array. We also make use of EXOSAT, ROSAT, and Chandra archival observations taken between 1983 and 2009. XMM-Newton detected 34 QPEs of which 8 have significantly lower amplitudes than the others. No correlated radio/X-ray variability was observed during QPEs. In terms of timing properties, the QPEs in RX J1301.9+2747 do not exhibit the striking regularity observed in the discovery source GSN 069. In fact there is no clear repetition pattern between QPEs: the average time separation between their peaks is about four hours, but it can be as short as one, and as long as six hours. The QPE spectral properties of RX J1301.9+2747 as a function of energy are however very similar to those of GSN 069 and of other QPE sources. The quiescent emission of RX J1301.9+2747 is more complex than that of GSN 069, as it requires a soft X-ray excess-like component in addition to the thermal emission from the accretion disk. Its long-term X-ray quiescent flux variations are of low-amplitude and not strictly monotonic, with a general decay over $\sim 22$ years. We discuss our observational results in terms of some of the ideas and models that have been proposed so far for the physical origin of QPEs., Comment: Accepted for publication in Astronomy & Astrophysics. 22 pages and 17 figures in the main text; 11 pages, 5 tables, and 4 figures in the Appendix. Abstract shortened to comply with arXiv requirements

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

Tidal disruption events (TDEs) can potentially probe low-mass black holes in host galaxies that might not adhere to bulge or stellar-dispersion relationships. At least initially, TDEs can also reveal super-Eddington accretion. X-ray spectroscopy can potentially constrain black hole masses, and reveal ionized outflows associated with super-Eddington accretion. Our analysis of XMM-Newton X-ray observations of the TDE AT2021ehb, around 300 days post-disruption, reveals a soft spectrum and can be fit with a combination of multi-color disk blackbody and power-law components. Using two independent disk models with properties suited to TDEs, we estimate a black hole mass at $M \simeq 10^{5.5}~M_{\odot}$, indicating AT2021ehb may expose the elusive low-mass end of the nuclear black hole population. These models offer simple yet robust characterization; more complicated models are not required, but provide important context and caveats in the limit of moderately sensitive data. If disk reflection is included, the disk flux is lower and inferred black hole masses are $\sim$ 0.35 dex higher. Simple wind formulations imply an extremely fast $v_{\mathrm{out}} = -0.2~c$ outflow and obviate a disk continuum component. Assuming a unity filling factor, such a wind implies an instantaneous mass outflow rate of $\dot{M} \simeq 5~M_{\odot}~{\rm yr}^{-1}$. Such a high rate suggests that the filling factor for the Ultra Fast Outflow (UFO) must be extremely low, and/or the UFO phase is ephemeral. We discuss the strengths and limitations of our analysis and avenues for future observations of TDEs., Comment: 19 pages, 4 figures

We consider the prediction of general tensor properties of crystalline materials, including dielectric, piezoelectric, and elastic tensors. A key challenge here is how to make the predictions satisfy the unique tensor equivariance to O(3) group and invariance to crystal space groups. To this end, we propose a General Materials Tensor Network (GMTNet), which is carefully designed to satisfy the required symmetries. To evaluate our method, we curate a dataset and establish evaluation metrics that are tailored to the intricacies of crystal tensor predictions. Experimental results show that our GMTNet not only achieves promising performance on crystal tensors of various orders but also generates predictions fully consistent with the intrinsic crystal symmetries. Our code is publicly available as part of the AIRS library (https://github.com/divelab/AIRS)., Comment: This paper has been accepted to ICML 24 as a poster. You are encouraged to cite the conference version of this paper

Fluid dynamics systems driven by dominant, nearly periodic large-scale dynamics are common across wakes, jets, rotating machinery, and high-speed flows. Traditional decomposition techniques such as proper orthogonal decomposition and dynamic mode decomposition have been used to gain insight into these flows, but can require many modes to represent physical processes. With the aim of generating modes that intuitively convey the underlying physical mechanisms, we propose an intrinsic phase-based proper orthogonal decomposition (IPhaB POD) method. IPhaB POD creates energetically ranked modes that evolve along a characteristic cycle of a driving near-periodic large scale. Our proposed formulation is set in the time domain, which is particularly useful in cases where the large-scale dynamics are imperfectly periodic. We formally derive IPhaB POD within a proper orthogonal decomposition framework, and it inherits the optimal representation inherent to POD. As part of this derivation, a dynamical systems representation of the large scale is utilized, facilitating a definition of phase relative to the large scale within the time domain. An expectation operator and inner product are also constructed relative to this definition of phase in a manner that allows for the various cycles within the data to demonstrate imperfect periodicity. The formulation is tested on two sample problems: a simple, low Reynolds number airfoil wake, and a complex, high-speed pulsating shock wave problem. The resulting modes are shown to better isolate the large-scale dynamics in the first mode than space-only proper orthogonal decomposition, and to highlight meaningful small-scale dynamics in higher modes for the shock flow problem.

We present detailed radio observations of the tidal disruption event (TDE) ASASSN-19bt/AT2019ahk, obtained with the Australia Telescope Compact Array (ATCA), the Atacama Large Millimeter/submillimeter Array (ALMA), and the MeerKAT radio telescopes, spanning 40 to 1464 days after the onset of the optical flare. We find that ASASSN-19bt displays unusual radio evolution compared to other TDEs, as the peak brightness of its radio emission increases rapidly until 457 days post-optical discovery and then plateaus. Using a generalized approach to standard equipartition techniques, we estimate the energy and corresponding physical parameters for two possible emission geometries: a non-relativistic spherical outflow and a relativistic outflow observed from an arbitrary viewing angle. We find that the non-relativistic solution implies a continuous energy rise in the outflow from $E\sim10^{46}$ erg to $E\sim10^{49}$ erg with $\beta \approx 0.05$, while the off-axis relativistic jet solution instead suggests $E\approx10^{52}$ erg with $\Gamma\sim10$ erg at late times in the maximally off-axis case. We find that neither model provides a holistic explanation for the origin and evolution of the radio emission, emphasizing the need for more complex models. ASASSN-19bt joins the population of TDEs that display unusual radio emission at late times. Conducting long-term radio observations of these TDEs, especially during the later phases, will be crucial for understanding how these types of radio emission in TDEs are produced., Comment: 25 pages. Submitted to ApJ

Neural algorithmic reasoning is an emerging research direction that endows neural networks with the ability to mimic algorithmic executions step-by-step. A common paradigm in existing designs involves the use of historical embeddings in predicting the results of future execution steps. Our observation in this work is that such historical dependence intrinsically contradicts the Markov nature of algorithmic reasoning tasks. Based on this motivation, we present our ForgetNet, which does not use historical embeddings and thus is consistent with the Markov nature of the tasks. To address challenges in training ForgetNet at early stages, we further introduce G-ForgetNet, which uses a gating mechanism to allow for the selective integration of historical embeddings. Such an enhanced capability provides valuable computational pathways during the model's early training phase. Our extensive experiments, based on the CLRS-30 algorithmic reasoning benchmark, demonstrate that both ForgetNet and G-ForgetNet achieve better generalization capability than existing methods. Furthermore, we investigate the behavior of the gating mechanism, highlighting its degree of alignment with our intuitions and its effectiveness for robust performance., Comment: To appear at ICLR 2024 (Spotlight paper). 17 pages, 10 figures

Quasi-Periodic Eruptions (QPEs) are luminous X-ray outbursts recurring on hour timescales, observed from the nuclei of a growing handful of nearby low-mass galaxies. Their physical origin is still debated, and usually modeled as (a) accretion disk instabilities or (b) interaction of a supermassive black hole (SMBH) with a lower mass companion in an extreme mass-ratio inspiral (EMRI). EMRI models can be tested with several predictions related to the short- and long-term behavior of QPEs. In this study, we report on the ongoing 3.5-year NICER and XMM-Newton monitoring campaign of eRO-QPE1, which is known to exhibit erratic QPEs that have been challenging for the simplest EMRI models to explain. We report 1) complex, non-monotonic evolution in the long-term trends of QPE energy output and inferred emitting area; 2) the disappearance of the QPEs (within NICER detectability) in October 2023, then reappearance by January 2024 at a luminosity $\sim$100x fainter (and temperature $\sim$3x cooler) than initial discovery; 3) radio non-detections with MeerKAT and VLA observations partly contemporaneous with our NICER campaign (though not during outbursts); and 4) the presence of a possible $\sim$6-day modulation of the QPE timing residuals, which aligns with the expected nodal precession timescale of the underlying accretion disk. Our results tentatively support EMRI-disk collision models powering the QPEs, and we demonstrate that the timing modulation of QPEs may be used to jointly constrain the SMBH spin and disk density profile., Comment: Accepted for publication in ApJ

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science.

We discuss the applicability of quasilinear-type approximations for a turbulent system with a large range of spatial and temporal scales. We consider a paradigm fluid system of rotating convection with a vertical and horizontal temperature gradients. In particular, the interaction of rotating with the horizontal temperature gradient drives a ``thermal wind'' shear flow whose strength is controlled by a horizontal temperature gradient. Varying the parameters systematically alters the ordering of the shearing timescale, the convective timescale, and the correlation timescale. We demonstrate that quasilinear-type approximations work well when the shearing timescale or the correlation timescale is sufficiently short. In all cases, the Generalised Quasilinear approximation (GQL) systematically outperforms the Quasilinear approximation (QL). We discuss the consequences for statistical theories of turbulence interacting with mean gradients. We conclude with comments about the general applicability of these ideas across a wide variety of non-linear physical systems., Comment: 5 pages, 3 figures

Detailed dynamical systems' models used in the life sciences may include hundreds of state variables and many input parameters, often with physical meaning. Therefore, efficient and unique input parameter identification, from experimental data, is an essential but challenging task for this class of model. To clarify our understating of the process (which within a clinical context amounts to a personalisation), we utilise the computational methods of Unscented Kalman filtration (UKF), sensitivity and orthogonality analysis. We have applied these three techniques to a test-bench model of a single ventricle, coupled, via Ohmic valves, to a Compliance-Resistor-Compliance (CRC) Windkessel electrical analogue model of the systemic circulation, chosen in view of its relative simplicity, interpretability and prior art. Utilising an efficient, novel and real-time implementation of the UKF (Code available at https://github.com/H-Sax/CMSB-2023), we show how, counter-intuitively, input parameters are efficiently recovered from experimental data \emph{even if they are not sensitive parameters in the currently accepted sense}. This result (i) exposes potential limitations in the standard interpretation of what it means for an input parameter to be designated identifiable and (ii) suggests that the concepts of sensitivity and identifiability may have a weaker relationship than commonly thought - at least in the presence of an appropriate data set. We rationalise these observations. Practically, we present results which show the UKF to be an efficient method for assigning personalised input parameters from experimental data in real-time, which enhances the clinical significance of our approach.

This research paper proposes a Latent Diffusion Model for 3D (LDM3D) that generates both image and depth map data from a given text prompt, allowing users to generate RGBD images from text prompts. The LDM3D model is fine-tuned on a dataset of tuples containing an RGB image, depth map and caption, and validated through extensive experiments. We also develop an application called DepthFusion, which uses the generated RGB images and depth maps to create immersive and interactive 360-degree-view experiences using TouchDesigner. This technology has the potential to transform a wide range of industries, from entertainment and gaming to architecture and design. Overall, this paper presents a significant contribution to the field of generative AI and computer vision, and showcases the potential of LDM3D and DepthFusion to revolutionize content creation and digital experiences. A short video summarizing the approach can be found at https://t.ly/tdi2.

X-ray quasi-periodic eruptions (QPEs) are intense repeating soft X-ray bursts from the nuclei of nearby galaxies. Their physical origin is still largely unconstrained, and several theoretical models have been proposed to date. We present here results from a recent XMM-Newton observation of GSN 069, the galactic nucleus where QPEs were first discovered. After about two years of absence, QPEs have reappeared in GSN 069, and we detect two consecutive QPEs separated by a much shorter recurrence time than ever before. Moreover, their intensities and peak temperatures are remarkably different, a novel addition to the QPE phenomenology. We study the QPE spectral properties from all XMM-Newton observations assuming QPEs to either represent an additional emission component superimposed on that from the disc, or the transient evolution of the disc emission itself. In the former scenario, QPEs are consistent with black-body emission from a region that expands by a factor of 2-3 during the individual QPE evolution with radius of the order of that of the Sun at QPE peak. In the alternative non-additive scenario, QPEs originate from a region with an area 6-30 times smaller than the quiescent state X-ray emission, with the smallest regions corresponding to the hottest and most luminous eruptions. The QPE reappearance reveals that eruptions are only present below a quiescent luminosity threshold corresponding to an Eddington ratio of the order of 0.4 for a one million solar masses black hole. The disappearance of QPEs above threshold is most likely driven by the ratio of QPE to quiescence temperatures approaching unity at high quiescent luminosity, making QPE detection challenging, if not impossible, above threshold. We briefly discuss some of the consequences of our results on the proposed models for the QPE physical origin. [abridged], Comment: accepted for publication as A&A Letter

A tidal disruption event (TDE) occurs when a star is destroyed by a supermassive black hole. Broadband radio spectral observations of TDEs trace the emission from any outflows or jets that are ejected from the vicinity of the supermassive black hole. However, radio detections of TDEs are rare, with less than 20 published to date, and only 11 with multi-epoch broadband coverage. Here we present the radio detection of the TDE AT2020vwl and our subsequent radio monitoring campaign of the outflow that was produced, spanning 1.5 years post-optical flare. We tracked the outflow evolution as it expanded between $10^{16}$ cm to $10^{17}$ cm from the supermassive black hole, deducing it was non-relativistic and launched quasi-simultaneously with the initial optical detection through modelling the evolving synchrotron spectra of the event. We deduce that the outflow is likely to have been launched by material ejected from stream-stream collisions (more likely), the unbound debris stream, or an accretion-induced wind or jet from the supermassive black hole (less likely). AT2020vwl joins a growing number of TDEs with well-characterised prompt radio emission, with future timely radio observations of TDEs required to fully understand the mechanism that produces this type of radio emission in TDEs., Comment: 15 pages, 9 figures, accepted for publication in MNRAS

We perform a comprehensive analysis of a population of 19 X-ray bright tidal disruption events (TDEs), fitting their X-ray spectra with a new, physically self consistent, relativistic accretion disc model. Not all of the TDEs inhabit regions of parameter space where the model is valid, or have sufficient data for a detailed analysis, and physically interpretable parameters for a sub-sample of 11 TDEs are determined. These sources have thermal (power-law free) X-ray spectra. The radial sizes measured from these spectra lie at values consistent with the inner-most stable circular orbit of black holes with masses given by the $M_{\rm BH}-\sigma$ relationship, and can be used as an independent measurement of $M_{\rm BH}$. The bolometric disc luminosity can also be inferred from X-ray data. All of the TDEs have luminosities which are sub-Eddington ($L_{\rm bol, disc} \lesssim L_{\rm edd}$), and larger than the typical hard-state transitional luminosity of X-ray binary discs ($L_{\rm bol, disc} \gtrsim 0.01 L_{\rm edd}$). The {\it peak} bolometric luminosity is found to be linearly correlated with the $M_{\rm BH}-\sigma$ mass. The TDE X-ray-to-bolometric correction can reach values up to $\sim 100$, and grows exponentially at late times, resolving the missing energy problem. We show that the peak disc luminosities of some TDEs are smaller than their observed optical luminosities, implying that not all of the early time optical emission can be sourced from reprocessed disc emission. Our results are supportive of the hypothesis that thermal X-ray bright TDEs are in accretion states analogous to the ``soft'' accretion state of X-ray binaries, and that black hole accretion processes are scale (mass) invariant., Comment: 17 pages + 3 appendices. 18 figures, 5 tables. Version as accepted for publication in MNRAS

A flat plate turbulent boundary layer (TBL) is experimentally subjected to a family of 22 favorable-adverse pressure gradients (FAPGs) by using a ceiling panel of variable convex curvature. We define a FAPG as a sequence of streamwise pressure gradients in the order of favorable followed by adverse, similar to the pressure gradient sequence over the suction side of an airfoil. The adverse pressure gradient region of this configuration is studied using particle image velocimetry (PIV) in the streamwise--wall-normal plane. The streamwise mean and Reynolds stresses are presented and discussed in this work. Although favorable and adverse pressure gradients are known to evoke opposing responses from a TBL, the current adverse pressure gradient (APG) that followed a favorable pressure gradient (FPG) was not found to 'reverse' the effect of the FPG. Instead, a complex response that is neither characteristic of a ZPG nor an APG TBL was observed. The Reynolds stresses showed a bimodal structure, with the outer-scaled stresses strengthening along the streamwise direction in $y < ~ 0.2 \delta$ and weakening in $y > ~ 0.2\delta$, where $y$ is the wall-normal coordinate, and $\delta$, the local boundary layer thickness. These were signatures of a new internal layer, previously observed in bump/hill flows, formed due to the pressure gradient sign change from favorable to adverse. The non-typical structure of and trends in the statistics were more significant in the stronger pressure gradient cases, where the stronger upstream FPG seemed to have exerted a stronger influence on the downstream APG. The current dataset with 22 cases provided a well-resolved picture of the breakdown of equilibrium of the ZPG TBL under an FAPG configuration. The statistics for all the cases are included under the ancillary file section.

Data stores are the foundation on which data science, in all its variations, is built upon. They provide a queryable interface to structured and unstructured data. Data science often starts by leveraging these query features to perform initial data preparation. However, most data stores are designed to run continuously to service disparate user requests with little or no downtime. Many HPC architectures process user requests by job queue scheduler and maintain a shard filesystem to store a jobs persistent data. We deploy a MongoDB sharded cluster with a run script that is designed to run a data science workload concurrently. As our test piece, we run data ingest and data queries to measure the performance with different configurations on the Blue Waters supper computer.

GSN 069 is the first galactic nucleus where quasi-periodic eruptions (QPEs) have been identified. These are high-amplitude, soft X-ray bursts recurring every ~9 hr, lasting ~1 hr, and during which the X-ray count rate increases by up to two orders of magnitude with respect to an otherwise stable quiescent level. The X-ray spectral properties and the long-term evolution of GSN 069 in the first few years are consistent with a long-lived tidal disruption event (TDE). Here we derive the properties of QPEs and of the long-term X-ray evolution in GSN 069 over the past 12 yr by studying timing and spectral X-ray data from 11 XMM-Newton, one Chandra, and 34 Swift observations on timescales ranging from minutes to years. QPEs in GSN 069 are a transient phenomenon with a lifetime > 1.05 yr. The QPE intensity and recurrence time oscillate and allow for alternating strong-weak QPEs and long-short recurrence times to be defined. In observations with QPEs, the quiescent level exhibits a quasi-periodic oscillation with a period equal to the average separation between consecutive QPEs. The QPE spectral evolution is consistent with thermal emission from a very compact region that heats up quickly and subsequently cools down via X-ray emission while expanding by a factor of ~3 in radius. The long-term evolution of the quiescent level is characterised by two repeating TDEs ~9 yr apart. We detect a precursor X-ray flare prior to the second TDE that may be associated with the circularisation phase during disc formation. A similar precursor flare is tentatively detected just before the first TDE. Future X-ray observations of GSN 069 promise that the QPE origin and the relation between QPEs and repeating TDEs in this galactic nucleus will be constrained, with consequences for the other sources where QPEs have been identified. [abridged], Comment: Revised and expanded version. Accepted for publication in A&A on 30 November 2022

We present a publicly accessible database designed to aid in the conception, training, demonstration, evaluation, and comparison of reduced-complexity models for fluid mechanics. Availability of high-quality flow data is essential for all of these aspects of model development for both data-driven and physics-based methods. The database contains time-resolved data for six distinct datasets: a large eddy simulation of a turbulent jet, direct numerical simulations of a zero-pressure-gradient turbulent boundary layer, particle-image-velocimetry measurements for the same boundary layer at several Reynolds numbers, direct numerical simulations of laminar stationary and pitching flat-plate airfoils, particle-image-velocimetry and force measurements of an airfoil encountering a gust, and a large eddy simulation of the separated, turbulent flow over an airfoil. These six cases span several key flow categories: laminar and turbulent, statistically stationary and transient, tonal and broadband spectral content, canonical and application-oriented, wall-bounded and free-shear flow, and simulation and experimental measurements. For each dataset, we describe the flow setup and computational/experimental methods, catalog the data available in the database, and provide examples of how these data can be used for reduced-complexity modeling. All data can be downloaded using a browser interface or Globus. Our vision is that the common testbed provided by this database will aid the fluid mechanics community in clarifying the distinct capabilities of new and existing methods.

On-road bicycle lanes improve safety for cyclists, and encourage participation in cycling for active transport and recreation. With many local authorities responsible for portions of the infrastructure, official maps and datasets of bicycle lanes may be out-of-date and incomplete. Even "crowdsourced" databases may have significant gaps, especially outside popular metropolitan areas. This thesis presents a method to create a map of bicycle lanes in a survey area by taking sample street scene images from each road, and then applying a deep learning model that has been trained to recognise bicycle lane symbols. The list of coordinates where bicycle lane markings are detected is then correlated to geospatial data about the road network to record bicycle lane routes. The method was applied to successfully build a map for a survey area in the outer suburbs of Melbourne. It was able to identify bicycle lanes not previously recorded in the official state government dataset, OpenStreetMap, or the "biking" layer of Google Maps., Comment: 77 pages, 24 figures. A minor thesis submitted in partial fulfilment of the requirements for the degree of Master of Data Science

We present the identifications of a flux-limited sample of highly variable X-ray sources on long time-scales from the second catalogue of the XMM$-$Newton SLew survey (XMMSL2). The carefully constructed sample, comprising 265 sources (2.5 per cent) selected from the XMMSL2 clean catalogue, displayed X-ray variability of a factor of more than 10 in 0.2$-$2 keV compared to the ROSAT All Sky Survey. Of the sample sources, 94.3 per cent are identified. The identification procedure follows a series of cross-matches with astronomical data bases and multiwavelength catalogues to refine the source position and identify counterparts to the X-ray sources. Assignment of source type utilizes a combination of indicators including counterparts offset, parallax measurement, spectral colours, X-ray luminosity, and light-curve behaviour. We identified 40 per cent of the variables with stars, 10 per cent with accreting binaries, and at least 30.4 per cent with active galactic nuclei. The rest of the variables are identified as galaxies. It is found that the mean effective temperatures of the highly variable stars are lower than those of less variable stars. Our sample of highly variable AGN tend to have lower black hole masses, redshifts, and marginally lower soft X-ray luminosities compared to the less variable ones, while no difference was found in the Eddington ratio distributions. Five flaring events are tidal disruption events published previously. This study has significantly increased the number of variable sources in XMMSL2 with identifications and provides greater insight on the nature of many of the sources, enabling further studies of highly variable X-ray sources., Comment: 20 pages, 18 figures, published in MNRAS