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Gross Primary Productivity (GPP), the amount of carbon plants fixed by photosynthesis, is pivotal for understanding the global carbon cycle and ecosystem functioning. Process-based models built on the knowledge of ecological processes are susceptible to biases stemming from their assumptions and approximations. These limitations potentially result in considerable uncertainties in global GPP estimation, which may pose significant challenges to our Net Zero goals. This study presents UFLUX v2.0, a process-informed model that integrates state-of-art ecological knowledge and advanced machine learning techniques to reduce uncertainties in GPP estimation by learning the biases between process-based models and eddy covariance (EC) measurements. In our findings, UFLUX v2.0 demonstrated a substantial improvement in model accuracy, achieving an R^2 of 0.79 with a reduced RMSE of 1.60 g C m^-2 d^-1, compared to the process-based model's R^2 of 0.51 and RMSE of 3.09 g C m^-2 d^-1. Our global GPP distribution analysis indicates that while UFLUX v2.0 and the process-based model achieved similar global total GPP (137.47 Pg C and 132.23 Pg C, respectively), they exhibited large differences in spatial distribution, particularly in latitudinal gradients. These differences are very likely due to systematic biases in the process-based model and differing sensitivities to climate and environmental conditions. This study offers improved adaptability for GPP modelling across diverse ecosystems, and further enhances our understanding of global carbon cycles and its responses to environmental changes.

Quasiparticle relaxation in pure superconductors is thought to be determined by the intrinsic inelastic scattering rate in the material. In certain applications, i.e. superconducting qubits and circuits, excess quasiparticles exist at densities far beyond the thermal equilibrium level, potentially leading to dephasing and energy loss. In order to engineer superconductors with shorter overall quasiparticle lifetimes, we consider the impact of a proximity layer on the transport of quasiparticles in a superconductor. We find that a normal metal layer can be used to significantly increase the relaxation rate of quasiparticles in a superconductor, as seen by a large reduction in the quasiparticle charge imbalance in a fully proximitized Cu/Al bilayer wire. The mechanism for this effect may be useful for preventing quasiparticle poisoning of qubits using carefully chosen proximity bilayers consisting of clean superconductors and disordered normal metals., Comment: 8 pages, 6 figures

Excited state spectra of $B$, $B_s$ and $B_c$ mesons are computed using lattice QCD. Working with a large basis of carefully constructed, approximately-local $q\bar{q}$-like operators we determine a rich spectrum of states up to $J=4$ in $B, B_s$ and $B_c$, that can be grouped into patterns matching those of the quark model. In addition, hybrid-like states are identified at approximately 1500 MeV above the ground states in each of $B, B_s$ and $B_c$, in a multiplet pattern already found in similar computations at a range of quark masses spanning light to bottom. This study is performed using an anisotropic, relativistic Wilson-clover action at a single spatial lattice spacing of $a_s\approx 0.12$ fm and with a light-quark mass corresponding to $m_\pi\approx 391$ MeV. We find good agreement with experimental results where known and discuss prospects for further work in interesting $J^P$ quantum numbers., Comment: 26 pages, 14 figures

Stepped wedge cluster randomized trials (SW-CRTs) are a form of randomized trial whereby clusters are progressively transitioned from control to intervention, with the timing of transition randomized for each cluster. An important task at the design stage is to ensure that the planned trial has sufficient power to observe a clinically meaningful effect size. While methods for determining study power have been well-developed for SW-CRTs with continuous and binary outcomes, limited methods for power calculation are available for SW-CRTs with censored time-to-event outcomes. In this article, we propose a stratified marginal Cox model to account for secular trend in cross-sectional SW-CRTs, and derive an explicit expression of the robust sandwich variance to facilitate power calculations without the need for computationally intensive simulations. Power formulas based on both the Wald and robust score tests are developed and validated via simulation under different finite-sample scenarios. Finally, we illustrate our methods in the context of a SW-CRT testing the effect of a new electronic reminder system on time to catheter removal in hospital settings. We also offer an R Shiny application to facilitate sample size and power calculations using our proposed methods., Comment: v2: under review; 49 pgs (main text - 19, supporting information - 30); 16 figures (main text - 4, supporting information - 12); 2 tables (main text - 2, supporting information - 0); 7 appendices v1: under review; 45 pgs (main text - 22, supporting information - 23); 18 figures (main text - 4, supporting information - 14); 2 tables (main text - 2, supporting information - 0); 5 appendices

The integration of multisource remote sensing data and deep learning models offers new possibilities for accurately mapping high spatial resolution forest height. We found that GEDI relative heights (RH) metrics exhibited strong correlation with the mean of the top 10 highest trees (dominant height) measured in situ at the corresponding footprint locations. Consequently, we proposed a novel deep learning framework termed the multi-modal attention remote sensing network (MARSNet) to estimate forest dominant height by extrapolating dominant height derived from GEDI, using Setinel-1 data, ALOS-2 PALSAR-2 data, Sentinel-2 optical data and ancillary data. MARSNet comprises separate encoders for each remote sensing data modality to extract multi-scale features, and a shared decoder to fuse the features and estimate height. Using individual encoders for each remote sensing imagery avoids interference across modalities and extracts distinct representations. To focus on the efficacious information from each dataset, we reduced the prevalent spatial and band redundancies in each remote sensing data by incorporating the extended spatial and band reconstruction convolution modules in the encoders. MARSNet achieved commendable performance in estimating dominant height, with an R2 of 0.62 and RMSE of 2.82 m, outperforming the widely used random forest approach which attained an R2 of 0.55 and RMSE of 3.05 m. Finally, we applied the trained MARSNet model to generate wall-to-wall maps at 10 m resolution for Jilin, China. Through independent validation using field measurements, MARSNet demonstrated an R2 of 0.58 and RMSE of 3.76 m, compared to 0.41 and 4.37 m for the random forest baseline. Our research demonstrates the effectiveness of a multimodal deep learning approach fusing GEDI with SAR and passive optical imagery for enhancing the accuracy of high resolution dominant height estimation.

Accurate quantification of forest aboveground biomass (AGB) is critical for understanding carbon accounting in the context of climate change. In this study, we presented a novel attention-based deep learning approach for forest AGB estimation, primarily utilizing openly accessible EO data, including: GEDI LiDAR data, C-band Sentinel-1 SAR data, ALOS-2 PALSAR-2 data, and Sentinel-2 multispectral data. The attention UNet (AU) model achieved markedly higher accuracy for biomass estimation compared to the conventional RF algorithm. Specifically, the AU model attained an R2 of 0.66, RMSE of 43.66 Mg ha-1, and bias of 0.14 Mg ha-1, while RF resulted in lower scores of R2 0.62, RMSE 45.87 Mg ha-1, and bias 1.09 Mg ha-1. However, the superiority of the deep learning approach was not uniformly observed across all tested models. ResNet101 only achieved an R2 of 0.50, an RMSE of 52.93 Mg ha-1, and a bias of 0.99 Mg ha-1, while the UNet reported an R2 of 0.65, an RMSE of 44.28 Mg ha-1, and a substantial bias of 1.84 Mg ha-1. Moreover, to explore the performance of AU in the absence of spatial information, fully connected (FC) layers were employed to eliminate spatial information from the remote sensing data. AU-FC achieved intermediate R2 of 0.64, RMSE of 44.92 Mgha-1, and bias of -0.56 Mg ha-1, outperforming RF but underperforming AU model using spatial information. We also generated 10m forest AGB maps across Guangdong for the year 2019 using AU and compared it with that produced by RF. The AGB distributions from both models showed strong agreement with similar mean values; the mean forest AGB estimated by AU was 102.18 Mg ha-1 while that of RF was 104.84 Mg ha-1. Additionally, it was observed that the AGB map generated by AU provided superior spatial information. Overall, this research substantiates the feasibility of employing deep learning for biomass estimation based on satellite data.

The two-stage preference design (TSPD) enables the inference for treatment efficacy while allowing for incorporation of patient preference to treatment. It can provide unbiased estimates for selection and preference effects, where a selection effect occurs when patients who prefer one treatment respond differently than those who prefer another, and a preference effect is the difference in response caused by an interaction between the patient's preference and the actual treatment they receive. One potential barrier to adopting TSPD in practice, however, is the relatively large sample size required to estimate selection and preference effects with sufficient power. To address this concern, we propose a group sequential two-stage preference design (GS-TSPD), which combines TSPD with sequential monitoring for early stopping. In the GS-TSPD, pre-planned sequential monitoring allows investigators to conduct repeated hypothesis tests on accumulated data prior to full enrollment to assess study eligibility for early trial termination without inflating type I error rates. Thus, the procedure allows investigators to terminate the study when there is sufficient evidence of treatment, selection, or preference effects during an interim analysis, thereby reducing the design resource in expectation. To formalize such a procedure, we verify the independent increments assumption for testing the selection and preference effects and apply group sequential stopping boundaries from the approximate sequential density functions. Simulations are then conducted to investigate the operating characteristics of our proposed GS-TSPD compared to the traditional TSPD. We demonstrate the applicability of the design using a study of Hepatitis C treatment modality., Comment: 27 pages, 7 tables, 5 figures, 4 appendices; under review at Statistics in Medicine

Large solar eruptions are often associated with long-duration gamma-ray emission extending well above 100 MeV. While this phenomenon is known to be caused by high-energy ions interacting with the solar atmosphere, the underlying dominant acceleration process remains under debate. Potential mechanisms include continuous acceleration of particles trapped within large coronal loops or acceleration at coronal mass ejection (CME)-driven shocks, with subsequent back-propagation towards the Sun. As a test of the latter scenario, previous studies have explored the relationship between the inferred particle population producing the high-energy gamma-rays, and the population of solar energetic particles (SEPs) measured in situ. However, given the significant limitations on available observations, these estimates unavoidably rely on a number of assumptions. In an effort to better constrain theories of the gamma-ray emission origin, we re-examine the calculation uncertainties and how they influence the comparison of these two proton populations. We show that, even accounting for conservative assumptions related to gamma-ray flare, SEP event and interplanetary scattering modeling, their statistical relationship is only poorly/moderately significant. However, though the level of correlation is of interest, it does not provide conclusive evidence for or against a causal connection. The main result of this investigation is that the fraction of the shock-accelerated protons required to account for the gamma-ray observations is >20-40% for six of the fourteen eruptions analyzed. Such high values argue against current CME-shock origin models, predicting a <2% back-precipitation, hence the computed numbers of high-energy SEPs appear to be greatly insufficient to sustain the measured gamma-ray emission., Comment: Accepted for publication in the Astrophysical Journal

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit., Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figures

Cluster randomized trials (CRTs) are studies where treatment is randomized at the cluster level but outcomes are typically collected at the individual level. When CRTs are employed in pragmatic settings, baseline population characteristics may moderate treatment effects, leading to what is known as heterogeneous treatment effects (HTEs). Pre-specified, hypothesis-driven HTE analyses in CRTs can enable an understanding of how interventions may impact subpopulation outcomes. While closed-form sample size formulas have recently been proposed, assuming known intracluster correlation coefficients (ICCs) for both the covariate and outcome, guidance on optimal cluster randomized designs to ensure maximum power with pre-specified HTE analyses has not yet been developed. We derive new design formulas to determine the cluster size and number of clusters to achieve the locally optimal design (LOD) that minimizes variance for estimating the HTE parameter given a budget constraint. Given the LODs are based on covariate and outcome-ICC values that are usually unknown, we further develop the maximin design for assessing HTE, identifying the combination of design resources that maximize the relative efficiency of the HTE analysis in the worst case scenario. In addition, given the analysis of the average treatment effect is often of primary interest, we also establish optimal designs to accommodate multiple objectives by combining considerations for studying both the average and heterogeneous treatment effects. We illustrate our methods using the context of the Kerala Diabetes Prevention Program CRT, and provide an R Shiny app to facilitate calculation of optimal designs under a wide range of design parameters., Comment: 25 pages, 6 figures, 5 tables, 3 appendices; clarified phrasing, typos corrected

The FASTSUM Collaboration has developed a comprehensive research programme in thermal QCD using 2+1 flavour, anisotropic ensembles. In this talk, we summarise some of our recent results including thermal hadron spectrum calculations using our ``Generation 2L'' ensembles which have pion masses of 239(1) MeV. These include open charm mesons and charm baryons. We also summarise our work using the Backus Gilbert approach to determining the spectral function of the NRQCD bottomonium system. Finally, we review our determination of the interquark potential in the same system, but using our ``Generation 2'' ensembles which have heavier pion masses of 384(4) MeV., Comment: 9 pages, Contribution to the 39th International Symposium on Lattice Field theory (LATTICE2022),8th-13th August, 2022, Bonn, Germany

The coupling of various charmonium mesons to a photon is studied using lattice QCD, giving access to radiative form factors and transitions, and probing the mesons' structure. Methods are developed which allow the robust determination of amplitudes, including those involving an excited state as well as multiple form factors, for a range of kinematics. These are applied in dynamical lattice QCD calculations using the distillation technique to compute the underlying three-point correlation functions. Form factors and amplitudes involving the low-lying charmonia, $\eta_c$, $J/\psi$, $\chi_{c0}$ and $\eta_c'$, are calculated, demonstrating the methods and providing the first dynamical lattice QCD results for some quantities., Comment: 39 pages, 15 figures, small changes to match published version

Phase transitions in a non-perturbative regime can be studied by ab initio Lattice Field Theory methods. The status and future research directions for LFT investigations of Quantum Chromo-Dynamics under extreme conditions are reviewed, including properties of hadrons and of the hypothesized QCD axion as inferred from QCD topology in different phases. We discuss phase transitions in strong interactions in an extended parameter space, and the possibility of model building for Dark Matter and Electro-Weak Symmetry Breaking. Methodological challenges are addressed as well, including new developments in Artificial Intelligence geared towards the identification of different phases and transitions., Comment: 94 pages, 23 figures, GGI workshop "Phase Transitions in Particle Physics" review - v2: minor revisions and typo corrected, matches accepted version on PPNP

We present the most recent results from the FASTSUM collaboration for hadron properties at high temperature. This includes the temperature dependence of the light and charmed meson and baryon spectrum, as well as properties of heavy quarkonia. The results are obtained using anisotropic lattices with a fixed scale approach. We also present the status of our next generation gauge ensembles., Comment: 8 pages, 8 figures. Contribution to the XVth Quark Confinement and the Hadron Spectrum, 1-6 August 2022, Stavanger, Norway

We study what happens to D and D_s mesons as the temperature increases, using lattice QCD simulations with N_f=2+1 dynamical flavours on anistropic lattices. We have access to five temperatures in the hadronic phase. Using the determined groundstate mass at the lowest temperature, we investigate the effect of rising temperature by analysing ratios of mesonic correlators, without the need for further fitting or spectral reconstruction. In the pseudoscalar and vector channels, we demonstrate that temperature effects are at the percent level and can be captured by a reduction of the groundstate mass as the thermal crossover is approached. In the axial-vector and scalar channels on the other hand, temperature effects are prominent throughout the hadronic phase., Comment: 35 pages, 25 pdf figures

Propensity scores are commonly used to reduce the confounding bias in non-randomized observational studies for estimating the average treatment effect. An important assumption underlying this approach is that all confounders that are associated with both the treatment and the outcome of interest are measured and included in the propensity score model. In the absence of strong prior knowledge about potential confounders, researchers may agnostically want to adjust for a high-dimensional set of pre-treatment variables. As such, variable selection procedure is needed for propensity score estimation. In addition, recent studies show that including variables related to treatment only in the propensity score model may inflate the variance of the treatment effect estimates, while including variables that are predictive of only the outcome can improve efficiency. In this paper, we propose a flexible approach to incorporating outcome-covariate relationship in the propensity score model by including the predicted binary outcome probability (OP) as a covariate. Our approach can be easily adapted to an ensemble of variable selection methods, including regularization methods and modern machine learning tools based on classification and regression trees. We evaluate our method to estimate the treatment effects on a binary outcome, which is possibly censored, among multiple treatment groups. Simulation studies indicate that incorporating OP for estimating the propensity scores can improve statistical efficiency and protect against model misspecification. The proposed methods are applied to a cohort of advanced stage prostate cancer patients identified from a private insurance claims database for comparing the adverse effects of four commonly used drugs for treating castration-resistant prostate cancer., Comment: 22 pages, 6 figures, 2 tables

Continued advances in superconducting qubit performance require more detailed understandings of the many sources of decoherence. Within these devices, two-level systems arise due to defects, interfaces, and grain boundaries, and are thought to be a major source of qubit decoherence at millikelvin temperatures. In addition to Al, Nb is a commonly used metalization layer for superconducting qubits. Consequently, a significant effort is required to develop and qualify processes that mitigate defects in Nb films. As the fabrication of complete superconducting qubits and their characterization at millikelvin temperatures is a time and resource intensive process, it is desirable to have measurement tools that can rapidly characterize the properties of films and evaluate different treatments. Here we show that measurements of the variation of the superconducting critical temperature $T_c$ with an applied external magnetic field $H$ (of the phase boundary $T_c - H$) performed with very high resolution show features that are directly correlated with the structure of the Nb films. In combination with x-ray diffraction measurements, we show that one can even distinguish variations quality and crystal orientation of the grains in a Nb film by small but reproducible changes in the measured superconducting phase boundary.

Psychiatric and social epidemiology often involves assessing the effects of environmental exposure on outcomes that are difficult to measure directly. To address this problem, it is common to measure outcomes using a comprehensive battery of different tests thought to be related to a common, underlying construct of interest. In the application that motivates our work, for example, researchers wanted to assess the impact of in utero alcohol exposure on child cognition and neuropsychological development, which were evaluated using a range of different tests. Statistical analysis of the resulting multiple outcomes data can be challenging, not only because of the need to account for the correlation between outcomes measured on the same individual, but because it is often unclear, a priori, which outcomes are impacted by the exposure under study. While researchers will generally have some hypotheses about which outcomes are important, a framework is needed to help identify outcomes that are sensitive to the exposure and to quantify the associated treatment or exposure effects of interest. We propose such a framework using a modification of stochastic search variable selection (SSVS), a popular Bayesian variable selection model and use it to quantify an overall effect of the exposure on the affected outcomes. We investigate the performance of the method via simulation and illustrate its application to data from a study involving the effects of prenatal alcohol exposure on child cognition.

The status and prospects for investigations of exotic and conventional hadrons with lattice QCD are discussed. The majority of hadrons decay strongly via one or multiple decay-channels, including most of the experimentally discovered exotic hadrons. Despite this difficult challenge, the properties of several hadronic resonances have been determined within lattice QCD. To further discern the spectroscopic properties of various hadrons and to help resolve their nature we present our suggestions for future analytic and lattice studies., Comment: 23 pages + references, Submitted to the Proceedings of the US Community Study on the Future of Particle Physics (Snowmass 2021); v2: few references added

We present results from the fastsum collaboration's programme to determine the spectrum of the bottomonium system as a function of temperature. Three different methods of extracting spectral information are discussed: a Maximum Likelihood approach using a Gaussian spectral function for the ground state, the Backus Gilbert method, and the Kernel Ridge Regression machine learning procedure. We employ the fastsum anisotropic lattices with 2+1 dynamical quark flavours, with temperatures ranging from 47 to 375 MeV., Comment: 8 pages, 4 figures. Presented at A Virtual Tribute to Quark Confinement and the Hadron Spectrum, August 2-6 2021, University of Stavanger, Stavanger, Norway

We discuss results for bottomonium at nonzero temperature obtained using NRQCD on Fastsum Generation 2L ensembles, as part of the Fastsum collaboration's programme to determine the spectrum of the bottomonium system as a function of temperature using a variety of approaches. Here we give an update on results for spectral functions obtained using Kernel Ridge Regression. We pay in particular attention to the generation of training data and introduce the notion of using lattice QCD ensembles to learn how to improve the generation of training data. A practical implementation is given., Comment: 9 pages, 10 figures, contribution to the 38th International Symposium on Lattice Field Theory, 26th-30th July 2021, Massachusetts Institute of Technology, USA

We present progress results from the FASTSUM collaboration's programme to determine the spectrum of the bottomonium system as a function of temperature using a variety of approaches. In this contribution, the Backus Gilbert method is used to reconstruct spectral functions from NRQCD meson correlator data from FASTSUM's anisotropic ensembles at nonzero temperature. We focus in particular on the resolving power of the method, providing a demonstration of how the underlying resolution functions can be probed by exploiting the Laplacian nature of the NRQCD kernel. We conclude with estimates of the bottomonium ground state mass and widths at nonzero temperature., Comment: 9 pages, 8 figures; Proceedings of the 38th International Symposium on Lattice Field Theory - Lattice 2021 26-30 July 2021, Zoom/Gather@MIT (USA) - PoS(LATTICE2021)134. Corrected T=214MeV datapoint in Figure 2

We present progress results from the Fastsum collaboration's programme to determine the spectrum of the bottomonium system as a function of temperature using a variety of approaches. In these proceedings, the Maximum Likelihood approach is used with an Ansatz comprising of a Gaussian spectral function for the ground state. Fastsum anisotropic lattices with 2+1 dynamical quark flavours were used with temperatures ranging from 47 to 375 MeV., Comment: 7 pages, 3 figures, The 38th International Symposium on Lattice Field Theory, LATTICE2021 26th-30th July, 2021 Zoom/Gather@MIT, USA

We discuss some selected recent developments in the field of lattice QCD at nonzero temperature and density, describing in particular the transition from the hadronic gas to the quark-gluon plasma, as seen in simulations using Wilson fermions., Comment: 9 pages, invited talk presented at the XXXII IUPAP Conference on Computational Physics 21, Coventry, UK, 1-5 August 2021

Modern radio interferometers such as the LOw Frequency ARray (LOFAR) are capable of producing data at hundreds of gigabits to terabits per second. This high data rate makes the analysis of radio data cumbersome and computationally expensive. While high performance computing facilities exist for large national and international facilities, that may not be the case for instruments operated by a single institution or a small consortium. Data rates for next generation radio telescopes are set to eclipse those currently in operation, hence local processing of data will become all the more important. Here, we introduce the REAL-time Transient Acquisition backend (REALTA), a computing backend at the Irish LOFAR station (I-LOFAR) which facilitates the recording of data in near real-time and post-processing. We also present first searches and scientific results of a number of radio phenomena observed by I-LOFAR and REALTA, including pulsars, fast radio bursts (FRBs), rotating radio transients (RRATs), the search for extraterrestrial intelligence (SETI), Jupiter, and the Sun., Comment: 12 pages, 10 figures, accepted for publication in Astronomical instrumentation section of Astronomy & Astrophysics 24/08/2021

We define a class of Riemannian and pseudo-Riemannian 2-step nilpotent Lie groups with nondegenerate centers that generalize the H-type groups of Kaplan. Examples are given and geometric properties are investigated., Comment: 23 pp

The widespread popularity of soccer across the globe has turned it into a multi-billion dollar industry. As a result, most professional clubs actively engage in talent identification and development programmes. Contemporary research has generally supported the use of executive functions - a class of neuropsychological processes responsible for cognitive behaviours - in predicting a soccer player's future success. However, studies on the developmental evolution of executive functions have yielded differing results in their structural form (such as inverted U-shapes, or otherwise). This article presents the first analysis of changes in the domain-generic and domain-specific executive functions based on longitudinal data measured on elite German soccer players. Results obtained from a latent variable model show that these executive functions experience noticeable growth from late childhood until pre-adolescence, but remain fairly stable in later growth stages. As a consequence, our results suggest that the role of executive functions in facilitating talent identification may have been overly emphasised.

A parallel implementation of a compatible discretization scheme for steady-state Stokes problems is presented in this work. The scheme uses generalized moving least squares to generate differential operators and apply boundary conditions. This meshless scheme allows a high-order convergence for both the velocity and pressure, while also incorporates finite-difference-like sparse discretization. Additionally, the method is inherently scalable: the stencil generation process requires local inversion of matrices amenable to GPU acceleration, and the divergence-free treatment of velocity replaces the traditional saddle point structure of the global system with elliptic diagonal blocks amenable to algebraic multigrid. The implementation in this work uses a variety of Trilinos packages to exploit this local and global parallelism, and benchmarks demonstrating high-order convergence and weak scalability are provided.

The solar corona is a highly-structured plasma which can reach temperatures of more than ~2 MK. At low frequencies (decimetric and metric wavelengths), scattering and refraction of electromagnetic waves are thought to considerably increase the imaged radio source sizes (up to a few arcminutes). However, exactly how source size relates to scattering due to turbulence is still subject to investigation. The theoretical predictions relating source broadening to propagation effects have not been fully confirmed by observations due to the rarity of high spatial resolution observations of the solar corona at low frequencies. Here, the LOw Frequency ARray (LOFAR) was used to observe the solar corona at 120-180 MHz using baselines of up to ~3.5 km (corresponding to a resolution of ~1-2') during the partial solar eclipse of 2015 March 20. A lunar de-occultation technique was used to achieve higher spatial resolution (~0.6') than that attainable via standard interferometric imaging (~2.4'). This provides a means of studying the contribution of scattering to apparent source size broadening. It was found that the de-occultation technique reveals a more structured quiet corona that is not resolved from standard imaging, implying scattering may be overestimated in this region when using standard imaging techniques. However, an active region source was measured to be ~4' using both de-occultation and standard imaging. This may be explained by the increased scattering of radio waves by turbulent density fluctuations in active regions, which is more severe than in the quiet Sun., Comment: 8 pages, 6 figures, 1 table

Isospin-1/2 $D\pi$ scattering amplitudes are computed using lattice QCD, working in a single volume of approximately $(3.6\; \mathrm{fm})^3$ and with a light quark mass corresponding to $m_\pi\approx239$ MeV. The spectrum of the elastic $D\pi$ energy region is computed yielding 20 energy levels. Using the L\"uscher finite-volume quantisation condition, these energies are translated into constraints on the infinite-volume scattering amplitudes and hence enable us to map out the energy dependence of elastic $D\pi$ scattering. By analytically continuing a range of scattering amplitudes, a $D_0^\ast$ resonance pole is consistently found strongly coupled to the $S$-wave $D\pi$ channel, with a mass $m\approx 2200$ MeV and a width $\Gamma\approx400$ MeV. Combined with earlier work investigating the $D_{s0}^\ast$, and $D_0^\ast$ with heavier light quarks, similar couplings between each of these scalar states and their relevant meson-meson scattering channels are determined. The mass of the $D_0^\ast$ is consistently found well below that of the $D_{s0}^\ast$, in contrast to the currently reported experimental result., Comment: 40 pages, 15 figures. Various small changes to match the version accepted by JHEP

The Facility for Antiproton and Ion Research (FAIR) in Darmstadt, Germany, provides unique possibilities for a new generation of hadron-, nuclear- and atomic physics experiments. The future antiProton ANnihilations at DArmstadt (PANDA or $\overline{\rm P}$ANDA) experiment at FAIR will offer a broad physics programme, covering different aspects of the strong interaction. Understanding the latter in the non-perturbative regime remains one of the greatest challenges in contemporary physics. The antiproton-nucleon interaction studied with PANDA provides crucial tests in this area. Furthermore, the high-intensity, low-energy domain of PANDA allows for searches for physics beyond the Standard Model, e.g. through high precision symmetry tests. This paper takes into account a staged approach for the detector setup and for the delivered luminosity from the accelerator. The available detector setup at the time of the delivery of the first antiproton beams in the HESR storage ring is referred to as the \textit{Phase One} setup. The physics programme that is achievable during Phase One is outlined in this paper., Comment: 35 pages, 15 figures

While it is well known that high levels of prenatal alcohol exposure (PAE) result in significant cognitive deficits in children, the exact nature of the dose response is less well understood. In particular, there is a pressing need to identify the levels of PAE associated with an increased risk of clinically significant adverse effects. To address this issue, data have been combined from six longitudinal birth cohort studies in the United States that assessed the effects of PAE on cognitive outcomes measured from early school age through adolescence. Structural equation models (SEMs) are commonly used to capture the association among multiple observed outcomes in order to characterise the underlying variable of interest (in this case, cognition) and then relate it to PAE. However, it was not possible to apply classic SEM software in our context because different outcomes were measured in the six studies. In this paper we show how a Bayesian approach can be used to fit a multi-group multi-level structural model that maps cognition to a broad range of observed variables measured at multiple ages. These variables map to several different cognitive subdomains and are examined in relation to PAE after adjusting for confounding using propensity scores. The model also tests the possibility of a change point in the dose-response function.

Gamma-ray emission during long-duration gamma-ray flare (LDGRF) events is thought to be caused mainly by $>$300 MeV protons interacting with the ambient plasma at or near the photosphere. Prolonged periods of the gamma-ray emission have prompted the suggestion that the source of the energetic protons is acceleration at a coronal mass ejection (CME)-driven shock, followed by particle back-precipitation onto the solar atmosphere over extended times. We study the latter hypothesis using test particle simulations, which allow us to investigate whether scattering associated with turbulence aids particles in overcoming the effect of magnetic mirroring, which impedes back-precipitation by reflecting particles as they travel sunwards. The instantaneous precipitation fraction, $P$, the proportion of protons that successfully precipitate for injection at a fixed height, $r_i$, is studied as a function of scattering mean free path, $\lambda$ and $r_i$. Upper limits to the total precipitation fraction, $\overline{P}$, were calculated for eight LDGRF events for moderate scattering conditions ($\lambda$=0.1 au). We find that the presence of scattering helps back-precipitation compared to the scatter-free case, although at very low $\lambda$ values outward convection with the solar wind ultimately dominates. For eight LDGRF events, due to strong mirroring, $\overline{P}$ is very small, between 0.56 and 0.93% even in the presence of scattering. Time-extended acceleration and large total precipitation fractions, as seen in the observations, cannot be reconciled for a moving shock source according to our simulations. Therefore, it is not possible to obtain both long duration $\gamma$ ray emission and efficient precipitation within this scenario. These results challenge the CME shock source scenario as the main mechanism for $\gamma$ ray production in LDGRFs., Comment: Accepted for publication in A&A

This paper discusses several modern approaches to regression analysis involving time series data where some of the predictor variables are also indexed by time. We discuss classical statistical approaches as well as methods that have been proposed recently in the machine learning literature. The approaches are compared and contrasted, and it will be seen that there are advantages and disadvantages to most currently available approaches. There is ample room for methodological developments in this area. The work is motivated by an application involving the prediction of water levels as a function of rainfall and other climate variables in an aquifer in eastern Australia.

Evidence from animal models and epidemiological studies has linked prenatal alcohol exposure (PAE) to a broad range of long-term cognitive and behavioral deficits. However, there is virtually no information in the scientific literature regarding the levels of PAE associated with an increased risk of clinically significant adverse effects. During the period from 1975-1993, several prospective longitudinal cohort studies were conducted in the U.S., in which maternal reports regarding alcohol use were obtained during pregnancy and the cognitive development of the offspring was assessed from early childhood through early adulthood. The sample sizes in these cohorts did not provide sufficient power to examine effects associated with different levels and patterns of PAE. To address this critical public health issue, we have developed a hierarchical meta-analysis to synthesize information regarding the effects of PAE on cognition, integrating data on multiple endpoints from six U.S. longitudinal cohort studies. Our approach involves estimating the dose-response coefficients for each endpoint and then pooling these correlated dose-response coefficients to obtain an estimated `global' effect of exposure on cognition. In the first stage, we use individual participant data to derive estimates of the effects of PAE by fitting regression models that adjust for potential confounding variables using propensity scores. The correlation matrix characterizing the dependence between the endpoint-specific dose-response coefficients estimated within each cohort is then run, while accommodating incomplete information on some endpoints. We also compare and discuss inferences based on the proposed approach to inferences based on a full multivariate analysis

Elastic scattering amplitudes for $I=0$ $DK$ and $I=0,1$ $D\bar{K}$ are computed in $S$, $P$ and $D$ partial waves using lattice QCD with light-quark masses corresponding to $m_\pi = 239$ MeV and $m_\pi = 391$ MeV. The $S$-waves contain interesting features including a near-threshold $J^P=0^+$ bound state in $I=0$ $DK$, corresponding to the $D_{s0}^\ast(2317)$, with an effect that is clearly visible above threshold, and suggestions of a $0^+$ virtual bound state in $I=0$ $D\bar{K}$. The $S$-wave $I=1$ $D\bar{K}$ amplitude is found to be weakly repulsive. The computed finite-volume spectra also contain a deeply-bound $D^\ast$ vector resonance, but negligibly small $P$-wave $DK$ interactions are observed in the energy region considered; the $P$ and $D$-wave $D\bar{K}$ amplitudes are also small. There is some evidence of $1^+$ and $2^+$ resonances in $I=0$ $DK$ at higher energies., Comment: 53 pages, 22 figures, small changes to match published version

We explore the spectrum of excited and exotic bottomonia using lattice QCD. Highly excited states are identified with masses up to 11,000 MeV, many of which can be grouped into supermultiplets matching those of the quark model while exotic spin--parity--charge-conjugation quantum numbers $J^{PC}=0^{+-},\,1^{-+},\,2^{+-}$ that cannot be formed from $\bar{q}q$ alone are also identified. Single-meson operator constructions are used that have good $J^{PC}$ in the continuum, these are found to overlap well onto heavy quark states with $J\le4$. A continuum $J^{PC}$ is assigned to each level, based on the distribution amongst lattice irreps and dominant operator overlaps. States with a dominant gluonic component are identified and form a hybrid supermultiplet with $J^{PC}=(0,1,2)^{-+},\, 1^{--}$, approximately 1500 MeV above the ground-state $\eta_b$, similar to previous computations with light, strange and charm quark systems., Comment: 22 pages, 9 figures

We study properties of the thermal transition in QCD, using anisotropic, fixed-scale lattice simulations with $N_f = 2+1$ flavours of Wilson fermion. Observables are compared for two values of the pion mass, focusing on chiral properties. Results are presented for the Polyakov loop, various susceptibilities, the chiral condensate and its susceptibility, and the onset of parity doubling in the light and strange baryonic sector., Comment: 12 pages, 11 figures, 5 tables

Mathematical models of diffusive transport underpin our understanding of chemical, biochemical and biological transport phenomena. Analysis of such models often focusses on relatively simple geometries and deals with diffusion through highly idealised homogeneous media. In contrast, practical applications of diffusive transport theory inevitably involve dealing with more complicated geometries as well as dealing with heterogeneous media. One of the most fundamental properties of diffusive transport is the concept of mean particle lifetime or mean exit time, which are particular applications of the concept of first passage time, and provide the mean time required for a diffusing particle to reach an absorbing boundary. Most formal analysis of mean particle lifetime applies to relatively simple geometries, often with homogeneous (spatially-invariant) material properties. In this work, we present a general framework that provides exact mathematical insight into the mean particle lifetime, and higher moments of particle lifetime, for point particles diffusing in heterogeneous discs and spheres with radial symmetry. Our analysis applies to geometries with an arbitrary number and arrangement of distinct layers, where transport in each layer is characterised by a distinct diffusivity. We obtain exact closed-form expressions for the mean particle lifetime for a diffusing particle released at an arbitrary location and we generalise these results to give exact, closed-form expressions for any higher-order moment of particle lifetime for a range of different boundary conditions. Finally, using these results we construct new homogenization formulae that provide an accurate simplified description of diffusion through heterogeneous discs and spheres., Comment: 19 pages, 3 figures, accepted version of paper published in The Journal of Chemical Physics

Preliminary results for the spectra of excited and exotic bottom mesons are presented. The calculation on a dynamical anisotropic ensemble exploits distillation, enabling the use of a large basis of interpolating operators including those proportional to the gluonic field strength which are relevant for hybrid states. A comparison of results with similar calculations in the light and charm sectors is discussed., Comment: 7 pages, 6 figures; presented at the 37th International Symposium on Lattice Field Theory (Lattice2019), 16-22 June 2019, Wuhan, China

New results on bottomonium at nonzero temperature are presented, using the FASTSUM Generation 2L ensembles. Preliminary results for spectral function reconstruction using Kernel Ridge Regression, a machine learning technique, are shown as well and compared to results from the Maximum Entropy Method., Comment: 7 pages, 6 figures; Proceedings of the 37th International Symposium on Lattice Field Theory - Lattice 2019, Wuhan (China)

In order to study spectral quantities in thermal QCD, the FASTSUM collaboration employs anisotropic lattice simulations with N_f=2+1 flavours of Wilson fermions. Here we discuss our Generation 2 and Generation 2L ensembles, which differ in the pion mass. The focus is on observables related to the light quarks and chiral symmetry restoration., Comment: 7 pages, 9 figures; Proceedings of the 37th International Symposium on Lattice Field Theory - Lattice 2019, Wuhan (China)

The detection of anomalies in real time is paramount to maintain performance and efficiency across a wide range of applications including web services and smart manufacturing. This paper presents a novel algorithm to detect anomalies in streaming time series data via statistical learning. We adapt the generalised extreme studentised deviate test [1] to streaming data by using a sliding window approach. This is made computationally feasible by recursive updates of the Grubbs test statistic [2]. Moreover, a priority queue [3] is employed to reduce memory requirements, where subsets of the required data streaming window are maintained in the algorithm rather than the full list. Our method is statistically principled. It is suitable for streaming data and it outperforms the AnomalyDetection software package, recently released by Twitter Inc. (Twitter) [4] and used by multiple teams at Twitter as their state of the art on a daily basis [5]. The methodology is demonstrated using an example of unlabelled data from the Twitter AnomalyDetection GitHub repository and using a real manufacturing example with labelled anomalies.

We report on the results of a survey to assess diversity and inclusivity in the Lattice community as one of the duties of a newly formed Committee on Diversity and Inclusivity in the Lattice community., Comment: 12 pages, 13 figures, 1 table. To appear in the Proceedings of 37th International Symposium on Lattice Field Theory - Lattice2019, 16-22 June 2019, Wuhan, China

Faltering growth among children is a nutritional problem prevalent in low to medium income countries; it is generally defined as a slower rate of growth compared to a reference healthy population of the same age and gender. As faltering is closely associated with reduced physical, intellectual and economic productivity potential, it is important to identify faltered children and be able to characterise different growth patterns so that targeted treatments can be designed and administered. We introduce a multiclass classification model for growth trajectory that flexibly extends a current classification approach called the broken stick model, which is a piecewise linear model with breaks at fixed knot locations. Heterogeneity in growth patterns among children is captured using mixture distributed random effects, whereby the mixture components determine the classification of children into subgroups. The mixture distribution is modelled using a Dirichlet process prior, which avoids the need to choose the "true" number of mixture components, and allows this to be driven by the complexity of the data. Because children have individual differences in the onset of growth stages, we introduce child-specific random change points. Simulation results show that the random change point model outperforms the broken stick model because it has fewer restrictions on knot locations. We illustrate our model on a longitudinal birth cohort from the Healthy Birth, Growth and Development knowledge integration project funded by the Bill and Melinda Gates Foundation. Analysis reveals 9 subgroups of children within the population which exhibit varying faltering trends between birth and age one.

Polysaccharides are semi-flexible polymers composed of sugar residues with a myriad of important functions including structural support, energy storage and immunogenicity. The local conformation of such chains is a crucial factor governing their interactions, where the relative orientation of adjacent sugar rings determines the propensity for hydrogen bonding and specific ion-mediated interactions with neighbouring chains. Traditionally this conformation has only been directly accessible in the solid-state, using crystallographic techniques such as fibre diffraction. Herein it is demonstrated that improvements in the quality of synchrotron-based x-ray scattering data means that conformation-dependent features, the positions of which are related to the linear repeating distance between single saccharide monomers, can now be measured in solution. This technique is expected to be universally applicable for polysaccharides that consist of comparatively stiff glycosidic linkages, and to have extensive relevance for a number of biological macromolecules, including glycosylated proteins., Comment: 11 pages, 6 figures total, including Supplemental Material on pages 6-11

Little is known about the origin of the high-energy and sustained emission from solar Long-Duration Gamma-Ray Flares (LDGRFs), identified with the Compton Gamma Ray Observatory (CGRO), the Solar Maximum Mission (SMM), and now Fermi. Though Fermi/Large Area Space Telescope (LAT) has identified dozens of flares with LDGRF signature, the nature of this phenomenon has been a challenge to explain both due to the extreme energies and long durations. The highest-energy emission has generally been attributed to pion production from the interaction of >300 MeV protons with the ambient matter. The extended duration suggests that particle acceleration occurs over large volumes extending high in the corona, either from stochastic acceleration within large coronal loops or from back precipitation from coronal mass ejection driven shocks. It is possible to test these models by making direct comparison between the properties of the accelerated ion population producing the gamma-ray emission derived from the Fermi/LAT observations, and the characteristics of solar energetic particles (SEPs) measured by the Payload for Matter-Antimatter Exploration and Light Nuclei Astrophysics (PAMELA) spacecraft in the energy range corresponding to the pion-related emission detected with Fermi. For fourteen of these events we compare the two populations -- SEPs in space and the interacting particles at the Sun -- and discuss the implications in terms of potential sources. Our analysis shows that the two proton numbers are poorly correlated, with their ratio spanning more than five orders of magnitude, suggesting that the back precipitation of shock-acceleration particles is unlikely the source of the LDGRF emission., Comment: Accepted for publication in ApJ

An interval of exceptional solar activity was registered in early September 2017, late in the decay phase of solar cycle 24, involving the complex Active Region 12673 as it rotated across the western hemisphere with respect to Earth. A large number of eruptions occurred between 4-10 September, including four associated with X-class flares. The X9.3 flare on 6 September and the X8.2 flare on 10 September are currently the two largest during cycle 24. Both were accompanied by fast coronal mass ejections and gave rise to solar energetic particle (SEP) events measured by near-Earth spacecraft. In particular, the partially-occulted solar event on 10 September triggered a ground level enhancement (GLE), the second GLE of cycle 24. A further, much less energetic SEP event was recorded on 4 September. In this work we analyze observations by the Advanced Composition Explorer (ACE) and the Geostationary Operational Environmental Satellites (GOES), estimating the SEP event-integrated spectra above 300 keV and carrying out a detailed study of the spectral shape temporal evolution. Derived spectra are characterized by a low-energy break at few/tens of MeV; the 10 September event spectrum, extending up to ~1 GeV, exhibits an additional rollover at several hundred MeV. We discuss the spectral interpretation in the scenario of shock acceleration and in terms of other important external influences related to interplanetary transport and magnetic connectivity, taking advantage of multi-point observations from the Solar Terrestrial Relations Observatory (STEREO). Spectral results are also compared with those obtained for the 17 May 2012 GLE event., Comment: Accepted for publication in Space Weather; 24 pages, 10 figures, 1 table

The conformation of polyelectrolytes in the solution state has long been of interest in polymer science. Herein we utilize all atom molecular dynamics simulations (MD) and small-angle x-ray scattering experiments (SAXS) to elucidate the molecular structure of the model polyelectrolyte homogalacturonan. Several degrees of polymerization were studied and in addition partial methylesterification of the otherwise charge-carrying carboxyl groups was used in order to generate samples with varying intra-chain charge distributions. It is shown that at length scales above around 1 nm the conformation of isolated chains has surprisingly little dependence on the charge distribution or the concentration of attendant monovalent salts, reflective of the intrinsic stiffness of the saccharide rings and the dynamical constraints of the glycosidic linkage. Indeed the conformation of isolated chains over all accessible length-scales is well described by the atomic co-ordinates available from fibre diffraction studies. Furthermore, in more concentrated systems it is shown that, after careful analysis of the SAXS data, the form of the inter-particle effects heralded by the emergence of a so-called polyelectrolyte peak, can be extracted, and that this phenomena can be reproduced by multiple chain MD simulations., Comment: 10 pages, 6 figures

A child is considered to have faltered growth when increases in their height or weight starts to decline relative to a suitable comparison population. However, there is currently a lack of consensus on both the choice of anthropometric indexes for characterizing growth over time and the operational definition of faltering. Cole's classic conditional standard deviation scores is a popular metric but can be problematic, since it only utilizes two data points and relies on having complete data. In the existing literature, arbitrary thresholds are often used to define faltering, which may not be appropriate for all populations. In this article, we propose to assess faltering via minimum random slopes (MRS) derived from a piecewise linear mixed model. When used in conjunction with mixture model-based classification, MRS provides a viable method for identifying children that have faltered, without being dependent upon arbitrary standards. We illustrate our work via a simulation study and apply it to a case study based on a birth cohort within the Healthy Birth, Growth and Development knowledge integration (HBGDki) project funded by the Bill and Melinda Gates Foundation.