Search

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.