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Channel sounding is essential for the development of radio systems. One flexible strategy is the switched-array-based channel sounding, where antenna elements are activated at different time instants to measure the channel spatial characteristics. Although its hardware complexity is decreased due to fewer radio-frequency (RF) chains, sequentially switching the antenna elements can result in aliasing in the joint estimation of angles and Doppler frequencies of multipath components (MPCs). Therefore, pseudo-random switching has been proposed to mitigate such aliasing and increase estimation accuracy in both angular and Doppler domains. Nevertheless, the increased Doppler resolution could cause additional post-processing complexity of parameter estimation, which is relevant when the Doppler frequencies are not of interest, e.g., for spatial channel modeling. This paper proposes an improved hybrid sequential and random switching scheme. The primary purpose is to maintain the estimation accuracy of angles of MPCs while decreasing the resolution of Doppler frequencies for minimized complexity of channel parameter estimation. A simulated-annealing algorithm is exploited to obtain an optimized switching sequence. The effectiveness of the proposed scheme is also demonstrated with a realistic antenna array., Comment: This paper has been accepted by IEEE Vehicular Technology Conference

Patient-reported outcome (PRO) measures are increasingly collected as a means of measuring healthcare quality and value. The capability to predict such measures enables patient-provider shared decision making and the delivery of patient-centered care. However, due to their voluntary nature, PRO measures often suffer from a high missing rate, and the missingness may depend on many patient factors. Under such a complex missing mechanism, statistical inference of the parameters in prediction models for PRO measures is challenging, especially when flexible imputation models such as machine learning or nonparametric methods are used. Specifically, the slow convergence rate of the flexible imputation model may lead to non-negligible bias, and the traditional missing propensity, capable of removing such a bias, is hard to estimate due to the complex missing mechanism. To efficiently infer the parameters of interest, we propose to use an informative surrogate that can lead to a flexible imputation model lying in a low-dimensional subspace. To remove the bias due to the flexible imputation model, we identify a class of weighting functions as alternatives to the traditional propensity score and estimate the low-dimensional one within the identified function class. Based on the estimated low-dimensional weighting function, we construct a one-step debiased estimator without using any information of the true missing propensity. We establish the asymptotic normality of the one-step debiased estimator. Simulation and an application to real-world data demonstrate the superiority of the proposed method., 20 pages, 1 figure, 2 tables

As machine learning becomes prevalent, mitigating any unfairness present in the training data becomes critical. Among the various notions of fairness, this paper focuses on the well-known individual fairness, which states that similar individuals should be treated similarly. While individual fairness can be improved when training a model (in-processing), we contend that fixing the data before model training (pre-processing) is a more fundamental solution. In particular, we show that label flipping is an effective pre-processing technique for improving individual fairness. Our system iFlipper solves the optimization problem of minimally flipping labels given a limit to the individual fairness violations, where a violation occurs when two similar examples in the training data have different labels. We first prove that the problem is NP-hard. We then propose an approximate linear programming algorithm and provide theoretical guarantees on how close its result is to the optimal solution in terms of the number of label flips. We also propose techniques for making the linear programming solution more optimal without exceeding the violations limit. Experiments on real datasets show that iFlipper significantly outperforms other pre-processing baselines in terms of individual fairness and accuracy on unseen test sets. In addition, iFlipper can be combined with in-processing techniques for even better results., 20 pages, 19 figures, 8 tables

Additional file 1: Includes frequency of patient censuses during the study period (Table S1); admission rates given patient census during rounding and daytime and during nighttime (Table S2); piecewise Poisson regression results (Table S3); sensitivity analysis with the different criterion of high mortality risk patients (Table S4); and sensitivity analysis including rare patient census occasions (Table S5).

The selection of an appropriate turbulence/transition model is critical when simulating the hypersonic flows based on the Reynolds-averaged Navier–Stokes (RANS) equation. In particular, a deep understanding of the validity, reliability, and limitations of existing models is an essential prerequisite to facilitate their further development. This paper reports on the assessment of two models dedicated to hypersonic boundary layer transition analysis. Both models are compared with two other models that are widely used in this field. The double ramp and shock wave laboratory scramjet intake cases are used for the validation and evaluation of the predictive performance of both models via comparison against experimental data. The results reveal that the appropriate selection of the transition model is critical to the attainment of accurate results. Moreover, the forebody of the scramjet combustion propulsion-01 (SCP-01) flight vehicle, which is a research model, is analyzed as a case study. The air intake performance of the SCP-01, as predicted by four models, is compared and analyzed at different flight altitude and Mach number conditions. The results reveal that the accuracy of the prediction of boundary layer and separated flow transitions significantly affects the flow field and corresponding air intake performance. Furthermore, the uncertainty of results obtained using the two models increases significantly with an increase in altitude. Finally, the reliability and limitations of the transition models considered in this study are examined.

Correlated outcomes are common in many practical problems. In some settings, one outcome is of particular interest, and others are auxiliary. To leverage information shared by all the outcomes, traditional multi-task learning (MTL) minimizes an averaged loss function over all the outcomes, which may lead to biased estimation for the target outcome, especially when the MTL model is mis-specified. In this work, based on a decomposition of estimation bias into two types, within-subspace and against-subspace, we develop a robust transfer learning approach to estimating a high-dimensional linear decision rule for the outcome of interest with the presence of auxiliary outcomes. The proposed method includes an MTL step using all outcomes to gain efficiency, and a subsequent calibration step using only the outcome of interest to correct both types of biases. We show that the final estimator can achieve a lower estimation error than the one using only the single outcome of interest. Simulations and real data analysis are conducted to justify the superiority of the proposed method., 19 pages, 2 figures

Diffusive-Time-of-Flight (DTOF), representing the travel time of pressure front propagation, has found many applications in unconventional reservoir performance analysis. The computation of DTOF typically involves upwind finite difference of the Eikonal equation and solution using the fast marching method (FMM). However, application of the finite difference based FMM to irregular grid systems remains a challenge. In this paper, we present a novel and robust method for solving the Eikonal equation using finite volume discretization and the FMM.The finite volume form of the Eikonal equation is derived by differential manipulation, volume integration and the divergence theorem. Using product rule, the differential term is first converted to the divergence form. Then volume integrals that contain divergence terms are converted to surface integrals using the divergence theorem. Consequently, the spatial coordinates are replaced by cell volumes and transmissibilities which are universal for both structured and unstructured grids in finite volume simulators. When applied with the upstream scheme, the finite volume form evolves into a set of quadratic equations, and fast marching method is implemented to solve these equations.The implementation is first validated with analytical solutions using isotropic and anisotropic models with homogeneous reservoir properties. Consistent DTOF distributions are obtained between the proposed approach and the analytical solutions. Next, the implementation is applied to unconventional reservoirs with hydraulic and natural fractures. Our approach relies on cell volumes and connections (transmissibilities) rather than the grid geometry, and thus can be easily applied to complex grid systems. For illustrative purposes, we present applications of the proposed method to embedded discrete fracture models (EDFM), dual-porosity dual-permeability models, and unstructured PEBI grids with heterogeneous reservoir properties. Visualization of the DTOF provides flow diagnostics such as evolution of the drainage volume of the wells and well interactions.The novelty of the proposed approach is its broad applicability to arbitrary grid systems and ease of implementation in commercial reservoir simulators. This makes the approach well-suited for field applications with complex grid geometry and complex well architecture.

「ら抜きことば」は実生活で幅広く使われている表現だが、「ら抜きことばは日本語の乱れ」といった否定的な認識がまだ残っているため、年配者相手や面接、教育などの改まったことばづかいが必要とされる場面では使わないのが礼儀とされている。本稿では、「ら抜きことば」が本当に標準語において認定されていない「乱れたことば」なのかを研究するため、「ら抜きことば」研究の権威である井上史雄氏の文献を参考に「ら抜きことば」の定義と変遷を調べた。さらに、今日の「ら抜きことば」の実態を把握するためアンケートを行って「ら抜きことば」の諸相を調査し、分析した。

We report the characterization of mixed oxides nanocomposite nanofibers of (1 − x) ZnO-(x)SnO2 (x ≤ 0.45) synthesized by electrospinning technique. The diameter of calcined nanofibers depends on Sn content. Other phases like SnO, ZnSnO3, and Zn2SnO4 were absent. Photoluminescence studies show that there is a change in the blue/violet luminescence confirming the presence of Sn in Zn-rich composition. Present study shows that the crystalline nanocomposite nanofibers with stoichiometry of (1 − x)ZnO-(x)SnO2 (x ≤ 0.45) stabilize after the calcination and possess some morphological and optical properties that strongly depend on Sn content.

We report experimental results validating the concept that plasma confinement is enhanced in a magnetic cusp configuration when beta (plasma pressure/magnetic field pressure) is order of unity. This enhancement is required for a fusion power reactor based on cusp confinement to be feasible. The magnetic cusp configuration possesses a critical advantage: the plasma is stable to large scale perturbations. However, early work indicated that plasma loss rates in a reactor based on a cusp configuration were too large for net power production. Grad and others theorized that at high beta a sharp boundary would form between the plasma and the magnetic field, leading to substantially smaller loss rates. The current experiment validates this theoretical conjecture for the first time and represents critical progress toward the Polywell fusion concept which combines a high beta cusp configuration with an electrostatic fusion for a compact, economical, power-producing nuclear fusion reactor., 12 pages, figures included. 5 movies in Ancillary files