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We address the problem of how to achieve optimal inference in distributed quantile regression without stringent scaling conditions. This is challenging due to the non-smooth nature of the quantile regression (QR) loss function, which invalidates the use of existing methodology. The difficulties are resolved through a double-smoothing approach that is applied to the local (at each data source) and global objective functions. Despite the reliance on a delicate combination of local and global smoothing parameters, the quantile regression model is fully parametric, thereby facilitating interpretation. In the low-dimensional regime, we establish a finite-sample theoretical framework for the sequentially defined distributed QR estimators. This reveals a trade-off between the communication cost and statistical error. We further discuss and compare several alternative confidence set constructions, based on inversion of Wald and score-type tests and resampling techniques, detailing an improvement that is effective for more extreme quantile coefficients. In high dimensions, a sparse framework is adopted, where the proposed doubly-smoothed objective function is complemented with an $\ell_1$-penalty. We show that the corresponding distributed penalized QR estimator achieves the global convergence rate after a near-constant number of communication rounds. A thorough simulation study further elucidates our findings.

This paper reports the bistability of curved compression ramp (CCR) flows. It reveals that both separation and attachment states can be stably established even for the same boundary conditions. Firstly, to investigate the effects of initial condition and evolutionary history, a thought experiment involving two processes with the same three steps but in different orders is designed, possibly constructing the two distinct-different stable CCR flows. Subsequently, three-dimensional direct numerical simulations are then performed to replicate the thought experiment, verifying the existence of the bistable states in CCR flows. In the end, the method of virtual separation disturbance is proposed to detect whether potential bistable states exist, theoretically demonstrating the bistability of CCR flows. As a canonical type of Shock waveBoundary layer interactions, local CCR flows often appear on aircraft, hence the bistability will certainly bring noteworthy changes to the global aerothermodynamic characteristics, which supersonic/hypersonic flight has to deal with.

Symmetries play a major role in identifying topological phases of matter and in establishing a direct connection between protected edge states and topological bulk invariants via the bulk-boundary correspondence. One-dimensional lattices are deemed to be protected by chiral symmetry, exhibiting quantized Zak phases and protected edge states, but not for all cases. Here, we experimentally realize an extended Su-Schrieffer-Heeger model with broken chiral symmetry by engineering one-dimensional zigzag photonic lattices, where the long-range hopping breaks chiral symmetry but ensures the existence of inversion symmetry. By the averaged mean displacement method, we detect topological invariants directly in the bulk through the continuous-time quantum walk of photons. Our results demonstrate that inversion symmetry protects the quantized Zak phase, but edge states can disappear in the topological nontrivial phase, thus breaking the conventional bulk-boundary correspondence. Our photonic lattice provides a useful platform to study the interplay among topological phases, symmetries, and the bulk-boundary correspondence., Comment: 6 pages, 4 figures, 53 references, 1 supplemental materials

$\ell_1$-penalized quantile regression is widely used for analyzing high-dimensional data with heterogeneity. It is now recognized that the $\ell_1$-penalty introduces non-negligible estimation bias, while a proper use of concave regularization may lead to estimators with refined convergence rates and oracle properties as the signal strengthens. Although folded concave penalized $M$-estimation with strongly convex loss functions have been well studied, the extant literature on quantile regression is relatively silent. The main difficulty is that the quantile loss is piecewise linear: it is non-smooth and has curvature concentrated at a single point. To overcome the lack of smoothness and strong convexity, we propose and study a convolution-type smoothed quantile regression with iteratively reweighted $\ell_1$-regularization. The resulting smoothed empirical loss is twice continuously differentiable and (provably) locally strongly convex with high probability. We show that the iteratively reweighted $\ell_1$-penalized smoothed quantile regression estimator, after a few iterations, achieves the optimal rate of convergence, and moreover, the oracle rate and the strong oracle property under an almost necessary and sufficient minimum signal strength condition. Extensive numerical studies corroborate our theoretical results., Comment: Main text is 27 pages, online supplementary materials are attached after the main text

The bistable states and separation hysteresis in curved compression ramp (CCR) flows, and the corresponding aerothermal characteristics (including wall friction, pressure and heat flux), are studied numerically and theoretically. Direct numerical simulations of separation hysteresis induced by variation of turning angle, as well as the influence of inflow Mach number and wall temperature on hysteresis loops, are carried out. Distributions of wall friction, pressure and heat flux are analyzed. Further, emergence of wall friction's first and second minima in the separation bubble is interpreted, revealing it is dominated by the adverse pressure gradient induced by separation and reattachment shocks. The present results and analysis indicate that the reversed-flow singularity of Smith (Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences, 1988, 420: 21-52) is less likely to occur in CCR flows. The prediction of peak pressure of separation states confirms the model based on the minimum viscous dissipation theorem (Physics of Fluids, 2020, 32(10):101702). While the pressure overshoot can be analyzed by shock-polars with pressure match of compression and expansion process. The correlation between peak heat flux and peak pressure rise of both separation and attachment states is also discussed in terms of the classical power relations.

A new spatial-related mechanism is proposed to understand separation hysteresis processes in curved compression ramp (CCR) flows discovered recently (Hu et al. Phy. Fluid, 32(11): 113601, 2020). Two separation hystereses, induced by variations of Mach number and wall temperature, are investigated numerically. The two hystereses indicate that there must exist parameter intervals of Mach number and wall temperature, wherein both attachment and separation states can be established stably. The relationships between the aerodynamic characteristics (including wall friction, pressure and heat flux) and the shock wave configurations in this two hystereses are analyzed. Further, the adverse pressure gradient (APG) Isb(x) induced by the upstream separation process and APG Icw(x) induced by the downstream isentropic compression process are estimated by classic theories. The trend of boundary layer APG resistence Ib(x) is evaluated from the spatial distributions of the physical quantities such as the shape factor and the height of the sound velocity line. With the stable conditions of separation and attachment, a self-consistent mechanism is obtained when Isb, Icw and Ib have appropriate spatial distributions.