Elastic Wave Eigenmode Solver for Acoustic Waveguides

A numerical solver for the elastic wave eigenmodes in acoustic waveguides of inhomogeneous cross-section is presented. Operating under the assumptions of linear, isotropic materials, it utilizes a finite-difference method on a staggered grid to solve for the acoustic eigenmodes of the vector-field elastic wave equation. Free, fixed, symmetry, and anti-symmetry boundary conditions are implemented, enabling efficient simulation of acoustic structures with geometrical symmetries and terminations. Perfectly matched layers are also implemented, allowing for the simulation of radiative (leaky) modes. The method is analogous to eigenmode solvers ubiquitously employed in electromagnetics to find waveguide modes, and enables design of acoustic waveguides as well as seamless integration with electromagnetic solvers for optomechanical device design. The accuracy of the solver is demonstrated by calculating eigenfrequencies and mode shapes for common acoustic modes in several simple geometries and comparing the results to analytical solutions where available or to numerical solvers based on more computationally expensive methods

Atherosclerotic conditions promote the packaging of functional microRNA-92 into endothelial microvesicles

Objectives: Microvesicle (MV)-incorporated microRNAs (miRs) are biomarkers and effectors of cardiovascular diseases. Whether MV-miRs expression is regulated in coronary artery disease (CAD) is unknown. We aimed to explore the expression of circulating MV-miRs in patients with CAD. Methods and results: Circulating MVs were isolated from patients’ plasma using ultracentrifugation. The electron microscope was used for MVs size characterization. Taqman miR array revealed that MV-miRs are significantly regulated in patients with stable CAD compared to ACS patients. To validate miR array results, 180 patients with angiographically excluded CAD (n=41), stable CAD (n=77) and acute coronary syndrome (ACS, n=62) were prospectively studied. Nine miRs involved in the regulation of vascular performance - miR-126, miR-222, miR-let7, miR-21, miR-26, miR-92, miR-133, miR-30 and miR-199 – were quantified in circulating MVs by real-time PCR. Among those, miR-92 was significantly increased in patients with CAD compared to non-CAD patients. MV-sorting experiments showed that endothelial cells (ECs) were the major cell source of MVs (EMVs) containing miR-92. In vitro, oxLDL stimulation dose-dependently increased miR-92 expression both in EMVs and ECs in a STAT3-dependent way. MiR-92 and EMV labeling demonstrated that functional miR-92 was transported into recipient ECs promoting EC migration and proliferation. Knockdown of miR-92 in EMVs abrogated EMV-mediated effects on EC migration and proliferation and blocked vascular network formation in matrigel plug. PCR-based gene profiling showed that the expression of THBS1, a target of miR-92 and an inhibitor of angiogenesis, was significantly reduced in ECs by EMVs. Knockdown of miR-92 in EMVs abrogated EMV-mediated inhibition of THBS1 gene and protein expression. Conclusion: Atherosclerotic conditions promote the packaging of endothelial miR-92 from ECs into EMVs. EMV-mediated transfer of functional miR-92 regulates angiogenesis in recipient ECs in a THBS1-dependent mechanism

Ultra-low-loss CMOS-Compatible Waveguide Crossing Arrays Based on Multimode Bloch Waves and Imaginary Coupling

We experimentally demonstrate broadband waveguide crossing arrays showing ultra low loss down to $0.04\,$dB/crossing ($0.9\%$), matching theory, and crosstalk suppression over $35\,$dB, in a CMOS-compatible geometry. The principle of operation is the tailored excitation of a low-loss spatial Bloch wave formed by matching the periodicity of the crossing array to the difference in propagation constants of the 1$^\text{st}$- and 3$^\text{rd}$-order TE-like modes of a multimode silicon waveguide. Radiative scattering at the crossing points acts like a periodic imaginary-permittivity perturbation that couples two supermodes, which results in imaginary (radiative) propagation-constant splitting and gives rise to a low-loss, unidirectional breathing Bloch wave. This type of crossing array provides a robust implementation of a key component enabling dense photonic integration

Damped Proximal Augmented Lagrangian Method for weakly-Convex Problems with Convex Constraints

We give a damped proximal augmented Lagrangian method (DPALM) for solving problems with a weakly-convex objective and convex linear/nonlinear constraints. Instead of taking a full stepsize, DPALM adopts a damped dual stepsize to ensure the boundedness of dual iterates. We show that DPALM can produce a (near) \vareps-KKT point within O(\vareps^{-2}) outer iterations if each DPALM subproblem is solved to a proper accuracy. In addition, we establish overall iteration complexity of DPALM when the objective is either a regularized smooth function or in a regularized compositional form. For the former case, DPALM achieves the complexity of $\widetilde{\mathcal{O}}\left(\varepsilon^{-2.5} \right)$ to produce an $\varepsilon$-KKT point by applying an accelerated proximal gradient (APG) method to each DPALM subproblem. For the latter case, the complexity of DPALM is $\widetilde{\mathcal{O}}\left(\varepsilon^{-3} \right)$ to produce a near $\varepsilon$-KKT point by using an APG to solve a Moreau-envelope smoothed version of each subproblem. Our outer iteration complexity and the overall complexity either generalize existing best ones from unconstrained or linear-constrained problems to convex-constrained ones, or improve over the best-known results on solving the same-structured problems. Furthermore, numerical experiments on linearly/quadratically constrained non-convex quadratic programs and linear-constrained robust nonlinear least squares are conducted to demonstrate the empirical efficiency of the proposed DPALM over several state-of-the art methods.Comment: 27 page

MST-Based Semi-Supervised Clustering Using M-Labeled Objects

Most of the existing semi-supervised clustering algorithms depend on pairwise constraints, and they usually use lots of priori knowledge to improve their accuracies. In this paper, we use another semi-supervised method called label propagation to help detect clusters. We propose two new semi-supervised algorithms named K-SSMST and M-SSMST. Both of them aim to discover clusters of diverse density and arbitrary shape. Based on Minimum Spanning Tree's algorithm variant, K-SSMST can automatically find natural clusters in a dataset by using K labeled data objects where K is the number of clusters. M-SSMST can detect new clusters with insufficient semi-supervised information. Our algorithms have been tested on various artificial and UCI datasets. The results demonstrate that the algorithm's accuracy is better than other supervised and semi-supervised approaches