Chirality-enabled unidirectional light emission and nanoparticle detection in parity-time-symmetric microcavity

Achieving unidirectional emission and manipulating waves in a microcavity are crucial for information processing and data transmission in next-generation photonic circuits (PCs). Here we show how to impose twin microcavities with opposite chirality by incorporating parity-time (PT) symmetry to realize unidirectional emission. Our numerical calculation results show that the opposite chirality in microcavities stems from the asymmetric coupling of the clockwise (CW) and counterclockwise (CCW) components carried by the attached waveguide to the left- or right-sided microcavities, respectively. Notably, by engineering PT symmetry in the coupled system via the gain-loss control, the clockwise component of the lossy cavity could be selectively suppressed, which leads to the unidirectional emission with an extinction ratio of up to -52 dB. Furthermore, the chirality and PT-symmetry breaking enabled unidirectional emission is extremely sensitive to external scatters, allowing the detection of nanoparticles with an ultrasmall radius of 5-50 nm by recording the extinction ratio change. The proposed system provides a simple yet general way to manipulate the standing waves in a microcavity and will be essential for advancing the potentials of the microcavity in PCs.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Detecting Extra Dimension By the Experiment of the Quantum Gravity Induced Entanglement of Masses

It is believed that gravity may be regarded as a quantum coherent mediator. In this work we propose a plan using the Quantum Gravity Induced Entanglement of Masses (QGEM) experiment to test the extra dimension. The experiment involves two freely falling test masses passing though a Stern-Gerlach-like device. We study the entanglement witness of them in the framework of Randall-Sundrum II model (RS-II). It turns out that the system would reach entangled more rapidly in the presence of extra dimension. In particular, this is more significant for large radius of extra dimension

InfeRE: Step-by-Step Regex Generation via Chain of Inference

Automatically generating regular expressions (abbrev. regexes) from natural language description (NL2RE) has been an emerging research area. Prior studies treat regex as a linear sequence of tokens and generate the final expressions autoregressively in a single pass. They did not take into account the step-by-step internal text-matching processes behind the final results. This significantly hinders the efficacy and interpretability of regex generation by neural language models. In this paper, we propose a new paradigm called InfeRE, which decomposes the generation of regexes into chains of step-by-step inference. To enhance the robustness, we introduce a self-consistency decoding mechanism that ensembles multiple outputs sampled from different models. We evaluate InfeRE on two publicly available datasets, NL-RX-Turk and KB13, and compare the results with state-of-the-art approaches and the popular tree-based generation approach TRANX. Experimental results show that InfeRE substantially outperforms previous baselines, yielding 16.3% and 14.7% improvement in DFA@5 accuracy on two datasets, respectively. Particularly, InfeRE outperforms the popular tree-based generation approach by 18.1% and 11.3% on both datasets, respectively, in terms of DFA@5 accuracy.Comment: This paper has been accepted by ASE'2

Semi-implicit Continuous Newton Method for Power Flow Analysis

This paper proposes a semi-implicit version of continuous Newton method (CNM) for power flow analysis. The proposed method succeeds the numerical robustness from the implicit CNM (ICNM) framework while prevents the iterative solution of nonlinear systems, hence revealing higher convergence speed and computation efficiency. The intractability of ICNM consists in its nonlinear implicit ordinary-differential-equation (ODE) nature. We circumvent this by introducing intermediate variables, hence converting the implicit ODEs into differential algebraic equations (DAEs), and solve the DAEs with a linear scheme, the stiffly accurate Rosenbrock type method (SARM). A new 4-stage 3rd-order hyper-stable SARM, together with a 2nd-order embedded formula to control the step size, is constructed. Case studies on system 9241pegase verified the alleged performance

Method to Annotate Arrhythmias by Deep Network

This study targets to automatically annotate on arrhythmia by deep network. The investigated types include sinus rhythm, asystole (Asys), supraventricular tachycardia (Tachy), ventricular flutter or fibrillation (VF/VFL), ventricular tachycardia (VT). Methods: 13s limb lead ECG chunks from MIT malignant ventricular arrhythmia database (VFDB) and MIT normal sinus rhythm database were partitioned into subsets for 5-fold cross validation. These signals were resampled to 200Hz, filtered to remove baseline wandering, projected to 2D gray spectrum and then fed into a deep network with brand-new structure. In this network, a feature vector for a single time point was retrieved by residual layers, from which latent representation was extracted by variational autoencoder (VAE). These front portions were trained to meet a certain threshold in loss function, then fixed while training procedure switched to remaining bidirectional recurrent neural network (RNN), the very portions to predict an arrhythmia category. Attention windows were polynomial lumped on RNN outputs for learning from details to outlines. And over sampling was employed for imbalanced data. The trained model was wrapped into docker image for deployment in edge or cloud. Conclusion: Promising sensitivities were achieved in four arrhythmias and good precision rates in two ventricular arrhythmias were also observed. Moreover, it was proven that latent representation by VAE, can significantly boost the speed of convergence and accuracy

Airframe-Propulsion Integration Design and Optimization

Airframe-propulsion integration design is one of the key technologies of the hypersonic vehicle. With the development of hypersonic vehicle design method, CFD technology, and optimization method, it is possible to improve the conceptual design of airframe-propulsion integration both in accuracy and efficiency. In this chapter, design methods of waverider airframes and propulsion systems, including inlets, nozzles, isolators, and combustors, are reviewed and discussed in the light of CFD analyses. Thereafter, the Busemann inlet, a three-dimensional flow-stream traced nozzle, and a circular combustor together with a cone-derived waverider are chosen to demonstrate the airframe-propulsion integration design. The propulsion system is optimized according to the overall performance, and then the component such as the nozzle is optimized to obtain a better conceptual configuration