Phenomenological modeling of Geometric Metasurfaces

Metasurfaces, with their superior capability in manipulating the optical wavefront at the subwavelength scale and low manufacturing complexity, have shown great potential for planar photonics and novel optical devices. However, vector field simulation of metasurfaces is so far limited to periodic-structured metasurfaces containing a small number of meta-atoms in the unit cell by using full-wave numerical methods. Here, we propose a general phenomenological method to analytically model metasurfaces made up of arbitrarily distributed meta-atoms based on the assumption that the meta-atoms possess localized resonances with Lorentz-Drude forms, whose exact form can be retrieved from the full wave simulation of a single element. Applied to phase modulated geometric metasurfaces, our analytical results show good agreement with full-wave numerical simulations. The proposed theory provides an efficient method to model and design optical devices based on metasurfaces.Comment: 16 pages, 8 figure

Simple method for measuring the linewidth enhancement factor of semiconductor lasers

A simple method for measuring the linewidth enhancement factor (LEF) of semiconductor lasers (SLs) is proposed and demonstrated in this paper. This method is based on the self-mixing effect when a small portion of optical signal intensity emitted by the SL reflected by the moving target re-enters the SL cavity, leading to a modulation in the SL\u27s output power intensity, in which the modulated envelope shape depends on the optical feedback strength as well as the LEF. By investigating the relationship between the light phase and power from the well-known Lang and Kobayashi equations, it was found that the LEF can be simply measured from the power value overlapped by two SLs\u27 output power under two different optical feedback strengths. Our proposed method is verified by both simulations and experiments. (C) 2015 Optical Society of Americ

Features of a Self-Mixing Laser Diode Operating Near Relaxation Oscillation

When a fraction of the light reflected by an external cavity re-enters the laser cavity, both the amplitude and the frequency of the lasing field can be modulated. This phenomenon is called the self-mixing effect (SME). A self-mixing laser diode (SM-LD) is a sensor using the SME. Usually, such LDs operate below the stability boundary where no relaxation oscillation happens. The boundary is determined by the operation condition including the injection current, optical feedback strength and external cavity length. This paper discovers the features of an SM-LD where the LD operates beyond the stability boundary, that is, near the relaxation oscillation (RO) status. We call the signals from such a SM-LD as RO-SM signals to differentiate them from the conventional SM signals reported in the literature. Firstly, simulations are made based on the well-known Lang and Kobayashi (L-K) equations. Then the experiments are conducted on different LDs to verify the simulation results. It shows that a RO-SM signal exhibits high frequency oscillation with its amplitude modulated by a slow time varying envelop which corresponds to the movement of the external target. The envelope has same fringe structure (half-wavelength displacement resolution) with the conventional SM signals. However, the amplitudes of the RO-SM signals are much higher compared to conventional SM signals. The results presented reveal that an SM-LD operating near the RO has potential for achieving sensing with improved sensitivity

GBG++: A Fast and Stable Granular Ball Generation Method for Classification

Granular ball computing (GBC), as an efficient, robust, and scalable learning method, has become a popular research topic of granular computing. GBC includes two stages: granular ball generation (GBG) and multi-granularity learning based on the granular ball (GB). However, the stability and efficiency of existing GBG methods need to be further improved due to their strong dependence on $k$-means or $k$-division. In addition, GB-based classifiers only unilaterally consider the GB's geometric characteristics to construct classification rules, but the GB's quality is ignored. Therefore, in this paper, based on the attention mechanism, a fast and stable GBG (GBG++) method is proposed first. Specifically, the proposed GBG++ method only needs to calculate the distances from the data-driven center to the undivided samples when splitting each GB instead of randomly selecting the center and calculating the distances between it and all samples. Moreover, an outlier detection method is introduced to identify local outliers. Consequently, the GBG++ method can significantly improve effectiveness, robustness, and efficiency while being absolutely stable. Second, considering the influence of the sample size within the GB on the GB's quality, based on the GBG++ method, an improved GB-based $k$-nearest neighbors algorithm (GB$k$NN++) is presented, which can reduce misclassification at the class boundary. Finally, the experimental results indicate that the proposed method outperforms several existing GB-based classifiers and classical machine learning classifiers on $24$ public benchmark datasets

Transient and Stable GFP Expression in Germ Cells by the vasa Regulatory Sequences from the Red Seabream (Pagrus major)

Primordial germ cells (PGCs) are the precursors of gametes responsible for genetic transmission to the next generation. They provide an ideal system for cryopreservation and restoration of biodiversity. Recently, considerable attention has been raised to visualize, isolate and transplant PGCs within and between species. In fish, stable PGC visualization in live embryo and individual has been limited to laboratory fish models such as medaka and zebrafish. One exception is the rainbow trout, which represents the only species with aquaculture importance and has GFP-labeled germ cells throughout development. PGCs can be transiently labeled by embryonic injection of mRNA containing green fluorescence protein gene (GFP) and 3'-untranslated region (3'-UTR) of a maternal germ gene such as vasa, nos1, etc. Stable PGC labeling can be achieved through production of transgenic animals by some transcriptional regulatory sequences from germ genes, such as the vasa promoter and 3'-UTR. In this study, we reported the functional analyses of the red seabream vasa (Pmvas) regulatory sequences, using medaka as a model system. It was showed that injection of GFP-Pmvas3'UTR mRNA was able to label medaka PGCs during embryogenesis. Besides, we have constructed pPmvasGFP transgenic vector, and established a stable transgenic medaka line exhibiting GFP expression in germ cells including PGCs, mitotic and meiotic germ cells of both sexes, under control of the Pmvas transcriptional regulatory sequences. It is concluded that the Pmvas regulatory sequences examined in this study are sufficient for germ cell expression and labeling

Reweighted Mixup for Subpopulation Shift

Subpopulation shift exists widely in many real-world applications, which refers to the training and test distributions that contain the same subpopulation groups but with different subpopulation proportions. Ignoring subpopulation shifts may lead to significant performance degradation and fairness concerns. Importance reweighting is a classical and effective way to handle the subpopulation shift. However, recent studies have recognized that most of these approaches fail to improve the performance especially when applied to over-parameterized neural networks which are capable of fitting any training samples. In this work, we propose a simple yet practical framework, called reweighted mixup (RMIX), to mitigate the overfitting issue in over-parameterized models by conducting importance weighting on the ''mixed'' samples. Benefiting from leveraging reweighting in mixup, RMIX allows the model to explore the vicinal space of minority samples more, thereby obtaining more robust model against subpopulation shift. When the subpopulation memberships are unknown, the training-trajectories-based uncertainty estimation is equipped in the proposed RMIX to flexibly characterize the subpopulation distribution. We also provide insightful theoretical analysis to verify that RMIX achieves better generalization bounds over prior works. Further, we conduct extensive empirical studies across a wide range of tasks to validate the effectiveness of the proposed method.Comment: Journal version of arXiv:2209.0892