LGBTQ Youth’s Development in Ontario Schools

Abstract In order to support lesbian, gay, bisexual, transgender and queer (LGBTQ) students, the Ontario government recently introduced a new sex education curriculum that seeks to educate all students about the LGBTQ community. Through a progressive initiative, the rise of social media and information technology have changed the way in which students interact and learn, so it is critical to develop a current understanding of struggles that LGBTQ students face, whether it be with conventional forms of discrimination and bullying, or instances of cyberbullying. It is likewise important to understand how these issues impact their self-perceptions and development. To understand these concerns, the current study employs an extensive literature review, then, through the lens of the anti-oppressive practice (AOP) and theory of change, explores potential solutions and considers the effectiveness of Ontario’s new sex education curriculum. The findings suggest that qualitative, longitudinal, and comparative research will need to be done in the future to determine the nature of the issues that current LGBTQ students face and the effectiveness of proposed solutions

High-Isolation Dual-Polarized Microstrip Antenna via Substrate Integrated Waveguide Technology

A dual-polarized microstrip antenna with high-isolation is proposed by the utilization of the substrate-integrated waveguide (SIW) technology. According to the SIW technology, the metalized holes (MHs) are inserted into the substrate for the proposed antenna and the electric fields of the feeding parts are enclosed, so the isolation of the antenna is enhanced. The bandwidth is improved due to the MHs in the four sides of the antenna. A prototype of the proposed antenna has been fabricated and measured. Experimental results indicate that the antenna obtains the isolation more than 40 dB and achieves the impedance bandwidth of 21.9% and 23.8%(11.8-14.6 GHz and 11.65-14.8 GHz for two ports) of the reflection coefficients less than -20 dB. The cross polarization with the main lobe remains less than -30 dB and the half-power beam width is about 70° for the proposed antenna. Meanwhile, the front-to-back ratio remains to be better than 20 dB. A good agreement between the measured and simulated results validates the proposed design

Waiting time distribution of solar energetic particle events modeled with a non-stationary Poisson process

We present a study of the waiting time distributions (WTDs) of solar energetic particle (SEP) events observed with the spacecraft $WIND$ and $GOES$. Both the WTDs of solar electron events (SEEs) and solar proton events (SPEs) display a power-law tail $\sim \Delta t^{-\gamma}$. The SEEs display a broken power-law WTD. The power-law index is $\gamma_{1} =$ 0.99 for the short waiting times ($$100 hours). The break of the WTD of SEEs is probably due to the modulation of the corotating interaction regions (CIRs). The power-law index $\gamma \sim$ 1.82 is derived for the WTD of SPEs that is consistent with the WTD of type II radio bursts, indicating a close relationship between the shock wave and the production of energetic protons. The WTDs of SEP events can be modeled with a non-stationary Poisson process which was proposed to understand the waiting time statistics of solar flares (Wheatland 2000; Aschwanden $\&$ McTiernan 2010). We generalize the method and find that, if the SEP event rate $\lambda = 1/\Delta t$ varies as the time distribution of event rate $f(\lambda) = A \lambda^{-\alpha}exp(-\beta \lambda)$, the time-dependent Poisson distribution can produce a power-law tail WTD $\sim \Delta t^{\alpha - 3}$, where $0 \leq \alpha < 2$.Comment: 10 pages, 4 figures, accepted for publication in ApJ Letter

Efficient Estimation Under Data Fusion

We aim to make inferences about a smooth, finite-dimensional parameter by fusing data from multiple sources together. Previous works have studied the estimation of a variety of parameters in similar data fusion settings, including in the estimation of the average treatment effect and average reward under a policy, with the majority of them merging one historical data source with covariates, actions, and rewards and one data source of the same covariates. In this work, we consider the general case where one or more data sources align with each part of the distribution of the target population, for example, the conditional distribution of the reward given actions and covariates. We describe potential gains in efficiency that can arise from fusing these data sources together in a single analysis, which we characterize by a reduction in the semiparametric efficiency bound. We also provide a general means to construct estimators that achieve these bounds. In numerical experiments, we illustrate marked improvements in efficiency from using our proposed estimators rather than their natural alternatives. Finally, we illustrate the magnitude of efficiency gains that can be realized in vaccine immunogenicity studies by fusing data from two HIV vaccine trials

Big Learning Expectation Maximization

Mixture models serve as one fundamental tool with versatile applications. However, their training techniques, like the popular Expectation Maximization (EM) algorithm, are notoriously sensitive to parameter initialization and often suffer from bad local optima that could be arbitrarily worse than the optimal. To address the long-lasting bad-local-optima challenge, we draw inspiration from the recent ground-breaking foundation models and propose to leverage their underlying big learning principle to upgrade the EM. Specifically, we present the Big Learning EM (BigLearn-EM), an EM upgrade that simultaneously performs joint, marginal, and orthogonally transformed marginal matchings between data and model distributions. Through simulated experiments, we empirically show that the BigLearn-EM is capable of delivering the optimal with high probability; comparisons on benchmark clustering datasets further demonstrate its effectiveness and advantages over existing techniques. The code is available at https://github.com/YulaiCong/Big-Learning-Expectation-Maximization.Comment: AAAI 202

Fractal Metamaterial Absorber with Three-Order Oblique Cross Dipole Slot Structure and its Application for In-band RCS Reduction of Array Antennas

To miniaturize the perfect metamaterial absorber, a fractal three-order oblique cross dipole slot structure is proposed and investigated in this paper. The fractal perfect metamaterial absorber (FPMA) consists of two metallic layers separated by a lossy dielectric substrate. The top layer etched a three-order oblique fractal-shaped cross dipole slot set in a square patch and the bottom one is a solid metal. The parametric study is performed for providing practical design guidelines. A prototype with a thickness of 0.0106λ (λ is the wavelength at 3.18 GHz) of the FPMA was designed, fabricated, measured, and is loaded on a 1×10 guidewave slot array antennas to reduce the in-band radar cross section (RCS) based on their surface current distribution. Experiments are carried out to verify the simulation results, and the experimental results show that the absorption at normal incidence is above 90% from 3.17 to 3.22GHz, the size for the absorber is 0.1λ×0.1λ, the three-order FPMA is miniaturized 60% compared with the zero-order ones, and the array antennas significantly obtain the RCS reduction without the radiation deterioration

Adversarial Training with OCR Modality Perturbation for Scene-Text Visual Question Answering

Scene-Text Visual Question Answering (ST-VQA) aims to understand scene text in images and answer questions related to the text content. Most existing methods heavily rely on the accuracy of Optical Character Recognition (OCR) systems, and aggressive fine-tuning based on limited spatial location information and erroneous OCR text information often leads to inevitable overfitting. In this paper, we propose a multimodal adversarial training architecture with spatial awareness capabilities. Specifically, we introduce an Adversarial OCR Enhancement (AOE) module, which leverages adversarial training in the embedding space of OCR modality to enhance fault-tolerant representation of OCR texts, thereby reducing noise caused by OCR errors. Simultaneously, We add a Spatial-Aware Self-Attention (SASA) mechanism to help the model better capture the spatial relationships among OCR tokens. Various experiments demonstrate that our method achieves significant performance improvements on both the ST-VQA and TextVQA datasets and provides a novel paradigm for multimodal adversarial training.Comment: 6 pages, 3 figures, accepted by 2024 IEEE International Conference on Multimedia and Exp