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This study presents an approach to analyze health disparities in Sexual and Gender Minority (SGM) populations, with a focus on the role of social support levels as an example to allow causal interpretations of regression models. We advocate for precisely defining the exposure variable and incorporating mediators into analyses, to address the limitations of comparing counterfactual outcomes solely between SGM and heterosexual populations. We define sexual orientation into domains (attraction, behavior, and identity), and emphasize a consideration of these elements either separately or together, depending on the research question. We also introduce social support measured before and after the disclosure of sexual orientation to facilitate inference. We illustrate this approach by examining the association between SGM status and depression diagnosis with data from the 2020 and 2021 National Health Interview Survey. We find a direct effect of SGM status on depression (OR: 3.07, 95% CI: 2.64 - 3.58) and no indirect effect through social support (OR: 1.07, 95% CI: 0.87-1.31). Our research emphasizes the necessity of the comprehensive measurement of sexual orientation and a focus on intervenable variables like social support in order to empower SGM communities and address SGM related health inequalities.

In this work, we utilize thin dielectric meta-atoms placed on a silver substrate to efficiently enhance and manipulate the third harmonic generation. We theoretically and experimentally reveal that when the structural symmetry of the meta-atom is incompatible with the lattice symmetry of an array, some generalized nonlinear geometric phases appear, which offers new possibilities for harmonic generation control beyond the accessible symmetries governed by the selection rule. The underlying mechanism is attributed to the modified rotation of the effective principal axis of a dense meta-atom array, where the strong coupling among the units gives rise to a generalized linear geometric phase modulation on the pump light. Therefore, nonlinear geometric phases carried by the third-harmonic emissions are the natural result of the wave-mixing process among the modes excited at the fundamental frequency. This mechanism further points out a new strategy to predict the nonlinear geometric phases delivered by the nanostructures according to their linear responses. Our design is simple and efficient, and offers alternatives for the nonlinear meta-devices that are capable of flexible photon generation and manipulation.

Deep neural networks (DNNs) can be easily fooled by adversarial attacks during inference phase when attackers add imperceptible perturbations to original examples, i.e., adversarial examples. Many works focus on adversarial detection and adversarial training to defend against adversarial attacks. However, few works explore the tool-chains behind adversarial examples, which can help defenders to seize the clues about the originator of the attack, their goals, and provide insight into the most effective defense algorithm against corresponding attacks. With such a gap, it is necessary to develop techniques that can recognize tool-chains that are leveraged to generate the adversarial examples, which is called Adversarial Attribution Problem (AAP). In this paper, AAP is defined as the recognition of three signatures, i.e., {\em attack algorithm}, {\em victim model} and {\em hyperparameter}. Current works transfer AAP into single label classification task and ignore the relationship between these signatures. The former will meet combination explosion problem as the number of signatures is increasing. The latter dictates that we cannot treat AAP simply as a single task problem. We first conduct some experiments to validate the attributability of adversarial examples. Furthermore, we propose a multi-task learning framework named Multi-Task Adversarial Attribution (MTAA) to recognize the three signatures simultaneously. MTAA contains perturbation extraction module, adversarial-only extraction module and classification and regression module. It takes the relationship between attack algorithm and corresponding hyperparameter into account and uses the uncertainty weighted loss to adjust the weights of three recognition tasks. The experimental results on MNIST and ImageNet show the feasibility and scalability of the proposed framework as well as its effectiveness in dealing with false alarms.

This paper consider a new secure communication scene where a full-duplex transmitter (Alan) need to transmit confidential information to a half-duplex receiver (Bob), with a silent eavesdropper (Eve) that tries to eavesdrop the confidential information. For realizing secure communication between Alan and Bob, a novel two phases communication scheme is proposed: in Phase 1, Alan and Bob send artificial noises (AN) simultaneously, while in Phase 2, Alan superimposes the AN received in Phase 1 with its confidential signal and sends the mixed signalto Bob. Since the mixed AN could degrade the SINR (Signal to Interference and Noise Ratio) of Eve, but does not affect the SINR of Bob, a secrecy capacity can be achieved. We also derive the conditions that the secrecy capacity of the proposed scheme exists, and analyze the secrecy outage probability under Rayleigh fading channel. Numerical results show that the secrecy capacity is about two times higher than without AN, even though in the proposed scheme half of the time is used to transmit ANs, and the outage probability is about five times lower than that without AN.