Metasurfaces offer a flexible framework for the manipulation of light
properties in the realm of thin film optics. Specifically, the polarization of
light can be effectively controlled through the use of thin phase plates. This
study aims to introduce a surrogate optimization framework for these devices.
The framework is applied to develop two kinds of vortex phase masks (VPMs)
tailored for application in astronomical high-contrast imaging. Computational
intelligence techniques are exploited to optimize the geometric features of
these devices. The large design space and computational limitations necessitate
the use of surrogate models like partial least squares Kriging, radial basis
functions, or neural networks. However, we demonstrate the inadequacy of these
methods in modeling the performance of VPMs. To address the shortcomings of
these methods, a data-efficient evolutionary optimization setup using a deep
neural network as a highly accurate and efficient surrogate model is proposed.
The optimization process in this study employs a robust particle swarm
evolutionary optimization scheme, which operates on explicit geometric
parameters of the photonic device. Through this approach, optimal designs are
developed for two design candidates. In the most complex case, evolutionary
optimization enables optimization of the design that would otherwise be
impractical (requiring too much simulations). In both cases, the surrogate
model improves the reliability and efficiency of the procedure, effectively
reducing the required number of simulations by up to 75% compared to
conventional optimization techniques.

利用元表面技术对薄膜光学进行光学特性的灵活操控，本研究提出了一种替代优化框架来开发适用于天文高对比度成像的两种旋涡相位面具，其中的几何特征通过计算智能技术进行优化。为解决传统方法的不足，引入一种数据高效的进化优化设置，利用深度神经网络作为高度精确和高效的替代模型，使用健壮的粒子群进化优化方案操作光子器件的几何参数进行优化，通过该方法，优化了两种设计方案的性能，有效地将所需的模拟次数与传统优化技术相比减少了 75%。

高效数据驱动深度学习优化的光子结构：纳米鳍片和环形凹槽相位掩模应用

Photonic Structures Optimization Using Highly Data-Efficient Deep  Learning: Application To Nanofin And Annular Groove Phase Masks

Interactions among humans on social media often convey intentions behind
their actions, yielding a psychological language resource for Mental Health
Analysis (MHA) of online users. The success of Computational Intelligence
Techniques (CIT) for inferring mental illness from such social media resources
points to NLP as a lens for causal analysis and perception mining. However, we
argue that more consequential and explainable research is required for optimal
impact on clinical psychology practice and personalized mental healthcare. To
bridge this gap, we posit two significant dimensions: (1) Causal analysis to
illustrate a cause and effect relationship in the user generated text; (2)
Perception mining to infer psychological perspectives of social effects on
online users intentions. Within the scope of Natural Language Processing (NLP),
we further explore critical areas of inquiry associated with these two
dimensions, specifically through recent advancements in discourse analysis.
This position paper guides the community to explore solutions in this space and
advance the state of practice in developing conversational agents for inferring
mental health from social media. We advocate for a more explainable approach
toward modeling computational psychology problems through the lens of language
as we observe an increased number of research contributions in dataset and
problem formulation for causal relation extraction and perception enhancements
while inferring mental states.

聊天软件等社交媒体的人际互动对个人心理状况具有暗示作用，而计算智能技术、自然语言处理等方面的持续发展为探讨导致用户在线上状况的原因与心理效应提供了一种全新的视角和工具。本文旨在进一步探讨其中两大关键方向：寻找因果关系与推理发生在用户意识中影响的各种心理角度，在自然语言处理的范畴内通过最新的话语分析技术拓展思路空间，以发掘心理计算学问题的更多潜在解决方案。