This paper introduced key aspects of applying Machine Learning (ML) models, improved trading strategies, and the Quasi-Reversibility Method (QRM) to optimize stock option forecasting and trading results. It presented the findings of the follow-up project of the research "Application of Convolutional Neural Networks with Quasi-Reversibility Method Results for Option Forecasting". First, the project included an application of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks to provide a novel way of predicting stock option trends. Additionally, it examined the dependence of the ML models by evaluating the experimental method of combining multiple ML models to improve prediction results and decision-making. Lastly, two improved trading strategies and simulated investing results were presented. The Binomial Asset Pricing Model with discrete time stochastic process analysis and portfolio hedging was applied and suggested an optimized investment expectation. These results can be utilized in real-life trading strategies to optimize stock option investment results based on historical data.

This paper presents a methodology for combining programming and mathematics to optimize elevator wait times. Based on simulated user data generated according to the canonical three-peak model of elevator traffic, we first develop a naive model from an intuitive understanding of the logic behind elevators. We take into consideration a general array of features including capacity, acceleration, and maximum wait time thresholds to adequately model realistic circumstances. Using the same evaluation framework, we proceed to develop a Deep Q Learning model in an attempt to match the hard-coded naive approach for elevator control. Throughout the majority of the paper, we work under a Markov Decision Process (MDP) schema, but later explore how the assumption fails to characterize the highly stochastic overall Elevator Group Control System (EGCS).

客户评论通常包含有关一个人在线购物体验的大量信息。尽管积极的评论对商店有益，但负面评论将在很大程度上影响消费者的决定，并可能导致销售下降。因此，仔细和有说服力地回答每个负面评论并最大程度地减少其不利影响至关重要。最近的研究考虑利用生成模型来帮助卖家做出回应。但是，此问题并不深入，因为评论可能包含问题的多个方面，这些方面应相应和有说服力地解决。在这项工作中，我们为有说服力的响应生成提出了一个多源多相关生成模型。提出的模型适当地获得和利用了各种信息来源，以产生更有信息和有说服力的响应。提出了一个多方面的细心网络，以自动参与审查中的不同方面，并确保解决大多数问题。在两个现实世界数据集上进行的广泛实验表明，我们的方法优于最先进的方法和在线测试，这证明我们的部署系统大大提高了商店处理负面评论的效率。

尽管条件变异自动编码器（CVAE）模型比传统的SEQ2SEQ模型可以产生更多的多样化响应，但响应通常与输入词的相关性低或与问题不合逻辑。进行因果分析以研究背后的原因，并提供了一种寻找调解人并减轻对话中混杂偏见的方法。具体而言，我们建议预测调解人，以保留相关信息，并自动将调解人纳入生成过程中。此外，动态主题图指导条件变异自动编码器（TGG-CVAE）模型用于补充语义空间并减少响应中的混杂偏置。广泛的实验表明，所提出的模型能够产生相关和信息性的响应，并且在自动指标和人类评估方面优于最先进的响应。

传统的细颗粒图像分类通常依赖于带注释的地面真相的大规模训练样本。但是，某些子类别在实际应用中可能几乎没有可用的样本。在本文中，我们建议使用多频邻域（MFN）和双交叉调制（DCM）提出一个新颖的几弹性细颗粒图像分类网络（FICNET）。采用模块MFN来捕获空间域和频域中的信息。然后，提取自相似性和多频成分以产生多频结构表示。 DCM使用分别考虑全球环境信息和类别之间的微妙关系来调节嵌入过程。针对两个少量任务的三个细粒基准数据集进行的综合实验验证了FICNET与最先进的方法相比具有出色的性能。特别是，在两个数据集“ Caltech-UCSD鸟”和“ Stanford Cars”上进行的实验分别可以获得分类精度93.17 \％和95.36 \％。它们甚至高于一般的细粒图像分类方法可以实现的。

评估生成的对抗网络（GANS）的表现是由于其实际意义的重要课题。虽然已经提出了几种评估指标，但它们通常会评估整个产生的图像分布的质量。对于参考标制图像合成（RIS）任务，即呈现另一参考图像的样式的源图像，其中，在评估单个生成图像的质量至关重要时，这些度量不适用于这些度量。在本文中，我们提出了一般学习的框架，参考引导图像合成评估（RISA）来定量地评估单个生成图像的质量。值得注意的是，RISA的培训不需要人类注释。具体而言，RISA的训练数据由RIS中的培训过程中的中间模型获取，并且基于图像质量与迭代之间的正相关性，通过模型迭代的数量弱写。由于该注释作为监督信号太粗糙，我们介绍了两种技术：1）一种像素 - 明智的插值方案，以改进粗标签，以及2）多个二进制分类器来替换NA \“IVE回归。此外，无人监督引入对比损失以有效地捕获所生成的图像及其参考图像之间的风格相似性。各种数据集的经验结果表明，RISA与人偏好和跨越模型的井中转移良好。

基于深入的学习的断层摄影图像重建一直在这些年来引起了很多关注。稀疏视图数据重建是典型的未确定逆问题之一，如何从数十个投影重建高质量CT图像仍然是实践中的挑战。为了解决这一挑战，在本文中，我们提出了一个多域一体化的Swin变压器网络（MIST-NET）。首先，使用灵活的网络架构，所提出的雾网掺入了来自数据，残差数据，图像和剩余图像的豪华域特征。这里，残差数据和残差 - 图像域网组件可以被认为是数据一致性模块，以消除残差数据和图像域中的插值误差，然后进一步保持图像细节。其次，为了检测图像特征和进一步保护图像边缘，将培训的Sobel滤波器结合到网络中以提高编码解码能力。第三，随着经典的Swin变压器，我们进一步设计了高质量的重建变压器（即，REFFORMER）来提高重建性能。 REFFORMER继承了SWIN变压器的功率以捕获重建图像的全局和本地特征。具有48种视图的数值数据集的实验证明了我们所提出的雾网提供更高的重建图像质量，具有小的特征恢复和边缘保护，而不是其他竞争对手，包括高级展开网络。定量结果表明，我们的雾网也获得了最佳性能。训练有素的网络被转移到真实的心脏CT数据集，48次视图，重建结果进一步验证了我们的雾网的优势，进一步证明了临床应用中雾的良好稳健性。

几乎所有现有的基于面部动作编码系统的数据集包括面部动作单元（AU）强度信息使用A-E级别分层地向强度值注释。然而，面部表情连续变化，并将从一个状态变为另一个状态。因此，将局部面部AU的强度值重新播出以表示整个面部表情的变化更有效，特别是在表达转移和面部动画的领域。我们将Feafa的扩展与重新标记的DISFA数据库相结合，可在HTTPS://www.iiplab.net/feafa+ /现在提供。扩展Feafa（Feafa +）包括来自Feafa和Disfa的150个视频序列，总共230,184帧，使用表达式定量工具手动注释24重新定义AU的浮点强度值。我们还列出了针对构成和自发子集的粗略数值结果，并为AU强度回归任务提供基线比较。

In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables. We formalize this scenario by building a new Chinese benchmark KnowSQL consisting of domain-specific questions covering various domains. We then address this problem by presenting formulaic knowledge, rather than by annotating additional data examples. More concretely, we construct a formulaic knowledge bank as a domain knowledge base and propose a framework (ReGrouP) to leverage this formulaic knowledge during parsing. Experiments using ReGrouP demonstrate a significant 28.2% improvement overall on KnowSQL.

Weakly-supervised object localization aims to indicate the category as well as the scope of an object in an image given only the image-level labels. Most of the existing works are based on Class Activation Mapping (CAM) and endeavor to enlarge the discriminative area inside the activation map to perceive the whole object, yet ignore the co-occurrence confounder of the object and context (e.g., fish and water), which makes the model inspection hard to distinguish object boundaries. Besides, the use of CAM also brings a dilemma problem that the classification and localization always suffer from a performance gap and can not reach their highest accuracy simultaneously. In this paper, we propose a casual knowledge distillation method, dubbed KD-CI-CAM, to address these two under-explored issues in one go. More specifically, we tackle the co-occurrence context confounder problem via causal intervention (CI), which explores the causalities among image features, contexts, and categories to eliminate the biased object-context entanglement in the class activation maps. Based on the de-biased object feature, we additionally propose a multi-teacher causal distillation framework to balance the absorption of classification knowledge and localization knowledge during model training. Extensive experiments on several benchmarks demonstrate the effectiveness of KD-CI-CAM in learning clear object boundaries from confounding contexts and addressing the dilemma problem between classification and localization performance.