Being able to forecast the popularity of new garment designs is very important in an industry as fast paced as fashion, both in terms of profitability and reducing the problem of unsold inventory. Here, we attempt to address this task in order to provide informative forecasts to fashion designers within a virtual reality designer application that will allow them to fine tune their creations based on current consumer preferences within an interactive and immersive environment. To achieve this we have to deal with the following central challenges: (1) the proposed method should not hinder the creative process and thus it has to rely only on the garment's visual characteristics, (2) the new garment lacks historical data from which to extrapolate their future popularity and (3) fashion trends in general are highly dynamical. To this end, we develop a computer vision pipeline fine tuned on fashion imagery in order to extract relevant visual features along with the category and attributes of the garment. We propose a hierarchical label sharing (HLS) pipeline for automatically capturing hierarchical relations among fashion categories and attributes. Moreover, we propose MuQAR, a Multimodal Quasi-AutoRegressive neural network that forecasts the popularity of new garments by combining their visual features and categorical features while an autoregressive neural network is modelling the popularity time series of the garment's category and attributes. Both the proposed HLS and MuQAR prove capable of surpassing the current state-of-the-art in key benchmark datasets, DeepFashion for image classification and VISUELLE for new garment sales forecasting.

In the cybersecurity setting, defenders are often at the mercy of their detection technologies and subject to the information and experiences that individual analysts have. In order to give defenders an advantage, it is important to understand an attacker's motivation and their likely next best action. As a first step in modeling this behavior, we introduce a security game framework that simulates interplay between attackers and defenders in a noisy environment, focusing on the factors that drive decision making for attackers and defenders in the variants of the game with full knowledge and observability, knowledge of the parameters but no observability of the state (``partial knowledge''), and zero knowledge or observability (``zero knowledge''). We demonstrate the importance of making the right assumptions about attackers, given significant differences in outcomes. Furthermore, there is a measurable trade-off between false-positives and true-positives in terms of attacker outcomes, suggesting that a more false-positive prone environment may be acceptable under conditions where true-positives are also higher.

重建和分类误差的关节优化是一个难题的问题，尤其是当使用非线性映射时。为了克服这一障碍，提出了一种新颖的优化策略，其中将降低维度的卷积自动编码器和由完全连接的网络组成的分类器组合在一起，以同时产生监督的维度降低和预测。事实证明，这种方法也可以极大地有益于深度学习体系结构的解释性。此外，可以利用针对分类任务进行优化的最终潜在空间来改善传统的，可解释的分类算法。实验结果表明，所提出的方法对最先进的深度学习方法实现了竞争结果，同时在参数计数方面更有效。最后，从经验上证明，所提出的方法论介绍了关于通过产生的潜在空间的数据结构，还涉及分类行为的高级解释性。

机器学习领域的最新进展打开了高性能计算的新时代。机器学习算法在开发复杂问题的准确和成本效益的替代物中的应用已经引起了科学家的主要关注。尽管具有强大的近似功能，但代理人仍无法为问题产生“精确”解决方案。为了解决此问题，本文利用了最新的ML工具，并提供了线性方程系统的自定义迭代求解器，能够在任何所需的准确性级别求解大规模参数化问题。具体而言，建议的方法包括以下两个步骤。首先，进行了一组减少的模型评估集，并使用相应的解决方案用于建立从问题的参数空间到其解决方案空间的近似映射，并使用深层馈电神经网络和卷积自动编码器。该映射是一种手段，可以以微不足道的计算成本来获得对系统对新查询点的响应的非常准确的初始预测。随后，开发了一种受代数多机方法启发的迭代求解器与适当的正交分解（称为pod-2g）相结合的迭代求解器，该迭代求解器被开发为依次完善对确切系统解决方案的初始预测。在大规模系统的几个数值示例中，证明了POD-2G作为独立求解器或作为预处理梯度方法的预处理，结果表明其优于常规迭代溶液方案。