尽管机器学习方法已在金融领域广泛使用，但在非常成功的学位上，这些方法仍然可以根据解释性，可比性和可重复性来定制特定研究和不透明。这项研究的主要目的是通过提供一种通用方法来阐明这一领域，该方法是调查 - 不合Snostic且可解释给金融市场从业人员，从而提高了其效率，降低了进入的障碍，并提高了实验的可重复性。提出的方法在两个自动交易平台组件上展示。也就是说，价格水平，众所周知的交易模式和一种新颖的2步特征提取方法。该方法依赖于假设检验，该假设检验在其他社会和科学学科中广泛应用，以有效地评估除简单分类准确性之外的具体结果。提出的主要假设是为了评估所选的交易模式是否适合在机器学习设置中使用。在整个实验中，我们发现在机器学习设置中使用所考虑的交易模式仅由统计数据得到部分支持，从而导致效果尺寸微不足道（反弹7- $ 0.64 \ pm 1.02 $，反弹11 $ 0.38 \ pm 0.98 $，并且篮板15- $ 1.05 \ pm 1.16 $），但允许拒绝零假设。我们展示了美国期货市场工具上的通用方法，并提供了证据表明，通过这种方法，我们可以轻松获得除传统绩效和盈利度指标之外的信息指标。这项工作是最早将这种严格的统计支持方法应用于金融市场领域的工作之一，我们希望这可能是更多研究的跳板。

This short report reviews the current state of the research and methodology on theoretical and practical aspects of Artificial Neural Networks (ANN). It was prepared to gather state-of-the-art knowledge needed to construct complex, hypercomplex and fuzzy neural networks. The report reflects the individual interests of the authors and, by now means, cannot be treated as a comprehensive review of the ANN discipline. Considering the fast development of this field, it is currently impossible to do a detailed review of a considerable number of pages. The report is an outcome of the Project 'The Strategic Research Partnership for the mathematical aspects of complex, hypercomplex and fuzzy neural networks' meeting at the University of Warmia and Mazury in Olsztyn, Poland, organized in September 2022.

Two approaches to AI, neural networks and symbolic systems, have been proven very successful for an array of AI problems. However, neither has been able to achieve the general reasoning ability required for human-like intelligence. It has been argued that this is due to inherent weaknesses in each approach. Luckily, these weaknesses appear to be complementary, with symbolic systems being adept at the kinds of things neural networks have trouble with and vice-versa. The field of neural-symbolic AI attempts to exploit this asymmetry by combining neural networks and symbolic AI into integrated systems. Often this has been done by encoding symbolic knowledge into neural networks. Unfortunately, although many different methods for this have been proposed, there is no common definition of an encoding to compare them. We seek to rectify this problem by introducing a semantic framework for neural-symbolic AI, which is then shown to be general enough to account for a large family of neural-symbolic systems. We provide a number of examples and proofs of the application of the framework to the neural encoding of various forms of knowledge representation and neural network. These, at first sight disparate approaches, are all shown to fall within the framework's formal definition of what we call semantic encoding for neural-symbolic AI.

We propose a method that leverages graph neural networks, multi-level message passing, and unsupervised training to enable real-time prediction of realistic clothing dynamics. Whereas existing methods based on linear blend skinning must be trained for specific garments, our method is agnostic to body shape and applies to tight-fitting garments as well as loose, free-flowing clothing. Our method furthermore handles changes in topology (e.g., garments with buttons or zippers) and material properties at inference time. As one key contribution, we propose a hierarchical message-passing scheme that efficiently propagates stiff stretching modes while preserving local detail. We empirically show that our method outperforms strong baselines quantitatively and that its results are perceived as more realistic than state-of-the-art methods.

The self-configuring nnU-Net has achieved leading performance in a large range of medical image segmentation challenges. It is widely considered as the model of choice and a strong baseline for medical image segmentation. However, despite its extraordinary performance, nnU-Net does not supply a measure of uncertainty to indicate its possible failure. This can be problematic for large-scale image segmentation applications, where data are heterogeneous and nnU-Net may fail without notice. In this work, we introduce a novel method to estimate nnU-Net uncertainty for medical image segmentation. We propose a highly effective scheme for posterior sampling of weight space for Bayesian uncertainty estimation. Different from previous baseline methods such as Monte Carlo Dropout and mean-field Bayesian Neural Networks, our proposed method does not require a variational architecture and keeps the original nnU-Net architecture intact, thereby preserving its excellent performance and ease of use. Additionally, we boost the segmentation performance over the original nnU-Net via marginalizing multi-modal posterior models. We applied our method on the public ACDC and M&M datasets of cardiac MRI and demonstrated improved uncertainty estimation over a range of baseline methods. The proposed method further strengthens nnU-Net for medical image segmentation in terms of both segmentation accuracy and quality control.

Hierarchical decomposition of control is unavoidable in large dynamical systems. In reinforcement learning (RL), it is usually solved with subgoals defined at higher policy levels and achieved at lower policy levels. Reaching these goals can take a substantial amount of time, during which it is not verified whether they are still worth pursuing. However, due to the randomness of the environment, these goals may become obsolete. In this paper, we address this gap in the state-of-the-art approaches and propose a method in which the validity of higher-level actions (thus lower-level goals) is constantly verified at the higher level. If the actions, i.e. lower level goals, become inadequate, they are replaced by more appropriate ones. This way we combine the advantages of hierarchical RL, which is fast training, and flat RL, which is immediate reactivity. We study our approach experimentally on seven benchmark environments.

The number of standardized policy documents regarding climate policy and their publication frequency is significantly increasing. The documents are long and tedious for manual analysis, especially for policy experts, lawmakers, and citizens who lack access or domain expertise to utilize data analytics tools. Potential consequences of such a situation include reduced citizen governance and involvement in climate policies and an overall surge in analytics costs, rendering less accessibility for the public. In this work, we use a Latent Dirichlet Allocation-based pipeline for the automatic summarization and analysis of 10-years of national energy and climate plans (NECPs) for the period from 2021 to 2030, established by 27 Member States of the European Union. We focus on analyzing policy framing, the language used to describe specific issues, to detect essential nuances in the way governments frame their climate policies and achieve climate goals. The methods leverage topic modeling and clustering for the comparative analysis of policy documents across different countries. It allows for easier integration in potential user-friendly applications for the development of theories and processes of climate policy. This would further lead to better citizen governance and engagement over climate policies and public policy research.

本文介绍了我们对Polval 2021任务2的贡献：翻译质量评估指标的评估。我们描述了使用预先训练的语言模型和最新框架进行的实验，以在任务的非盲和盲版中进行翻译质量评估。我们的解决方案在非蓝色版本中排名第二，在盲版中排名第三。

最近，由于其对交通清算的重大影响，交通事故风险预测的问题一直引起了智能运输系统社区的关注。通过使用数据驱动的方法来对空间和时间事件的影响进行建模，因此在文献中通常可以解决此问题，因为它们被证明对于交通事故风险预测问题至关重要。为了实现这一目标，大多数方法构建了不同的体系结构以捕获时空相关性功能，从而使它们对大型交通事故数据集效率低下。因此，在这项工作中，我们提出了一个新颖的统一框架，即是上下文视觉变压器，可以通过端到端的方法进行培训，该方法可以有效地建议问题的空间和时间方面，同时提供准确的交通事故。风险预测。我们评估并比较了我们提出的方法的性能与来自两个不同地理位置的两个大规模交通事故数据集的文献的基线方法。结果表明，与文献中先前的最新作品（SOTA）相比，RMSE得分的重大改善大约为2 \％。此外，我们提出的方法在两个数据集上优于SOTA技术，而仅需要少23倍的计算要求。

由于时空事件发生的随机性，在报告的交通中断开始时缺乏信息，并且缺乏运输工程的高级方法来从过去中获得见解，因此预测交通事故持续时间是一个难题事故。本文提出了一个新的Fusion框架，用于通过将机器学习与交通流量/速度和事件描述作为功能进行集成来预测有限信息的事件持续时间，并通过多种深度​​学习方法编码（ANN AUTOCONEDER和角色级别的LSTM-ANN情绪分类器）。该论文在运输和数据科学中构建了跨学科建模方法。该方法提高了适用于基线事件报告的最佳表现ML模型的入射持续时间预测准确性。结果表明，与标准线性或支持矢量回归模型相比，我们提出的方法可以提高准确性$ 60 \％$，并且相对于混合深度学习自动编码的GBDT模型的另外7美元\％$改进，这似乎胜过表现所有其他模型。应用区是旧金山市，富含交通事件日志（全国交通事故数据集）和过去的历史交通拥堵信息（Caltrans绩效测量系统的5分钟精度测量）。