传统的脑电脑接口（BCI）需要在使用之前为每个用户提供完整的数据收集，训练和校准阶段。近年来，已经开发了许多主题独立的（SI）BCI。与受试者依赖性（SD）方法相比，这些方法中的许多方法产生较弱的性能，有些方法是计算昂贵的。潜在的真实世界应用程序将极大地受益于更准确，紧凑，并计算高效的主题的BCI。在这项工作中，我们提出了一个名为CCSPNET（卷积公共空间模式网络）的新型主题独立的BCI框架，该框架被训练在大型脑电图（EEG）信号数据库中的电动机图像（MI）范例上，由400个试验组成每54名科目执行两班手机MI任务。所提出的框架应用小波核卷积神经网络（WKCNN）和时间卷积神经网络（TCNN），以表示和提取EEG信号的光谱特征。对于空间特征提取来实现公共空间模式（CSP）算法，并且通过密集的神经网络减少了CSP特征的数量。最后，类标签由线性判别分析（LDA）分类器确定。 CCSPNET评估结果表明，可以具有紧凑的BCI，可实现与复杂和计算昂贵的模型相当的SD和SI最先进的性能。

Designing a local planner to control tractor-trailer vehicles in forward and backward maneuvering is a challenging control problem in the research community of autonomous driving systems. Considering a critical situation in the stability of tractor-trailer systems, a practical and novel approach is presented to design a non-linear MPC(NMPC) local planner for tractor-trailer autonomous vehicles in both forward and backward maneuvering. The tractor velocity and steering angle are considered to be control variables. The proposed NMPC local planner is designed to handle jackknife situations, avoiding multiple static obstacles, and path following in both forward and backward maneuvering. The challenges mentioned above are converted into a constrained problem that can be handled simultaneously by the proposed NMPC local planner. The direct multiple shooting approach is used to convert the optimal control problem(OCP) into a non-linear programming problem(NLP) that IPOPT solvers can solve in CasADi. The controller performance is evaluated through different backup and forward maneuvering scenarios in the Gazebo simulation environment in real-time. It achieves asymptotic stability in avoiding static obstacles and accurate tracking performance while respecting path constraints. Finally, the proposed NMPC local planner is integrated with an open-source autonomous driving software stack called AutowareAi.

Numerous models have tried to effectively embed knowledge graphs in low dimensions. Among the state-of-the-art methods, Graph Neural Network (GNN) models provide structure-aware representations of knowledge graphs. However, they often utilize the information of relations and their interactions with entities inefficiently. Moreover, most state-of-the-art knowledge graph embedding models suffer from scalability issues because of assigning high-dimensional embeddings to entities and relations. To address the above limitations, we propose a scalable general knowledge graph encoder that adaptively involves a powerful tensor decomposition method in the aggregation function of RGCN, a well-known relational GNN model. Specifically, the parameters of a low-rank core projection tensor, used to transform neighborhood entities in the encoder, are shared across relations to benefit from multi-task learning and incorporate relations information effectively. Besides, we propose a low-rank estimation of the core tensor using CP decomposition to compress the model, which is also applicable, as a regularization method, to other similar linear models. We evaluated our model on knowledge graph completion as a common downstream task. We train our model for using a new loss function based on contrastive learning, which relieves the training limitation of the 1-N method on huge graphs. We improved RGCN performance on FB15-237 by 0.42% with considerably lower dimensionality of embeddings.

The Universal Feature Selection Tool (UniFeat) is an open-source tool developed entirely in Java for performing feature selection processes in various research areas. It provides a set of well-known and advanced feature selection methods within its significant auxiliary tools. This allows users to compare the performance of feature selection methods. Moreover, due to the open-source nature of UniFeat, researchers can use and modify it in their research, which facilitates the rapid development of new feature selection algorithms.

Cartoons are an important part of our entertainment culture. Though drawing a cartoon is not for everyone, creating it using an arrangement of basic geometric primitives that approximates that character is a fairly frequent technique in art. The key motivation behind this technique is that human bodies - as well as cartoon figures - can be split down into various basic geometric primitives. Numerous tutorials are available that demonstrate how to draw figures using an appropriate arrangement of fundamental shapes, thus assisting us in creating cartoon characters. This technique is very beneficial for children in terms of teaching them how to draw cartoons. In this paper, we develop a tool - shape2toon - that aims to automate this approach by utilizing a generative adversarial network which combines geometric primitives (i.e. circles) and generate a cartoon figure (i.e. Mickey Mouse) depending on the given approximation. For this purpose, we created a dataset of geometrically represented cartoon characters. We apply an image-to-image translation technique on our dataset and report the results in this paper. The experimental results show that our system can generate cartoon characters from input layout of geometric shapes. In addition, we demonstrate a web-based tool as a practical implication of our work.

The cover is the face of a book and is a point of attraction for the readers. Designing book covers is an essential task in the publishing industry. One of the main challenges in creating a book cover is representing the theme of the book's content in a single image. In this research, we explore ways to produce a book cover using artificial intelligence based on the fact that there exists a relationship between the summary of the book and its cover. Our key motivation is the application of text-to-image synthesis methods to generate images from given text or captions. We explore several existing text-to-image conversion techniques for this purpose and propose an approach to exploit these frameworks for producing book covers from provided summaries. We construct a dataset of English books that contains a large number of samples of summaries of existing books and their cover images. In this paper, we describe our approach to collecting, organizing, and pre-processing the dataset to use it for training models. We apply different text-to-image synthesis techniques to generate book covers from the summary and exhibit the results in this paper.

在过去的十年中，我们看到了工业数据，计算能力的巨大改善以及机器学习的重大理论进步。这为在大规模非线性监控和控制问题上使用现代机器学习工具提供了机会。本文对过程行业的应用进行了对最新结果的调查。

本文提出了一种快速准确的表面正常估计方法，可以直接在深度图（有组织的点云）上使用。表面正常估计过程被配制为封闭形式的表达。为了减少测量噪声的效果，平均操作以多方向方式使用。多方向正常估计过程将在要有效实施的下一步中进行重新重新制定。最后，提出了一种简单而有效的方法，以消除深度不连续性下错误的正常估计。将所提出的方法与众所周知的表面正常估计算法进行比较。结果表明，所提出的算法不仅在准确性方面优于基线算法，而且还足够快，可以在实时应用中使用。

可解释的人工智能（XAI）方法旨在帮助人类用户更好地了解AI代理的决策。但是，许多现代的XAI方法对最终用户，尤其是那些没有先前AI或ML知识的用户都不纯粹。在本文中，我们提出了一种新颖的XAI方法，我们称为责任，标识了特定决定的最负责任的培训示例。然后可以将此示例显示为一个解释：“这是我（AI）学到的使我做到的。”我们介绍了许多领域的实验结果，以及亚马逊机械Turk用户研究的结果，比较了责任和图像分类任务上的现有XAI方法。我们的结果表明，责任可以帮助提高人类最终用户和次要ML模型的准确性。

尽管在利用深度学习来自动化胸部X光片解释和疾病诊断任务方面取得了进展，但顺序胸部X射线（CXR）之间的变化受到了有限的关注。监测通过胸部成像可视化的病理的进展在解剖运动估计和图像注册中构成了几个挑战，即在空间上对齐这两个图像并在变化检测中对时间动力学进行建模。在这项工作中，我们提出了Chexrelnet，这是一种可以跟踪两个CXR之间纵向病理关系的神经模型。Chexrelnet结合了局部和全球视觉特征，利用图像间和图像内的解剖信息，并学习解剖区域属性之间的依赖性，以准确预测一对CXR的疾病变化。与基准相比，胸部成像组数据集的实验结果显示下游性能提高。代码可从https://github.com/plan-lab/chexrelnet获得