在本文中，我们提出了一种新的手工识别方法，以便犯罪调查，因为手形象往往是在严重犯罪如性虐待中的唯一可用信息。我们提出的方法，使用注意网络（MBA-Net）多分支，除了全球（不受注意）分支之外，还包含了分支机构中的通道和空间注意模块，以捕获歧视特征学习的全局结构信息。注意力模块侧重于手形图像的相关特征，同时抑制无关背景。为了克服注意力机制的弱点，等离性体到像素混洗，我们将相对位置编码集成到空间注意模块中以捕获像素的空间位置。对两个大型多民族和公共手部数据集进行广泛的评估表明，我们的提出方法实现了最先进的性能，超越了现有的基于手的识别方法。

在严重犯罪的情况下，包括性虐待，往往是唯一可以证明识别潜力的可用信息是手的图像。由于这种证据在不受控制的情况下捕获，因此难以分析。随着全局对特征比较的方法在这种情况下有限，重要的是要考虑当地信息。在这项工作中，我们通过学习全球和地方深度特征表示来提出基于手的人识别。我们提出的方法，全局和部分感知网络（GPA-Net），在Conv-Tother上创建全局和本地分支，以学习强大的歧视全局和零级功能。为了学习本地（零件级别）功能，我们在水平和垂直方向上对CONC层执行统一分区。我们通过进行软分区检索零件，而无需明确地分区图像或需要外部提示，例如姿势估计。我们对两个大型多民族和公开的手部数据集进行了广泛的评估，表明我们所提出的方法显着优于竞争方法。

This study introduces and examines the potential of an AI system to generate health awareness messages. The topic of folic acid, a vitamin that is critical during pregnancy, served as a test case. Using prompt engineering, we generated messages that could be used to raise awareness and compared them to retweeted human-generated messages via computational and human evaluation methods. The system was easy to use and prolific, and computational analyses revealed that the AI-generated messages were on par with human-generated ones in terms of sentiment, reading ease, and semantic content. Also, the human evaluation study showed that AI-generated messages ranked higher in message quality and clarity. We discuss the theoretical, practical, and ethical implications of these results.

本文解决了机器人的问题，可以协作将电缆带到指定的目标位置，同时避免实时碰撞。引入电缆（与刚性链接相反）使机器人团队能够通过电缆的松弛/拉特开关更改其内在尺寸，从而使机器人团队能够穿越狭窄的空间。但是，这是一个具有挑战性的问题，因为混合模式开关以及多个机器人和负载之间的动态耦合。以前解决此类问题的尝试是离线执行的，并且不考虑避免在线障碍。在本文中，我们介绍了一个级联的计划方案，并采用平行的集中式轨迹优化，涉及混合模式开关。我们还每个机器人开发了一组分散的计划者，这使我们可以解决在线协作负载操作问题的方法。我们开发并演示了第一个能够移动有线电视载荷的首个协作自治框架之一，该框架太重了，无法通过一个机器人移动，通过狭窄空间，具有实时反馈和实验中的反应性计划。

旨在为通用机器人铺平道路的边界研究，视觉和语言导航（VLN）一直是计算机视觉和自然语言处理社区的热门话题。 VLN任务要求代理在不熟悉的环境中按照自然语言说明导航到目标位置。最近，基于变压器的模型已在VLN任务上获得了重大改进。由于变压器体系结构中的注意力机制可以更好地整合视觉和语言的模式内和模式信息。但是，当前基于变压器的模型中存在两个问题。 1）模型独立处理每个视图，而无需考虑对象的完整性。 2）在视觉模态的自我注意操作期间，在空间上遥远的视图可以彼此交织而无需明确的限制。这种混合可能会引入额外的噪音而不是有用的信息。为了解决这些问题，我们建议1）基于插槽注意的模块，以合并来自同一对象的分割的信息。 2）局部注意力掩模机制限制视觉注意力跨度。所提出的模块可以轻松地插入任何VLN体系结构中，我们将复发的VLN-Bert用作基本模型。 R2R数据集的实验表明，我们的模型已达到最新结果。

我们可以通过机器学习（ml）改善城市陆地面积的建模吗？在预测所有常见表面通量的情况下，城市陆地表面模型（ULSMS）的比较发现，没有单一模型是“最好”。在这里，我们开发了一个城市神经网络（UNN），在一个网站上的22个ULSMS的平均预测助焊剂训练。UNN准确地模拟ULSMS的平均输出。与参考ulsm（城镇能量平衡; TEB）相比，UNN相对于通量观察，计算成本较少，并且需要较少的输入参数具有更高的准确性。当使用TensoRFlow绑定耦合到天气研究预测（WRF）模型时，WRF-UNN比参考WRF-TEB稳定，更准确。虽然申请目前受到培训数据（1个网站）的限制，但我们展示了一种新的方法来通过将几个ULSMS的强度与使用ML的强度组合成一个方法来改善表面助熔剂的建模。

迅速严重性评估患有传染病感染的确诊患者的评估模型可以实现高效的诊断和减轻医疗系统的负担。本文利用机器学习技术提供了严重性评估模型的开发过程及其在SARS-COV-2患者的应用。在这里，我们强调我们的模型只需要基本患者的基本个人数据，从而允许他们判断自己的严重程度。我们选择了基于升级的决策树模型作为分类器，并将死亡率解释为建模后的概率分数。具体而言，使用贝叶斯优化技术调整确定树模型结构的超参数，而不知道医疗信息。结果，我们测量了模型性能并识别通过模型影响严重性的变量。最后，我们的目标是建立一个允许患者检查自己的严重性的医疗系统，并根据其他患者的过去的治疗细节来访问他们访问适当的诊所中心。

Deep-learning of artificial neural networks (ANNs) is creating highly functional tools that are, unfortunately, as hard to interpret as their natural counterparts. While it is possible to identify functional modules in natural brains using technologies such as fMRI, we do not have at our disposal similarly robust methods for artificial neural networks. Ideally, understanding which parts of an artificial neural network perform what function might help us to address a number of vexing problems in ANN research, such as catastrophic forgetting and overfitting. Furthermore, revealing a network's modularity could improve our trust in them by making these black boxes more transparent. Here we introduce a new information-theoretic concept that proves useful in understanding and analyzing a network's functional modularity: the relay information $I_R$. The relay information measures how much information groups of neurons that participate in a particular function (modules) relay from inputs to outputs. Combined with a greedy search algorithm, relay information can be used to {\em identify} computational modules in neural networks. We also show that the functionality of modules correlates with the amount of relay information they carry.

Embedding words in vector space is a fundamental first step in state-of-the-art natural language processing (NLP). Typical NLP solutions employ pre-defined vector representations to improve generalization by co-locating similar words in vector space. For instance, Word2Vec is a self-supervised predictive model that captures the context of words using a neural network. Similarly, GLoVe is a popular unsupervised model incorporating corpus-wide word co-occurrence statistics. Such word embedding has significantly boosted important NLP tasks, including sentiment analysis, document classification, and machine translation. However, the embeddings are dense floating-point vectors, making them expensive to compute and difficult to interpret. In this paper, we instead propose to represent the semantics of words with a few defining words that are related using propositional logic. To produce such logical embeddings, we introduce a Tsetlin Machine-based autoencoder that learns logical clauses self-supervised. The clauses consist of contextual words like "black," "cup," and "hot" to define other words like "coffee," thus being human-understandable. We evaluate our embedding approach on several intrinsic and extrinsic benchmarks, outperforming GLoVe on six classification tasks. Furthermore, we investigate the interpretability of our embedding using the logical representations acquired during training. We also visualize word clusters in vector space, demonstrating how our logical embedding co-locate similar words.

Medical image segmentation (MIS) is essential for supporting disease diagnosis and treatment effect assessment. Despite considerable advances in artificial intelligence (AI) for MIS, clinicians remain skeptical of its utility, maintaining low confidence in such black box systems, with this problem being exacerbated by low generalization for out-of-distribution (OOD) data. To move towards effective clinical utilization, we propose a foundation model named EvidenceCap, which makes the box transparent in a quantifiable way by uncertainty estimation. EvidenceCap not only makes AI visible in regions of uncertainty and OOD data, but also enhances the reliability, robustness, and computational efficiency of MIS. Uncertainty is modeled explicitly through subjective logic theory to gather strong evidence from features. We show the effectiveness of EvidenceCap in three segmentation datasets and apply it to the clinic. Our work sheds light on clinical safe applications and explainable AI, and can contribute towards trustworthiness in the medical domain.