Parkinson's Disease (PD) is a progressive nervous system disorder that has affected more than 5.8 million people, especially the elderly. Due to the complexity of its symptoms and its similarity to other neurological disorders, early detection requires neurologists or PD specialists to be involved, which is not accessible to most old people. Therefore, we integrate smart mobile devices with AI technologies. In this paper, we introduce the framework of our developed PD early detection system which combines different tasks evaluating both motor and non-motor symptoms. With the developed model, we help users detect PD punctually in non-clinical settings and figure out their most severe symptoms. The results are expected to be further used for PD rehabilitation guidance and detection of other neurological disorders.

This paper introduces a structure-deformable land-air robot which possesses both excellent ground driving and flying ability, with smooth switching mechanism between two modes. The elaborate coupled dynamics model of the proposed robot is established, including rotors, chassis, especially the deformable structures. Furthermore, taking fusion locomotion and complex near-ground situations into consideration, a model based controller is designed for landing and mode switching under various harsh conditions, in which we realise the cooperation between fused two motion modes. The entire system is implemented in ADAMS/Simulink simulation and in practical. We conduct experiments under various complex scenarios. The results show our robot can accomplish land-air switching swiftly and smoothly, and the designed controller can effectively improve the landing flexibility and reliability.

In this paper, we are interested in learning a generalizable person re-identification (re-ID) representation from unlabeled videos. Compared with 1) the popular unsupervised re-ID setting where the training and test sets are typically under the same domain, and 2) the popular domain generalization (DG) re-ID setting where the training samples are labeled, our novel scenario combines their key challenges: the training samples are unlabeled, and collected form various domains which do no align with the test domain. In other words, we aim to learn a representation in an unsupervised manner and directly use the learned representation for re-ID in novel domains. To fulfill this goal, we make two main contributions: First, we propose Cycle Association (CycAs), a scalable self-supervised learning method for re-ID with low training complexity; and second, we construct a large-scale unlabeled re-ID dataset named LMP-video, tailored for the proposed method. Specifically, CycAs learns re-ID features by enforcing cycle consistency of instance association between temporally successive video frame pairs, and the training cost is merely linear to the data size, making large-scale training possible. On the other hand, the LMP-video dataset is extremely large, containing 50 million unlabeled person images cropped from over 10K Youtube videos, therefore is sufficient to serve as fertile soil for self-supervised learning. Trained on LMP-video, we show that CycAs learns good generalization towards novel domains. The achieved results sometimes even outperform supervised domain generalizable models. Remarkably, CycAs achieves 82.2% Rank-1 on Market-1501 and 49.0% Rank-1 on MSMT17 with zero human annotation, surpassing state-of-the-art supervised DG re-ID methods. Moreover, we also demonstrate the superiority of CycAs under the canonical unsupervised re-ID and the pretrain-and-finetune scenarios.

在许多综合设置（例如视频游戏）和GO中，增强学习（RL）超出了人类的绩效。但是，端到端RL模型的现实部署不太常见，因为RL模型对环境的轻微扰动非常敏感。强大的马尔可夫决策过程（MDP）框架（其中的过渡概率属于名义模型设置的不确定性）提供了一种开发健壮模型的方法。虽然先前的分析表明，RL算法是有效的，假设访问生成模型，但尚不清楚RL在更现实的在线设置下是否可以有效，这需要在探索和开发之间取得仔细的平衡。在这项工作中，我们通过与未知的名义系统进行互动来考虑在线强大的MDP。我们提出了一种强大的乐观策略优化算法，该算法可有效。为了解决由对抗性环境引起的其他不确定性，我们的模型具有通过Fenchel Conjugates得出的新的乐观更新规则。我们的分析确定了在线强大MDP的第一个遗憾。

在本文中，我们将解决方案介绍给Muse-Humor的多模式情感挑战（MUSE）2022的邮件，库穆尔人子挑战的目标是发现幽默并从德国足球馆的视听录音中计算出AUC新闻发布会。它是针对教练表现出的幽默的注释。对于此子挑战，我们首先使用变压器模块和BilstM模块构建一个判别模型，然后提出一种混合融合策略，以使用每种模式的预测结果来提高模型的性能。我们的实验证明了我们提出的模型和混合融合策略对多模式融合的有效性，并且我们在测试集中提出的模型的AUC为0.8972。

本文提出了一个4D主链，以供长期点云视频理解。捕获空间上下文的一种典型方法是使用无层次结构的4DCONV或变压器。但是，由于相机运动，场景变化，采样模式和4D数据的复杂性，这些方法既没有有效也没有高效的效率。为了解决这些问题，我们利用原始平面作为中层表示，以捕获4D点云视频中的长期空间上下文，并提出了一个名为Point Point Primitive Transformer（PPTR）的新型层次骨架，主要由该骨架组成，该骨架主要由主要的点变压器和原始变压器。广泛的实验表明，PPTR在不同任务上优于先前的艺术状态

Kripke模型对于表达静态知识或信念很有用。另一方面，动作模型描述了信息流和动态知识或信念。精炼分区的技术已用于在分分后找到最小的kripke模型，这对于Kripke模型的语义等效性是足够和必要的。在本文中，我们将改进分区推广到动作模型，以在命题动作仿真下找到最小的动作模型，这足以满足动作模型的语义等效性，如果需要动作模型为命题，则足够且必要。

统计建模和数据驱动学习是吸引许多关注的两个重要领域。统计模型打算捕获和解释变量之间的关系，而基于数据的学习尝试直接从数据中提取信息而无需通过复杂模型预先处理。鉴于两个字段中的广泛研究，一个微妙的问题是如何正确地整合基于数据的方法现有知识或模型。在本文中，基于时间序列数据，我们提出了两种不同的方向来集成两者，基于分解的方法和利用数据特征的统计提取方法。第一个将数据分解成线性稳定，非线性稳定和不稳定部件，其中合适的统计模型用于线性稳定和非线性稳定部件，而适当的机器学习工具用于不稳定部件。第二个应用统计模型来提取数据的统计特征，并将其作为额外的输入送入机器学习平台进行培训。最关键和具有挑战性的是如何从数学或统计模型中确定和提取有价值的信息，以提高机器学习算法的性能。我们使用具有不同程度的稳定性的时间序列数据评估该提案。性能结果表明，两种方法都可以优于使用模型和单独学习的现有方案，而改进可能超过60％。我们所提出的方法都具有促进拓展模型和数据驱动的方案之间的差距，并集成了两个，以提供全面的高等学校性能。

Digital engineering transformation is a crucial process for the engineering paradigm shifts in the fourth industrial revolution (4IR), and artificial intelligence (AI) is a critical enabling technology in digital engineering transformation. This article discusses the following research questions: What are the fundamental changes in the 4IR? More specifically, what are the fundamental changes in engineering? What is digital engineering? What are the main uncertainties there? What is trustworthy AI? Why is it important today? What are emerging engineering paradigm shifts in the 4IR? What is the relationship between the data-intensive paradigm and digital engineering transformation? What should we do for digitalization? From investigating the pattern of industrial revolutions, this article argues that ubiquitous machine intelligence (uMI) is the defining power brought by the 4IR. Digitalization is a condition to leverage ubiquitous machine intelligence. Digital engineering transformation towards Industry 4.0 has three essential building blocks: digitalization of engineering, leveraging ubiquitous machine intelligence, and building digital trust and security. The engineering design community at large is facing an excellent opportunity to bring the new capabilities of ubiquitous machine intelligence and trustworthy AI principles, as well as digital trust, together in various engineering systems design to ensure the trustworthiness of systems in Industry 4.0.

Transformers are becoming increasingly popular due to their superior performance over conventional convolutional neural networks(CNNs). However, transformers usually require a much larger amount of memory to train than CNNs, which prevents their application in many low resource settings. Local learning, which divides the network into several distinct modules and trains them individually, is a promising alternative to the end-to-end (E2E) training approach to reduce the amount of memory for training and to increase parallelism. This paper is the first to apply Local Learning on transformers for this purpose. The standard CNN-based local learning method, InfoPro [32], reconstructs the input images for each module in a CNN. However, reconstructing the entire image does not generalize well. In this paper, we propose a new mechanism for each local module, where instead of reconstructing the entire image, we reconstruct its input features, generated from previous modules. We evaluate our approach on 4 commonly used datasets and 3 commonly used decoder structures on Swin-Tiny. The experiments show that our approach outperforms InfoPro-Transformer, the InfoPro with Transfomer backbone we introduced, by at up to 0.58% on CIFAR-10, CIFAR-100, STL-10 and SVHN datasets, while using up to 12% less memory. Compared to the E2E approach, we require 36% less GPU memory when the network is divided into 2 modules and 45% less GPU memory when the network is divided into 4 modules.