近年来，破坏预测取得了迅速的进展，尤其是在机器学习（ML）的方法中。理解为什么预测因子使某个预测与未来Tokamak破坏预测指标的预测准确性一样至关重要。大多数破坏预测因素的目的是准确性或跨机能力。但是，如果可以解释中断预测模型，则可以说明为什么某些样品被归类为中断前体。这使我们能够说出传入的破坏类型，并使我们深入了解破坏机制。本文根据J-TEXT上的物理引导特征提取（IDP-PGFE）设计了一种称为可解释的破坏预测变量的破坏预测变量。通过提取物理引导的特征有效地改善了模型的预测性能。需要高性能模型来确保解释结果的有效性。 IDP-PGFE的可解释性研究提供了对J-Text破坏的理解，并且通常与现有的破坏理解一致。 IDP-PGFE已被应用于破坏，因为在J文本上的密度极限实验的密度不断增加。 PGFE的时间演变具有贡献，表明ECRH的应用触发了辐射引起的破坏，从而降低了破坏时的密度。虽然RMP的应用确实提高了J文本中的密度极限。解释性研究指导了RMP不仅会影响MHD不稳定性，而且还会影响辐射轮廓的密度极限破坏的物理机制，从而延迟了密度极限的破坏。

预测不同托卡马克人的破坏是要克服的巨大障碍。未来的Tokamaks在高性能排放时几乎无法忍受中断。很少有高性能的破坏排放几乎无法构成丰富的训练集，这使得当前数据驱动的方法难以获得可接受的结果。能够将在一个Tokamak训练的中断预测模型转移到另一种训练的机器学习方法以解决该问题。关键是一个包含特征提取器的破坏预测模型，该模型能够在Tokamak诊断数据中提取常见的破坏前体痕迹，并具有可转移的破坏分类器。基于上面的问题，该论文首先提出了专门针对Tokamaks上的普通诊断中的破坏前体特征而设计的深融合功能提取器，该特征是根据当前已知的破坏前体，为可转移模型提供了有希望的基础。通过与J-Text上的手动特征提取进行比较，可以证明融合功能提取器。基于在J-TEXT上训练的功能提取器，将中断预测模型转移到East数据中，仅来自East实验的20次放电。该性能与经过1896年出院的模型相当。从其他模型培训方案之间的比较，转移学习表明了其在预测不同托卡马克人的破坏方面的潜力。

求解部分微分方程（PDE）是物理，生物学和化学领域的重要研究手段。作为数值方法的近似替代方法，Pinn受到了广泛的关注，并在许多领域发挥了重要作用。但是，Pinn使用完全连接的网络作为其模型，在时间和空间中，其合适能力和有限的外推能力有限。在本文中，我们提出了用于求解图形神经网络基础的部分微分方程的phygnnet，该方程由编码器，处理器和解码器块组成。特别是，我们将计算区域划分为常规网格，在网格上定义部分差分运算符，然后构建PDE损失以使网络优化以构建Phygnnet模型。更重要的是，我们对汉堡方程和热方程式进行比较实验以验证我们的方法，结果表明，与PINN相比，我们的方法在时间和空间区域具有更好的拟合能力和外推能力。

In this chapter, we review and discuss the transformation of AI technology in HCI/UX work and assess how AI technology will change how we do the work. We first discuss how AI can be used to enhance the result of user research and design evaluation. We then discuss how AI technology can be used to enhance HCI/UX design. Finally, we discuss how AI-enabled capabilities can improve UX when users interact with computing systems, applications, and services.

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.

The visual dimension of cities has been a fundamental subject in urban studies, since the pioneering work of scholars such as Sitte, Lynch, Arnheim, and Jacobs. Several decades later, big data and artificial intelligence (AI) are revolutionizing how people move, sense, and interact with cities. This paper reviews the literature on the appearance and function of cities to illustrate how visual information has been used to understand them. A conceptual framework, Urban Visual Intelligence, is introduced to systematically elaborate on how new image data sources and AI techniques are reshaping the way researchers perceive and measure cities, enabling the study of the physical environment and its interactions with socioeconomic environments at various scales. The paper argues that these new approaches enable researchers to revisit the classic urban theories and themes, and potentially help cities create environments that are more in line with human behaviors and aspirations in the digital age.

Data-centric AI has shed light on the significance of data within the machine learning (ML) pipeline. Acknowledging its importance, various research and policies are suggested by academia, industry, and government departments. Although the capability of utilizing existing data is essential, the capability to build a dataset has become more important than ever. In consideration of this trend, we propose a "Data Management Operation and Recipes" that will guide the industry regardless of the task or domain. In other words, this paper presents the concept of DMOps derived from real-world experience. By offering a baseline for building data, we want to help the industry streamline its data operation optimally.

In this tutorial paper, we look into the evolution and prospect of network architecture and propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed architecture has two key elements, i.e., holistic network virtualization and pervasive artificial intelligence (AI). The holistic network virtualization consists of network slicing and digital twin, from the aspects of service provision and service demand, respectively, to incorporate service-centric and user-centric networking. The pervasive network intelligence integrates AI into future networks from the perspectives of networking for AI and AI for networking, respectively. Building on holistic network virtualization and pervasive network intelligence, the proposed architecture can facilitate three types of interplay, i.e., the interplay between digital twin and network slicing paradigms, between model-driven and data-driven methods for network management, and between virtualization and AI, to maximize the flexibility, scalability, adaptivity, and intelligence for 6G networks. We also identify challenges and open issues related to the proposed architecture. By providing our vision, we aim to inspire further discussions and developments on the potential architecture of 6G.

Drug dosing is an important application of AI, which can be formulated as a Reinforcement Learning (RL) problem. In this paper, we identify two major challenges of using RL for drug dosing: delayed and prolonged effects of administering medications, which break the Markov assumption of the RL framework. We focus on prolongedness and define PAE-POMDP (Prolonged Action Effect-Partially Observable Markov Decision Process), a subclass of POMDPs in which the Markov assumption does not hold specifically due to prolonged effects of actions. Motivated by the pharmacology literature, we propose a simple and effective approach to converting drug dosing PAE-POMDPs into MDPs, enabling the use of the existing RL algorithms to solve such problems. We validate the proposed approach on a toy task, and a challenging glucose control task, for which we devise a clinically-inspired reward function. Our results demonstrate that: (1) the proposed method to restore the Markov assumption leads to significant improvements over a vanilla baseline; (2) the approach is competitive with recurrent policies which may inherently capture the prolonged effect of actions; (3) it is remarkably more time and memory efficient than the recurrent baseline and hence more suitable for real-time dosing control systems; and (4) it exhibits favorable qualitative behavior in our policy analysis.

We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain 3D cuboid annotations for 26 object categories, all of which are sufficiently-sampled to support training and evaluation of 3D perception models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. This dataset is the largest ever collection of lidar sensor data and supports self-supervised learning and the emerging task of point cloud forecasting. Finally, the Motion Forecasting Dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene. Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category. In all three datasets, each scenario contains its own HD Map with 3D lane and crosswalk geometry - sourced from data captured in six distinct cities. We believe these datasets will support new and existing machine learning research problems in ways that existing datasets do not. All datasets are released under the CC BY-NC-SA 4.0 license.