图表神经网络（GNN）基于故障诊断（FD）近年来收到了越来越多的关注，因为来自来自多个应用域的数据可以有利地表示为图。实际上，与传统的FD方法相比，这种特殊的代表性表格导致了卓越的性能。在本次审查中，给出了GNN，对故障诊断领域的潜在应用以及未来观点的简单介绍。首先，通过专注于它们的数据表示，即时间序列，图像和图形，回顾基于神经网络的FD方法。其次，引入了GNN的基本原则和主要架构，注意了图形卷积网络，图注意网络，图形样本和聚合，图形自动编码器和空间 - 时间图卷积网络。第三，通过详细实验验证基于GNN的最相关的故障诊断方法，结论是基于GNN的方法可以实现良好的故障诊断性能。最后，提供了讨论和未来的挑战。

Dry Eye Disease (DED) is one of the most common ocular diseases: over five percent of US adults suffer from DED. Tear film instability is a known factor for DED, and is thought to be regulated in large part by the thin lipid layer that covers and stabilizes the tear film. In order to aid eye related disease diagnosis, this work proposes a novel paradigm in using computer vision techniques to numerically analyze the tear film lipid layer (TFLL) spread. Eleven videos of the tear film lipid layer spread are collected with a micro-interferometer and a subset are annotated. A tracking algorithm relying on various pillar computer vision techniques is developed. Our method can be found at https://easytear-dev.github.io/.

安全的加强学习（RL）旨在学习在将其部署到关键安全应用程序中之前满足某些约束的政策。以前的原始双重风格方法遭受了不稳定性问题的困扰，并且缺乏最佳保证。本文从概率推断的角度克服了问题。我们在政策学习过程中介绍了一种新颖的期望最大化方法来自然纳入约束：1）在凸优化（E-step）后，可以以封闭形式计算可证明的最佳非参数变异分布； 2）基于最佳变异分布（M-step），在信任区域内改进了策略参数。提出的算法将安全的RL问题分解为凸优化阶段和监督学习阶段，从而产生了更稳定的培训性能。对连续机器人任务进行的广泛实验表明，所提出的方法比基线获得了更好的约束满意度和更好的样品效率。该代码可在https://github.com/liuzuxin/cvpo-safe-rl上找到。

外科模拟器不仅允许规划和培训复杂的程序，而且还提供了为算法开发产生结构化数据的能力，这可以应用于图像引导的计算机辅助干预措施。虽然在外科医生或数据生成引擎的发展培训平台上，但我们知识的这两个功能尚未一起提供。我们展示了我们的开发成本效益和协同框架，命名为异步多体框架加（AMBF +），它与练习其外科技能的用户同时生成下游算法开发的数据。 AMBF +在虚拟现实（VR）设备上提供立体显示器，并触觉外科仿真的触觉反馈。它还可以生成不同的数据，例如对象姿势和分段图。 AMBF +采用柔性插件设置设计，可允许仿真仿真不同外科手术。我们将AMBF +的一个用例显示为虚拟钻探模拟器，用于横向颅底手术，用户可以使用虚拟手术钻积极地修改患者解剖结构。我们进一步演示如何生成的数据可用于验证和培训下游计算机视觉算法

Different people speak with diverse personalized speaking styles. Although existing one-shot talking head methods have made significant progress in lip sync, natural facial expressions, and stable head motions, they still cannot generate diverse speaking styles in the final talking head videos. To tackle this problem, we propose a one-shot style-controllable talking face generation framework. In a nutshell, we aim to attain a speaking style from an arbitrary reference speaking video and then drive the one-shot portrait to speak with the reference speaking style and another piece of audio. Specifically, we first develop a style encoder to extract dynamic facial motion patterns of a style reference video and then encode them into a style code. Afterward, we introduce a style-controllable decoder to synthesize stylized facial animations from the speech content and style code. In order to integrate the reference speaking style into generated videos, we design a style-aware adaptive transformer, which enables the encoded style code to adjust the weights of the feed-forward layers accordingly. Thanks to the style-aware adaptation mechanism, the reference speaking style can be better embedded into synthesized videos during decoding. Extensive experiments demonstrate that our method is capable of generating talking head videos with diverse speaking styles from only one portrait image and an audio clip while achieving authentic visual effects. Project Page: https://github.com/FuxiVirtualHuman/styletalk.

Unmanned aerial vehicle (UAV) swarms are considered as a promising technique for next-generation communication networks due to their flexibility, mobility, low cost, and the ability to collaboratively and autonomously provide services. Distributed learning (DL) enables UAV swarms to intelligently provide communication services, multi-directional remote surveillance, and target tracking. In this survey, we first introduce several popular DL algorithms such as federated learning (FL), multi-agent Reinforcement Learning (MARL), distributed inference, and split learning, and present a comprehensive overview of their applications for UAV swarms, such as trajectory design, power control, wireless resource allocation, user assignment, perception, and satellite communications. Then, we present several state-of-the-art applications of UAV swarms in wireless communication systems, such us reconfigurable intelligent surface (RIS), virtual reality (VR), semantic communications, and discuss the problems and challenges that DL-enabled UAV swarms can solve in these applications. Finally, we describe open problems of using DL in UAV swarms and future research directions of DL enabled UAV swarms. In summary, this survey provides a comprehensive survey of various DL applications for UAV swarms in extensive scenarios.

Reading comprehension of legal text can be a particularly challenging task due to the length and complexity of legal clauses and a shortage of expert-annotated datasets. To address this challenge, we introduce the Merger Agreement Understanding Dataset (MAUD), an expert-annotated reading comprehension dataset based on the American Bar Association's 2021 Public Target Deal Points Study, with over 39,000 examples and over 47,000 total annotations. Our fine-tuned Transformer baselines show promising results, with models performing well above random on most questions. However, on a large subset of questions, there is still room for significant improvement. As the only expert-annotated merger agreement dataset, MAUD is valuable as a benchmark for both the legal profession and the NLP community.

In this work, we focus on instance-level open vocabulary segmentation, intending to expand a segmenter for instance-wise novel categories without mask annotations. We investigate a simple yet effective framework with the help of image captions, focusing on exploiting thousands of object nouns in captions to discover instances of novel classes. Rather than adopting pretrained caption models or using massive caption datasets with complex pipelines, we propose an end-to-end solution from two aspects: caption grounding and caption generation. In particular, we devise a joint Caption Grounding and Generation (CGG) framework based on a Mask Transformer baseline. The framework has a novel grounding loss that performs explicit and implicit multi-modal feature alignments. We further design a lightweight caption generation head to allow for additional caption supervision. We find that grounding and generation complement each other, significantly enhancing the segmentation performance for novel categories. We conduct extensive experiments on the COCO dataset with two settings: Open Vocabulary Instance Segmentation (OVIS) and Open Set Panoptic Segmentation (OSPS). The results demonstrate the superiority of our CGG framework over previous OVIS methods, achieving a large improvement of 6.8% mAP on novel classes without extra caption data. Our method also achieves over 15% PQ improvements for novel classes on the OSPS benchmark under various settings.

Non-line-of-sight (NLOS) imaging aims to reconstruct the three-dimensional hidden scenes from the data measured in the line-of-sight, which uses photon time-of-flight information encoded in light after multiple diffuse reflections. The under-sampled scanning data can facilitate fast imaging. However, the resulting reconstruction problem becomes a serious ill-posed inverse problem, the solution of which is of high possibility to be degraded due to noises and distortions. In this paper, we propose two novel NLOS reconstruction models based on curvature regularization, i.e., the object-domain curvature regularization model and the dual (i.e., signal and object)-domain curvature regularization model. Fast numerical optimization algorithms are developed relying on the alternating direction method of multipliers (ADMM) with the backtracking stepsize rule, which are further accelerated by GPU implementation. We evaluate the proposed algorithms on both synthetic and real datasets, which achieve state-of-the-art performance, especially in the compressed sensing setting. All our codes and data are available at https://github.com/Duanlab123/CurvNLOS.

We present Second Thought, a new learning paradigm that enables language models (LMs) to re-align with human values. By modeling the chain-of-edits between value-unaligned and value-aligned text, with LM fine-tuning and additional refinement through reinforcement learning, Second Thought not only achieves superior performance in three value alignment benchmark datasets but also shows strong human-value transfer learning ability in few-shot scenarios. The generated editing steps also offer better interpretability and ease for interactive error correction. Extensive human evaluations further confirm its effectiveness.