我们提出了仅使用目标文本提示的3D模型的零击生成技术。在没有任何3D监督的情况下，我们的方法变形了极限细分表面的控制形状及其纹理地图和正常地图，以获得与输入文本提示相对应的3D资产，并且可以轻松地部署到游戏或建模应用程序中。我们仅依靠预先训练的剪辑模型，该模型将输入文本提示与我们3D模型的渲染图像进行了分化。虽然先前的作品集中在风格化或对生成模型的必要培训上，但我们直接对网格参数进行优化，以生成形状，纹理或两者兼而有之。为了限制优化以产生合理的网格和纹理，我们使用图像增强量引入了许多技术，并使用预验证的先验，该技术在给定文本嵌入的情况下生成了剪贴图像嵌入。

Given a few seed entities of a certain type (e.g., Software or Programming Language), entity set expansion aims to discover an extensive set of entities that share the same type as the seeds. Entity set expansion in software-related domains such as StackOverflow can benefit many downstream tasks (e.g., software knowledge graph construction) and facilitate better IT operations and service management. Meanwhile, existing approaches are less concerned with two problems: (1) How to deal with multiple types of seed entities simultaneously? (2) How to leverage the power of pre-trained language models (PLMs)? Being aware of these two problems, in this paper, we study the entity set co-expansion task in StackOverflow, which extracts Library, OS, Application, and Language entities from StackOverflow question-answer threads. During the co-expansion process, we use PLMs to derive embeddings of candidate entities for calculating similarities between entities. Experimental results show that our proposed SECoExpan framework outperforms previous approaches significantly.

庞大的石油和天然气传输管道需要定期监测维护和危险检查，以避免设备故障和潜在事故。严重的Covid-19大流行情况迫使公司缩小了他们的团队的规模。面对现场的一种风险由不受控制的油气和天然气的不受控制的释放来表示。在许多检测方法中，无人驾驶飞行器系统含有柔韧性和稳定性。无人驾驶飞行器可以实时转移数据，而他们正在进行监控任务。本文专注于配备光学传感和人工智能的无人机车辆，尤其是具有深入学习技术的图像识别，用于管道监测。无人驾驶飞行器可用于定期巡逻职责，以识别和捕获感兴趣领域的图像和视频。难以达到的地方将进入更快，更便宜，风险较少。目前的论文基于捕获基于无人机的检验视频和图像的想法，这可能在危险之前发现几个潜在的危险问题。由于外管绝缘材料的包层弱化，损坏可以出现。当通过外部腐蚀的管道厚度可能发生时，也可能存在这种情况。本文介绍了石油和天然气行业专家完成的调查，用于寻找所提出的系统的功能和非功能性要求。

诸如自然灾害，全球大流行和社会动荡等危机不断威胁到我们的世界，并以不同的方式影响了全世界的数百万人。了解人们在大规模危机期间表达的情绪有助于告知政策制定者和急救人员有关人口的情绪状态，并为需要这种支持的人提供情感支持。我们介绍了Covidemo，〜3K英语推文标有情感，并在18个月内分布时间。我们的分析揭示了Covid-19造成的情感损失，以及随着时间的推移社会叙事和相关情绪的变化。由危机的时间敏感性和大规模注释努力的成本的激励，我们研究了在Covid-19的感知情绪预测的任务中，大型的预训练的语言模型在跨领域和时间表中的范围很好。我们的分析表明，跨域信息传输发生，但仍然存在很大的差距。我们提出了半监督的学习，作为弥合这一差距的一种方式，使用来自目标域的未标记数据获得了明显更好的性能。

The last decade witnessed increasingly rapid progress in self-driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence. The objective of this paper is to survey the current state-of-the-art on deep learning technologies used in autonomous driving. We start by presenting AI-based self-driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration and motion control algorithms. We investigate both the modular perception-planning-action pipeline, where each module is built using deep learning methods, as well as End2End systems, which directly map sensory information to steering commands. Additionally, we tackle current challenges encountered in designing AI architectures for autonomous driving, such as their safety, training data sources and computational hardware. The comparison presented in this survey helps to gain insight into the strengths and limitations of deep learning and AI approaches for autonomous driving and assist with design choices. 1