Traditional power grid systems have become obsolete under more frequent and extreme natural disasters. Reinforcement learning (RL) has been a promising solution for resilience given its successful history of power grid control. However, most power grid simulators and RL interfaces do not support simulation of power grid under large-scale blackouts or when the network is divided into sub-networks. In this study, we proposed an updated power grid simulator built on Grid2Op, an existing simulator and RL interface, and experimented on limiting the action and observation spaces of Grid2Op. By testing with DDQN and SliceRDQN algorithms, we found that reduced action spaces significantly improve training performance and efficiency. In addition, we investigated a low-rank neural network regularization method for deep Q-learning, one of the most widely used RL algorithms, in this power grid control scenario. As a result, the experiment demonstrated that in the power grid simulation environment, adopting this method will significantly increase the performance of RL agents.

我们介绍了一项对自然语言（NL）推理的人类通知，开放域和逻辑上复杂且多样的数据集，配备了一阶逻辑（fol）注释。对开本由1,435个示例（独特的结论）组成，每个示例与487组前提之一搭配，这些场所作为规则，可用于演绎理由，以理解每个结论的有效性。前提和结论的逻辑正确性是通过其平行注释来确保的，这些注释会自动由我们的FOL推理引擎验证。除了主要的NL推理任务外，对开本中的NL-FOL对自动构成了使用FOL作为逻辑形式的新的NL-FOL翻译数据集。我们对广泛的实验系统地评估了对中型语言模型（BERT，ROBERTA）进行微调的FOL推理能力，并且在大型语言模型（GPT-NEOX，OPT，OPT，GPT-3，Codex）上促成了很少的射击。对于NL-FOL翻译，我们尝试使用GPT-3和Codex。我们的结果表明，公开可用的最强大的大语言模型之一（LLM），GPT-3 Davinci，仅比随机结果略好，而在一部分集的一部分中，该模型尤其不好，并且在预测该模型方面尤其不好。纠正虚假和未知结论的真实价值。我们的数据集和代码可在https://github.com/yale-lily/folio上找到。

该技术报告提出了一种有效的自动驾驶运动预测方法。我们开发了一种基于变压器的方法，用于输入编码和轨迹预测。此外，我们提出了时间流动头来增强轨迹编码。最后，使用了有效的K均值集合方法。使用我们的变压器网络和集合方法，我们以1.90的最新Brier-Minfde得分赢得了Argoverse 2 Motion预测挑战的第一名。

多机器人系统（MRS）是一组协调的机器人，旨在相互合作并完成给定的任务。由于操作环境中的不确定性，该系统可能会遇到紧急情况，例如未观察到的障碍物，移动车辆和极端天气。蜂群等动物群体会引发集体紧急反应行为，例如绕过障碍和避免掠食者，类似于肌肉条件的反射，该反射组织局部肌肉以避免在第一反应中避免危害，而不会延迟通过大脑的危害。受此启发，我们开发了一种类似的集体反射机制，以使多机器人系统应对紧急情况。在这项研究中，基于动物集体行为分析和多代理增强学习（MARL），开发了一种由生物启发的紧急反应机制（MARL）开发的集体条件反射（CCR）。该算法使用物理模型来确定机器人是否经历了紧急情况。然后，通过相应的启发式奖励增强了涉及紧急情况的机器人的奖励，该奖励评估紧急情况和后果并决定当地机器人的参与。 CCR在三个典型的紧急情况下进行了验证：\ textit {湍流，强风和隐藏障碍物}。仿真结果表明，与基线方法相比，CCR以更快的反应速度和更安全的轨迹调整来提高机器人团队的紧急反应能力。

We conduct a systematic study of backdoor vulnerabilities in normally trained Deep Learning models. They are as dangerous as backdoors injected by data poisoning because both can be equally exploited. We leverage 20 different types of injected backdoor attacks in the literature as the guidance and study their correspondences in normally trained models, which we call natural backdoor vulnerabilities. We find that natural backdoors are widely existing, with most injected backdoor attacks having natural correspondences. We categorize these natural backdoors and propose a general detection framework. It finds 315 natural backdoors in the 56 normally trained models downloaded from the Internet, covering all the different categories, while existing scanners designed for injected backdoors can at most detect 65 backdoors. We also study the root causes and defense of natural backdoors.

视频中的战斗检测是当今监视系统和流媒体的流行率的新兴深度学习应用程序。以前的工作主要依靠行动识别技术来解决这个问题。在本文中，我们提出了一种简单但有效的方法，该方法从新的角度解决了任务：我们将战斗检测模型设计为动作感知功能提取器和异常得分生成器的组成。另外，考虑到视频收集帧级标签太费力了，我们设计了一个弱监督的两阶段训练计划，在此我们使用在视频级别标签上计算出的多个实体学习损失来培训得分生成器，并采用自我训练的技术以进一步提高其性能。在公开可用的大规模数据集（UBI-Fights）上进行了广泛的实验，证明了我们方法的有效性，并且数据集的性能超过了几种先前的最先进的方法。此外，我们收集了一个新的数据集VFD-2000，该数据集专门研究视频战斗检测，比现有数据集更大，场景更大。我们的方法的实现和拟议的数据集将在https://github.com/hepta-col/videofightdetection上公开获得。

Compressed videos often exhibit visually annoying artifacts, known as Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual quality. Subjective and objective measures capable of identifying and quantifying various types of PEAs are critical in improving visual quality. In this paper, we investigate the influence of four spatial PEAs (i.e. blurring, blocking, bleeding, and ringing) and two temporal PEAs (i.e. flickering and floating) on video quality. For spatial artifacts, we propose a visual saliency model with a low computational cost and higher consistency with human visual perception. In terms of temporal artifacts, self-attention based TimeSFormer is improved to detect temporal artifacts. Based on the six types of PEAs, a quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement (SSTAM) is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics. We believe that SSTAM will be beneficial for optimizing video coding techniques.

As one of the most important psychic stress reactions, micro-expressions (MEs), are spontaneous and transient facial expressions that can reveal the genuine emotions of human beings. Thus, recognizing MEs (MER) automatically is becoming increasingly crucial in the field of affective computing, and provides essential technical support in lie detection, psychological analysis and other areas. However, the lack of abundant ME data seriously restricts the development of cutting-edge data-driven MER models. Despite the recent efforts of several spontaneous ME datasets to alleviate this problem, it is still a tiny amount of work. To solve the problem of ME data hunger, we construct a dynamic spontaneous ME dataset with the largest current ME data scale, called DFME (Dynamic Facial Micro-expressions), which includes 7,526 well-labeled ME videos induced by 671 participants and annotated by more than 20 annotators throughout three years. Afterwards, we adopt four classical spatiotemporal feature learning models on DFME to perform MER experiments to objectively verify the validity of DFME dataset. In addition, we explore different solutions to the class imbalance and key-frame sequence sampling problems in dynamic MER respectively on DFME, so as to provide a valuable reference for future research. The comprehensive experimental results show that our DFME dataset can facilitate the research of automatic MER, and provide a new benchmark for MER. DFME will be published via https://mea-lab-421.github.io.

Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, recent research generally focuses on short-distance applications (i.e., phone unlocking) while lacking consideration of long-distance scenes (i.e., surveillance security checks). In order to promote relevant research and fill this gap in the community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under 40 surveillance scenes, which has 101 subjects from different age groups with 232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and screens), and 2 adversarial attacks. In this scene, low image resolution and noise interference are new challenges faced in surveillance FAS. Together with the SuHiFiMask dataset, we propose a Contrastive Quality-Invariance Learning (CQIL) network to alleviate the performance degradation caused by image quality from three aspects: (1) An Image Quality Variable module (IQV) is introduced to recover image information associated with discrimination by combining the super-resolution network. (2) Using generated sample pairs to simulate quality variance distributions to help contrastive learning strategies obtain robust feature representation under quality variation. (3) A Separate Quality Network (SQN) is designed to learn discriminative features independent of image quality. Finally, a large number of experiments verify the quality of the SuHiFiMask dataset and the superiority of the proposed CQIL.

Interview has been regarded as one of the most crucial step for recruitment. To fully prepare for the interview with the recruiters, job seekers usually practice with mock interviews between each other. However, such a mock interview with peers is generally far away from the real interview experience: the mock interviewers are not guaranteed to be professional and are not likely to behave like a real interviewer. Due to the rapid growth of online recruitment in recent years, recruiters tend to have online interviews, which makes it possible to collect real interview data from real interviewers. In this paper, we propose a novel application named EZInterviewer, which aims to learn from the online interview data and provides mock interview services to the job seekers. The task is challenging in two ways: (1) the interview data are now available but still of low-resource; (2) to generate meaningful and relevant interview dialogs requires thorough understanding of both resumes and job descriptions. To address the low-resource challenge, EZInterviewer is trained on a very small set of interview dialogs. The key idea is to reduce the number of parameters that rely on interview dialogs by disentangling the knowledge selector and dialog generator so that most parameters can be trained with ungrounded dialogs as well as the resume data that are not low-resource. Evaluation results on a real-world job interview dialog dataset indicate that we achieve promising results to generate mock interviews. With the help of EZInterviewer, we hope to make mock interview practice become easier for job seekers.