随着深度学习技术的快速发展，各种最近的工作试图应用图形神经网络（GNN）来解决诸如布尔满足（SAT）之类的NP硬问题，这表明了桥接机器学习与象征性差距的潜力。然而，GNN预测的解决方案的质量并未在文献中进行很好地研究。在本文中，我们研究了GNNS在学习中解决最大可满足性（MaxSAT）问题的能力，从理论和实践角度来看。我们构建了两种GNN模型来学习来自基准的MaxSAT实例的解决方案，并显示GNN通过实验评估解决MaxSAT问题的有吸引力。我们还基于算法对准理论，我们还提出了GNNS可以在一定程度上学会解决MaxSAT问题的影响的理论解释。

时域仿真是电力系统瞬态稳定性分析的基础。准确可靠的模拟取决于准确的动态分量建模。在实用电力系统中，动态元件建模长期面临模型测定和模型校准的挑战，特别是随着可再生于可再生发电和电力电子产品的快速发展。本文基于神经常规差分方程（ODES）的一般框架，提出了一种具有外部输入和神经差分 - 代数方程（DAES）模块的神经竞争模块，用于电力系统动态分量模型。提出了基于AutoEncoder的框架，以提高培训型号的性能。还证明了将所提出的神经模块训练的神经动态模型集成到瞬态稳定性模拟的方法。对于由输入变量和输出变量的采样曲线组成的数据集，所提出的模块可用于满足黑匣子建模，物理数据集成建模，参数推断等的任务。测试是在IEEE-39中进行的测试系统证明提出的模块的有效性和潜力。

随着人工智能（AI）的迅速发展，可以预见的是，动态模拟器和AI的整合将大大提高对未来电力系统的动态分析的准确性和效率。为了探索电力系统动态模拟的交互机制和AI的相互作用机制，提出了面向AI的动力系统动态模拟器的一般设计，该设计由具有神经网络支持性的高性能模拟器和灵活的外部和内部应用程序编程接口（APIS）组成（APIS（APIS） ）。在API的支持下，模拟辅助AI和AI辅助模拟形成了功率系统动态模拟与AI之间的全面交互机制。该设计的原型由基于高效的机电模拟器实施并公开。该原型的测试是在四种情况下进行的，包括样本生成，基于AI的稳定性预测，数据驱动的动态组件建模和AI AIDED稳定性控制，这证明了设计和实施的有效性，灵活性和效率面向AI的动力系统动态模拟器。

For Prognostics and Health Management (PHM) of Lithium-ion (Li-ion) batteries, many models have been established to characterize their degradation process. The existing empirical or physical models can reveal important information regarding the degradation dynamics. However, there is no general and flexible methods to fuse the information represented by those models. Physics-Informed Neural Network (PINN) is an efficient tool to fuse empirical or physical dynamic models with data-driven models. To take full advantage of various information sources, we propose a model fusion scheme based on PINN. It is implemented by developing a semi-empirical semi-physical Partial Differential Equation (PDE) to model the degradation dynamics of Li-ion-batteries. When there is little prior knowledge about the dynamics, we leverage the data-driven Deep Hidden Physics Model (DeepHPM) to discover the underlying governing dynamic models. The uncovered dynamics information is then fused with that mined by the surrogate neural network in the PINN framework. Moreover, an uncertainty-based adaptive weighting method is employed to balance the multiple learning tasks when training the PINN. The proposed methods are verified on a public dataset of Li-ion Phosphate (LFP)/graphite batteries.

Deep Neural Networks have been widely used in many fields. However, studies have shown that DNNs are easily attacked by adversarial examples, which have tiny perturbations and greatly mislead the correct judgment of DNNs. Furthermore, even if malicious attackers cannot obtain all the underlying model parameters, they can use adversarial examples to attack various DNN-based task systems. Researchers have proposed various defense methods to protect DNNs, such as reducing the aggressiveness of adversarial examples by preprocessing or improving the robustness of the model by adding modules. However, some defense methods are only effective for small-scale examples or small perturbations but have limited defense effects for adversarial examples with large perturbations. This paper assigns different defense strategies to adversarial perturbations of different strengths by grading the perturbations on the input examples. Experimental results show that the proposed method effectively improves defense performance. In addition, the proposed method does not modify any task model, which can be used as a preprocessing module, which significantly reduces the deployment cost in practical applications.

Recovering the skeletal shape of an animal from a monocular video is a longstanding challenge. Prevailing animal reconstruction methods often adopt a control-point driven animation model and optimize bone transforms individually without considering skeletal topology, yielding unsatisfactory shape and articulation. In contrast, humans can easily infer the articulation structure of an unknown animal by associating it with a seen articulated character in their memory. Inspired by this fact, we present CASA, a novel Category-Agnostic Skeletal Animal reconstruction method consisting of two major components: a video-to-shape retrieval process and a neural inverse graphics framework. During inference, CASA first retrieves an articulated shape from a 3D character assets bank so that the input video scores highly with the rendered image, according to a pretrained language-vision model. CASA then integrates the retrieved character into an inverse graphics framework and jointly infers the shape deformation, skeleton structure, and skinning weights through optimization. Experiments validate the efficacy of CASA regarding shape reconstruction and articulation. We further demonstrate that the resulting skeletal-animated characters can be used for re-animation.

深入学习在许多任务中获得了越来越优秀的表现，例如自主驾驶和面部识别，并且也受到不同类型的攻击挑战。在图像中添加人类视力不可察觉的扰动可以误导神经网络模型以高信任获得错误的结果。对手示例是已经添加的图像，其具有特定的噪声来误导深神经网络模型，但是向图像添加噪声会破坏原始数据，使得在数字取证和其他字段中无用。为了防止非法或未授权访问图像数据，例如人面，并确保不对法律使用的情感可逆的逆势攻击技术是升高的。原始图像可以从其可逆的对抗性示例中恢复。然而，现有的可逆对抗例子生成策略均为传统的难以察觉的对抗性扰动设计。如何获得局部可见的对抗性扰动的可逆性？在本文中，我们提出了一种基于局部视觉逆势扰动产生可逆的对抗性实例的新方法。通过可逆数据隐藏技术将图像恢复所需的信息嵌入到超出对抗贴片之外的区域。为了降低图像失真并提高视觉质量，采用无损压缩和嵌入原理。 Imagenet DataSet上的实验表明，我们的方法可以在确保攻击性能的同时恢复无错误的原始图像。

在合作的多代理增强学习（MARL）中，代理只能获得部分观察，有效利用本地信息至关重要。在长期观察期间，代理可以构建\ textit {意识}，使队友减轻部分可观察性问题。但是，以前的MAL方法通常忽略了对本地信息的这种利用。为了解决这个问题，我们提出了一个新颖的框架，多代理\ textit {本地信息分解，以意识到队友}（linda），代理商通过该框架学会分解本地信息并为每个队友建立意识。我们将意识模拟为随机随机变量并执行表示学习，以确保意识表示的信息，通过最大程度地提高意识与相应代理的实际轨迹之间的相互信息。 Linda对特定算法是不可知论的，可以灵活地集成到不同的MARL方法中。足够的实验表明，所提出的框架从当地的部分观察结果中学习了信息丰富的意识，以更好地协作并显着提高学习绩效，尤其是在具有挑战性的任务上。

虽然对理解计算机视觉中的手对象交互进行了重大进展，但机器人执行复杂的灵巧操纵仍然非常具有挑战性。在本文中，我们提出了一种新的平台和管道DEXMV（来自视频的Dexerous操纵）以进行模仿学习。我们设计了一个平台：（i）具有多指机器人手和（ii）计算机视觉系统的复杂灵巧操纵任务的仿真系统，以记录进行相同任务的人类手的大规模示范。在我们的小说管道中，我们从视频中提取3D手和对象姿势，并提出了一种新颖的演示翻译方法，将人类运动转换为机器人示范。然后，我们将多个仿制学习算法与演示进行应用。我们表明，示威活动确实可以通过大幅度提高机器人学习，并解决独自增强学习无法解决的复杂任务。具有视频的项目页面：https://yzqin.github.io/dexmv

This paper focuses on designing efficient models with low parameters and FLOPs for dense predictions. Even though CNN-based lightweight methods have achieved stunning results after years of research, trading-off model accuracy and constrained resources still need further improvements. This work rethinks the essential unity of efficient Inverted Residual Block in MobileNetv2 and effective Transformer in ViT, inductively abstracting a general concept of Meta-Mobile Block, and we argue that the specific instantiation is very important to model performance though sharing the same framework. Motivated by this phenomenon, we deduce a simple yet efficient modern \textbf{I}nverted \textbf{R}esidual \textbf{M}obile \textbf{B}lock (iRMB) for mobile applications, which absorbs CNN-like efficiency to model short-distance dependency and Transformer-like dynamic modeling capability to learn long-distance interactions. Furthermore, we design a ResNet-like 4-phase \textbf{E}fficient \textbf{MO}del (EMO) based only on a series of iRMBs for dense applications. Massive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, \eg, our EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass \textbf{SoTA} CNN-/Transformer-based models, while trading-off the model accuracy and efficiency well.