Recent studies have demonstrated that smart grids are vulnerable to stealthy false data injection attacks (SFDIAs), as SFDIAs can bypass residual-based bad data detection mechanisms. The SFDIA detection has become one of the focuses of smart grid research. Methods based on deep learning technology have shown promising accuracy in the detection of SFDIAs. However, most existing methods rely on the temporal structure of a sequence of measurements but do not take account of the spatial structure between buses and transmission lines. To address this issue, we propose a spatiotemporal deep network, PowerFDNet, for the SFDIA detection in AC-model power grids. The PowerFDNet consists of two sub-architectures: spatial architecture (SA) and temporal architecture (TA). The SA is aimed at extracting representations of bus/line measurements and modeling the spatial structure based on their representations. The TA is aimed at modeling the temporal structure of a sequence of measurements. Therefore, the proposed PowerFDNet can effectively model the spatiotemporal structure of measurements. Case studies on the detection of SFDIAs on the benchmark smart grids show that the PowerFDNet achieved significant improvement compared with the state-of-the-art SFDIA detection methods. In addition, an IoT-oriented lightweight prototype of size 52 MB is implemented and tested for mobile devices, which demonstrates the potential applications on mobile devices. The trained model will be available at \textit{https://github.com/HubYZ/PowerFDNet}.

潜在的指纹对于识别犯罪嫌疑人很重要。但是，认识到参考指纹集中的潜在指纹仍然是一个挑战。现有方法的大多数（如果不是全部）将独立提取每个指纹的表示特征，然后比较这些表示特征在不同过程中识别的相似性。如果没有对特征提取过程的相似性的监督，则很难在最佳地反映两种指纹的相似性，这是匹配决策的基础。在本文中，我们提出了一种新方案，可以将两个指纹的配对关系建模为识别的相似性功能。配对关系是由混合深网建模的，该网络可以处理随机大小的困难和潜在指纹的损坏区域。两个数据库的实验结果表明，所提出的方法的表现优于最新技术。

潜在的指纹增强是潜在指纹识别的必要预处理步骤。大多数潜在的指纹增强方法试图恢复损坏的灰色山脊/山谷。在本文中，我们提出了一种新方法，该方法将潜在的指纹增强增强为生成对抗网络（GAN）框架中的指纹生成问题。我们将提议的网络命名为Fingergan。它可以在与相应的地面真相实例上强制执行其生成的指纹（即增强的潜在指纹），该实例无法与相应的地面真相实例相互区分。由于细节是指纹识别的主要特征，并且可以直接从指纹骨架图中检索细节，因此我们提供了一个整体框架，可以在直接优化细节信息的情况下执行潜在的指纹增强。这将有助于显着提高潜在的指纹识别性能。两个公共潜在指纹数据库的实验结果表明，我们的方法的表现大大优于艺术状态。这些代码可从\ url {https://github.com/hubyz/latentenhancement}提供非商业目的。

Reorienting objects using extrinsic supporting items on the working platform is a meaningful, nonetheless challenging manipulation task, considering the elaborate geometry of the objects and the robot's feasible motions. In this work, we propose a pipeline using the RGBD camera's perception results to predict objects' stable placements afforded by supporting items, including a generation stage, a refinement stage, and a classification stage. Then, we construct manipulation graphs that enclose shared grasp configurations to transform objects' stable placements. The robot can reorient objects through sequential pick-and-place operations based on the manipulation graphs. We show in experiments that our approach is effective and efficient. The simulation experiments demonstrate that our pipeline can generalize to novel objects in random start poses on the working platform, generating diverse placements with high accuracy. Moreover, the manipulation graphs are conducive to providing collision-free motions for the robot to reorient objects. We also employ a robot in real-world experiments to perform sequential pick-and-place operations, indicating that our method can transfer objects' placement poses in real scenes.

为了促进机器人技术和AI的最新进展，以进行人类和机器之间的微妙合作，我们提出了Kinova Gemini，这是一种原始的机器人系统，它整合了对话式AI对话和视觉推理，以使Kinova gen3 Lite机器人帮助人们撤回基于观念的对象或完全基于感知的对象选择任务。当一个人走到Kinova Gen3 Lite时，我们的Kinova Gemini能够在三种不同的应用程序中满足用户的要求：（1）它可以与人们进行自然对话，以互动并协助人类检索物体并将其交给用户，并将其交给用户。逐个。 （2）它通过Yolo V3检测到不同的对象，并识别物品的颜色属性，以询问人们是否想通过对话来掌握它，或者使用户可以选择需要哪个特定的特定特定的对象。 （3）它应用了Yolo V3来识别多个对象，并让您选择两个基于感知的挑选和位置任务，例如“将香蕉放入碗中”，并具有视觉推理和对话性交互。

本文在移动平台上介绍了四摩托车的自动起飞和着陆系统。设计的系统解决了三个具有挑战性的问题：快速姿势估计，受限的外部定位和有效避免障碍物。具体而言，首先，我们基于Aruco标记设计了着陆识别和定位系统，以帮助四极管快速计算相对姿势。其次，我们利用基于梯度的本地运动计划者快速生成无冲突的参考轨迹；第三，我们构建了一台自主状态机器，使四极管能够完全自治完成其起飞，跟踪和着陆任务；最后，我们在模拟，现实世界和室外环境中进行实验，以验证系统的有效性并证明其潜力。

基于采样的路径规划算法通常实现均匀的采样方法来搜索状态空间。然而，统一的采样可能导致许多情况下不必要的探索，例如具有几个死角的环境。我们以前的工作建议使用有希望的区域来指导采样过程来解决问题。然而，预测的有希望区域通常是断开连接，这意味着它们无法连接到开始和目标状态，导致缺乏概率完整性。这项工作侧重于提高预测有前途地区的连通性。我们所提出的方法在x和y方向上回归边缘的连接概率。此外，它可以计算丢失中有希望的边缘的重量，以引导神经网络更加关注有前景区域的连通性。我们进行一系列仿真实验，结果表明，有前途地区的连接性显着提高。此外，我们分析了连接基于采样的路径规划算法的影响，并得出结论，连接在维护算法性能方面发挥着重要作用。

基于图形的群集在群集任务中扮演着重要作用。作为图形卷积网络（GCN），图形类型数据上的神经网络的变体已经实现了令人印象深刻的性能，发现GCN是否可用于在非图形数据上增加基于图形的聚类方法，即，一般数据。但是，鉴于$ N $示例，基于图形的聚类方法通常需要至少$ O（n ^ 2）$时间来构建图形，图表卷积需要密集图和$ uyn $ o（n ^ 2）$。 o（| \ mathcal {e} |）$ for稀疏的$ | \ mathcal {e} | $边。换句话说，基于图形的聚类和GCN患有严重的低效率问题。为了解决这个问题，进一步雇用GCN促进基于图形的聚类的能力，我们提出了一种新的聚类方法，奇迹。由于常规群集方案中未提供图形结构，首先通过引入生成图模型来展示如何将非图形数据集转换为图形，该模型用于构建GCN。从原始数据生成锚来构建二分的图形，使得图表卷积的计算复杂度从$ O（n ^ 2）$和$ o（| \ mathcal {e} |）$到$ o（n） $。群集的后续步骤可以轻松设计为$ O（n）$操作。有趣的是，锚天然导致暹罗的GCN架构。由锚构造的二分图是动态更新的，以利用数据后面的高级信息。最终，我们理论上证明简单的更新将导致退化，因此设计了特定的策略。

Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones. We systematically study different options in the distillation framework, including distilling targets, losses, input, network regularization, sequential distillation, etc, revealing that: 1) Distilling token relations is more effective than CLS token- and feature-based distillation; 2) An intermediate layer of the teacher network as target perform better than that using the last layer when the depth of the student mismatches that of the teacher; 3) Weak regularization is preferred; etc. With these findings, we achieve significant fine-tuning accuracy improvements over the scratch MIM pre-training on ImageNet-1K classification, using all the ViT-Tiny, ViT-Small, and ViT-base models, with +4.2%/+2.4%/+1.4% gains, respectively. Our TinyMIM model of base size achieves 52.2 mIoU in AE20K semantic segmentation, which is +4.1 higher than the MAE baseline. Our TinyMIM model of tiny size achieves 79.6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget. This strong performance suggests an alternative way for developing small vision Transformer models, that is, by exploring better training methods rather than introducing inductive biases into architectures as in most previous works. Code is available at https://github.com/OliverRensu/TinyMIM.

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.