Due to the issue that existing wireless sensor network (WSN)-based anomaly detection methods only consider and analyze temporal features, in this paper, a self-supervised learning-based anomaly node detection method based on an autoencoder is designed. This method integrates temporal WSN data flow feature extraction, spatial position feature extraction and intermodal WSN correlation feature extraction into the design of the autoencoder to make full use of the spatial and temporal information of the WSN for anomaly detection. First, a fully connected network is used to extract the temporal features of nodes by considering a single mode from a local spatial perspective. Second, a graph neural network (GNN) is used to introduce the WSN topology from a global spatial perspective for anomaly detection and extract the spatial and temporal features of the data flows of nodes and their neighbors by considering a single mode. Then, the adaptive fusion method involving weighted summation is used to extract the relevant features between different models. In addition, this paper introduces a gated recurrent unit (GRU) to solve the long-term dependence problem of the time dimension. Eventually, the reconstructed output of the decoder and the hidden layer representation of the autoencoder are fed into a fully connected network to calculate the anomaly probability of the current system. Since the spatial feature extraction operation is advanced, the designed method can be applied to the task of large-scale network anomaly detection by adding a clustering operation. Experiments show that the designed method outperforms the baselines, and the F1 score reaches 90.6%, which is 5.2% higher than those of the existing anomaly detection methods based on unsupervised reconstruction and prediction. Code and model are available at https://github.com/GuetYe/anomaly_detection/GLSL

非凸松弛方法已被广泛用于张量恢复问题，并且与凸松弛方法相比，可以实现更好的恢复结果。在本文中，提出了一种新的非凸函数，最小值对数凹点（MLCP）函数，并分析了其某些固有属性，其中有趣的是发现对数函数是MLCP的上限功能。所提出的功能概括为张量病例，得出张量MLCP和加权张量$ l \ gamma $ -norm。考虑到将其直接应用于张量恢复问题时无法获得其明确解决方案。因此，给出了解决此类问题的相应等效定理，即张量等效的MLCP定理和等效加权张量$ l \ gamma $ -norm定理。此外，我们提出了两个基于EMLCP的经典张量恢复问题的模型，即低秩量张量完成（LRTC）和张量稳健的主组件分析（TRPCA）以及设计近端替代线性化最小化（棕榈）算法以单独解决它们。此外，基于Kurdyka - {\ l} ojasiwicz属性，证明所提出算法的溶液序列具有有限的长度并在全球范围内收敛到临界点。最后，广泛的实验表明，提出的算法取得了良好的结果，并证实MLCP函数确实比最小化问题中的对数函数更好，这与理论特性的分析一致。

张量恢复是计算机视觉和机器学习中的重要问题。它通常使用张量排名的凸松弛和$ l_ {0} $ norm，即分别为核定标准和$ l_ {1} $ norm，以解决此类问题。已知凸的近似值会产生偏置的估计量。为了克服这个问题，采用并设计了相应的非凸照器。受到最近开发的矩阵等效最小值凸额（EMCP）定理的启发，本文确定了张量当量的最小值 - concave惩罚（TEMCP）的定理。张量当量MCP（TEMCP）作为非凸照正规器零件和等效加权张量$ \ gamma $ norm（EWTGN）作为低级别部分的构建，两者都可以实现权重适应性。同时，我们提出了两个相应的自适应模型，用于两个经典的张量恢复问题，即低级张量完成（LRTC）和张量鲁棒的主成分分析（TRPCA），其中优化算法基于交替的方向乘数（ADMM）。设计了这种新型的迭代自适应算法，可以产生更准确的张量恢复效果。对于张量的完成模型，考虑了多光谱图像（MSI），磁共振成像（MRI）和彩色视频（CV）数据，而对于张量的稳定性主成分分析模型，高光谱图像（HSI）在高斯噪声和盐和盐和盐和盐和盐和盐和盐和盐和盐和考虑了胡椒噪声。所提出的算法优于ARTS方法，并且通过实验保证其降低和收敛性。

张量稀疏建模是一种有希望的方法，在整个科学和工程学中，取得了巨大的成功。众所周知，实际应用中的各种数据通常由多种因素产生，因此使用张量表示包含多个因素内部结构的数据。但是，与矩阵情况不同，构建合理的稀疏度量张量是一项相对困难且非常重要的任务。因此，在本文中，我们提出了一种称为张量全功能度量（FFM）的新张量稀疏度度量。它可以同时描述张量的每个维度的特征信息以及两个维度之间的相关特征，并将塔克等级与张量管等级连接。这种测量方法可以更全面地描述张量的稀疏特征。在此基础上，我们建立了其非凸放松，并将FFM应用于低级张量完成（LRTC）和张量鲁棒的主成分分析（TRPCA）。提出了基于FFM的LRTC和TRPCA模型，并开发了两种有效的交替方向乘数法（ADMM）算法来求解所提出的模型。各种实际数值实验证实了超出最先进的方法的优势。

低等级张量完成（LRTC）问题引起了计算机视觉和信号处理的极大关注。如何获得高质量的图像恢复效果仍然是目前要解决的紧急任务。本文提出了一种新的张量$ l_ {2,1} $最小化模型（TLNM），该模型（TLNM）集成了总和核标准（SNN）方法，与经典的张量核定常（TNN）基于张量的张量完成方法不同，与$ L_ { 2,1} $ norm和卡塔尔里亚尔分解用于解决LRTC问题。为了提高图像的局部先验信息的利用率，引入了总变化（TV）正则化项，从而导致一类新的Tensor $ L_ {2,1} $ NORM Minimization，总变量模型（TLNMTV）。两个提出的模型都是凸，因此具有全局最佳解决方案。此外，我们采用交替的方向乘数法（ADMM）来获得每个变量的封闭形式解，从而确保算法的可行性。数值实验表明，这两种提出的算法是收敛性的，比较优于方法。特别是，当高光谱图像的采样率为2.5 \％时，我们的方法显着优于对比方法。

图形卷积神经网络（GCN）吸引了越来越多的注意力，并在各种计算机视觉任务中取得了良好的表现，但是，对GCN的内部机制缺乏明确的解释。对于标准的卷积神经网络（CNN），通常使用类激活映射（CAM）方法通过生成热图来可视化CNN的决策和图像区域之间的连接。尽管如此，当这些凸轮直接应用于GCN时，这种热图通常会显示出语义 - chaos。在本文中，我们提出了一种新颖的可视化方法，特别适用于GCN，顶点语义类激活映射（VS-CAM）。 VS-CAM包括两个独立的管道，分别制作一组语义探针图和一个语义基映射。语义探针图用于检测语义信息从语义碱图图中的语义信息，以汇总语义感知的热图。定性结果表明，VS-CAM可以获得与基于CNN的CAM更精确地匹配对象的热图。定量评估进一步证明了VS-CAM的优势。

在弱光条件下获得的图像将严重影响图像的质量。解决较差的弱光图像质量的问题可以有效地提高图像的视觉质量，并更好地改善计算机视觉的可用性。此外，它在许多领域都具有非常重要的应用。本文提出了基于视网膜的Deanet，以增强弱光图像。它将图像的频率信息和内容信息结合到三个子网络中：分解网络，增强网络和调整网络。这三个子网络分别用于分解，变形，对比度增强和细节保存，调整和图像产生。我们的模型对于所有低光图像都具有良好的良好结果。该模型对公共数据集进行了培训，实验结果表明，就视力和质量而言，我们的方法比现有的最新方法更好。

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.

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.