Real-time parameter estimation captures the timevarying behavior of line parameters, preventing attackers from maintaining an accurate system model. As attackers rely on outdated parameter information, the measurement effects of their injections no longer align with the attacker’s intended outcomes, rendering perfectly stealthy False Data Injection Attacks (FDIAs) ineffective. This paper introduces Confusion False Data Injection Attacks (CFDIAs), which exploit the mismatch between an attacker’s outdated model and the system’s time-varying reality. CFDIAs reshape the resulting measurement discrepancies so they fall within the statistical behavior of normal noise, enabling approximate stealthiness under residual-based detection. A bi-level optimization framework is established to quantify the economic impacts of CFDIAs. The upper level designs approximately stealthy attacks, while the lower level performs optimal power flow. A Conservative Chance-Constraint Linearization (CCCL) technique is incorporated to provide a geometric and tractable approximation of nonlinear chance constraints. The results show that real-time parameter updates can create a false sense of protection, as some lines that appear secure remain covertly exploitable by CFDIAs. Experiments further demonstrate that such attacks can raise dispatch costs by 33.16% in the IEEE 30-bus system and 27.21% in the 89-bus system.

Given an object mask, Semi-supervised Video Object Segmentation (SVOS) technique aims to track and segment the object across video frames, serving as a fundamental task in computer vision. Although recent memory-based methods demonstrate potential, they often struggle with scenes involving occlusion, particularly in handling object interactions and high feature similarity. To address these issues and meet the real-time processing requirements of downstream applications, in this paper, we propose a novel bOundary Amendment video object Segmentation method with Inherent Structure refinement, hereby named OASIS. Specifically, a lightweight structure refinement module is proposed to enhance segmentation accuracy. With the fusion of rough edge priors captured by the Canny filter and stored object features, the module can generate an object-level structure map and refine the representations by highlighting boundary features. Evidential learning for uncertainty estimation is introduced to further address challenges in occluded regions. The proposed method, OASIS, maintains an efficient design, yet extensive experiments on challenging benchmarks demonstrate its superior performance and competitive inference speed compared to other state-of-the-art methods, i.e., achieving the F values of 91.6 (vs. 89.7 on DAVIS-17 validation set) and G values of 86.6 (vs. 86.2 on YouTubeVOS 2019 validation set) while maintaining a competitive speed of 48 FPS on DAVIS.

Learning an effective manipulation policy with high efficiency in robotics continues to be a significant challenge. In this paper, we propose E-GAIL, which aims to learn manipulation policies efficiently from a limited set of demonstrations with negative corruption and long-term rewards under the framework of GAIL. Specifically, we propose two techniques: 1) Utilizing both short-term and long-term observations to offer additional rewards for training, accelerating convergence. 2) Incorporating negative actions into generated trajectories for corruption to improve data effectiveness and increase success rates. E-GAIL achieves a 25% improvement in success rates across multiple manipulation tasks, requiring 70% fewer episodes for policy convergence, highlighting its efficiency with limited demonstrations. Our video is available at https://youtu.be/bIDfOjYcY54 .

Point-based interactive editing serves as an essential tool to complement the controllability of existing generative mod-els. A concurrent work, DragD iffus ion, updates the diffusion latent map in response to user inputs, causing global latent map alterations. This results in imprecise preservation of the original content and unsuccessful editing due to gradient vanishing. In contrast, we present DragNoise, offering ro-bust and accelerated editing without retracing the latent map. The core rationale of DragNoise lies in utilizing the predicted noise output of each U-Net as a semantic editor. This approach is grounded in two critical observations: firstly, the bottleneck features of U-Net inherently possess semantically rich features ideal for interactive editing; secondly, high-level semantics, established early in the denoising process, show minimal variation in subsequent stages. Leveraging these insights, DragNoise edits diffusion semantics in a sin-gle denoising step and efficiently propagates these changes, ensuring stability and efficiency in diffusion editing. Compar-ative experiments reveal that DragNoise achieves superior control and semantic retention, reducing the optimization time by over 50% compared to DragDiffusion. Our codes are available at https://github.com/haofenglIDragNoise.

This paper investigates various receiver architectures for uplink non-orthogonal multiple access (NOMA)-based integrated sensing and communication (ISaC) systems. Specifi-cally, a novel signaling approach is considered whereby mutual interference between the radar and communication signals is canceled by alternately reversing the radar symbols and phase-rotating the transmitted data symbols in consecutive periods. While such a signaling approach eliminates both detection ambiguity and radar interference at the receiver, this requires the receiver to detect the targets and data over two symbol periods at a time. To this end, in this paper, we adapt various well-known receivers, such as the maximum ratio combining (MRC), zero forcing (ZF), successive interference cancellation (SIC) and max-imum likelihood (ML) receivers, and compare their performance under a variety of conditions. When evaluating the bit error rate (BER), the achievable sum rate (ASR), and the radar channel estimation (RCE) accuracy, it is found that the proposed ISaC system exhibits remarkable performance regardless of the radar signal's power compared to other ISaC signaling approaches. It is also found that the MRC receiver suffers from error floors when the system loading is relatively high, while ZF and ML provide comparable and superior performances regardless of the system load.

Abstract The current instance segmentation method can achieve satisfactory results in common scenarios. However, under the overlap or partial occlusion between targets caused by the complex scenes, accurate segmentation of pigs remains a challenging task. To address the problem, the authors propose an instance segmentation method based on Mask Scoring region‐based convolutional neural networks (R‐CNN) (MS R‐CNN), which creates the adversarial network called MaskDis in the head branch of MS R‐CNN. The MaskDis is trained as a discriminator using a generative adversarial network, and the MS R‐CNN model is used as a generator during model training. The adversarial training enables the generator to learn context information and features at the pixel level, which effectively improves the segmentation quality under pigs’ overlapping or dense occlusions scenes. Experimental conducted on the pig object segmentation dataset show that the proposed approach achieves a precision of 92.03%, a recall of 92.18%, and an F1 score of 0.9210. Compared with the basic MS R‐CNN model, the approach achieved a 2.25% improvement in precision and 1.18% improvement in F1 score. Furthermore, the improved approach outperformed advanced instance segmentation methods such as YOLACT, Swin Transformer, YOLOv5‐seg, and SOLOv2 on COCO evaluation metrics. These experimental results demonstrate the effectiveness of the proposed approach in instance segmentation of pigs in complex scenes, providing technical support for non‐contact pig automatic management.

In recent years, with the development of social economy and technology, the brain-computer interface based on motor imagery(MI-BCI) has gradually become the focus content of many re-searchers. However, the motor imagery EEG signal (MI-EEG) itself has the characteristics of non-linearity and low signal-to-noise ratio, and because the characteristics of different domains of MI-EEG cannot be effectively combined, the recognition rate of MI-EEG is unsatisfactory. To overcome the above problems, this paper proposes a Transformer-based one-dimensional convolutional neural network model(CNN-Transformer) for the classification and recognition of four types of motor imagery EEG signals. Firstly, the artifacts of the original EEG are removed and new time-space-frequency features are constructed by preprocessing such as bandpass filtering and PCA dimensionality; then, the local features in the temporal dimension are extracted through the convolution and pooling operations of 1D-CNN, while reducing the dimension of the time feature; next, the Transformer based on the attention mechanism is used to extract more abstract and high-level temporal features from multiple perspectives; finally, the classification results are integrated and output through the fully connected layer. The performance of the CNN-Transformer model is evaluated using the competition dataset 2008 BCI-Competition 2A. The results show that the average accuracy and kappa value of the CNN-Transformer model are as high as 99.29%(±0.07%) and 98.43%(±0.21), respectively, which are 3.72% and 7.68% higher than the classical architecture (CNN-LSTM). This model provides a design idea for improving the accuracy of MI-EEG classification and recognition, and also lays a foundation for the wide application of MI-BCI.

Cataracts are the leading cause of vision loss worldwide. Restoration algorithms are developed to improve the readability of cataract fundus images in order to increase the certainty in diagnosis and treatment for cataract patients. Unfortunately, the requirement of annotation limits the application of these algorithms in clinics. This paper proposes a network to annotation-freely restore cataractous fundus images (ArcNet) so as to boost the clinical practicability of restoration. Annotations are unnecessary in ArcNet, where the high-frequency component is extracted from fundus images to replace segmentation in the preservation of retinal structures. The restoration model is learned from the synthesized images and adapted to real cataract images. Extensive experiments are implemented to verify the performance and effectiveness of ArcNet. Favorable performance is achieved using ArcNet against state-of-the-art algorithms, and the diagnosis of ocular fundus diseases in cataract patients is promoted by ArcNet. The capability of properly restoring cataractous images in the absence of annotated data promises the proposed algorithm outstanding clinical practicability.