The security of AI-generated content (AIGC) detection is crucial for ensuring multimedia content credibility. To enhance detector security, research on adversarial attacks has become essential. However, most existing adversarial attacks focus only on GAN-generated facial images detection, struggle to be effective on multi-class natural images and diffusion-based detectors, and exhibit poor invisibility. To fill this gap, we first conduct an in-depth analysis of the vulnerability of AIGC detectors and discover the feature that detectors vary in vulnerability to different post-processing. Then, considering that the detector is agnostic in real-world scenarios and given this discovery, we propose a Realistic-like Robust Black-box Adversarial attack (R2BA) with post-processing fusion optimization. Unlike typical perturbations, R2BA uses real-world post-processing, i.e., Gaussian blur, JPEG compression, Gaussian noise and light spot to generate adversarial examples. Specifically, we use a stochastic particle swarm algorithm with inertia decay to optimize post-processing fusion intensity and explore the detector’s decision boundary. Guided by the detector’s fake probability, R2BA enhances/weakens the detector-vulnerable/detector-robust post-processing intensity to strike a balance between adversariality and invisibility. Extensive experiments on popular/commercial AIGC detectors and datasets demonstrate that R2BA exhibits impressive anti-detection performance, excellent invisibility, and strong robustness in GAN-based and diffusion-based cases. Compared to state-of-the-art white-box and black-box attacks, R2BA shows significant improvements of 15%–72% and 21%–47% in anti-detection performance under the original and robust scenario respectively, offering valuable insights for the security of AIGC detection in real-world applications.

Understanding protein function is essential for decoding cellular mechanisms and addressing a wide range of biological challenges. However, current protein function prediction methods face significant limitations due to data silos, heterogeneity, and privacy constraints, which hinder the integration and utilization of large-scale, multimodal datasets across institutions. Moreover, existing approaches lack personalization, making them less effective in adapting to institution-specific data characteristics and prediction needs. To address these challenges, we introduce a novel framework based on personalized federated learning that enables collaborative model training without compromising data privacy. The proposed framework, named PFLGO, incorporates a multi-center aggregation strategy and leverages largescale pre-trained models with knowledge transfer mechanisms to support efficient and accurate protein function prediction. By integrating heterogeneous multimodal data-including sequence, structure, and contextual embeddings-from multiple institutions, our method significantly enhances generalization performance. Extensive experiments conducted on two benchmark datasets demonstrate that PFLGO consistently outperforms both traditional centralized and existing multimodal approaches in terms of accuracy, convergence speed, and communication efficiency. This work highlights the potential of federated learning in advancing collaborative, privacy-preserving protein function prediction.

GNSS/INS integrated navigation positioning methods are widely used in vehicle positioning services. However, high-accuracy and reliable vehicle localization remains a challenge under GNSS signal degradation or INS drift. We propose PUKF, a novel fusion algorithm combining Nonlinear Predictive Filtering (NPF) with Unscented Kalman Filtering (UKF), to address model error-induced degradation in GNSS/INS integrated navigation. PUKF introduces real-time model error estimation into the prediction phase of UKF to dynamically adapt to complex environments. Preliminary results from simulation and KITTI dataset-based experiments show that PUKF outperforms the traditional UKF in high-dynamic environments, especially when model errors are large, significantly enhancing positioning accuracy and system stability under abrupt dynamics and low-cost sensors. The proposed method has promising applications in realtime web-based vehicle services.

Artificial Intelligence Generated Content (AIGC) techniques, represented by text-to-image generation, have led to a malicious use of deep forgeries, raising concerns about the trustworthiness of multimedia content. Experimental results demonstrate that traditional forgery detection methods perform poorly in adapting to diffusion model-generated scenarios, while existing diffusion-specific techniques lack robustness against post-processed images. In response, we propose the Trinity Detector, which integrates coarse-grained text features from a Contrastive Language-Image Pretraining (CLIP) encoder with fine-grained artifacts in the pixel domain to achieve semantic-level image detection, significantly enhancing model robustness. To enhance sensitivity to diffusion-generated image features, a Multi-spectral Channel Attention Fusion Unit (MCAF) is designed. It adaptively fuses multiple preset frequency bands, dynamically adjusting the weight of each band, and then integrates the fused frequency-domain information with the spatial co-occurrence of the two modalities. Extensive experiments validate that our Trinity Detector improves transfer detection performance across black-box datasets by an average of 14.3% compared to previous diffusion detection models and demonstrating superior performance on post-processed image datasets.

Spaceborne synthetic aperture radar (SAR) image ship detection is an important tool to ensure the safety of sea areas and improve the efficiency of maritime traffic. Due to the sparse distribution of ships in the vast ocean, many imaging results are redundant. Furthermore, SAR imaging consumes huge computing, storage, and communication resources. The range-compressed data, without azimuth compression calculation, has caught our attention. Nevertheless, the echo energy of the ship is scattered in the azimuth direction, making it difficult to detect. Several deep learning-based methods are proposed, yet their performance is constrained by labeled datasets. As they neglect sea clutter interference, these methods are also impractical. To address these issues, this article proposes a constant false alarm rate (CFAR)-based ship detector for range-compressed SAR data. First, the imaging process of spaceborne SAR signal is reviewed and analyzed. Then, a generalized Gamma distribution (G$\Gamma $D)-based sea clutter model is proposed for the SAR range-focused domain. Next, a CFAR-based method for detecting ships in range-compressed SAR data is customized. Finally, experiments are conducted on Sentinel-1 and ERS-2 SAR data. The results show that the proposed sea clutter model has high goodness-of-fit, and the customized CFAR-based method effectively detects ship targets. In summary, ship detection in range-compressed SAR data is very promising research.

High-performance classical simulator for quantum circuits, in particular the tensor network contraction algorithm, has become an important tool for the validation of noisy quantum computing. In order to address the memory limitations, the slicing technique is used to reduce the tensor dimensions, but it could also lead to additional computation overhead that greatly slows down the overall performance. This paper proposes novel lifetime-based methods to reduce the slicing overhead and improve the computing efficiency, including, an interpretation method to deal with slicing overhead, an inplace slicing strategy to find the smallest slicing set and an adaptive tensor network contraction path refiner customized for Sunway architecture. Experiments show that in most cases the slicing overhead with our inplace slicing strategy would be less than the Cotengra, which is the most used graph path optimization software at present. Finally, the resulting simulation time is reduced to 96.1s for the Sycamore quantum processor RQC, with a sustainable single-precision performance of 308.6Pflops using over 41M cores to generate 1M correlated samples, which is more than 5 times performance improvement compared to 60.4 Pflops in 2021 Gordon Bell Prize work.

This article is concerned with both consensus and coordinated path-following control for multiple nonholonomic wheeled mobile robots. In the design, the path-following control is decoupled into the longitudinal control (speed control) and the lateral control (heading control) for the convenience of implementation. Different from coordinated trajectory tracking control schemes, the proposed control scheme removes the temporal constraint, which greatly improves the coordination robustness. In particular, two new coordinated error variables describing a chasing-and-waiting strategy are introduced in the proposed coordinated path-following control for injective paths and circular paths, respectively. All the closed-loop signals have proved to be asymptotically stable in the Lyapunov sense. Finally, simulation results under three typical paths are presented to verify the proposed coordination controllers.

This article presents Jdebug, a fast, non-intrusive and scalable fault locating tool for extreme-scale parallel applications. Large-scale debugging has drawn more attention with the increasing scale of supercomputers and applications. To eliminate program intrusion caused by traditional instrumentation or interception during debugging information acquisition, we introduce the out-of-band management into large-scale debugging. We propose a rapid information gathering scheme that separates user and debugging traffic to solve scalability problem and to eliminate program interference during merging data. Observations of Program Counters (PC) and performance characteristics in suspended applications find abnormalities and help locate abnormal threads caused by software errors or hardware failures effectively. Evaluation shows that Jdebug collects PCs of over 20 million cores on the new Sunway supercomputer within 1.97 seconds, and can locate the abnormal threads in 1.4 seconds with an accuracy of 92.5%. In the running test of three fundamental benchmarks (HPL, HPCG, Graph500) and seventeen real-world applications, Jdebug quickly and accurately locates abnormal threads to help find scalability errors and hardware failures including memory access failures, communication failures, and execution component failures, which validates its effectiveness.

This article addresses the robust fixed-time stabilization control problem for generic linear systems with both matched and mismatched disturbances. A new observer-based fixed-time control technique is proposed to solve this robust stabilization problem, provided that the system matrix pair$(A,B)$is controllable. The ultimate boundedness of the closed-loop system in the presence of mismatched disturbances is proven. An upper bound of the convergence time is provided, which is irrelevant to initial conditions. Finally, a simulation example is presented to show the efficiency of the proposed control design method.

A systematic differentiator-based adaptive backstepping control methodology is proposed for a class of sandwich-like nonlinear system with unknown state-dependent deadzone nonlinearity and parametric uncertainties. The novelty of our approach is that a high-order sliding mode differentiator is utilized to estimate the nonstrict feedback coupling term resulting from the sandwiched deadzone, and all the outputs of the differentiator are integrated into the backstepping procedure based on Lyapunov functions with flat zone recursively. By this approach, all the unknown parameters are estimated online, the discontinuity of the virtual input caused by bound estimations is avoided. It is shown that the ultimate boundedness of all the closed-loop signals is achieved and the output tracking error converges to a preset set. Simulation is performed to verify the theoretical findings.

We develop a high-performance tensor-based simulator for random quantum circuits(RQCs) on the new Sunway supercomputer. Our major innovations include: (1) a near-optimal slicing scheme, and a path-optimization strategy that considers both complexity and compute density; (2) a three-level parallelization scheme that scales to about 42 million cores; (3) a fused permutation and multiplication design that improves the compute efficiency for a wide range of tensor contraction scenarios; and (4) a mixed-precision scheme to further improve the performance. Our simulator effectively expands the scope of simulatable RQCs to include the 10X10(qubits)X(1+40+1)(depth) circuit, with a sustained performance of 1.2 Eflops (single-precision), or 4.4 Eflops (mixed-precision)as a new milestone for classical simulation of quantum circuits; and reduces the simulation sampling time of Google Sycamore to 304 seconds, from the previously claimed 10,000 years.