Despite the rapid progress of deep learning in video action recognition (VAR) in recent years, privacy leakage in videos remains a critical concern. Current state-of-the-art privacy-preserving methods often rely on anonymization. These methods suffer from (1) low concealment, where producing visually distorted videos that attract attackers’ attention during transmission, and (2) spatiotemporal disruption, where degrading essential spatiotemporal features for accurate VAR. To address these issues, we propose StegaVAR, a novel framework that embeds action videos into ordinary cover videos and directly performs VAR in the steganographic domain for the first time. Throughout both data transmission and action analysis, the spatiotemporal information of hidden secret video remains complete, while the natural appearance of cover videos ensures the concealment of transmission. Considering the difficulty of steganographic domain analysis, we propose Secret Spatio-Temporal Promotion (STeP) and Cross-Band Difference Attention (CroDA) for analysis within the steganographic domain. STeP uses the secret video to guide spatiotemporal feature extraction in the steganographic domain during training. CroDA suppresses cover interference by capturing cross-band semantic differences. Experiments demonstrate that StegaVAR achieves superior VAR and privacy-preserving performance on widely used datasets. Moreover, our framework is effective for multiple steganographic models.

Recently, with the increasing capabilities of Large Language Models (LLMs), AI applications have gradually emerged to solve various problems in people's daily lives, so accurately measuring their performance and reliability is paramount. However, existing benchmarks predominantly rely on closed-ended, multiple-choice or short-answer question formats. While useful for assessment, these formats exhibit a significant gap compared to the diverse and open-ended nature of questions posed by real-world users. To bridge this gap, we produce OmniBench, a comprehensive open-domain benchmark. OmniBench is uniquely composed of authentic, user-generated questions harvested from real-world interactions on various websites and applications, covering 16 rigorously defined knowledge domains and 5 crucial user intents derived from a large-scale analysis of the mass corpus. Crucially, we propose three automated data construction pipelines that enable the continuous and periodic updating of the benchmark dataset. This approach not only ensures that the questions can keep up with current events, but also effectively mitigates the critical issue of data contamination prevalent in static benchmarks. Moreover, a multi-dimensional hybrid evaluation framework named OmniEval is proposed for evaluating the responses. This framework combines diverse metrics and evaluation methods to capture nuanced aspects of answer performance. Extensive validation demonstrates that this evaluation framework exhibits strong alignment with human judgments, ensuring the reliability of the benchmark results.

As integrated circuit (IC) designs continue to scale in complexity, global routing faces increasing challenges in managing timing and congestion simultaneously. This paper proposes a GPU-accelerated global routing framework that effectively balances timing optimization and congestion mitigation. The proposed framework begins with a preprocessing stage that partitions ultralarge nets, followed by timing path construction, and decomposes nets based on estimated pin slack to enhance scalability and timing sensitivity. For critical nets, we propose a timing and congestion driven GPU-accelerated hybrid 3D pattern routing method. Specifically, an Elmore-based timing weight calculation method is proposed to efficiently capture the timing criticality of routing paths, and the resulting weights are ordered for more targeted and effective timing optimization. Then, a well-designed cost scheme is proposed to better balance timing and congestion. Finally, we develop a GPU-accelerated hybrid 3D pattern routing strategy that combines L -shape and sparse Z -shape patterns to improve routing efficiency. After routing critical nets, the remaining non-critical nets are routed using a congestion-driven GPU-accelerated routing engine that supports flexible detours to alleviate congestion and utilize residual routing resources. Compared with the champion of the ISPD 2025 contest, experimental results on the ISPD 2025 contest benchmarks show that our algorithm achieves 19.4% better weighted scores and 1 6. 2 % faster runtime.

Large language models (LLMs) generate highdimensional embeddings that capture rich semantic and syntactic information.However, high-dimensional embeddings exacerbate computational complexity and storage requirements, thereby hindering practical deployment.To address these challenges, we propose a novel training framework named Sequential Matryoshka Embedding Compression (SMEC).This framework introduces the Sequential Matryoshka Representation Learning(SMRL) method to mitigate gradient variance during training, the Adaptive Dimension Selection (ADS) module to reduce information degradation during dimension pruning, and the Selectable Cross-batch Memory (S-XBM) module to enhance unsupervised learning between high-and low-dimensional embeddings.Experiments on image, text, and multimodal datasets demonstrate that SMEC achieves significant dimensionality reduction while maintaining performance.For instance, on the BEIR dataset, our approach improves the performance of compressed LLM2Vec embeddings (256 dimensions) by 1.1 points and 2.7 points compared to the Matryoshka-Adaptor and Search-Adaptor models, respectively.

Partial point attack approaches focus on leveraging the fewest points to achieve the best attack efficiency for easy implementation in the physical domain. For the first time, this paper proposes that the partial point attack strategy should pay attention to not only the selection and disturbance of points, but also the penetration of current defense methods. By re-examining characteristics of previous partial point attack approaches leading to performance improvement, we discover two fundamental principles: first, the selection of attacked points should consider not only the favourable visual salience but also the proper position concentration, thus to acquire effective structural destruction on the basis of remaining imperceptible; second, the perturbation of target points should form meaningful structures rather than outliers. To achieve this, we first propose a novel distributed-concentrated point selection (DPS) strategy, which is easier to concentrate salient points containing rich local information in a few tiny regions. Additionally, to enhance the penetration efficacy and real-time performance of attack point clouds against defenses, we further design a perturbation network based on the multi-scale penetration loss (L_msp), which can generate adversarial samples with as few outliers as possible only through a single forward propagation. Experimental results demonstrate that the real-time distributed-concentrated penetration attack (RDPA) framework can achieve state-of-the-art (SOTA) success rates by perturbing only 3.5% of points, and have the best penetration for mainstream defense methods such as SRS and SOR.

Shared mobility is becoming increasingly popular in the construction of smart cities. However, there is no relevant research on battery swapping task of shared electric bikes (e-bikes) at present. In this article, the battery swapping problem is transformed into a power failure probability prediction problem. Unlike traditional machine learning models, the goal is to simultaneously estimate when and whether a power failure will occur, in other words, to estimate the distribution function of power failure time for each e-bike. To solve this problem, an innovative Mixture Survival Classifiation Neueral Network (MSCN) model is proposed, which expands the traditional survival model and integrates the characteristics of Neural Network. Furthermore, for a more accurate prediction, we mine the temporal and spatial correlation of e-bikes flow between sites and model this correlation using graph networks and Sequeeze-and-Excitation Network (SENet). Experiments are conducted on a real shared e-bikes riding data set from Hellobike, one of the largest sharing mobility service provider in China. The results show that MSCN has excellent predictive performance and can provide data support for the formulation of battery swapping strategy.

Frame rate up-conversion (FRUC) usually suffers from unreliable motion vectors due to the absence of the current frame to be interpolated. In addition, since the majority of video sequences are usually compressed by various coding standards to reduce the data volume, the quality of the generated frames in the FRUC will be further impaired. To address this problem, we proposed two FRUC algorithms based on deep residual network. We first present a deep residual network for the FRUC (DRNFRUC), which consists of feature extraction, feature recursive analysis, and image restoration parts with a skip connection between the input and the output of the network. The proposed DRNFRUC takes the result of an arbitrary existing FRUC method as the input and is able to significantly reduce the edge blurring and blocking artifacts when the motion of the block is violent. In addition, we proposed a deep residual network with weighted convolutional motion compensation (DRNWCMC) for the FRUC, where the convolution operations can be embedded into the motion compensation interpolation (MCI) in any existing MCI-based FRUC method. In DRNWCMC, we first devise two convolutional neural networks corresponding to the forward and backward motion compensated frames, respectively. And then, the adaptive interpolation coefficients for motion compensation are designed as two$1\times1$convolutional kernels. Finally, the interpolation result of WCMC is fed into another convolutional neural network to further improve the performance. All the parameters involved in the DRNWCMC are trained simultaneously under the same cost function. The experimental results show that the two proposed algorithms can remarkably improve both the objective and subjective quality of the interpolated frames.