Single-cell RNA sequencing (scRNA-seq) is essential for decoding tumor heterogeneity. However, pan-cancer research still faces two key challenges: learning discriminative and efficient single-cell representations, and establishing a comprehensive evaluation benchmark. In this paper, we introduce \algoname, a lightweight hybrid neural network that combines the strengths of Transformers and state-space models to achieve a balance between performance and efficiency. \algoname consists of a front-end local-context encoder with shared self-attention layers to capture complex, order-independent gene interactions; and a back-end global sequential feature decoder that efficiently integrates global context using a linear-time state-space model. This modular design preserves the expressive power of Transformers while leveraging the scalability of Mamba to enable transcriptome modeling, effectively capturing both local and global regulatory signals. To enable robust evaluation, we also construct a large-scale pan-cancer single-cell benchmark, \algoname Bench, containing over 3.5 million high-quality cells across 33 cancer subtypes, curated through a rigorous preprocessing pipeline. Experimental results show that \algoname outperforms state-of-the-art models on our pan-cancer benchmark (+4.0\%) and across multiple public tasks, including cell type annotation (+7.4\%), batch integration (+4.0\%) and multi-omics integration (+3.1\%).

Infrared and visible image fusion aims to generate fused images with rich textures and clear target representations. Existing methods generally assume high-quality input images, thus overlooking issues such as reduced contrast and loss of details in visible images under low-light conditions. The naive enhance-then-fuse strategy cannot perform fuse-oriented image enhancement, which always reaches a sub-optimal result. To address this challenge, we propose a perceptual transform fusion of infrared and visible images, which simultaneously optimizes low-light enhancement and image fusion. Specifically, to improve computational efficiency and optimize key feature representations while suppressing noise interactions caused by lighting variations, we introduce a lightweight adaptive sparse Transformer block (ASTBlock). This model adaptively integrates sparse and dense attention mechanisms to enhance feature representations and employs a feed-forward network to eliminate redundant information, thereby ensuring the quality of image fusion. Subsequently, to retain significant details while reducing the impact of noise introduced by low-light enhancement, we incorporate discrete wavelet transform (DWT) for feature decomposition and fusion, further enhancing the representation capability and feature preservation of fused images. Meanwhile, to tackle the issues of insufficient contrast and hidden details in low-light conditions, we design an illumination perception module and an illumination consistency loss to improve the contrast and clarity of fused images. Experimental results on multiple public benchmark datasets for quality assessment and downstream tasks, e.g., pedestrian detection, demonstrate that our method significantly outperforms the state-of-the-art (SOTA) methods. The code is available at https://github.com/hinmouc/PIVFusion.

Light field (LF) image data potentially involves a lot of sensitive information about users. Its transmission channel breaches could compromise user privacy and implicate illegal activities. Therefore, the confidentiality and integrity of LF image data transmission are crucial. However, most of the existing watermarking techniques may not adequately consider the confidentiality and integrity authentication of LF image data transmission. To address this problem, this article employs Generic Radial Harmonic Fourier Moments in Radon space (GRHFMR) to propose a robust zero-watermarking scheme of the LF image, called LFIZW-GRHFMR. Specifically, the radial harmonic Fourier coefficients of sub-aperture images (SAIs) of LF image are calculated by taking into full consideration the characteristics of the GRHFMR variable weight basis function, and then the corresponding watermarks are combined to generate zero-watermarks with stronger robustness. For yielding more watermarking information and enhancing security authentication, the 32 key SAIs of LF image at a sampling rate of 0.5 are selected to act with GRHFMR. Meanwhile, the key SAIs are converted as video streaming with high efficiency video coding for transmission to improve the data transmission efficiency. Extensive experimental results demonstrate that the proposed LFIZW-GRHFMR outperforms the latest watermarking methods.

Abstract This paper pays close attention to the stabilization issue for delayed uncertain semi-Markovian jumping complex-valued networks via sliding mode control. The concerned corresponding transition rates depend on a positive constant, i.e., sojourn-time, which is not required to obey the general exponential distribution. Combine the generalized Dynkin’s formula with Lyapunov stability theory as well as the characteristics of cumulative distribution functions, a few sufficient criteria are proposed to ascertain the stochastic stability of the obtained sliding mode dynamical system. In addition, design a novel sliding mode controller to ensure all state trajectories of the potential closed-loop system can reach the synthesized sliding mode switching surface in a finite time and maintain there in the subsequent time. In the end of paper, one simple example is presented to verify superiority and feasibility of the provided controller design scheme.