Conventional tampering localization only extracts features in the image domain, which makes it hard to capture the subtle tampering traces. In this paper, we propose a wavelet convolution and multi-scale attention network (WCMA-Net) for image tampering detection and localization, in which a wavelet convolution module (WCM) branch and a multi-scale attention module (MSAM) branch are integrated following the backbone. In the WCM branch, wavelet decomposition is utilized to enhance high-frequency details and enhance the detection of subtle tampering traces. In the MSAM branch, a multi-scale attention operation is employed to extract global and local features, which are then combined according to their similarity to capture long-range dependencies among pixels. Finally, an adaptive weight strategy is employed to fuse the features from both branches for binary pixel-level tampering mask prediction. Experimental results on various public datasets demonstrate that the proposed method achieves superior precise pixel-level image tampering localization over state-of-the-art methods. Codes and models are available at https://github.com/csust-sonie/WCMA-Net.

Graph Neural Networks (GNNs) learn from graph-structured data by passing messages between neighboring nodes along edges on certain topological layouts. While layouts can be essential to GNNs' performance, extant methods generally consider obtaining layouts from limited perspectives. In this paper, we introduce Distributional Edge Layouts (DELs), a first-of-its-kind method to sample a collection of topological layouts from a Boltzmann distribution under physical energies. By integrating DELs into GNNs, a wide landscape of feasible graph layouts can be captured from a holistic perspective, overcoming the intrinsic drawbacks in existing GNN designs.In practice, DELs can complement various GNN architectures with high versatility. Our theoretical analysis proves that GNNs equipped with DELs maintain at least the same expressive as their original counterparts, with empirical potential offering extra expressivity. Extensive experiments demonstrate that DELs consistently and substantially improve the performance of a wide range of GNN baselines across multiple datasets, achieving state-of-the-art results. This improvement suggests that DELs capture important distributional information previously overlooked by traditional GNN approaches. DEL is open-sourced at https://github.com/LOGO-CUHKSZ/DEL.

Understanding the dynamic behavior of proteins is critical to elucidating their functional mechanisms, yet generating realistic, temporally coherent trajectories of protein ensembles remains a significant challenge. In this work, we introduce a novel hierarchical autoregressive framework for modeling protein dynamics that leverages the intrinsic multi-scale organization of molecular motions. Unlike existing methods that focus on generating static conformational ensembles or treat dynamic sampling as an independent process, our approach characterizes protein dynamics as a Markovian process. The framework employs a two-scale architecture: a low-resolution model captures slow, collective motions driving major conformational transitions, while a high-resolution model generates detailed local fluctuations conditioned on these large-scale movements. This hierarchical design ensures that the causal dependencies inherent in protein dynamics are preserved, enabling the generation of temporally coherent and physically realistic trajectories. By bridging high-level biophysical principles with state-of-the-art generative modeling, our approach provides an efficient framework for simulating protein dynamics that balances computational efficiency with physical accuracy.

Graph Neural Networks operate through bottom-up message-passing, fundamentally differing from human visual perception, which intuitively captures global structures first. We investigate the underappreciated potential of vision models for graph understanding, finding they achieve performance comparable to GNNs on established benchmarks while exhibiting distinctly different learning patterns. These divergent behaviors, combined with limitations of existing benchmarks that conflate domain features with topological understanding, motivate our introduction of GraphAbstract. This benchmark evaluates models' ability to perceive global graph properties as humans do: recognizing organizational archetypes, detecting symmetry, sensing connectivity strength, and identifying critical elements. Our results reveal that vision models significantly outperform GNNs on tasks requiring holistic structural understanding and maintain generalizability across varying graph scales, while GNNs struggle with global pattern abstraction and degrade with increasing graph size. This work demonstrates that vision models possess remarkable yet underutilized capabilities for graph structural understanding, particularly for problems requiring global topological awareness and scale-invariant reasoning. These findings open new avenues to leverage this underappreciated potential for developing more effective graph foundation models for tasks dominated by holistic pattern recognition.

In the realm of single-cell analysis, accurately predicting gene expressions is crucial for understanding cellular functions and interactions. Traditional approaches often face significant challenges due to intrinsic noise, high dimensionality, and limited data availability in single-cell datasets. On the other hand, deep learning methods are prone to overfitting and perform poorly with limited data. This paper introduces a novel framework, Prototype-based Proximal Neural Factorization (PPNF), which harnesses the power of prototype learning and neural factorization to address these issues. Our method leverages a robust learning paradigm that identifies representative prototypes from single-cell data, facilitating a more resilient and interpretable data representations. We validate our approach using a diverse set of single-cell datasets, demonstrating that our method significantly outperforms existing techniques in terms of both robustness and accuracy. PPNF shows its effectiveness even with limited data, thereby reducing the financial and computational burden associated with high-throughput technologies. By enhancing the robustness and generalizability of single-cell gene expression predictions, our framework provides significant benefits for advancing the analysis and interpretation of single-cell gene expression data, particularly in data-limited scenarios, demonstrating its potential for more cost-effective applications.