Lip synchronization is the task of aligning a speaker’s lip movements in video with corresponding speech audio, and it is essential for creating realistic, expressive video content. However, existing methods often rely on reference frames and masked-frame inpainting, which limit their robustness to identity consistency, pose variations, facial occlusions, and stylized content. In addition, since audio signals provide weaker conditioning than visual cues, lip shape leakage from the original video will affect lip sync quality. In this paper, we present OmniSync, a universal lip synchronization framework for diverse visual scenarios. Our approach introduces a mask-free training paradigm using Diffusion Transformer models for direct frame editing without explicit masks, enabling unlimited-duration inference while maintaining natural facial dynamics and preserving character identity. During inference, we propose a flow-matching-based progressive noise initialization to ensure pose and identity consistency, while allowing precise mouth-region editing. To address the weak conditioning signal of audio, we develop a Dynamic Spatiotemporal Classifier-Free Guidance (DS-CFG) mechanism that adaptively adjusts guidance strength over time and space. We also establish the AIGC-LipSync Benchmark, the first evaluation suite for lip synchronization in diverse AI-generated videos. Extensive experiments demonstrate that OmniSync significantly outperforms prior methods in both visual quality and lip sync accuracy, achieving superior results in both real-world and AI-generated videos.

Abstract With the popularity of handheld devices, video stabilization is becoming increasingly important. In previous studies, many methods have been proposed to stabilize shaky videos. However, these methods fail to balance between image content integrity and stability. Some methods sacrifice image content for better stability. Other methods ignore the subtle jitters, which leads to poor stability. This work innovatively proposes a video stabilization method based on decomposed motion compensation. First, a grid‐based motion statistics method is adopted for motion estimation, which obtains more accurate motion vectors according to matched likelihood estimates. Then, the motion compensation is inherently decomposed into two parts: linear motion compensation and auxiliary motion compensation. Linear motion compensation removes complex jitter by constructing linear path constraints to obtain a more stable camera path. Auxiliary motion compensation uses a moving average filter to remove the high‐frequency jitter as a supplement and preserve more image content. The two components are combined with individual weights to derive the final transform matrix and warp the original frames. Experimental results show that our method outperforms the previous methods on NUS and DeepStab datasets qualitatively and quantitatively.

A growing number of studies have confirmed the important role of microRNAs (miRNAs) in human diseases and the aberrant expression of miRNAs affects the onset and progression of human diseases. The discovery of disease-associated miRNAs as new biomarkers promote the progress of disease pathology and clinical medicine. However, only a small proportion of miRNA-disease correlations have been validated by biological experiments. And identifying miRNA-disease associations through biological experiments is both expensive and inefficient. Therefore, it is important to develop efficient and highly accurate computational methods to predict miRNA-disease associations. A miRNA-disease associations prediction algorithm based on Graph Convolutional neural Networks and Principal Component Analysis (GCNPCA) is proposed in this paper. Specifically, the deep topological structure information is extracted from the heterogeneous network composed of miRNA and disease nodes by a Graph Convolutional neural Network (GCN) with an additional attention mechanism. The internal attribute information of the nodes is obtained by the Principal Component Analysis (PCA). Then, the topological structure information and the node attribute information are combined to construct comprehensive feature descriptors. Finally, the Random Forest (RF) is used to train and classify these feature descriptors. In the five-fold cross-validation experiment, the AUC and AUPR for the GCNPCA algorithm are 0.983 and 0.988 respectively.

Solutions to timing relationship analysis problems are often reported using symbolic variables and inequalities which specify linear relationships between the variables. Complex relationships can be expressed using Presburger formulas which allow Boolean relations to be specified between the inequalities. This paper develops and applies a highly effective simplification approach for Presburger formulas based on logic minimization techniques.

We present a novel set of tools for performing symbolic timing verificationof timing diagrams. The tools are multi-purpose with usesin verification, derivation of synthesis constraints, and design evaluation.Our methodology is based on using techniques for manipulatingPresburger formulas. We demonstrate using several interestingexamples that the method is efficient in practice and should beconsidered for inclusion in commercial tools.