The precise control of cell fate is driven by a hierarchical regulatory network (HRNet) where transcription factors (TFs) and cis-regulatory elements (CREs) orchestrate the expression of target genes (TGs) through complex causal actions. While single-cell multi-omics technologies provide multi-dimensional data to resolve regulatory networks, existing methods often fail to capture their hierarchical and causal properties. We propose SMOGT (Single-cell Multi-Omics Graph Transformer), a graph representation learning method to decipher HRNet. SMOGT embeds epigenetic mechanism into Heterogeneous Graph Transformer (HGT) by structuring information flow along a hierarchical-guided meta-path (TF-TF → TF-CRE → CRE-CRE → CRE-TG), and employs a semi-supervised strategy to ensure network accuracy. Validated against ChIP-seq and HiC-seq benchmarked datasets, SMOGT showed significantly higher accuracy in predicting transcriptional regulation (TF-CRE) and long-range chromatin conformation (CRE-CRE). The HRNet scaffolds downstream modules that mechanistically link network architecture to cell fate. The multi-layer random walk (MRWR) module identifies driver regulators and their TGs. The BioStreamNet module predicts shifts in cell fate trajectories following in silico perturbations within gene-specific HRNet formed by extracting regulatory weights during TG expression prediction. In hematopoietic stem cell differentiation, SMOGT elucidated the hierarchical causal cascade from driver TFs that governs lineage commitment. In melanoma epithelial-to-mesenchymal transition (EMT), it revealed a critical therapeutic window for reversing the process, and in Acute Myeloid Leukemia (AML), it uncovered hub-CREs with significant prognostic value. By accurately modeling hierarchical causality, SMOGT provides a robust tool to dissect and predict cell fate dynamics in both development and disease.

We introduce Loki, a new framework for robust simulation of fluid, rigid, and deformable objects with non-compromising fidelity on any single element, and capabilities for coupling and representation transitions across multiple elements. Loki adapts multiple best-in-class solvers into a unified framework driven by a declarative state machine where users declare 'what' is simulated but not 'when,' so an automatic scheduling system takes care of mixing any combination of objects. This leads to intuitive setups for coupled simulations such as hair in the wind or objects transitioning from one representation to another, for example bulk water FLIP particles to SPH spray particles to volumetric mist. We also provide a consistent treatment for components used in several domains, such as unified collision and attachment constraints across 1D, 2D, 3D deforming and rigid objects. Distribution over MPI, custom linear equation solvers, and aggressive application of sparse techniques keep performance within production requirements. We demonstrate a variety of solvers within the framework and their interactions, including FLIPstyle liquids, spatially adaptive volumetric fluids, SPH, MPM, and mesh-based solids, including but not limited to discrete elastic rods, elastons, and FEM with state-of-the-art constitutive models. Our framework has proven powerful and intuitive enough for voluntary artist adoption and has delivered creature and FX simulations for multiple major movie productions in the preceding four years.

This paper presents a vector painting system for digital artworks. We first propose Temporal Diffusion Curve (TDC), a new form of vector graphics, and a novel random-access solver for modeling the evolution of strokes. With the help of a procedural stroke processing function, the TDC strokes can achieve various shapes and effects for multiple art styles. Based on these, we build a painting system of great potential. Thanks to the random-access solver, our method has real-time performance regardless of the rendering resolution, provides straightforward editing possibilities on strokes both at runtime and afterward, and is effective and straightforward for art production. Compared with the previous Diffusion Curve, our method uses strokes as the basic graphics primitives, which are able to intersect each other and much more consistent with the intuition and painting habits of human. We finally demonstrate that professional artists can create multiple genres of artworks with our painting system.

This paper extends the recently proposed power-particle-based fluid simulation method with staggered discretization, GPU implementation, and adaptive sampling, largely enhancing the efficiency and usability of the method. In contrast to the original formulation which uses co-located pressures and velocities, in this paper, a staggered scheme is adapted to the Power Particles to benefit visual details and computing efficiency. Meanwhile, we propose a novel facet-based power diagrams construction algorithm suitable for parallelization and explore its GPU implementation, achieving an order of magnitude boost in performance over the existing code library. In addition, to utilize the potential of Power Particles to control individual cell volume, we apply adaptive particle sampling to improve the detail level with varying resolution. The proposed method can be entirely carried out on GPUs, and our extensive experiments validate our method both in terms of efficiency and visual quality.

Abstract In this paper, we present a new strategy, a joint deep learning architecture, for two classic tasks in computer graphics: water surface reconstruction and water image synthesis. Modeling water surfaces from single images can be regarded as the inverse of image rendering, which converts surface geometries into photorealistic images. On the basis of this fact, we therefore consider these two problems as a cycle image‐to‐image translation and propose to tackle them together using a pair of neural networks, with the three‐dimensional surface geometries being represented as two‐dimensional surface normal maps. Furthermore, we also estimate the imaging parameters from the existing water images with a subnetwork to reuse the lighting conditions when synthesizing new images. Experiments demonstrate that our method achieves an accurate reconstruction of surfaces from monocular images efficiently and produces visually plausible new images under variable lighting conditions.

BACKGROUND: Brain disorders are one of the top causes of human death. Generally, neurologists analyze brain medical images for diagnosis. In the image analysis field, corners are one of the most important features, which makes corner detection and matching studies essential. However, existing corner detection studies do not consider the domain information of brain. This leads to many useless corners and the loss of significant information. Regarding corner matching, the uncertainty and structure of brain are not employed in existing methods. Moreover, most corner matching studies are used for 3D image registration. They are inapplicable for 2D brain image diagnosis because of the different mechanisms. To address these problems, we propose a novel corner-based brain medical image classification method. Specifically, we automatically extract multilayer texture images (MTIs) which embody diagnostic information from neurologists. Moreover, we present a corner matching method utilizing the uncertainty and structure of brain medical images and a bipartite graph model. Finally, we propose a similarity calculation method for diagnosis. RESULTS: Brain CT and MRI image sets are utilized to evaluate the proposed method. First, classifiers are trained in N-fold cross-validation analysis to produce the best θ and K. Then independent brain image sets are tested to evaluate the classifiers. Moreover, the classifiers are also compared with advanced brain image classification studies. For the brain CT image set, the proposed classifier outperforms the comparison methods by at least 8% on accuracy and 2.4% on F1-score. Regarding the brain MRI image set, the proposed classifier is superior to the comparison methods by more than 7.3% on accuracy and 4.9% on F1-score. Results also demonstrate that the proposed method is robust to different intensity ranges of brain medical image. CONCLUSIONS: In this study, we develop a robust corner-based brain medical image classifier. Specifically, we propose a corner detection method utilizing the diagnostic information from neurologists and a corner matching method based on the uncertainty and structure of brain medical images. Additionally, we present a similarity calculation method for brain image classification. Experimental results on two brain image sets show the proposed corner-based brain medical image classifier outperforms the state-of-the-art studies.

Abstract This paper advocates a novel method for modelling physically realistic flow from captured incompressible gas sequence via modal analysis in frequency‐constrained subspace. Our analytical tool is uniquely founded upon empirical mode decomposition (EMD) and modal reduction for fluids, which are seamlessly integrated towards a powerful, style‐controllable flow modelling approach. We first extend EMD, which is capable of processing 1D time series but has shown inadequacies for 3D graphics earlier, to fit gas flows in 3D. Next, frequency components from EMD are adopted as candidate vectors for bases of modal reduction. The prerequisite parameters of the Navier–Stokes equations are then optimized to inversely model the physically realistic flow in the frequency‐constrained subspace. The estimated parameters can be utilized for re‐simulation, or be altered toward fluid editing. Our novel inverse‐modelling technique produces real‐time gas sequences after precomputation, and is convenient to couple with other methods for visual enhancement and/or special visual effects. We integrate our new modelling tool with a state‐of‐the‐art fluid capturing approach, forming a complete pipeline from real‐world fluid to flow re‐simulation and editing for various graphics applications.

many methods have been developed for corner detection and matching. However, these detection methods do not take the domain knowledge of brain medical images into account. They produce some useless corners and lose essential domain information. Moreover, existing corner matching methods do not consider the uncertainty and structure of brain medical images. And most of them are developed for 3D medical image registration, which are not applicable for 2D image classification. To address these problems, a corner detection method is firstly proposed based on hierarchical textures. Then, based on the uncertainty and structure of brain medical images, a corner matching method is developed to yield an initial and furthermore a maximum matched corner pair sequences. Finally, a similarity function is presented for classification. Experimental results show the proposed corner detection method outperforms the existing method and the classification results based on the maximum matched corner pair sequence are better than the state-of-the-art brain medical image classification methods.

Medical images are one of the most important tools in doctors' diagnostic decision-making. It has been a research hotspot in medical big data that how to effectively represent medical images and find essential patterns hidden in them to assist doctors to achieve a better diagnosis. Several graph models have been developed to represent medical images. However, the unique structures of domain-specific images are not considered well to lose some essential information. Thus, aiming at brain CT images, we first construct a graph about the Topological Relations between Ventricles and Lesions (TRVL) and present the graph modeling process. Then we propose a method named Frequent Approximate Subgraph Mining based on Graph Edit Distance (FASMGED). This method uses an error-tolerant graph matching strategy that is accordant with ubiquitous noise in practice. Experimental results show that the graph modeling process is computationally scalable and FASMGED can find more significant patterns than current algorithms.