JGS2 is a Jacobi-like GPU simulation algorithm. It avoids the overshooting issue by augmenting each subproblem with a perturbation subspace that predicts the global influence of the local solve. The efficiency of JGS2 is due to Cubature-based subspace integration at each subproblem. Being a data-driven method, Cubature requires a set of representative deformed poses that cover deformations likely to occur in the simulation. This requirement is unlikely for high-resolution deformation with rich local details. Therefore, simulation performance and convergence degenerate when Cubature extrapolates. This paper proposes a training-free subspace integration algorithm based on classic Gaussian quadrature (GQ). We leverage the fact that the subproblem's subspace bases can be well-approximated by a low-degree multivariable polynomial, which suggests GQ an excellent candidate for Cubature substitute. To this end, we introduce a novel algorithm that adaptively generates the integration region for each subproblem. As a result, GQ integration can be analytically retrieved without cumbersome data generation and training. We also show how to handle frictional contact by modifying the pre-computed perturbation subspace. The resulting JGS2-GQ framework is more versatile than the vanilla JGS2 method. It is more stable for large and novel deformations, and is free of data generation and expensive training, while maintaining a near second-order convergence that is comparable to Newton. Performance-wise, JGS2-GQ is as efficient as JGS2, which is three orders faster than classic CPU methods and up to two orders faster than classic GPU algorithms. When novel deformation occurs, JGS2-GQ outperforms JGS2 over 50%.

We present an efficient B-spline finite element method (FEM) for cloth simulation. While higher-order FEM has long promised higher accuracy, its adoption in cloth simulators has been limited by its larger computational costs while generating results with similar visual quality. Our contribution is a full algorithmic pipeline that makes cloth simulation using quadratic B-spline surfaces faster than standard linear FEM in practice while consistently improving accuracy and visual fidelity. Using quadratic B-spline basis functions, we obtain a globally C 1 -continuous displacement field that supports consistent discretization of both membrane and bending energies, effectively reducing locking artifacts and mesh dependence common to linear elements. To close the performance gap, we introduce a reduced integration scheme that separately optimizes quadrature rules for membrane and bending energies, an accelerated Hessian assembly procedure tailored to the spline structure, and an optimized linear solver based on partial factorization. Together, these optimizations make high-order, smooth cloth simulation competitive at scale, yielding an average 2× speedup over linear FEM in our tests. Extensive experiments demonstrate improved accuracy, wrinkle detail, and robustness, including contact-rich scenarios, relative to linear FEM and recent higher-order approaches. Our method enables realistic wrinkling dynamics across a wide range of material parameters and supports practical garment animation, providing a new promising spatial discretization for high-quality cloth simulation.

Neural Radiance Fields (NeRF) has demonstrated its ability to generate high-quality synthesized views. Nonetheless, due to its slow inference speed, there is a need to explore faster inference methods. In this paper, we propose RT-Octree, which uses batched regular tracking based on PlenOctree with neural denoising to achieve better real-time performance. We achieve this by modifying the volume rendering algorithm to regular tracking. We batch all samples for each pixel in one single ray-voxel intersection process to further improve the real-time performance. To reduce the variance caused by insufficient samples while ensuring real-time speed, we propose a lightweight neural network named GuidanceNet, which predicts the guidance map and weight maps utilized for the subsequent multi-layer denoising module. We evaluate our method on both synthetic and real-world datasets, obtaining a speed of 100 + frames per second (FPS) with a resolution of 1920 × 1080. Compared to PlenOctree, our method is 1.5 to 2 times faster in inference time and significantly outperforms NeRF by several orders of magnitude. The experimental results demonstrate the effectiveness of our approach in achieving real-time performance while maintaining similar rendering quality.

Time picking is of great concern in the processing of microseismic data. However, the traditional method based on time/frequency domain cannot pick the first arrival time accurately in low signal-to-noise ratio. Besides, the traditional time picking methods which based on clustering are sensitive to selecting the initial clustering centers and easy to converge to local optimal value. To solve the above problems, we propose a time picking method for microseismic data based on locally linear embedding (LLE) and improved particle swarm optimization (PSO) clustering algorithm. First, the LLE algorithm can obtain the inherent characteristics and the rules hidden in high-dimensional data by calculating Euclidean distances and reconstruction weights between microseismic data points. The input is represented in a low-dimensional form. Then, the improved PSO clustering algorithm is used to select the optimal clustering centers from low-dimensional data through global search method. After that, the low-dimensional data can be classified into noise cluster and signal cluster by the K-means algorithm. Finally, the initial time of the signal cluster can be considered as the first arrival time of microseismic data. The experimental results show that accuracy of the proposed method is higher than that of the improved PSO clustering algorithm, Akaike information criterion method, and short- and long-time window ratio method (short-time window averaging/long-time window averaging).

P-wave time picking is of great significance in microseismic data processing. However, traditional time picking methods do not consider the difference of low-dimensional manifold features between signal and noise which can be extracted more effectively in low signal to noise ratio scenarios. In this letter, we develop a new method named spectral multimanifold clustering for picking P-wave arrivals. It can extract the low-dimensional manifold features from a suitable affinity matrix. In this approach, the manifold features of data are concentrated by residual statics estimation and a suitable affinity matrix is constructed using structural similarity and local similarity. Then, by using unnormalized spectral clustering, the low-dimensional manifold features extracted from the affinity matrix can be classified into noise cluster and signal cluster. Finally, the initial time of the signal cluster is considered to be the first arrival time in microseismic data. We design a series of experiments using both synthetic and field microseismic data. Our proposed method demonstrates higher accuracy, better stability, and noise immunity than either the short and long time average method or the akaike information criterion method.

Bioinformatics is a fast-growing interdisciplinary field in which the demand for quality education exceeds the supply, especially in developing regions and countries.A massive open online course (MOOC) is a new model for education that delivers videotaped lectures and other course materials over the Internet for all interested persons around the globe to learn for free.Here we present our MOOC ''Bioinformatics: Introduction and Methods,'' which is the second bioinformatics MOOC in the world and one of the first batch of seven MOOCs from China.In the first two runs of this bilingual MOOC, more than 30,000 students with diverse backgrounds registered from 110 countries and regions.In this manuscript, we present the content design of the MOOC, the demographic profiles and learning patterns of the students, the requirement for English support, and feedback from on-campus students.We offer a few suggestions to other scientists who may be interested in creating a MOOC.We also remember the S* course, a successful open online bioinformatics course that ran from 2001 to 2007, long before the current wave of MOOCs.We believe that MOOC education has great potential to enhance global bioinformatics education.