CTSN: Predicting Cloth Deformation for Skeleton-based Characters with a Two-stream Skinning Network

We present a novel learning method to predict the cloth deformation for skeleton-based characters with a two-stream network. The characters processed in our approach are not limited to humans, and can be other skeletal-based representations of non-human targets such as fish or pets. We use a novel network architecture which consists of skeleton-based and mesh-based residual networks to learn the coarse and wrinkle features as the overall residual from the template cloth mesh. Our network is used to predict the deformation for loose or tight-fitting clothing or dresses. We ensure that the memory footprint of our network is low, and thereby result in reduced storage and computational requirements. In practice, our prediction for a single cloth mesh for the skeleton-based character takes about 7 milliseconds on an NVIDIA GeForce RTX 3090 GPU. Compared with prior methods, our network can generate fine deformation results with details and wrinkles.Comment: 13 page

Hypovirus infection induces proliferation and perturbs functions of mitochondria in the chestnut blight fungus

IntroductionThe chestnut blight fungus, Cryphonectria parasitica, and hypovirus have been used as a model to probe the mechanism of virulence and regulation of traits important to the host fungus. Previous studies have indicated that mitochondria could be the primary target of the hypovirus.MethodsIn this study, we report a comprehensive and comparative study comprising mitochondrion quantification, reactive oxygen species (ROS) and respiratory efficiency, and quantitative mitochondrial proteomics of the wild-type and virus-infected strains of the chestnut blight fungus.Results and discussionOur data show that hypovirus infection increases the total number of mitochondria, lowers the general ROS level, and increases mitochondrial respiratory efficiency. Quantification of mitochondrial proteomes revealed that a set of proteins functioning in energy metabolism and mitochondrial morphogenesis, as well as virulence, were regulated by the virus. In addition, two viral proteins, p29 and p48, were found to co-fractionate with the mitochondrial membrane and matrix. These results suggest that hypovirus perturbs the host mitochondrial functions to result in hypovirulence

Classification of Complete Regular Minimal Surfaces in ℝ<i>ⁿ</i> with Total Curvature −6<i>π</i>

In this paper, we classify the complete regular orientable minimal surfaces in Rn with total curvature −6π and give a method to construct a series of complete non-holomorphic minimal surfaces with total curvature −6π. Specially, we give a simplified classification in another method if the surfaces lie in R4

The 18th Biennial Conference of International Society for Ecological Modelling Fully-Coupled Modeling of Shallow Water Flow and Pollutant Transport on Unstructured Grids

Abstract Understanding the space-time dynamics of pollutant transport remains an essential impediment to accurate prediction of impacts on the ecology of rivers and coastal areas and also for establishing efficient strategies for pollution control and environmental protection. Numerical models are a powerful tool to study the water flows and pollutant transport, and recently a new generation of models is being developed to simulate the coupled flow and pollutant transport in shallow water. In this paper, a two-dimensional fully-coupled model of shallow water flows and pollutant transport was developed using a triangular unstructured grid (TIN: triangular irregular network), which is also an important module of the PIHM-Hydro modeling system. The model is based on a cell-centered upwind finite volume method using the HLL approximate Riemann solver. A multidimensional linear reconstruction technique and multidimensional slope limiter was implemented to achieve a second-order spatial accuracy. In order to make the model efficient and stable, an explicit-implicit method was used in temporal discretization by an operator splitting technique. A test case of the pollutant transport in a square cavity is used to validate the model. Then the model was further applied to two pollutant transport scenarios: microscale pollutant transport following dam break and mesoscale pollutant transport driven by storm surge in Galveston Bay. The numerical results show that the model could accurately predict the flow dynamics and pollutant transport in extreme events such as a dam break and a storm surge. According to the prediction of the model, the storm surge caused by the Hurricane Ike significantly extended the polluted area

N-Cloth: Predicting 3D Cloth Deformation with Mesh-Based Networks

We present a novel mesh-based learning approach (N-Cloth) for plausible 3D cloth deformation prediction. Our approach is general and can handle cloth or obstacles represented by triangle meshes with arbitrary topologies. We use graph convolution to transform the cloth and object meshes into a latent space to reduce the non-linearity in the mesh space. Our network can predict the target 3D cloth mesh deformation based on the initial state of the cloth mesh template and the target obstacle mesh. Our approach can handle complex cloth meshes with up to 100K triangles and scenes with various objects corresponding to SMPL humans, non-SMPL humans or rigid bodies. In practice, our approach can be used to generate plausible cloth simulation at 30-45 fps on an NVIDIA GeForce RTX 3090 GPU. We highlight its benefits over prior learning-based methods and physically-based cloth simulators.Comment: 12 page

Agricultural risk modeling challenges in China : probabilistic modeling of rice losses in Hunan province

This article summarizes a joint research project undertaken under the Risk Management Solutions, Inc. (RMS) banner to investigate some of the possible approaches for agricultural risk modeling in China. Two modeling approaches were investigated—the simulated weather crop index and the burn yield analysis approach. The study was limited to Hunan Province and a single crop—rice. Both modeling approaches were dealt with probabilistically and were able to produce probabilistic risk metrics. Illustrative model outputs are also presented. The article discusses the robustness of the modeling approaches and their dependence on the availability, access to, and quality of weather and yield data. We offer our perspective on the requirements for models and platforms for agricultural risk quantification in China in order to respond to the needs of all stakeholders in agricultural risk transfer.Published versio

The RMS US inland flood model

The RMS US inland flood model provides flood hazard data of up to 10×10m resolution for the Contiguous United States for different return periods. The flood maps were developed using a series of physically based models. First, several thousand years of precipitation were simulated using principal component analysis coupled to a tropical cyclone precipitation model. Then, discharge and runoff were calculated using a semi-distributed rainfall runoff and routing model based on the TOPMODEL approach run at an hourly time step. This in turn forms the input to the fluvial and pluvial inundation models, which uses the shallow water equation to simulate flood propagation. Each of the individual model components such as precipitation, discharge and flood extent and depth were validated individually. The model generally performed very well compared to available flood maps, especially in the high exposure areas, even if it has some difficulties in the dry low exposure areas of the United States, which are heavily influenced by water management. The flood maps will be the base for the fully probabilistic loss model including a financial model. Via the simulated Hurricane track data set the flood model will be coupled to the RMS North Atlantic Hurricane model