Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data can significantly reduce MR scan times, leading to improved equipment utilization and enhanced patient comfort. Applying federated learning (FL) in MR image reconstruction enables multiple hospitals to collaboratively train a model in a distributed manner without aggregating local data, thereby protecting patient privacy. Prototype-based FL further optimizes communication costs by sharing lightweight representatives as global knowledge for multisite collaboration. However, to our knowledge, existing prototypebased FL methods have not been explored in the context of image generation tasks. Moreover, tackling domain shifts in multi-site MR image data due to differences in sequences, scanners, and diseases presents a significant challenge. To address these issues, we propose FedPMR, the first personalized prototype-based FL framework for accelerated MR image reconstruction. FedPMR collaboratively searches semantic features in the approximate direction across multiple sites and improves generalization of local models by integrating common knowledge. Extensive experiments conducted on three public datasets and one private dataset demonstrate that FedPMR not only reduces communication costs ($236.70 \text{Mb} \rightarrow 8 ~\text{Kb}$), but also achieves approximately a 3.0 % improvement in SSIM and a 4.9 % improvement in PSNR for clients experiencing greater domain shift. Additionally, we explore the optimal selection of collaborative prototypes in image generation, including the effects of location, scale, dimensionality reduction methods, and multi-scale fusion.

We present a GPU-friendly framework for real-time implicit simulation of elastic material in the presence of frictional contacts. The integration of hyperelasticity, non-interpenetration contact, and friction in real-time simulations presents formidable nonlinear and non-smooth problems, which are highly challenging to solve. By incorporating nonlinear complementarity conditions within the local-global framework, we achieve rapid convergence in addressing these challenges. While the structure of local-global methods is not fully GPU-friendly, our proposal of a simple yet efficient solver with sparse presentation of the system inverse enables highly parallel computing while maintaining a fast convergence rate. Moreover, our novel splitting strategy for non-smooth indicators not only amplifies overall performance but also refines the complementarity preconditioner, enhancing the accuracy of frictional behavior modeling. Through extensive experimentation, the robustness of our framework in managing real-time contact scenarios, ranging from large-scale systems and extreme deformations to non-smooth contacts and precise friction interactions, has been validated. Compatible with a wide range of hyperelastic models, our approach maintains efficiency across both low and high stiffness materials. Despite its remarkable efficiency, robustness, and generality, our method is elegantly simple, with its core contributions grounded solely on standard matrix operations.

We present Jade, a differentiable physics engine for articulated rigid bodies. Jade models contacts as the Linear Complementarity Problem (LCP). Compared to existing differentiable simulations, Jade offers features including intersection-free collision simulation and stable LCP solutions for multiple frictional contacts. We use continuous collision detection to detect the time of impact and adopt the backtracking strategy to prevent intersection between bodies with complex geometry shapes. We derive the gradient calculation to ensure the whole simulation process is differentiable under the backtracking mechanism. We modify the popular Dantzig’s algorithm to get valid solutions under multiple frictional contacts. We conduct extensive experiments to demonstrate the effectiveness of our differentiable physics simulation over a variety of contact-rich tasks. Supplemental materials and videos are available on our project webpage at https://sites.google.com/view/diffsim

Differentiable physics simulation provides an avenue to tackle previously intractable challenges through gradient-based optimization, thereby greatly improving the efficiency of solving robotics-related problems. To apply differentiable simulation in diverse robotic manipulation scenarios, a key challenge is to integrate various materials in a unified framework. We present SoftMAC, a differentiable simulation framework that couples soft bodies with articulated rigid bodies and clothes. SoftMAC simulates soft bodies with the continuum-mechanics-based Material Point Method (MPM). We provide a novel forecast-based contact model for MPM, which effectively reduces penetration without introducing other artifacts like unnatural rebound. To couple MPM particles with deformable and non-volumetric clothes meshes, we also propose a penetration tracing algorithm that reconstructs the signed distance field in local area. Diverging from previous works, SoftMAC simulates the complete dynamics of each modality and incorporates them into a cohesive system with an explicit and differentiable coupling mechanism. The feature empowers SoftMAC to handle a broader spectrum of interactions, such as soft bodies serving as manipulators and engaging with underactuated systems. We conducted comprehensive experiments to validate the effectiveness and accuracy of the proposed differentiable pipeline in downstream robotic manipulation applications. Supplementary materials are available on our project website at https://damianliumin.github.io/SoftMAC.

Computational biologists are frequently engaged in collaborative data analysis with wet lab researchers. These interdisciplinary projects, as necessary as they are to the scientific endeavor, can be surprisingly challenging due to cultural differences in operations and values. In this Ten Simple Rules guide, we aim to help dry lab researchers identify sources of friction and provide actionable tools to facilitate respectful, open, transparent, and rewarding collaborations.

We present ClothesNet: a large-scale dataset of 3D clothes objects with information-rich annotations. Our dataset consists of around 4400 models covering 11 categories annotated with clothes features, boundary lines, and keypoints. ClothesNet can be used to facilitate a variety of computer vision and robot interaction tasks. Using our dataset, we establish benchmark tasks for clothes perception, including classification, boundary line segmentation, and keypoint detection, and develop simulated clothes environments for robotic interaction tasks, including rearranging, folding, hanging, and dressing. We also demonstrate the efficacy of our ClothesNet in real-world experiments. Supplemental materials and dataset are available on our project webpage at https://sites.google.com/view/clothesnet.

Differentiable Simulations have recently proven useful for various robotic manipulation tasks, including cloth manipulation. In robotic cloth simulation, it is crucial to maintain intersection-free properties. We present DiffClothAI, a differentiable cloth simulation with intersection-free friction contact and two-way coupling with articulated rigid bodies. DiffClothAI integrates the Project Dynamics and Incremental Potential Contact coherently and proposes an effective method to derive gradients in the Cloth Simulation. It also establishes the differentiable coupling mechanism between articulated rigid bodies and cloth. We conduct a comprehensive evaluation of DiffClothAI's effectiveness and accuracy and perform a variety of experiments in downstream robotic manipulation tasks. Supplemental materials and videos are available on our project webpage at https://sites.google.com/view/diffsimcloth.