Intersection-free Robot Manipulation with Soft-Rigid Coupled Incremental Potential Contact

This paper presents a novel simulation platform, ZeMa, designed for robotic manipulation tasks concerning soft objects. Such simulation ideally requires three properties: two-way soft-rigid coupling, intersection-free guarantees, and frictional contact modeling, with acceptable runtime suitable for deep and reinforcement learning tasks. Current simulators often satisfy only a subset of these needs, primarily focusing on distinct rigid-rigid or soft-soft interactions. The proposed ZeMa prioritizes physical accuracy and integrates the incremental potential contact method, offering unified dynamics simulation for both soft and rigid objects. It efficiently manages soft-rigid contact, operating 75x faster than baseline tools with similar methodologies like IPC-GraspSim. To demonstrate its applicability, we employ it for parallel grasp generation, penetrated grasp repair, and reinforcement learning for grasping, successfully transferring the trained RL policy to real-world scenarios

Enhancing the fairness of AI prediction models by Quasi-Pareto improvement among heterogeneous thyroid nodule population

Abstract Artificial Intelligence (AI) models for medical diagnosis often face challenges of generalizability and fairness. We highlighted the algorithmic unfairness in a large thyroid ultrasound dataset with significant diagnostic performance disparities across subgroups linked causally to sample size imbalances. To address this, we introduced the Quasi-Pareto Improvement (QPI) approach and a deep learning implementation (QP-Net) combining multi-task learning and domain adaptation to improve model performance among disadvantaged subgroups without compromising overall population performance. On the thyroid ultrasound dataset, our method significantly mitigated the area under curve (AUC) disparity for three less-prevalent subgroups by 0.213, 0.112, and 0.173 while maintaining the AUC for dominant subgroups; we also further confirmed the generalizability of our approach on two public datasets: the ISIC2019 skin disease dataset and the CheXpert chest radiograph dataset. Here we show the QPI approach to be widely applicable in promoting AI for equitable healthcare outcomes

Light Regimes Regulate Leaf and Twigs Traits of Camellia oleifera (Abel) in Pinus massoniana Plantation Understory

Camellia oleifera (Abel) is an economic tree species and one of the four largest oil plants in the world. The leaf and twig responses and plasticity indices of C. oleifera were investigated under four light regimes in Pinus massoniana understory plantations, namely, 100% light intensity (CK), 75% of CK (HL), 50% of CK (ML), and 30% of CK (LL). The morphological characteristics, biomass allocation, and physiological characteristics of C. oleifera leaves and twigs under different light regimes, as well as their plasticity indexes, were comprehensively evaluated. The results showed that leaf area, and specific leaf area, leaf total carbon, total nitrogen, total phosphorus and chlorophyll contents, and photosynthesis increased, which indicates that plants have the strongest adaptability under HL. No fruit appeared in twigs under LL and ML. The plastic morphological traits were greater than the biomass allocation and physiological traits. The plasticity of palisade/sponge tissue thickness and lower epidermis thickness were the lowest. In conclusion, C. oleifera have differences in sensitivity and regulation mechanism according to their differences in leaf morphological characteristics, biomass allocation physiological indicators, and response to light regimes. C. oleifera plants showed obvious phenotypic inhibition under CK, while they can adjust their strategies for using light energy to maintain their own growth and development under HL. The wide range of light adaptation and strong plasticity of C. oleifera may be two important reasons for its existence in heterogeneous habitats, but it needs at least 75% light regimes to complete its normal growth development and fruit setting. The study provides insights into the optimum light regimes for the improvement of the quality and efficiency of C. oleifera in P. massoniana understory plantations