Rearrangement Planning for General Part Assembly

Most successes in autonomous robotic assembly have been restricted to single target or category. We propose to investigate general part assembly, the task of creating novel target assemblies with unseen part shapes. As a fundamental step to a general part assembly system, we tackle the task of determining the precise poses of the parts in the target assembly, which we we term ``rearrangement planning''. We present General Part Assembly Transformer (GPAT), a transformer-based model architecture that accurately predicts part poses by inferring how each part shape corresponds to the target shape. Our experiments on both 3D CAD models and real-world scans demonstrate GPAT's generalization abilities to novel and diverse target and part shapes.Comment: Project website: https://general-part-assembly.github.io

Data Valuation and Detections in Federated Learning

Federated Learning (FL) enables collaborative model training while preserving the privacy of raw data. A challenge in this framework is the fair and efficient valuation of data, which is crucial for incentivizing clients to contribute high-quality data in the FL task. In scenarios involving numerous data clients within FL, it is often the case that only a subset of clients and datasets are pertinent to a specific learning task, while others might have either a negative or negligible impact on the model training process. This paper introduces a novel privacy-preserving method for evaluating client contributions and selecting relevant datasets without a pre-specified training algorithm in an FL task. Our proposed approach FedBary, utilizes Wasserstein distance within the federated context, offering a new solution for data valuation in the FL framework. This method ensures transparent data valuation and efficient computation of the Wasserstein barycenter and reduces the dependence on validation datasets. Through extensive empirical experiments and theoretical analyses, we demonstrate the potential of this data valuation method as a promising avenue for FL research.Comment: Fixed some experimental errors and typo

Research on a UAV spray system combined with grid atomized droplets

BackgroundsUAVs for crop protection hold significant potential for application in mountainous orchard areas in China. However, certain issues pertaining to UAV spraying need to be addressed for further technological advancement, aimed at enhancing crop protection efficiency and reducing pesticide usage. These challenges include the potential for droplet drift, limited capacity for pesticide solution. Consequently, efforts are required to overcome these limitations and optimize UAV spraying technology.MethodsIn order to balance high deposition and low drift in plant protection UAV spraying, this study proposes a plant protection UAV spraying method. In order to study the operational effects of this spraying method, this study conducted a UAV spray and grid impact test to investigate the effects of different operational parameters on droplet deposition and drift. Meanwhile, a spray model was constructed using machine learning techniques to predict the spraying effect of this method.Results and discussionThis study investigated the droplet deposition rate and downwind drift rate on three types of citrus trees: traditional densely planted trees, dwarf trees, and hedged trees, considering different particle sizes and UAV flight altitudes. Analyzing the effect of increasing the grid on droplet coverage and deposition density for different tree forms. The findings demonstrated a significantly improved droplet deposition rate on dwarf and hedged citrus trees compared to traditional densely planted trees and adopting a fixed-height grid increased droplet coverage and deposition density for both the densely planted and trellised citrus trees, but had the opposite effect on dwarfed citrus trees. When using the grid system. Among the factors examined, the height of the sampling point exhibited the greatest influence on the droplet deposition rate, whereas UAV flight height and droplet particle size had no significant impact. The distance in relation to wind direction had the most substantial effect on droplet drift rate. In terms of predicting droplet drift rate, the BP neural network performed inadequately with a coefficient of determination of 0.88. Conversely, REGRESS, ELM, and RBFNN yielded similar and notably superior results with a coefficient of determination greater than 0.95. Notably, ELM demonstrated the smallest root mean square error

Case Report: Prenatal Diagnosis and Treatment of Fetal Autoimmune-Associated First-Degree Atrioventricular Block: First Report From China

Background: The rapid progression from fetal first-degree atrioventricular block (AVB) to third-degree AVB had been reported. However, how to define fetal first-degree AVB with proper technique and the necessity of the treatment in utero for fetal autoimmune-associated first-degree AVB are still controversial.Purpose: To explore the diagnosis and the effect of treatment for fetal first-degree AVB.Cases Presentation: Four pregnant women with positive autoantibodies anti-SSA/Ro were admitted into our hospital with complaints of rapid prolonged atrioventricular (AV) intervals of their fetuses. Fetal AV intervals were re-measured by tissue Doppler imaging (TDI) from the onset of atrial contraction to ventricular systole (Aa-Sa), which were 170 ms (case 1-twin A), 160 ms (case 1-twin B), 163 ms (case 2) and 172 ms (case 3) and 170 ms (case 4), respectively. The histories of medication usage or infection during gestation were denied. Amniotic fluid genetic screenings and virological tests were negative in all cases. No structural cardiac disorders were found and the cardiovascular profile scores were 10 for each fetus. Oral dexamethasone (initial dose of 4.5 mg daily) and hydroxychloroquine (200 mg bid) plus weekly follow-up surveillance were suggested. The dosage of dexamethasone was adjusted according to the changes of the AV intervals and fetal development of biparietal diameters (BPD) and femur lengths (FL). All fetal AV intervals were controlled well. Maternal and fetal adverse effects were noted as diabetes in 1 mother and growth retardation in all fetuses. All fetuses were delivered via cesarean section at 35+4, 37, 38, and 37+1 gestational weeks, with 10 scores of Apgar score. Postnatally, positive anti-SSA/Ro was found in all neonates. However, there were no clinical or laboratory evidence of neonatal lupus syndrome. No abnormal signs were found on postnatal electrocardiogram and echocardiography for all neonates. With a follow-up of 8–53 months, there was no progression of disease and all infants demonstrated normal physical, mental, and motor development.Conclusion: Prenatal treatment for fetal autoimmune-associated first-degree AVB could be an alternative. Strict surveillance and timely adjustment of the treatment according to the conditions of the mother and the fetus are indicated. Further studies are necessary to prove our concept

Aridity-driven shift in biodiversity–soil multifunctionality relationships

From Springer Nature via Jisc Publications RouterHistory: received 2021-01-07, accepted 2021-08-12, registration 2021-08-25, pub-electronic 2021-09-09, online 2021-09-09, collection 2021-12Publication status: PublishedFunder: National Natural Science Foundation of China (National Science Foundation of China); doi: https://doi.org/10.13039/501100001809; Grant(s): 31770430Abstract: Relationships between biodiversity and multiple ecosystem functions (that is, ecosystem multifunctionality) are context-dependent. Both plant and soil microbial diversity have been reported to regulate ecosystem multifunctionality, but how their relative importance varies along environmental gradients remains poorly understood. Here, we relate plant and microbial diversity to soil multifunctionality across 130 dryland sites along a 4,000 km aridity gradient in northern China. Our results show a strong positive association between plant species richness and soil multifunctionality in less arid regions, whereas microbial diversity, in particular of fungi, is positively associated with multifunctionality in more arid regions. This shift in the relationships between plant or microbial diversity and soil multifunctionality occur at an aridity level of ∼0.8, the boundary between semiarid and arid climates, which is predicted to advance geographically ∼28% by the end of the current century. Our study highlights that biodiversity loss of plants and soil microorganisms may have especially strong consequences under low and high aridity conditions, respectively, which calls for climate-specific biodiversity conservation strategies to mitigate the effects of aridification