Proprioceptive Learning with Soft Polyhedral Networks

Proprioception is the "sixth sense" that detects limb postures with motor neurons. It requires a natural integration between the musculoskeletal systems and sensory receptors, which is challenging among modern robots that aim for lightweight, adaptive, and sensitive designs at a low cost. Here, we present the Soft Polyhedral Network with an embedded vision for physical interactions, capable of adaptive kinesthesia and viscoelastic proprioception by learning kinetic features. This design enables passive adaptations to omni-directional interactions, visually captured by a miniature high-speed motion tracking system embedded inside for proprioceptive learning. The results show that the soft network can infer real-time 6D forces and torques with accuracies of 0.25/0.24/0.35 N and 0.025/0.034/0.006 Nm in dynamic interactions. We also incorporate viscoelasticity in proprioception during static adaptation by adding a creep and relaxation modifier to refine the predicted results. The proposed soft network combines simplicity in design, omni-adaptation, and proprioceptive sensing with high accuracy, making it a versatile solution for robotics at a low cost with more than 1 million use cycles for tasks such as sensitive and competitive grasping, and touch-based geometry reconstruction. This study offers new insights into vision-based proprioception for soft robots in adaptive grasping, soft manipulation, and human-robot interaction.Comment: 20 pages, 10 figures, 2 tables, submitted to the International Journal of Robotics Research for revie

Climatic change controls productivity variation in global grasslands.

Detection and identification of the impacts of climate change on ecosystems have been core issues in climate change research in recent years. In this study, we compared average annual values of the normalized difference vegetation index (NDVI) with theoretical net primary productivity (NPP) values based on temperature and precipitation to determine the effect of historic climate change on global grassland productivity from 1982 to 2011. Comparison of trends in actual productivity (NDVI) with climate-induced potential productivity showed that the trends in average productivity in nearly 40% of global grassland areas have been significantly affected by climate change. The contribution of climate change to variability in grassland productivity was 15.2-71.2% during 1982-2011. Climate change contributed significantly to long-term trends in grassland productivity mainly in North America, central Eurasia, central Africa, and Oceania; these regions will be more sensitive to future climate change impacts. The impacts of climate change on variability in grassland productivity were greater in the Western Hemisphere than the Eastern Hemisphere. Confirmation of the observed trends requires long-term controlled experiments and multi-model ensembles to reduce uncertainties and explain mechanisms

Technological Innovation: A Case Study of Mobile Internet Information Technology Applications in Community Management

The Mobile Internet Information Technology MIIT has been widely accepted as one of the most promising technologies in the next decades, having various applications and different value positions. However, few published studies explore and examine the effects of MIIT on community management. Based on the Dramaturgical Theory, this article uses a case study method to get an insightful understanding of MIIT. This article found that the MIIT was used by grid organizations to realize technological innovation and change organizational routines and structures, but eventually it was shaped by them, so this new technology was only able to embed itself into the public service model as a secondary or complementary role. Copyright: © 2018 IGA Globa

CD133 expression in cancer cells predicts poor prognosis of non-mucin producing intrahepatic cholangiocarcinoma

Background; CD133 is a marker of stem cells as well cancer stem cells. This study investigated the association between CD133 expression in cancer cells and the clinical outcome of non-mucin producing intrahepatic cholangiocarcinoma (ICC). Methods: Fifty-seven non-mucin producing ICC patients were enrolled in this study. Immunohistochemistry (IHC) and immunofluorescence staining for CD133 as well as other cancer-associated proteins, including cytokeratin 19, TGF-β1, p-Smad2 and epithelial–mesenchymal transition (EMT) markers S100A4, E-Cadherin and Vimentin were analyzed. Results: IHC staining showed that tumor cells in 52.6% of patients expressed CD133. The CD133+ patients had significantly higher metastasis rate than those without CD133+ tumor cells (36.7% vs. 10.1%, p = 0.03). The CD133+ patients had shorter overall and disease-free survival time as compared to the CD133− patients. Furthermore, 90.9% of CD133+ patients developed cancer recurrence, as compared to 64.3% of CD133− patients (p = 0.02). As compared to CD133− patients, tumor cells in CD133+ patients demonstrated high levels of TGF-β/p-Smad2 as well as EMT-like alteration, characterized by loss of E-Cadherin and expression of Vimentin and S100A4. Conclusions: CD133 expression in ICC tumor cells indicates poor prognosis of the disease and might be associated with TGF-β related EMT alterations

Second generation Dirac cones and inversion symmetry breaking induced gaps in graphene/hexagonal boron nitride

Graphene/h-BN has emerged as a model van der Waals heterostructure, and the band structure engineering by the superlattice potential has led to various novel quantum phenomena including the self-similar Hofstadter butterfly states. Although newly generated second generation Dirac cones (SDCs) are believed to be crucial for understanding such intriguing phenomena, so far fundamental knowledge of SDCs in such heterostructure, e.g. locations and dispersion of SDCs, the effect of inversion symmetry breaking on the gap opening, still remains highly debated due to the lack of direct experimental results. Here we report first direct experimental results on the dispersion of SDCs in 0$^\circ$ aligned graphene/h-BN heterostructure using angle-resolved photoemission spectroscopy. Our data reveal unambiguously SDCs at the corners of the superlattice Brillouin zone, and at only one of the two superlattice valleys. Moreover, gaps of $\approx$ 100 meV and $\approx$ 160 meV are observed at the SDCs and the original graphene Dirac cone respectively. Our work highlights the important role of a strong inversion symmetry breaking perturbation potential in the physics of graphene/h-BN, and fills critical knowledge gaps in the band structure engineering of Dirac fermions by a superlattice potential.Comment: Nature Physics 2016, In press, Supplementary Information include