We demonstrate model-based, visual robot manipulation of linear deformable objects. Our approach is based on a state-space representation of the physical system that the robot aims to control. This choice has multiple advantages, including the ease of incorporating physics priors in the dynamics model and perception model, and the ease of planning manipulation actions. In addition, physical states can naturally represent object instances of different appearances. Therefore, dynamics in the state space can be learned in one setting and directly used in other visually different settings. This is in contrast to dynamics learned in pixel space or latent space, where generalization to visual differences are not guaranteed. Challenges in taking the statespace approach are the estimation of the high-dimensional state of a deformable object from raw images, where annotations are very expensive on real data, and finding a dynamics model that is both accurate, generalizable, and efficient to compute. We are the first to demonstrate self-supervised training of rope state estimation on real images, without requiring expensive annotations. This is achieved by our novel self-supervising learning objective, which is generalizable across a wide range of visual appearances. With estimated rope states, we train a fast and differentiable neural network dynamics model that encodes the physics of mass-spring systems. Our method has a higher accuracy in predicting future states compared to models that do not involve explicit state estimation and do not use any physics prior, while only using 3% of training data. We also show that our approach achieves more efficient manipulation, both in simulation and on a real robot, when used within a model predictive controller.
Previous studies in multimodal sentiment analysis have used limited datasets, which only contain unified multimodal annotations. However, the unified annotations do not always reflect the independent sentiment of single modalities and limit the model to capture the difference between modalities. In this paper, we introduce a Chinese single-and multimodal sentiment analysis dataset, CH-SIMS, which contains 2,281 refined video segments in the wild with both multimodal and independent unimodal annotations. It allows researchers to study the interaction between modalities or use independent unimodal annotations for unimodal sentiment analysis. Furthermore, we propose a multi-task learning framework based on late fusion as the baseline. Extensive experiments on the CH-SIMS show that our methods achieve state-of-the-art performance and learn more distinctive unimodal representations. The full dataset and codes are available for use at https://github.com/ thuiar/MMSA.
The renin-angiotensin system (RAS) plays a critical role in chronic renal failure associated with heart failure. In the past few years, angiotensin (Ang) (1-7) have been reported to counteract the effects of angiotensin II (Ang II) and were even considered as a new therapeutical target in RAS. The purposes of this study were to examine whether the Ang (1-7) improves the heart function and remodeling of the left ventricle (LV) in mice with 5/6 nephrectomy (NC). We used a 5/6 nephrectomy to induce significant renal dysfunction in wildtype mice (WT). Twelve weeks after NC, WT showed high blood pressure, significant leftventricular dilation and dysfunction, which were accompanied by cardiomyocyte hypertrophy, diffuse interstitial fibrosis and oxidative damage of cardiomyocytes. Exogenous Ang (1-7) injection improved the heart function and remodeling of LV in mice with 5/6 NC accompanied by a reduction in cardiac interstitial fibrosis, inflammatory cytokine expression and oxidative damage levels of cardiomyocytes, decrease in the profibrotic signaling molecule transforming growth factor (TGF)-b and increase in the collagen degradation signaling molecule matrix metalloproteinase (MMP)-2, -9. However, these beneficial effects did not occur in hydralazine-treated mice. These findings suggest that (1) Exogenous Ang (1-7) injection improve the heart function and remodeling of LV in mice with 5/6 NC. (2) These beneficial effects are independent of its anti-blood pressure effect.
With the aim of creating virtual cloth deformations more similar to real world clothing, we propose a new computational framework that recasts three dimensional cloth deformation as an RGB image in a two dimensional pattern space. Then a three dimensional animation of cloth is equivalent to a sequence of two dimensional RGB images, which in turn are driven/choreographed via animation parameters such as joint angles. This allows us to leverage popular CNNs to learn cloth deformations in image space. The two dimensional cloth pixels are extended into the real world via standard body skinning techniques, after which the RGB values are interpreted as texture offsets and displacement maps. Notably, we illustrate that our approach does not require accurate unclothed body shapes or robust skinning techniques. Additionally, we discuss how standard image based techniques such as image partitioning for higher resolution, GANs for merging partitioned image regions back together, etc., can readily be incorporated into our framework.
High-volume hemofiltration was associated with improved clinical outcome in acute pancreatitis patients, and should be initiated before kidney injury appearance.
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