ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics

Physical simulators have been widely used in robot planning and control. Among them, differentiable simulators are particularly favored, as they can be incorporated into gradient-based optimization algorithms that are efficient in solving inverse problems such as optimal control and motion planning. Simulating deformable objects is, however, more challenging compared to rigid body dynamics. The underlying physical laws of deformable objects are more complex, and the resulting systems have orders of magnitude more degrees of freedom and therefore they are significantly more computationally expensive to simulate. Computing gradients with respect to physical design or controller parameters is typically even more computationally challenging. In this paper, we propose a real-time, differentiable hybrid Lagrangian-Eulerian physical simulator for deformable objects, ChainQueen, based on the Moving Least Squares Material Point Method (MLS-MPM). MLS-MPM can simulate deformable objects including contact and can be seamlessly incorporated into inference, control and co-design systems. We demonstrate that our simulator achieves high precision in both forward simulation and backward gradient computation. We have successfully employed it in a diverse set of control tasks for soft robots, including problems with nearly 3,000 decision variables.Comment: In submission to ICRA 2019. Supplemental Video: https://www.youtube.com/watch?v=4IWD4iGIsB4 Project Page: https://github.com/yuanming-hu/ChainQuee

Effects of taurine on male reproduction in rats of different ages

<p>Abstract</p> <p>Background</p> <p>It has been demonstrated that taurine is one of the most abundant free amino acids in the male reproductive system, and can be biosynthesized by male reproductive organs. But the effect of taurine on male reproduction is poorly understood.</p> <p>Methods</p> <p>Taurine and β-alanine (taurine transport inhibitor) were offered in water to male rats of different ages. The effects of taurine on reproductive hormones, testis marker enzymes, antioxidative ability and sperm quality were investigated.</p> <p>Results</p> <p>The levels of T and LH were obviously increased by taurine supplementation in rats of different ages, and the level of E was also significantly elevated in baby rats. The levels of SOD, ACP, SDH and NOS were obviously increased by taurine administration in adult rats, but the levels of AKP, AST, ALT and NO were significantly decreased. The levels of SOD, ACP, LDH, SDH, NOS, NO and GSH were significantly elevated by taurine administration in aged rats, but the levels of AST and ALT were significantly decreased. The motility of spermatozoa was obviously increased by taurine supplement in adult rats. The numbers and motility of spermatozoa, the rate of live spermatozoa were significantly increased by taurine supplement in aged rats.</p> <p>Conclusions</p> <p>The present study demonstrated that a taurine supplement could stimulate the secretion of LH and T, increase the levels of testicular marker enzymes, elevate testicular antioxidation and improve sperm quality. The results imply that taurine plays important roles in male reproduction especially in aged animals.</p

Image Clustering with External Guidance

The core of clustering is incorporating prior knowledge to construct supervision signals. From classic k-means based on data compactness to recent contrastive clustering guided by self-supervision, the evolution of clustering methods intrinsically corresponds to the progression of supervision signals. At present, substantial efforts have been devoted to mining internal supervision signals from data. Nevertheless, the abundant external knowledge such as semantic descriptions, which naturally conduces to clustering, is regrettably overlooked. In this work, we propose leveraging external knowledge as a new supervision signal to guide clustering, even though it seems irrelevant to the given data. To implement and validate our idea, we design an externally guided clustering method (Text-Aided Clustering, TAC), which leverages the textual semantics of WordNet to facilitate image clustering. Specifically, TAC first selects and retrieves WordNet nouns that best distinguish images to enhance the feature discriminability. Then, to improve image clustering performance, TAC collaborates text and image modalities by mutually distilling cross-modal neighborhood information. Experiments demonstrate that TAC achieves state-of-the-art performance on five widely used and three more challenging image clustering benchmarks, including the full ImageNet-1K dataset

Kinases and pseudokinases: Lessons from RAF

Protein kinases are thought to mediate their biological effects through their catalytic activity. The large number of pseudokinases in the kinome and an increasing appreciation that they have critical roles in signaling pathways, however, suggest that catalyzing protein phosphorylation may not be the only function of protein kinases. Using the principle of hydrophobic spine assembly, we interpret how kinases are capable of performing a dual function in signaling. Its first role is that of a signaling enzyme (classical kinases; canonical), while its second role is that of an allosteric activator of other kinases or as a scaffold protein for signaling in a manner that is independent of phosphoryl transfer (classical pseudokinases; noncanonical). As the hydrophobic spines are a conserved feature of the kinase domain itself, all kinases carry an inherent potential to play both roles in signaling. This review focuses on the recent lessons from the RAF kinases that effectively toggle between these roles and can be “frozen” by introducing mutations at their hydrophobic spines

Differences in diversity and community assembly processes between planktonic and benthic diatoms in the upper reach of the Jinsha River, China

Comparing spatio-temporal patterns between planktonic and benthic algae is helpful for understanding their associations and differences. However, such studies are still rare especially in large rivers. We used a dataset collected in the upper reach of the Jinsha River in different seasons to explore biodiversity and assembly processes of planktonic and benthic diatom assemblages. We found that planktonic and benthic diatoms presented different seasonal variation in species richness and community compositions. We also found evidence that planktonic and benthic diatoms were coupled in the summer. Planktonic diatom assemblages were mainly affected by spatial processes via directional spatial dispersal, especially in the summer. By comparison, benthic diatom assemblages were more affected by environmental processes. Our findings suggest that mass effect and species sorting paradigms explain the assembly processes of planktonic and benthic diatom assemblages, respectively, but the explanatory powers of these two paradigms vary seasonally. To effectively monitor and assess ecological conditions of large rivers, we recommend using benthic algae as a biotic indicator group as they had stronger correlations with environmental factors.Peer reviewe

Inter-Calibration of Satellite Passive Microwave Land Observations from AMSR-E and AMSR2 Using Overlapping FY3B-MWRI Sensor Measurements

The development and continuity of consistent long-term data records from similar overlapping satellite observations is critical for global monitoring and environmental change assessments. We developed an empirical approach for inter-calibration of satellite microwave brightness temperature (Tb) records over land from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and Microwave Scanning Radiometer 2 (AMSR2) using overlapping Tb observations from the Microwave Radiation Imager (MWRI). Double Differencing (DD) calculations revealed significant AMSR2 and MWRI biases relative to AMSR-E. Pixel-wise linear relationships were established from overlapping Tb records and used for calibrating MWRI and AMSR2 records to the AMSR-E baseline. The integrated multi-sensor Tb record was largely consistent over the major global vegetation and climate zones; sensor biases were generally well calibrated, though residual Tb differences inherent to different sensor configurations were still present. Daily surface air temperature estimates from the calibrated AMSR2 Tb inputs also showed favorable accuracy against independent measurements from 142 global weather stations (R2 ≥ 0.75, RMSE ≤ 3.64 °C), but with slightly lower accuracy than the AMSR-E baseline (R2 ≥ 0.78, RMSE ≤ 3.46 °C). The proposed method is promising for generating consistent, uninterrupted global land parameter records spanning the AMSR-E and continuing AMSR2 missions

Complex Locomotion Skill Learning via Differentiable Physics

Differentiable physics enables efficient gradient-based optimizations of neural network (NN) controllers. However, existing work typically only delivers NN controllers with limited capability and generalizability. We present a practical learning framework that outputs unified NN controllers capable of tasks with significantly improved complexity and diversity. To systematically improve training robustness and efficiency, we investigated a suite of improvements over the baseline approach, including periodic activation functions, and tailored loss functions. In addition, we find our adoption of batching and an Adam optimizer effective in training complex locomotion tasks. We evaluate our framework on differentiable mass-spring and material point method (MPM) simulations, with challenging locomotion tasks and multiple robot designs. Experiments show that our learning framework, based on differentiable physics, delivers better results than reinforcement learning and converges much faster. We demonstrate that users can interactively control soft robot locomotion and switch among multiple goals with specified velocity, height, and direction instructions using a unified NN controller trained in our system. Code is available at https://github.com/erizmr/Complex-locomotion-skill-learning-via-differentiable-physics

Establishing an Efficient Way to Utilize the Drought Resistance Germplasm Population in Wheat

Drought resistance breeding provides a hopeful way to improve yield and quality of wheat in arid and semiarid regions. Constructing core collection is an efficient way to evaluate and utilize drought-resistant germplasm resources in wheat. In the present research, 1,683 wheat varieties were divided into five germplasm groups (high resistant, HR; resistant, R; moderate resistant, MR; susceptible, S; and high susceptible, HS). The least distance stepwise sampling (LDSS) method was adopted to select core accessions. Six commonly used genetic distances (Euclidean distance, Euclid; Standardized Euclidean distance, Seuclid; Mahalanobis distance, Mahal; Manhattan distance, Manhat; Cosine distance, Cosine; and Correlation distance, Correlation) were used to assess genetic distances among accessions. Unweighted pair-group average (UPGMA) method was used to perform hierarchical cluster analysis. Coincidence rate of range (CR) and variable rate of coefficient of variation (VR) were adopted to evaluate the representativeness of the core collection. A method for selecting the ideal constructing strategy was suggested in the present research. A wheat core collection for the drought resistance breeding programs was constructed by the strategy selected in the present research. The principal component analysis showed that the genetic diversity was well preserved in that core collection