Integrating Symmetry into Differentiable Planning with Steerable Convolutions

We study how group symmetry helps improve data efficiency and generalization for end-to-end differentiable planning algorithms when symmetry appears in decision-making tasks. Motivated by equivariant convolution networks, we treat the path planning problem as \textit{signals} over grids. We show that value iteration in this case is a linear equivariant operator, which is a (steerable) convolution. This extends Value Iteration Networks (VINs) on using convolutional networks for path planning with additional rotation and reflection symmetry. Our implementation is based on VINs and uses steerable convolution networks to incorporate symmetry. The experiments are performed on four tasks: 2D navigation, visual navigation, and 2 degrees of freedom (2DOFs) configuration space and workspace manipulation. Our symmetric planning algorithms improve training efficiency and generalization by large margins compared to non-equivariant counterparts, VIN and GPPN.Comment: Restructured main text and appendix. Renamed from "Integrating Symmetry into Differentiable Planning

A General Theory of Correct, Incorrect, and Extrinsic Equivariance

Although equivariant machine learning has proven effective at many tasks, success depends heavily on the assumption that the ground truth function is symmetric over the entire domain matching the symmetry in an equivariant neural network. A missing piece in the equivariant learning literature is the analysis of equivariant networks when symmetry exists only partially in the domain. In this work, we present a general theory for such a situation. We propose pointwise definitions of correct, incorrect, and extrinsic equivariance, which allow us to quantify continuously the degree of each type of equivariance a function displays. We then study the impact of various degrees of incorrect or extrinsic symmetry on model error. We prove error lower bounds for invariant or equivariant networks in classification or regression settings with partially incorrect symmetry. We also analyze the potentially harmful effects of extrinsic equivariance. Experiments validate these results in three different environments.Comment: Published at NeurIPS 202

Joint Exposure to Ambient Air Pollutants Might Elevate the Risk of Small for Gestational Age (SGA) Infants in Wuhan: Evidence From a Cross-Sectional Study

Objective: To investigate the effect of exposure to multiple ambient air pollutants during pregnancy on the risk of children being born small for gestational age (SGA).Methods: An Air Pollution Score (APS) was constructed to assess the effects of being exposed to six air pollutants simultaneously, PM2.5, PM10, SO2, NO2, CO, and O3 (referred to as joint exposure). A logistic regression model was applied to estimate the associations of APS and SGA.Results: The adjusted odds ratios (ORs) of SGA per 10 ug/m3 increased in APS during the first and second trimesters and the entire pregnancy were 1.003 [95% confidence intervals (CIs): 1.000, 1.007], 1.018 (1.012, 1.025), and 1.020 (1.009, 1.031), respectively. The ORs of SGA for each 10 μg/m3 elevated in APS during the whole pregnancy were 1.025 (1.005, 1.046) for mothers aged over 35 years old vs. 1.018 (1.005, 1.031) for mothers aged under 35 years old. Women who were pregnant for the first time were more vulnerable to joint ambient air pollution.Conclusion: In summary, the results of the present study suggested that joint exposure to ambient air pollutants was associated with the increment in the risks of SGA

Recent Progress in Graphene-Based Electrocatalysts for Hydrogen Evolution Reaction

Hydrogen is regarded as a key renewable energy source to meet future energy demands. Moreover, graphene and its derivatives have many advantages, including high electronic conductivity, controllable morphology, and eco-friendliness, etc., which show great promise for electrocatalytic splitting of water to produce hydrogen. This review article highlights recent advances in the synthesis and the applications of graphene-based supported electrocatalysts in hydrogen evolution reaction (HER). Herein, powder-based and self-supporting three-dimensional (3D) electrocatalysts with doped or undoped heteroatom graphene are highlighted. Quantum dot catalysts such as carbon quantum dots, graphene quantum dots, and fullerenes are also included. Different strategies to tune and improve the structural properties and performance of HER electrocatalysts by defect engineering through synthetic approaches are discussed. The relationship between each graphene-based HER electrocatalyst is highlighted. Apart from HER electrocatalysis, the latest advances in water electrolysis by bifunctional oxygen evolution reaction (OER) and HER performed by multi-doped graphene-based electrocatalysts are also considered. This comprehensive review identifies rational strategies to direct the design and synthesis of high-performance graphene-based electrocatalysts for green and sustainable applications

Phylogenomic analyses provide insights into primate evolution

Comparative analysis of primate genomes within a phylogenetic context is essential for understanding the evolution of human genetic architecture and primate diversity. We present such a study of 50 primate species spanning 38 genera and 14 families, including 27 genomes first reported here, with many from previously less well represented groups, the New World monkeys and the Strepsirrhini. Our analyses reveal heterogeneous rates of genomic rearrangement and gene evolution across primate lineages. Thousands of genes under positive selection in different lineages play roles in the nervous, skeletal, and digestive systems and may have contributed to primate innovations and adaptations. Our study reveals that many key genomic innovations occurred in the Simiiformes ancestral node and may have had an impact on the adaptive radiation of the Simiiformes and human evolution

Molecular characterization of CO2 sequestration and assimilation in microalgae and its biotechnological applications

Microalgae are renewable feedstock for sustainable biofuel production, cell factory for valuable chemicals and promising in alleviation of greenhouse gas CO2. However, the carbon assimilation capacity is still the bottleneck for higher productivity. Molecular characterization of CO2 sequestration and assimilation in microalgae has advanced in the past few years and are reviewed here. In some cyanobacteria, genes for 2-oxoglytarate dehydrogenase was replaced by four alternative mechanisms to fulfill TCA cycle. In green algae Coccomyxa subellipsoidea C-169, alternative carbon assimilation pathway was upregulated under high CO2 conditions. These advances thus provide new insights and new targets for accelerating CO2 sequestration rate and enhancing bioproduct synthesis in microalgae. When integrated with conventional parameter optimization, molecular approach for microalgae modification targeting at different levels is promising in generating value-added chemicals from green algae and cyanobacteria efficiently in the near future. (c) 2017 Elsevier Ltd. All rights reserved

A Unitary Transform Based Generalized Approximate Message Passing

We consider the problem of recovering an unknown signal from general nonlinear measurements obtained through a generalized linear model (GLM). Based on the unitary transform approximate message passing (UAMP) and expectation propagation, a unitary transform based generalized AMP (GUAMP) algorithm is proposed for general measurement matrices, in particular highly correlated matrices. Experimental results on quantized compressed sensing demonstrate that the proposed GUAMP significantly outperforms state-of-the-art Generalized AMP (AMP) and generalized vector AMP (GVAMP) under correlated matrices

A Method for Yarns Calculation in Sock production

In socks production, enterprises always confuse how to estimate the quantity of the yarns rapidly and correctly for different kinds of socks. It results in the socks’ costs cannot be calculated in time. This paper aims at the calculation method of yarn used in the socks based on their pattern files and development of the corresponding software so that the quantity of yarns consumption can be calculated timely before the sock production. The composition of sock was introduced and the pattern file for sock production was analysed firstly. Then a concept of Thousand Stitches Weight(TSW) was proposed and the TSWs were determined for some yarns with different knitting structures. On this basis, a process was proposed for the calculation of yarn expending in a sock with different knitting structures before production. Finally, a software was developed to get the results fast and easily. The example shows that the calculation method proposed in this paper is reliable and valuable for hosiery enterprises

On-Robot Learning With Equivariant Models

Recently, equivariant neural network models have been shown to improve sample efficiency for tasks in computer vision and reinforcement learning. This paper explores this idea in the context of on-robot policy learning in which a policy must be learned entirely on a physical robotic system without reference to a model, a simulator, or an offline dataset. We focus on applications of Equivariant SAC to robotic manipulation and explore a number of variations of the algorithm. Ultimately, we demonstrate the ability to learn several non-trivial manipulation tasks completely through on-robot experiences in less than an hour or two of wall clock time