Semantic-Aware Fine-Grained Correspondence

Establishing visual correspondence across images is a challenging and essential task. Recently, an influx of self-supervised methods have been proposed to better learn representations for visual correspondence. However, we find that these methods often fail to leverage semantic information and over-rely on the matching of low-level features. In contrast, human vision is capable of distinguishing between distinct objects as a pretext to tracking. Inspired by this paradigm, we propose to learn semantic-aware fine-grained correspondence. Firstly, we demonstrate that semantic correspondence is implicitly available through a rich set of image-level self-supervised methods. We further design a pixel-level self-supervised learning objective which specifically targets fine-grained correspondence. For downstream tasks, we fuse these two kinds of complementary correspondence representations together, demonstrating that they boost performance synergistically. Our method surpasses previous state-of-the-art self-supervised methods using convolutional networks on a variety of visual correspondence tasks, including video object segmentation, human pose tracking, and human part tracking.Comment: 26 page

Policy Contrastive Imitation Learning

Adversarial imitation learning (AIL) is a popular method that has recently achieved much success. However, the performance of AIL is still unsatisfactory on the more challenging tasks. We find that one of the major reasons is due to the low quality of AIL discriminator representation. Since the AIL discriminator is trained via binary classification that does not necessarily discriminate the policy from the expert in a meaningful way, the resulting reward might not be meaningful either. We propose a new method called Policy Contrastive Imitation Learning (PCIL) to resolve this issue. PCIL learns a contrastive representation space by anchoring on different policies and generates a smooth cosine-similarity-based reward. Our proposed representation learning objective can be viewed as a stronger version of the AIL objective and provide a more meaningful comparison between the agent and the policy. From a theoretical perspective, we show the validity of our method using the apprenticeship learning framework. Furthermore, our empirical evaluation on the DeepMind Control suite demonstrates that PCIL can achieve state-of-the-art performance. Finally, qualitative results suggest that PCIL builds a smoother and more meaningful representation space for imitation learning

Numerical Simulation of Dynamic Response of Fiber Reinforced Ceramic Matrix Composite Beam with Matrix Cracks Using Multiscale Modeling

AbstractA multiscale method for simulating the dynamic response of ceramic matrix composite (CMC) with matrix cracks is developed. At the global level, the finite element method is employed to simulate the dynamic response of a CMC beam. While at the local level, the multiscale mechanical method is used to estimate the stress/strain response of the material. A distributed computing system is developed to speed up the simulation. The simulation of dynamic response of a Nicalon/CAS-II beam being subjected to harmonic loading is performed as a numerical example. The results show that both the stress/strain responses under tension and compressive loading are nonlinear. These conditions result in a different response compared with that of elastic beam, such as: 1) the displacement response is not symmetric about the axis of time; 2) in the condition of small external load, the response at first order natural frequency is limited within a finite range; 3) decreasing the matrix crack space will increase the displacement response of the beam

For Pre-Trained Vision Models in Motor Control, Not All Policy Learning Methods are Created Equal

In recent years, increasing attention has been directed to leveraging pre-trained vision models for motor control. While existing works mainly emphasize the importance of this pre-training phase, the arguably equally important role played by downstream policy learning during control-specific fine-tuning is often neglected. It thus remains unclear if pre-trained vision models are consistent in their effectiveness under different control policies. To bridge this gap in understanding, we conduct a comprehensive study on 14 pre-trained vision models using 3 distinct classes of policy learning methods, including reinforcement learning (RL), imitation learning through behavior cloning (BC), and imitation learning with a visual reward function (VRF). Our study yields a series of intriguing results, including the discovery that the effectiveness of pre-training is highly dependent on the choice of the downstream policy learning algorithm. We show that conventionally accepted evaluation based on RL methods is highly variable and therefore unreliable, and further advocate for using more robust methods like VRF and BC. To facilitate more universal evaluations of pre-trained models and their policy learning methods in the future, we also release a benchmark of 21 tasks across 3 different environments alongside our work

A Universal Semantic-Geometric Representation for Robotic Manipulation

Robots rely heavily on sensors, especially RGB and depth cameras, to perceive and interact with the world. RGB cameras record 2D images with rich semantic information while missing precise spatial information. On the other side, depth cameras offer critical 3D geometry data but capture limited semantics. Therefore, integrating both modalities is crucial for learning representations for robotic perception and control. However, current research predominantly focuses on only one of these modalities, neglecting the benefits of incorporating both. To this end, we present Semantic-Geometric Representation (SGR), a universal perception module for robotics that leverages the rich semantic information of large-scale pre-trained 2D models and inherits the merits of 3D spatial reasoning. Our experiments demonstrate that SGR empowers the agent to successfully complete a diverse range of simulated and real-world robotic manipulation tasks, outperforming state-of-the-art methods significantly in both single-task and multi-task settings. Furthermore, SGR possesses the unique capability to generalize to novel semantic attributes, setting it apart from the other methods

Direct observation of ordered configurations of hydrogen adatoms on graphene

Ordered configurations of hydrogen adatoms on graphene have long been proposed, calculated and searched for. Here we report direct observation of several ordered configurations of H adatoms on graphene by scanning tunneling microscopy. On the top side of the graphene plane, H atoms in the configurations appear to stick to carbon atoms in the same sublattice. A gap larger than 0.6 eV in the local density of states of the configurations was revealed by scanning tunneling spectroscopy measurements. These findings can be well explained by density functional theory calculations based on double sided H configurations. In addition, factors that may influence H ordering are discussed

Tuning the selectivity of natural oils and fatty acids/esters deoxygenation to biofuels and fatty alcohols : A review

The chemical transformation of natural oils provides alternatives to limited fossil fuels and produces compounds with added value for the chemical industries. The selective deoxygenation of natural oils to diesel-ranged hydrocarbons, bio-jet fuels, or fatty alcohols with controllable selectivity is especially attractive in natural oil feedstock biorefineries. This review presents recent progress in catalytic deoxygenation of natural oils or related model compounds (e.g., fatty acids) to renewable liquid fuels (green diesel and bio-jet fuels) and valuable fatty alcohols (unsaturated and saturated fatty alcohols). Besides, it discusses and compares the existing and potential strategies to control the product selectivity over heterogeneous catalysts. Most research conducted and reviewed has only addressed the production of one category; therefore, a new integrative vision exploring how to direct the process toward fuel and/or chemicals is urgently needed. Thus, work conducted to date addressing the development of new catalysts and studying the influence of the reaction parameters (e.g., temperature, time and hydrogen pressure) is summarized and critically discussed from a green and sustainable perspective using efficiency indicators (e.g., yields, selectivity, turnover frequencies and catalysts lifetime). Special attention has been given to the chemical transformations occurring to identify key descriptors to tune the selectivity toward target products by manipulating the reaction conditions and the structures of the catalysts. Finally, the challenges and future research goals to develop novel and holistic natural oil biorefineries are proposed. As a result, this critical review provides the readership with appropriate information to selectively control the transformation of natural oils into either biofuels and/or value-added chemicals. This new flexible vision can help pave the wave to suit the present and future market needs

Comparison of the gut microbiota and untargeted gut tissue metabolome of Chinese mitten crabs (Eriocheir sinensis) with different shell colors

IntroductionThe Chinese mitten crab (Eriocheir sinensis) is a highly valued freshwater crustacean in China. While the natural shell color of E. sinensis is greenish brown (GH), we found a variety with a brownish-orange shell color (RH). Although RH is more expensive, it exhibits a lower molting frequency and growth rate compared with GH, which significantly reduces its yield and hinders large-scale farming. The growth and development of animals are closely related to their gut microbiota and gut tissue metabolic profiles.MethodsIn this study, we compared the gut microbiome communities and metabolic profiles of juvenile RH and GH crabs using 16S rRNA gene sequencing and liquid chromatography–mass spectrometry (LC–MS), respectively.ResultsOur findings indicated that the intestinal microbial composition and metabolic characteristics of E. sinensis differed significantly between RH and GH. At the operational taxonomic unit (OTU) level, the α-diversity of the gut microbiota did not differ significantly between RH and GH, while the β-diversity of the RH gut microbiota was higher than that of the GH gut microbiota. At the species level, the richness of unclassified_c_Alphaproteobacteria was significantly higher in the GH group, while the RH group had a significantly higher richness of three low-abundance species, Flavobacteria bacterium BAL38, Paraburkholderia ferrariae, and uncultured_bacterium_g__Legionella. In the current study, 598 gut tissue metabolites were identified, and 159 metabolites were significantly different between GH and RH. The metabolite profile of RH was characteristic of a low level of most amino acids and lipid metabolites and a high level of several pigments compared with that of GH. These metabolites were enriched in 102 KEGG pathways. Four pathways, including (1) Central carbon metabolism in cancer, (2) protein digestion and absorption, (3) alanine, aspartate and glutamate metabolism, and (4) aminoacyl-tRNA biosynthesis, were significantly enriched. The correlation analysis between metabolites and microbiotas indicated that most key differential metabolites were positively correlated with the abundance of Shewanella_sp_MR-7.DiscussionThis research provided a greater understanding of the physiological conditions of E. sinensis varieties with different shell colors by comparing the gut microbiota and gut tissue metabolome

Selection and validation of endogenous reference genes using a high throughput approach

BACKGROUND: Endogenous reference genes are commonly used to normalize expression levels of other genes with the assumption that the expression of the former is constant in different tissues and in different physiopathological conditions. Whether this assumption is correct it is, however, still matter of debate. In this study, we searched for stably expressed genes in 384 cDNA array hybridization experiments encompassing different tissues and cell lines. RESULTS: Several genes were identified whose expression was highly stable across all samples studied. The usefulness of 8 genes among them was tested by normalizing the relative gene expression against test genes whose expression pattern was known. The range of accuracy of individual endogenous reference genes was wide whereas consistent information could be obtained when information pooled from different endogenous reference genes was used. CONCLUSIONS: This study suggests that even when the most stably expressed genes in array experiments are used as endogenous reference, significant variation in test gene expression estimates may occur and the best normalization is achieved when data from several endogenous reference genes are pooled together to minimize minimal but significant variation among samples. We are presently optimizing strategies for the preparation of endogenous reference gene mixtures that could yield information comparable to that of data pooled from individual endogenous reference gene normalizations