Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D input

Great success has been achieved in the 6-DoF grasp learning from the point cloud input, yet the computational cost due to the point set orderlessness remains a concern. Alternatively, we explore the grasp generation from the RGB-D input in this paper. The proposed solution, Keypoint-GraspNet, detects the projection of the gripper keypoints in the image space and then recover the SE(3) poses with a PnP algorithm. A synthetic dataset based on the primitive shape and the grasp family is constructed to examine our idea. Metric-based evaluation reveals that our method outperforms the baselines in terms of the grasp proposal accuracy, diversity, and the time cost. Finally, robot experiments show high success rate, demonstrating the potential of the idea in the real-world applications.Comment: Submitted to ICRA202

WDiscOOD: Out-of-Distribution Detection via Whitened Linear Discriminant Analysis

Deep neural networks are susceptible to generating overconfident yet erroneous predictions when presented with data beyond known concepts. This challenge underscores the importance of detecting out-of-distribution (OOD) samples in the open world. In this work, we propose a novel feature-space OOD detection score based on class-specific and class-agnostic information. Specifically, the approach utilizes Whitened Linear Discriminant Analysis to project features into two subspaces - the discriminative and residual subspaces - for which the in-distribution (ID) classes are maximally separated and closely clustered, respectively. The OOD score is then determined by combining the deviation from the input data to the ID pattern in both subspaces. The efficacy of our method, named WDiscOOD, is verified on the large-scale ImageNet-1k benchmark, with six OOD datasets that cover a variety of distribution shifts. WDiscOOD demonstrates superior performance on deep classifiers with diverse backbone architectures, including CNN and vision transformer. Furthermore, we also show that WDiscOOD more effectively detects novel concepts in representation spaces trained with contrastive objectives, including supervised contrastive loss and multi-modality contrastive loss.Comment: Accepted by ICCV 2023. Code is available at: https://github.com/ivalab/WDiscOOD.gi

Effect of nonlinear and noncollinear transformation strain pathways in phase-field modeling of nucleation and growth during martensite transformation

The phase-field microelasticity theory has exhibited great capacities in studying elasticity and its effects on microstructure evolution due to various structural and chemical non-uniformities (impurities and defects) in solids. However, the usually adopted linear and/or collinear coupling between eigen transformation strain tensors and order parameters in phase-field microelasticity have excluded many nonlinear transformation pathways that have been revealed in many atomistic calculations. Here we extend phase-field microelasticity by adopting general nonlinear and noncollinear eigen transformation strain paths, which allows for the incorporation of complex transformation pathways and provides a multiscale modeling scheme linking atomistic mechanisms with overall kinetics to better describe solid-state phase transformations. Our case study on a generic cubic to tetragonal martensitic transformation shows that nonlinear transformation pathways can significantly alter the nucleation and growth rates, as well as the configuration and activation energy of the critical nuclei. It is also found that for a pure-shear martensitic transformation, depending on the actual transformation pathway, the nuclei and austenite/martensite interfaces can have nonzero far-field hydrostatic stress and may thus interact with other crystalline defects such as point defects and/or background tension/compression field in a more profound way than what is expected from a linear transformation pathway. Further significance is discussed on the implication of vacancy clustering at austenite/martensite interfaces and segregation at coherent precipitate/matrix interfaces.National Science Foundation (U.S.). Division of Materials Research (DMR-1410322)National Science Foundation (U.S.). Division of Materials Research (DMR-1410636

Schema-aware Reference as Prompt Improves Data-Efficient Relational Triple and Event Extraction

Information Extraction, which aims to extract structural relational triple or event from unstructured texts, often suffers from data scarcity issues. With the development of pre-trained language models, many prompt-based approaches to data-efficient information extraction have been proposed and achieved impressive performance. However, existing prompt learning methods for information extraction are still susceptible to several potential limitations: (i) semantic gap between natural language and output structure knowledge with pre-defined schema; (ii) representation learning with locally individual instances limits the performance given the insufficient features. In this paper, we propose a novel approach of schema-aware Reference As Prompt (RAP), which dynamically leverage schema and knowledge inherited from global (few-shot) training data for each sample. Specifically, we propose a schema-aware reference store, which unifies symbolic schema and relevant textual instances. Then, we employ a dynamic reference integration module to retrieve pertinent knowledge from the datastore as prompts during training and inference. Experimental results demonstrate that RAP can be plugged into various existing models and outperforms baselines in low-resource settings on four datasets of relational triple extraction and event extraction. In addition, we provide comprehensive empirical ablations and case analysis regarding different types and scales of knowledge in order to better understand the mechanisms of RAP. Code is available in https://github.com/zjunlp/RAP.Comment: Work in progres

Effect of astragaloside on vitamin d-receptor expression after endothelin-1-induced cardiomyocyte injury

Background: Astragaloside, which is one of the main components of Astragalus membranaceus, has been widely used in the treatment of congestive heart failure in China, and it can protect cardiomyocytes. Its mechanism of action remains unclear. Therefore, the present study was carried out to investigate the influence of astragaloside on rat cardiomyocytes stimulated with endothelin-1 (ET-1), and explored the underlying mechanism.Materials and Methods: ET-1 was used to stimulate primary rat cardiomyocytes and establish a cardiomyocyte hypertrophy model. Different astragaloside doses were administered in combination with ET-1. Cardiomyocyte hypertrophy and apoptosis were examined using transmission electron microscopy (TEM) and flow cytometry, respectively. The molecular mechanism was explored by analyzing the mRNA of the vitamin D receptor (VDR), cytochrome P450 family 27 subfamily B member 1(CYP27B), cytochrome P450 family 24 subfamily A member 1(CYP24A) and renin mRNA levels by quantificational real-time polymerase chain reaction(qRT-PCR).Results: Rat cardiomyocyte hypertrophy model was established successfully. Astragaloside administration significantly affected cell apoptosis and significantly inhibited ET-1-induced cardiomyocyte hypertrophy in a dose-dependent manner. Astragaloside treatment affected the expression of signaling molecules in the vitamin D axis.Conclusion: Astragaloside inhibits ET-1-induced cardiomyocyte hypertrophy. This effect can be reversed by regulating the levels of the relevant factors in the vitamin D axis.Keywords: cardiomyocyte hypertrophy, Astragaloside, Vitamin D Receptor, reni