GPA-3D: Geometry-aware Prototype Alignment for Unsupervised Domain Adaptive 3D Object Detection from Point Clouds

LiDAR-based 3D detection has made great progress in recent years. However, the performance of 3D detectors is considerably limited when deployed in unseen environments, owing to the severe domain gap problem. Existing domain adaptive 3D detection methods do not adequately consider the problem of the distributional discrepancy in feature space, thereby hindering generalization of detectors across domains. In this work, we propose a novel unsupervised domain adaptive \textbf{3D} detection framework, namely \textbf{G}eometry-aware \textbf{P}rototype \textbf{A}lignment (\textbf{GPA-3D}), which explicitly leverages the intrinsic geometric relationship from point cloud objects to reduce the feature discrepancy, thus facilitating cross-domain transferring. Specifically, GPA-3D assigns a series of tailored and learnable prototypes to point cloud objects with distinct geometric structures. Each prototype aligns BEV (bird's-eye-view) features derived from corresponding point cloud objects on source and target domains, reducing the distributional discrepancy and achieving better adaptation. The evaluation results obtained on various benchmarks, including Waymo, nuScenes and KITTI, demonstrate the superiority of our GPA-3D over the state-of-the-art approaches for different adaptation scenarios. The MindSpore version code will be publicly available at \url{https://github.com/Liz66666/GPA3D}.Comment: Accepted by ICCV 202

Operation strategies of capital-constrained small and medium-sized enterprises based on blockchain technology

Introduction: In reality, due to the low credit rating of small and medium-sized enterprises (SMEs), it is difficult for them to obtain sufficient financing from a single financier. This paper considers a dual-channel supply chain consisting of a capital-constrained manufacturer, an e-commerce platform (ECP), a third-party logistics company (3PL) and consumers. There are two innovations in this paper: the manufacturer obtains sufficient production funds through hybrid financing of the ECP and 3PL, and consumers want to know product information and compare prices. The contributions of this paper are to investigate new applications of blockchain in both hybrid financing and meeting consumer information search needs.Methodology: We discuss the operation and pricing decisions of supply chain in two scenarios. These two scenarios are without adopting blockchain (N) and with adopting blockchain (B). Then, we compare the equilibrium decisions in two scenarios.Results: The results show that the supply chain will adopt blockchain when certain conditions are met. The initial adoption of blockchain is bad for the ECP and 3PL. Further, we find that with the increase of financing ratio, the optimal financing interest rate of the ECP decreases, while the optimal financing interest rate of the 3PL increases.Discussion: The numerical analysis shows that the adoption of blockchain can be more profitable when the cost of information search is high.Management insights: In order to achieve supply chain coordination, the manufacturer should give subsidies the ECP and 3PL

Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective

Graph contrastive learning (GCL) emerges as the most representative approach for graph representation learning, which leverages the principle of maximizing mutual information (InfoMax) to learn node representations applied in downstream tasks. To explore better generalization from GCL to downstream tasks, previous methods heuristically define data augmentation or pretext tasks. However, the generalization ability of GCL and its theoretical principle are still less reported. In this paper, we first propose a metric named GCL-GE for GCL generalization ability. Considering the intractability of the metric due to the agnostic downstream task, we theoretically prove a mutual information upper bound for it from an information-theoretic perspective. Guided by the bound, we design a GCL framework named InfoAdv with enhanced generalization ability, which jointly optimizes the generalization metric and InfoMax to strike the right balance between pretext task fitting and the generalization ability on downstream tasks. We empirically validate our theoretical findings on a number of representative benchmarks, and experimental results demonstrate that our model achieves state-of-the-art performance.Comment: 25 pages, 7 figures, 6 table

Learning Effective NeRFs and SDFs Representations with 3D Generative Adversarial Networks for 3D Object Generation: Technical Report for ICCV 2023 OmniObject3D Challenge

In this technical report, we present a solution for 3D object generation of ICCV 2023 OmniObject3D Challenge. In recent years, 3D object generation has made great process and achieved promising results, but it remains a challenging task due to the difficulty of generating complex, textured and high-fidelity results. To resolve this problem, we study learning effective NeRFs and SDFs representations with 3D Generative Adversarial Networks (GANs) for 3D object generation. Specifically, inspired by recent works, we use the efficient geometry-aware 3D GANs as the backbone incorporating with label embedding and color mapping, which enables to train the model on different taxonomies simultaneously. Then, through a decoder, we aggregate the resulting features to generate Neural Radiance Fields (NeRFs) based representations for rendering high-fidelity synthetic images. Meanwhile, we optimize Signed Distance Functions (SDFs) to effectively represent objects with 3D meshes. Besides, we observe that this model can be effectively trained with only a few images of each object from a variety of classes, instead of using a great number of images per object or training one model per class. With this pipeline, we can optimize an effective model for 3D object generation. This solution is one of the final top-3-place solutions in the ICCV 2023 OmniObject3D Challenge

DyTed: Disentangled Representation Learning for Discrete-time Dynamic Graph

Unsupervised representation learning for dynamic graphs has attracted a lot of research attention in recent years. Compared with static graph, the dynamic graph is a comprehensive embodiment of both the intrinsic stable characteristics of nodes and the time-related dynamic preference. However, existing methods generally mix these two types of information into a single representation space, which may lead to poor explanation, less robustness, and a limited ability when applied to different downstream tasks. To solve the above problems, in this paper, we propose a novel disenTangled representation learning framework for discrete-time Dynamic graphs, namely DyTed. We specially design a temporal-clips contrastive learning task together with a structure contrastive learning to effectively identify the time-invariant and time-varying representations respectively. To further enhance the disentanglement of these two types of representation, we propose a disentanglement-aware discriminator under an adversarial learning framework from the perspective of information theory. Extensive experiments on Tencent and five commonly used public datasets demonstrate that DyTed, as a general framework that can be applied to existing methods, achieves state-of-the-art performance on various downstream tasks, as well as be more robust against noise

Complete mitochondrial genome sequences of Physogyra lichtensteini (Milne Edwards & Haime, 1851) and Plerogyra sinuosa (Dana, 1846) (Scleractinia, Plerogyridae): characterisation and phylogenetic analysis

In this study, the whole mitochondrial genomes of Physogyra lichtensteini and Plerogyra sinuosa have been sequenced for the first time. The length of their assembled mitogenome sequences were 17,286 bp and 17,586 bp, respectively, both including 13 protein-coding genes, two tRNAs, and two rRNAs. Their mitogenomes offered no distinct structure and their gene order were the same as other typical scleractinians. Based on 13 protein-coding genes, a maximum likelihood phylogenetic analysis showed that Physogyra lichtensteini and Plerogyra sinuosa are clustered in the family Plerogyridae, which belongs to the “Robust” clade. The 13 tandem mitogenome PCG sequences used in this research can provide important molecular information to clarify the evolutionary relationships amongst stony corals, especially at the family level. On the other hand, more advanced markers and more species need to be used in the future to confirm the evolutionary relationships of all the scleractinians

Crystal structure of the N‐terminal region of human Ash2L shows a winged‐helix motif involved in DNA binding

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102216/1/embr2011101-sup-0001.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/102216/2/embr2011101.reviewer_comments.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/102216/3/embr2011101.pd