115 research outputs found
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
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
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
ï»żComplete 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 âRobustâ 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 Nâterminal region of human Ash2L shows a wingedâhelix 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
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