We introduce GEM3D -- a new deep, topology-aware generative model of 3D
shapes. The key ingredient of our method is a neural skeleton-based
representation encoding information on both shape topology and geometry.
Through a denoising diffusion probabilistic model, our method first generates
skeleton-based representations following the Medial Axis Transform (MAT), then
generates surfaces through a skeleton-driven neural implicit formulation. The
neural implicit takes into account the topological and geometric information
stored in the generated skeleton representations to yield surfaces that are
more topologically and geometrically accurate compared to previous neural field
formulations. We discuss applications of our method in shape synthesis and
point cloud reconstruction tasks, and evaluate our method both qualitatively
and quantitatively. We demonstrate significantly more faithful surface
reconstruction and diverse shape generation results compared to the
state-of-the-art, also involving challenging scenarios of reconstructing and
synthesizing structurally complex, high-genus shape surfaces from Thingi10K and
ShapeNet.

GEM3D 是一种新的深度、拓扑感知的 3D 形状生成模型，通过神经骨架为基础的表示编码形状拓扑和几何信息，生成更准确的拓扑和几何信息的表面，并应用于形状综合和点云重建任务，取得了显著的表面重建和多样化形状生成结果。

GEM3D：三维形状合成的生成方法

GEM3D: GEnerative Medial Abstractions for 3D Shape Synthesis

With the rapid growth of edge intelligence, the deployment of federated
learning (FL) over wireless networks has garnered increasing attention, which
is called Federated Edge Learning (FEEL). In FEEL, both mobile devices
transmitting model parameters over noisy channels and collecting data in
diverse environments pose challenges to the generalization of trained models.
Moreover, devices can engage in decentralized FL via Device-to-Device
communication while the communication topology of connected devices also
impacts the generalization of models. Most recent theoretical studies overlook
the incorporation of all these effects into FEEL when developing generalization
analyses. In contrast, our work presents an information-theoretic
generalization analysis for topology-aware FEEL in the presence of data
heterogeneity and noisy channels. Additionally, we propose a novel
regularization method called Federated Global Mutual Information Reduction
(FedGMIR) to enhance the performance of models based on our analysis. Numerical
results validate our theoretical findings and provide evidence for the
effectiveness of the proposed method.

我们的工作针对数据异构性和噪声通道的情况，在拓扑感知的边缘学习中提出了一种信息论泛化分析，并提出了一种名为联合全局互信息缩减（FedGMIR）的新型正则化方法，通过数值结果验证了这种方法的有效性。

基于信息论的拓扑感知异构联邦边缘学习在噪声信道上的泛化分析

Information-Theoretic Generalization Analysis for Topology-aware  Heterogeneous Federated Edge Learning over Noisy Channels

Multi-modal knowledge graph (MKG) includes triplets that consist of entities
and relations and multi-modal auxiliary data. In recent years, multi-hop
multi-modal knowledge graph reasoning (MMKGR) based on reinforcement learning
(RL) has received extensive attention because it addresses the intrinsic
incompleteness of MKG in an interpretable manner. However, its performance is
limited by empirically designed rewards and sparse relations. In addition, this
method has been designed for the transductive setting where test entities have
been seen during training, and it works poorly in the inductive setting where
test entities do not appear in the training set. To overcome these issues, we
propose TMR (Topology-aware Multi-hop Reasoning), which can conduct MKG
reasoning under inductive and transductive settings. Specifically, TMR mainly
consists of two components. (1) The topology-aware inductive representation
captures information from the directed relations of unseen entities, and
aggregates query-related topology features in an attentive manner to generate
the fine-grained entity-independent features. (2) After completing multi-modal
feature fusion, the relation-augment adaptive RL conducts multi-hop reasoning
by eliminating manual rewards and dynamically adding actions. Finally, we
construct new MKG datasets with different scales for inductive reasoning
evaluation. Experimental results demonstrate that TMP outperforms
state-of-the-art MKGR methods under both inductive and transductive settings.

本文提出了一种拓扑感知的、适用于归纳和推理环境下的多模态知识图谱推理方法 TMR，该方法主要由任务感知归纳表示和关系增强自适应强化学习两个组件构成，并在不同规模的归纳推理数据集上进行了评估。实验表明，TMR 在归纳和推理环境下均优于现有的多模态知识图谱推理方法。