The integrated satellite-terrestrial network (ISTN) system has experienced
significant growth, offering seamless communication services in remote areas
with limited terrestrial infrastructure. However, designing a routing scheme
for ISTN is exceedingly difficult, primarily due to the heightened complexity
resulting from the inclusion of additional ground stations, along with the
requirement to satisfy various constraints related to satellite service
quality. To address these challenges, we study packet routing with ground
stations and satellites working jointly to transmit packets, while prioritizing
fast communication and meeting energy efficiency and packet loss requirements.
Specifically, we formulate the problem of packet routing with constraints as a
max-min problem using the Lagrange method. Then we propose a novel constrained
Multi-Agent reinforcement learning (MARL) dynamic routing algorithm named
CMADR, which efficiently balances objective improvement and constraint
satisfaction during the updating of policy and Lagrange multipliers. Finally,
we conduct extensive experiments and an ablation study using the OneWeb and
Telesat mega-constellations. Results demonstrate that CMADR reduces the packet
delay by a minimum of 21% and 15%, while meeting stringent energy consumption
and packet loss rate constraints, outperforming several baseline algorithms.

通过提出一种名为 CMADR 的新型约束多智能体强化学习动态路由算法，以高效地平衡目标改进与约束满足，该算法能有效减少数据包延迟最少 21% 和 15%，并满足严格的能量消耗和丢包率要求，优于多个基准算法。

集成卫星地面网络的动态路由：一种受限多智能体强化学习方法

Dynamic Routing for Integrated Satellite-Terrestrial Networks: A  Constrained Multi-Agent Reinforcement Learning Approach

Capsule networks, which incorporate the paradigms of connectionism and
symbolism, have brought fresh insights into artificial intelligence. The
capsule, as the building block of capsule networks, is a group of neurons
represented by a vector to encode different features of an entity. The
information is extracted hierarchically through capsule layers via routing
algorithms. Here, we introduce a quantum capsule network (dubbed QCapsNet)
together with an efficient quantum dynamic routing algorithm. To benchmark the
performance of the QCapsNet, we carry out extensive numerical simulations on
the classification of handwritten digits and symmetry-protected topological
phases, and show that the QCapsNet can achieve an enhanced accuracy and
outperform conventional quantum classifiers evidently. We further unpack the
output capsule state and find that a particular subspace may correspond to a
human-understandable feature of the input data, which indicates the potential
explainability of such networks. Our work reveals an intriguing prospect of
quantum capsule networks in quantum machine learning, which may provide a
valuable guide towards explainable quantum artificial intelligence.

介绍了一种名为 QCapsNet 的量子胶囊网络，该网络通过一种高效的量子动态路由算法从胶囊层层抽取信息， 实现了对手写数字和对称保护拓扑相的分类任务。进一步分析发现，输出胶囊状态的一个子空间可能对应于输入数据的一个人类可理解的特征，暗示了这种网络的可解释性和价值。

量子胶囊网络

Quantum Capsule Networks

Deep convolutional neural networks (CNN) have massively influenced recent
advances in large-scale image classification. More recently, a dynamic routing
algorithm with capsules (groups of neurons) has shown state-of-the-art
recognition performance. However, the behavior of such networks in the presence
of a degrading signal (noise) is mostly unexplored. An analytical study on
different network architectures toward noise robustness is essential for
selecting the appropriate model in a specific application scenario. This paper
presents an extensive performance analysis of six deep architectures for image
classification on six most common image degradation models. In this study, we
have compared VGG-16, VGG-19, ResNet-50, Inception-v3, MobileNet and CapsuleNet
architectures on Gaussian white, Gaussian color, salt-and-pepper, Gaussian
blur, motion blur and JPEG compression noise models.

本文通过对几种深度学习算法在不同图像噪声下的表现进行评估，研究了针对噪声鲁棒性的不同网络架构，以辅助在特定应用环境下选择最佳模型。