为了在非结构化环境中安全，成功地导航自动驾驶汽车，地形的穿越性应根据车辆的驾驶能力而变化。实际的驾驶经验可以以自我监督的方式使用来学习特定的轨迹。但是，现有的学习自我监督的方法对于学习各种车辆的遍历性并不可扩展。在这项工作中，我们引入了一个可扩展的框架，用于学习自我监督的遍历性，该框架可以直接从车辆 - 泰林的互动中学习遍历性，而无需任何人类监督。我们训练一个神经网络，该神经网络可以预测车辆从3D点云中经历的本体感受体验。使用一种新颖的PU学习方法，网络同时确定了不可转化的区域，其中估计可以过度自信。通过从模拟和现实世界中收集的各种车辆的驾驶数据，我们表明我们的框架能够学习各种车辆的自我监督的越野性。通过将我们的框架与模型预测控制器整合在一起，我们证明了估计的遍历性会导致有效的导航，从而根据车辆的驾驶特性实现了不同的操作。此外，实验结果验证了我们方法识别和避免不可转化区域的能力。

LIDAR广泛用于捕获准确的3D室外场景结构。但是，LiDAR在雪天气中产生许多不良的噪音点，这阻碍了有意义的3D场景结构。带有积雪标签的语义分割将是清除它们的简单解决方案，但需要艰苦的点注释。为了解决这个问题，我们提出了一个新颖的自我监督学习框架，用于在激光雷德点云中清除降雪点。我们的方法利用了噪声点的结构特征：与邻居的低空间相关性。我们的方法由两个深神经网络组成：点重建网络（PR-NET）从其邻居中重建每个点；重建难度网络（RD-NET）预测了PR-NET重建的重点难度，我们称之为重建难度。通过简单的后处理，我们的方法有效地检测了没有任何标签的雪点。我们的方法在无标签方法中实现了最新的性能，并且可以与完全监督的方法相媲美。此外，我们证明我们的方法可以被利用为提高监督登记培训的标签效率的借口。

Metaverse over wireless networks is an emerging use case of the sixth generation (6G) wireless systems, posing unprecedented challenges in terms of its multi-modal data transmissions with stringent latency and reliability requirements. Towards enabling this wireless metaverse, in this article we propose a novel semantic communication (SC) framework by decomposing the metaverse into human/machine agent-specific semantic multiverses (SMs). An SM stored at each agent comprises a semantic encoder and a generator, leveraging recent advances in generative artificial intelligence (AI). To improve communication efficiency, the encoder learns the semantic representations (SRs) of multi-modal data, while the generator learns how to manipulate them for locally rendering scenes and interactions in the metaverse. Since these learned SMs are biased towards local environments, their success hinges on synchronizing heterogeneous SMs in the background while communicating SRs in the foreground, turning the wireless metaverse problem into the problem of semantic multiverse communication (SMC). Based on this SMC architecture, we propose several promising algorithmic and analytic tools for modeling and designing SMC, ranging from distributed learning and multi-agent reinforcement learning (MARL) to signaling games and symbolic AI.

While witnessing the noisy intermediate-scale quantum (NISQ) era and beyond, quantum federated learning (QFL) has recently become an emerging field of study. In QFL, each quantum computer or device locally trains its quantum neural network (QNN) with trainable gates, and communicates only these gate parameters over classical channels, without costly quantum communications. Towards enabling QFL under various channel conditions, in this article we develop a depth-controllable architecture of entangled slimmable quantum neural networks (eSQNNs), and propose an entangled slimmable QFL (eSQFL) that communicates the superposition-coded parameters of eS-QNNs. Compared to the existing depth-fixed QNNs, training the depth-controllable eSQNN architecture is more challenging due to high entanglement entropy and inter-depth interference, which are mitigated by introducing entanglement controlled universal (CU) gates and an inplace fidelity distillation (IPFD) regularizer penalizing inter-depth quantum state differences, respectively. Furthermore, we optimize the superposition coding power allocation by deriving and minimizing the convergence bound of eSQFL. In an image classification task, extensive simulations corroborate the effectiveness of eSQFL in terms of prediction accuracy, fidelity, and entropy compared to Vanilla QFL as well as under different channel conditions and various data distributions.

Few-shot learning (FSL), which aims to classify unseen classes with few samples, is challenging due to data scarcity. Although various generative methods have been explored for FSL, the entangled generation process of these methods exacerbates the distribution shift in FSL, thus greatly limiting the quality of generated samples. To these challenges, we propose a novel Information Bottleneck (IB) based Disentangled Generation Framework for FSL, termed as DisGenIB, that can simultaneously guarantee the discrimination and diversity of generated samples. Specifically, we formulate a novel framework with information bottleneck that applies for both disentangled representation learning and sample generation. Different from existing IB-based methods that can hardly exploit priors, we demonstrate our DisGenIB can effectively utilize priors to further facilitate disentanglement. We further prove in theory that some previous generative and disentanglement methods are special cases of our DisGenIB, which demonstrates the generality of the proposed DisGenIB. Extensive experiments on challenging FSL benchmarks confirm the effectiveness and superiority of DisGenIB, together with the validity of our theoretical analyses. Our codes will be open-source upon acceptance.

Partial Label (PL) learning refers to the task of learning from the partially labeled data, where each training instance is ambiguously equipped with a set of candidate labels but only one is valid. Advances in the recent deep PL learning literature have shown that the deep learning paradigms, e.g., self-training, contrastive learning, or class activate values, can achieve promising performance. Inspired by the impressive success of deep Semi-Supervised (SS) learning, we transform the PL learning problem into the SS learning problem, and propose a novel PL learning method, namely Partial Label learning with Semi-supervised Perspective (PLSP). Specifically, we first form the pseudo-labeled dataset by selecting a small number of reliable pseudo-labeled instances with high-confidence prediction scores and treating the remaining instances as pseudo-unlabeled ones. Then we design a SS learning objective, consisting of a supervised loss for pseudo-labeled instances and a semantic consistency regularization for pseudo-unlabeled instances. We further introduce a complementary regularization for those non-candidate labels to constrain the model predictions on them to be as small as possible. Empirical results demonstrate that PLSP significantly outperforms the existing PL baseline methods, especially on high ambiguity levels. Code available: https://github.com/changchunli/PLSP.

机器人的大多数对象操纵策略都是基于以下假设：对象是刚性（即具有固定几何形状），并且目标的细节已完全指定（例如，确切的目标姿势）。但是，有许多任务涉及人类环境中的空间关系，这些条件可能难以满足，例如弯曲和将电缆放入未知容器中。为了在非结构化的环境中开发先进的机器人操纵功能，以避免这些假设，我们提出了一个新颖的长马框架，该框架利用了对比计划来寻找有希望的协作行动。使用随机操作收集的仿真数据，我们以对比方式学习一个嵌入模型，该模型从成功的体验中编码时空信息，从而通过在潜在空间中的聚类来促进次目标计划。基于基于KePoint对应的操作参数化，我们为双臂之间的协作设计了领导者追随者控制方案。我们政策的所有模型均经过模拟自动培训，可以直接传输到现实世界环境中。为了验证所提出的框架，我们对模拟和真实环境中的环境和可及性约束，对复杂场景进行了详细的实验研究。

尽管量子至高无上尚未到来，但最近在实用量子计算的迫在眉睫的时代，人们对​​确定量子机学习的潜力（QML）的兴趣越来越大。由此激励，在本文中，我们基于具有两个单独的可训练参数的单独维度的量子神经网络（QNN）的独特特征重新设计多代理增强学习（MARL）：影响输出Qubit状态和极点参数的角度参数：与输出测量基础相关。我们提出了将这种二元训练性作为元学习能力，我们提出了量子元marl（QM2ARL），该量子元MARL（QM2ARL）首先应用角度训练进行元学习，然后进行极点训练，以进行几次射击或局部QNN培训。为了避免过度拟合，我们在角度训练期间开发了一种将噪声注入到极域中的角度正则化技术。此外，通过将极点作为每个受过训练的QNN的内存地址利用，我们介绍了极点内存的概念，允许仅使用两参数极点值保存和加载经过训练的QNN。从理论上讲，我们证明了角度到极正则化下的角度训练的收敛性，并通过模拟证实了QM2ARL在获得高奖励和快速收敛方面的有效性，以及在快速适应时间变化环境中的极点记忆。

量子联合学习（QFL）最近受到了越来越多的关注，其中量子神经网络（QNN）集成到联邦学习（FL）中。与现有的静态QFL方法相反，我们在本文中提出了可靠的QFL（SLIMQFL），这是一个动态QFL框架，可以应对时变的通信通道和计算能量限制。通过利用QNN的独特性质，可以分别训练并动态利用其角度参数，从而使其可行。模拟结果证实了SLIMQFL比香草QFL更高的分类精度，尤其是在较差的通道条件下。

经典的媒体访问控制（MAC）协议是可解释的，但是它们的任务不可能控制信号传导消息（CMS）不适合新兴任务 - 关键任务应用程序。相比之下，基于神经网络（NN）协议模型（NPM）学会生成特定于任务的CMS，但其理由和影响缺乏可解释性。为了填补这一空白，在本文中，我们首次提出了通过将NPM转换为概率逻辑编程语言（ProBlog）编写的可解释的符号图来构建的语义协议模型（SPM）。通过在将NPM视为CM发生器的同时提取和合并共同的CM及其连接，可以可行。通过广泛的模拟，我们证实了SPM在仅占据0.02％内存的同时紧密近似其原始NPM。通过利用其可解释性和记忆效率，我们演示了几种支持SPM的应用程序，例如SPM重新配置，以避免碰撞，并通过语义熵计算和存储多个SPM来比较不同的SPM，以应对非平稳环境。