Variational autoencoder (VAE)-based frameworks possess a natural advantage in modeling the shared and private information inherent in multimodal data. However, current models focus on improving the quality of shared representations from the reconstruction perspective, lacking explicit mechanisms to model their underlying semantic structure. In this paper, we propose the multimodal Gaussian mixture variational autoencoder with consistency regularizations, which introduces a Gaussian mixture prior over the shared latent space to enhance its semantic structure and encourage the formation of cluster-aware latent representations. To address the cross-modal inconsistency problem under missing modality conditions, we propose a cluster-guided regularization strategy that enforces the cross-modal consistency using the pseudo-category labels from unsupervised clustering. Additionally, we design a self-supervised contrastive regularization strategy to align semantically similar representations across modalities. Extensive experiments on MNIST-SVHN and MNIST-CDCB datasets demonstrate that our method significantly outperforms prior state-of-the-art models in generation, classification, and retrieval tasks.

This study aims to explore how to improve the reliability of the next-generation mobility model—Mobility as a Service (MaaS) based on autonomous vehicles, with a particular focus on the system’s resilience to uncertainty. Currently, the application of reliability engineering in the field of smart mobility services is primarily concentrated on technical details, lacking unified standards and methods to enhance the reliability of service levels. This paper attempts to fill this research gap. In this study, we adopt a combined approach of system analysis and optimization algorithms. First, we design a system reliability analysis method by examining the potential discrepancies between the system’s capability to provide mobility services and stakeholder demands. Subsequently, we propose a reinforcement learning-based system service capacity optimization algorithm aimed at enhancing the system’s resilience at the service level to tackle challenges posed by uncertainty. To validate the effectiveness of the proposed method, we conduct a case study on a practical intelligent mobility service framework. Through system simulation, we generate and collect data on system service capacity, demand discrepancies, and uncertainty, as well as stakeholders’ expectations for the MaaS framework evaluation. Case studies and experimental data analysis confirm that the proposed resilience engineering approach effectively identifies potential risks in system service capacity and provides a compromise system resilience engineering solution in the context of conflicting stakeholder demands. To facilitate reproducibility and further research, the core code is available at https://github.com/zzs-code/MaaS-RE-MARL.git.

Despite the significant advances of Convolutional neural networks (CNNs) and Transformers in image deraining, they either suffer from limited receptive fields or incur quadratic complexity, leading to an imbalance between performance and efficiency. Recently, state space models (SSMs) have demonstrated significant potential in modeling long-range dependencies while maintaining linear complexity. However, existing Mamba-based approaches lack the exploration of useful complementary information from multiple image scales, which could be beneficial for facilitating rain removal. In this paper, we propose an effective multi-scale state-space model-based framework (MS-RainMamba) to explore richer scale-space information for better image deraining. Specifically, we design a local-enhanced state space module to better aggregate rich local and global information. In contrast to existing methods that adopt fixed-scale scanning for feature extraction, we develop a multi-scale hierarchical 2D scanning technique to better help image restoration. Experimental results on six benchmarks show that the proposed method performs favorably against state-of-the-art models.

Deep reinforcement learning (DRL) provides an agent with an optimal policy so as to maximize the cumulative rewards. The policy defined in DRL mainly depends on the state, historical memory, and policy model parameters. However, we humans usually take actions according to our own intentions, such as moving fast or slow, besides the elements included in the traditional policy models. In order to make the action-choosing mechanism more similar to humans and make the agent to select actions that incorporate intentions, we propose an intention-aware policy learning method in this letter To formalize this process, we first define an intention-aware policy by incorporating the intention information into the policy model, which is learned by maximizing the cumulative rewards with the mutual information (MI) between the intention and the action. Then we derive an approximation of the MI objective that can be optimized efficiently. Finally, we demonstrate the effectiveness of the intention-aware policy in the classical MuJoCo control task and the multigoal continuous chain walking task.

Text matching is a critical task in natural language processing to measure semantic similarity between two texts. A significant portion of online texts are labeled with a variety of coarse topic responses. These supervised topic indicators can provide prior structured and explicable semantics for textual similarity modeling. However, most existing state-of-the-art neural network methods cannot benefit from such complementary topic signals. Therefore, we propose a novel Topic Supervision BERT-based model (TSB) for text matching. TSB provides a reference multi-task joint training framework involving two types of topic supervision, including explicit and implicit topic supervision. To constrain consistent topic correspondences between texts, we introduce a supervised auxiliary learning task to incorporate explicit pre-defined topic supervision. Furthermore, to adapt to latent topic structures for mutual benefit between text representations and multiple tasks, we integrate a topic model into a contextual text representation model BERT to mine and incorporate implicit self-learnable topic supervision. Experimental results show that TSB supplements explicit and implicit topic information through a multi-task learning approach, which significantly improves the performance of text matching on two public datasets, especially on challenging short text matches.

We consider the learning problem under an online Markov decision process (MDP) aimed at learning the time-dependent decision-making policy of an agent that minimizes the regret-the difference from the best fixed policy. The difficulty of online MDP learning is that the reward function changes over time. In this letter, we show that a simple online policy gradient algorithm achieves regret O(√T) for T steps under a certain concavity assumption and O(log T) under a strong concavity assumption. To the best of our knowledge, this is the first work to present an online MDP algorithm that can handle continuous state, action, and parameter spaces with guarantee. We also illustrate the behavior of the proposed online policy gradient method through experiments.

The policy gradient approach is a flexible and powerful reinforcement learning method particularly for problems with continuous actions such as robot control. A common challenge is how to reduce the variance of policy gradient estimates for reliable policy updates. In this letter, we combine the following three ideas and give a highly effective policy gradient method: (1) policy gradients with parameter-based exploration, a recently proposed policy search method with low variance of gradient estimates; (2) an importance sampling technique, which allows us to reuse previously gathered data in a consistent way; and (3) an optimal baseline, which minimizes the variance of gradient estimates with their unbiasedness being maintained. For the proposed method, we give a theoretical analysis of the variance of gradient estimates and show its usefulness through extensive experiments.

Policy gradient is a useful model-free reinforcement learning approach, but it tends to suffer from instability of gradient estimates. In this paper, we analyze and improve the stability of policy gradient methods. We first prove that the variance of gradient estimates in the PGPE(policy gradients with parameter-based exploration) method is smaller than that of the classical REINFORCE method under a mild assumption. We then derive the optimal baseline for PGPE, which contributes to further reducing the variance. We also theoretically show that PGPE with the optimal baseline is more preferable than REINFORCE with the optimal baseline in terms of the variance of gradient estimates. Finally, we demonstrate the usefulness of the improved PGPE method through experiments.