Parameter sharing is a widely adopted approach in cooperative Multi-Agent Reinforcement Learning (MARL), often achieving strong performance. However, its effectiveness can vary, as the policy’s similarity induced by parameter sharing may hinder performance in certain tasks. In this study, we propose a novel framework, termed Composite Shared Policy (CSP), to enhance the cross-task applicability of parameter sharing. CSP is designed to model multiple diverse policies concurrently, thereby introducing inherent policy diversity without relying on task-specific designs. By increasing the differences among the policies of individual agents, CSP effectively mitigates the policy similarity problem commonly associated with parameter sharing. These characteristics collectively enable CSP to improve the cross-task applicability of parameter sharing. To empirically validate the effectiveness of CSP, we implement it based on QMIX, a classic cooperative MARL method, and conduct experiments across two widely used MARL testbeds. The experimental results demonstrate that CSP significantly enhances the cross-task applicability of parameter sharing. Additionally, we conduct ablation studies to evaluate the contributions of each component within CSP. The results highlight that each component plays a critical role in the overall effectiveness of the framework. The source code is available at https://github.com/Yurui-Li/CSP.

While convolution neural networks (CNNs) and vision transformers (ViTs) dominate visual representation learning, the growing model depth causes difficulty for interpretability. Although their internal mechanisms are inspiration from human visual sensation, the entire model fails to emulate human perceptual process, particularly the ability to conceptualize abstract visual elements, discover concept compositionality, and leverage analogical reasoning for real-world interaction. Existing feature visualization methods offer post-hoc insights into model perception but struggle to bridge the gap between statistical patterns and symbolic knowledge. Addressing this, we propose ConceptVQ, a symbolic interpreter that distills pretrained features into visual concepts via a learnable codebook, trained through a von Mises-Fisher Vector Quantized Variational Autoencoder (vMF-VQVAE) framework. By unifying multi-granular features to hyperspherical latent prior, ConceptVQ simulates concept abstraction by bottom-up feature clustering and analogy-making by cross-stage Codebook redistribution, mimicking two important cognitive mechanisms. Experiments demonstrate that our framework outperforms standard VQVAE series in both perceptual reconstruction and semantic fidelity, while its codebook establishes direct, interpretable mappings between pretrained features and discrete visual concepts, revealing hierarchical semantics through locally clustered visual patterns. Moreover, our model works as an architecture-agnostic extension which preserves the pretrained model’s semantic capabilities and maintains high compression rates like VQVAE series, enabling efficient adaptation to downstream symbolic tasks. Our work advances interpretability research by unifying representation learning with cognitively inspired symbolic abstraction, offering a pathway toward human-aligned visual representation.

Significant progress has been made in image inpainting methods in recent years. However, they are incapable of producing inpainting results with reasonable structures, rich detail, and sharpness at the same time. In this paper, we propose the Pyramid-VAE-GAN network for image inpainting to address this limitation. Our network is built on a variational autoencoder (VAE) backbone that encodes high-level latent variables to represent complicated high-dimensional prior distributions of images. The prior assists in reconstructing reasonable structures when inpainting. We also adopt a pyramid structure in our model to maintain rich detail in low-level latent variables. To avoid the usual incompatibility of requiring both reasonable structures and rich detail, we propose a novel cross-layer latent variable transfer module. This transfers information about long-range structures contained in high-level latent variables to low-level latent variables representing more detailed information. We further use adversarial training to select the most reasonable results and to improve the sharpness of the images. Extensive experimental results on multiple datasets demonstrate the superiority of our method. Our code is available at https://github.com/thy960112/Pyramid-VAE-GAN .

BACKGROUND: Medical information has rapidly increased on the internet and has become one of the main targets of search engine use. However, medical information on the internet is subject to the problems of quality and accessibility, so ordinary users are unable to obtain answers to their medical questions conveniently. As a solution, researchers build medical question answering (QA) systems. However, research on medical QA in the Chinese language lags behind work on English-based systems. This lag is mainly due to the difficulty of constructing a high-quality knowledge base and the underutilization of medical corpora in the Chinese language. RESULTS: This study developed an end-to-end solution to implement a medical QA system for the Chinese language with low cost and time. First, we created a high-quality medical knowledge graph from hospital data (electronic health/medical records) in a nearly automatic manner that trained a supervised model based on data labeled using bootstrapping techniques. Then, we designed a QA system based on a memory-based neural network and attention mechanism. Finally, we trained the system to generate answers from the knowledge base and a QA corpus on the internet. CONCLUSIONS: Bootstrapping and deep neural network techniques can construct a knowledge graph from electronic health/medical records with satisfactory precision and coverage. Our proposed context bridge mechanisms perform training with a variety of language features. Our QA system can achieve state-of-the-art quality in answering medical questions with constrained topics. As we evaluated, complex Chinese language processing techniques, such as segmentation and parsing, were not necessary for practice and complex architectures were not necessary to build the QA system. Lastly, we created an application using our method for internet QA usage.

Smart environments require to percept conditions of people. Current context-aware systems mainly model limited user situations, which constrains their coverage and effect in real world usage. This paper proposes an encyclopedic knowledge network to enable practical context inference in our daily life by: 1) expressing essential semantics of contextual concepts and relations into a well-informed relational network, and 2) exploiting relational semantics to infer various contexts simultaneously. The performance of the approach is validated in real challenging problems and compared with inference of human being.

The vision of the Internet of things is very appealing, and gains more and more attention. Since mobile devices in the Internet become more complex and heterogeneous, device collaboration will be full of technical challenges. How to integrate different systems and heterogeneous devices is a big problem. In order to overcome the problem, it is very important to describe and match the heterogeneous device services with semantics. We use web service interface specification to wrap device, and OWL to describe services in semantic. In this paper we introduce a semantic device bus for Internet of things, which will allow service creation of different devices, management of device services, semantic description and matching of device services, and complex collaboration of device services. The bus provides a fundamental platform for large-scale applications of the Internet of things.

This paper attempts to model pervasive computing environments as a user-centric ldquoSmartShadowrdquo using the BDP (belief-desire-plan) user model, which maps pervasive computing environments into a dynamic virtual user space. SmartShadow will follow the user to provide him with pervasive services, just like his shadow in the physical world. In the BDP model, desires of a user are inferred from his belief set, and plans are made to satisfy each desire. Pervasive service is introduced to describe computing resources in the cyberspace, which can be organized by the user's BDP to accomplish his desires. The composition process maps pervasive services into a user's SmartShadow. The model is logically natural and simple, and can flexibly model dynamics of pervasive computing spaces. In addition, we implement a simulation system to verify and evaluate the SmartShadow model.