SafeDiffuser: Safe Planning with Diffusion Probabilistic Models

Diffusion model-based approaches have shown promise in data-driven planning, but there are no safety guarantees, thus making it hard to be applied for safety-critical applications. To address these challenges, we propose a new method, called SafeDiffuser, to ensure diffusion probabilistic models satisfy specifications by using a class of control barrier functions. The key idea of our approach is to embed the proposed finite-time diffusion invariance into the denoising diffusion procedure, which enables trustworthy diffusion data generation. Moreover, we demonstrate that our finite-time diffusion invariance method through generative models not only maintains generalization performance but also creates robustness in safe data generation. We test our method on a series of safe planning tasks, including maze path generation, legged robot locomotion, and 3D space manipulation, with results showing the advantages of robustness and guarantees over vanilla diffusion models.Comment: 19 pages, website: https://safediffuser.github.io/safediffuser

Partition Function Expansion on Region-Graphs and Message-Passing Equations

Disordered and frustrated graphical systems are ubiquitous in physics, biology, and information science. For models on complete graphs or random graphs, deep understanding has been achieved through the mean-field replica and cavity methods. But finite-dimensional `real' systems persist to be very challenging because of the abundance of short loops and strong local correlations. A statistical mechanics theory is constructed in this paper for finite-dimensional models based on the mathematical framework of partition function expansion and the concept of region-graphs. Rigorous expressions for the free energy and grand free energy are derived. Message-passing equations on the region-graph, such as belief-propagation and survey-propagation, are also derived rigorously.Comment: 10 pages including two figures. New theoretical and numerical results added. Will be published by JSTAT as a lette

Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning

For graph self-supervised learning (GSSL), masked autoencoder (MAE) follows the generative paradigm and learns to reconstruct masked graph edges or node features. Contrastive Learning (CL) maximizes the similarity between augmented views of the same graph and is widely used for GSSL. However, MAE and CL are considered separately in existing works for GSSL. We observe that the MAE and CL paradigms are complementary and propose the graph contrastive masked autoencoder (GCMAE) framework to unify them. Specifically, by focusing on local edges or node features, MAE cannot capture global information of the graph and is sensitive to particular edges and features. On the contrary, CL excels in extracting global information because it considers the relation between graphs. As such, we equip GCMAE with an MAE branch and a CL branch, and the two branches share a common encoder, which allows the MAE branch to exploit the global information extracted by the CL branch. To force GCMAE to capture global graph structures, we train it to reconstruct the entire adjacency matrix instead of only the masked edges as in existing works. Moreover, a discrimination loss is proposed for feature reconstruction, which improves the disparity between node embeddings rather than reducing the reconstruction error to tackle the feature smoothing problem of MAE. We evaluate GCMAE on four popular graph tasks (i.e., node classification, node clustering, link prediction, and graph classification) and compare with 14 state-of-the-art baselines. The results show that GCMAE consistently provides good accuracy across these tasks, and the maximum accuracy improvement is up to 3.2% compared with the best-performing baseline

Understanding Status Update in Microblog: A Perspective on Media Needs

Microblog has grown popularly as a seminal social medium for timely information seeking and sharing. However, the reason why individuals update real-time information in microblog has not been well understood, and empirical research to address this specific information behavior is hardly available. As a felt urge can be conceptualized as a precursor of real-time updating in the microblog, we attempt to capture the underlying mechanism in causing this less reflective behavior urge. We apply the media needs theory to investigate how the individuals’ media needs spark their urge to update personal status in the microblog. In particular, we conceptualize the cognitive needs as related to information uniqueness, personal integrative needs as related to connectivity, social integrative needs as a unidirectional relationship, affective needs as positive emotions and tension release needs as negative emotions. An online survey was employed to validate the proposed model within 523 microblog users in China. The results suggest that the users’ behavior urge is significantly influenced by information uniqueness, connectivity, unidirectional relationship and positive emotions. Furthermore, among the five media needs, the affective and social integrative related factors strongly determine the personal real-time status update in microblog. The theoretical and practical implications are discussed in this paper

Enhanced antitumor immunity by targeting dendritic cells with tumor cell lysate-loaded chitosan nanoparticles vaccine

Whole tumor cell lysates (TCL) have been implemented as tumor antigens for cancer vaccine development, although clinical outcomes of TCL-based antitumor immunotherapy remain unsatisfactory. In order to improve the efficacy of TCL-based vaccines, biomaterials have been employed to enhance antigen delivery and presentation. Here, we have developed chitosan nanoparticles (CTS NPs) with surface mannose (Man) moieties for specific dendritic cells (DCs) targeting (Man-CTS NPs). The Man-CTS NPs were then loaded with TCL generated from B16 melanoma cells (Man-CTS-TCL NPs) for in vitro and in vivo assessment. Potency of the Man-CTS-TCL NPs as cancer vaccine was also assessed in vivo by immunization of mice with Man-CTS-TCL NPs followed by re-challenge with B16 melanoma cell inoculation. We have shown here that Man-CTS-TCL NPs promote bone marrow-derived dendritic cells (BMDCs) maturation and antigen presentation in vitro. In vivo evaluation further demonstrated that the Man-CTS-TCL NPs were readily taken up by endogenous DCs within the draining lymph node (DLN) following subcutaneous administration accompanied by increasing in serum IFN-γ and IL-4 levels. Tumor growth was also significantly delayed in mice primed with Man-CTS-TCL NPs vaccine, attributable at least in part to cytotoxic T lymphocytes response. Moreover, Man-CTS-TCL NPs vaccine also exhibited therapeutic effects in mice with melanoma. Thus, we report here the Man-CTS-TCL NPs as effective anti-tumor vaccine for cancer immunotherapy