The Cultural Recreation of the Traditional Working and Living Appliances in Tibetan and Qiang Culture

Confronting the contradiction of the global trend of cultural homogenization and the appeals for the independence of national culture, this paper put forwards the new design concept of “Green Humanity”. Guided by the “green humanity” philosophy, our research on the living and working appliances typical of Tibetan and Qiang people, and on the practice of the localization strategy of cultural recreation of Tibetan and Qiang culture would make our national culture more and more prosperous. It is a conscious measure to make use of design strategy, but for the local people who use the improved appliances, the process of “using” would be an unconscious heritage of culture, a process of evolution in a quiet way more beneficial to the natural heritage of culture

Towards Few-shot Out-of-Distribution Detection

Out-of-distribution (OOD) detection is critical for ensuring the reliability of open-world intelligent systems. Despite the notable advancements in existing OOD detection methodologies, our study identifies a significant performance drop under the scarcity of training samples. In this context, we introduce a novel few-shot OOD detection benchmark, carefully constructed to address this gap. Our empirical analysis reveals the superiority of ParameterEfficient Fine-Tuning (PEFT) strategies, such as visual prompt tuning and visual adapter tuning, over conventional techniques, including fully fine-tuning and linear probing tuning in the few-shot OOD detection task. Recognizing some crucial information from the pre-trained model, which is pivotal for OOD detection, may be lost during the fine-tuning process, we propose a method termed DomainSpecific and General Knowledge Fusion (DSGF). This approach is designed to be compatible with diverse fine-tuning frameworks. Our experiments show that the integration of DSGF significantly enhances the few-shot OOD detection capabilities across various methods and fine-tuning methodologies, including fully fine-tuning, visual adapter tuning, and visual prompt tuning. The code will be released

GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner

Graph self-supervised learning (SSL), including contrastive and generative approaches, offers great potential to address the fundamental challenge of label scarcity in real-world graph data. Among both sets of graph SSL techniques, the masked graph autoencoders (e.g., GraphMAE)--one type of generative method--have recently produced promising results. The idea behind this is to reconstruct the node features (or structures)--that are randomly masked from the input--with the autoencoder architecture. However, the performance of masked feature reconstruction naturally relies on the discriminability of the input features and is usually vulnerable to disturbance in the features. In this paper, we present a masked self-supervised learning framework GraphMAE2 with the goal of overcoming this issue. The idea is to impose regularization on feature reconstruction for graph SSL. Specifically, we design the strategies of multi-view random re-mask decoding and latent representation prediction to regularize the feature reconstruction. The multi-view random re-mask decoding is to introduce randomness into reconstruction in the feature space, while the latent representation prediction is to enforce the reconstruction in the embedding space. Extensive experiments show that GraphMAE2 can consistently generate top results on various public datasets, including at least 2.45% improvements over state-of-the-art baselines on ogbn-Papers100M with 111M nodes and 1.6B edges.Comment: Accepted to WWW'2

Paeoniflorin inhibits the growth of bladder carcinoma via deactivation of STAT3

Bladder cancer (BCa) is one of the most common urinary cancers. The present study aims to investigate whether Paeoniflorin (Pae) can exert inhibitory effects on BCa. The results showed that Pae inhibited proliferation of human BCa cell lines in a concentration- and time-dependent manner. Pae and cisplatin (Cis) synergistically inhibited the growth of tumours in RT4-bearing mice. Pae treatment neutralized the body loss induced by Cis. Moreover, Pae induced apoptosis in RT4 cells and increased the activities of caspase3, caspase8 and caspase9. Western blotting and immunohistochemical analysis revealed that the phosphorylated signal transducer and activator of transcription-3 (p-STAT3) level were decreased in Pae-treated RT4 cells and Pae-treated tumour-bearing mice. Furthermore, STAT3 transcriptional target B-cell lymphoma-2 was decreased in Pae-treated RT4 cells. Interestingly, Pae prevented translocation of STAT3 to the nucleus in RT4 cells. Collectively, Pae inhibits the growth of BCa, at least in part, via a STAT3 pathway