The status change of culture and education in the traditional Chinese city landscape after the Song and Yuan Dynasty

After the Song and Yuan dynasties, the development of the imperial examination system was witnessed by the spread of the Neo-Confucianism of the Song and Ming dynasties. This was accompanied by the position of culture and education buildings in the local urban landscape system that was greatly improved, some even dominating the performance of the urban landscape. The resulted structure of the urban landscape before the Song Dynasty is described as the so-called status change of the "The Status Change of Culture and Education." Studies have shown that "The Status Change" during the Ming and Qing Dynasties could be found here and there. This work took the City of Yangzhou Prefecture in the Ming and Qing Dynasties as the research object. Starting from the background of the development of culture and education, this paper expounds the process and characteristics of such a status change during this period

Coreset selection can accelerate quantum machine learning models with provable generalization

Quantum neural networks (QNNs) and quantum kernels stand as prominent figures in the realm of quantum machine learning, poised to leverage the nascent capabilities of near-term quantum computers to surmount classical machine learning challenges. Nonetheless, the training efficiency challenge poses a limitation on both QNNs and quantum kernels, curbing their efficacy when applied to extensive datasets. To confront this concern, we present a unified approach: coreset selection, aimed at expediting the training of QNNs and quantum kernels by distilling a judicious subset from the original training dataset. Furthermore, we analyze the generalization error bounds of QNNs and quantum kernels when trained on such coresets, unveiling the comparable performance with those training on the complete original dataset. Through systematic numerical simulations, we illuminate the potential of coreset selection in expediting tasks encompassing synthetic data classification, identification of quantum correlations, and quantum compiling. Our work offers a useful way to improve diverse quantum machine learning models with a theoretical guarantee while reducing the training cost.Comment: 25 pages, 7 figure

KCNK9 mediates the inhibitory effects of genistein on hepatic metastasis from colon cancer

Objective: The tyrosine-protein kinase inhibitor, genistein, can inhibit cell malignant transformation and has an antitumor effect on various types of cancer. It has been shown that both genistein and KNCK9 can inhibit colon cancer. This research aimed to investigate the suppressive effects of genistein on colon cancer cells and the association between the application of genistein and KCNK9 expression level. Methods: The Cancer Genome Atlas (TCGA) database was used to study the correlation between the KCNK9 expression level and the prognosis of colon cancer patients. HT29 and SW480 colon cancer cell lines were cultured to examine the inhibitory effects of KCNK9 and genistein on colon cancer in vitro, and a mouse model of colon cancer with liver metastasis was established to verify the inhibitory effect of genistein in vivo. Results: KCNK9 was overexpressed in colon cancer cells and was associated with a shorter Overall Survival (OS), a shorter Disease-Specific Survival (DFS), and a shorter Progression-Free Interval (PFI) of colon cancer patients. In vitro experiments showed that downregulation of KCNK9 or genistein application could suppress cell proliferation, migration, and invasion abilities, induce cell cycle quiescence, promote cell apoptosis, and reduce epithelial-mesenchymal transition of the colon cancer cell line. In vivo experiments revealed that silencing of KCNK9 or application of genistein could inhibit hepatic metastasis from colon cancer. Additionally, genistein could inhibit KCNK9 expression, thereby attenuating Wnt/β-catenin signaling pathway. Conclusion: Genistein inhibited the occurrence and progression of colon cancer through Wnt/β-catenin signaling pathway that could be mediated by KCNK9

Setting the Trap: Capturing and Defeating Backdoors in Pretrained Language Models through Honeypots

In the field of natural language processing, the prevalent approach involves fine-tuning pretrained language models (PLMs) using local samples. Recent research has exposed the susceptibility of PLMs to backdoor attacks, wherein the adversaries can embed malicious prediction behaviors by manipulating a few training samples. In this study, our objective is to develop a backdoor-resistant tuning procedure that yields a backdoor-free model, no matter whether the fine-tuning dataset contains poisoned samples. To this end, we propose and integrate a honeypot module into the original PLM, specifically designed to absorb backdoor information exclusively. Our design is motivated by the observation that lower-layer representations in PLMs carry sufficient backdoor features while carrying minimal information about the original tasks. Consequently, we can impose penalties on the information acquired by the honeypot module to inhibit backdoor creation during the fine-tuning process of the stem network. Comprehensive experiments conducted on benchmark datasets substantiate the effectiveness and robustness of our defensive strategy. Notably, these results indicate a substantial reduction in the attack success rate ranging from 10\% to 40\% when compared to prior state-of-the-art methods