MDAPT: Multi-Modal Depth Adversarial Prompt Tuning to Enhance the Adversarial Robustness of Visual Language Models.

Large visual language models like Contrastive Language-Image Pre-training (CLIP), despite their excellent performance, are highly vulnerable to the influence of adversarial examples. This work investigates the accuracy and robustness of visual language models (VLMs) from a novel multi-modal perspective. We propose a multi-modal fine-tuning method called Multi-modal Depth Adversarial Prompt Tuning (MDAPT), which guides the generation of visual prompts through text prompts to improve the accuracy and performance of visual language models.

Identification effect of least square fitting method in archives management.

Archives management plays an important role in the current information age. Solving the problem of identifying and classifying archives is essential for promoting the development of archives management. The Least Squares Support Vector Machine (LS-SVM) is obtained by introducing the least squares fitting method into SVM, which is good at solving nonlinear classification.

Photocatalytic degradation of air pollutant by modified nano titanium oxide (TiO)in a fluidized bed photoreactor: Optimizing and kinetic modeling.

Volatile organic compound (VOC) removal by photocatalytic oxidation (PCO) is the practical and economical process to reduce air pollutants. Many conditions, such as temperature, initial concentration of VOC, relative humidity, gas flow rate, and light intensity, affected this process. Therefore, finding the optimal operating conditions for the PCO process can increase the efficiency of the process and also operate the process more economically.

Prediction on transmission trajectory of COVID-19 based on particle swarm algorithm.

This study aimed to predict the transmission trajectory of the 2019 Corona Virus Disease (COVID-19). The particle swarm optimization (PSO) algorithm was combined with the traditional susceptible exposed infected recovered (SEIR) infectious disease prediction model to propose a SEIR-PSO prediction model on the COVID-19. In addition, the domestic epidemic data from February 25, 2020 to March 20, 2020 in China were selected as the training set for analysis.