Abstract: Systems, methods and computer readable storage mediums are provided. Data comprising raw images and their corresponding pixel-level semantic labels can be obtained. The data can be preprocessed to remove noisy samples or to augment the data with the technique of data augmentation. The data can be split into training and validation sets. A model and loss functions can be designed so first and second level semantic information is leveraged to boost first-level semantic segmentation. The model can comprise main and auxiliary branches. The first level semantic information can be utilized in the main branch and the second level semantic information can be utilized by the auxiliary branch. The model can be trained by the training set to iteratively minimize the loss functions. Model parameters can be updated as the loss function is minimized. The auxiliary branch can be removed after the model is trained. Results can be predicted using the trained model on samples in the validation set.

Abstract: Systems, methods, and computer-readable media for implementing a teaching system focused on the topic of communication via lip-reading using AI-based (automated) visual speech recognition (e.g., VSR) technology, both for developing relevant lesson content and for evaluating user progress. More particularly, the present embodiments can implement AI-based automated lip-reading (also called visual speech recognition or VSR) algorithms in combination with other image processing and machine learning tools to create a teaching system for helping a user learn how to understand conversations through lip-reading and/or how to produce tailored or silent speech so as to be more easily understood through lip-reading.

Abstract: Systems, methods, and computer-readable media for reducing a bias in visual speech recognition (VSR). In the present embodiments, a comprehensive analysis of the bias (e.g., determining type and severity of the bias) can be performed for each sample in the training data, such as age, gender, and ethnicity, for example. Further, synthetic training data can be generated for under-represented groups using various techniques, such as generative adversarial networks (GANs), for example. Additionally, synthetic video generation can be performed using different modes (e.g., six modes) to ensure quantities and diversity in the synthetic samples. A combination of the real data and the synthetic training data generated can be used to train a VSR model.

Abstract: A real time and quantitative method of measuring traction force of living cells include the following procedures. Place AT-cut and BT-cut quartz crystals of the same frequency, surface morphology and/or modified with the same cell adhesion molecules in petri dishes or detection cells; add the cells to the petri dishes or detection cells, the cell traction force at arbitrary time t during adhesion of the cells or under different internal/external environmental stimulations is estimated by the following equation: ?St=(KAT?KBT)?1[tqAT?ftAT/frAT?tqBT?ftBT/frBT]. The method can be used to track the dynamic changes of cells generated force during the adhesion of cells and under different internal/external environmental stimulations, such as the effects of drugs. The drugs can be added before or after the adhesion of the cells. This method is suitable for all adherent cells, including primary cells and passage cells.

Abstract: A real time and quantitative method of measuring traction force of living cells include the following procedures. Place AT-cut and BT-cut quartz crystals of the same frequency, surface morphology and/or modified with the same cell adhesion molecules in petri dishes or detection cells; add the cells to the petri dishes or detection cells, the cell traction force at arbitrary time t during adhesion of the cells or under different internal/external environmental stimulations is estimated by the following equation: ?St=(KAT?KBT)?1[tqAT?ftAT/frAT?tqBT?ftBT/frBT]. The method can be used to track the dynamic changes of cells generated force during the adhesion of cells and under different internal/external environmental stimulations, such as the effects of drugs. The drugs can be added before or after the adhesion of the cells. This method is suitable for all adherent cells, including primary cells and passage cells.