Prompting-based Temporal Domain Generalization

Machine learning traditionally assumes that the training and testing data are distributed independently and identically. However, in many real-world settings, the data distribution can shift over time, leading to poor generalization of trained models in future time periods. This paper presents a novel prompting-based approach to temporal domain generalization that is parameter-efficient, time-efficient, and does not require access to future data during training. Our method adapts a trained model to temporal drift by learning global prompts, domain-specific prompts, and drift-aware prompts that capture underlying temporal dynamics. Experiments on classification, regression, and time series forecasting tasks demonstrate the generality of the proposed approach. The code repository will be publicly shared

Object detection in surveillance video from dense trajectories

Detecting objects such as humans or vehicles is a central problem in surveillance video. Myriad standard approaches exist for this problem. At their core, approaches consider either the appearance of people, patterns of their motion, or differences from the background. In this paper we build on dense trajectories, a state-of-the-art approach for describing spatio-temporal patterns in video sequences. We demonstrate an application of dense trajectories to object detection in surveillance video, showing that they can be used to both regress estimates of object locations and accurately classify objects

DDCNNC: Dilated and depthwise separable convolutional neural Network for diagnosis COVID-19 via chest X-ray images

Purpose: As of December 21, 2020, a total of 77,670,400 cases of coronavirus disease 2019 (COVID-19) have been confirmed worldwide, 53,825,243 cases have been cured and 1,693,253 cases have died. Among the diagnostic methods of COVID-19, chest X-ray images have the advantages of fast imaging, low cost and high accuracy of single plane lesions recognition. The current COVID-19 detection models have shortcomings such as weak robustness, unreliable generalization ability, and long training time. Methods: To solve the above problems, our team proposed two novel frameworks and five methods to diagnose COVID-19 based on chest X-ray images. (i) A novel framework – depthwise separable convolutional neural network (DCNN), and we tested Three methods, viz., using LeNet-5, VGG-16, and ResNet-18 as backbones. (ii) A novel framework – dilated and depthwise separable convolutional neural network (DDCNN), and we tested Two methods, viz., using VGG-16 and ResNet-18 as backbones. Results: Experiment results show that our models not only improve the detection accuracy, but also reduce the training time. Conclusions: Our methods are superior to state-of-the-art methods in both above aspects

The Relationship between Physical Activity, Mobile Phone Addiction, and Irrational Procrastination in Chinese College Students

The aim of the current study was to examine the associations between physical activity, mobile phone addiction, and irrational procrastination after adjustment for potential confounding variables. The participants were 6294 first- and second-year students recruited as a cluster sample from three public universities in Shanghai, China. Physical activity, mobile phone use, and irrational procrastination were assessed using the International Physical Activity Questionnaire-Short Form (IPAQ-SF), the mobile phone addiction index scale (MPAI), and the irrational procrastination scale (IPS). The participants were divided into four groups according to their mobile phone usage status and physical activity level. The binary logistic regression model was used to predict the probability of serious irrational procrastination among different groups. The emergence of serious of irrational procrastination under physical activity of different intensity and different mobile phone addiction statuses was predicted by a multiple linear regression model. In this study, the combination of insufficient physical activity and mobile phone addiction is positively associated with high levels of irrational procrastination. Furthermore, students who exhibited both mobile phone addiction behaviors and insufficient physical activity tended to have significantly higher odds of reporting high levels of irrational procrastination than those students who exhibited one behavior or neither behavior. After adjusting for the effects of age, BMI, tobacco, alcohol use, and sedentary time, the result is consistent with previous outcomes. These findings suggest that intervention efforts should focus on the promotion of physical activity and reduction of mobile phone addiction

Object detection in surveillance video from dense trajectories

Detecting objects such as humans or vehicles is a central problem in video surveillance. Myriad stan-dard approaches exist for this problem. At their core, approaches consider either the appearance of people, patterns of their motion, or differences from the back-ground. In this paper we build on dense trajectories, a state-of-the-art approach for describing spatio-temporal patterns in video sequences. We demonstrate an ap-plication of dense trajectories to object detection in surveillance video, showing that they can be used to both regress estimates of object locations and accurately classify objects.