Test-Time Adaptation for Optical Flow Estimation Using Motion Vectors.

Due to the prohibitive cost as well as technical challenges in annotating ground-truth optical flow for large-scale realistic video datasets, the existing deep learning models for optical flow estimation mostly rely on synthetic data for training, which in turn may lead to significant performance degradation under test-data distribution shift in real-world environments. In this work, we propose the methodology to tackle this important problem. We design a self-supervised learning task for adjusting the optical flow estimation model at test time. We exploit the fact that most videos are stored in compressed formats, from which compact information on motion, in the form of motion vectors and residuals, can be made readily available. We formulate the self-supervised task as motion vector prediction, and link this task to optical flow estimation. To the best of our knowledge, our Test-Time Adaption guided with Motion Vectors (TTA-MV), is the first work to perform such adaptation for optical flow. The experimental results demonstrate that TTA-MV can improve the generalization capability of various well-known deep learning methods for optical flow estimation, such as FlowNet, PWCNet, and RAFT.

Cabazitaxel prodrug nanoassemblies with branched chain modifications: Narrowing the gap between efficacy and safety.

The clinical application of cabazitaxel (CTX) is restricted by severe dose-related toxicity, failing to considering therapeutic efficacy and safety together. Self-assembled prodrugs promote new drug delivery paradigms as they can self-deliver and self-formulate. However, the current studies mainly focused on the use of straight chains to construct self-assembled prodrugs, and the role of branched chains in prodrug nanoassemblies remains to be clarified. In this study, we systematically explored the structure-function relationship of prodrug nanoassemblies using four CTX prodrugs that contained branched chain aliphatic alcohols (BAs) with different alkyl lengths. Overall, CTX-SS-BA20 NPs with the proper alkyl length exhibited significant improvements in both antitumor efficacy and biosafety. Furthermore, compared with straight chain (SC) modified prodrug nanoassemblies (CTX-SS-SC20 NPs), CTX-SS-BA20 NPs still hold great therapeutic promise due to its good biosafety. These findings illustrated the significance of BAs as modified chains in designing prodrug nanoassemblies for narrowing the efficacy-to-safety gap of cancer therapy.

Stress Detection Through Wrist-Based Electrodermal Activity Monitoring and Machine Learning.

Stress is an inevitable part of modern life. While stress can negatively impact a person's life and health, positive and under-controlled stress can also enable people to generate creative solutions to problems encountered in their daily lives. Although it is hard to eliminate stress, we can learn to monitor and control its physical and psychological effects. It is essential to provide feasible and immediate solutions for more mental health counselling and support programs to help people relieve stress and improve their mental health. Popular wearable devices, such as smartwatches with several sensing capabilities, including physiological signal monitoring, can alleviate the problem. This work investigates the feasibility of using wrist-based electrodermal activity (EDA) signals collected from wearable devices to predict people's stress status and identify possible factors impacting stress classification accuracy. We use data collected from wrist-worn devices to examine the binary classification discriminating stress from non-stress. For efficient classification, five machine learning-based classifiers were examined. We explore the classification performance on four available EDA databases under different feature selections. According to the results, Support Vector Machine (SVM) outperforms the other machine learning approaches with an accuracy of 92.9 for stress prediction. Additionally, when the subject classification included gender information, the performance analysis showed significant differences between males and females. We further examine a multimodal approach for stress classifications. The results indicate that wearable devices with EDA sensors have a great potential to provide helpful insight for improved mental health monitoring.