Multi-Camera View Based Proactive BS Selection and Beam Switching for V2X

Due to the short wavelength and large attenuation of millimeter-wave (mmWave), mmWave BSs are densely distributed and require beamforming with high directivity. When the user moves out of the coverage of the current BS or is severely blocked, the mmWave BS must be switched to ensure the communication quality. In this paper, we proposed a multi-camera view based proactive BS selection and beam switching that can predict the optimal BS of the user in the future frame and switch the corresponding beam pair. Specifically, we extract the features of multi-camera view images and a small part of channel state information (CSI) in historical frames, and dynamically adjust the weight of each modality feature. Then we design a multi-task learning module to guide the network to better understand the main task, thereby enhancing the accuracy and the robustness of BS selection and beam switching. Using the outputs of all tasks, a prior knowledge based fine tuning network is designed to further increase the BS switching accuracy. After the optimal BS is obtained, a beam pair switching network is proposed to directly predict the optimal beam pair of the corresponding BS. Simulation results in an outdoor intersection environment show the superior performance of our proposed solution under several metrics such as predicting accuracy, achievable rate, harmonic mean of precision and recall

Preventive Effects of a Chinese Herbal Formula, Shengjiang Xiexin Decoction, on Irinotecan-Induced Delayed-Onset Diarrhea in Rats

Irinotecan is a well-known chemotherapy drug for the treatment of various cancers. However, delayed-onset diarrhea is a common adverse reaction, limiting the application of the drug. The study presented was designed to evaluate the preventive effects of Shengjiang Xiexin decoction (SXD) on irinotecan-induced diarrhea and to explore the possible mechanisms of this action. We established a diarrhea rat model. The condition of the rats was observed. The proliferation and apoptosis of intestinal cells were measured using immunohistochemical assays and a caspase-3 activity assay, respectively. The expression of Lgr5 and CD44 staining were used to observe intestinal stem cells (ISCs). In addition, the activity of β-glucuronidase in the rats’ feces was measured. Our results showed that the number of proliferating intestinal cells in the SXD groups was obviously higher, while the activity of caspase-3 was lower. The expression of Lgr5 and the integrated option density (IOD) of CD44 stain were increased significantly by SXD. Additionally, SXD decreased the activity of β-glucuronidase after irinotecan administration. In conclusion, SXD exhibited preventive effects on irinotecan-induced diarrhea, and this action was associated with an inhibitory effect on intestinal apoptosis and β-glucuronidase and a promotive effect on intestinal cell proliferation due to increased maintenance of ISCs

High reward enhances perceptual learning.

Studies of perceptual learning have revealed a great deal of plasticity in adult humans. In this study, we systematically investigated the effects and mechanisms of several forms (trial-by-trial, block, and session rewards) and levels (no, low, high, subliminal) of monetary reward on the rate, magnitude, and generalizability of perceptual learning. We found that high monetary reward can greatly promote the rate and boost the magnitude of learning and enhance performance in untrained spatial frequencies and eye without changing interocular, interlocation, and interdirection transfer indices. High reward per se made unique contributions to the enhanced learning through improved internal noise reduction. Furthermore, the effects of high reward on perceptual learning occurred in a range of perceptual tasks. The results may have major implications for the understanding of the nature of the learning rule in perceptual learning and for the use of reward to enhance perceptual learning in practical applications

Daily commute time prediction based on genetic algorithm

This paper presents a joint discrete-continuous model for activity-travel time allocation by employing the ordered probit model for departure time choice and the hazard model for travel time prediction. Genetic algorithm GA is employed for optimizing the parameters in the hazard model. The joint model is estimated using data collected in Beijing, 2005. With the developed model, departure and travel times for the daily commute trips are predicted and the influence of sociodemographic variables on activity-travel timing decisions is analyzed. Then the whole time allocation for the typical daily commute activities and trips is derived. The results indicate that the discrete choice model and the continuous model match well in the calculation of activity-travel schedule. The results also show that the genetic algorithm contributes to the optimization and thus the high accuracy of the hazard model. The developed joint discrete-continuous model can be used to predict the agenda of a simple daily activity-travel pattern containing only work, and it provides potential for transportation demand management policy analysis