SYENet: A Simple Yet Effective Network for Multiple Low-Level Vision Tasks with Real-time Performance on Mobile Device

With the rapid development of AI hardware accelerators, applying deep learning-based algorithms to solve various low-level vision tasks on mobile devices has gradually become possible. However, two main problems still need to be solved: task-specific algorithms make it difficult to integrate them into a single neural network architecture, and large amounts of parameters make it difficult to achieve real-time inference. To tackle these problems, we propose a novel network, SYENet, with only $~$6K parameters, to handle multiple low-level vision tasks on mobile devices in a real-time manner. The SYENet consists of two asymmetrical branches with simple building blocks. To effectively connect the results by asymmetrical branches, a Quadratic Connection Unit(QCU) is proposed. Furthermore, to improve performance, a new Outlier-Aware Loss is proposed to process the image. The proposed method proves its superior performance with the best PSNR as compared with other networks in real-time applications such as Image Signal Processing(ISP), Low-Light Enhancement(LLE), and Super-Resolution(SR) with 2K60FPS throughput on Qualcomm 8 Gen 1 mobile SoC(System-on-Chip). Particularly, for ISP task, SYENet got the highest score in MAI 2022 Learned Smartphone ISP challenge

Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report

The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based image signal processing (ISP) pipeline replacing the standard mobile ISPs that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The runtime of the resulting models was evaluated on the Snapdragon's 8 Gen 1 GPU that provides excellent acceleration results for the majority of common deep learning ops. The proposed solutions are compatible with all recent mobile GPUs, being able to process Full HD photos in less than 20-50 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper

A Comparative Study of Parking Occupancy Prediction Methods considering Parking Type and Parking Scale

Parking issues have been receiving increasing attention. An accurate parking occupancy prediction is considered to be a key prerequisite to optimally manage limited parking resources. However, parking prediction research that focuses on estimating the occupancy for various parking lots, which is critical to the coordination management of multiple parks (e.g., district-scale or city-scale), is relatively limited. This study aims to analyse the performance of different prediction methods with regard to parking occupancy, considering parking type and parking scale. Two forecasting methods, FM1 and FM2, and four predicting models, linear regression (LR), support vector machine (SVR), backpropagation neural network (BPNN), and autoregressive integrated moving average (ARIMA), were proposed to build models that can predict the parking occupancy of different parking lots. To compare the predictive performances of these models, real-world data of four parks in Shenzhen, Shanghai, and Dongguan were collected over 8 weeks to estimate the correlation between the parking lot attributes and forecast results. As per the case studies, among the four models considered, SVM offers stable and accurate prediction performance for almost all types and scales of parking lots. For commercial, mixed functional, and large-scale parking lots, FM1 with SVM made the best prediction. For office and medium-scale parking lots, FM2 with SVM made the best prediction

Neural-Network-Based Dynamic Distribution Model of Parking Space Under Sharing and Non-Sharing Modes

In recent years, with the rapid development of China’s automobile industry, the number of vehicles in China has been increasing steadily. Vehicles represent a convenient mode of travel, but the growth rate of the number of urban motor vehicles far exceeds the construction rate of parking facilities. The continuous improvement of parking allocation methods has always been key for ensuring sustainable city management. Thus, developing an efficient and dynamic parking distribution algorithm will be an important breakthrough to alleviate the urban parking shortage problem. However, the existing parking distribution models do not adequately consider the influence of real-time changes in parking demand and supply on parking space assignment. Therefore, this study proposed a method for dynamic parking allocation using parking demand predictions and a predictive control method. A neural-network-based dynamic parking distribution model was developed considering seven influencing factors: driving duration, walking distance, parking fee, traffic congestion, possibility of finding a parking space in the target parking lot and adjacent parking lot, and parking satisfaction degree. Considering whether the parking spaces in the targeted parking lots are shared or not, two allocation modes—sharing mode and non-sharing mode—were proposed and embedded into the model. At the experimental stage, a simulation case and a real-time case were performed to evaluate the developed models. The experimental results show that the dynamic parking distribution model based on neural networks can not only allocate parking spaces in real time but also improve the utilisation rate of different types of parking spaces. The performance score of the dynamic parking distribution model for a time interval of 2–20 min was maintained above 80%. In addition, the distribution performance of the sharing mode was better than that of the non-sharing mode and contributed to a better overall effectiveness. This model can effectively improve the utilisation rate of resources and the uniformity of distribution and can reduce the failure rate of parking; thus, it significantly contributes to more smart and sustainable urban parking management

Water Price Prediction for Increasing Market Efficiency Using Random Forest Regression: A Case Study in the Western United States

The existence of water markets establishes water prices, promoting trading of water from low- to high-valued uses. However, market participants can face uncertainty when asking and offering prices because water rights are heterogeneous, resulting in inefficiency of the market. This paper proposes three random forest regression models (RFR) to predict water price in the western United States: a full variable set model and two reduced ones with optimal numbers of variables using a backward variable elimination (BVE) approach. Transactions of 12 semiarid states, from 1987 to 2009, and a dataset containing various predictors, were assembled. Multiple replications of k-fold cross-validation were applied to assess the model performance and their generalizability was tested on unused data. The importance of price influencing factors was then analyzed based on two plausible variable importance rankings. Results show that the RFR models have good predictive power for water price. They outperform a baseline model without leading to overfitting. Also, the higher degree of accuracy of the reduced models is insignificant, reflecting the robustness of RFR to including lower informative variables. This study suggests that, due to its ability to automatically learn from and make predictions on data, RFR-based models can aid water market participants in making more efficient decisions

The role of small airway function parameters in preschool asthmatic children

Abstract Background Small airways are the major sites of inflammation and airway remodeling in all severities of asthma patients. However, whether small airway function parameters could reflect the airway dysfunction feature in preschool asthmatic children remain unclear. We aim to investigate the role of small airway function parameters in evaluating airway dysfunction, airflow limitation and airway hyperresponsiveness (AHR). Methods Eight hundred and fifty-one preschool children diagnosed with asthma were enrolled retrospectively to investigate the characteristics of small airway function parameters. Curve estimation analysis was applied to clarify the correlation between small and large airway dysfunction. Spearman’s correlation and receiver-operating characteristic (ROC) curves were employed to evaluate the relationship between small airway dysfunction (SAD) and AHR. Results The prevalence of SAD was 19.5% (166 of 851) in this cross-sectional cohort study. Small airway function parameters (FEF25-75%, FEF50%, FEF75%) showed strong correlations with FEV1% (r = 0.670, 0.658, 0.609, p<0.001, respectively), FEV1/FVC% (r = 0.812, 0.751, 0.871, p<0.001, respectively) and PEF% (r = 0.626, 0.635, 0.530, p<0.01, respectively). Moreover, small airway function parameters and large airway function parameters (FEV1%, FEV1/FVC%, PEF%) were curve-associated rather than linear-related (p<0.001). FEF25-75%, FEF50%, FEF75% and FEV1% demonstrated a positive correlation with PC20 (r = 0.282, 0.291, 0.251, 0.224, p<0.001, respectively). Interestingly, FEF25-75% and FEF50% exhibited a higher correlation coefficient with PC20 than FEV1% (0.282 vs. 0.224, p = 0.031 and 0.291 vs. 0.224, p = 0.014, respectively). ROC curve analysis for predicting moderate to severe AHR showed that the area under the curve (AUC) was 0.796, 0.783, 0.738, and 0.802 for FEF25-75%, FEF50%, FEF75%, and the combination of FEF25-75% and FEF75%, respectively. When Compared to children with normal lung function, patients with SAD were slightly older, more likely to have a family history of asthma and airflow obstruction with lower FEV1% and FEV1/FVC%, lower PEF% and more severe AHR with lower PC20 ( all p<0.05). Conclusion Small airway dysfunction is highly correlated with large airway function impairment, severe airflow obstruction and AHR in preschool asthmatic children. Small airway function parameters should be utilized in the management of preschool asthma

Vitamin C injection improves antioxidant stress capacity through regulating blood metabolism in post-transit yak

Abstract Transportation stress is one of the most serious issues in the management of yak. Previous studies have demonstrated that transport stress is caused by a pro-oxidant state in the animal resulting from an imbalance between pro-oxidant and antioxidant status. In this context, vitamin C has the ability to regulate reactive oxygen species (ROS) synthesis and alleviate oxidative stress. Although this effect of vitamin C is useful in pigs, goats and cattle, the effect of vitamin C on the mitigation of transport stress in yaks is still unclear. The purpose of this study was to better assess the metabolic changes induced by the action of vitamin C in yaks under transportation stress, and whether these changes can influence antioxidant status. After the yaks arrived at the farm, control or baseline blood samples were collected immediately through the jugular vein (VC_CON). Then, 100 mg/kg VC was injected intramuscularly, and blood samples were collected on the 10th day before feeding in the morning (VC). Relative to the control group, the VC injection group had higher levels of VC. Compared with VC_CON, VC injection significantly (P  0.05) in GGT, ALP, TBA, TP, ALBII, GLO, A/G, TC, TG, HDL-C, LDL-C, GLU and l-lactate between VC_CON and VC. The injection of VC led to greater (P  0.05) the serum concentrations of LPO and ROS. The injection of VC led to greater (P  0.05) in GSH, GSH-ST and GR was observed between VC_CON and VC. Compared with the control group, metabolomics using liquid chromatography tandem–mass spectrometry identified 156 differential metabolites with P  1.5 in the VC injection group. The injection of VC resulted in significant changes to the intracellular amino acid metabolism of glutathione, glutamate, cysteine, methionine, glycine, phenylalanine, tyrosine, tryptophan, alanine and aspartate. Overall, our study indicated that VC injections were able to modulate antioxidant levels by affecting metabolism to resist oxidative stress generated during transport