In recent years, deep learning has dramatically improved state of the art in many practical applications. However, this utility is highly dependent on fine-tuning of hyperparameters, including learning rate, batch size, and network initialization. Although many first-order adaptive gradient algorithms (e.g., Adam, AdaGrad) have been proposed to adjust the learning rate, they are vulnerable to the initial learning rate and network structure in the training over-parameterized models, especially in the dynamic online setting. Therefore, the main challenge of using deep learning in practice is how to reduce the cost of tuning hyperparameters. To address this problem, we integrate the adaptive strategy of Radhakrishnan et al. and the acceleration strategy of Ghadimi et al. to propose a fast adaptive online gradient algorithm, FAOGD. The adaptive strategy we adopt only adjusts the learning rate according to the historical gradient and training loss value, while the acceleration strategy is the heavy-ball momentum used to accelerate the training of deep models. The proposed FAOGD enjoys merit that there is no need to tune hyperparameters related to the learning rate, which thus saves much unnecessary computational overhead. It is also shown that FAOGD can obtain the regret bound of O( √(T)) , matching the Adam and AdaGrad using the empirical learning rate. Simulation results in the over-parameterized neural networks clearly show that FAOGD outperforms existing algorithms. Furthermore, FAOGD is also robust to network structures and batch size.

Since the breakout of Corona Virus Disease 2019 (COVID-19), the global fight against influenza has begun. Var-ious technologies have been developed to support the fast-growing contactless service market, and hence contactless services are rapidly becoming a new growth strategy. In particular, the retail service industry most urgently needs contactless service technology. A representative technical case is the self-checkout machine, which can reduce labor costs and provide customer satisfaction. We present a solution in this article. We propose a hand gesture recognition contactless self-checkout system, which is a hand gesture recognition model based on YOLOv5s. The hand gesture recognition mAP (0.5) value reaches 0.995, the mAP (0.5:0.95) value reaches 0.865, and the Fl score is 0.96, together with the accuracy and recall rate is close to 1. Compared with the excellent algorithm YOLOx-s, the FPS value of YOLOv5s can reach 123 (YOLOx-s is 108). In addition, the model can be used to detect recorded static and dynamic hand gestures in real-time. Practical results show that the YOLOv5s can effectively recognize hand gestures and realize the contactless checkout process.

The Hadamard product parametrization (HPP), together with the profile LASSO, are combined to achieve sparse signal recovery in signal process. Compared with the tail-ℓ1 min-imization algorithm, the tail Hadamard product parametrization (tail-HPP) algorithm can obtain faster signal reconstruction speed and higher signal recovery accuracy. In numerical experiments, the proposed tail Hadamard product parametrization algorithm achieves excellent sparse signal estimation performance, which forcefully shows that the tail Hadamard product parametrization algorithm is a promising method for compressed sensing.