Social media popularity prediction task aims to predict the popularity of posts on social media platforms, which has a positive driving effect on application scenarios such as content optimization, digital marketing and online advertising. Though many studies have made significant progress, few of them pay much attention to the integration between popularity prediction with temporal alignment. In this paper, with exploring YouTube’s multilingual and multi-modal content, we construct a new social media temporal popularity prediction benchmark, namely SMTPD, and suggest a baseline framework for temporal popularity prediction. Through data analysis and experiments, we verify that temporal alignment and early popularity play crucial roles in social media popularity prediction for not only deepening the understanding of temporal dynamics of popularity in social media but also offering a suggestion about developing more effective prediction models in this field. Code is available at https://github.com/zhuwei321/SMTPD

A training set with sufficient quantity and reliable quality is crucial for achieving satisfactory results in data-driven acoustic impedance inversion. However, effective data augmentation still faces significant challenges when labeled well data are sparse. In this study, it is proposed a novel impedance sequence augmentation strategy based on the SinGAN multiscale generative adversarial networks under few well conditions. SinGAN requires only a single impedance sequence for training and supports two augmentation modes: Random Impedance Generation Mode (RIGM) and Controllable Impedance Generation Mode (CIGM). RIGM controls diversity between synthetic and true impedance by adjusting the Start Generation Scale (SGS), while CIGM synthesizes impedance by fusing a known low-frequency reference model with high-frequency details derived from well data, the SGS determines the proportion of their integration. Three training sets were established through data augmentation using broadcasting, RIGM, and CIGM on the Marmousi2 model, and were subsequently fed into a CNN-GRU fusion network with identical hyperparameters. Experimental results show that the correlation coefficients (R²) between the estimated and true impedance values reach 0.9111, 0.9282, and 0.9423 for the broadcasting, RIGM, and CIGM methods, respectively. Meanwhile, the CIGM-based model achieves the best overall performance, with an MSE of 0.006 and an SSIM of 0.966, and it accurately characterizes impedance variations across stratigraphic layers and clearly delineates the water–strata interface and associated sand bodies. These findings verify that the proposed augmentation strategy effectively expands the training sample space and enhance impedance prediction accuracy, offering a new promising approach for seismic inversion tasks with sparse labeled data.

For a magnetic coupling resonant wireless power transfer (MCR-WPT) system, the most challenging design issue is to maintain the reasonable transfer efficiency and the output power, especially over varying transmitting distances. To address this issue, this study proposes an adaptive impedance matching (AIM) system with a fast optimization control algorithm for the MCR-WPT system. By dynamically monitoring system input impedance variations at a given frequency, the matching system can be automatically activated. The matching precision is enabled by combining an amplitude-phase measurement circuit, a Pi-type impedance matching (IM) network, and the local optimization algorithm. Experimental comparisons show a significant improvement in the transfer efficiency. The maximum improvement is over 32.6%. The results also show that the local optimization algorithm helps the MCR-WPT system to effectively overcome the efficiency challenges caused by parasitic parameters, resulting in a maximum improvement of 19.8%. The stability of the output power is also significantly improved, with a 27.8% reduction in oscillation amplitude compared to the scenario without the local optimization algorithm, and a 60.0% reduction compared to the scenario without the IM network. The output power range (1.55 - 2.21 W) could meet the power requirements of most implantable medical devices. Moreover, this system adopts a control algorithm of tracking the theoretically optimal matching solution through Microcontroller Unit (MCU) computation, leading to a notable enhancement in matching accuracy.

An interference suppression algorithm is proposed for canceling the spatially correlated background noise and interference in EEG/MEG source localization applications. Rather than using the standard prewhitening approach, the proposed algorithm attempts to directly null interference using a projection operator obtained from a set of secondary, control-state data. The use of direct interference cancellation is shown to be significantly more robust than prewhitening, due to the elimination of the need for computing covariance matrices, and the relaxation of the assumption of temporal stationarity. Simulation examples are presented to demonstrate the robust performance of the algorithm.