From time series analysis to a modified ordinary differential equation

In understanding Big Data, people are interested to obtain the trend and dynamics of a given set of temporal data, which in turn can be used to predict possible futures. This paper examines a time series analysis method and an ordinary differential equation approach in modeling the price movements of petroleum price and of three different bank stock prices over a time frame of three years. Computational tests consist of a range of data fitting models in order to understand the advantages and disadvantages of these two approaches. A modified ordinary differential equation model, with different forms of polynomials and periodic functions, is proposed. Numerical tests demonstrated the advantage of the modified ordinary differential equation approach. Computational properties of the modified ordinary differential equation are studied

Dynamic Self-adaptive Multiscale Distillation from Pre-trained Multimodal Large Model for Efficient Cross-modal Representation Learning

In recent years, pre-trained multimodal large models have attracted widespread attention due to their outstanding performance in various multimodal applications. Nonetheless, the extensive computational resources and vast datasets required for their training present significant hurdles for deployment in environments with limited computational resources. To address this challenge, we propose a novel dynamic self-adaptive multiscale distillation from pre-trained multimodal large model for efficient cross-modal representation learning for the first time. Unlike existing distillation methods, our strategy employs a multiscale perspective, enabling the extraction structural knowledge across from the pre-trained multimodal large model. Ensuring that the student model inherits a comprehensive and nuanced understanding of the teacher knowledge. To optimize each distillation loss in a balanced and efficient manner, we propose a dynamic self-adaptive distillation loss balancer, a novel component eliminating the need for manual loss weight adjustments and dynamically balances each loss item during the distillation process. Our methodology streamlines pre-trained multimodal large models using only their output features and original image-level information, requiring minimal computational resources. This efficient approach is suited for various applications and allows the deployment of advanced multimodal technologies even in resource-limited settings. Extensive experiments has demonstrated that our method maintains high performance while significantly reducing model complexity and training costs. Moreover, our distilled student model utilizes only image-level information to achieve state-of-the-art performance on cross-modal retrieval tasks, surpassing previous methods that relied on region-level information.Comment: 10 page

Enhancing Spatial Perception for Satellite Video Target Tracking

In recent years, Transformer-based target tracking algorithms have performed well in ordinary scenarios. However, when applied to satellite video scenarios, the tracking effect of the algorithms is not satisfactory due to the small size of satellite video targets, blurred features, and complex background interference. To address this issue, this paper proposes an algorithm for Enhancing Spatial Perception for Satellite Video Target Tracking (ESPTrack). This algorithm, through the spatial collaborative attention module, integrates local and global spatial information to enhance the multi-level representation of the target’s detailed features and overall structure. Meanwhile, a Gaussian prior cross-attention module is constructed. The Gaussian distribution weighting is utilized to enhance the key context information, improving the model’s ability to perceive the target’s spatial position and reducing the impact of background interference. To verify the effectiveness of the algorithm proposed in this paper, experiments were conducted on the satellite video datasets SatSOT and OOTB. The results show that the proposed algorithm has better performance compared with the existing target tracking algorithms, and it is verified that enhancing spatial perception in complex satellite video scenarios can effectively improve tracking performance

The dynamic interplay between neuroticism, extraversion, and problematic gaming in adolescents: A 4-wave longitudinal study

Background and aims Adolescent problematic gaming is a global public health issue, and is associated with numerous negative outcomes. The Big Two personality traits, neuroticism and extraversion, have been identified as significant predictors of problematic gaming in adolescents. However, most previous studies have been cross-sectional, limiting the ability to explore their mutual influences or causality inference. This study addresses this gap by employing a longitudinal design and utilizing the Random Intercept Cross-Lagged Panel Model (RI-CLPM) to examine the bidirectional relations between the Big Two personality traits and problematic gaming at the within-person level. Methods This study included 3,307 students (Mean age = 11.30, SD = 0.48, 43.6% being girls). Participants were assessed annually, completing a total of four assessments over the course of the study. Results The RI-CLPM analyses revealed that neuroticism and problematic gaming significantly predict each other. Extraversion acts as a protective factor against adolescent problematic gaming, whereas problematic gaming leads to a decrease in extraversion levels. Additionally, the longitudinal relations between neuroticism and problematic gaming exhibit significant sex differences. Discussion and conclusions This study provides insights into the interplay between the Big Two personality traits and problematic gaming in adolescents. These findings emphasize the need for prevention and intervention strategies that address personality traits as risk factors while recognizing how problematic gaming can influence personality, promoting a more holistic approach. The observed sex differences highlight the importance of integrating sex-specific considerations in interventions