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Sentiment classification is a key task in exploring people’s opinions; improved sentiment classification can help individuals make better decisions. Social media users are increasingly using both images and text to express their opinions and share their experiences, instead of only using text in conventional social media. As a result, understanding how to fully utilize them is critical in a variety of activities, including sentiment classification. In this work, we provide a fresh multimodal sentiment classification approach: visual distillation and attention network or VisdaNet. First, this method proposes a knowledge augmentation module, which overcomes the lack of information in short text by integrating the information of image captions and short text; secondly, aimed at the information control problem in the multi-modal fusion process in the product review scene, this paper proposes a knowledge distillation based on the CLIP module to reduce the noise information of the original modalities and improve the quality of the original modal information. Finally, regarding the single-text multi-image fusion problem in the product review scene, this paper proposes visual aspect attention based on the CLIP module, which correctly models the text-image interaction relationship in special scenes and realizes feature-level fusion across modalities. The results of the experiment on the Yelp multimodal dataset reveal that our model outperforms the previous SOTA model. Furthermore, the ablation experiment results demonstrate the efficacy of various tactics in the suggested model.

In sentiment analysis, biased user reviews can have a detrimental impact on a company’s evaluation. Therefore, identifying such users can be highly beneficial as their reviews are not based on reality but on their characteristics rooted in their psychology. Furthermore, biased users may be seen as instigators of other prejudiced information on social media. Thus, proposing a method to help detect polarized opinions in product reviews would offer significant advantages. This paper proposes a new method for sentiment classification of multimodal data, which is called UsbVisdaNet (User Behavior Visual Distillation and Attention Network). The method aims to identify biased user reviews by analyzing their psychological behaviors. It can identify both positive and negative users and improves sentiment classification results that may be skewed due to subjective biases in user opinions by leveraging user behavior information. Through ablation and comparison experiments, the effectiveness of UsbVisdaNet is demonstrated, achieving superior sentiment classification performance on the Yelp multimodal dataset. Our research pioneers the integration of user behavior features, text features, and image features at multiple hierarchical levels within this domain.

When multiple electric vehicles need to be charged, it will take more time and money for the electric vehicles to randomly enter the charging stations during the disorderly scheduling process. In the meantime, the utilization rate of charging piles is different, and the load of power grid is heavier. In this paper, a charging scheduling strategy is designed considering of the requests of multiple electric vehicles, which schedule in a way of overall parallel. In this charging scheduling strategy, electric vehicles will cost less time and money, the utilization rate of charging piles is more equal, and the power grid has minimum load. According to the charging scheduling strategy, a vehicle charging scheduling model is established based on multi-objective optimization. Technique for order preference by similarity to ideal solution is used to eliminate the dimensions of multiple objectives, and the genetic algorithm is used to solve the model. The simulation results show that the charging scheduling strategy can select appropriate charging stations for electric vehicles and achieve the goal of multi-objective optimization.

Global shortening of 3′UTRs by alternative polyadenylation (APA) has been observed in cancer cells. However, the role of APA in cancer remains unknown. CCND1 is a proto-oncogene that regulates progression through the G1-S phase of the cell cycle; moreover, it has been observed to be switching to proximal APA sites in cancer cells. To investigate the biological function of the APA of CCND1, we edited the weak poly(A) signal (PAS) of the proximal APA site to a canonical PAS using the CRISPR/Cas9 method, which can force the cells to use a proximal APA site. Cell cycle profiling and proliferation assays revealed that the proximal APA sites of CCND1 accelerated the cell cycle and promoted cell proliferation, but UTR-APA and CR-APA act via different molecular mechanisms. These results indicate that PAS editing with CRISPR/Cas9 provides a good method by which to study the biological function of APA.

Recent advances in multimodal large language models unlock unprecedented opportunities for GUI automation. However, a fundamental challenge remains: how to efficiently acquire high-quality training data while maintaining annotation reliability? We introduce a self-evolving training pipeline powered by the Calibrated Step Reward System, which converts model-generated trajectories into reliable training signals through trajectory-level calibration, achieving >90% annotation accuracy with 10-100x lower cost. Leveraging this pipeline, we introduce Step-GUI, a family of models (4B/8B) that achieves state-of-the-art GUI performance (8B: 80.2% AndroidWorld, 48.5% OSWorld, 62.6% ScreenShot-Pro) while maintaining robust general capabilities. As GUI agent capabilities improve, practical deployment demands standardized interfaces across heterogeneous devices while protecting user privacy. To this end, we propose GUI-MCP, the first Model Context Protocol for GUI automation with hierarchical architecture that combines low-level atomic operations and high-level task delegation to local specialist models, enabling high-privacy execution where sensitive data stays on-device. Finally, to assess whether agents can handle authentic everyday usage, we introduce AndroidDaily, a benchmark grounded in real-world mobile usage patterns with 3146 static actions and 235 end-to-end tasks across high-frequency daily scenarios (8B: static 89.91%, end-to-end 52.50%). Our work advances the development of practical GUI agents and demonstrates strong potential for real-world deployment in everyday digital interactions.