Compared with botanical medicines, the intelligent identification of animal-derived medicines has developed relatively slowly and presents greater challenges due to species diversity, substantial morphological variations after processing, and the prevalence of adulteration. Tortoise plastron is a representative animal-derived medicine whose subtle morphological differences complicate reliable authentication. This study aimed to establish a standardized image-based framework to achieve accurate and reproducible identification of tortoise plastron and its common adulterants. An RGB image dataset covering Chinemys reevesii , Mauremys mutica , Ocadia sinensis , Malayemys subtrijuga , and Trachemys scripta elegans was constructed. Images were enhanced through geometric transformations, color jittering, and Gaussian blurring to simulate diverse acquisition conditions. Segmentation was performed using the SAM2 model to remove background noise and extract core regions, and classification was conducted using YOLO11 models of three scales (m, l, x). Training and validation were carried out on datasets with and without segmentation-based augmentation, each repeated three times, with average performance recorded. Statistical analysis included comparison of model performance metrics across datasets and model scales. The combined preprocessing–segmentation–classification workflow effectively captured both global and fine-grained features of tortoise plastron. YOLO11m with segmentation-based augmentation achieved the most balanced performance across accuracy and robustness. Eight technical modules, including attention-enhanced feature extraction and multi-scale pooling, contributed to improved classification precision. A practical recognition application was developed to facilitate user-friendly deployment. This study established a comprehensive digital framework for the objective identification of tortoise plastron and adulterants, transforming subjective trait-based evaluation into quantitative image analysis. The integration of advanced segmentation and multi-scale feature fusion provides a transferable paradigm for the intelligent identification of animal-derived medicines, with potential to enhance quality control and authenticity assurance in traditional Chinese medicine.

Creating high-dynamic videos such as motion-rich actions and sophisticated visual effects poses a significant challenge in the field of artificial intelligence. Unfortu-nately, current state-of-the-art video generation methods, primarily focusing on text-to- video generation, tend to pro-duce video clips with minimal motions despite maintaining high fidelity. We argue that relying solely on text in-structions is insufficient and suboptimal for video generation. In this paper, we introduce PixelDance, a novel approach based on diffusion models that incorporates image instructions for both the first and last frames in conjunction with text instructions for video generation. Comprehensive experimental results demonstrate that PixelDance trained with public data exhibits significantly better proficiency in synthesizing videos with complex scenes and intricate motions, setting a new standard for video generation.

Generating rich and controllable motion is a pivotal challenge in video synthesis. We propose Boximator, a new approach for fine-grained motion control. Boximator introduces two constraint types: hard box and soft box. Users select objects in the conditional frame using hard boxes and then use either type of boxes to roughly or rigorously define the object’s position, shape, or motion path in future frames. Boximator functions as a plug-in for existing video diffusion models. Its training process preserves the base model’s knowledge by freezing the original weights and training only the control module. To address training challenges, we introduce a novel self-tracking technique that greatly simplifies the learning of box-object correlations. Empirically, Boximator achieves state-of-the-art video quality (FVD) scores, improving on two base models, and further enhanced after incorporating box constraints. Its robust motion controllability is validated by drastic increases in the bounding box alignment metric. Human evaluation also shows that users favor Boximator generation results over the base model.

With the promotion of the pre-training paradigm, researchers are increasingly focusing on injecting external knowledge, such as entities and triplets from knowledge graphs, into pre-trained language models (PTMs) to improve their understanding and logical reasoning abilities. This results in significant improvements in natural language understanding and generation tasks and some level of interpretability. In this paper, we propose a novel two-stage entity knowledge enhancement pipeline for Chinese pre-trained models based on “bidirectional” prompt tuning. The pipeline consists of a “forward” stage, in which we construct fine-grained entity type prompt templates to boost PTMs injected with entity knowledge, and a “backward” stage, where the trained templates are used to generate type-constrained context-dependent negative samples for contrastive learning. Experiments on six classification tasks in the Chinese Language Understanding Evaluation (CLUE) benchmark demonstrate that our approach significantly improves upon the baseline results in most datasets, particularly those that have a strong reliance on diverse and extensive knowledge.

Session-based Recommendation (SBR) aims to predict the next item for the current session, which consists of several clicked items in a short period by an anonymous user. Most of the sequential modeling approaches to SBR are focusing on adopting advanced Deep Neural Networks (DNNs), and these methods require increasingly longer training times. Existing studies have shown that some traditional SBR methods can outperform some DNN-based sequential models, however, few studies have attempted to investigate the effectiveness of traditional methods in recent years. In this paper, we propose a novel and concise SBR model inspired by the basic concept of autocorrelation in the Stochastic Process. Autocorrelation measures the correlation of a process at different moments. Therefore, it is natural to use it to model the correlation of clicked item sequences at different time shifts. Specifically, we use Fast Fourier Transforms (FFT) to compute the autocorrelation and combine it with several linear transformations to enhance the session representation. By this means, our proposed method can learn better session preferences and is more efficient than most DNN-based models. Extensive experiments on two public datasets show that the proposed method outperforms state-of-the-art models in both effectiveness and efficiency.