Soccer is a globally popular sporting event, typically characterized by long matches and distinctive highlight moments. Recent advances in Multimodal Large Language Models (MLLMs) show promising capabilities in temporal grounding and video understanding. However, generating soccer commentary requires both precise temporal localization and semantically rich descriptions over long-form videos. Existing soccer MLLMs often rely on temporal priors for caption generation, which limits their ability to process the entire video in an end-to-end manner. Traditional approaches, on the other hand, follow a complex two-step paradigm that fails to capture the global context, leading to suboptimal performance. To solve the above issues, we present TimeSoccer, the first end-to-end soccer MLLM for Single-anchor Dense Video Captioning (SDVC) in full-match soccer videos. TimeSoccer jointly predicts timestamps and generates captions in a single pass, enabling global context modeling across 45-minute matches. To support long video understanding of soccer matches, we introduce MoFA-Select, a training-free, motion-aware frame compression module that adaptively selects representative frames via a coarse-to-fine strategy, and incorporates complementary training paradigms to strengthen the model's ability to handle long temporal sequences. Extensive experiments demonstrate that our TimeSoccer achieves State-of-The-Art (SoTA) performance on the SDVC task in an end-to-end form, generating high-quality commentary with accurate temporal alignment and strong semantic relevance. For more information, please visit: https://vpx-ecnu.github.io/TimeSoccer-Website/.

The increasing global aging brings the substantial demand for healthcare knowledge among the elderly. Large Language Models (LLMs) based Conversation Agents (CAs) hold significant promise for addressing the elderly’s healthcare knowledge inquiries. Yet, general LLMs often fall short in providing professional and practically usable healthcare conversations due to the lack of specific knowledge, possible hallucination issues and contextual comprehension biases. To address these challenges, we first propose a cost-effective, domain-specific questioning-answering (QA) generation framework based on knowledge distillation (KD). Based on this framework, we then built CareQA, the first Chinese healthcare QA dataset specifically for the elderly, with 41,694 QA pairs spanning geriatric diseases covering multiple categories. A comprehensive benchmarking experiment, including both automated and human evaluation, is conducted to examine the usability of CareQA. The results demonstrate that the LLMs fine-tuned on CareQA perform better in answering elderly healthcare-related questions.

Dementia has increasingly impacted the health of older adults, necessitating precise prediction of cognitive levels to deliver targeted healthcare services and mitigate disease progression. In recent years, although there has been surging interest in research on AI-driven dementia prediction, existing methods commonly rely on background-specific datasets and utilize multi-models with equal weights for predictions. These methods limit the result to the inherent features of the datasets on the one hand and ignore the differences among various feature channels on the other hand. To address these limitations, this paper proposes a dynamic composite ensemble learning framework, including the following three novel modules. Firstly, based on the selected common feature set, we construct a top-level feature set through dynamically determined screening thresholds. Subsequently, we deploy transfer learning models for knowledge complementation to enhance model generalization. In addition, we purposefully designed a dynamic weighting module to prioritize feature channels with stronger relevance to the target task. We deploy our model on the ELSA-HCAP dataset and conduct a series of experiments to evaluate its practical effectiveness. The results demonstrate that the proposed feature engineering, dynamic weighting, and knowledge transfer modules collectively enhance the overall model performance. Moreover, the combination of these three modules in the fusion model attains optimal performance, achieving an accuracy rate of 95.76%.

Prediction of response to input drivers by unmonitored entities has been recognized as one of the most important problems in many scientific problems. This problem is challenging due to the non-stationary processes that underlie the dynamics of data observations over space and time. Hence, directly transferring models from well-observed data entities to unmonitored target entity often lead to sub-optimal performance due to the shift in data distribution. This paper proposes a new meta-transfer learning framework that automatically estimates the similarity amongst entities to transfer knowledge from well-observed entities to unmonitored entities. A sequence autoencoder embeds temporal behaviors of time series data and simulations generated by traditional physics-based models. This embedding model is trained in a meta-transfer learning framework under the guidance of source-to-source transferring experiences. We tested this method in streamflow prediction for multiple river segments in the Delaware River Basin, an ecologically diverse region along the eastern coast of the United States. The experimental results demonstrate the superiority of the proposed method in predicting streamflow for unmonitored stream segments compared to a diverse set of baselines. Our method also creates meaningful similarity estimates amongst segments to guide the transfer learning process.