Document chunking is a critical preprocessing step in dense retrieval systems, yet the design space of chunking strategies remains poorly understood. Recent research has proposed several concurrent approaches, including LLM-guided methods (e.g., DenseX and LumberChunker) and contextualized strategies (e.g., Late Chunking), which generate embeddings before segmentation to preserve contextual information. However, these methods emerged independently and were evaluated on benchmarks with minimal overlap, making direct comparisons difficult.

• Hearing loss lowered the prosody and semantics emotion perception in older adults. • Hearing loss affected emotion prosody more than emotion semantics in older adults. • Unlike younger participants, older adults showed faster responses to happy stimuli. • Age, hearing, and cognition abilities were predictive of emotion identification. Challenges in the ability of older adults to comprehend vocal emotion have been documented. However, limited research has investigated the combined effects of aging and age-related hearing loss. The present study aimed to bridge this research gap by comparing the performance of three participant groups (younger adults with normal hearing, older adults with hearing loss, and older adults without hearing loss) in identification of “happy” and “sad” emotions via prosodic and semantic channels. We conducted regression models to investigate the relationship between age, hearing threshold, cognitive abilities and overall emotion perception performance. Results of emotion identification accuracy demonstrated that older adults with hearing loss performed worse than other two normal hearing groups in both channels. Additionally, only older adults with hearing loss showed lower accuracy in the emotional prosody than semantics, indicating only this group is influenced by channel. As for response time, both older listener groups responded more slowly than younger listeners in both channels. They also exhibited faster responses to “happy” compared to “sad”, supporting the positivity effect on emotion perception in older participants. Moreover, the regression models indicated that age, hearing threshold and working memory (measured by Digit Span test) were predictive of participants' overall identification accuracy, and selective attention (measured by Stroop test) was predictive of participants’ overall reaction time. These findings suggest that the degraded emotion perception among older adults is attributed to complex underlying mechanisms, which can be reasonably explained by not only aging but also the decline in hearing sensitivity and cognitive functions.

Non-suicidal self-injury (NSSI) is a serious threat to the physical and mental health of adolescents, significantly increasing the risk of suicide and attracting widespread public concern. Electroencephalography (EEG), as an objective tool for identifying brain disorders, holds great promise. However, extracting meaningful and reliable features from high-dimensional EEG data, especially by integrating spatiotemporal brain dynamics into informative representations, remains a major challenge. In this study, we introduce an advanced semi-supervised adversarial network, NSSI-Net, to effectively model EEG features related to NSSI. NSSI-Net consists of two key modules: a spatial-temporal feature extraction module and a multi-concept discriminator. In the spatial-temporal feature extraction module, an integrated 2D convolutional neural network (2D-CNN) and a bi-directional Gated Recurrent Unit (BiGRU) are used to capture both spatial and temporal dynamics in EEG data. In the multi-concept discriminator, signal, gender, domain, and disease levels are fully explored to extract meaningful EEG features, considering individual, demographic, disease variations across a diverse population. Based on self-collected NSSI data (n=114), the model's effectiveness and reliability are demonstrated, with a 5.44% improvement in performance compared to existing machine learning and deep learning methods. This study advances the understanding and early diagnosis of NSSI in adolescents with depression, enabling timely intervention.