Yulan Wang

Jianfeng Zhu, Hailong Jiang, Yulan Wang et al.

Early substance use during adolescence increases the risk of later substance use disorders and mental health problems, yet the emotional and contextual factors driving these behaviors remain poorly understood. This study analyzed 23000 substance-use related posts and an equal number of non-substance posts from Reddit's r/teenagers community (2018-2022). Posts were annotated for six discrete emotions (sadness, anger, joy, guilt, fear, disgust) and contextual factors (family, peers, school) using large language models (LLMs). Statistical analyses compared group differences, and interpretable machine learning (SHAP) identified key predictors of substance-use discussions. LLM-assisted thematic coding further revealed latent psychosocial themes linking emotions with contexts. Negative emotions, especially sadness, guilt, fear, and disgust, were significantly more common in substance-use posts, while joy dominated non-substance discussions. Guilt and shame diverged in function: guilt often reflected regret and self-reflection, whereas shame reinforced risky behaviors through peer performance. Peer influence emerged as the strongest contextual factor, closely tied to sadness, fear, and guilt. Family and school environments acted as both risk and protective factors depending on relational quality and stress levels. Overall, adolescent substance-use discussions reflected a dynamic interplay of emotion, social context, and coping behavior. By integrating statistical analysis, interpretable models, and LLM-based thematic exploration, this study demonstrates the value of mixed computational approaches for uncovering the emotional and contextual mechanisms underlying adolescent risk behavior.

Susanne Schaub, Florentin Bieder, Matheus L. Oliveira et al.

Cone-beam computed tomography (CBCT) is a widely used 3D imaging technique in dentistry, offering high-resolution images while minimising radiation exposure for patients. However, CBCT is highly susceptible to artefacts arising from high-density objects such as dental implants, which can compromise image quality and diagnostic accuracy. To reduce artefacts, implant inpainting in the sequence of projections plays a crucial role in many artefact reduction approaches. Recently, diffusion models have achieved state-of-the-art results in image generation and have widely been applied to image inpainting tasks. However, to our knowledge, existing diffusion-based methods for implant inpainting operate on independent 2D projections. This approach neglects the correlations among individual projections, resulting in inconsistencies in the reconstructed images. To address this, we propose a 3D dental implant inpainting approach based on perpendicular score-based diffusion models, each trained in two different planes and operating in the projection domain. The 3D distribution of the projection series is modelled by combining the two 2D score-based diffusion models in the sampling scheme. Our results demonstrate the method's effectiveness in producing high-quality, artefact-reduced 3D CBCT images, making it a promising solution for improving clinical imaging.

Tao Yang, Shikai Fang, Shibo Li et al.

Leveraging biased click data for optimizing learning to rank systems has been a popular approach in information retrieval. Because click data is often noisy and biased, a variety of methods have been proposed to construct unbiased learning to rank (ULTR) algorithms for the learning of unbiased ranking models. Among them, automatic unbiased learning to rank (AutoULTR) algorithms that jointly learn user bias models (i.e., propensity models) with unbiased rankers have received a lot of attention due to their superior performance and low deployment cost in practice. Despite their differences in theories and algorithm design, existing studies on ULTR usually use uni-variate ranking functions to score each document or result independently. On the other hand, recent advances in context-aware learning-to-rank models have shown that multivariate scoring functions, which read multiple documents together and predict their ranking scores jointly, are more powerful than uni-variate ranking functions in ranking tasks with human-annotated relevance labels. Whether such superior performance would hold in ULTR with noisy data, however, is mostly unknown. In this paper, we investigate existing multivariate scoring functions and AutoULTR algorithms in theory and prove that permutation invariance is a crucial factor that determines whether a context-aware learning-to-rank model could be applied to existing AutoULTR framework. Our experiments with synthetic clicks on two large-scale benchmark datasets show that AutoULTR models with permutation-invariant multivariate scoring functions significantly outperform those with uni-variate scoring functions and permutation-variant multivariate scoring functions.