Neighborhood environments play a critical role in shaping youth development. Although prior research has focused on individual and family-level factors, there remains a gap in understanding neighborhood-level protective mechanisms for adolescents exposed to child maltreatment. This study examined two forms of Neighborhood Informal Social Control of Child Maltreatment (ISC_CM) – protective and punitive ISC_CM – as potential moderators of the relationship between child maltreatment and adolescent depression, aggressive behavior, and suicidal risk among middle and high school students in South Korea utilizing a cross-sectional survey design. Structural equation modeling results indicated that punitive ISC_CM, such as taking firm action to address punitive parenting, marginally moderated the association between child maltreatment and aggressive behavior, but not depression or suicidal risk. Protective ISC_CM, defined as offering supportive resources in instances of harmful parenting, demonstrated a main effect on all outcomes, but no significant moderation effect was observed. These findings suggest that protective neighborhood interventions are generally beneficial for youth mental health, even if they do not specifically buffer the effects of maltreatment. Community-based programs addressing adolescent mental and emotional health may benefit from encouraging neighbors to establish shared social norms regarding child maltreatment and to intervene in cases of paternal maltreating behaviors. Major keywords. Neighborhood protective factors, collective efficacy, informal social control, suicidal risk, child maltreatment, ISC_CM.
Vision-Language-Action models have recently emerged as a powerful paradigm for general-purpose robot learning, enabling agents to map visual observations and natural-language instructions into executable robotic actions. Though popular, they are primarily trained via supervised fine-tuning or training-time reinforcement learning, requiring explicit fine-tuning phases, human interventions, or controlled data collection. Consequently, existing methods remain unsuitable for challenging simulated- or physical-world deployments, where robots must respond autonomously and flexibly to evolving environments. To address this limitation, we introduce a Test-Time Reinforcement Learning for VLAs (TT-VLA), a framework that enables on-the-fly policy adaptation during inference. TT-VLA formulates a dense reward mechanism that leverages step-by-step task-progress signals to refine action policies during test time while preserving the SFT/RL-trained priors, making it an effective supplement to current VLA models. Empirical results show that our approach enhances overall adaptability, stability, and task success in dynamic, previously unseen scenarios under simulated and real-world settings. We believe TT-VLA offers a principled step toward self-improving, deployment-ready VLAs.
Digital media, including television, the internet, social media, video games, and interactive assistants, form the digital ecosystem. When this digital ecosystem is designed with children's unique developmental needs in mind, it can support learning and well-being. In contrast, digital ecosystems that prioritize engagement and commercialization often encourage prolonged use, which in turn can displace healthy behaviors (eg, movement behaviors, sleep), and contribute to negative outcomes. This policy statement follows the conceptual framework of the socioecological model, depicting nested circles of care including: children's own characteristics, their caregivers, the digital ecosystem, as well as broader societal systems. Given the interconnected nature of these influences and systems, "media and children" cannot be viewed solely through the lens of individual child behaviors or screen limits alone. Recommendations are provided for families, pediatric providers, practitioners (eg, psychologists, social workers, counselors, educators, researchers), industry, and policy makers, aiming to provide strengths-based solutions and promote a more child-centered digital ecosystem.
3D Gaussian Splatting (3DGS) has emerged as a leading technology for high-quality 3D scene reconstruction. However, the iterative refinement and densification process leads to the generation of a large number of primitives, each contributing to the reconstruction to a substantially different extent. Estimating primitive importance is thus crucial, both for removing redundancy during reconstruction and for enabling efficient compression and transmission.Existing methods typically rely on rendering-based analyses, where each primitive is evaluated through its contribution across multiple camera viewpoints. However, such methods are 1) sensitive to the number and selection of views; 2) rely on specialized differentiable rasterizers; and 3) have long calculation times that grow linearly with view count, making them difficult to integrate as plug-and-play modules, as well as resulting in limited scalability and generalization.To address these issues, we propose RAP — a fast feedforward Rendering-free Attribute-guided method for efficient importance score Prediction in 3DGS. RAP infers primitive significance directly from intrinsic Gaussian attributes and local neighborhood statistics, avoiding any rendering-based or visibility-dependent computations. A compact MLP is trained to predict per-primitive importance scores using a combination of rendering loss, pruning-aware loss, and significance distribution regularization loss. After being trained on a small set of scenes, RAP generalizes effectively to unseen data and can be seamlessly integrated into reconstruction, compression, and transmission pipelines, providing a unified and efficient pruning solution.
Large language models (LLMs) offer new opportunities for constructing knowledge graphs (KGs) from unstructured clinical narratives. However, existing approaches often rely on structured inputs and lack robust validation of factual accuracy and semantic consistency, limitations that are especially problematic in oncology. We introduce an end-to-end framework for clinical KG construction and evaluation directly from free text using multi-agent prompting and a schema-constrained Retrieval-Augmented Generation (KG-RAG) strategy. Our pipeline integrates (1) prompt-driven entity, attribute, and relation extraction; (2) entropy-based uncertainty scoring; (3) ontology-aligned RDF/OWL schema generation; and (4) multi-LLM consensus validation for hallucination detection and semantic refinement. Beyond static graph construction, the framework supports continuous refinement and self-supervised evaluation, enabling iterative improvement of graph quality. Applied to two oncology cohorts (PDAC and BRCA), our method produces interpretable, SPARQL-compatible, and clinically grounded knowledge graphs without relying on gold-standard annotations. Experimental results demonstrate consistent gains in precision, relevance, and ontology compliance over baseline methods.
