Online communities serve as essential support channels for People Who Use Drugs (PWUD), providing access to peer support and harm reduction information. The moderation of these communities involves consequential decisions affecting member safety, yet existing sociotechnical systems provide insufficient support for moderators. Through interviews with experienced moderators from PWUD forums on Reddit, we examine the unique nature of this work and its implications for HCI and content moderation research. We demonstrate that this work constitutes a distinct form of public health intervention characterised by three challenges: (1) high-stakes risk evaluation requiring pharmacological expertise, (2) time-critical crisis intervention spanning platform content and external drug market surveillance, and (3) navigation of structural conflicts where platform policies designed to minimise legal liability directly oppose community harm reduction goals. Our findings extend existing HCI moderation frameworks by revealing how legal liability structures can systematically undermine expert moderators' protective work, with implications for other marginalised communities facing similar regulatory tensions, including abortion care and sex work contexts. We identify two necessary shifts in sociotechnical design: moving from binary classification to multi-dimensional approaches that externalise competing factors moderators must balance, and shifting from low-level rule programming to high-level example-based instruction. However, we surface unresolved tensions around volunteer labour sustainability and risks of incorporating automated systems in high-stakes health contexts, identifying open questions requiring HCI research attention. These findings inform the design of platforms that better accommodate vulnerable populations whose health needs conflict with regulatory frameworks.

Existing arbitrary-scale super-resolution (ASSR) methods suffer from quadratic computational complexity w.r.t. image scale due to the reliance on multi-layer perceptrons (MLPs) to query dense spatial coordinate matrices. The inefficiency becomes particularly pronounced when extending to high-dimensional imaging modalities. To address these limitations, we propose a novel functional tensor decomposition (FTD) framework that fundamentally reconfigures the computational paradigm for ASSR. Specifically, we propose 1) a separation mechanism that employs distinct MLPs to query separable spatial coordinate vectors, substantially reducing decoder MLP invocations, and 2) functional tensor Tucker or CP decompositions for efficient factor matrix integration. The FTD framework delivers three key advantages: 1) Superior scalability to high-dimensional imaging modalities, such as hyperspectral images (HSIs), by virtue of the FTD design; 2) Significantly enhanced inference speed across scales; 3) Faster convergence towards a desired training model. Extensive experiments validate FTD's exceptional performance in HSI joint spatial-spectral ASSR, achieving up to 90.04% reduction in inference time and substantial performance improvements. For conventional image ASSR, our method improves both inference speed and convergence efficiency, achieving up to 88.82% inference time reduction and superior few-shot generalization capabilities due to faster convergence.