Seyoung Jin

Gyutaek Oh, Baekgyu Choi, Seyoung Jin et al.

Single-nucleus RNA sequencing (snRNA-seq) has significantly advanced our understanding of the disease etiology of neurodegenerative disorders. However, the low quality of specimens derived from postmortem brain tissues, combined with the high variability caused by disease heterogeneity, makes it challenging to integrate snRNA-seq data from multiple sources for precise analyses. To address these challenges, we present scMamba, a pre-trained model designed to improve the quality and utility of snRNA-seq analysis, with a particular focus on neurodegenerative diseases. Inspired by the recent Mamba model, scMamba introduces a novel architecture that incorporates a linear adapter layer, gene embeddings, and bidirectional Mamba blocks, enabling efficient processing of snRNA-seq data while preserving information from the raw input. Notably, scMamba learns generalizable features of cells and genes through pre-training on snRNA-seq data, without relying on dimension reduction or selection of highly variable genes. We demonstrate that scMamba outperforms benchmark methods in various downstream tasks, including cell type annotation, doublet detection, imputation, and the identification of differentially expressed genes.

Nivedita Singh, Seyoung Jin, Hyoungshick Kim

To comply with data protection regulations such as the EU General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), websites widely deploy cookie consent banners to collect users' privacy preferences. In practice, however, these interfaces often embed dark patterns that undermine informed and freely given consent. As regulatory scrutiny increases, such patterns have not disappeared but have evolved into subtler and more legally ambiguous forms, making existing detection approaches outdated. We present UMBRA, a consent management platform (CMP)-agnostic system that detects both previously studied patterns (DP1-DP10) and nine newly evolved patterns (DP11-DP19) targeting information disclosure, consent revocation, and legal ambiguity, including pay-to-opt-out schemes, revocation barriers, and fake opt-outs. UMBRA combines text analysis, visual heuristics, interaction tracing, and cookie-state monitoring to capture multi-step consent flows missed by prior tools. We evaluate UMBRA on a manually annotated ground-truth dataset and achieve 99% detection accuracy. We further conduct a large-scale compliance-oriented measurement across 14,000 websites spanning the EU, the US, and top-ranked global domains. Our results show that evolved dark patterns are pervasive: revocation is often obstructed, cookies are set before consent or despite explicit rejection, and opt-out interfaces often fail to prevent third-party tracking. On sites with revocation barriers, cookies increase by 25% on average, and many use insecure attributes that increase exposure to attacks such as XSS and CSRF. Overall, our findings provide evidence of systematic non-compliance and show how evolving consent manipulation erodes user autonomy while amplifying privacy and security risks.