Shoe print evidence recovered from crime scenes plays a key role in forensic investigations. By examining shoe prints, investigators can determine details of the footwear worn by suspects. However, establishing that a suspect's shoes match the make and model of a crime scene print may not be sufficient. Typically, thousands of shoes of the same size, make, and model are manufactured, any of which could be responsible for the print. Accordingly, a popular approach used by investigators is to examine the print for signs of ``accidentals,'' i.e., cuts, scrapes, and other features that accumulate on shoe soles after purchase due to wear. While some patterns of accidentals are common on certain types of shoes, others are highly distinctive, potentially distinguishing the suspect's shoe from all others. Quantifying the rarity of a pattern is thus essential to accurately measuring the strength of forensic evidence. In this study, we address this task by developing a hierarchical Bayesian model. Our improvement over existing methods primarily stems from two advancements. First, we frame our approach in terms of a latent Gaussian model, thus enabling inference to be efficiently scaled to large collections of annotated shoe prints via integrated nested Laplace approximations. Second, we incorporate spatially varying coefficients to model the relationship between shoes' tread patterns and accidental locations. We demonstrate these improvements through superior performance on held-out data, which enhances accuracy and reliability in forensic shoe print analysis.

These authors contributed equally to this work.

Graph Contrastive Learning (GCL) has shown superior performance in representation learning in graph-structured data. Despite their success, most existing GCL methods rely on prefabricated graph augmentation and homophily assumptions. Thus, they fail to generalize well to heterophilic graphs where connected nodes may have different class labels and dissimilar features.

Landmark cases on social media's impact on children begin this week in US Two lawsuits accusing the world's largest social media companies of harming children begin this week, marking the first legal efforts to hold companies like Meta responsible for the effects their products have on young users. Opening arguments began today in a case brought by New Mexico's attorney general's office, which alleges that Meta failed to protect children from sexually explicit material. A separate case in Los Angeles, which accuses Meta and the Google-owned YouTube of deliberately designing their platforms to be addictive for children, is set to begin later this week. The New Mexico and California lawsuits are the first of a wave of 40 lawsuits filed by state attorneys general around the US against Meta, specifically, that allege that the social media giant is harming the mental health of young Americans. In the opening argument in the New Mexico case, which was first filed in 2023, prosecutors told jurors on Monday that Meta - Facebook and Instagram's parent company - had failed to disclose its platforms' harmful effects on kids.

Despite significant efforts, both practical and theoretical, the problem remains open.

Unsupervised domain adaptation (UDA)enables cross-domain learning without target domain labels by transferring knowledge from a labeled source domain whose distribution differs from that of the target. However, UDA is not always successful and several accounts of'negative transfer' have been reported in the literature.