Shwe Kokko city, a casino, entertainment, and tourism complex,from Thailand's side of the border after Bangkok said it would suspend electricity supply to some border areas with Myanmar to try to curb scam centers, in the Mae Sot district, Thailand, on Feb. 5, 2025 | REUTERS Criminals in Southeast Asia are harnessing inexpensive artificial intelligence tools to target bigger pools of potential victims at high speed, keeping scam centers humming even as governments try and crack down, senior officials at Interpol say. Previously, some scams were easy to spot -- from poor quality online ads luring people to work in such centers to the scams themselves, typically designed to make people part with their money through the promise of romance or investment returns. Now, scammers are using large language models and other AI tools to make their cons more sophisticated. Artificial intelligence also allows them to change course quickly, shifting to newer targets and from fresh locations. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

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.

Choosing relevant predictors is central to the analysis of biomedical time-to-event data. Classical frequentist inference, however, presumes that the set of covariates is fixed in advance and does not account for data-driven variable selection. As a consequence, naive post-selection inference may be biased and misleading. In right-censored survival settings, these issues may be further exacerbated by the additional uncertainty induced by censoring. We investigate several inference procedures applied after variable selection for the coefficients of the Lasso and its extension, the adaptive Lasso, in the context of the Cox model. The methods considered include sample splitting, exact post-selection inference, and the debiased Lasso. Their performance is examined in a neutral simulation study reflecting realistic covariate structures and censoring rates commonly encountered in biomedical applications. To complement the simulation results, we illustrate the practical behavior of these procedures in an applied example using a publicly available survival dataset.

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.

WhilecurrentHDmap constructors perform well under ideal conditions, their resilience to real-world challenges,e.g.,adverseweather andsensor failures, isnotwellunderstood, raising safety concerns.

Depending on the sequence in question the length of this list can vary by some orders of magnitude.