MOTIVATION: Efforts to address health disparities are often limited by the lack of robust computational tools for inferring genetic ancestry by calculating an individual's genetic similarity to continental groups. We have already shown that a preferred alternative to self-described race is using ancestry-informative markers (AIMs) that can be classified into ancestral components and used to estimate their similarity to those of known populations to identify continental groups. However, real-world genomic data can present challenges, including limited availability of germline DNA, a small number of AIMs for each sample, and the use of different variant calling software, limiting the application of existing solutions. RESULTS: Here, we describe a novel supervised machine-learning tool AncestryGeni, which infers genetic ancestry for samples with even a hundred markers and is applicable to any genomic data, including whole exome sequencing (WES) and RNA sequencing (RNA-Seq) data. Applying AncestryGeni to a real-world genomic dataset obtained from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study, we show that it is more accurate than the commonly used FastNGSadmix when using nonstandard genomic material. We also demonstrate that when using AncestryGeni, the tumor-derived sequence obtained from WES and RNA-Seq can be a robust data source to accurately estimate an individual's genetic similarity to a continental group. AVAILABILITY AND IMPLEMENTATION: AncestryGeni pipeline is available at https://github.com/eelhaik/AncestryGeni/tree/main.

Email is a critical Internet application, and its security is important. The Sender Policy Framework (SPF), DomainKeys Identified Mail (DKIM), and Domain-based Message Authentication, Reporting, and Conformance (DMARC) were developed to enable mail servers to detect and reject email coming from fraudulent sources. In this paper we study the state of SPF, DKIM, and DMARC validation across a large number of mail servers, the first such study at scale that we know of. We consider two behaviors of sender-validating mail servers: behavior when an email with a valid sender is received and behavior when an email from a invalid sender is received. Our techniques allow us to elicit SPF, DKIM, and DMARC validation behavior of the servers without spam. We find that as many as 85% of mail servers are deploying SPF validation, and over half are deploying all three mechanisms: SPF, DKIM, and DMARC. We also observe there are some nuanced behaviors with regard to adherence to the SPF specification.

We discuss the problem of assessing and aiding user performance in dynamic tasks that require rapid selection among multiple information sources. Motivated by research in human sequential learning, we develop a system that learns by observation to predict the information a user desires in different contexts. The model decides when the display should be updated, which is akin to the problem of scene segmentation, and then selects the situationally relevant information display. The model reduces the cognitive burden of selecting situation-relevant displays. We evaluate the system in a tank video game environment and find that the system boosts user performance. The fit of the model to user data provides a quantitative assessment of user behavior, which is useful in assessing individual differences and the progression from novice- to expert-level proficiency. We discuss the relative benefits of adopting a learning approach to predicting information preferences and possible avenues to reduce the negative consequences of automation.