When the rapper Fenix Flexin débuted a new accent on his single "Rubberz," he stumbled into a century-old fight over musical fraudulence. Startups including Suno allow users to create songs with A.I., and some A.I. entities have started to attract followings on Spotify. Earlier this summer, a small night club in Passaic, New Jersey, hosted a brief performance by one of the year's most talked-about musicians. A post on Instagram had promised attendees "an unforgettable night," although not necessarily a long one: the only assurance was that the Los Angeles rapper known as Fenix Flexin would be "performing his viral hit'Rubberz' live!" Until recently, Fenix was known for high-spirited and slightly mush-mouthed hip-hop tracks, but "Rubberz," which was released in June, marked a dramatic change: it was sad but sprightly synth pop, like an unheard outtake from the nineteen-eighties; the vocals were crisp, with a hint of vibrato and a faintly British accent. Listeners seemed to delight in the incongruity--a semipopular rapper suddenly sounded nothing like himself.

We consider the problem of embedding entities and relationships of multirelational data in low-dimensional vector spaces. Our objective is to propose a canonical model which is easy to train, contains a reduced number of parameters and can scale up to very large databases. Hence, we propose TransE, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities. Despite its simplicity, this assumption proves to be powerful since extensive experiments show that TransE significantly outperforms state-of-the-art methods in link prediction on two knowledge bases. Besides, it can be successfully trained on a large scale data set with 1M entities, 25k relationships and more than 17M training samples.

When COVID-19 first started spreading and quarantine was implemented, the Society for Industrial and Applied Mathematics (SIAM) Student Chapter at the University of Minnesota-Twin Cities began a collaboration with Ecolab to use our skills as data scientists and mathematicians to extract useful insights from relevant data relating to the pandemic. This collaboration consisted of multiple groups working on different projects. In this write-up we focus on using clustering techniques to help us find groups of similar counties in the US and use that to help us understand the pandemic. Our team for this project consisted of University of Minnesota students Cora Brown, Sarah Milstein, Tianyi Sun, and Cooper Zhao, with help from Ecolab Data Scientist Jimmy Broomfield and University of Minnesota student Skye Ke. In the sections below we describe all of the work done for this project. In Section 2, we list the data we gathered, as well as the feature engineering we performed. In Section 3, we describe the metrics we used for evaluating our models. In Section 4, we explain the methods we used for interpreting the results of our various clustering approaches. In Section 5, we describe the different clustering methods we implemented. In Section 6, we present the results of our clustering techniques and provide relevant interpretation. Finally, in Section 7, we provide some concluding remarks comparing the different clustering methods.