This special edition is part of our Guest Prudie series, where we ask smart, thoughtful people to step in as Prudie for the day and give you advice. Today's columnist is number one New York Times-bestselling author Leigh Bardugo. She is the author of the books The Familiar, Ninth House and the creator of the Grishaverse (now a Netflix original series) which spans the Shadow and Bone trilogy, the Six of Crows duology, the King of Scars duology. Her short fiction has appeared in multiple anthologies including The Best American Science Fiction and Fantasy. She lives in Los Angeles and is an associate fellow of Pauli Murray College at Yale University. We asked Bardugo to weigh in on "romantic" gestures gone wrong, conversational vampires, and vocal dogs: I recently met a man on a dating app. We hit it off quickly. We were texting all of the time about work, writing, and the world--often getting pretty flirty. I was having tons of fun. He was charming and seemed to me conspicuously brilliant.

Abstract: Contrastive Learning has recently achieved state-of-the-art performance in a wide range of tasks. Many contrastive learning approaches use mined hard negatives to make batches more informative during training but these approaches are inefficient as they increase epoch length proportional to the number of mined negatives and require frequent updates of nearest neighbor indices or mining from recent batches. In this work, we provide an alternative to hard negative mining in supervised contrastive learning, Tail Batch Sampling (TBS), an efficient approximation to the batch assignment problem that upper bounds the gap between the global and training losses, LGlobal LTrain. TBS \textbf{improves state-of-the-art performance} in sentence embedding ( 0.37 Spearman) and code-search tasks ( 2.2\% MRR), is easy to implement -- requiring only a few additional lines of code, does not maintain external data structures such as nearest neighbor indices, is more computationally efficient when compared to the most minimal hard negative mining approaches, and makes no changes to the model being trained. Abstract: Collaborative filtering (CF) models easily suffer from popularity bias, which makes recommendation deviate from users' actual preferences.

A robot with a history of running away from its creators may seem like an odd choice to welcome travellers on Russia's transport network. But Moscow's Metro bosses have decided to employ the services of Metrosha, a quirky machine who will greet people arriving at the city's main subway station. The unusual android is based on the promobot, or promotional robot, which hit headlines last summer when it made a break for freedom from its testing laboratory. Moscow's Metro bosses are employing the services of Metrosha, a quirky robot who will greet people arriving at the city's main subway station on special occasions and public holidays Metrosha is based on promobot - short for Promotional Robot - a unique robot created by Russian scientists and is designed to work in customer relations. Promobot was designed for companies to use it to attract new customers.

If you've ever caught yourself thinking, "She looks like a Sue," or "He doesn't look like a Bob," a new study may back up your instincts about whether people's names suit them. In fact, people often do "look like their names," perhaps especially those named Tom or Veronique, the research suggests. In the study, researchers found that people could correctly match an unfamiliar face to that person's name at a rate higher than expected due to chance, according to a new study. In two experiments involving 185 participants in Israel and France, people were shown only color headshot photographs of 25 total strangers, and the researchers asked them to guess the stranger's name from a list of four or five name possibilities. For example, a participant who is shown a face and given four names to choose from has a 25 percent chance of guessing the right name.