In many real-world applications of machine learning, we are interested to know if it is possible to train on the data that we have gathered so far, and obtain accurate predictions on a new test data subset that is qualitatively different in some respect (time period, geographic region, etc). Another question is whether data subsets are similar enough so that it is beneficial to combine subsets during model training. We propose SOAK, Same/Other/All K-fold cross-validation, a new method which can be used to answer both questions. SOAK systematically compares models which are trained on different subsets of data, and then used for prediction on a fixed test subset, to estimate the similarity of learnable/predictable patterns in data subsets. We show results of using SOAK on six new real data sets (with geographic/temporal subsets, to check if predictions are accurate on new subsets), 3 image pair data sets (subsets are different image types, to check that we get smaller prediction error on similar images), and 11 benchmark data sets with predefined train/test splits (to check similarity of predefined splits).

Recent studies of large-scale contrastive pretraining in the text embedding domain show that using single-source minibatches, rather than mixed-source minibatches, can substantially improve overall model accuracy. In this work, we explore extending training data stratification beyond source granularity by leveraging a pretrained text embedding model and the classic k-means clustering algorithm to further split training data apart by the semantic clusters within each source. Experimentally, we observe a notable increase in NDCG@10 when pretraining a BERT-based text embedding model on query-passage pairs from the MSMARCO passage retrieval dataset. Additionally, we conceptually connect our clustering approach to both the Topic Aware Sampling (TAS) aspect of the TAS-B methodology and the nearest-neighbor-based hard-negative mining aspect of the ANCE methodology and discuss how this unified view motivates future lines of research on the organization of contrastive pretraining data.

Roughly 100 alien abduction cases spanning over the past 30 years are set to be investigated by scientists with hopes of unraveling the mysterious experiences. Researchers at Rice University in Texas plan to analyze a trove of archives consisting of thousands of documents in the form of books, journals, photographs, slides, reports, meeting notes and letters. The cases include the famous story of Betty and Barney Hill, a New Hampshire couple who say they were abducted in 1961 while driving up a mountain. Other cases include one that inspired a new Netflix show - the Pascagoula abduction. The'Archive of the Impossible' at Rice University plays host to material from Jacques Vallee (whose work inspired Steven Spielberg's'Close Encounters of the Third Kind') and author Whitley Streiber, who claimed to have been abducted by aliens in his 1987 book Communion among hundreds of other abduction stories.

A Tulane University student was fatally struck at a rest stop on Tuesday by two wheels that had reportedly flown off a tractor-trailer. A Tulane University student was killed at a highway rest stop in Mississippi on Tuesday after a pair of tires flew off a passing tractor-trailer and struck her. The victim was Margaret Maurer, an ecology and evolutionary biology major originally from Forest Lake, Minnesota. Mauer and two friends were about to get back into their car at rest stop on eastbound I-10 in Gautier when the tires hit her, their car and a nearby SUV, authorities said. UBER WON'T FACE CRIMINAL CHARGES IN DEADLY SELF-DRIVING CAR CRASH, PROSECUTOR SAYS The tractor-trailer that struck her was traveling westbound when it lost the wheels, then turned around and drove east.

A few years ago Eric Schadt met a woman who had cancer. It was an aggressive form of colon cancer that had come on quickly and metastasized to her liver. She was a young war widow from Mississippi, the mother of two girls she was raising alone, and she had only the health care that her husband's death benefits afforded her--an overburdened oncologist at a military hospital, the lowest rung on the health care ladder. To walk into such a facility with stage 4 metastatic disease is to walk back in time to the world of the unmapped human genome, when "colon cancer" was understood to have a single cause instead of millions of causes resulting in unique variations, when treatment was the same bag of poison, whether you were in Ocean Springs, Mississippi, or Timbuktu. A time without big data, machine learning, or hope.