Entities, as important carriers of real-world knowledge, play a key role in many NLP tasks. We focus on incorporating entity knowledge into an encoder-decoder framework for informative text generation. Existing approaches tried to index, retrieve, and read external documents as evidence, but they suffered from a large computational overhead. In this work, we propose an encoder-decoder framework with an entity memory, namely EDMem. The entity knowledge is stored in the memory as latent representations, and the memory is pre-trained on Wikipedia along with encoder-decoder parameters. To precisely generate entity names, we design three decoding methods to constrain entity generation by linking entities in the memory. EDMem is a unified framework that can be used on various entity-intensive question answering and generation tasks. Extensive experimental results show that EDMem outperforms both memory-based auto-encoder models and non-memory encoder-decoder models.

With the increasing availability of wearable devices, continuous monitoring of individuals' physiological and behavioral patterns has become significantly more accessible. Access to these continuous patterns about individuals' statuses offers an unprecedented opportunity for studying complex diseases and health conditions such as type 2 diabetes (T2D). T2D is a widely common chronic disease that its roots and progression patterns are not fully understood. Predicting the progression of T2D can inform timely and more effective interventions to prevent or manage the disease. In this study, we have used a dataset related to 63 patients with T2D that includes the data from two different types of wearable devices worn by the patients: continuous glucose monitoring (CGM) devices and activity trackers (ActiGraphs). Using this dataset, we created a model for predicting the levels of four major biomarkers related to T2D after a one-year period. We developed a wide and deep neural network and used the data from the demographic information, lab tests, and wearable sensors to create the model. The deep part of our method was developed based on the long short-term memory (LSTM) structure to process the time-series dataset collected by the wearables. In predicting the patterns of the four biomarkers, we have obtained a root mean square error of 1.67% for HBA1c, 6.22 mg/dl for HDL cholesterol, 10.46 mg/dl for LDL cholesterol, and 18.38 mg/dl for Triglyceride. Compared to existing models for studying T2D, our model offers a more comprehensive tool for combining a large variety of factors that contribute to the disease.

In the 11 months since 17 teachers and students were killed at Marjory Stoneman Douglas High School in Parkland, Florida, campuses across the country have started spending big on surveillance technology. The Lockport, New York, school district spent $1.4 million in state funds on a facial-recognition system. Schools in Michigan, Massachusetts, and Los Angeles have adopted artificial-intelligence software--prone to false positives--that scans students' Facebook and Twitter accounts for signs that they might become a shooter. In New Mexico, students as young as 6 are under acoustic surveillance, thanks to a gunshot-detection program originally developed for use by the military to track enemy snipers. Earlier this month, the Marjory Stoneman Douglas High School Public Safety Commission released its report on the safety and security failures that contributed to fatalities during last year's shooting.

In this July 10, 2018 photo, a camera with facial recognition capabilities hangs from a wall while being installed at Lockport High School in Lockport, N.Y. The surveillance system that has kept watch on students entering Lockport schools for over a decade is getting a novel upgrade. Facial recognition technology soon will check each face against a database of expelled students, sex offenders and other possible troublemakers. It could be the start of a trend as more schools fearful of shootings consider adopting the technology, which has been gaining ground on city streets and in some businesses and government agencies. Just last week, Seattle-based digital software company RealNetworks began offering a free version of its facial recognition system to schools nationwide.