New Release Books by Jason Wang

The Big Fat Notebook series for high school takes on Pre-Algebra & Algebra I, often a student's first high school-level math course, and a big challenge.

Jason, who grew up in Shekou since childhood, uses warm hand paintings to make more people feel Shekou: this place full of happiness. This is the first hand drawn city tour in China. Please open this book and come with us to Shekou to experience the slow pace!

The long-awaited cookbook from an iconic New York restaurant, revealing never-before-published recipes Since its humble opening in 2005, Xi’an Famous Foods has expanded from one stall in Flushing to 14 locations in Manhattan, Brooklyn, and Queens. CEO Jason Wang divulges the untold story of how this empire came to be, alongside the never-before-published recipes that helped create this New York City icon. From heavenly ribbons of liang pi doused in a bright vinegar sauce to flatbread ï¬?lled with caramelized pork to cumin lamb over hand-pulled Biang Biang noodles, this cookbook helps home cooks make the dishes that fans of Xi’an Famous Foods line up for while also exploring the vibrant cuisine and culture of Xi’an. Transporting readers to the streets of Xi’an and the kitchens of New York’s Chinatown, Xi’an Famous Foods is the cookbook that fans of Xi’an Famous Foods have been waiting for.

Need a quick laugh? Want to master the art of Dad jokes? Just want to make your coworkers groan? Fear not, 101 Preposterous Puns is for you! This book contains over 101 jokes (actually, over 200!) on topics like food, work, and the stress of life, tailor-made to get your friends and family to facepalm, chuckle, or roll on the floor laughing. PIck it up today and get ready for a journey that will have you giggling all the way to the end.

Examines interventions in the healthcare system that use Electronic Medical Record Systems (EMR-S) to affect patient trajectories--i.e., the sequence of encounters a patient has with the healthcare system--by improving health and thereby reducing healthcare utilization, or by reducing a costly form of utilization (e.g., inpatient stays) and increasing a more economical form (e.g., office visits to physicians, or prescription medications).

Topic models are probabilistic models used to abstract topical information from collections of text documents. Documents are modeled as probability distributions over latent topics and topics are modeled as probability distributions over words. In a single collection of documents, topics are global, that is, they are shared across the multiple documents in the collection. A nested document collection has documents that are nested inside a higher order structure, for example, stories in a book, articles in a journal, or web pages in a web site. Regular topic models ignore the nesting and treat all documents as distinct. In contrast, a nested document collection, such as web pages nested in web sites, web pages of the same web sites share similarities with each other that they do not share with web pages of other web sites. Regular topic models allow inferences about each web page individually; they are not suited for making inferences about an entire web site. We propose hierarchical local topic models that place a hierarchical prior on web page topic distributions and explicitly model local topics, or topics that are unique to one web site. The hierarchical prior asserts that web page topic distributions vary around their web site topic distribution and that web site topic distributions vary around a global topic distribution.Explicitly modeling local topics reduces the number of global topics needed, identifies the local topics and their owning web site, and lets us adjust inferences about how topics are covered. We propose hierarchical topic presence models that place a sparsity inducing prior on topic distributions; they let us model the presence of topics in web sites, web pages, or both web sites and web pages. Topic presence in a web site can be modeled with logistic regression, as a function of covariates. We apply hierarchical topic presence models to identify health topics in United States county health department web sites, estimate the percent of web sites that cover particular health topics, and identify demographic predictors of topic presence for human immunodeficiency virus (HIV) and opioid use disorder (OUD) topics.