There has recently been growing interest in the automatic generation of cooking recipes that satisfy some form of dietary restrictions, thanks in part to the availability of online recipe data. Prior studies have used pre-trained language models, or relied on small paired recipe data (e.g., a recipe paired with a similar one that satisfies a dietary constraint). However, pre-trained language models generate inconsistent or incoherent recipes, and paired datasets are not available at scale. We address these deficiencies with RecipeCrit, a hierarchical denoising auto-encoder that edits recipes given ingredient-level critiques. The model is trained for recipe completion to learn semantic relationships within recipes. Our work's main innovation is our unsupervised critiquing module that allows users to edit recipes by interacting with the predicted ingredients; the system iteratively rewrites recipes to satisfy users' feedback. Experiments on the Recipe1M recipe dataset show that our model can more effectively edit recipes compared to strong language-modeling baselines, creating recipes that satisfy user constraints and are more correct, serendipitous, coherent, and relevant as measured by human judges.

Check out their product page … link Get the Chemometrics and Spectroscopy News in real time on Twitter @ CalibModel and follow us. Near-Infrared Spectroscopy (NIRS) "NIR Spectroscopic Techniques for Quality and Process Control in the Meat Industry" LINK "Estimating coefficient of linear extensibility using Vis–NIR reflectance spectral data: Comparison of model validation approaches" LINK "NIR spectroscopy and chemometric tools to identify high content of deoxynivalenol in barley" LINK "Combining multivariate method and spectral variable selection for soil total nitrogen estimation by Vis–NIR spectroscopy" LINK "Multi-task deep learning of near infrared spectra for improved grain quality trait predictions" LINK "Multi-factor Fusion Models for Soluble Solid Content Detection in Pear (Pyrus bretschneideri'Ya') Using Vis/NIR Online Half-transmittance Technique" LINK "Determining regression equations for predicting the metabolic energy values of barley-producing cultivars in Iran and ...

In hypothesis testing, a false discovery occurs when a hypothesis is incorrectly rejected due to noise in the sample. When adaptively testing multiple hypotheses, the probability of a false discovery increases as more tests are performed. Thus the problem of False Discovery Rate (FDR) control is to find a procedure for testing multiple hypotheses that accounts for this effect in determining the set of hypotheses to reject. The goal is to minimize the number (or fraction) of false discoveries, while maintaining a high true positive rate (i.e., correct discoveries). In this work, we study False Discovery Rate (FDR) control in multiple hypothesis testing under the constraint of differential privacy for the sample. Unlike previous work in this direction, we focus on the online setting, meaning that a decision about each hypothesis must be made immediately after the test is performed, rather than waiting for the output of all tests as in the offline setting. We provide new private algorithms based on state-of-the-art results in non-private online FDR control. Our algorithms have strong provable guarantees for privacy and statistical performance as measured by FDR and power. We also provide experimental results to demonstrate the efficacy of our algorithms in a variety of data environments.

If you're lucky, you can even taste it. It is the start of grilling season. Colemans and Kenmores are being brought out of hibernation, as barbecue fans across the country scramble to find their favorite tongs, while butchers get busy prepping ribs, steaks and more. Yes, backyard cooking can imply a lot of work, but it doesn't have to be as time consuming as it's been in the past. Thanks to chefs and barbecue pros willing to share their tricks and tips, there are dozens of ways to cut corners at your next cookout.