A code repository and accompanying data for incorporating imperfect theory into machine learning for improved prediction and explainability. Specifically, it focuses on the case study of the dimensions of a polymer chain in different solvent qualities. Jupyter Notebooks for quickly testing concepts and reproducing figures, as well as source code that computes the mean squared error as a function of dataset size for various machine learning models are included.For additional details on the data, please refer to the README.md associated with the data.

SolDet is an object-oriented package for solitonic feature detection in absorption images of Bose-Einstein condensate. with wider use for cold atom image analysis. Featured with classifier, object detector, and Mexican hat metric methods. Technical details are explained in https://arxiv.org/abs/2111.04881.

This research software package contains Python code to execute experiments on deep generative modeling of classical random process models for noise time series. Specifically, it includes Pytorch implementations of two generative adversarial network (GAN) models for time series based on convolutational neural networks (CNNs): WaveGAN, a 1-D CNN model, and STFT-GAN, a 2-D CNN model. In addition, there are methods for generating and evaluating noise time series defined several by classical random process models.

Lipid nanoparticles (LNPs) were prepared as described (https://doi.org/10.1038/s42003-021-02441-2) using the lipids DLin-KC2-DMA, DSPC, cholesterol, and PEG-DMG2000 at mol ratios of 50:10:38.5:1.5. Four sample types were prepared: LNPs in the presence and absence of RNA, and with LNPs ejected into pH 4 and pH 7.4 buffer after microfluidic assembly. To prepare samples for imaging, 3 ?L of LNP formulation was applied to holey carbon grids (Quantifoil, R3.5/1, 200 mesh copper).

Monthly and annual Canadian arrivals of one or more nights to the U.S. are provided by Statistics Canada for analysis and reporting. A limited amount of U.S. resident travel to Canada is also reported at a monthly level. Monthly level data are reported by mode of transportation with a 3-4 month lag time. Annual data are made available to Tourism Industries at the end of May and a written report with graphics and spreadsheets is generally available in the late summer. The annual report analyzes travelers by province of origin, season of travel, mode of transportation, etc.

An econometric-based forecast of tourism arrivals to the U.S. from more than 20 primary and secondary international markets. Provides forecasted arrivals for a 3 year horizon. Outbound international travel is forecast or reported for Mexico, Canada and overseas travel only.

Monthly international visitation data are collected and reported by the National Travel and Tourism Office's (NTTO) Visitor Arrivals Program (I-94 Record).

This program provides information on U.S. travel and tourism receipts (exports), payments (imports), and the balance of trade for major world regions and select countries. The National Travel and Tourism Office (NTTO) obtains these data from the Bureau of Economic Analysis (BEA) and publishes preliminary data in March, revised data in June, and final annual data in October.

The National Travel and Tourism Office (NTTO) works cooperatively with the U.S. Department of Homeland Security (DHS)/U.S. Customs and Border Protection (CBP)to release arrivals data, providing a comprehensive count of all travelers entering the United States.

Monthly U.S. citizen departures are collected and reported in Tourism Industries U.S. International Air Travel Statistics (I-92 data) Program.