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).

Enforcement and Compliance's Global Steel Trade Monitor provides global import and export trends for the top countries trading in steel mill products.To supplement ITA's core trade statistics in the Steel Import Monitoring and Analysis (SIMA) system, quarterly country reports for the top 20 steel importing and top 20 steel exporting countries will be published.Each country report includes export and import trends by country and aggregate product, production and consumption data, and where available, information about trade remedy actions taken on steel products.

The Steel Import Monitoring and Analysis (SIMA) System, under the Department of Commerce, collects and publishes data of steel mill product imports. By design this information provides stakeholders and public valuable information on steel trade with the United States.

Table of U.S. exports of steel mill products for all steel mill products by quantity in metric tons.

The ‘Import Monitor’ aggregates detailed license information and makes it available for public analysis on a weekly basis. All imported steel products under the U.S. Bureau of the Census (Census) definition of steel mill products are required to have an import license effective June 9, 20051. The data from the licensing system are used in conjunction with similar Census data to put together the ‘Import Monitor’. This Monitor helps alert users to changes in import patterns.

Frequently Asked Questions covering the Steel Import Monitoring system, which includes both the steel import monitoring system and the import monitor

Contains Artificial Intelligence Patent Landscape data classifying 13,244,037 granted patents and PGPubs published from 1976 through 2021 in eight AI component technologies using state-of-the art machine learning based models.