MS-nowcasting: Operational Precipitation Nowcasting with Convolutional LSTMs at Microsoft Weather

We present the encoder-forecaster convolutional long short-term memory (LSTM) deep-learning model that powers Microsoft Weather's operational precipitation nowcasting product. This model takes as input a sequence of weather radar mosaics and deterministically predicts future radar reflectivity at lead times up to 6 hours. By stacking a large input receptive field along the feature dimension and conditioning the model's forecaster with predictions from the physics-based High Resolution Rapid Refresh (HRRR) model, we are able to outperform optical flow and HRRR baselines by 20-25% on multiple metrics averaged over all lead times.Comment: Minor updates to reflect final submission to NeurIPS worksho

Machine Learning at Microsoft with ML .NET

Machine Learning is transitioning from an art and science into a technology available to every developer. In the near future, every application on every platform will incorporate trained models to encode data-based decisions that would be impossible for developers to author. This presents a significant engineering challenge, since currently data science and modeling are largely decoupled from standard software development processes. This separation makes incorporating machine learning capabilities inside applications unnecessarily costly and difficult, and furthermore discourage developers from embracing ML in first place. In this paper we present ML .NET, a framework developed at Microsoft over the last decade in response to the challenge of making it easy to ship machine learning models in large software applications. We present its architecture, and illuminate the application demands that shaped it. Specifically, we introduce DataView, the core data abstraction of ML .NET which allows it to capture full predictive pipelines efficiently and consistently across training and inference lifecycles. We close the paper with a surprisingly favorable performance study of ML .NET compared to more recent entrants, and a discussion of some lessons learned

Comparison Of Lung Function Tests Between Healthy And Asthmatic Individuals: Comparison of Pulmonary Functions Tests

Background: Comparison of pulmonary function tests (PFTs) were achieved between asthmatic case and Controls. Materials & Methods: Case-control study conducted at Baqai Medical University (50 cases and 50 control). PFTs in both the participants were estimated by spirometry; FEC, FEV1 and their ratios were estimated. Results: According to the findings all spirometric values were lower in asthmatic patients as compared to healthy subjects. Moreover the asthamatic subjects had lower lung values when compared with healthy subjects. Conclusion: The current study evaluated spirometric values in asthmatic patients in Gadap area. This study's findings can be applied to the treatment of asthma in people of all ages