4 research outputs found
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