An Error Analysis Toolkit for Binned Counting Experiments

Error Analysis of Experiments

A Toolkit for Analysis of Deep Learning Experiments

Learning experiments are complex procedures which generate high volumes of data due to the number of updates which occur during training and the number of trials necessary for hyper-parameter selection. Often during runtime, interim result data is purged as the experiment progresses. This purge makes rolling-back to interim experiments, restarting at a specific point or discovering trends and p...

Data Exploration Toolkit for serial diffraction experiments

Ultrafast diffraction at X-ray free-electron lasers (XFELs) has the potential to yield new insights into important biological systems that produce radiation-sensitive crystals. An unavoidable feature of the `diffraction before destruction' nature of these experiments is that images are obtained from many distinct crystals and/or different regions of the same crystal. Combined with other sources...

Metro: An Analysis Toolkit for Template Semantics

We describe the Metro toolkit, which supports software modelling and analysis for requirements notations that have configurable semantics. Metro is based on a formalism, called template semantics, which structures the operational semantics of a family of notations as a predefined parameterized template that is instantiated with user-provided parameter values. Thus, the semantics of a single not...

On the Estimation Error in Binned Lo al Linear Regression