Szymon

Do you need high-resolution data for your machine learning, but you have only areal aggregates? Would you like to present continuous maps instead of choropleth maps? We can transform county-level data into smaller blocks with Pyinterpolate. We will learn how to perform Poisson Kriging on the areal dataset during workshops.

Choropleth maps representing areal aggregates are standard in the social sciences. We aggregate data over areas for administrative purposes and protect citizens' privacy. Unfortunately, those aggregated datasets can be misleading: - Administrative units, especially in Europe, vary significantly in shape and size, - Large units tend to be visually more important than smaller areas, - It is hard to integrate areal data into machine learning pipelines with data at a smaller and regular scale. There is a solution for the processes that are spatially correlated and represent rates. One example is the disease incidence rate map. An incidence rate is the number of disease cases per area divided by the total population in this area and multiplied by the constant number of 100,000. Through the denominator (total population), we can divide our space into smaller blocks – in this case, the population blocks. Then we regularize the semivariogram of areal data with the population density semivariogram to obtain a final model that considers fine-scale population blocks and can predict disease rates at a smaller scale. After this transformation, we can: - show a continuous map of disease rates, - avoid problems with the visual discrepancy between different areas' sizes, - use data with better spatial resolution as an input for machine learning pipelines; for example, we can merge data with the remotely sensed information. We will learn how to transform areal aggregates into smaller blocks during workshops. We will use the Pyinterpole package. We will discuss the most dangerous modeling pitfalls and what can be done with the output data. If you are an expert in the economy, social sciences, public health, or similar fields, this workshop is for you. Pyinterpolate is a Python package for spatial interpolation. It is available here: https://pypi.org/project/pyinterpolate/

There's a variety of places - on Earth and beyond - that pose challenging conditions to the ever-shrinking digital circuits of today. Making those tiny transistors work reliably when bombarded with charged particles in the vacuum of space, in the underground tunnels of CERN or in your local hospital's X-ray machine is not an easy feat. This talk is going to shed some light on what can be done to keep particles from messing up your ones and zeroes, how errors in digital circuits can be detected and corrected, and how you may even re-purpose those flipped bits in your RAM as a particle detector.

This talk will introduce the audience to the class of problems that digital circuits are faced with in challenging radiation environments. Such environments include satellites in space, the electronics inside particle accelerators and also a variety of medical applications. After giving an overview of the various effects that may cause malfunctions, different techniques for detection and mitigation of such effects are presented. Some of these techniques concern the transistor-level design of digital circuits, others include triple modular redundancy (TMR) and correction codes. Some open source software solutions that aid in the design and verification of circuits hardened against such problems are presented, and of course a 'lessons learned' from our experiences in the field of particle detector electronics will be shared.

Other people: thasti