The Long Wait (Part I): A Personal Account of Infantry Training in Britain, June 1942–June 1943

In the early summer of 1942, Harold (Hal) MacDonald, a young infantry officer from Saint John, New Brunswick, was posted overseas to join the North Shore (New Brunswick) Regiment, then stationed in Great Britain. The North Shores were part of a growing Canadian military presence in Britain, preparing for the day when the Allies would return to the continent to help defeat the armies of Adolf Hitler’s Third Reich. Canadian troops had begun to arrive in England in 1939, and indeed, after the fall of France in the late spring of 1940, formed an important part of Britain’s defence forces at a time when it and the Commonwealth stood alone against the combined might of Germany and Italy. By the time that MacDonald arrived, the number of Canadian troops had swelled to some 130,000, for the most part concentrated in the south of England, where they underwent rigorous training exercises and highly realistic simulated battles designed to prepare them to meet the enemy

Combining Terrier with Apache Spark to Create Agile Experimental Information Retrieval Pipelines

Experimentation using IR systems has traditionally been a procedural and laborious process. Queries must be run on an index, with any parameters of the retrieval models suitably tuned. With the advent of learning-to-rank, such experimental processes (including the appropriate folding of queries to achieve cross-fold validation) have resulted in complicated experimental designs and hence scripting. At the same time, machine learning platforms such as Scikit Learn and Apache Spark have pioneered the notion of an experimental pipeline , which naturally allows a supervised classification experiment to be expressed a series of stages, which can be learned or transformed. In this demonstration, we detail Terrier-Spark, a recent adaptation to the Terrier Information Retrieval platform which permits it to be used within the experimental pipelines of Spark. We argue that this (1) provides an agile experimental platform for information retrieval, comparable to that enjoyed by other branches of data science; (2) aids research reproducibility in information retrieval by facilitating easily-distributable notebooks containing conducted experiments; and (3) facilitates the teaching of information retrieval experiments in educational environments