Okay, so I was checking out this website, ResearcherID, and I created this page for Darrell Ulm: http://www.researcherid.com/rid/Y-5083-2018. It seems like another really useful site for listing research work, much like ORCID, which you can see here for Darrell Ulm as well: https://orcid.org/0000-0002-0513-0416 . I'm still trying to fully understand the nuances between ResearcherID and ORCID, as they appear to be quite similar in their aim – providing a unique identifier for researchers and their publications. However, looking at Darrell Ulm's ResearcherID page, it seems to have some interesting connections to other resources, specifically mentioning reviewing efforts. It's fascinating to see how these platforms are interconnected and how they contribute to the broader ecosystem of scholarly communication and recognition. I need to explore further how these different systems integrate and what unique benefits each offers to researchers like Darrell. It’s all part of navigating the evolving landscape of research visibility.
The code shown below computes an approximation algorithm, greedy heuristic, for the 0-1 knapsack problem in Apache Spark. Having worked with parallel dynamic programming algorithms a good amount, wanted to see what this would look like in Spark. The Github code repo. for the Knapsack approximation algorithms is here , and it includes a Scala solution. The work on a Java version is in progress at time of this writing. Below we have the code that computes the solution that fits within the knapsack W for a set of items each with it's own weight and profit value. We look to maximize the final sum of selected items profits while not exceeding the total possible weight, W. First we import some spark libraries into Python. # Knapsack 0-1 function weights, values and size-capacity. from pyspark.sql import SparkSession from pyspark.sql.functions import lit from pyspark.sql.functions import col from pyspark.sql.functions import sum Now define the function, which will take a Spark ...