MIDAS Reproducibility Challenge Showcase: #5
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What Works Best When? A Systematic Evaluation of Heuristics for Max-Cut and QUBO
Iain Dunning (Hudson River Trading), Swati Gupta (Assistant Professor, Industrial and Systems Engineering, Georgia Tech), John Silberholz (Assistant Professor, Ross School of Business, University of Michigan)
A Multi-informatic Cellular Visualization tool for interactively interrogating high-dimensional datasets
Nigel Michki (Doctoral Student – Biophysics, University of Michigan)
What Works Best When? A Systematic Evaluation of Heuristics for Max-Cut and QUBO: Though empirical testing is broadly used to evaluate heuristics, there are shortcomings with how it is often applied in practice. In a systematic review of Max-Cut and Quadratic Unconstrained Binary Optimization (QUBO) heuristics papers, we found only 4% publish source code, only 14% compare heuristics with identical termination criteria, and most experiments are performed with an artificial, homogeneous set of problem instances. To address these limitations, we implement and release as open-source a code-base of 10 MaxCut and 27 QUBO heuristics, which we evaluated using cloud computing on a diverse library of 3,296 instances.
A Multi-informatic Cellular Visualization tool for interactively interrogating high-dimensional datasets: Biologists looking to scRNA-seq as a high-throughput exploratory method are often bogged down by the multitude of bioinformatics tools and pipelines available today. This is unfortunate because many tools have matured beyond the shortcomings of their predecessors and are capable of drawing informative suggestions without the need for elaborate parameter tuning. MiCV, the Multi-informatic Cellular Visualization tool that we have built in this work, is our answer to the questions: “Which pipeline should I be using? How long will it take me to learn how to use it? And, how can I communicate with my colleagues or reviewers if they do not know the same tools?” By providing a uniform web interface to a set of essential analytical tools, we aim to accelerate the pace at which biologists can make novel discoveries on their own datasets, validate other researchers’ findings, and dig deeper into the many massive scRNA-seq datasets being rapidly generated around the globe.