We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors ...

On the other hand, Embedding is more suited for large datasets with high-cardinality categorical features. It is beneficial when capturing relationships between categories is important. One-hot ...

One-hot encoding and dummy variable trap are two important concepts in data science, especially when dealing with categorical variables. In this article, I will explain what they are, why they matter, ...

Mellon, J., and Worrell, C., 2023: Explainability in Cybersecurity Data Science. Software Engineering Institute blog, Accessed June 24, 2026, https://doi.org/10.58012 ...

We review encoding and hardware-independent formulations of optimization problems for quantum computing. Using this generalized approach, an extensive library of optimization problems from the ...

Abstract: In this article, we propose a novel method of formulating an NP-hard wireless channel assignment problem as a higher-order unconstrained binary optimization (HUBO), where the Grover adaptive ...

Because machine learning with deep neural techniques has advanced quickly, our resident data scientist updates binary classification techniques and best practices based on experience over the past two ...

This repository contains the code for the paper "Efficient Interactive LLM Serving with Proxy Model-based Sequence Length Prediction" (link). The key idea/observation is that a small proxy model (i.e.

Abstract: The differences in performance among binary-integer encodings in an Ising machine, which can solve combinatorial optimization problems, are investigated. Many combinatorial optimization ...

Quantum annealing is a heuristic algorithm for solving combinatorial optimization problems, and hardware for implementing this algorithm has been developed by D-Wave Systems Inc. The current version ...