LIMBO is a tool for the creation of load profiles with variable intensity over time both from scratch and from existing data. Primarily, LIMBO’s intended use is the description of load arrival behaviours in open workloads. Specifically, LIMBO can be employed for the creation of custom request or user arrival time-stamps or for the re-parameterization of existing traces. LIMBO bases on the Desca...

Clustering is a problem of great practical importance in numerous applications. The problem of clustering becomes more challenging when the data is categorical, that is, when there is no inherent distance measure between data values. We introduce LIMBO, a scalable hierarchical categorical clustering algorithm that builds on the Information Bottleneck (IB) framework for quantifying the relevant ...

Clustering is a problem of great practical importance in numerous applications. The problem of clustering becomes more challenging when the data is categorical, that is, when there is no inherent distance measure between data values. In this work, we introduce LIMBO, a scalable hierarchical categorical clustering algorithm that builds on the Information Bottleneck (IB) framework for quantifying...

Web services are increasingly adopted as a service provision mechanism in pervasive computing environments. Implementing web services on networked, embedded devices raises a number of challenges, for example efficiency of web services, handling of variability and dependencies of hardware and software platforms, and of device state and context changes. To address these challenges, we developed a...

WE-14 is generated in subpopulations of chromogranin A immunopositive endocrine cells and neurons including those innervating the anterior uvea. This study investigated WE-14 in intact sclero-limbo-corneal tissue from embryonic (E17), neonatal (N0-N16), and adult mice using immunocytochemistry and confocal scanning laser microscopy. Weak WE-14 immunostaining was observed at birth in nerve fibre...

Limbo is an open-source C++11 library for Bayesian optimization which is designed to be both highly flexible and very fast. It can be used to optimize functions for which the gradient is unknown, evaluations are expensive, and runtime cost matters (e.g., on embedded systems or robots). Benchmarks on standard functions show that Limbo is about 2 times faster than BayesOpt (another C++ library) f...