The Dice score is widely used for binary segmentation due to its robustness to class imbalance. Soft generalisations of the Dice score allow it to be used as a loss function for training convolutional neural networks (CNN). Although CNNs trained using mean-class Dice score achieve state-of-the-art results on multi-class segmentation, this loss function does neither take advantage of inter-class...

DICE (the DSPCAD Integrative Command Line Environment) is a package of utilities that facilitates efficient management of software projects. Key areas of emphasis in DICE are cross-platform operation, support for projects that integrate heterogeneous programming languages, and support for applying and integrating different kinds of design and testing methodologies. The package is being develope...

Most RPGs (role-playing games) use some sort of randomizer when resolving actions. Most often dice are used for this, but a few games use cards, rock-paper-scissors or other means of randomization. There are dozens of different ways dice have been used in RPGs, and we are likely to see many more in the future. This is not an evolution from bad methods to better methods – there is no such thing ...

There are two players playing the game, player U and player I. Each player chooses a (distinct) die and rolls – the player with the higher value is the winner. I kindly gives player U the first choice of any one of the three dice. Let us say that U chooses die X. After some thought, I chooses die Z, and then both players roll their dice. What are the possibilities? Because both dice have 6 side...

We develop a theoretical model about how organizations cope with the bullwhip effect created by consumer demand uncertainty through product modularity and information sharing across the supply chain. Unpredictability of consumer demand is likely to accentuate inventory flows in the supply chain. Information sharing and product modularity can be used by organizations to mediate the impact of unc...

The overall research in Reinforcement Learning (RL) concentrates on discrete sets of actions, but for certain real-world problems it is important to have methods which are able to find good strategies using actions drawn from continuous sets. This paper describes a simple control task called direction finder and its known optimal solution for both discrete and continuous actions. It allows for ...