108,692 research outputs found
Allocation in Practice
How do we allocate scarcere sources? How do we fairly allocate costs? These
are two pressing challenges facing society today. I discuss two recent projects
at NICTA concerning resource and cost allocation. In the first, we have been
working with FoodBank Local, a social startup working in collaboration with
food bank charities around the world to optimise the logistics of collecting
and distributing donated food. Before we can distribute this food, we must
decide how to allocate it to different charities and food kitchens. This gives
rise to a fair division problem with several new dimensions, rarely considered
in the literature. In the second, we have been looking at cost allocation
within the distribution network of a large multinational company. This also has
several new dimensions rarely considered in the literature.Comment: To appear in Proc. of 37th edition of the German Conference on
Artificial Intelligence (KI 2014), Springer LNC
Parrondo Strategies for Artificial Traders
On markets with receding prices, artificial noise traders may consider
alternatives to buy-and-hold. By simulating variations of the Parrondo
strategy, using real data from the Swedish stock market, we produce first
indications of a buy-low-sell-random Parrondo variation outperforming
buy-and-hold. Subject to our assumptions, buy-low-sell-random also outperforms
the traditional value and trend investor strategies. We measure the success of
the Parrondo variations not only through their performance compared to other
kinds of strategies, but also relative to varying levels of perfect
information, received through messages within a multi-agent system of
artificial traders.Comment: 10 pages, 4 figure
A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases
Machine learning has provided, over the last decades, tools for knowledge extraction in complex medical domains. Most of these tools, though, are ad hoc solutions and lack the systematic approach that would be required to become mainstream in medical practice. In this brief paper, we define a machine learning-based analysis pipeline for helping in a difficult problem in the field of neuro-oncology, namely the discrimination of brain glioblastomas from single brain metastases. This pipeline involves source extraction using k-Meansinitialized Convex Non-negative Matrix Factorization and a collection of classifiers, including Logistic Regression, Linear Discriminant Analysis, AdaBoost, and Random Forests.Peer ReviewedPostprint (published version
A discussion on the validation tests employed to compare human action recognition methods using the MSR Action3D dataset
This paper aims to determine which is the best human action recognition
method based on features extracted from RGB-D devices, such as the Microsoft
Kinect. A review of all the papers that make reference to MSR Action3D, the
most used dataset that includes depth information acquired from a RGB-D device,
has been performed. We found that the validation method used by each work
differs from the others. So, a direct comparison among works cannot be made.
However, almost all the works present their results comparing them without
taking into account this issue. Therefore, we present different rankings
according to the methodology used for the validation in orden to clarify the
existing confusion.Comment: 16 pages and 7 table
On Pruning for Score-Based Bayesian Network Structure Learning
Many algorithms for score-based Bayesian network structure learning (BNSL),
in particular exact ones, take as input a collection of potentially optimal
parent sets for each variable in the data. Constructing such collections
naively is computationally intensive since the number of parent sets grows
exponentially with the number of variables. Thus, pruning techniques are not
only desirable but essential. While good pruning rules exist for the Bayesian
Information Criterion (BIC), current results for the Bayesian Dirichlet
equivalent uniform (BDeu) score reduce the search space very modestly,
hampering the use of the (often preferred) BDeu. We derive new non-trivial
theoretical upper bounds for the BDeu score that considerably improve on the
state-of-the-art. Since the new bounds are mathematically proven to be tighter
than previous ones and at little extra computational cost, they are a promising
addition to BNSL methods
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