Color Classification and Object Recognition for Robot Soccer Under Variable Illumination

Griffith Sciences, School of Information and Communication TechnologyFull Tex

A System Dynamics Model Approach for Simulating Hyper-inflammation in Different COVID-19 Patient Scenarios

The exceptionally high virulence of COVID-19 and the patients' precondition seem to constitute primary factors in how pro-inflammatory cytokines production evolves during the course of an infection. We present a System Dynamics Model approach for simulating the patient reaction using two key control parameters (i) virulence, which can be moderate or high and (ii) patient precondition, which can be healthy, not so healthy or serious preconditions. In particular, we study the behaviour of Inflammatory (M1) Alveolar Macrophages, IL6 and Active Adaptive Immune system as indicators of the immune system response, together with the COVID viral load over time. The results show that it is possible to build an initial model of the system to explore the behaviour of the key attributes involved in the patient condition, virulence and response. The model suggests aspects that need further study so that it can then assist in choosing the correct immunomodulatory treatment, for instance the regime of application of an Interleukin 6 (IL-6) inhibitor (tocilizumab) that corresponds to the projected immune status of the patients. We introduce machine learning techniques to corroborate aspects of the model and propose that a dynamic model and machine learning techniques could provide a decision support tool to ICU physicians

Non-Monotonic Reasoning on Board a Sony AIBO

Griffith Sciences, School of Information and Communication TechnologyFull Tex

Computing nash equilibria gets harder : new results show hardness even for parameterized complexity

In this paper we show that some decision problems regarding the computation of Nash equilibria are to be considered particularly hard. Most decision problems regarding Nash equilibria have been shown to be NP-complete. While some NP-complete problems can find an alternative to tractability with the tools of Parameterized Complexity Theory, it is also the case that some classes of problems do not seem to have fixed-parameter tractable algorithms. We show that k-Uniform Nash and k-Minimal Nash support are W[2]-hard. Given a game G=(A,B) and a nonnegative integer k, the k-Uniform Nash problem asks whether G has a uniform Nash equilibrium of size k. The k-Minimal Nash support asks whether has Nash equilibrium such that the support of eacGh player’s Nash strategy has size equal to or less than k. First, we show that k-Uniform Nash (with k as the parameter) is W[2]-hard even when we have 2 players, or fewer than 4 different integer values in the matrices. Second, we illustrate that even in zerosum games k-Minimal Nash support is W[2]-hard (a sample Nash equilibrium in a zero-sum 2-player game can be found in polynomial time (von Stengel 2002)). Thus, it must be the case that other more general decision problems are also W[2]-hard. Therefore, the possible parameters for fixed parameter tractability in those decision problems regarding Nash equilibria seem elusive

Cluster validity using support vector machines

Abstract- Gaining confidence that a clustering algorithm has produced meaningful results and not an accident of its usually heuristic optimization is central to data analysis. This is the issue of validity and we propose here a method by which Support Vector Machines are used to evaluate the separation in the clustering results. However, we not only obtain a method to compare clustering results from different algorithms or different runs of the same algorithm, but we can also filter noise and outliers. Thus, for a fixed data set we can identify what is the most robust and potentially meaningful clustering result. A set of experiments illustrates the steps of our approach