A voter model of the spatial prisoner's dilemma

The prisoner's dilemma (PD) involves contests between two players and may naturally be played on a spatial grid using voter model rules. In the model of spatial PD discussed here, the sites of a two-dimensional lattice are occupied by strategies. At each time step, a site is chosen to play a PD game with one of its neighbors. The strategy of the chosen site then invades its neighbor with a probability that is proportional to the payoff from the game. Using results from the analysis of voter models, it is shown that with simple linear strategies, this scenario results in the long-term survival of only one strategy. If three nonlinear strategies have a cyclic dominance relation between one another, then it is possible for relatively cooperative strategies to persist indefinitely. With the voter model dynamics, however, the average level of cooperation decreases with time if mutation of the strategies is included. Spatial effects are not in themselves sufficient to lead to the maintenance of cooperation     

Dynamic mechanical compression influences nitric oxide production by articular chondrocytes seeded in agarose

Nitric oxide (NO) has been implicated in the inhibition of cell proliferation in cytokine and lipopolysaccharide (LPS)-stimulated chondrocytes and is known to be influenced by physical forces in several tissues. In this study, a well-characterized model system utilizing bovine chondrocytes embedded in 3% agarose constructs has been used to investigate the effect of dynamic strain at 0.3, 1, or 3 Hz on NO production. LPS induced a significant increase in nitrite levels, which was reversed by both L-NAME and dexamethasone. Dynamic compressive strain produced a significant reduction in nitrite production. The effect was partially blocked by L-NAME but unaffected by dexamethasone. L-NAME also reversed dynamic compression-induced stimulation of [3H]-thymidine incorporation. NO appears to be a constituent of mechanotransduction pathways which influence proliferation of bovine chondrocytes seeded within agarose constructs. The inhibitor experiments also infer that alterations in cNOS activity primarily determine the response.     

Adapting modularity during learning in cooperative co-evolutionary recurrent neural networks

Adaptation during evolution has been an important focus of research in training neural networks. Cooperative coevolution has played a significant role in improving standard evolution of neural networks by organizing the training problem into modules and independently solving them. The number of modules required to represent a neural network is critical to the success of evolution. This paper proposes a framework for the adaptation of the number of modules during evolution. The framework is called adaptive modularity cooperative coevolution. It is used for training recurrent neural networks on grammatical inference problems. The results shows that the proposed approach performs better than its counterparts as the dimensionality of the problem increases.     

A divide-and-conquer approach for content replication in WMNs

Wireless Mesh Networks (WMNs) extend Internet access in areas where the wired infrastructure is not available. A problem that arises is the congestion around gateways, delayed access latency and low throughput. Therefore, object replication and placement is essential for multi-hop wireless networks. Many replication schemes are proposed for the Internet, but they are designed for CDNs that have both high bandwidth and high server capacity, which makes them unsuitable for the wireless environment. Object replication has received comparatively less attention from the research community when it comes to WMNs. In this paper, we propose an object replication and placement scheme for WMNs. In our scheme, each mesh router acts as a replica server in a peer-to-peer fashion. The scheme exploits graph partitioning to build a hierarchy from fine-grained to coarse-grained partitions. The challenge is to replicate content as close as possible to the requesting clients and thus reduce the access latency per object, while minimizing the number of replicas. Using simulation tests, we demonstrate that our scheme is scalable, performing well with respect to the number of replica servers and the number of objects. The simulation results show that our proposed scheme has better performance compared to other replication schemes.     

Catastrophic forgetting in simple networks: an analysis of the pseudorehearsal solution.

Catastrophic forgetting is a major problem for sequential learning in neural networks. One very general solution to this problem, known as 'pseudorehearsal', works well in practice for nonlinear networks but has not been analysed before. This paper formalizes pseudorehearsal in linear networks. We show that the method can fail in low dimensions but is guaranteed to succeed in high dimensions under fairly general conditions. In this case an optimal version of the method is equivalent to a simple modification of the 'delta rule'.     

Population-based continuous optimization, probabilistic modelling and mean shift

Evolutionary algorithms perform optimization using a population of sample solution points. An interesting development has been to view population-based optimization as the process of evolving an explicit, probabilistic model of the search space. This paper investigates a formal basis for continuous, population-based optimization in terms of a stochastic gradient descent on the Kullback-Leibler divergence between the model probability density and the objective function, represented as an unknown density of assumed form. This leads to an update rule that is related and compared with previous theoretical work, a continuous version of the population-based incremental learning algorithm, and the generalized mean shift clustering framework. Experimental results are presented that demonstrate the dynamics of the new algorithm on a set of simple test problems.