Information-Theoretic Inference of Large Transcriptional Regulatory Networks

The paper presents MRNET, an original method for inferring genetic networks from microarray data. The method is based on maximum relevance/minimum redundancy (MRMR), an effective information-theoretic technique for feature selection in supervised learning. The MRMR principle consists in selecting among the least redundant variables the ones that have the highest mutual information with the target. MRNET extends this feature selection principle to networks in order to infer gene-dependence relationships from microarray data. The paper assesses MRNET by benchmarking it against RELNET, CLR, and ARACNE, three state-of-the-art information-theoretic methods for large (up to several thousands of genes) network inference. Experimental results on thirty synthetically generated microarray datasets show that MRNET is competitive with these methods.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information

SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Particle algorithms for optimization on binary spaces

We discuss a unified approach to stochastic optimization of pseudo-Boolean objective functions based on particle methods, including the cross-entropy method and simulated annealing as special cases. We point out the need for auxiliary sampling distributions, that is parametric families on binary spaces, which are able to reproduce complex dependency structures, and illustrate their usefulness in our numerical experiments. We provide numerical evidence that particle-driven optimization algorithms based on parametric families yield superior results on strongly multi-modal optimization problems while local search heuristics outperform them on easier problems

Analytical approaches to protection planning in rail-truck intermodal transportation

A significant volume of traffic uses a rail-truck intermodal transportation network, making it the preferred transportation medium for customers. Thus, the associated infrastructure of rail-truck intermodal transportation should be considered critical, i.e., systems and assets whose destruction (or disruption) would have a crippling effect on security, economy, public health, and safety. Disruptions could be induced by nature such as hurricane Katrina in 2005, or man-made disturbances such as the 9/11 terrorist attacks in the United States. This thesis proposes an analytical approach to preserve, as much as possible, the functionality of a rail-truck intermodal transportation system in the wake of worst-case attacks. As such, it will serves as an aid to the top managers to compare the cost of implementing protective measures with the benefits that such measures could bring. A tri-level Defender-Attacker-Defender (DAD) approach is proposed to model this situation, where the outermost problem belongs to the network operator with a limited budget to protect some of the terminals, the middle level problem belongs to the attacker with enough resources to interdict some of the un-protected terminals, and the innermost problem belongs to the intermodal operator who attempts to meet the demand on a reduced network with the minimum cost. Since the resulting model is very difficult to solve by any optimization package, efficient solution techniques have been developed for solving this model. Finally, the proposed framework is applied to the rail-truck intermodal transportation network of a Class I railroad operator in North America to discover the optimal way to protect the system

Low complexity turbo equalization for coded intersymbol interference channels

Master'sMASTER OF ENGINEERIN

Road network recovery from concurrent capacity-reducing incidents : model development and optimisation

Local and regional economies are highly dependent on the road network. The concurrent closure of multiple sections of the network following a hazardous event is likely to have significant negative consequences for those using the network. In situations such as these, infrastructure managers must decide how best to restore the network to protect users, maximise connectivity and minimise overall disruption. Furthermore, many hazardous events are forecast to become more frequent and extreme in the future as a result of climate change. Extensive research has been undertaken to understand how to improve the resilience of degraded transport networks. Whilst network robustness (that is, the ability of a network to withstand stress) has been considered in numerous studies, the recovery of the network has captured less attention among researchers. Methodologies developed to date are overly simplistic, especially when simulating the dynamics of traffic demand and drivers’ decision-making in multi-day situations where there is considerable interplay between actual and perceived network states and behaviour. This thesis presents a decision-support tool that optimises the recovery of road transport networks after major day-to-day disruptions, maximising network connectivity and minimising total travel costs. This work expands upon previous efforts by introducing a new approach that models the damage-capacity-time relationship and improves the existing reinforcement-learning traffic-assignment models to be applicable to disrupted scenarios. An efficient metaheuristic approach (NSGA-II) is proposed to find optimal solutions for the recovery problem. The model is also applied to a real-world scenario based on the Scottish road network. Results from this case study clearly highlight the potential applicability of this model to evaluate different recovery strategies and optimise the recovery of road networks after multi-day major disruptions.Local and regional economies are highly dependent on the road network. The concurrent closure of multiple sections of the network following a hazardous event is likely to have significant negative consequences for those using the network. In situations such as these, infrastructure managers must decide how best to restore the network to protect users, maximise connectivity and minimise overall disruption. Furthermore, many hazardous events are forecast to become more frequent and extreme in the future as a result of climate change. Extensive research has been undertaken to understand how to improve the resilience of degraded transport networks. Whilst network robustness (that is, the ability of a network to withstand stress) has been considered in numerous studies, the recovery of the network has captured less attention among researchers. Methodologies developed to date are overly simplistic, especially when simulating the dynamics of traffic demand and drivers’ decision-making in multi-day situations where there is considerable interplay between actual and perceived network states and behaviour. This thesis presents a decision-support tool that optimises the recovery of road transport networks after major day-to-day disruptions, maximising network connectivity and minimising total travel costs. This work expands upon previous efforts by introducing a new approach that models the damage-capacity-time relationship and improves the existing reinforcement-learning traffic-assignment models to be applicable to disrupted scenarios. An efficient metaheuristic approach (NSGA-II) is proposed to find optimal solutions for the recovery problem. The model is also applied to a real-world scenario based on the Scottish road network. Results from this case study clearly highlight the potential applicability of this model to evaluate different recovery strategies and optimise the recovery of road networks after multi-day major disruptions