Rank discriminants for predicting phenotypes from RNA expression

Statistical methods for analyzing large-scale biomolecular data are commonplace in computational biology. A notable example is phenotype prediction from gene expression data, for instance, detecting human cancers, differentiating subtypes and predicting clinical outcomes. Still, clinical applications remain scarce. One reason is that the complexity of the decision rules that emerge from standard statistical learning impedes biological understanding, in particular, any mechanistic interpretation. Here we explore decision rules for binary classification utilizing only the ordering of expression among several genes; the basic building blocks are then two-gene expression comparisons. The simplest example, just one comparison, is the TSP classifier, which has appeared in a variety of cancer-related discovery studies. Decision rules based on multiple comparisons can better accommodate class heterogeneity, and thereby increase accuracy, and might provide a link with biological mechanism. We consider a general framework ("rank-in-context") for designing discriminant functions, including a data-driven selection of the number and identity of the genes in the support ("context"). We then specialize to two examples: voting among several pairs and comparing the median expression in two groups of genes. Comprehensive experiments assess accuracy relative to other, more complex, methods, and reinforce earlier observations that simple classifiers are competitive.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS738 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Hierarchical D ∗ algorithm with materialization of costs for robot path planning

In this paper a new hierarchical extension of the D ∗ algorithm for robot path planning is introduced. The hierarchical D ∗ algorithm uses a down-top strategy and a set of precalculated paths (materialization of path costs) in order to improve performance. This on-line path planning algorithm allows optimality and specially lower computational time. H-Graphs (hierarchical graphs) are modified and adapted to support on-line path planning with materialization of costs and multiple hierarchical levels. Traditional on-line robot path planning focused in horizontal spaces is also extended to vertical and interbuilding spaces. Some experimental results are showed and compared to other path planning algorithms

Post-Westgate SWAT : C4ISTAR Architectural Framework for Autonomous Network Integrated Multifaceted Warfighting Solutions Version 1.0 : A Peer-Reviewed Monograph

Police SWAT teams and Military Special Forces face mounting pressure and challenges from adversaries that can only be resolved by way of ever more sophisticated inputs into tactical operations. Lethal Autonomy provides constrained military/security forces with a viable option, but only if implementation has got proper empirically supported foundations. Autonomous weapon systems can be designed and developed to conduct ground, air and naval operations. This monograph offers some insights into the challenges of developing legal, reliable and ethical forms of autonomous weapons, that address the gap between Police or Law Enforcement and Military operations that is growing exponentially small. National adversaries are today in many instances hybrid threats, that manifest criminal and military traits, these often require deployment of hybrid-capability autonomous weapons imbued with the capability to taken on both Military and/or Security objectives. The Westgate Terrorist Attack of 21st September 2013 in the Westlands suburb of Nairobi, Kenya is a very clear manifestation of the hybrid combat scenario that required military response and police investigations against a fighting cell of the Somalia based globally networked Al Shabaab terrorist group.Comment: 52 pages, 6 Figures, over 40 references, reviewed by a reade

Hierarchical Path Search with Partial Materialization of Costs for a Smart Wheelchair

In this paper, the off-line path planner module of a smart wheelchair aided navigation system is described. Environmental information is structured into a hierarchical graph (H-graph) and used either by the user interface or the path planner module. This information structure facilitates efficient path search and easier information access and retrieval. Special path planning issues like planning between floors of a building (vertical path planning) are also viewed. The H-graph proposed is modelled by a tree. The hierarchy of abstractions contained in the tree has several levels of detail. Each abstraction level is a graph whose nodes can represent other graphs in a deeper level of the hierarchy. Path planning is performed using a path skeleton which is built from the deepest abstraction levels of the hierarchy to the most upper levels and completed in the last step of the algorithm. In order not to lose accuracy in the path skeleton generation and speed up the search, a set of optimal subpaths are previously stored in some nodes of the H-graph (path costs are partially materialized). Finally, some experimental results are showed and compared to traditional heuristic search algorithms used in robot path planning.Comisión Interministerial de Ciencia y Tecnología TER96-2056-C02-0