CP-nets: From Theory to Practice

Conditional preference networks (CP-nets) exploit the power of ceteris paribus rules to represent preferences over combinatorial decision domains compactly. CP-nets have much appeal. However, their study has not yet advanced sufficiently for their widespread use in real-world applications. Known algorithms for deciding dominance---whether one outcome is better than another with respect to a CP-net---require exponential time. Data for CP-nets are difficult to obtain: human subjects data over combinatorial domains are not readily available, and earlier work on random generation is also problematic. Also, much of the research on CP-nets makes strong, often unrealistic assumptions, such as that decision variables must be binary or that only strict preferences are permitted. In this thesis, I address such limitations to make CP-nets more useful. I show how: to generate CP-nets uniformly randomly; to limit search depth in dominance testing given expectations about sets of CP-nets; and to use local search for learning restricted classes of CP-nets from choice data

Computational analysis of the synergy among multiple interacting genes

Diseases such as cancer are often related to collaborative effects involving interactions of multiple genes within complex pathways, or to combinations of multiple SNPs. To understand the structure of such mechanisms, it is helpful to analyze genes in terms of the purely cooperative, as opposed to independent, nature of their contributions towards a phenotype. Here, we present an information-theoretic analysis that provides a quantitative measure of the multivariate synergy and decomposes sets of genes into submodules each of which contains synergistically interacting genes. When the resulting computational tools are used for the analysis of gene expression or SNP data, this systems-based methodology provides insight into the biological mechanisms responsible for disease

Dissociation between the Activity of the Right Middle Frontal Gyrus and the Middle Temporal Gyrus in Processing Semantic Priming

The aim of this event-related functional magnetic resonance imaging (fMRI) study was to test whether the right middle frontal gyrus (MFG) and middle temporal gyrus (MTG) would show differential sensitivity to the effect of prime-target association strength on repetition priming. In the experimental condition (RP), the target occurred after repetitive presentation of the prime within an oddball design. In the control condition (CTR), the target followed a single presentation of the prime with equal probability of the target as in RP. To manipulate semantic overlap between the prime and the target both conditions (RP and CTR) employed either the onomatopoeia “oink” as the prime and the referent “pig” as the target (OP) or vice-versa (PO) since semantic overlap was previously shown to be greater in OP. The results showed that the left MTG was sensitive to release of adaptation while both the right MTG and MFG were sensitive to sequence regularity extraction and its verification. However, dissociated activity between OP and PO was revealed in RP only in the right MFG. Specifically, target “pig” (OP) and the physically equivalent target in CTR elicited comparable deactivations whereas target “oink” (PO) elicited less inhibited response in RP than in CTR. This interaction in the right MFG was explained by integrating these effects into a competition model between perceptual and conceptual effects in priming processing

Normal basis exhaustive search: 10 years later

This paper concerns an exhaustive search for normal bases with minimum complexity in finite fields F2n over F2 for n≤46. This is a followup paper to [11], which appeared one decade ago in 2008 and completed the cases n≤39. We extend the results in [11] by taking advantage of a combination of algorithmic improvements, more efficient implementations and massive parallelism

An efficient framework for dynamic reconfiguration of instruction-set customization

10.1007/s10617-008-9035-xDesign Automation for Embedded Systems131-291-113DAES

Urinary biomarkers of physical activity: candidates and clinical utility

Chronic physical inactivity is a major risk factor for a number of important lifestyle diseases, while inappropriate exposure to high physical demands is a risk factor for musculoskeletal injury and fatigue. Proteomic and metabolomic investigations of the physical activity continuum - extreme sedentariness to extremes in physical performance - offer increasing insight into the biological impacts of physical activity. Moreover, biomarkers, revealed in such studies, may have utility in the monitoring of metabolic and musculoskeletal health or recovery following injury. As a diagnostic matrix, urine is non-invasive to collect and it contains many biomolecules, which reflect both positive and negative adaptations to physical activity exposure. This review examines the utility and landscape of biomarkers of physical activity with particular reference to those found in urine