8,625 research outputs found
GRASP/VND Optimization Algorithms for Hard Combinatorial Problems
Two hard combinatorial problems are addressed in this thesis. The first one is known as the ”Max CutClique”, a combinatorial problem introduced by P. Martins in 2012. Given a simple graph, the goal is to
find a clique C such that the number of links shared between C and its complement C
C is maximum.
In a first contribution, a GRASP/VND methodology is proposed to tackle the problem. In a second
one, the N P-Completeness of the problem is mathematically proved. Finally, a further generalization
with weighted links is formally presented with a mathematical programming formulation, and the
previous GRASP is adapted to the new problem.
The second problem under study is a celebrated optimization problem coming from network
reliability analysis. We assume a graph G with perfect nodes and imperfect links, that fail independently
with identical probability ρ ∈ [0,1]. The reliability RG(ρ), is the probability that the resulting subgraph
has some spanning tree. Given a number of nodes and links, p and q, the goal is to find the (p,q)-graph
that has the maximum reliability RG(ρ), uniformly in the compact set ρ ∈ [0,1]. In a first contribution,
we exploit properties shared by all uniformly most-reliable graphs such as maximum connectivity and
maximum Kirchhoff number, in order to build a novel GRASP/VND methodology. Our proposal finds
the globally optimum solution under small cases, and it returns novel candidates of uniformly
most-reliable graphs, such as Kantor-Mobius and Heawood graphs. We also offer a literature review, ¨
and a mathematical proof that the bipartite graph K4,4 is uniformly most-reliable.
Finally, an abstract mathematical model of Stochastic Binary Systems (SBS) is also studied. It is a
further generalization of network reliability models, where failures are modelled by a general logical
function. A geometrical approximation of a logical function is offered, as well as a novel method to find
reliability bounds for general SBS. This bounding method combines an algebraic duality, Markov
inequality and Hahn-Banach separation theorem between convex and compact sets
Maturation trajectories of cortical resting-state networks depend on the mediating frequency band
The functional significance of resting state networks and their abnormal manifestations in psychiatric disorders are firmly established, as is the importance of the cortical rhythms in mediating these networks. Resting state networks are known to undergo substantial reorganization from childhood to adulthood, but whether distinct cortical rhythms, which are generated by separable neural mechanisms and are often manifested abnormally in psychiatric conditions, mediate maturation differentially, remains unknown. Using magnetoencephalography (MEG) to map frequency band specific maturation of resting state networks from age 7 to 29 in 162 participants (31 independent), we found significant changes with age in networks mediated by the beta (13–30 Hz) and gamma (31–80 Hz) bands. More specifically, gamma band mediated networks followed an expected asymptotic trajectory, but beta band mediated networks followed a linear trajectory. Network integration increased with age in gamma band mediated networks, while local segregation increased with age in beta band mediated networks. Spatially, the hubs that changed in importance with age in the beta band mediated networks had relatively little overlap with those that showed the greatest changes in the gamma band mediated networks. These findings are relevant for our understanding of the neural mechanisms of cortical maturation, in both typical and atypical development.This work was supported by grants from the Nancy Lurie Marks Family Foundation (TK, SK, MGK), Autism Speaks (TK), The Simons Foundation (SFARI 239395, TK), The National Institute of Child Health and Development (R01HD073254, TK), National Institute for Biomedical Imaging and Bioengineering (P41EB015896, 5R01EB009048, MSH), and the Cognitive Rhythms Collaborative: A Discovery Network (NFS 1042134, MSH). (Nancy Lurie Marks Family Foundation; Autism Speaks; SFARI 239395 - Simons Foundation; R01HD073254 - National Institute of Child Health and Development; P41EB015896 - National Institute for Biomedical Imaging and Bioengineering; 5R01EB009048 - National Institute for Biomedical Imaging and Bioengineering; NFS 1042134 - Cognitive Rhythms Collaborative: A Discovery Network
Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification
Detecting faults in electrical power grids is of paramount importance, either
from the electricity operator and consumer viewpoints. Modern electric power
grids (smart grids) are equipped with smart sensors that allow to gather
real-time information regarding the physical status of all the component
elements belonging to the whole infrastructure (e.g., cables and related
insulation, transformers, breakers and so on). In real-world smart grid
systems, usually, additional information that are related to the operational
status of the grid itself are collected such as meteorological information.
Designing a suitable recognition (discrimination) model of faults in a
real-world smart grid system is hence a challenging task. This follows from the
heterogeneity of the information that actually determine a typical fault
condition. The second point is that, for synthesizing a recognition model, in
practice only the conditions of observed faults are usually meaningful.
Therefore, a suitable recognition model should be synthesized by making use of
the observed fault conditions only. In this paper, we deal with the problem of
modeling and recognizing faults in a real-world smart grid system, which
supplies the entire city of Rome, Italy. Recognition of faults is addressed by
following a combined approach of multiple dissimilarity measures customization
and one-class classification techniques. We provide here an in-depth study
related to the available data and to the models synthesized by the proposed
one-class classifier. We offer also a comprehensive analysis of the fault
recognition results by exploiting a fuzzy set based reliability decision rule
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