We consider the task of finding frequent parallel episodes in parallel point processes (or event sequences), allowing for imprecise synchrony of the events constituting occurrences (temporal imprecision) as well as in...
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We consider the task of finding frequent parallel episodes in parallel point processes (or event sequences), allowing for imprecise synchrony of the events constituting occurrences (temporal imprecision) as well as in...
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ISBN:
(纸本)9781509019687
We consider the task of finding frequent parallel episodes in parallel point processes (or event sequences), allowing for imprecise synchrony of the events constituting occurrences (temporal imprecision) as well as incomplete occurrences (selective participation). The temporal imprecision problem is tackled by frequent pattern mining using a graded notion of synchrony that captures both the number of instances of a pattern as well as the precision of synchrony of its events. To cope with selective participation, a reduction sequence of items (or event types) is formed based on found frequent patterns and guided by pattern overlap. We evaluate the performance of this method on a large number of data sets with injected parallel episodes. We demonstrate that, in contrast to binary synchrony where it pays to consider the pattern instances, graded synchrony performs better with a pattern-based scheme than with an instance-based one, thus simplifying the procedure.
The Barabási-Albert-model is commonly used to generate scale-free graphs, like social networks. To generate dynamics in these networks, methods for altering such graphs are needed. Growing and shrinking is done s...
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The Barabási-Albert-model is commonly used to generate scale-free graphs, like social networks. To generate dynamics in these networks, methods for altering such graphs are needed. Growing and shrinking is done simply by doing further generation iterations or undo them. In our paper we present four methods to merge two graphs based on the Barabási-Albert-model, and five strategies to reverse them. First we compared these algorithms by edge preservation, which describes the ratio of the inner structure kept after altering. To check if hubs in the initial graphs are hubs in the resulting graphs as well, we used the node-degree rank correlation. Finally we tested how well the node-degree distribution follows the power-law function from the Barabási-Albert-model.
Soft computing refers to a collection of computational techniques in computer science, artificial intelligence, machine learning and some engineering disciplines, which attempt to study, model, and analyze very comple...
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ISBN:
(纸本)9781479925056
Soft computing refers to a collection of computational techniques in computer science, artificial intelligence, machine learning and some engineering disciplines, which attempt to study, model, and analyze very complex phenomena: those for which more conventional methods have not yielded low cost, analytic, and complete solutions. Earlier computational approaches could model and precisely analyze only relatively simple systems. More complex systems arising in biology, medicine, the humanities, management sciences, and similar fields often remained intractable to conventional mathematical and analytical methods. That said, it should be pointed out that simplicity and complexity of systems are relative, and many conventional mathematical models have been both challenging and very productive. To react quickly and successfully is a matter of knowledge and the task to provide relevant, updated and useful knowledge for management is the arena for developing, building and implementing intelligent support systems.
Introduction Extrapyramidal, cognitive, and sensory deficits are increasingly recognised in patients with amyotrophic lateral sclerosis (ALS), suggesting dysfunction of specific frontostriatal, nigrostriatal and corti...
Introduction Extrapyramidal, cognitive, and sensory deficits are increasingly recognised in patients with amyotrophic lateral sclerosis (ALS), suggesting dysfunction of specific frontostriatal, nigrostriatal and corticobasal circuits. While significant basal ganglia involvement has been demonstrated by recent histopathology and neuroimaging studies, subcortical pathology in ALS has not been studied in relation to cognitive and behavioural deficits. Here, we evaluate basal ganglia involvement along the continuum of ALS, ALS with cognitive and behavioural deficits, and ALS with comorbid frontotemporal dementia (ALS-FTD), using multiple, complementary imaging techniques. Methods Volumetric, shape, and density analyses were performed for seven subcortical structures: thalamus, amygdala, nucleus accumbens, hippocampus, caudate nucleus, pallidum, and putamen. Patients were allocated into three study-groups: C9orf72 -negative ALS patients without cognitive or behavioural impairment (“ALS-Nci”, n = 42), ALS patients with cognitive and/or behavioural impairment (“ALS-Plus”, n = 18), and ALS patients with comorbid FTD (“ALS-FTD”, n = 7), matched for disease duration and severity. An age-, gender-, and education matched group of healthy controls was also included (“HC”, n = 39). Results Volumetric analyses revealed intergroup differences for six subcortical structures when adjusting for total intracranial volume and controlling for age: caudate nucleus ( p = 0.023), thalamus ( p < 0.001), nucleus accumbens ( p < 0.001), hippocampus ( p < 0.001), putamen ( p < 0.001), and pallidum ( p = 0.033). No amygdala volume differences were observed between study-groups ( p = 0.406). However, shape analysis revealed right ventral amygdala atrophy in the ALS-Nci group compared to controls ( p < 0.05, corrected for multiple comparisons). Hippocampal atrophy was identified in ALS-Plus patients in comparison to controls, affecting the head and b
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