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Discrete scale invariance, which corresponds to a partial breaking of the scaling symmetry, is reflected in the existence of a hierarchy of characteristic scales l0,l0λ,l0λ2,…, where λ is a preferred scaling ratio and l0 a microscopic cut-off. Signatures of discrete scale invariance have recently been found in a variety of systems ranging from rupture, earthquakes, Laplacian growth phenomena, "animals" in percolation to financial market crashes. We believe it to be a quite general, albeit subtle phenomenon. Indeed, the practical problem in uncovering an underlying discrete scale invariance is that standard ensemble averaging procedures destroy it as if it was pure noise. This is due to the fact, that while λ only depends on the underlying physics, l0 on the contrary is realization-dependent. Here, we adapt and implement a novel so-called "canonical" averaging scheme which re-sets the l0 of different realizations to approximately the same value. The method is based on the determination of a realization-dependent effective critical point obtained from, e.g., a maximum susceptibility criterion. We demonstrate the method on diffusion limited aggregation and a model of rupture.
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Least association rules are corresponded to the rarity or irregularity relationship among itemset in database. Mining these rules is very difficult and rarely focused since it always involves with infrequent and exceptional cases. In certain medical data, detecting these rules is very critical and most valuable. However, mathematical formulation and evaluation of the new proposed measurement are not really impressive. Therefore, in this paper we applied our novel measurement called Critical Relative Support (CRS) to mine the critical least association rules from medical dataset. We also employed our scalable algorithm called Significant Least Pattern Growth algorithm (SLP-Growth) to mine the respective association rules. Experiment with two benchmarked medical datasets, Breast Cancer and Cardiac Single Proton Emission Computed Tomography (SPECT) Images proves that CRS can be used to detect to the pertinent rules and thus verify its scalability.
Storytelling has been understood as more than a way of promoting a re-storying of the past, in which one single actor would be “the teller of the past.” Storytelling has progressed into a process in that a variety of voices are negotiating in the present how to tell the history in the future. In this context, a variety of tensions can take place. Such tensions can support versions of the past or promote forget-fulness about pieces of the story. However, the storytelling around political and power disputes between groups that have divergent interests have to be studied. In this study, I attempt to promote a political understanding of the relationship between businesses in the extractive industry, local governments, and communities. To understand the context of extractive industries in Brazil, I am exploring a link between antenarrative theory and decolonial thought. I analyze the narratives of different actors that are disputing discourses around the impacts of mining operations. The findings show that communities located in Brazilian mining areas have been concerned about water issues, environmental disasters, and economic welfare. These issues, in particular, have sparked most of the resistance against the expansion of iron ore production. There is a narrative that opposes the type of economic development and well-being that is currently promoted through mineral extraction activities. In conclusion, general dissatisfaction with the current model of development of societies is observed. However, mining companies have many economic and political resources to maintain a privileged relationship with governments and states. Therefore, they are more likely to be the guardians of the story that will be told in the future.