1,555 research outputs found

    Network Density of States

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    Spectral analysis connects graph structure to the eigenvalues and eigenvectors of associated matrices. Much of spectral graph theory descends directly from spectral geometry, the study of differentiable manifolds through the spectra of associated differential operators. But the translation from spectral geometry to spectral graph theory has largely focused on results involving only a few extreme eigenvalues and their associated eigenvalues. Unlike in geometry, the study of graphs through the overall distribution of eigenvalues - the spectral density - is largely limited to simple random graph models. The interior of the spectrum of real-world graphs remains largely unexplored, difficult to compute and to interpret. In this paper, we delve into the heart of spectral densities of real-world graphs. We borrow tools developed in condensed matter physics, and add novel adaptations to handle the spectral signatures of common graph motifs. The resulting methods are highly efficient, as we illustrate by computing spectral densities for graphs with over a billion edges on a single compute node. Beyond providing visually compelling fingerprints of graphs, we show how the estimation of spectral densities facilitates the computation of many common centrality measures, and use spectral densities to estimate meaningful information about graph structure that cannot be inferred from the extremal eigenpairs alone.Comment: 10 pages, 7 figure

    Computational Models (of Narrative) for Literary Studies

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    In the last decades a growing body of literature in Artificial Intelligence (AI) and Cognitive Science (CS) has approached the problem of narrative understanding by means of computational systems. Narrative, in fact, is an ubiquitous element in our everyday activity and the ability to generate and understand stories, and their structures, is a crucial cue of our intelligence. However, despite the fact that - from an historical standpoint - narrative (and narrative structures) have been an important topic of investigation in both these areas, a more comprehensive approach coupling them with narratology, digital humanities and literary studies was still lacking. With the aim of covering this empty space, in the last years, a multidisciplinary effort has been made in order to create an international meeting open to computer scientist, psychologists, digital humanists, linguists, narratologists etc.. This event has been named CMN (for Computational Models of Narrative) and was launched in the 2009 by the MIT scholars Mark A. Finlayson and Patrick H. Winston1

    Collaboration and Coordination in Process-Centered Software Development Environments

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    How Prediction Based on Sequence Detection in the Cerebellum Led to the Origins of Stone Tools, Language, and Culture and, Thereby, to the Rise of Homo sapiens

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    This article extends Leiner et al.'s watershed position that cerebellar mechanisms played prominent roles in the evolution of the manipulation and refinement of ideas and language. First it is shown how cerebellar mechanism of sequence-detection may lead to the foundational learning of a predictive working memory in the infant. Second, it is argued how this same cerebellar mechanism may have led to the adaptive selection toward the progressively predictive phonological loop in the evolution of working memory of pre-humans. Within these contexts, cerebellar sequence detection is then applied to an analysis of leading anthropologists Stout and Hecht's cerebral cortex-based explanation of the evolution of culture and language through the repetitious rigors of stone-tool knapping. It is argued that Stout and Hecht's focus on the roles of areas of the brain's cerebral cortex is seriously lacking, because it can be readily shown that cerebellar sequence detection importantly (perhaps predominantly) provides more fundamental explanations for the origins of culture and language. It is shown that the cerebellum does this in the following ways: (1) through prediction-enhancing silent speech in working memory, (2) through prediction in observational learning, and (3) through prediction leading to accuracy in stone-tool knapping. It is concluded, in agreement with Leiner et al. that the more recently proposed mechanism of cerebellar sequence-detection has played a prominent role in the evolution of culture, language, and stone-tool technology, the earmarks of Homo sapiens. It is further concluded that through these same mechanisms the cerebellum continues to play a prominent role in the relentless advancement of culture

    Towards a rigorous use of SPEM

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    International audienceModeling software process is a good way to improve development and thus quality of resulting applications. The OMG proposes the SPEM metamodel to describe software processes. Its concepts are described through class diagrams. Unfortunately, it lacks a formal description of its semantics that makes it hard to use. So, we propose a specialization of SPEM that clarifies it and we use OCL to formally express constraints on it

    Chemical Characterisation and Source Attribution of Inorganic Nitrate-based Homemade Explosives

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    This thesis presents a comprehensive investigation into the chemical characterisation, identification, and source attribution of inorganic nitrate-based homemade explosives. The findings demonstrate the potential of linking pre- and post-blast residues to their precursor source using spectroscopic and trace elemental techniques coupled with chemometrics. This provides a significant addition to the forensic intelligence literature and provides a basis for the continuous improvement of forensic chemical analysis and source determination of homemade explosives
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