662 research outputs found
Graph product multilayer networks: spectral properties and applications
This article aims to establish theoretical foundations of graph product multilayer networks (GPMNs), a family of multilayer networks that can be obtained as a graph product of two or more factor networks. Cartesian, direct (tensor), and strong product operators are considered, and then generalized. We first describe mathematical relationships between GPMNs and their factor networks regarding their degree/strength, adjacency, and Laplacian spectra, and then show that those relationships can still hold for non-simple and generalized GPMNs. Applications of GPMNs are discussed in three areas: predicting epidemic thresholds, modelling propagation in non-trivial space and time, and analysing higher-order properties of self-similar networks. Directions of future research are also discussed
Mapping the Curricular Structure and Contents of Network Science Courses
As network science has matured as an established field of research, there are
already a number of courses on this topic developed and offered at various
higher education institutions, often at postgraduate levels. In those courses,
instructors adopted different approaches with different focus areas and
curricular designs. We collected information about 30 existing network science
courses from various online sources, and analyzed the contents of their syllabi
or course schedules. The topics and their curricular sequences were extracted
from the course syllabi/schedules and represented as a directed weighted graph,
which we call the topic network. Community detection in the topic network
revealed seven topic clusters, which matched reasonably with the concept list
previously generated by students and educators through the Network Literacy
initiative. The minimum spanning tree of the topic network revealed typical
flows of curricular contents, starting with examples of networks, moving onto
random networks and small-world networks, then branching off to various
subtopics from there. These results illustrate the current state of consensus
formation (including variations and disagreements) among the network science
community on what should be taught about networks and how, which may also be
informative for K--12 education and informal education.Comment: 17 pages, 11 figures, 2 tables; to appear in Cramer, C. et al.
(eds.), Network Science in Education -- Tools and Techniques for Transforming
Teaching and Learning (Springer, 2017, in press
The Effect of Sensory Blind Zones on Milling Behavior in a Dynamic Self-Propelled Particle Model
Emergent pattern formation in self-propelled particle (SPP) systems is
extensively studied because it addresses a range of swarming phenomena which
occur without leadership. Here we present a dynamic SPP model in which a
sensory blind zone is introduced into each particle's zone of interaction.
Using numerical simulations we discovered that the degradation of milling
patterns with increasing blind zone ranges undergoes two distinct transitions,
including a new, spatially nonhomogeneous transition that involves cessation of
particles' motion caused by broken symmetries in their interaction fields. Our
results also show the necessity of nearly complete panoramic sensory ability
for milling behavior to emerge in dynamic SPP models, suggesting a possible
relationship between collective behavior and sensory systems of biological
organisms.Comment: 12 pages, 4 figure
Cervical spine metastases: techniques for anterior reconstruction and stabilization
pre-printThe surgical management of cervical spine metastases continues to evolve and improve. The authors provide an overview of the various techniques for anterior reconstruction and stabilization of the subaxial cervical spine after corpectomy for spinal metastases. Vertebral body reconstruction can be accomplished using a variety of materials such as bone autograft/allograft, polymethylmethacrylate, interbody spacers, and/or cages with or without supplemental anterior cervical plating. In some instances, posterior instrumentation is needed for additional stabilization
Formation of regular spatial patterns in ratio-dependent predator-prey model driven by spatial colored-noise
Results are reported concerning the formation of spatial patterns in the
two-species ratio-dependent predator-prey model driven by spatial
colored-noise. The results show that there is a critical value with respect to
the intensity of spatial noise for this system when the parameters are in the
Turing space, above which the regular spatial patterns appear in two
dimensions, but under which there are not regular spatial patterns produced. In
particular, we investigate in two-dimensional space the formation of regular
spatial patterns with the spatial noise added in the side and the center of the
simulation domain, respectively.Comment: 4 pages and 3 figure
Local Susceptibility Against Soft Errors in Dynamic Random Access Memories (DRAMs) Analyzed by Nuclear Microprobes
A novel evaluation technique for soft errors in Mbit DRAMs (dynamic random access memories) has been developed using a 400 keV proton microprobe system. This technique, which is called soft error mapping, consists of a bit-state mapping image and a secondary electron mapping image, and can reveal the correlation between the incident position of protons and susceptibility against soft errors in DRAMs. Soft errors are found to be induced by proton incidence at 400 keV within about 6 μm around the memory cell in the case of DRAMs with a conventional well. The susceptible area against proton incidence is much larger than the memory cell size. It is found that the area within 4 μm around the memory cell is, in particular, highly sensitive to 400 keV protons. A threshold dose to radiation hardness is estimated by deterioration of the DRAMs during soft error mapping. A buried barrier layer, formed by high-energy ion-implantation, was found to control the charge collection of induced carriers and to suppress soft errors by 400 keV proton microprobes
Complexity, Development, and Evolution in Morphogenetic Collective Systems
Many living and non-living complex systems can be modeled and understood as
collective systems made of heterogeneous components that self-organize and
generate nontrivial morphological structures and behaviors. This chapter
presents a brief overview of our recent effort that investigated various
aspects of such morphogenetic collective systems. We first propose a
theoretical classification scheme that distinguishes four complexity levels of
morphogenetic collective systems based on the nature of their components and
interactions. We conducted a series of computational experiments using a
self-propelled particle swarm model to investigate the effects of (1)
heterogeneity of components, (2) differentiation/re-differentiation of
components, and (3) local information sharing among components, on the
self-organization of a collective system. Results showed that (a) heterogeneity
of components had a strong impact on the system's structure and behavior, (b)
dynamic differentiation/re-differentiation of components and local information
sharing helped the system maintain spatially adjacent, coherent organization,
(c) dynamic differentiation/re-differentiation contributed to the development
of more diverse structures and behaviors, and (d) stochastic re-differentiation
of components naturally realized a self-repair capability of self-organizing
morphologies. We also explored evolutionary methods to design novel
self-organizing patterns, using interactive evolutionary computation and
spontaneous evolution within an artificial ecosystem. These self-organizing
patterns were found to be remarkably robust against dimensional changes from 2D
to 3D, although evolution worked efficiently only in 2D settings.Comment: 13 pages, 8 figures, 1 table; submitted to "Evolution, Development,
and Complexity: Multiscale Models in Complex Adaptive Systems" (Springer
Proceedings in Complexity Series
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