http://www.maths.qmul.ac.uk/%7Elatora/report_06.pdf

Complex networks: Structure and dynamics

J. Appl. Cryst.の発刊に際して

https://deim.urv.cat/~alexandre.arenas/publicacions/pdf/review.pdf

Synchronization in complex networks

The origin of large but rare cascades that are triggered by small initial shocks is a phenomenon that manifests itself as diversely as cultural fads, collective action, the diffusion of norms and innovations, and cascading failures in infrastructure and organizational networks. This paper presents a possible explanation of this phenomenon in terms of a sparse, random network of interacting agents whose decisions are determined by the actions of their neighbors according to a simple threshold rule. Two regimes are identified in which the network is susceptible to very large cascades—herein called global cascades—that occur very rarely. When cascade propagation is limited by the connectivity of the network, a power law distribution of cascade sizes is observed, analogous to the cluster size distribution in standard percolation theory and avalanches in self-organized criticality. But when the network is highly connected, cascade propagation is limited instead by the local stability of the nodes themselves, and the size distribution of cascades is bimodal, implying a more extreme kind of instability that is correspondingly harder to anticipate. In the first regime, where the distribution of network neighbors is highly skewed, it is found that the most connected nodes are far more likely than average nodes to trigger cascades, but not in the second regime. Finally, it is shown that heterogeneity plays an ambiguous role in determining a system's stability: increasingly heterogeneous thresholds make the system more vulnerable to global cascades; but an increasingly heterogeneous degree distribution makes it less vulnerable.

/pdf/a-simple-model-of-global-cascades-on-random-networks-zv4xfy8ajb.pdf

A simple model of global cascades on random networks

Stable non-Gaussian random processes , by G. Samorodnitsky and M. S. Taqqu. Pp. 632. £49.50. 1994. ISBN 0-412-05171-0 (Chapman and Hall).

The new generation of neutron scattering instruments being built are higher resolution and produce one or more orders of magnitude larger data than the previous generation of instruments. For instance, we have grown out of being able to perform some important tasks with our laptops. The data sizes are too big and the computational time would be too long. These large datasets can be problematic as facility users now begin to struggle with many of the same issues faced by more established computing communities. These issues include data access, management, and movement, data format standards, distributed computing, and collaboration with others. The Neutron Science Portal has been designed, and implemented to provide users with an easy-to-use interface for managing and processing data, while also keeping an eye on meeting modern computer security requirements that are currently being imposed on institutions. Users can browse or search for data which they are allowed to see, run data reduction and analysis applications, perform sample activation calculations and perform McStas simulations. Collaboration is facilitated by providing users a read/writeable common area, shared across all experiment team members. The portal currently has over 370 registered users; almost 7TB of experiment and user data, approximately 1,000,000 files cataloged, and had almost 10,000 unique visits last year. Future directions for enhancing portal robustness include examining how to mirror data and portal services, better facilitation of collaborations via virtual organizations, enhancing disconnected service via "thick client" applications, and better inter-facility connectivity to support cross-cutting research.

The SNS/HFIR Web Portal System for SANS

Magnetic field ripple from a tokamak bundle divertor is localized to a small toroidal sector and must be treated differently from the usual (distributed) toroidal field (TF) coil ripple. Generally, in a tokamak with an unoptimized divertor design, all of the banana-trapped fast ions are quickly lost due to banana drift diffusion or to trapping between the 1/R variation in |B| Ξ B and local field maxima due to the divertor.Studying the full three-dimensional shape of the B-surfaces allows quick evaluation of many inferior designs. For designs that eliminate any large maxima in B, low on-axis ripple is a good indicator of avoiding deleterious effects due to the divertor. However, this must be achieved while satisfying many engineering constraints.A computer code has been written to optimize automatically on-axis ripple subject to these constraints, while varying up to nine design parameters. Optimum configurations have low on-axis ripple (<0.2%) so that, now, most banana-trapped fast ions are confin...

Constrained Ripple Optimization of Tokamak Bundle Divertors

To predict the size, expense, and performance of mag netic fusion reactors, it is critical to know the scaling of particle transport and losses across the magnetic field with machine and plasma parameters. The mechanism for transport in magnetically confined plasmas is not yet known. However, at the plasma edge, losses larger than those predicted by classical theory have been shown to be induced by fluctuations. Numerical calculations of plasma turbulence are expected to lead to the identifica tion of the transport scaling parameters. The fluid ap proach to plasma dynamics makes tractable some plasma turbulence problems for which kinetic theory calculations are, at present, too complex. Only with present supercomputers can this problem be treated for the edge plasma.

/pdf/plasma-turbulence-calculations-on-supercomputers-3k1n06like.pdf

Plasma turbulence calculations on supercomputers

Crystallography is the powerhouse technique for molecular structure determination, with applications in fields ranging from energy storage to drug design. Accurate structure determination, however, relies partly on determining the precise locations and integrated intensities of Bragg peaks in the resulting data. Here, we describe a method for Bragg peak integration that is accomplished using neural networks. The network is based on a U-Net and identifies peaks in three-dimensional reciprocal space through segmentation, allowing prediction of the full 3D peak shape from noisy data that is commonly difficult to process. The procedure for generating appropriate training sets is detailed. Trained networks achieve Dice coefficients of 0.82 and mean IoUs of 0.69. Carrying out integration over entire datasets, it is demonstrated that integrating neural network-predicted peaks results in improved intensity statistics. Furthermore, using a second dataset, the possibility of transfer learning between datasets is shown. Given the ubiquity and growing complexity of crystallography, we anticipate integration by machine learning to play an increasingly important role across the physical sciences. These early results demonstrate the applicability of deep learning techniques for integrating crystallography data and suggest a possible role in the next generation of crystallography experiments.

Volumetric Segmentation via Neural Networks Improves Neutron Crystallography Data Analysis

The TeraGrid's outreach effort to the neutron science community is creating an environment that is encouraging the exploration of advanced cyberinfrastructure being incorporated into facility operations in a way that leverages facility operations to multiply the scientific output of its users, including many NSF supported scientists in many disciplines The Neutron Science TeraGrid Gateway serves as an exploratory incubator for several TeraGrid projects Virtual neutron scattering experiments from one exploratory project will be highlighted

Vickie E. Lynch

Papers

The SNS/HFIR Web Portal System for SANS

Constrained Ripple Optimization of Tokamak Bundle Divertors

Plasma turbulence calculations on supercomputers

Volumetric Segmentation via Neural Networks Improves Neutron Crystallography Data Analysis

Virtual Experiments on the Neutron Science TeraGrid Gateway