Stress concentrations around voids in three dimensions : The roots of failure

Funding This work forms part of a NERC New Investigator award for DH (NE/I001743/1), which is gratefully acknowledged. Acknowledgments The authors would like to acknowledge the reviewers, Elizabeth Ritz and Phillip Resor. Their reviews were very constructive, both helping to improve the manuscripts consistency and highlighting a number of errors in the initial submission. The authors would also like to thank Lydia Jagger's keen eye and patience, she helped greatly in removing a number of grammatical errors from the initial draft.Peer reviewedPublisher PD

Pore geometry as a control on rock strength

This study was funded via RJW's University of Leicester start-up fund, as part of AAB's PhD project. We thank Don Swanson and Mike Poland at HVO, Hawai'i, for their help and advice during fieldwork planning and sample collection in the Koa'e fault system, and the National Park Service for granting a research permit to collect rock samples. Sergio Vinciguerra is thanked for access to the Rock Mechanics and Physics lab at the British Geological Survey and Audrey Ougier-Simonin is thanked for her help preparing samples and advice during testing. We thank Mike Heap (EOST Strasbourg) and an anonymous reviewer for their detailed and careful comments that greatly improved the manuscript.Peer reviewedPostprin

k-server via multiscale entropic regularization

We present an $O((\log k)^2)$-competitive randomized algorithm for the $k$-server problem on hierarchically separated trees (HSTs). This is the first $o(k)$-competitive randomized algorithm for which the competitive ratio is independent of the size of the underlying HST. Our algorithm is designed in the framework of online mirror descent where the mirror map is a multiscale entropy. When combined with Bartal's static HST embedding reduction, this leads to an $O((\log k)^2 \log n)$-competitive algorithm on any $n$-point metric space. We give a new dynamic HST embedding that yields an $O((\log k)^3 \log \Delta)$-competitive algorithm on any metric space where the ratio of the largest to smallest non-zero distance is at most $\Delta$

The finite mass mesh method

The finite mass method is a purely Lagrangian scheme for the spatial discretisation of the macroscopic phenomenological laws that govern the flow of compressible fluids. In this article we investigate how to take into account long range gravitational forces in the framework of the finite mass method. This is achieved by incorporating an extra discrete potential energy of the gravitational field into the Lagrangian that underlies the finite mass method. The discretisation of the potential is done in an Eulerian fashion and employs an adaptive tensor product mesh fixed in space, hence the name finite mass mesh method for the new scheme. The transfer of information between the mass packets of the finite mass method and the discrete potential equation relies on numerical quadrature, for which different strategies will be proposed. The performance of the extended finite mass method for the simulation of two-dimensional gas pillars under self-gravity will be reporte

Single File Diffusion enhancement in a fluctuating modulated 1D channel

We show that the diffusion of a single file of particles moving in a fluctuating modulated 1D channel is enhanced with respect to the one in a bald pipe. This effect, induced by the fluctuations of the modulation, is favored by the incommensurability between the channel potential modulation and the moving file periodicity. This phenomenon could be of importance in order to optimize the critical current in superconductors, in particular in the case where mobile vortices move in 1D channels designed by adapted patterns of pinning sites.Comment: 4 pages, 4 figure

Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies

Pandemic influenza has the epidemic potential to kill millions of people. While various preventive measures exist (i.a., vaccination and school closures), deciding on strategies that lead to their most effective and efficient use remains challenging. To this end, individual-based epidemiological models are essential to assist decision makers in determining the best strategy to curb epidemic spread. However, individual-based models are computationally intensive and it is therefore pivotal to identify the optimal strategy using a minimal amount of model evaluations. Additionally, as epidemiological modeling experiments need to be planned, a computational budget needs to be specified a priori. Consequently, we present a new sampling technique to optimize the evaluation of preventive strategies using fixed budget best-arm identification algorithms. We use epidemiological modeling theory to derive knowledge about the reward distribution which we exploit using Bayesian best-arm identification algorithms (i.e., Top-two Thompson sampling and BayesGap). We evaluate these algorithms in a realistic experimental setting and demonstrate that it is possible to identify the optimal strategy using only a limited number of model evaluations, i.e., 2-to-3 times faster compared to the uniform sampling method, the predominant technique used for epidemiological decision making in the literature. Finally, we contribute and evaluate a statistic for Top-two Thompson sampling to inform the decision makers about the confidence of an arm recommendation

Discovering Valuable Items from Massive Data

Suppose there is a large collection of items, each with an associated cost and an inherent utility that is revealed only once we commit to selecting it. Given a budget on the cumulative cost of the selected items, how can we pick a subset of maximal value? This task generalizes several important problems such as multi-arm bandits, active search and the knapsack problem. We present an algorithm, GP-Select, which utilizes prior knowledge about similarity be- tween items, expressed as a kernel function. GP-Select uses Gaussian process prediction to balance exploration (estimating the unknown value of items) and exploitation (selecting items of high value). We extend GP-Select to be able to discover sets that simultaneously have high utility and are diverse. Our preference for diversity can be specified as an arbitrary monotone submodular function that quantifies the diminishing returns obtained when selecting similar items. Furthermore, we exploit the structure of the model updates to achieve an order of magnitude (up to 40X) speedup in our experiments without resorting to approximations. We provide strong guarantees on the performance of GP-Select and apply it to three real-world case studies of industrial relevance: (1) Refreshing a repository of prices in a Global Distribution System for the travel industry, (2) Identifying diverse, binding-affine peptides in a vaccine de- sign task and (3) Maximizing clicks in a web-scale recommender system by recommending items to users

Structure and Melting of Two-Species Charged Clusters in a Parabolic Trap

We consider a system of charged particles interacting with an unscreened Coulomb repulsion in a two-dimensional parabolic confining trap. The static charge on a portion of the particles is twice as large as the charge on the remaining particles. The particles separate into a shell structure with those of greater charge situated farther from the center of the trap. As we vary the ratio of the number of particles of the two species, we find that for certain configurations, the symmetry of the arrangement of the inner cluster of singly-charged particles matches the symmetry of the outer ring of doubly-charged particles. These matching configurations have a higher melting temperature and a higher thermal threshold for intershell rotation between the species than the nonmatching configurations.Comment: 4 pages, 6 postscript figure

Yield conditions for deformation of amorphous polymer glasses

Shear yielding of glassy polymers is usually described in terms of the pressure-dependent Tresca or von Mises yield criteria. We test these criteria against molecular dynamics simulations of deformation in amorphous polymer glasses under triaxial loading conditions that are difficult to realize in experiments. Difficulties and ambiguities in extending several standard definitions of the yield point to triaxial loads are described. Two definitions, the maximum and offset octahedral stresses, are then used to evaluate the yield stress for a wide range of model parameters. In all cases, the onset of shear is consistent with the pressure-modified von Mises criterion, and the pressure coefficient is nearly independent of many parameters. Under triaxial tensile loading, the mode of failure changes to cavitation.Comment: 9 pages, 8 figures, revte