3,648 research outputs found
U.S. Population, Energy & Climate Change
Explains how U.S. population trends tend to exacerbate both the causes and effects of climate change. Outlines how population density and composition affect energy and land use, the role each U.S. region plays in climate change, and the risks they face
Inferring bulk self-assembly properties from simulations of small systems with multiple constituent species and small systems in the grand canonical ensemble
In this paper we generalize a methodology [T. E. Ouldridge, A. A. Louis, and
J. P. K. Doye, J. Phys.: Condens. Matter {\bf 22}, 104102 (2010)] for dealing
with the inference of bulk properties from small simulations of self-assembling
systems of characteristic finite size. In particular, schemes for extrapolating
the results of simulations of a single self-assembling object to the bulk limit
are established in three cases: for assembly involving multiple particle
species, for systems with one species localized in space and for simulations in
the grand canonical ensemble. Furthermore, methodologies are introduced for
evaluating the accuracy of these extrapolations. Example systems demonstrate
that differences in cluster concentrations between simulations of a single
self-assembling structure and bulk studies of the same model under identical
conditions can be large, and that convergence on bulk results as system size is
increased can be slow and non-trivial.Comment: Accepted by J. Chem. Phy
Entanglement Generation from Thermal Spin States via Unitary Beam Splitters
We suggest a method of generating distillable entanglement form mixed states
unitarily, by utilizing the flexibility of dimension od occupied Hilbert space.
We present a model of a thermal spin state entering a beam splitter generating
entanglement. It is the truncation of the state that allows for entanglement
generation. The output entanglement is investigated for different temperatures
and it is found that more randomness - in the form of higher temperature - is
better for this set up.Comment: 4 pages, 3 figures. Small changes in accordance with journal advice
to make more readable. Improved discussion on implemetability of scheme, and
references adde
Entanglement of multiparty stabilizer, symmetric, and antisymmetric states
We study various distance-like entanglement measures of multipartite states
under certain symmetries. Using group averaging techniques we provide
conditions under which the relative entropy of entanglement, the geometric
measure of entanglement and the logarithmic robustness are equivalent. We
consider important classes of multiparty states, and in particular show that
these measures are equivalent for all stabilizer states, symmetric basis and
antisymmetric basis states. We rigorously prove a conjecture that the closest
product state of permutation symmetric states can always be chosen to be
permutation symmetric. This allows us to calculate the explicit values of
various entanglement measures for symmetric and antisymmetric basis states,
observing that antisymmetric states are generally more entangled. We use these
results to obtain a variety of interesting ensembles of quantum states for
which the optimal LOCC discrimination probability may be explicitly determined
and achieved. We also discuss applications to the construction of optimal
entanglement witnesses
Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus
In systems of multiple agents, identifying the cause of observed agent
dynamics is challenging. Often, these agents operate in diverse, non-stationary
environments, where models rely on hand-crafted environment-specific features
to infer influential regions in the system's surroundings. To overcome the
limitations of these inflexible models, we present GP-LAPLACE, a technique for
locating sources and sinks from trajectories in time-varying fields. Using
Gaussian processes, we jointly infer a spatio-temporal vector field, as well as
canonical vector calculus operations on that field. Notably, we do this from
only agent trajectories without requiring knowledge of the environment, and
also obtain a metric for denoting the significance of inferred causal features
in the environment by exploiting our probabilistic method. To evaluate our
approach, we apply it to both synthetic and real-world GPS data, demonstrating
the applicability of our technique in the presence of multiple agents, as well
as its superiority over existing methods.Comment: KDD '18 Proceedings of the 24th ACM SIGKDD International Conference
on Knowledge Discovery & Data Mining, Pages 1254-1262, 9 pages, 5 figures,
conference submission, University of Oxford. arXiv admin note: text overlap
with arXiv:1709.0235
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