52,875 research outputs found
Supersolid and charge density-wave states from anisotropic interaction in an optical lattice
We show anisotropy of the dipole interaction between magnetic atoms or polar
molecules can stabilize new quantum phases in an optical lattice. Using a well
controlled numerical method based on the tensor network algorithm, we calculate
phase diagram of the resultant effective Hamiltonian in a two-dimensional
square lattice - an anisotropic Hubbard model of hard-core bosons with
attractive interaction in one direction and repulsive interaction in the other
direction. Besides the conventional superfluid and the Mott insulator states,
we find the striped and the checkerboard charge density wave states and the
supersolid phase that interconnect the superfluid and the striped solid states.
The transition to the supersolid phase has a mechanism different from the case
of the soft-core Bose Hubbard model.Comment: 5 pages, 5 figures
Ambulance Emergency Response Optimization in Developing Countries
The lack of emergency medical transportation is viewed as the main barrier to
the access of emergency medical care in low and middle-income countries
(LMICs). In this paper, we present a robust optimization approach to optimize
both the location and routing of emergency response vehicles, accounting for
uncertainty in travel times and spatial demand characteristic of LMICs. We
traveled to Dhaka, Bangladesh, the sixth largest and third most densely
populated city in the world, to conduct field research resulting in the
collection of two unique datasets that inform our approach. This data is
leveraged to develop machine learning methodologies to estimate demand for
emergency medical services in a LMIC setting and to predict the travel time
between any two locations in the road network for different times of day and
days of the week. We combine our robust optimization and machine learning
frameworks with real data to provide an in-depth investigation into three
policy-related questions. First, we demonstrate that outpost locations
optimized for weekday rush hour lead to good performance for all times of day
and days of the week. Second, we find that significant improvements in
emergency response times can be achieved by re-locating a small number of
outposts and that the performance of the current system could be replicated
using only 30% of the resources. Lastly, we show that a fleet of small
motorcycle-based ambulances has the potential to significantly outperform
traditional ambulance vans. In particular, they are able to capture three times
more demand while reducing the median response time by 42% due to increased
routing flexibility offered by nimble vehicles on a larger road network. Our
results provide practical insights for emergency response optimization that can
be leveraged by hospital-based and private ambulance providers in Dhaka and
other urban centers in LMICs
Kinetics and mechanism of formic acid decomposition on Ru(001)
The steady-state rate of decomposition of formic acid on
Ru(001) has been measured as a function of surface temperature, parametric in the pressure of formic acid. The
products of the decomposition reaction are C0_2, H_2, CO,
and H_2)0, i.e., both dehydrogenation and dehydration occur
on Ru (001). A similar product distribution has been observed on Ni(110), Ni(100), Ru(100), Fe(100), and
Ni(111) surfaces; whereas only dehydrogenation to C0_2
and H_2 occurs on the Cu(100), Cu(110), and Pt(111)
surfaces. Only reversible adsorption and desorption of formic acid is observed on the less reactive Ag(110) surface at low temperatures, whereas the more reactive Mo(100) surface is oxidized by formic acid at low temperatures with the products of this reaction being H_2, CO, and H_(2)O (Ref. 10). We report here the confirmation of earlier observations of the occurrence of both dehydrogenation and dehydration of formic acid on Ru(001), and more importantly, we provide a detailed mechanistic description of the steady-state decomposition reaction on this surface in terms of elementary steps
Power-law Behavior of High Energy String Scatterings in Compact Spaces
We calculate high energy massive scattering amplitudes of closed bosonic
string compactified on the torus. We obtain infinite linear relations among
high energy scattering amplitudes. For some kinematic regimes, we discover that
some linear relations break down and, simultaneously, the amplitudes enhance to
power-law behavior due to the space-time T-duality symmetry in the compact
direction. This result is consistent with the coexistence of the linear
relations and the softer exponential fall-off behavior of high energy string
scattering amplitudes as we pointed out prevously. It is also reminiscent of
hard (power-law) string scatterings in warped spacetime proposed by Polchinski
and Strassler.Comment: 6 pages, no figure. Talk presented by Jen-Chi Lee at Europhysics
Conference (EPS2007), Manchester, England, July 19-25, 2007. To be published
by Journal of Physics: Conference Series
A Hybrid Differential Evolution Approach to Designing Deep Convolutional Neural Networks for Image Classification
Convolutional Neural Networks (CNNs) have demonstrated their superiority in
image classification, and evolutionary computation (EC) methods have recently
been surging to automatically design the architectures of CNNs to save the
tedious work of manually designing CNNs. In this paper, a new hybrid
differential evolution (DE) algorithm with a newly added crossover operator is
proposed to evolve the architectures of CNNs of any lengths, which is named
DECNN. There are three new ideas in the proposed DECNN method. Firstly, an
existing effective encoding scheme is refined to cater for variable-length CNN
architectures; Secondly, the new mutation and crossover operators are developed
for variable-length DE to optimise the hyperparameters of CNNs; Finally, the
new second crossover is introduced to evolve the depth of the CNN
architectures. The proposed algorithm is tested on six widely-used benchmark
datasets and the results are compared to 12 state-of-the-art methods, which
shows the proposed method is vigorously competitive to the state-of-the-art
algorithms. Furthermore, the proposed method is also compared with a method
using particle swarm optimisation with a similar encoding strategy named IPPSO,
and the proposed DECNN outperforms IPPSO in terms of the accuracy.Comment: Accepted by The Australasian Joint Conference on Artificial
Intelligence 201
Stabilization of the p-wave superfluid state in an optical lattice
It is hard to stabilize the p-wave superfluid state of cold atomic gas in
free space due to inelastic collisional losses. We consider the p-wave Feshbach
resonance in an optical lattice, and show that it is possible to have a stable
p-wave superfluid state where the multi-atom collisional loss is suppressed
through the quantum Zeno effect. We derive the effective Hamiltonian for this
system, and calculate its phase diagram in a one-dimensional optical lattice.
The results show rich phase transitions between the p-wave superfluid state and
different types of insulator states induced either by interaction or by
dissipation.Comment: 5 pages, 5 figure
Iterative solution of perturbation equations
Iterative solution of perturbation equation
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