736 research outputs found
Quantum widening of CDT universe
The physical phase of Causal Dynamical Triangulations (CDT) is known to be
described by an effective, one-dimensional action in which three-volumes of the
underlying foliation of the full CDT play a role of the sole degrees of
freedom. Here we map this effective description onto a statistical-physics
model of particles distributed on 1d lattice, with site occupation numbers
corresponding to the three-volumes. We identify the emergence of the quantum
de-Sitter universe observed in CDT with the condensation transition known from
similar statistical models. Our model correctly reproduces the shape of the
quantum universe and allows us to analytically determine quantum corrections to
the size of the universe. We also investigate the phase structure of the model
and show that it reproduces all three phases observed in computer simulations
of CDT. In addition, we predict that two other phases may exists, depending on
the exact form of the discretised effective action and boundary conditions. We
calculate various quantities such as the distribution of three-volumes in our
model and discuss how they can be compared with CDT.Comment: 19 pages, 13 figure
METHODS OF CALCULATION OF MSW STRUCTURES
The paper reviews the existing methods for the solution of structures supporting propaga-
tion of magnetostatic waves. Due to the fact that these are mostly multilayered structures
the mostly used numerical techniques for their calculation are the method of the surface
permeability, finite element method and the boundary element method. Because each
of them is more or less suitable in special cases, the advantages of each are discussed
and pointed out in the paper. The general magnetic anisotropy formulation has been
introduced into boundary element method
Zero-range process with long-range interactions at a T-junction
A generalized zero-range process with a limited number of long-range
interactions is studied as an example of a transport process in which particles
at a T-junction make a choice of which branch to take based on traffic levels
on each branch. The system is analysed with a self-consistent mean-field
approximation which allows phase diagrams to be constructed. Agreement between
the analysis and simulations is found to be very good.Comment: 21 pages, 6 figure
Pair-factorized steady states on arbitrary graphs
Stochastic mass transport models are usually described by specifying hopping
rates of particles between sites of a given lattice, and the goal is to predict
the existence and properties of the steady state. Here we ask the reverse
question: given a stationary state that factorizes over links (pairs of sites)
of an arbitrary connected graph, what are possible hopping rates that converge
to this state? We define a class of hopping functions which lead to the same
steady state and guarantee current conservation but may differ by the induced
current strength. For the special case of anisotropic hopping in two dimensions
we discuss some aspects of the phase structure. We also show how this case can
be traced back to an effective zero-range process in one dimension which is
solvable for a large class of hopping functions.Comment: IOP style, 9 pages, 1 figur
Speed and Accuracy of Static Image Discrimination by Rats
When discriminating dynamic noisy sensory signals, human and primate subjects
achieve higher accuracy when they take more time to decide, an effect
attributed to accumulation of evidence over time to overcome neural noise. We
measured the speed and accuracy of twelve freely behaving rats discriminating
static, high contrast photographs of real-world objects for water reward in a
self-paced task. Response latency was longer in correct trials compared to
error trials. Discrimination accuracy increased with response latency over the
range of 500-1200ms. We used morphs between previously learned images to vary
the image similarity parametrically, and thereby modulate task difficulty from
ceiling to chance. Over this range we find that rats take more time before
responding in trials with more similar stimuli. We conclude that rats'
perceptual decisions improve with time even in the absence of temporal
information in the stimulus, and that rats modulate speed in response to
discrimination difficulty to balance speed and accuracy
The NuMAX Long Baseline Neutrino Factory Concept
A Neutrino Factory where neutrinos of all species are produced in equal
quantities by muon decay is described as a facility at the intensity frontier
for exquisite precision providing ideal conditions for ultimate neutrino
studies and the ideal complement to Long Baseline Facilities like LBNF at
Fermilab. It is foreseen to be built in stages with progressively increasing
complexity and performance, taking advantage of existing or proposed facilities
at an existing laboratory like Fermilab. A tentative layout based on a
recirculating linac providing opportunities for considerable saving is
discussed as well as its possible evolution toward a muon collider if and when
requested by Physics. Tentative parameters of the various stages are presented
as well as the necessary R&D to address the technological issues and
demonstrate their feasibility.Comment: JINST Special Issue on Muon Accelerators. arXiv admin note: text
overlap with arXiv:1308.0494, arXiv:1502.0164
Predicting the effects of deep brain stimulation using a reduced coupled oscillator model
This is the final version. Available on open access from Public Library of Science via the DOI in this recordData Availability: The data analysed in this manuscript is available from MRC BNDU Data Sharing platform at: https://data.mrc.ox.ac.uk/data-set/tremor-data-measured-essential-tremor-patients-subjected-phase-locked-deep-brain DOI: 10.5287/bodleian:xq24eN2KmDeep brain stimulation (DBS) is known to be an effective treatment for a variety of neurological disorders, including Parkinson’s disease and essential tremor (ET). At
present, it involves administering a train of pulses with constant frequency via electrodes implanted into the brain. New ‘closed-loop’ approaches involve delivering
stimulation according to the ongoing symptoms or brain activity and have the potential to provide improvements in terms of efficiency, efficacy and reduction of side effects. The success of closed-loop DBS depends on being able to devise a stimulation strategy that minimizes oscillations in neural activity associated with symptoms of motor disorders. A useful stepping stone towards this is to construct a mathematical model, which can describe how the brain oscillations should change when stimulation is applied at a particular state of the system. Our work focuses on the use of coupled oscillators to represent neurons in areas generating pathological oscillations. Using a reduced form of the Kuramoto model, we analyse how a patient should respond to stimulation when neural oscillations have a given phase and amplitude, provided a number of conditions are satisfied. For such patients, we predict that the best stimulation strategy should be phase specific but also that stimulation should have a greater effect if applied when the amplitude of brain oscillations is lower. We compare this surprising prediction with data obtained from ET patients. In light of our predictions, we also propose a new hybrid strategy which effectively combines two of the closed-loop strategies found in the
literature, namely phase-locked and adaptive DBS
Optimal learning rules for familiarity detection
It has been suggested that the mammalian memory system has both familiarity and recollection components. Recently, a high-capacity network to store familiarity has been proposed. Here we derive analytically the optimal learning rule for such a familiarity memory using a signalto- noise ratio analysis. We find that in the limit of large networks the covariance rule, known to be the optimal local, linear learning rule for pattern association, is also the optimal learning rule for familiarity discrimination. The capacity is independent of the sparseness of the patterns, as long as the patterns have a fixed number of bits set. The corresponding information capacity is 0.057 bits per synapse, less than typically found for associative networks
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