64 research outputs found
A new method for identifying vertebrates using only their mitochondrial DNA
A new method for determining whether or not a mitrochondrial DNA (mtDNA)
sequence belongs to a vertebrate is described and tested. This method only
needs the mtDNA sequence of the organism in question, and unlike alignment
based methods, it does not require it to be compared with anything else. The
method is tested on all 1877 mtDNA sequences that were on NCBI's nucleotide
database on August 12, 2009, and works in 94.57% of the cases. Furthermore, all
organisms on which this method failed are closely related phylogenetically in
comparison to all other organisms included in the study. A list of potential
extensions to this method and open problems that emerge out of this study is
presented at the end.Comment: 7 Pages, 6 Figure
Beryllium monohydride (BeH): Where we are now, after 86 years of spectroscopy
BeH is one of the most important benchmark systems for ab initio methods and
for studying Born-Oppenheimer breakdown. However the best empirical potential
and best ab initio potential for the ground electronic state to date give
drastically different predictions in the long-range region beyond which
measurements have been made, which is about \sim1000 cm^{-1} for ^{9} BeH,
\sim3000 cm^{-1} for ^{9} BeD, and \sim13000 cm^{-1} for ^{9} BeT. Improved
empirical potentials and Born-Oppenheimer breakdown corrections have now been
built for the ground electronic states X(1^{2}\Sigma^{+}) of all three
isotopologues. The predicted dissociation energy for ^{9} BeH from the new
empirical potential is now closer to the current best ab initio prediction by
more than 66% of the discrepancy between the latter and the previous best
empirical potential. The previous best empirical potential predicted the
existence of unobserved vibrational levels for all three isotopologues, and the
current best ab initio study also predicted the existence of all of these
levels, and four more. The present empirical potential agrees with the ab
initio prediction of all of these extra levels not predicted by the earlier
empirical potential. With one exception, all energy spacings between
vibrational energy levels for which measurements have been made, are predicted
with an agreement of better than 1 cm^{-1} between the new empirical potential
and the current best ab initio potential, but some predictions for unobserved
levels are still in great disagreement, and the equilibrium bond lengths are
different by orders of magnitude.Comment: Feedback encouraged. 9 Pages, 4 Figures, 4 Tables. The author thanks
JSPS for financial suppor
Numerical Feynman integrals for density operator dynamics using master equation interpolants: faster convergence and significant reduction of computational cost
The Feynman integral is one of the most accurate methods for calculating
density operator dynamics in open quantum systems. However, the number of time
steps that can realistically be used is always limited, therefore one often
obtains an approximation of the density operator at a sparse grid of points in
time. Instead of relying only on \textit{ad hoc} interpolation methods such as
splines to estimate the system density operator in between these points, I
propose a method that uses physical information to assist with this
interpolation. This method is tested on a physically significant system, on
which its use allows important qualitative features of the density operator
dynamics to be captured with as little as 2 time steps in the Feynman integral.
This method allows for an enormous reduction in the amount of memory and CPU
time required for approximating density operator dynamics within a desired
accuracy. Since this method does not change the way the Feynman integral itself
is calculated, the value of the density operator approximation at the points in
time used to discretize the Feynamn integral will be the same whether or not
this method is used, but its approximation in between these points in time is
considerably improved by this method.Comment: 5 pages, 4 figures. v2: no change in results, no change in figures,
fixed formatting on page 3, some word changes, version of abstract appearing
in paper has been shortene
Analytic potentials and vibrational energies for Li states dissociating to \mbox{Li}\left(2S\right)+\mbox{Li}\left(3P\right). Part 1: The states
Analytic potentials are built for all four states of
Li dissociating to Li + Li: ,
, and . These
potentials include the effect of spin-orbit coupling for large internuclear
distances, and include state of the art long-range constants. This is the first
successful demonstration of fully analytic diatomic potentials that capture
features that are usually considered too difficult to capture without a
point-wise potential, such as multiple minima, and shelves. Vibrational
energies for each potential are presented for the isotopologues
Li, Li, Li, and the elusive `halo
nucleonic molecule' Li. These energies are claimed to be
accurate enough for new high-precision experimental setups such as the one
presented in {[}Sebastian \emph{et al.} Phys. Rev. A, \textbf{90}, 033417
(2014){]} to measure and assign energy levels of these electronic states, all
of which have not yet been explored in the long-range region. Measuring
energies in the long-range region of these electronic states may be significant
for studying the \emph{ab initio} vs experiment discrepancy discussed in
{[}Tang \emph{et al.} Phys. Rev. A, \textbf{84}, 052502 (2014){]} for the
long-range constant of Lithium, which has significance for improving
the SI definition of the second.Comment: Feedback encouraged. 19 pages, 4 figure
Modeling of neuron-semiconductor interactions in neuronal networks interfaced with silicon chips
Recent developments in the interfacing of neurons with silicon chips may pave
the way for progress in constructing scalable neurocomputers. The assembly of
synthetic neuronal networks with predefined synaptic connections and controlled
geometric structure has been realized experimentally within the last decade.
Furthermore, when such neuronal networks are interfaced with semiconductors,
action potentials in neurons of the network can be elicited by capacitative
stimulators, and voltage measurements can be made by transistors incorporated
into the associated silicon chip. Despite the impressive progress, such
preliminary devices have not yet demonstrated the performance of useful
computations, and constructing larger devices can be both expensive and
time-consuming. Accordingly, an appropriate modeling framework with the
capability to simulate current experimental results in such devices may be used
to make useful predictions regarding their potential computational power. A
proposed modeling framework for functional neuronal networks interfaced with
silicon chips is presented below.Comment: 14 Pages, 4 Figure
Simulating neurobiological localization of acoustic signals based on temporal and volumetric differentiations
The localization of sound sources by the human brain is computationally
simulated from a neurobiological perspective. The simulation includes the
neural representation of temporal differences in acoustic signals between the
ipsilateral and contralateral ears for constant sound intensities (angular
localization), and of volumetric differences in acoustic signals for constant
azimuthal angles (radial localization). The transmission of the original
acoustic signal from the environment, through each significant stage of
intermediate neurons, to the primary auditory cortex, is also simulated. The
errors that human brains make in attempting to localize sounds in
evolutionarily uncommon environments (such as when one ear is in water and one
ear is in air) are then mathematically predicted. A basic overview of the
physiology behind sound localization in the brain is also provided.Comment: 26 pages, 18 figure
Linear Multistep Numerical Methods for Ordinary Differential Equations
A review of the most popular Linear Multistep (LM) Methods for solving
Ordinary Differential Equations numerically is presented. These methods are
first derived from first principles, and are discussed in terms of their order,
consistency, and various types of stability. Particular varieties of stability
that may not be familiar, are briefly defined first. The methods that are
included are the Adams-Bashforth Methods, Adams-Moulton Methods, and Backwards
Differentiation Formulas. Advantages and disadvantages of these methods are
also described. Not much prior knowledge of numerical methods or ordinary
differential equations is required, although knowledge of basic topics from
calculus is assumed.Comment: A general review that does not require much prior knowledge in
numerical ODEs. 10 page
State of the art for ab initio vs empirical potentials for predicting 6e excited state molecular energies: Application to Li
We build the first analytic empirical potential for the most deeply bound
\mbox{Li}_{2} state: . Our potential is based on
experimental energy transitions covering , and very high precision
theoretical long-range constants. It provides high accuracy predictions up to
which pave the way for high-precision long-range measurements, and
hopefully an eventual resolution of the age old discrepancy between experiment
and theory for the \mbox{Li}\left(2^{2}S\right)+\mbox{Li}\left(2^{2}P\right)
value. State of the art ab initio calculations predict vibrational
energy spacings that are all in at most 0.8 cm disagreement with the
empirical potential.Comment: Feedback encouraged. 16 pages, 3 figure
Reducing multi-qubit interactions in adiabatic quantum computation without adding auxiliary qubits. Part 2: The "split-reduc" method and its application to quantum determination of Ramsey numbers
Quantum annealing has recently been used to determine the Ramsey numbers
R(m,2) for 3 < m < 9 and R(3,3) [Bian et al. (2013) PRL 111, 130505]. This was
greatly celebrated as the largest experimental implementation of an adiabatic
evolution algorithm to that date. However, in that computation, more than 66%
of the qubits used were auxiliary qubits, so the sizes of the Ramsey number
Hamiltonians used were tremendously smaller than the full 128-qubit capacity of
the device used. The reason these auxiliary qubits were needed was because the
best quantum annealing devices at the time (and still now) cannot implement
multi-qubit interactions beyond 2-qubit interactions, and they are also limited
in their capacity for 2-qubit interactions. We present a method which allows
the full qubit capacity of a quantum annealing device to be used, by reducing
multi-qubit and 2-qubit interactions. With our method, the device used in the
2013 Ramsey number quantum computation could have determined R(16,2) and R(4,3)
with under 10 minutes of runtime.Comment: Feedback encouraged. 5 page
Crushing runtimes in adiabatic quantum computation with Energy Landscape Manipulation (ELM): Application to Quantum Factoring
We introduce two methods for speeding up adiabatic quantum computations by
increasing the energy between the ground and first excited states. Our methods
are even more general. They can be used to shift a Hamiltonian's density of
states away from the ground state, so that fewer states occupy the low-lying
energies near the minimum, hence allowing for faster adiabatic passages to find
the ground state with less risk of getting caught in an undesired low-lying
excited state during the passage. Even more generally, our methods can be used
to transform a discrete optimization problem into a new one whose unique
minimum still encodes the desired answer, but with the objective function's
values forming a different landscape. Aspects of the landscape such as the
objective function's range, or the values of certain coefficients, or how many
different inputs lead to a given output value, can be decreased *or* increased.
One of the many examples for which these methods are useful is in finding the
ground state of a Hamiltonian using NMR: If it is difficult to find a molecule
such that the distances between the spins match the interactions in the
Hamiltonian, the interactions in the Hamiltonian can be changed without at all
changing the ground state. We apply our methods to an AQC algorithm for integer
factorization, and the first method reduces the maximum runtime in our example
by up to 754%, and the second method reduces the maximum runtime of another
example by up to 250%. These two methods may also be combined.Comment: Feedback Encourage
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