10,293 research outputs found
Rapid Computation of Thermodynamic Properties Over Multidimensional Nonbonded Parameter Spaces using Adaptive Multistate Reweighting
We show how thermodynamic properties of molecular models can be computed over
a large, multidimensional parameter space by combining multistate reweighting
analysis with a linear basis function approach. This approach reduces the
computational cost to estimate thermodynamic properties from molecular
simulations for over 130,000 tested parameter combinations from over a thousand
CPU years to tens of CPU days. This speed increase is achieved primarily by
computing the potential energy as a linear combination of basis functions,
computed from either modified simulation code or as the difference of energy
between two reference states, which can be done without any simulation code
modification. The thermodynamic properties are then estimated with the
Multistate Bennett Acceptance Ratio (MBAR) as a function of multiple model
parameters without the need to define a priori how the states are connected by
a pathway. Instead, we adaptively sample a set of points in parameter space to
create mutual configuration space overlap. The existence of regions of poor
configuration space overlap are detected by analyzing the eigenvalues of the
sampled states' overlap matrix. The configuration space overlap to sampled
states is monitored alongside the mean and maximum uncertainty to determine
convergence, as neither the uncertainty or the configuration space overlap
alone is a sufficient metric of convergence.
This adaptive sampling scheme is demonstrated by estimating with high
precision the solvation free energies of charged particles of Lennard-Jones
plus Coulomb functional form. We also compute entropy, enthalpy, and radial
distribution functions of unsampled parameter combinations using only the data
from these sampled states and use the free energies estimates to examine the
deviation of simulations from the Born approximation to the solvation free
energy
Symmetries of differential-difference dynamical systems in a two-dimensional lattice
Classification of differential-difference equation of the form
are considered
according to their Lie point symmetry groups. The set represents the
point and its six nearest neighbors in a two-dimensional triangular
lattice. It is shown that the symmetry group can be at most 12-dimensional for
abelian symmetry algebras and 13-dimensional for nonsolvable symmetry algebras.Comment: 24 pages, 1 figur
Quantum Gravity at the Planck Length
I describe our understanding of physics near the Planck length, in particular
the great progress in the last four years in string theory. These are lectures
presented at the 1998 SLAC Summer Institute.Comment: 33 pages, LaTeX, 11 epsf figure
Classification of five-point differential-difference equations
Using the generalized symmetry method, we carry out, up to autonomous point
transformations, the classification of integrable equations of a subclass of
the autonomous five-point differential-difference equations. This subclass
includes such well-known examples as the Itoh-Narita-Bogoyavlensky and the
discrete Sawada-Kotera equations. The resulting list contains 17 equations some
of which seem to be new. We have found non-point transformations relating most
of the resulting equations among themselves and their generalized symmetries.Comment: 29 page
Expanding perfect fluid generalizations of the C-metric
We reexamine Petrov type D gravitational fields generated by a perfect fluid
with spatially homogeneous energy density and in which the flow lines form a
timelike non-shearing and non-rotating congruence. It is shown that the
anisotropic such spacetimes, which comprise the vacuum C-metric as a limit
case, can have \emph{non-zero} expansion, contrary to the conclusion in the
original investigation by Barnes (Gen. Rel. Grav. 4, 105 (1973)). This class
consists of cosmological models with generically one and at most two Killing
vectors. We construct their line element and discuss some important properties.
The methods used in this investigation incite to deduce testable criteria
regarding shearfree normality and staticity op Petrov type spacetimes in
general, which we add in an appendix.Comment: 16 pages, extended and amended versio
Automated Identification of Unhealthy Drinking Using Routinely Collected Data: A Machine Learning Approach
Background: Unhealthy drinking is prevalent in the United States and can lead to serious health and social consequences, yet it is under-diagnosed and under-treated. Identifying unhealthy drinkers can be time-consuming for primary care providers. An automated tool for identification would allow attention to be focused on patients most likely to need care and therefore increase efficiency and effectiveness.
Objectives: To build a clinical prediction tool for unhealthy drinking based solely on routinely collected demographic and laboratory data.
Methods: We obtained demographic and laboratory data on 89,325 adults seen at the University of Vermont Medical Center from 2011-2017. Logistic regression, support vector machines (SVM), k-nearest neighbor, and random forests were each used to build clinical prediction models. The model with the largest area under the receiver operator curve (AUC) was selected.
Results: SVM with polynomials of degree 3 produced the largest AUC. The most influential predictors were alkaline phosphatase, gender, glucose, and serum bicarbonate. The optimum operating point had sensitivity 31.1%, specificity 91.2%, positive predictive value 50.4%, and negative predictive value 82.1%. Application of the tool increased the prevalence of unhealthy drinking from 18.3% to 32.4%, while reducing the target population by 22%.
Limitations: Universal screening was not used during the time data was collected. The prevalence of unhealthy drinking among those screened was 60% suggesting the AUDIT-C was administered to confirm rather than screen for unhealthy drinking.
Conclusion: An automated tool, using commonly available data, can identify a subset of patients who appear to warrant clinical attention for unhealthy drinking
Introduction: What is Meaning in a Legal Text? A First Dialogue for Law and Linguistics
This Article begins by describing the development of interest within linguistics over the last two decades in the language of legal processes, continues by tracing the evolution of the conference from a 1993 collaborative research project carried out by one law professor and three linguists, and concludes with some personal observations of the author on the benefits that linguists like herself stand to gain from further interdisciplinary efforts in this domain
Reparametrization-Invariant Path Integral in GR and "Big Bang" of Quantum Universe
The reparametrization-invariant generating functional for the unitary and
causal perturbation theory in general relativity in a finite space-time is
obtained. The region of validity of the Faddeev-Popov-DeWitt functional is
studied. It is shown that the invariant content of general relativity as a
constrained system can be covered by two "equivalent" unconstrained systems:
the "dynamic" (with "dynamic" evolution parameter as the metric scale factor)
and "geometric" (given by the Levi-Civita type canonical transformation to the
action-angle variables where the energy constraint converts into a new
momentum). "Big Bang", the Hubble evolution, and creation of matter fields by
the "geometric" vacuum are described by the inverted Levi-Civita (LC)
transformation of the geomeric system into the dynamic one. The particular case
of the LC transformations are the Bogoliubov ones of the particle variables
(diagonalizing the dynamic Hamiltonian) to the quasiparticles (diagonalizing
the equations of motion). The choice of initial conditions for the "Big Bang"
in the form of the Bogoliubov (squeezed) vacuum reproduces all stages of the
evolution of the Friedmann-Robertson-Walker Universe in their conformal
(Hoyle-Narlikar) versions.Comment: 21 pages, latex, 4 figures in postscrip
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