10,379 research outputs found
Convergence improvement for coupled cluster calculations
Convergence problems in coupled-cluster iterations are discussed, and a new
iteration scheme is proposed. Whereas the Jacobi method inverts only the
diagonal part of the large matrix of equation coefficients, we invert a matrix
which also includes a relatively small number of off-diagonal coefficients,
selected according to the excitation amplitudes undergoing the largest change
in the coupled cluster iteration. A test case shows that the new IPM (inversion
of partial matrix) method gives much better convergence than the
straightforward Jacobi-type scheme or such well-known convergence aids as the
reduced linear equations or direct inversion in iterative subspace methods.Comment: 7 pages, IOPP styl
Investigation, Testing, and Selection of Slip-ring Lead Wires for Use in High-precision Slip-ring Capsules Final Report
Evaluation of corrosion resistant silver alloys for use in lead wires for slip-ring assemblies of Saturn guidance and control system
An Email Attachment is Worth a Thousand Words, or Is It?
There is an extensive body of research on Social Network Analysis (SNA) based
on the email archive. The network used in the analysis is generally extracted
either by capturing the email communication in From, To, Cc and Bcc email
header fields or by the entities contained in the email message. In the latter
case, the entities could be, for instance, the bag of words, url's, names,
phones, etc. It could also include the textual content of attachments, for
instance Microsoft Word documents, excel spreadsheets, or Adobe pdfs. The nodes
in this network represent users and entities. The edges represent communication
between users and relations to the entities. We suggest taking a different
approach to the network extraction and use attachments shared between users as
the edges. The motivation for this is two-fold. First, attachments represent
the "intimacy" manifestation of the relation's strength. Second, the
statistical analysis of private email archives that we collected and Enron
email corpus shows that the attachments contribute in average around 80-90% to
the archive's disk-space usage, which means that most of the data is presently
ignored in the SNA of email archives. Consequently, we hypothesize that this
approach might provide more insight into the social structure of the email
archive. We extract the communication and shared attachments networks from
Enron email corpus. We further analyze degree, betweenness, closeness, and
eigenvector centrality measures in both networks and review the differences and
what can be learned from them. We use nearest neighbor algorithm to generate
similarity groups for five Enron employees. The groups are consistent with
Enron's organizational chart, which validates our approach.Comment: 12 pages, 4 figures, 7 tables, IML'17, Liverpool, U
A molecular perspective on the limits of life: Enzymes under pressure
From a purely operational standpoint, the existence of microbes that can grow
under extreme conditions, or "extremophiles", leads to the question of how the
molecules making up these microbes can maintain both their structure and
function. While microbes that live under extremes of temperature have been
heavily studied, those that live under extremes of pressure have been
neglected, in part due to the difficulty of collecting samples and performing
experiments under the ambient conditions of the microbe. However, thermodynamic
arguments imply that the effects of pressure might lead to different organismal
solutions than from the effects of temperature. Observationally, some of these
solutions might be in the condensed matter properties of the intracellular
milieu in addition to genetic modifications of the macromolecules or repair
mechanisms for the macromolecules. Here, the effects of pressure on enzymes,
which are proteins essential for the growth and reproduction of an organism,
and some adaptations against these effects are reviewed and amplified by the
results from molecular dynamics simulations. The aim is to provide biological
background for soft matter studies of these systems under pressure.Comment: 16 pages, 8 figure
The Barrier Method: A Technique for Calculating Very Long Transition Times
In many dynamical systems there is a large separation of time scales between
typical events and "rare" events which can be the cases of interest. Rare-event
rates are quite difficult to compute numerically, but they are of considerable
practical importance in many fields: for example transition times in chemical
physics and extinction times in epidemiology can be very long, but are quite
important. We present a very fast numerical technique that can be used to find
long transition times (very small rates) in low-dimensional systems, even if
they lack detailed balance. We illustrate the method for a bistable
non-equilibrium system introduced by Maier and Stein and a two-dimensional (in
parameter space) epidemiology model.Comment: 20 pages, 8 figure
Application of asymptotic expansions of maximum likelihood estimators errors to gravitational waves from binary mergers: the single interferometer case
In this paper we describe a new methodology to calculate analytically the
error for a maximum likelihood estimate (MLE) for physical parameters from
Gravitational wave signals. All the existing litterature focuses on the usage
of the Cramer Rao Lower bounds (CRLB) as a mean to approximate the errors for
large signal to noise ratios. We show here how the variance and the bias of a
MLE estimate can be expressed instead in inverse powers of the signal to noise
ratios where the first order in the variance expansion is the CRLB. As an
application we compute the second order of the variance and bias for MLE of
physical parameters from the inspiral phase of binary mergers and for noises of
gravitational wave interferometers . We also compare the improved error
estimate with existing numerical estimates. The value of the second order of
the variance expansions allows to get error predictions closer to what is
observed in numerical simulations. It also predicts correctly the necessary SNR
to approximate the error with the CRLB and provides new insight on the
relationship between waveform properties SNR and estimation errors. For example
the timing match filtering becomes optimal only if the SNR is larger than the
kurtosis of the gravitational wave spectrum
Generalization Error in Deep Learning
Deep learning models have lately shown great performance in various fields
such as computer vision, speech recognition, speech translation, and natural
language processing. However, alongside their state-of-the-art performance, it
is still generally unclear what is the source of their generalization ability.
Thus, an important question is what makes deep neural networks able to
generalize well from the training set to new data. In this article, we provide
an overview of the existing theory and bounds for the characterization of the
generalization error of deep neural networks, combining both classical and more
recent theoretical and empirical results
Endogenous Quasicycles and Stochastic Coherence in a Closed Endemic Model
We study the role of demographic fluctuations in typical endemics as
exemplified by the stochastic SIRS model. The birth-death master equation of
the model is simulated using exact numerics and analysed within the linear
noise approximation. The endemic fixed point is unstable to internal
demographic noise, and leads to sustained oscillations. This is ensured when
the eigenvalues () of the linearised drift matrix are complex, which
in turn, is possible only if detailed balance is violated. In the oscillatory
state, the phases decorrelate asymptotically, distinguishing such oscillations
from those produced by external periodic forcing. These so-called quasicycles
are of sufficient strength to be detected reliably only when the ratio
is of order unity. The coherence or regularity of
these oscillations show a maximum as a function of population size, an effect
known variously as stochastic coherence or coherence resonance. We find that
stochastic coherence can be simply understood as resulting from a non-monotonic
variation of with population size. Thus, within the
linear noise approximation, stochastic coherence can be predicted from a purely
deterministic analysis. The non-normality of the linearised drift matrix,
associated with the violation of detailed balance, leads to enhanced
fluctuations in the population amplitudes.Comment: 21 pages, 8 figure
Predictive coupled-cluster isomer orderings for some SiC () clusters; A pragmatic comparison between DFT and complete basis limit coupled-cluster benchmarks
The accurate determination of the preferred
isomer is important to guide experimental efforts directed towards synthesizing
SiC nano-wires and related polymer structures which are anticipated to be
highly efficient exciton materials for opto-electronic devices. In order to
definitively identify preferred isomeric structures for silicon carbon
nano-clusters, highly accurate geometries, energies and harmonic zero point
energies have been computed using coupled-cluster theory with systematic
extrapolation to the complete basis limit for set of silicon carbon clusters
ranging in size from SiC to . It is found that
post-MBPT(2) correlation energy plays a significant role in obtaining converged
relative isomer energies, suggesting that predictions using low rung density
functional methods will not have adequate accuracy. Utilizing the best
composite coupled-cluster energy that is still computationally feasible,
entailing a 3-4 SCF and CCSD extrapolation with triple- (T) correlation,
the {\it closo} isomer is identified to be the
preferred isomer in support of previous calculations [J. Chem. Phys. 2015, 142,
034303]. Additionally we have investigated more pragmatic approaches to
obtaining accurate silicon carbide isomer energies, including the use of frozen
natural orbital coupled-cluster theory and several rungs of standard and
double-hybrid density functional theory. Frozen natural orbitals as a way to
compute post MBPT(2) correlation energy is found to be an excellent balance
between efficiency and accuracy
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