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 ($\lambda$) 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 $|Im(\lambda)/Re(\lambda)|$ 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 $|Im(\lambda)/Re(\lambda)|$ 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 Sin{}_nCm{}_m (m,n≤12m, n\le 12) clusters; A pragmatic comparison between DFT and complete basis limit coupled-cluster benchmarks

The accurate determination of the preferred ${\rm Si}_{12}{\rm C}_{12}$ 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$_3$ to ${\rm Si}_{12}{\rm C}_{12}$. 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-$\zeta$ (T) correlation, the {\it closo} ${\rm Si}_{12}{\rm C}_{12}$ 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