169 research outputs found
Correlation effects in ionic crystals: I. The cohesive energy of MgO
High-level quantum-chemical calculations, using the coupled-cluster approach
and extended one-particle basis sets, have been performed for (Mg2+)n (O2-)m
clusters embedded in a Madelung potential. The results of these calculations
are used for setting up an incremental expansion for the correlation energy of
bulk MgO. This way, 96% of the experimental cohesive energy of the MgO crystal
is recovered. It is shown that only 60% of the correlation contribution to the
cohesive energy is of intra-ionic origin, the remaining part being caused by
van der Waals-like inter-ionic excitations.Comment: LaTeX, 20 pages, no figure
Bayesian joint estimation of non-Gaussianity and the power spectrum
We propose a rigorous, non-perturbative, Bayesian framework which enables one
jointly to test Gaussianity and estimate the power spectrum of CMB
anisotropies. It makes use of the Hilbert space of an harmonic oscillator to
set up an exact likelihood function, dependent on the power spectrum and on a
set of parameters , which are zero for Gaussian processes. The latter
can be expressed as series of cumulants; indeed they perturbatively reduce to
cumulants. However they have the advantage that their variation is essentially
unconstrained. Any truncation(i.e.: finite set of ) therefore still
produces a proper distribution - something which cannot be said of the only
other such tool on offer, the Edgeworth expansion. We apply our method to Very
Small Array (VSA) simulations based on signal Gaussianity, showing that our
algorithm is indeed not biased.Comment: 11pages, 4 figures, submitted to MNRA
Towards Machine Wald
The past century has seen a steady increase in the need of estimating and
predicting complex systems and making (possibly critical) decisions with
limited information. Although computers have made possible the numerical
evaluation of sophisticated statistical models, these models are still designed
\emph{by humans} because there is currently no known recipe or algorithm for
dividing the design of a statistical model into a sequence of arithmetic
operations. Indeed enabling computers to \emph{think} as \emph{humans} have the
ability to do when faced with uncertainty is challenging in several major ways:
(1) Finding optimal statistical models remains to be formulated as a well posed
problem when information on the system of interest is incomplete and comes in
the form of a complex combination of sample data, partial knowledge of
constitutive relations and a limited description of the distribution of input
random variables. (2) The space of admissible scenarios along with the space of
relevant information, assumptions, and/or beliefs, tend to be infinite
dimensional, whereas calculus on a computer is necessarily discrete and finite.
With this purpose, this paper explores the foundations of a rigorous framework
for the scientific computation of optimal statistical estimators/models and
reviews their connections with Decision Theory, Machine Learning, Bayesian
Inference, Stochastic Optimization, Robust Optimization, Optimal Uncertainty
Quantification and Information Based Complexity.Comment: 37 page
Tabu assisted guided local search approaches for freight service network design
The service network design problem (SNDP) is a core problem in freight transportation. It involves the determination of the most cost-effective transportation network and the character- istics of the corresponding services, subject to various constraints. The scale of the problem in real-world applications is usually very large, especially when the network contains both the geographical information and the temporal constraints which are necessary for modelling mul- tiple service-classes and dynamic events. The development of time-efficient algorithms for this problem is, therefore, crucial for successful real-world applications. Earlier research indicated that guided local search (GLS) was a promising solution method for this problem. One of the advantages of GLS is that it makes use of both the information collected during the search as well as any special structures which are present in solutions. Building upon earlier research, this paper carries out in-depth investigations into several mechanisms that could potentially speed up the GLS algorithm for the SNDP. Specifically, the mechanisms that we have looked at in this paper include a tabu list (as used by tabu search), short-term memory, and an aspiration crite- rion. An efficient hybrid algorithm for the SNDP is then proposed, based upon the results of these experiments. The algorithm combines a tabu list within a multi-start GLS approach, with an efficient feasibility-repairing heuristic. Experimental tests on a set of 24 well-known service network design benchmark instances have shown that the proposed algorithm is superior to a previously proposed tabu search method, reducing the computation time by over a third. In ad- dition, we also show that far better results can be obtained when a faster linear program solver is adopted for the sub-problem solution. The contribution of this paper is an efficient algorithm, along with detailed analyses of effective mechanisms which can help to increase the speed of the GLS algorithm for the SNDP
Genuine Correlations of Like-Sign Particles in Hadronic Z0 Decays
Correlations among hadrons with the same electric charge produced in Z0
decays are studied using the high statistics data collected from 1991 through
1995 with the OPAL detector at LEP. Normalized factorial cumulants up to fourth
order are used to measure genuine particle correlations as a function of the
size of phase space domains in rapidity, azimuthal angle and transverse
momentum. Both all-charge and like-sign particle combinations show strong
positive genuine correlations. One-dimensional cumulants initially increase
rapidly with decreasing size of the phase space cells but saturate quickly. In
contrast, cumulants in two- and three-dimensional domains continue to increase.
The strong rise of the cumulants for all-charge multiplets is increasingly
driven by that of like-sign multiplets. This points to the likely influence of
Bose-Einstein correlations. Some of the recently proposed algorithms to
simulate Bose-Einstein effects, implemented in the Monte Carlo model PYTHIA,
are found to reproduce reasonably well the measured second- and higher-order
correlations between particles with the same charge as well as those in
all-charge particle multiplets.Comment: 26 pages, 6 figures, Submitted to Phys. Lett.
An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics
For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types
Innovating clinical trials for amyotrophic lateral sclerosis : challenging the established order
Development of effective treatments for amyotrophic lateral sclerosis (ALS) has been hampered by disease heterogeneity, a limited understanding of underlying pathophysiology, and methodologic design challenges. We have evaluated 2 major themes in the design of pivotal, phase 3 clinical trials for ALSâ(1) patient selection and (2) analytical strategyâand discussed potential solutions with the European Medicines Agency. Several design considerations were assessed using data from 5 placebo-controlled clinical trials (n = 988), 4 population-based cohorts (n = 5,100), and 2,436 placebo-allocated patients from the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database. The validity of each proposed design modification was confirmed by means of simulation and illustrated for a hypothetical setting. Compared to classical trial design, the proposed design modifications reduce the sample size by 30.5% and placebo exposure time by 35.4%. By making use of prognostic survival models, one creates a potential to include a larger proportion of the population and maximize generalizability. We propose a flexible design framework that naturally adapts the trial duration when inaccurate assumptions are made at the design stage, such as enrollment or survival rate. In case of futility, the follow-up time is shortened and patient exposure to ineffective treatments or placebo is minimized. For diseases such as ALS, optimizing the use of resources, widening eligibility criteria, and minimizing exposure to futile treatments and placebo is critical to the development of effective treatments. Our proposed design modifications could circumvent important pitfalls and may serve as a blueprint for future clinical trials in this population
Stochastic thermodynamics of holonomic systems
International audienc
Track E Implementation Science, Health Systems and Economics
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138412/1/jia218443.pd
- âŚ