14,503 research outputs found
State space and movement specification in open population spatial capture-recapture models.
With continued global changes, such as climate change, biodiversity loss, and habitat fragmentation, the need for assessment of long-term population dynamics and population monitoring of threatened species is growing. One powerful way to estimate population size and dynamics is through capture-recapture methods. Spatial capture (SCR) models for open populations make efficient use of capture-recapture data, while being robust to design changes. Relatively few studies have implemented open SCR models, and to date, very few have explored potential issues in defining these models. We develop a series of simulation studies to examine the effects of the state-space definition and between-primary-period movement models on demographic parameter estimation. We demonstrate the implications on a 10-year camera-trap study of tigers in India. The results of our simulation study show that movement biases survival estimates in open SCR models when little is known about between-primary-period movements of animals. The size of the state-space delineation can also bias the estimates of survival in certain cases.We found that both the state-space definition and the between-primary-period movement specification affected survival estimates in the analysis of the tiger dataset (posterior mean estimates of survival ranged from 0.71 to 0.89). In general, we suggest that open SCR models can provide an efficient and flexible framework for long-term monitoring of populations; however, in many cases, realistic modeling of between-primary-period movements is crucial for unbiased estimates of survival and density
Sensitivity analysis of a branching process evolving on a network with application in epidemiology
We perform an analytical sensitivity analysis for a model of a
continuous-time branching process evolving on a fixed network. This allows us
to determine the relative importance of the model parameters to the growth of
the population on the network. We then apply our results to the early stages of
an influenza-like epidemic spreading among a set of cities connected by air
routes in the United States. We also consider vaccination and analyze the
sensitivity of the total size of the epidemic with respect to the fraction of
vaccinated people. Our analysis shows that the epidemic growth is more
sensitive with respect to transmission rates within cities than travel rates
between cities. More generally, we highlight the fact that branching processes
offer a powerful stochastic modeling tool with analytical formulas for
sensitivity which are easy to use in practice.Comment: 17 pages (30 with SI), Journal of Complex Networks, Feb 201
Fault-tolerant quantum computation versus Gaussian noise
We study the robustness of a fault-tolerant quantum computer subject to
Gaussian non-Markovian quantum noise, and we show that scalable quantum
computation is possible if the noise power spectrum satisfies an appropriate
"threshold condition." Our condition is less sensitive to very-high-frequency
noise than previously derived threshold conditions for non-Markovian noise.Comment: 30 pages, 6 figure
Non-Markovian Quantum Probes
We review the most recent developments in the theory of open quantum systems
focusing on situations in which the reservoir memory effects, due to
long-lasting and non-negligible correlations between system and environment,
play a crucial role. These systems are often referred to as non-Markovian
systems. After a brief summary of different measures of non-Markovianity that
have been introduced over the last few years we restrict our analysis to the
investigation of information flow between system and environment. Within this
framework we introduce an important application of non-Markovianity, namely its
use as a quantum probe of complex quantum systems. To illustrate this point we
consider quantum probes of ultracold gases, spin chains, and trapped ion
crystals and show how properties of these systems can be extracted by means of
non-Markovianity measures.Comment: 31 pages, 7 figures. To appear in a special volume of OSID on open
system
Markovian Dynamics on Complex Reaction Networks
Complex networks, comprised of individual elements that interact with each
other through reaction channels, are ubiquitous across many scientific and
engineering disciplines. Examples include biochemical, pharmacokinetic,
epidemiological, ecological, social, neural, and multi-agent networks. A common
approach to modeling such networks is by a master equation that governs the
dynamic evolution of the joint probability mass function of the underling
population process and naturally leads to Markovian dynamics for such process.
Due however to the nonlinear nature of most reactions, the computation and
analysis of the resulting stochastic population dynamics is a difficult task.
This review article provides a coherent and comprehensive coverage of recently
developed approaches and methods to tackle this problem. After reviewing a
general framework for modeling Markovian reaction networks and giving specific
examples, the authors present numerical and computational techniques capable of
evaluating or approximating the solution of the master equation, discuss a
recently developed approach for studying the stationary behavior of Markovian
reaction networks using a potential energy landscape perspective, and provide
an introduction to the emerging theory of thermodynamic analysis of such
networks. Three representative problems of opinion formation, transcription
regulation, and neural network dynamics are used as illustrative examples.Comment: 52 pages, 11 figures, for freely available MATLAB software, see
http://www.cis.jhu.edu/~goutsias/CSS%20lab/software.htm
Simultaneous computation of dynamical and equilibrium information using a weighted ensemble of trajectories
Equilibrium formally can be represented as an ensemble of uncoupled systems
undergoing unbiased dynamics in which detailed balance is maintained. Many
non-equilibrium processes can be described by suitable subsets of the
equilibrium ensemble. Here, we employ the "weighted ensemble" (WE) simulation
protocol [Huber and Kim, Biophys. J., 1996] to generate equilibrium trajectory
ensembles and extract non-equilibrium subsets for computing kinetic quantities.
States do not need to be chosen in advance. The procedure formally allows
estimation of kinetic rates between arbitrary states chosen after the
simulation, along with their equilibrium populations. We also describe a
related history-dependent matrix procedure for estimating equilibrium and
non-equilibrium observables when phase space has been divided into arbitrary
non-Markovian regions, whether in WE or ordinary simulation. In this
proof-of-principle study, these methods are successfully applied and validated
on two molecular systems: explicitly solvated methane association and the
implicitly solvated Ala4 peptide. We comment on challenges remaining in WE
calculations
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