2,463 research outputs found
Optimizing experimental parameters for tracking of diffusing particles
We describe how a single-particle tracking experiment should be designed in
order for its recorded trajectories to contain the most information about a
tracked particle's diffusion coefficient. The precision of estimators for the
diffusion coefficient is affected by motion blur, limited photon statistics,
and the length of recorded time-series. We demonstrate for a particle
undergoing free diffusion that precision is negligibly affected by motion blur
in typical experiments, while optimizing photon counts and the number of
recorded frames is the key to precision. Building on these results, we describe
for a wide range of experimental scenarios how to choose experimental
parameters in order to optimize the precision. Generally, one should choose
quantity over quality: experiments should be designed to maximize the number of
frames recorded in a time-series, even if this means lower information content
in individual frames
Temporal Gillespie algorithm: Fast simulation of contagion processes on time-varying networks
Stochastic simulations are one of the cornerstones of the analysis of
dynamical processes on complex networks, and are often the only accessible way
to explore their behavior. The development of fast algorithms is paramount to
allow large-scale simulations. The Gillespie algorithm can be used for fast
simulation of stochastic processes, and variants of it have been applied to
simulate dynamical processes on static networks. However, its adaptation to
temporal networks remains non-trivial. We here present a temporal Gillespie
algorithm that solves this problem. Our method is applicable to general Poisson
(constant-rate) processes on temporal networks, stochastically exact, and up to
multiple orders of magnitude faster than traditional simulation schemes based
on rejection sampling. We also show how it can be extended to simulate
non-Markovian processes. The algorithm is easily applicable in practice, and as
an illustration we detail how to simulate both Poissonian and non-Markovian
models of epidemic spreading. Namely, we provide pseudocode and its
implementation in C++ for simulating the paradigmatic
Susceptible-Infected-Susceptible and Susceptible-Infected-Recovered models and
a Susceptible-Infected-Recovered model with non-constant recovery rates. For
empirical networks, the temporal Gillespie algorithm is here typically from 10
to 100 times faster than rejection sampling.Comment: Minor changes and updates to reference
How memory generates heterogeneous dynamics in temporal networks
Empirical temporal networks display strong heterogeneities in their dynamics,
which profoundly affect processes taking place on these networks, such as rumor
and epidemic spreading. Despite the recent wealth of data on temporal networks,
little work has been devoted to the understanding of how such heterogeneities
can emerge from microscopic mechanisms at the level of nodes and links. Here we
show that long-term memory effects are present in the creation and
disappearance of links in empirical networks. We thus consider a simple
generative modeling framework for temporal networks able to incorporate these
memory mechanisms. This allows us to study separately the role of each of these
mechanisms in the emergence of heterogeneous network dynamics. In particular,
we show analytically and numerically how heterogeneous distributions of contact
durations, of inter-contact durations and of numbers of contacts per link
emerge. We also study the individual effect of heterogeneities on dynamical
processes, such as the paradigmatic Susceptible-Infected epidemic spreading
model. Our results confirm in particular the crucial role of the distributions
of inter-contact durations and of the numbers of contacts per link
Panel performance: Modelling variation in sensory profiling data by multiway analysis
Sensory profiling data is essentially three-way data where samples, attributes and assessors are the three dimensions of information. It is common practice to average over the assessors and focus the analysis on the relations between samples and sensory descriptors. However, since assessor reliability can not be controlled in advance, posthoc analysis on assessors is needed to assess performance of the individual and at the panel level. For this purpose, multiway analysis is a very efficient data method as it provides information on samples, attributes and assessors, simultaneously [1]. PARAllel FACtor (PARAFAC) analysis is one of the most used multiway methods in sensory analysis [2][3]. It is based on two basic assumptions: 1) there exist latent variables behind the identified sensory descriptors describing the variation among the products; 2) assessors have different sensitivities to these common latent variables. However, assessors may perceive the factors differently, so the assumption of “common latent variables” becomes questionable. This may happen when the panel is not well trained and/or the samples present subtle differences difficult to detect.
In this work a more flexible approach to the analysis of sensory data is presented. Specifically, the work proposes to use PARAFAC2 modelling [4] as it allows each assessor to have an individual idiosyncratic perceptive model. The data was obtained from a descriptive sensory analysis of organic milk samples. Results show that PARAFAC2 is very useful to highlight disagreement in the panel on specific attributes and to detect outlying assessors. In addition, by using PARAFAC2 an improvement in the description of samples is also achieved. On the other hand, PARAFAC has to be preferred to PARAFAC2 when a good panel agreement is observed, since it provides more stable solutions and no further gain in information is obtained from PARAFAC2. Finally, the work proposes an index to measure the performance of each assessor based on individual sensitivity and reproducibility
Compensating for population sampling in simulations of epidemic spread on temporal contact networks
Data describing human interactions often suffer from incomplete sampling of
the underlying population. As a consequence, the study of contagion processes
using data-driven models can lead to a severe underestimation of the epidemic
risk. Here we present a systematic method to alleviate this issue and obtain a
better estimation of the risk in the context of epidemic models informed by
high-resolution time-resolved contact data. We consider several such data sets
collected in various contexts and perform controlled resampling experiments. We
show how the statistical information contained in the resampled data can be
used to build a series of surrogate versions of the unknown contacts. We
simulate epidemic processes on the resulting reconstructed data sets and show
that it is possible to obtain good estimates of the outcome of simulations
performed using the complete data set. We discuss limitations and potential
improvements of our method
Technical Efficiency of the Danish Trawl fleet: Are the Industrial Vessels Better than Others?
Technical efficiency in the Danish trawl fishery in the North Sea is estimated for 1997 and 1998 by a stochastic production frontier model. This model allows noise when the frontier and the technical efficiency is found, which for fisheries is a reasonable assumption. The results show that the production frontier can be modelled by a translog function without time effects and a technical ineffi-ciency function. The type of fishery (industrial or consumption), size of vessel (greater or lesser than 60 GRT) and year give a good explanation for the ineffi-ciency in the fleet. The average technical efficiency is estimated to be 0.82. On average, industrial vessels have a higher technical efficiency than human con-sumption vessels, and smaller vessels have higher technical efficiency than lar-ger vessels. In sum, the analysis reveals that vessel larger than 60 GRT and fishing industrial species are the most efficient.Technical efficiency, stochastic production frontier, Danish trawl fishery
Composition of volatile compounds in bovine milk heat treated by instant infusion pasteurization and correlation to sensory analysis
Volatile compounds in skim milk and nonstandardised milk subjected to instant infusion pasteurisation at 80°C, 100°C and 120°C were compared with raw milk, high temperature short time pasteurised milk and milk pasteurised at 85°C/30 s. The composition of volatile compounds differed between infusion pasteurisation treated samples and the reference pasteurisations. The sensory properties of skim milk subjected to instant infusion pasteurisation were described by negative attributes, such as cardboard sour and plastic flavours, which are not associated normally with fresh milk. Partial least squares modelling showed good correlation between the volatile compounds and the sensory properties, indicating the predictive and possible causal importance of the volatile compounds for the sensory characteristics
High Redshift Standard Candles: Predicted Cosmological Constraints
We investigate whether future measurements of high redshift standard candles
(HzSCs) will be a powerful probe of dark energy, when compared to other types
of planned dark energy measurements. Active galactic nuclei and gamma ray
bursts have both been proposed as potential HzSC candidates. Due to their high
luminosity, they can be used to probe unexplored regions in the expansion
history of the universe. Information from these regions can help constrain the
properties of dark energy, and in particular, whether it varies over time.
We consider both linear and piecewise parameterizations of the dark energy
equation of state, , and assess the optimal redshift distribution a
high-redshift standard-candle survey could take to constrain these models.
The more general the form of the dark energy equation of state being
tested, the more useful high-redshift standard candles become. For a linear
parameterization of , HzSCs give only small improvements over planned
supernova and baryon acoustic oscillation measurements; a wide redshift range
with many low redshift points is optimal to constrain this linear model.
However to constrain a general, and thus potentially more informative, form of
, having many HzSCs can significantly improve limits on the nature of
dark energy.Comment: Accepted MNRAS, 27 Pages, 15 figures, matches published versio
Monitoring panel performance within and between sensory experiments by multi-way analysis
In sensory analysis a panel of trained assessors evaluates a set of samples
according to specific sensory descriptors. The training improves objectivity and
reliability of assessments. However, there can be individual differences between
assessors left after the training that should be taken into account in the analysis.
Monitoring panel performance is then crucial for optimal sensory evaluations. The
quality of the results is strongly dependent on the performance of each assessor and
of the panel as a whole. The present work proposes to analyze the panel performance
within single sensory evaluations and between consecutive evaluations. The
basic idea is to use multi-way models to handle the three-way nature of the sensory
data. Specifically, a PARAFAC model is used to investigate the panel performance
in the single experiment. N-PLS model is used to test the predictive ability of the
panel on each experiment. A PARAFAC model is also used for monitoring panel
performance over different experiments
Impact of spatially constrained sampling of temporal contact networks on the evaluation of the epidemic risk
The ability to directly record human face-to-face interactions increasingly
enables the development of detailed data-driven models for the spread of
directly transmitted infectious diseases at the scale of individuals. Complete
coverage of the contacts occurring in a population is however generally
unattainable, due for instance to limited participation rates or experimental
constraints in spatial coverage. Here, we study the impact of spatially
constrained sampling on our ability to estimate the epidemic risk in a
population using such detailed data-driven models. The epidemic risk is
quantified by the epidemic threshold of the
susceptible-infectious-recovered-susceptible model for the propagation of
communicable diseases, i.e. the critical value of disease transmissibility
above which the disease turns endemic. We verify for both synthetic and
empirical data of human interactions that the use of incomplete data sets due
to spatial sampling leads to the underestimation of the epidemic risk. The bias
is however smaller than the one obtained by uniformly sampling the same
fraction of contacts: it depends nonlinearly on the fraction of contacts that
are recorded and becomes negligible if this fraction is large enough. Moreover,
it depends on the interplay between the timescales of population and spreading
dynamics.Comment: 21 pages, 7 figure
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