531 research outputs found
Sequential Data-Adaptive Bandwidth Selection by Cross-Validation for Nonparametric Prediction
We consider the problem of bandwidth selection by cross-validation from a
sequential point of view in a nonparametric regression model. Having in mind
that in applications one often aims at estimation, prediction and change
detection simultaneously, we investigate that approach for sequential kernel
smoothers in order to base these tasks on a single statistic. We provide
uniform weak laws of large numbers and weak consistency results for the
cross-validated bandwidth. Extensions to weakly dependent error terms are
discussed as well. The errors may be {\alpha}-mixing or L2-near epoch
dependent, which guarantees that the uniform convergence of the cross
validation sum and the consistency of the cross-validated bandwidth hold true
for a large class of time series. The method is illustrated by analyzing
photovoltaic data.Comment: 26 page
On the Computational Complexity of Measuring Global Stability of Banking Networks
Threats on the stability of a financial system may severely affect the
functioning of the entire economy, and thus considerable emphasis is placed on
the analyzing the cause and effect of such threats. The financial crisis in the
current and past decade has shown that one important cause of instability in
global markets is the so-called financial contagion, namely the spreading of
instabilities or failures of individual components of the network to other,
perhaps healthier, components. This leads to a natural question of whether the
regulatory authorities could have predicted and perhaps mitigated the current
economic crisis by effective computations of some stability measure of the
banking networks. Motivated by such observations, we consider the problem of
defining and evaluating stabilities of both homogeneous and heterogeneous
banking networks against propagation of synchronous idiosyncratic shocks given
to a subset of banks. We formalize the homogeneous banking network model of
Nier et al. and its corresponding heterogeneous version, formalize the
synchronous shock propagation procedures, define two appropriate stability
measures and investigate the computational complexities of evaluating these
measures for various network topologies and parameters of interest. Our results
and proofs also shed some light on the properties of topologies and parameters
of the network that may lead to higher or lower stabilities.Comment: to appear in Algorithmic
Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies
Pandemic influenza has the epidemic potential to kill millions of people.
While various preventive measures exist (i.a., vaccination and school
closures), deciding on strategies that lead to their most effective and
efficient use remains challenging. To this end, individual-based
epidemiological models are essential to assist decision makers in determining
the best strategy to curb epidemic spread. However, individual-based models are
computationally intensive and it is therefore pivotal to identify the optimal
strategy using a minimal amount of model evaluations. Additionally, as
epidemiological modeling experiments need to be planned, a computational budget
needs to be specified a priori. Consequently, we present a new sampling
technique to optimize the evaluation of preventive strategies using fixed
budget best-arm identification algorithms. We use epidemiological modeling
theory to derive knowledge about the reward distribution which we exploit using
Bayesian best-arm identification algorithms (i.e., Top-two Thompson sampling
and BayesGap). We evaluate these algorithms in a realistic experimental setting
and demonstrate that it is possible to identify the optimal strategy using only
a limited number of model evaluations, i.e., 2-to-3 times faster compared to
the uniform sampling method, the predominant technique used for epidemiological
decision making in the literature. Finally, we contribute and evaluate a
statistic for Top-two Thompson sampling to inform the decision makers about the
confidence of an arm recommendation
Morphosyntactic processing in late second-language learners
The goal of the present study was to investigate the electro- physiological correlates of second-language (L2) morphosyn- tactic processing in highly proficient late learners of an L2 with long exposure to the L2 environment. ERPs were col- lected from 22 English–Spanish late learners while they read sentences in which morphosyntactic features of the L2 present or not present in the first language (number and gender agree- ment, respectively) were manipulated at two different sentence positions—within and across phrases. The results for a control group of age-matched native-speaker Spanish participants in- cluded an ERP pattern of LAN-type early negativity followed by P600 effect in response to both agreement violations and for both sentence positions. The late L2 learner results included a similar pattern, consisting of early negativity followed by P600, in the first sentence position (within-phrase agreement viola- tions) but only P600 effects in the second sentence position (across-phrase agreement violation), as well as significant am- plitude and onset latency differences between the gender and the number violation effects in both sentence positions. These results reveal that highly proficient learners can show electro- physiological correlates during L2 processing that are qualita- tively similar to those of native speakers, but the results also indicate the contribution of factors such as age of acquisition and transfer processes from first language to L
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PLT neutral injector performance
The experience with respect to beamline operation on PLT and on the Princeton test stand is reviewed. We discuss the performance of the injectors, beam energy distributions as measured by two techniques, beam-associated impurities, control of gas evolution in the drift duct by titanium evaporation, reionization in the drift duct, and the computer archiving and control system currently under development
Using random networks to study the dynamics of respiratory syncytial virus (RSV) in the Spanish region of Valencia
[EN] Seasonal fluctuations in the incidence of several respiratory infections are a feature of epidemiological surveys all around the world. This phenomenon is characteristic of influenza and respiratory syncytial virus pandemics. However, the explanation of the seasonal outbreaks of these diseases remains poorly understood. Many statistical studies have been carried out in order to provide a correlation of the outbreaks with climatic or social factors without achieving a definitive conclusion. Here we show that, in a random social network, self-sustained seasonal epidemics emerge as a process modulated by the infection probability and the immunity period after recovering from the infection. This is a purely endogenous phenomenon that does not require any exogenous forcing. Assuming that this is the dominant mechanism for seasonal epidemics, many implications for public health policies for infectious respiratory diseases could be drawn. (C) 2010 Elsevier Ltd. All rights reserved.Supported by a grant from the Universidad Politecnica de Valencia PAID-06-09 ref: 2588.Acedo Rodríguez, L.; Moraño Fernández, JA.; Villanueva Micó, RJ.; Villanueva Oller, FJ.; Díez Domingo, J. (2011). Using random networks to study the dynamics of respiratory syncytial virus (RSV) in the Spanish region of Valencia. Mathematical and Computer Modelling. 54(7-8):1650-1654. https://doi.org/10.1016/j.mcm.2010.11.068S16501654547-
Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees
The spread of infectious diseases crucially depends on the pattern of
contacts among individuals. Knowledge of these patterns is thus essential to
inform models and computational efforts. Few empirical studies are however
available that provide estimates of the number and duration of contacts among
social groups. Moreover, their space and time resolution are limited, so that
data is not explicit at the person-to-person level, and the dynamical aspect of
the contacts is disregarded. Here, we want to assess the role of data-driven
dynamic contact patterns among individuals, and in particular of their temporal
aspects, in shaping the spread of a simulated epidemic in the population.
We consider high resolution data of face-to-face interactions between the
attendees of a conference, obtained from the deployment of an infrastructure
based on Radio Frequency Identification (RFID) devices that assess mutual
face-to-face proximity. The spread of epidemics along these interactions is
simulated through an SEIR model, using both the dynamical network of contacts
defined by the collected data, and two aggregated versions of such network, in
order to assess the role of the data temporal aspects.
We show that, on the timescales considered, an aggregated network taking into
account the daily duration of contacts is a good approximation to the full
resolution network, whereas a homogeneous representation which retains only the
topology of the contact network fails in reproducing the size of the epidemic.
These results have important implications in understanding the level of
detail needed to correctly inform computational models for the study and
management of real epidemics
Dynamical Patterns of Cattle Trade Movements
Despite their importance for the spread of zoonotic diseases, our
understanding of the dynamical aspects characterizing the movements of farmed
animal populations remains limited as these systems are traditionally studied
as static objects and through simplified approximations. By leveraging on the
network science approach, here we are able for the first time to fully analyze
the longitudinal dataset of Italian cattle movements that reports the mobility
of individual animals among farms on a daily basis. The complexity and
inter-relations between topology, function and dynamical nature of the system
are characterized at different spatial and time resolutions, in order to
uncover patterns and vulnerabilities fundamental for the definition of targeted
prevention and control measures for zoonotic diseases. Results show how the
stationarity of statistical distributions coexists with a strong and
non-trivial evolutionary dynamics at the node and link levels, on all
timescales. Traditional static views of the displacement network hide important
patterns of structural changes affecting nodes' centrality and farms' spreading
potential, thus limiting the efficiency of interventions based on partial
longitudinal information. By fully taking into account the longitudinal
dimension, we propose a novel definition of dynamical motifs that is able to
uncover the presence of a temporal arrow describing the evolution of the system
and the causality patterns of its displacements, shedding light on mechanisms
that may play a crucial role in the definition of preventive actions
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