1,309 research outputs found
Organizational Chart Inference
Nowadays, to facilitate the communication and cooperation among employees, a
new family of online social networks has been adopted in many companies, which
are called the "enterprise social networks" (ESNs). ESNs can provide employees
with various professional services to help them deal with daily work issues.
Meanwhile, employees in companies are usually organized into different
hierarchies according to the relative ranks of their positions. The company
internal management structure can be outlined with the organizational chart
visually, which is normally confidential to the public out of the privacy and
security concerns. In this paper, we want to study the IOC (Inference of
Organizational Chart) problem to identify company internal organizational chart
based on the heterogeneous online ESN launched in it. IOC is very challenging
to address as, to guarantee smooth operations, the internal organizational
charts of companies need to meet certain structural requirements (about its
depth and width). To solve the IOC problem, a novel unsupervised method Create
(ChArT REcovEr) is proposed in this paper, which consists of 3 steps: (1)
social stratification of ESN users into different social classes, (2)
supervision link inference from managers to subordinates, and (3) consecutive
social classes matching to prune the redundant supervision links. Extensive
experiments conducted on real-world online ESN dataset demonstrate that Create
can perform very well in addressing the IOC problem.Comment: 10 pages, 9 figures, 1 table. The paper is accepted by KDD 201
Forecasting the price of gold
This article seeks to evaluate the appropriateness of a variety of existing forecasting techniques (17 methods) at providing accurate and statistically significant forecasts for gold price. We report the results from the nine most competitive techniques. Special consideration is given to the ability of these techniques to provide forecasts which outperforms the random walk (RW) as we noticed that certain multivariate models (which included prices of silver, platinum, palladium and rhodium, besides gold) were also unable to outperform the RW in this case. Interestingly, the results show that none of the forecasting techniques are able to outperform the RW at horizons of 1 and 9 steps ahead, and on average, the exponential smoothing model is seen providing the best forecasts in terms of the lowest root mean squared error over the 24-month forecasting horizons. Moreover, we find that the univariate models used in this article are able to outperform the Bayesian autoregression and Bayesian vector autoregressive models, with exponential smoothing reporting statistically significant results in comparison with the former models, and classical autoregressive and the vector autoregressive models in most cases
Measures of Model Performance Based On the Log Accuracy Ratio
Quantitative assessment of modeling and forecasting of continuous quantities uses a variety of approaches. We review existing literature describing metrics for forecast accuracy and bias, concentrating on those based on relative errors and percentage errors. Of these accuracy metrics, the mean absolute percentage error (MAPE) is one of the most common across many fields and has been widely applied in recent space science literature and we highlight the benefits and drawbacks of MAPE and proposed alternatives. We then introduce the log accuracy ratio and derive from it two metrics: the median symmetric accuracy and the symmetric signed percentage bias. Robust methods for estimating the spread of a multiplicative linear model using the log accuracy ratio are also presented. The developed metrics are shown to be easy to interpret, robust, and to mitigate the key drawbacks of their more widely used counterparts based on relative errors and percentage errors. Their use is illustrated with radiation belt electron flux modeling examples.Peer reviewe
Stochastic Feedback and the Regulation of Biological Rhythms
We propose a general approach to the question of how biological rhythms
spontaneously self-regulate, based on the concept of ``stochastic feedback''.
We illustrate this approach by considering the neuroautonomic regulation of the
heart rate. The model generates complex dynamics and successfully accounts for
key characteristics of cardiac variability, including the power spectrum,
the functional form and scaling of the distribution of variations, and
correlations in the Fourier phases. Our results suggest that in healthy systems
the control mechanisms operate to drive the system away from extreme values
while not allowing it to settle down to a constant output.Comment: 15 pages, latex2e using rotate and epsf, with 4 ps figures. Submitted
to PR
Open TURNS: An industrial software for uncertainty quantification in simulation
The needs to assess robust performances for complex systems and to answer
tighter regulatory processes (security, safety, environmental control, and
health impacts, etc.) have led to the emergence of a new industrial simulation
challenge: to take uncertainties into account when dealing with complex
numerical simulation frameworks. Therefore, a generic methodology has emerged
from the joint effort of several industrial companies and academic
institutions. EDF R&D, Airbus Group and Phimeca Engineering started a
collaboration at the beginning of 2005, joined by IMACS in 2014, for the
development of an Open Source software platform dedicated to uncertainty
propagation by probabilistic methods, named OpenTURNS for Open source Treatment
of Uncertainty, Risk 'N Statistics. OpenTURNS addresses the specific industrial
challenges attached to uncertainties, which are transparency, genericity,
modularity and multi-accessibility. This paper focuses on OpenTURNS and
presents its main features: openTURNS is an open source software under the LGPL
license, that presents itself as a C++ library and a Python TUI, and which
works under Linux and Windows environment. All the methodological tools are
described in the different sections of this paper: uncertainty quantification,
uncertainty propagation, sensitivity analysis and metamodeling. A section also
explains the generic wrappers way to link openTURNS to any external code. The
paper illustrates as much as possible the methodological tools on an
educational example that simulates the height of a river and compares it to the
height of a dyke that protects industrial facilities. At last, it gives an
overview of the main developments planned for the next few years
The substantive and practical significance of citation impact differences between institutions: Guidelines for the analysis of percentiles using effect sizes and confidence intervals
In our chapter we address the statistical analysis of percentiles: How should
the citation impact of institutions be compared? In educational and
psychological testing, percentiles are already used widely as a standard to
evaluate an individual's test scores - intelligence tests for example - by
comparing them with the percentiles of a calibrated sample. Percentiles, or
percentile rank classes, are also a very suitable method for bibliometrics to
normalize citations of publications in terms of the subject category and the
publication year and, unlike the mean-based indicators (the relative citation
rates), percentiles are scarcely affected by skewed distributions of citations.
The percentile of a certain publication provides information about the citation
impact this publication has achieved in comparison to other similar
publications in the same subject category and publication year. Analyses of
percentiles, however, have not always been presented in the most effective and
meaningful way. New APA guidelines (American Psychological Association, 2010)
suggest a lesser emphasis on significance tests and a greater emphasis on the
substantive and practical significance of findings. Drawing on work by Cumming
(2012) we show how examinations of effect sizes (e.g. Cohen's d statistic) and
confidence intervals can lead to a clear understanding of citation impact
differences
Cross-continental emergence of Nannizziopsis barbatae disease may threaten wild Australian lizards
Members of the genus Nannizziopsis are emerging fungal pathogens of reptiles that have been documented as the cause of fatal mycoses in a wide range of reptiles in captivity. Cases of severe, proliferative dermatitis, debility and death have been detected in multiple free-living lizard species from locations across Australia, including a substantial outbreak among Eastern water dragons (Intellagama lesueurii) in Brisbane, Queensland. We investigated this disease in a subset of severely affected lizards and identified a clinically consistent syndrome characterized by hyperkeratosis, epidermal hyperplasia, dermal inflammation, necrosis, ulceration, and emaciation. Using a novel fungal isolation method, histopathology, and molecular techniques, we identified the etiologic agent as Nannizziopsis barbatae, a species reported only once previously from captive lizards in Australia. Here we report severe dermatomycosis caused by N. barbatae in five species of Australian lizard, representing the first cases of Nannizziopsis infection among free-living reptiles, globally. Further, we evaluate key pathogen and host characteristics that indicate N. barbatae-associated dermatomycosis may pose a concerning threat to Australian lizards
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