pinecone/core-2020-05-10-deduplication huggingface dataset:
How useful could Arabic documentary sources be for reconstructing past climate?

Trees, corals, ice cores and documentary\ud
evidence provide high-resolution proxies\ud
that allow past climate to be reconstructed\ud
(Jones et al., 2009). Documentary evidence\ud
includes all forms of written historical information\ud
about past climate or weather, but\ud
its use for climate reconstruction is restricted\ud
to locations for which there is a rich documentary\ud
legacy. Several climate analyses\ud
and reconstructions have been undertaken\ud
using documentary evidence from Europe\ud
(Brázdil et al., 2005; 2010), North and South\ud
America (Dupigny-Giroux and Mock, 2009;\ud
Prieto and García-Herrera, 2009; Neukom\ud
et al., 2010), Asia (Ge et al., 2005; 2010; Aono\ud
and Kazui, 2008; Hirano and Mikami, 2008)\ud
and oceanic areas (García-Herrera et al.,\ud
2005). The Islamic World is a region for\ud
which such sources could also be used to\ud
reconstruct past climate, as indeed they\ud
have been widely used for astronomy and\ud
geophysics. Astronomers have used the\ud
observations made by Arab astronomers for\ud
centuries, with some remarkable examples.\ud
Historical eclipse observations, recorded by\ud
various ancient and mediaeval cultures\ud
including Arabs, have enabled changes in\ud
the Earth’s rate of rotation to be monitored\ud
with fair precision as far back as around\ud
700 AD (Stephenson, 2003). Rada and\ud
Stephenson (1992) catalogued meteor\ud
showers using mediaeval Arab chronicles\ud
and Ahn (2003) investigated the spatial distribution\ud
of meteor streams crossing the orbit of the Earth from the tenth to the fourteenth\ud
centuries using chronicles from\ud
Korea, Japan, China, Arabia and Europe.\ud
Documentary sources from Iberia have been\ud
used to identify and date such astronomical\ud
phenomena as eclipses and comets (Vernet,\ud
1982), naked-eye sunspots (Vaquero and\ud
Gallego, 2002) and aurora sightings\ud
(Vaquero and Gallego, 2001). The work of\ud
Basurah (2006) provides descriptions for 18\ud
aurora displays on various dates at low latitudes\ud
in the Mediterranean area taken from\ud
Islamic chronicles (ninth to sixteenth centuries).\ud
In seismology, Arabic chronicles\ud
were extensively used to prepare the\ud
Seismic Catalogue of the Iberian Peninsula\ud
(Martínez and Mezcua, 2002) and to study\ud
seismic activity in Syria and Palestine\ud
(Ambraseseys, 2005).\ud
However, the use of these sources to\ud
recover climate information is, to the best\ud
of our knowledge, still very limited (Bulliet,\ud
2009; Jones et al., 2009). Here we describe\ud
a preliminary inquiry based on Arabic documentary\ud
sources from Iraq. We would stress\ud
the antiquity of the documents used, with\ud
dates in the period 816–1009 AD. The\ud
sources consulted are Arabic chronicles that\ud
narrate the social, political and religious history\ud
of different regions in a form very characteristic\ud
of Arab culture.Peer reviewe
Energy productivity and decarbonization of the global economy: Factor five resource productivity

In the 21st Century much of the world will experience untold wealth and prosperity that could not even be conceived only some three centuries before. However as with most, if not all, of the human civilisations, increases in prosperity have accumulated significant environmental impacts that threaten to result in environmentally induced economic decline. A key part of the world’s response to this challenge is to rapidly decarbonise economies around the world, with options to achieve 60-80 per cent improvements (i.e. in the order of Factor 5) in energy and water productivity now available and proven in every sector. Drawing upon the 2009 publication “Factor 5”, in this paper we discuss how to realise such large-scale improvements, involving complexity beyond technical and process innovation.  We begin by considering the concept of greenhouse gas stabilisation trajectories that include reducing current greenhouse gas emissions to achieve a ‘peaking’ of global emissions, and subsequent ‘tailing’ of emissions to the desired endpoint in ‘decarbonising’ the economy. Temporal priorities given to peaking and tailing have significant implications for the mix of decarbonising solutions and the need for government and market assistance in causing them to be implemented, requiring careful consideration upfront. Within this context we refer to a number of examples of Factor 5 style opportunities for energy productivity and decarbonisation, and then discuss the need for critical economic contributions to take such success from examples to central mechanisms in decarbonizing the global economy
A critical review of modern approaches for multidimensional energy poverty measurement

Recent efforts to measure energy poverty more comprehensively attempt to redress the shortcomings of binary metrics that remain in common use. However, significant challenges remain both with the construction of the new measurement frameworks and their application. The paper presents an analysis of recent multidimensional measurement approaches and applications to draw inferences on the implications of applying these for the measurement of energy access and in informing policies aimed at improving it. The assessment suggests that despite progress having been made in capturing the multidimensional nature of energy poverty, the new measures are currently too complex to operationalize at the global level and too prescriptive to gain acceptance in diverse national contexts. Further efforts are thus required to consolidate and simplify the new frameworks for global tracking purposes, and to adapt and modify these to specific country contexts to inform national policy and planning. A subset of key energy poverty dimensions and uniform set of indicators need to be shortlisted for the purposes of global comparisons, while specific national tracking efforts can apply dimensions and thresholds most suited to accurately capture energy poverty and its drivers in a given context
Metallic and plastic dye solar cells

Dye solar cells (DSCs) are quite a new technology in photovoltaics. The traditional DSCs are prepared on conductively coated glass substrates in high temperature using a batch process. Manufacturing the cells on low-cost metal and plastic substrates would enable significant cost reductions as well as roll-to-roll mass production. There is a selection of metals and possible conducting coatings for plastics with varying electrical, optical, and chemical properties and price. The substrate has a dominant impact on the methods and materials that can be applied to make the cell and consequently on the resulting performance of the device. Furthermore, the substrates influence significantly the stability of the device. The main issue with plastics is their permeability whereas with metals, chemical stability in the electrolyte is themain concern. The leakage of electrolyte and the impact of water intake through the plastics can be affected by the material choices in particular with the electrolyte and dye composition. In the case of the metallic electrodes, the chemical stability can be improved by choosing a corrosion-resistant metal, applying a blocking layer or changing to a less aggressive electrolyte. One major focus of the current research of the flexible DSCs is increasing the efficiency by improved low-temperature preparation methods and materials especially for the photoelectrode. Another significant challenge is the development of noncorrosive electrolyte and dye combinations that work well even in the presence of significant amounts of water.Peer reviewe
Crop coefficients of Jatropha (Jatropha curcas) and Pongamia (Pongamia pinnata) using water balance approach

Jatropha and Pongamia are a potential source of biodiesel and grow in a wide range of agroclimatic zones and soil conditions. Data and knowledge available on water requirement of Jatropha and Pongamia are very scarce. Crop coefficients are important parameters used for assessing water requirement and irrigation scheduling. In the present study, crop coefficients of Jatropha and Pongamia were estimated using water balance approach. Temporal data on soil moisture at different depths in block plantations of Jatropha and Pongamia at International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) farm, Patancheru in India, were collected at 15 days interval between 2007 and 2010. Measured soil moisture data were analyzed using one-dimensional water balance model. Results showed that annual water requirement of Jatropha is 750 mm and of Pongamia is about 950 mm in semi-arid tropics. Crop coefficients of Jatropha ranged from 0.10 to 0.95 and of Pongamia from 0.30 to 1.10 depending on plant growth stage in different months. ICRISAT received 820 mm of rainfall in a normal year (data between 2001 and 2010) during the monsoon, of which 52% (430 mm) contributed to evapotranspiration (ET), 34% (280 mm) was stored in soil, and 14% (110 mm) was lost through surface runoff. Stored soil moisture during monsoon season was subsequently utilized by the Jatropha and 270 mm converted into ET during nonmonsoonal period. Pongamia utilized stored soil moisture more effectively than Jatropha as it could remove water from deeper soil layers even at high levels of soil moisture suction
Quantifying the variability of wind energy

This is the pre-peer reviewed version of the following article: WATSON, J., 2014. Quantifying the variability of wind energy.  Wiley Interdisciplinary Reviews: Energy and Environment, 3 (4), pp. 330 - 342 which has been published in final form at http://dx.doi.org/10.1002/wene.95. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Wind by its very nature is a variable element. Its variation is different on different timescales and spatially its magnitude can change dramatically depending on local climatology and terrain. This has implications in a variety of sectors, not least in the wind energy sector. The accuracy of weather forecasting models has increased significantly in the last few decades and these models are able to give an insight into variability on the hourly and daily timescales. On shorter timescales, predicting chaotic turbulent fluctuations is far more challenging. Similarly, the ability to make seasonal forecasts is extremely limited. General circulation models (GCMs) can give insights into possible future decadal fluctuations, but there are still large uncertainties. Observational data can give useful information concerning variation on a variety of timescales, but data quality and spatial coverage can be variable. An understanding of local scale spatial variations in wind is extremely important in wind farm siting. In the last 40 years, there have been significant advances in predicting these variations using computer models, although there remain significant challenges in understanding the behavior of the wind in certain environments. Both the spatial and temporal variations of wind are important considerations when wind power is integrated into electricity networks, and this will become an ever more important consideration as wind generation makes an increasing contribution to our global energy needs
Heavy-tailed densities

The concept of heavy- or long-tailed densities (or distributions) has attracted
much well-deserved attention in the literature. A quick search in Google using
the keywords long-tailed statistics retrieves almost 12 million items. The concept
has become a pillar of the theory of extremes, and through its connection with
outlier-prone distributions, long-tailed distributions also play a central role in the
theory of robustness.
The concept of tail heaviness is by now ubiquitous, appearing in a diverse set
of disciplines that includes: economics, communications, atmospheric sciences,
climate modeling, social sciences, physics, modeling of complex systems, etc.
Nevertheless, the precise meaning of ‘long-’ or ‘heavy tails’ remains somewhat
elusive. Thus, in a substantial portion of the early literature, long-tailednessmeant
that the underlying distributionwas capable of producing anomalous observations
in the sense that they were ‘too far’ from themain body of observations. Implicit in
these informal definitions was the notion that any distribution that behaved that
way had to do so because its tails were longer than those of the normal distribution.
This paper discusses tail orderings and several approaches for the classification
of probability distributions according to tail heaviness. It is concluded that an
approach based on the limiting behavior of the residual life function, and its
corresponding characterizations based on functions of regular variation and
asymptotic distribution of extreme spacings, provides the more natural and
illuminating concepts of tail behavior
Semi-supervised clustering methods

Cluster analysis methods seek to partition a data set into homogeneous
subgroups. It is useful in a wide variety of applications, including document
processing and modern genetics. Conventional clustering methods are
unsupervised, meaning that there is no outcome variable nor is anything known
about the relationship between the observations in the data set. In many
situations, however, information about the clusters is available in addition to
the values of the features. For example, the cluster labels of some
observations may be known, or certain observations may be known to belong to
the same cluster. In other cases, one may wish to identify clusters that are
associated with a particular outcome variable. This review describes several
clustering algorithms (known as "semi-supervised clustering" methods) that can
be applied in these situations. The majority of these methods are modifications
of the popular k-means clustering method, and several of them will be described
in detail. A brief description of some other semi-supervised clustering
algorithms is also provided.Comment: 28 pages, 5 figure
Bias in parametric estimation: reduction and useful side-effects

The bias of an estimator is defined as the difference of its expected value
from the parameter to be estimated, where the expectation is with respect to
the model. Loosely speaking, small bias reflects the desire that if an
experiment is repeated indefinitely then the average of all the resultant
estimates will be close to the parameter value that is estimated. The current
paper is a review of the still-expanding repository of methods that have been
developed to reduce bias in the estimation of parametric models. The review
provides a unifying framework where all those methods are seen as attempts to
approximate the solution of a simple estimating equation. Of particular focus
is the maximum likelihood estimator, which despite being asymptotically
unbiased under the usual regularity conditions, has finite-sample bias that can
result in significant loss of performance of standard inferential procedures.
An informal comparison of the methods is made revealing some useful practical
side-effects in the estimation of popular models in practice including: i)
shrinkage of the estimators in binomial and multinomial regression models that
guarantees finiteness even in cases of data separation where the maximum
likelihood estimator is infinite, and ii) inferential benefits for models that
require the estimation of dispersion or precision parameters
Computational finance: correlation, volatility, and markets

Financial data by nature are inter-related and should be analyzed using multivariate methods. Many models exist for the joint analysis of multiple financial instruments. Early models often assumed some type of constant behavior between the instruments over the time period of analysis. But today, time-varying covariance models are a key component of financial time series analysis leading to a deeper understanding of changing market conditions. Models for covolatility of financial data quickly grow in their complexity and parameters, and 20 years of research offers a variety of solutions to this complexity. After a short introduction of univariate volatility models, this article begins with the basic multivariate formulation for time series covariance modeling and moves to leading time series tools that address this complexity. Coupling these models with regime switching via a Markov process extends the features that can be understood from market behavior. We ground this review in an example of modeling the covariance of securities within sectors and sectors within markets, with dynamics that allow for two different market regimes. Specifically, we simultaneously model individual daily stock data that belong to one of three market sectors and examine the behavior of the market as a whole as well as the behavior of the market sectors over time. A motivation for this characterization concerns portfolio diversification and stock anomalies, and we capture the changing comovement of stocks within and between sectors as market conditions change. For example, some of these market conditions include market crashes or collapses and common external influences
Frameworks for prior-free posterior probabilistic inference

The development of statistical methods for valid and efficient probabilistic
inference without prior distributions has a long history. Fisher's fiducial
inference is perhaps the most famous of these attempts. We argue that, despite
its seemingly prior-free formulation, fiducial and its various extensions are
not prior-free and, therefore, do not meet the requirements for prior-free
probabilistic inference. In contrast, the inferential model (IM) framework is
genuinely prior-free and is shown to be a promising new method for generating
both valid and efficient probabilistic inference. With a brief introduction to
the two fundamental principles, namely, the validity and efficiency principles,
the three-step construction of the basic IM framework is discussed in the
context of the validity principle. Efficient IM methods, based on conditioning
and marginalization are illustrated with two benchmark examples, namely, the
bivariate normal with unknown correlation coefficient and the Behrens--Fisher
problem.Comment: 14 pages, 1 figure; to appear in WIREs Computational Statistic
Minimum Covariance Determinant and Extensions

The Minimum Covariance Determinant (MCD) method is a highly robust estimator
of multivariate location and scatter, for which a fast algorithm is available.
Since estimating the covariance matrix is the cornerstone of many multivariate
statistical methods, the MCD is an important building block when developing
robust multivariate techniques. It also serves as a convenient and efficient
tool for outlier detection. The MCD estimator is reviewed, along with its main
properties such as affine equivariance, breakdown value, and influence
function. We discuss its computation, and list applications and extensions of
the MCD in applied and methodological multivariate statistics. Two recent
extensions of the MCD are described. The first one is a fast deterministic
algorithm which inherits the robustness of the MCD while being almost affine
equivariant. The second is tailored to high-dimensional data, possibly with
more dimensions than cases, and incorporates regularization to prevent singular
matrices
Spatial modeling with R-INLA : a review

Coming up with Bayesian models for spatial data is easy, but performing inference with them can be challenging. Writing fast inference code for a complex spatial model with realistically‐sized datasets from scratch is time‐consuming, and if changes are made to the model, there is little guarantee that the code performs well. The key advantages of R‐INLA are the ease with which complex models can be created and modified, without the need to write complex code, and the speed at which inference can be done even for spatial problems with hundreds of thousands of observations. R‐INLA handles latent Gaussian models, where fixed effects, structured and unstructured Gaussian random effects are combined linearly in a linear predictor, and the elements of the linear predictor are observed through one or more likelihoods. The structured random effects can be both standard areal model such as the Besag and the BYM models, and geostatistical models from a subset of the Matérn Gaussian random fields. In this review, we discuss the large success of spatial modeling with R‐INLA and the types of spatial models that can be fitted, we give an overview of recent developments for areal models, and we give an overview of the stochastic partial differential equation (SPDE) approach and some of the ways it can be extended beyond the assumptions of isotropy and separability. In particular, we describe how slight changes to the SPDE approach leads to straight‐forward approaches for nonstationary spatial models and nonseparable space–time models.PostprintPeer reviewe
Overview of Random Forest Methodology and Practical
Guidance with Emphasis on Computational Biology and
Bioinformatics

The Random Forest (RF) algorithm by Leo Breiman has become a
standard data analysis tool in bioinformatics. It has shown excellent performance in settings where the number of variables is much larger than the number of observations, can cope with complex interaction structures as well as highly correlated variables and returns measures of variable importance. This paper synthesizes ten years of RF development with emphasis on applications to bioinformatics and computational biology. Special attention is given to practical aspects such as the selection of parameters, available RF implementations, and important pitfalls and biases of RF and its variable importance measures (VIMs). The paper surveys recent developments of the methodology relevant to bioinformatics as well as some representative examples of RF applications in this context and possible directions for future research.
An overview of Inference Methods in Probabilistic Classifier Chains for Multi-label classification

This study presents a review of the recent advances in performing inference in probabilistic classifier chains for multilabel classification. The interest of performing such inference arises in an attempt of improving the performance of the approach based on greedy search (the well‐known CC method) and simultaneously reducing the computational cost of an exhaustive search (the well‐known PCC method). Unlike PCC and as CC, inference techniques do not explore all the possible solutions, but they increase the performance of CC, sometimes reaching the optimal solution in terms of subset 0/1 loss, as PCC does. The ε‐approximate algorithm, the method based on a beam search and Monte Carlo sampling are those techniques. An exhaustive set of experiments over a wide range of datasets are performed to analyze not only to which extent these techniques tend to produce optimal solutions, otherwise also to study their computational cost, both in terms of solutions explored and execution time. Only ε‐approximate algorithm with ε=.0 theoretically guarantees reaching an optimal solution in terms of subset 0/1 loss. However, the other algorithms provide solutions close to an optimal solution, despite the fact they do not guarantee to reach an optimal solution. The ε‐approximate algorithm is the most promising to balance the performance in terms of subset 0/1 loss against the number of solutions explored and execution time. The value of ε determines a degree to which one prefers to guarantee to reach an optimal solution at the expense of increasing the computational cos