Parallel random prism: a computationally efficient ensemble learner for classification

Generally classifiers tend to overfit if there is noise in the training data or there are missing values. Ensemble learning methods are often used to improve a classifier's classification accuracy. Most ensemble learning approaches aim to improve the classification accuracy of decision trees. However, alternative classifiers to decision trees exist. The recently developed Random Prism ensemble learner for classification aims to improve an alternative classification rule induction approach, the Prism family of algorithms, which addresses some of the limitations of decision trees. However, Random Prism suffers like any ensemble learner from a high computational overhead due to replication of the data and the induction of multiple base classifiers. Hence even modest sized datasets may impose a computational challenge to ensemble learners such as Random Prism. Parallelism is often used to scale up algorithms to deal with large datasets. This paper investigates parallelisation for Random Prism, implements a prototype and evaluates it empirically using a Hadoop computing cluster.

Probability of survival in a random exchange economy with dependent agents

In this paper I analyze the general equilibrium in a random Walrasian economy. Dependence among agents is introduced in the form of dependency neighborhoods. Under the uncertainty, an agent may fail to survive due to a meager endowment in a particular state (direct effect), as well as due to unfavorable equilibrium price system at which the value of the endowment falls short of the minimum needed for survival (indirect terms-of-trade effect). To illustrate the main result I compute the stochastic limit of equilibrium price and probability of survival of an agent in a large Cobb-Douglas economy.

Validating the reported random errors of ACE‐FTS measurements

In order to validate the reported precision of space‐based atmospheric composition measurements, validation studies often focus on measurements in the tropical stratosphere, where natural variability is weak. The scatter in tropical measurements can then be used as an upper limit on single‐profile measurement precision. Here we introduce a method of quantifying the scatter of tropical measurements which aims to minimize the effects of short‐term atmospheric variability while maintaining large enough sample sizes that the results can be taken as representative of the full data set. We apply this technique to measurements of O3, HNO3, CO, H2O, NO, NO2, N2O, CH4, CCl2F2, and CCl3F produced by the Atmospheric Chemistry Experiment–Fourier Transform Spectrometer (ACE‐FTS). Tropical scatter in the ACE‐FTS retrievals is found to be consistent with the reported random errors (RREs) for H2O and CO at altitudes above 20 km, validating the RREs for these measurements. Tropical scatter in measurements of NO, NO2, CCl2F2, and CCl3F is roughly consistent with the RREs as long as the effect of outliers in the data set is reduced through the use of robust statistics. The scatter in measurements of O3, HNO3, CH4, and N2O in the stratosphere, while larger than the RREs, is shown to be consistent with the variability simulated in the Canadian Middle Atmosphere Model. This result implies that, for these species, stratospheric measurement scatter is dominated by natural variability, not random error, which provides added confidence in the scientific value of single‐profile measurements.

Effects of variety and nutrient availability on the acrylamide-forming potential of rye grain

Acrylamide is a probable human carcinogen that forms in plant-derived foods when free asparagine and reducing sugars react at high temperatures. The identification of rye varieties with low acrylamide-forming potential or agronomic conditions that produce raw material with low acrylamide precursor concentrations would reduce the acrylamide formed in baked rye foods without the need for additives or potentially costly changes to processes. This work compared five commercial rye varieties grown under a range of fertilisation regimes to investigate the effects of genotype and nutrient (nitrogen and sulphur) availability on the accumulation of acrylamide precursors. A strong correlation was established between the free asparagine concentration of grain and the acrylamide formed upon heating. The five rye varieties accumulated different concentrations of free asparagine in the grain, indicating that there is genetic control of this trait and that variety selection could be useful in reducing acrylamide levels in rye products. High levels of nitrogen fertilisation were found to increase the accumulation of free asparagine, showing that excessive nitrogen application should be avoided in order not to exacerbate the problem of acrylamide formation. This effect of nitrogen was mitigated in two of the varieties by the application of sulphur.

Random Prism: a noise-tolerant alternative to Random Forests

Ensemble learning can be used to increase the overall classification accuracy of a classifier by generating multiple base classifiers and combining their classification results. A frequently used family of base classifiers for ensemble learning are decision trees. However, alternative approaches can potentially be used, such as the Prism family of algorithms that also induces classification rules. Compared with decision trees, Prism algorithms generate modular classification rules that cannot necessarily be represented in the form of a decision tree. Prism algorithms produce a similar classification accuracy compared with decision trees. However, in some cases, for example, if there is noise in the training and test data, Prism algorithms can outperform decision trees by achieving a higher classification accuracy. However, Prism still tends to overfit on noisy data; hence, ensemble learners have been adopted in this work to reduce the overfitting. This paper describes the development of an ensemble learner using a member of the Prism family as the base classifier to reduce the overfitting of Prism algorithms on noisy datasets. The developed ensemble classifier is compared with a stand-alone Prism classifier in terms of classification accuracy and resistance to noise.

Remarks concerning the double cascade as a necessary mechanism for the instability of steady equivalent-barotropic flows

n a recent paper, Petroniet al. claim that a necessary condition for the instability of two-dimensional steady flows is a «double cascade» of energy and enstrophy respectively to larger and to smaller scales of motion. It is shown here that the analytical reasoning employed by Petroniet al. is flawed and that their conclusions are incorrect. What is true is that in any scale interaction (whether an instability or not), neither energy nor enstrophy can be transferred in one spectral direction only, but this result is extremely well known.

Sugar signaling in root responses to low phosphorus availability

Over the last decade, major advances have been made in our understanding of how plants sense, signal, and respond to soil phosphorus (P) availability (Amtmann et al., 2006; White and Hammond, 2008; Nilsson et al., 2010; Yang and Finnegan, 2010; Vance, 2010; George et al., 2011). Previously, we have reviewed the potential for shoot-derived carbohydrate signals to initiate acclimatory responses in roots to low P availability. In this context, these carbohydrates act as systemic plant growth regulators (Hammond and White, 2008). Photosynthate is transported primarily to sink tissues as Suc via the phloem. Under P starvation, plants accumulate sugars and starch in their leaves. Increased loading of Suc to the phloem under P starvation primarily functions to relocate carbon resources to the roots, which increases their size relative to the shoot (Hermans et al., 2006). The translocation of sugars via the phloem also has the potential to initiate sugar signaling cascades that alter the expression of genes involved plant responses to low P availability. These include optimizing root biochemistry to acquire soil P, through increased expression and activity of inorganic phosphate (Pi) transporters, the secretion of acid phosphatases and organic acids to release P from the soil, and the optimization of internal P use (Hammond and White, 2008). Here, we provide an Update to the field of plant signaling responses to low P availability and the interactions with sugar signaling components. Advances in the P signaling pathways and the roles of hormones in signaling plant responses to low P availability are also reviewed, and where possible their interactions with potential sugar signaling pathways.

Distribution and availability of trace elements in municipal solid waste composts

Trace element contamination is one of the main problems linked to the quality of compost, especially when it is produced from urban wastes, which can lead to high levels of some potentially toxic elements such as Cu, Pb or Zn. In this work, the distribution and bioavailability of five elements (Cu, Zn, Pb, Cr and Ni) were studied in five Spanish composts obtained from different feedstocks (municipal solid waste, garden trimmings, sewage sludge and mixed manure). The five composts showed high total concentrations of these elements, which in some cases limited their commercialization due to legal imperatives. First, a physical fractionation of the composts was performed, and the five elements were determined in each size fraction. Their availability was assessed by several methods of extraction (water, CaCl2–DTPA, the PBET extract, the TCLP extract, and sodium pyrophosphate), and their chemical distribution was assessed using the BCR sequential extraction procedure. The results showed that the finer fractions were enriched with the elements studied, and that Cu, Pb and Zn were the most potentially problematic ones, due to both their high total concentrations and availability. The partition into the BCR fractions was different for each element, but the differences between composts were scarce. Pb was evenly distributed among the four fractions defined in the BCR (soluble, oxidizable, reducible and residual); Cu was mainly found in the oxidizable fraction, linked to organic matter, and Zn was mainly associated to the reducible fraction (iron oxides), while Ni and Cr were mainly present almost exclusively in the residual fraction. It was not possible to establish a univocal relation between trace elements availability and their BCR fractionation. Given the differences existing for the availability and distribution of these elements, which not always were related to their total concentrations, we think that legal limits should consider availability, in order to achieve a more realistic assessment of the risks linked to compost use.

Sparse polynomial approximation in positive order Sobolev spaces with bounded mixed derivatives and applications to elliptic problems with random loading

In the present paper we study the approximation of functions with bounded mixed derivatives by sparse tensor product polynomials in positive order tensor product Sobolev spaces. We introduce a new sparse polynomial approximation operator which exhibits optimal convergence properties in L2 and tensorized View the MathML source simultaneously on a standard k-dimensional cube. In the special case k=2 the suggested approximation operator is also optimal in L2 and tensorized H1 (without essential boundary conditions). This allows to construct an optimal sparse p-version FEM with sparse piecewise continuous polynomial splines, reducing the number of unknowns from O(p2), needed for the full tensor product computation, to View the MathML source, required for the suggested sparse technique, preserving the same optimal convergence rate in terms of p. We apply this result to an elliptic differential equation and an elliptic integral equation with random loading and compute the covariances of the solutions with View the MathML source unknowns. Several numerical examples support the theoretical estimates.

Carbon stable isotope analysis of cereal remains as a way to reconstruct water availability: preliminary results

Reconstructing past water availability, both as rainfall and irrigation, is important to answer questions about the way society reacts to climate and its changes and the role of irrigation in the development of social complexity. Carbon stable isotope analysis of archaeobotanical remains is a potentially valuable method for reconstructing water availability. To further define the relationship between water availability and plant carbon isotope composition and to set up baseline values for the Southern Levant, grains of experimentally grown barley and sorghum were studied. The cereal crops were grown at three stations under five different irrigation regimes in Jordan. Results indicate that a positive but weak relationship exists between irrigation regime and total water input of barley grains, but no relationship was found for sorghum. The relationship for barley is site-specific and inter-annual variation was present at Deir ‘Alla, but not at Ramtha and Khirbet as-Samra.

An exploration of the equivalent weights particle filter

Particle filters are fully non-linear data assimilation techniques that aim to represent the probability distribution of the model state given the observations (the posterior) by a number of particles. In high-dimensional geophysical applications the number of particles required by the sequential importance resampling (SIR) particle filter in order to capture the high probability region of the posterior, is too large to make them usable. However particle filters can be formulated using proposal densities, which gives greater freedom in how particles are sampled and allows for a much smaller number of particles. Here a particle filter is presented which uses the proposal density to ensure that all particles end up in the high probability region of the posterior probability density function. This gives rise to the possibility of non-linear data assimilation in large dimensional systems. The particle filter formulation is compared to the optimal proposal density particle filter and the implicit particle filter, both of which also utilise a proposal density. We show that when observations are available every time step, both schemes will be degenerate when the number of independent observations is large, unlike the new scheme. The sensitivity of the new scheme to its parameter values is explored theoretically and demonstrated using the Lorenz (1963) model.

Quantifying errors due to frequency changes and target location uncertainty for radar refractivity retrievals

Radar refractivity retrievals can capture near-surface humidity changes, but noisy phase changes of the ground clutter returns limit the accuracy for both klystron- and magnetron-based systems. Observations with a C-band (5.6 cm) magnetron weather radar indicate that the correction for phase changes introduced by local oscillator frequency changes leads to refractivity errors no larger than 0.25 N units: equivalent to a relative humidity change of only 0.25% at 20°C. Requested stable local oscillator (STALO) frequency changes were accurate to 0.002 ppm based on laboratory measurements. More serious are the random phase change errors introduced when targets are not at the range-gate center and there are changes in the transmitter frequency (ΔfTx) or the refractivity (ΔN). Observations at C band with a 2-μs pulse show an additional 66° of phase change noise for a ΔfTx of 190 kHz (34 ppm); this allows the effect due to ΔN to be predicted. Even at S band with klystron transmitters, significant phase change noise should occur when a large ΔN develops relative to the reference period [e.g., ~55° when ΔN = 60 for the Next Generation Weather Radar (NEXRAD) radars]. At shorter wavelengths (e.g., C and X band) and with magnetron transmitters in particular, refractivity retrievals relative to an earlier reference period are even more difficult, and operational retrievals may be restricted to changes over shorter (e.g., hourly) periods of time. Target location errors can be reduced by using a shorter pulse or identified by a new technique making alternate measurements at two closely spaced frequencies, which could even be achieved with a dual–pulse repetition frequency (PRF) operation of a magnetron transmitter.