Finite sample weighting of recursive forecast errors

This paper proposes and tests a new framework for weighting recursive out-of-sample prediction errors according to their corresponding levels of in-sample estimation uncertainty. In essence, we show how to use the maximum possible amount of information from the sample in the evaluation of the prediction accuracy, by commencing the forecasts at the earliest opportunity and weighting the prediction errors. Via a Monte Carlo study, we demonstrate that the proposed framework selects the correct model from a set of candidate models considerably more often than the existing standard approach when only a small sample is available. We also show that the proposed weighting approaches result in tests of equal predictive accuracy that have much better sizes than the standard approach. An application to an exchange rate dataset highlights relevant differences in the results of tests of predictive accuracy based on the standard approach versus the framework proposed in this paper.

Yield and nitrogen accumulation and partitioning in winter wheat under elevated CO2: A 3-year free-air CO2 enrichment experiment

Fossil fuel combustion and deforestation have resulted in a rapid increase in atmospheric [CO2] since the 1950’s, and it will reach about 550 μmol mol−1 in 2050. Field experiments were conducted at the Free-air CO2 Enrichment facility in Beijing, China. Winter wheat was grown to maturity under elevated [CO2] (550 ± 17 μmol mol−1) and ambient [CO2] (415 ± 16 μmol mol−1), with high nitrogen (N) supply (HN, 170 kg N ha−1) and low nitrogen supply (LN, 100 kg N ha−1) for three growing seasons from 2007 to 2010. Elevated [CO2] increased wheat grain yield by 11.4% across the three years. [CO2]-induced yield enhancements were 10.8% and 11.9% under low N and high N supply, respectively. Nitrogen accumulation under elevated [CO2] was increased by 12.9% and 9.2% at the half-way anthesis and ripening stage across three years, respectively. Winter wheat had higher nitrogen demand under elevated [CO2] than ambient [CO2], and grain yield had a stronger correlation with plant N uptake after anthesis than before anthesis at high [CO2]. Our results suggest that regulating on the N application rate and time, is likely important for sustainable grain production under future CO2 climate.

Search engine liability for autocomplete suggestions: personality, privacy and the power of the algorithm

This article is concerned with the liability of search engines for algorithmically produced search suggestions, such as through Google’s ‘autocomplete’ function. Liability in this context may arise when automatically generated associations have an offensive or defamatory meaning, or may even induce infringement of intellectual property rights. The increasing number of cases that have been brought before courts all over the world puts forward questions on the conflict of fundamental freedoms of speech and access to information on the one hand, and personality rights of individuals— under a broader right of informational self-determination—on the other. In the light of the recent judgment of the Court of Justice of the European Union (EU) in Google Spain v AEPD, this article concludes that many requests for removal of suggestions including private individuals’ information will be successful on the basis of EU data protection law, even absent prejudice to the person concerned.

Differential uptake, partitioning and transfer of Cd and Zn in the soil-pea plant-aphid system

The biomagnification of trace metals during transfer from contaminated soil to higher trophic levels may potentially result in the exposure of predatory arthropods to toxic concentrations of these elements. This study examined the transfer of Cd and Zn in a soil−plant−arthropod system grown in series of field plots that had received two annual applications of municipal biosolids with elevated levels of Cd and Zn. Results showed that biosolids amendment significantly increased the concentration of Cd in the soil and the shoots of pea plants and the concentration of Zn in the soil, pea roots, shoots, and pods. In addition, the ratio of Cd to Zn concentration showed that Zn was preferentially transferred compared to Cd through all parts of the system. As a consequence, Zn was biomagnified by the system whereas Cd was biominimized. Cd and Zn are considered to exhibit similar behaviors in biological systems. However, the Cd/Zn ratios demonstrated that in this system, Cd is much less labile in the root−shoot−pod and shoot−aphid pathways than Zn.

Differences between the CME fronts tracked by an expert, an automated algorithm, and the Solar Stormwatch project

Observations from the Heliospheric Imager (HI) instruments aboard the twin STEREO spacecraft have enabled the compilation of several catalogues of coronal mass ejections (CMEs), each characterizing the propagation of CMEs through the inner heliosphere. Three such catalogues are the Rutherford Appleton Laboratory (RAL)-HI event list, the Solar Stormwatch CME catalogue, and, presented here, the J-tracker catalogue. Each catalogue uses a different method to characterize the location of CME fronts in the HI images: manual identification by an expert, the statistical reduction of the manual identifications of many citizen scientists, and an automated algorithm. We provide a quantitative comparison of the differences between these catalogues and techniques, using 51 CMEs common to each catalogue. The time-elongation profiles of these CME fronts are compared, as are the estimates of the CME kinematics derived from application of three widely used single-spacecraft-fitting techniques. The J-tracker and RAL-HI profiles are most similar, while the Solar Stormwatch profiles display a small systematic offset. Evidence is presented that these differences arise because the RAL-HI and J-tracker profiles follow the sunward edge of CME density enhancements, while Solar Stormwatch profiles track closer to the antisunward (leading) edge. We demonstrate that the method used to produce the time-elongation profile typically introduces more variability into the kinematic estimates than differences between the various single-spacecraft-fitting techniques. This has implications for the repeatability and robustness of these types of analyses, arguably especially so in the context of space weather forecasting, where it could make the results strongly dependent on the methods used by the forecaster.

A robust sclera segmentation algorithm

Sclera segmentation is shown to be of significant importance for eye and iris biometrics. However, sclera segmentation has not been extensively researched as a separate topic, but mainly summarized as a component of a broader task. This paper proposes a novel sclera segmentation algorithm for colour images which operates at pixel-level. Exploring various colour spaces, the proposed approach is robust to image noise and different gaze directions. The algorithm’s robustness is enhanced by a two-stage classifier. At the first stage, a set of simple classifiers is employed, while at the second stage, a neural network classifier operates on the probabilities’ space generated by the classifiers at stage 1. The proposed method was ranked the 1st in Sclera Segmentation Benchmarking Competition 2015, part of BTAS 2015, with a precision of 95.05% corresponding to a recall of 94.56%.

A Genetic Algorithm for the selection of structural MRI features for classification of Mild Cognitive Impairment and Alzheimer's Disease

This work investigates the problem of feature selection in neuroimaging features from structural MRI brain images for the classification of subjects as healthy controls, suffering from Mild Cognitive Impairment or Alzheimer’s Disease. A Genetic Algorithm wrapper method for feature selection is adopted in conjunction with a Support Vector Machine classifier. In very large feature sets, feature selection is found to be redundant as the accuracy is often worsened when compared to an Support Vector Machine with no feature selection. However, when just the hippocampal subfields are used, feature selection shows a significant improvement of the classification accuracy. Three-class Support Vector Machines and two-class Support Vector Machines combined with weighted voting are also compared with the former and found more useful. The highest accuracy achieved at classifying the test data was 65.5% using a genetic algorithm for feature selection with a three-class Support Vector Machine classifier.

Modelling personal thermal sensations using C-Support Vector Classification (C-SVC) algorithm

The personalised conditioning system (PCS) is widely studied. Potentially, it is able to reduce energy consumption while securing occupants’ thermal comfort requirements. It has been suggested that automatic optimised operation schemes for PCS should be introduced to avoid energy wastage and discomfort caused by inappropriate operation. In certain automatic operation schemes, personalised thermal sensation models are applied as key components to help in setting targets for PCS operation. In this research, a novel personal thermal sensation modelling method based on the C-Support Vector Classification (C-SVC) algorithm has been developed for PCS control. The personal thermal sensation modelling has been regarded as a classification problem. During the modelling process, the method ‘learns’ an occupant’s thermal preferences from his/her feedback, environmental parameters and personal physiological and behavioural factors. The modelling method has been verified by comparing the actual thermal sensation vote (TSV) with the modelled one based on 20 individual cases. Furthermore, the accuracy of each individual thermal sensation model has been compared with the outcomes of the PMV model. The results indicate that the modelling method presented in this paper is an effective tool to model personal thermal sensations and could be integrated within the PCS for optimised system operation and control.

Bio-inspired ant colony optimization based clustering algorithm with mobile sinks for applications in consumer home automation networks

With the fast development of wireless communications, ZigBee and semiconductor devices, home automation networks have recently become very popular. Since typical consumer products deployed in home automation networks are often powered by tiny and limited batteries, one of the most challenging research issues is concerning energy reduction and the balancing of energy consumption across the network in order to prolong the home network lifetime for consumer devices. The introduction of clustering and sink mobility techniques into home automation networks have been shown to be an efficient way to improve the network performance and have received significant research attention. Taking inspiration from nature, this paper proposes an Ant Colony Optimization (ACO) based clustering algorithm specifically with mobile sink support for home automation networks. In this work, the network is divided into several clusters and cluster heads are selected within each cluster. Then, a mobile sink communicates with each cluster head to collect data directly through short range communications. The ACO algorithm has been utilized in this work in order to find the optimal mobility trajectory for the mobile sink. Extensive simulation results from this research show that the proposed algorithm significantly improves home network performance when using mobile sinks in terms of energy consumption and network lifetime as compared to other routing algorithms currently deployed for home automation networks.

A tillering inhibition gene influences root-shoot carbon partitioning and pattern of water use to improve wheat productivity in rainfed environments

Genetic modification of shoot and root morphology has potential to improve water and nutrient 19 uptake of wheat crops in rainfed environments. Near-isogenic lines (NILs) varying for a tillering 20 inhibition (tin) gene and representing multiple genetic backgrounds were investigated in contrasting 21 controlled environments for shoot and root growth. Leaf area, shoot and root biomass were similar 22 until tillering whereupon reduced tillering in tin-containing NILs produced reductions of up to 60% in 23 total leaf area and biomass, and increases in total root length of up to 120% and root biomass to 24 145%. Together, root-to-shoot ratio increased two-fold with the tin gene. The influence of tin on shoot 25 and root growth was greatest in the cv. Banks genetic background, particularly in the biculm-selected 26 NIL, and was typically strongest in cooler environments. A separate de-tillering study confirmed 27 greater root-to-shoot ratios with regular tiller removal in non-tin containing genotypes. In validating 28 these observations in a rainfed field study, the tin allele had a negligible effect on seedling growth but 29 was associated with significantly (P<0.05) reduced tiller number (-37%), leaf area index (-26%) and 30 spike number (-35%) to reduce plant biomass (-19%) at anthesis. Root biomass, root-to-shoot ratio at 31 early stem elongation and root depth at maturity were increased in tin-containing NILs. Soil water use 32 was slowed in tin-containing NILs resulting in greater water availability, greater stomatal 33 conductance, cooler canopy temperatures and maintenance of green leaf area during grain-filling. 34 Together these effects contributed to increases in harvest index and grain yield. In both the controlled 35 and field environments, the tin gene was commonly associated with increased root length and biomass 36 but the significant influence of genetic background and environment suggests careful assessment of 37 tin-containing progeny in selection for genotypic increases in root growth.

Zero attracting recursive least squares algorithms

The l1-norm sparsity constraint is a widely used technique for constructing sparse models. In this contribution, two zero-attracting recursive least squares algorithms, referred to as ZA-RLS-I and ZA-RLS-II, are derived by employing the l1-norm of parameter vector constraint to facilitate the model sparsity. In order to achieve a closed-form solution, the l1-norm of the parameter vector is approximated by an adaptively weighted l2-norm, in which the weighting factors are set as the inversion of the associated l1-norm of parameter estimates that are readily available in the adaptive learning environment. ZA-RLS-II is computationally more efficient than ZA-RLS-I by exploiting the known results from linear algebra as well as the sparsity of the system. The proposed algorithms are proven to converge, and adaptive sparse channel estimation is used to demonstrate the effectiveness of the proposed approach.

A fast adaptive tunable RBF network for nonstationary systems

This paper describes a novel on-line learning approach for radial basis function (RBF) neural network. Based on an RBF network with individually tunable nodes and a fixed small model size, the weight vector is adjusted using the multi-innovation recursive least square algorithm on-line. When the residual error of the RBF network becomes large despite of the weight adaptation, an insignificant node with little contribution to the overall system is replaced by a new node. Structural parameters of the new node are optimized by proposed fast algorithms in order to significantly improve the modeling performance. The proposed scheme describes a novel, flexible, and fast way for on-line system identification problems. Simulation results show that the proposed approach can significantly outperform existing ones for nonstationary systems in particular.

Sparse density estimator with tunable kernels

A new sparse kernel density estimator with tunable kernels is introduced within a forward constrained regression framework whereby the nonnegative and summing-to-unity constraints of the mixing weights can easily be satisfied. Based on the minimum integrated square error criterion, a recursive algorithm is developed to select significant kernels one at time, and the kernel width of the selected kernel is then tuned using the gradient descent algorithm. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing very sparse kernel density estimators with competitive accuracy to existing kernel density estimators.

A new adaptive multiple modelling approach for non-linear and non-stationary systems

This paper proposes a novel adaptive multiple modelling algorithm for non-linear and non-stationary systems. This simple modelling paradigm comprises K candidate sub-models which are all linear. With data available in an online fashion, the performance of all candidate sub-models are monitored based on the most recent data window, and M best sub-models are selected from the K candidates. The weight coefficients of the selected sub-model are adapted via the recursive least square (RLS) algorithm, while the coefficients of the remaining sub-models are unchanged. These M model predictions are then optimally combined to produce the multi-model output. We propose to minimise the mean square error based on a recent data window, and apply the sum to one constraint to the combination parameters, leading to a closed-form solution, so that maximal computational efficiency can be achieved. In addition, at each time step, the model prediction is chosen from either the resultant multiple model or the best sub-model, whichever is the best. Simulation results are given in comparison with some typical alternatives, including the linear RLS algorithm and a number of online non-linear approaches, in terms of modelling performance and time consumption.