Fast kernel classifier construction using orthogonal forward selection to minimise leave-one-out misclassification rate

We propose a simple yet computationally efficient construction algorithm for two-class kernel classifiers. In order to optimise classifier's generalisation capability, an orthogonal forward selection procedure is used to select kernels one by one by minimising the leave-one-out (LOO) misclassification rate directly. It is shown that the computation of the LOO misclassification rate is very efficient owing to orthogonalisation. Examples are used to demonstrate that the proposed algorithm is a viable alternative to construct sparse two-class kernel classifiers in terms of performance and computational efficiency.

A fast linear-in-the-parameters classifier construction algorithm using orthogonal forward selection to minimize leave-one-out misclassification rate

We propose a simple and computationally efficient construction algorithm for two class linear-in-the-parameters classifiers. In order to optimize model generalization, a forward orthogonal selection (OFS) procedure is used for minimizing the leave-one-out (LOO) misclassification rate directly. An analytic formula and a set of forward recursive updating formula of the LOO misclassification rate are developed and applied in the proposed algorithm. Numerical examples are used to demonstrate that the proposed algorithm is an excellent alternative approach to construct sparse two class classifiers in terms of performance and computational efficiency.

A-optimality orthogonal forward regression algorithm using branch and bound

In this brief, we propose an orthogonal forward regression (OFR) algorithm based on the principles of the branch and bound (BB) and A-optimality experimental design. At each forward regression step, each candidate from a pool of candidate regressors, referred to as S, is evaluated in turn with three possible decisions: 1) one of these is selected and included into the model; 2) some of these remain in S for evaluation in the next forward regression step; and 3) the rest are permanently eliminated from S. Based on the BB principle in combination with an A-optimality composite cost function for model structure determination, a simple adaptive diagnostics test is proposed to determine the decision boundary between 2) and 3). As such the proposed algorithm can significantly reduce the computational cost in the A-optimality OFR algorithm. Numerical examples are used to demonstrate the effectiveness of the proposed algorithm.

Orthogonal space-time block codes for free-space IM/DD optical links

Use of orthogonal space-time block codes (STBCs) with multiple transmitters and receivers can improve signal quality. However, in optical intensity modulated signals, output of the transmitter is non-negative and hence standard orthogonal STBC schemes need to be modified. A generalised framework for applying orthogonal STBCs for free-space IM/DD optical links is presented.

Deterministic equalization and results of a DVB-T multipath equalizer for both 16-QAM and 64-QAM operation

Recent developments in the UK concerning the reception of Digital Terrestrial Television (DTT) have indicated that, as it currently stands, DVB-T receivers may not be sufficient to maintain adequate quality of digital picture information to the consumer. There are many possible reasons why such large errors are being introduced into the system preventing reception failure. It has been suggested that one possibility is that the assumptions concerning the immunity to multipath that Coded Orthogonal Frequency Division Multiplex (COFDM) is expected to have, may not be entirely accurate. Previous research has shown that multipath can indeed have an impact on a DVB-T receiver performance. In the UK, proposals have been made to change the modulation from 64-QAM to 16-QAM to improve the immunity to multipath, but this paper demonstrates that the 16-QAM performance may again not be sufficient. To this end, this paper presents a deterministic approach to equalization such that a 64-QAM receiver with the simple equalizer presented in this paper has the same order of MPEG-2 BER performance as that to a 16-QAM receiver without equalization. Thus, alleviating the requirement in the broadcasters to migrate from 64-QAM to 16-QAM Of course, by adding the equalizer to a 16-QAM receiver then the BER is also further improved and thus creating one more step to satisfying the consumers(1).

Enhancing MB-OFDM throughput with dual circular 32-QAM

Quadrature Phase Shift Keying (QPSK) and Dual Carrier Modulation (DCM) are currently used as the modulation schemes for Multiband Orthogonal Frequency Division Multiplexing (MB-OFDM) in the ECMA-368 defined Ultra-Wideband (UWB) radio platform. ECMA-368 has been chosen as the physical radio platform for many systems including Wireless USB (W-USB), Bluetooth 3.0 and Wireless HDMI; hence ECMA-368 is an important issue to consumer electronics and the users experience of these products. To enable the transport of high-rate USB, ECMA-368 offers up to 480 Mb/s instantaneous bit rate to the Medium Access Control (MAC) layer, but depending on radio channel conditions dropped packets unfortunately result in a lower throughput. This paper presents an alternative high data rate modulation scheme that fits within the configuration of the current standard increasing system throughput by achieving 600 Mb/s (reliable to 3.1 meters) thus maintaining the high rate USB throughput even with a moderate level of dropped packets. The modulation system is termed Dual Circular 32-QAM (DC 32-QAM). The system performance for DC 32-QAM modulation is presented and compared with 16-QAM and DCM1.

Dynamic modulation of human motor activity when observing actions

Previous studies have demonstrated that when we observe somebody else executing an action many areas of our own motor systems are active. It has been argued that these motor activations are evidence that we motorically simulate observed actions; this motoric simulation may support various functions such as imitation and action understanding. However, whether motoric simulation is indeed the function of motor activations during action observation is controversial, due to inconsistency in findings. Previous studies have demonstrated dynamic modulations in motor activity when we execute actions. Therefore, if we do motorically simulate observed actions, our motor systems should also be modulated dynamically, and in a corresponding fashion, during action observation. Using magnetoencephalography (MEG), we recorded the cortical activity of human participants while they observed actions performed by another person. Here, we show that activity in the human motor system is indeed modulated dynamically during action observation. The finding that activity in the motor system is modulated dynamically when observing actions can explain why studies of action observation using functional magnetic resonance imaging (fMRI) have reported conflicting results, and is consistent with the hypothesis that we motorically simulate observed actions.

In vitro evaluation of the microbiota modulation abilities of different sized whole oat grain flakes.

Epidemiological studies and healthy eating guidelines suggest a positive correlation between ingestion of whole grain cereal and food rich in fibre with protection from chronic diseases. The prebiotic potential of whole grains may be related, however, little is known about the microbiota modulatory capability of oat grain or the impact processing has on this ability. In this study the fermentation profile of whole grain oat flakes, processed to produce two different sized flakes (small and large), by human faecal microbiota was investigated in vitro. Simulated digestion and subsequent fermentation by gut bacteria was investigated using pH controlled faecal batch cultures inoculated with human faecal slurry. The different sized oat flakes, Oat 23’s (0.53–0.63 mm) and Oat 25’s/26’s (0.85–1.0 mm) were compared to oligofructose, a confirmed prebiotic, and cellulose, a poorly fermented carbohydrate. Bacterial enumeration was carried out using the culture independent technique, fluorescent in situ hybridisation, and short chain fatty acid (SCFA) production monitored by gas chromatography. Significant changes in total bacterial populations were observed after 24 h incubation for all substrates except Oat 23’s and cellulose. Oats 23’s fermentation resulted in a significant increase in the Bacteroides–Prevotella group. Oligofructose and Oats 25’s/26’s produced significant increases in Bifidobacterium in the latter stages of fermentation while numbers declined for Oats 23’s between 5 h and 24 h. This is possibly due to the smaller surface area of the larger flakes inhibiting the simulated digestion, which may have resulted in increased levels of resistant starch (Bifidobacterium are known to ferment this dietary fibre). Fermentation of Oat 25’s/26’s resulted in a propionate rich SCFA profile and a significant increase in butyrate, which have both been linked to benefiting host health. The smaller sized oats did not produce a significant increase in butyrate concentration. This study shows for the first time the impact of oat grain on the microbial ecology of the human gut and its potential to beneficially modulate the gut microbiota through increasing Bifidobacterium population.

Particle swarm optimization aided orthogonal forward regression for unified data modeling

We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density function estimation. A particle swarm optimization (PSO) aided orthogonal forward regression (OFR) algorithm based on leave-one-out (LOO) criteria is developed to construct parsimonious radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines the center vector and diagonal covariance matrix of one RBF node by minimizing the LOO statistics. For regression applications, the LOO criterion is chosen to be the LOO mean square error, while the LOO misclassification rate is adopted in two-class classification applications. By adopting the Parzen window estimate as the desired response, the unsupervised density estimation problem is transformed into a constrained regression problem. This PSO aided OFR algorithm for tunable-node RBF networks is capable of constructing very parsimonious RBF models that generalize well, and our analysis and experimental results demonstrate that the algorithm is computationally even simpler than the efficient regularization assisted orthogonal least square algorithm based on LOO criteria for selecting fixed-node RBF models. Another significant advantage of the proposed learning procedure is that it does not have learning hyperparameters that have to be tuned using costly cross validation. The effectiveness of the proposed PSO aided OFR construction procedure is illustrated using several examples taken from regression and classification, as well as density estimation applications.

Probability density estimation with tunable kernels using orthogonal forward regression

A generalized or tunable-kernel model is proposed for probability density function estimation based on an orthogonal forward regression procedure. Each stage of the density estimation process determines a tunable kernel, namely, its center vector and diagonal covariance matrix, by minimizing a leave-one-out test criterion. The kernel mixing weights of the constructed sparse density estimate are finally updated using the multiplicative nonnegative quadratic programming algorithm to ensure the nonnegative and unity constraints, and this weight-updating process additionally has the desired ability to further reduce the model size. The proposed tunable-kernel model has advantages, in terms of model generalization capability and model sparsity, over the standard fixed-kernel model that restricts kernel centers to the training data points and employs a single common kernel variance for every kernel. On the other hand, it does not optimize all the model parameters together and thus avoids the problems of high-dimensional ill-conditioned nonlinear optimization associated with the conventional finite mixture model. Several examples are included to demonstrate the ability of the proposed novel tunable-kernel model to effectively construct a very compact density estimate accurately.

Radial basis function classifier construction using particle swarm optimisation aided orthogonal forward regression

We develop a particle swarm optimisation (PSO) aided orthogonal forward regression (OFR) approach for constructing radial basis function (RBF) classifiers with tunable nodes. At each stage of the OFR construction process, the centre vector and diagonal covariance matrix of one RBF node is determined efficiently by minimising the leave-one-out (LOO) misclassification rate (MR) using a PSO algorithm. Compared with the state-of-the-art regularisation assisted orthogonal least square algorithm based on the LOO MR for selecting fixednode RBF classifiers, the proposed PSO aided OFR algorithm for constructing tunable-node RBF classifiers offers significant advantages in terms of better generalisation performance and smaller model size as well as imposes lower computational complexity in classifier construction process. Moreover, the proposed algorithm does not have any hyperparameter that requires costly tuning based on cross validation.

Intestinal microflora of human infants and current trends for its nutritional modulation

Diet, among other environmental and genetic factors, is currently recognised to have an important role in health and disease. There is increasing evidence that the human colonic microbiota can contribute positively towards host nutrition and health. As such, dietary modulation has been proposed as important for improved gut health, especially during the highly sensitive stage of infancy. Differences in gut microflora composition and incidence of infection occur between breast- and formula-fed infants. Human milk components that cannot be duplicated in infant formulae could possibly account for these differences. However, various functional food ingredients such as oligosaccharides, prebiotics, proteins and probiotics could effect a beneficial modification in the composition and activities of gut microflora of infants. The aim of the present review is to describe existing knowledge on the composition and metabolic activities of the gastrointestinal microflora of human infants and discuss various possibilities and opportunities for its nutritional modulation.

Pleiotropic effects of TCF7L2 gene variants and its modulation in the metabolic syndrome: from the LIPGENE study

Aims/hypothesis: Variants of the TCF7L2 gene predict the development of type 2 diabetes mellitus (T2DM). We investigated the associations between gene variants of TCF7L2 and clinical features of the metabolic syndrome (MetS) (an entity often preceeding T2DM), and their interaction with non-genetic factors, including plasma saturated fatty acids (SFA) concentration and insulin resistance (IR). Methods: Fasting lipid profiles, insulin sensitivity, insulin secretion, anthropometrics, blood pressure and 10 gene variations of the TCF7L2 gene were determined in 450 subjects with MetS. Results: Several single nucleotide polymorphisms (SNP) showed phenotypic associations independent of SFA or IR. Carriers of the rare T allele of rs7903146, and of three other SNPs in linkage disequilibrium with rs7903146, had lower blood pressure and insulin secretion. High IR and the presence of the T-allele of rs7903146 acted synergistically to define those with reduced insulin secretion. Carriers of the minor allele of rs290481 exhibited an altered lipid profile, with increased plasma levels of apolipoprotein B, non-esterified fatty acids, cholesterol and apolipoprotein B in triglyceride rich lipoproteins, and LDL cholesterol. Carriers of the minor allele of rs11196224 that had higher plasma SFA levels showed elevated procoagulant/proinflammatory biomarkers, impaired insulin secretion and increased IR, whereas carriers of the minor allele of rs17685538 with high plasma SFA levels exhibited higher blood pressure. Conclusions/interpretation: SNP in the TCF7L2 gene are associated with differences in insulin secretion, blood pressure, blood lipids and coagulation in MetS patients, and may be modulated by SFA in plasma or IR.