Improving support vector solutions by selecting a sequence of training subsets

In this paper we demonstrate that it is possible to gradually improve the performance of support vector machine (SVM) classifiers by using a genetic algorithm to select a sequence of training subsets from the available data. Performance improvement is possible because the SVM solution generally lies some distance away from the Bayes optimal in the space of learning parameters. We illustrate performance improvements on a number of benchmark data sets.

Authorship Attribution with Support Vector Machines

In this paper we explore the use of text-mining methods for the identification of the author of a text. We apply the support vector machine (SVM) to this problem, as it is able to cope with half a million of inputs it requires no feature selection and can process the frequency vector of all words of a text. We performed a number of experiments with texts from a German newspaper. With nearly perfect reliability the SVM was able to reject other authors and detected the target author in 60–80% of the cases. In a second experiment, we ignored nouns, verbs and adjectives and replaced them by grammatical tags and bigrams. This resulted in slightly reduced performance. Author detection with SVMs on full word forms was remarkably robust even if the author wrote about different topics.

Short-term electricity price forecasting using wavelet and SVM techniques

In deregulated electricity market, modeling and forecasting the spot price present a number of challenges. By applying wavelet and support vector machine techniques, a new time series model for short term electricity price forecasting has been developed in this paper. The model employs both historical price and other important information, such as load capacity and weather (temperature), to forecast the price of one or more time steps ahead. The developed model has been evaluated with the actual data from Australian National Electricity Market. The simulation results demonstrated that the forecast model is capable of forecasting the electricity price with a reasonable forecasting accuracy.

Computational methods for the detection of facial palsy

We are developing a telemedicine application which offers automated diagnosis of facial (Bell's) palsy through a Web service. We used a test data set of 43 images of facial palsy patients and 44 normal people to develop the automatic recognition algorithm. Three different image pre-processing methods were used. Machine learning techniques (support vector machine, SVM) were used to examine the difference between the two halves of the face. If there was a sufficient difference, then the SVM recognized facial palsy. Otherwise, if the halves were roughly symmetrical, the SVM classified the image as normal. It was found that the facial palsy images had a greater Hamming Distance than the normal images, indicating greater asymmetry. The median distance in the normal group was 331 (interquartile range 277-435) and the median distance in the facial palsy group was 509 (interquartile range 334-703). This difference was significant (P

Real-Time Gene-Expression Extraction with Fast Classification (FC) Networks

Fast Classification (FC) networks were inspired by a biologically plausible mechanism for short term memory where learning occurs instantaneously. Both weights and the topology for an FC network are mapped directly from the training samples by using a prescriptive training scheme. Only two presentations of the training data are required to train an FC network. Compared with iterative learning algorithms such as Back-propagation (which may require many hundreds of presentations of the training data), the training of FC networks is extremely fast and learning convergence is always guaranteed. Thus FC networks may be suitable for applications where real-time classification is needed. In this paper, the FC networks are applied for the real-time extraction of gene expressions for Chlamydia microarray data. Both the classification performance and learning time of the FC networks are compared with the Multi-Layer Proceptron (MLP) networks and support-vector-machines (SVM) in the same classification task. The FC networks are shown to have extremely fast learning time and comparable classification accuracy.

Enhancement or modulation of the vector competence of ochlerotatus vigilax (Diptera : Culicidae) for Ross River virus by temperature

Two different doses of Ross River virus (1111) were fed to Ochlerotatus vigilax (Skuse), the primary coastal vector in Australia; and blood engorged females were held at different temperatures up to 35 d. After ingesting 10(4.3) CCID50/Mosquito, mosquitoes reared at 18 and 25degreesC (and held at the same temperature) had higher body remnant and head and salivary gland titers than those held at 32degreesC, although infection rates were comparable. At 18, 25, and 32degreesC, respectively, virus was first detected in the salivary glands on days 3, 2, and 3. Based on a previously demonstrated 98.7% concordance between salivary gland infection and transmission, the extrinsic incubation periods were estimated as 5, 4, and 3 d, respectively, for these three temperatures. When Oc. vigilax reared at 18, 25, or 32degreesC were fed a lower dosage of 10(3.3) CCID50 RR/mosquito, and assayed after 7 d extrinsic incubation at these (or combinations of these) temperatures, infection rates and titers were similar. However, by 14 d, infection rates and titers of those reared and held at 18 and 32degreesC were significantly higher and lower, respectively. However, this process was reversible when the moderate 25degreesC was involved, and intermediate infection rates and titers resulted. These data indicate that for the strains of RR and Oc. vigilax used, rearing temperature is unimportant to vector competence in the field, and that ambient temperature variations will modulate or enhance detectable infection rates only after 7 d: extrinsic incubation. Because of the short duration of extrinsic incubation, however, this will do little to influence RR epidemiology, because by this time some Oc. vigilax could be seeking their third blood meal, the latter two being infectious.

Relationship of glycosaminoglycan and matrix changes to vascular smooth cell phenotype modulation in rabbit arteries after acute injury

Purpose: The phenotype of vascular smooth muscle cells (SMCs) is altered in several arterial pathologies, including the neointima formed after acute arterial injury. This study examined the time course of this phenotypic change in relation to changes in the amount and distribution of matrix glycosaminoglycans. Methods: The immunochemical staining of heparan sulphates (HS) and chondroitin sulphates (CS) in the extracellular matrix of the arterial wall was examined at early points after balloon catheter injury of the rabbit carotid artery. SMC phenotype was assessed by means of ultrastructural morphometry of the cytoplasmic volume fraction of myofilaments. The proportions of cell and matrix components in the media were analyzed with similar morphometric techniques. Results: HS and CS were shown in close association with SMCs of the uninjured arterial media as well as being more widespread within the matrix. Within 6 hours after arterial injury, there was loss of the regular pericellular distribution of both HS and CS, which was associated with a significant expansion in the extracellular space. This preceded the change in ultrastructural phenotype of the SMCs. The glycosaminoglycan loss was most exaggerated at 4 days, after which time the HS and CS reappeared around the medial SMCs. SMCs of the recovering media were able to rapidly replace their glycosaminoglycans, whereas SMCs of the developing neointima failed to produce HS as readily as they produced CS. Conclusions: These studies indicate that changes in glycosaminoglycans of the extracellular matrix precede changes in SMC phenotype after acute arterial injury. In the recovering arterial media, SMCs replace their matrix glycosaminoglycans rapidly, whereas the newly established neointima fails to produce similar amounts of heparan sulphates.

Controlling the complex Lorenz equations by modulation

We demonstrate that a system obeying the complex Lorenz equations in the deep chaotic regime can be controlled to periodic behavior by applying a modulation to the pump parameter. For arbitrary modulation frequency and amplitude there is no obvious simplification of the dynamics. However, we find that there are numerous windows where the chaotic system has been controlled to different periodic behaviors. The widths of these windows in parameter space are narrow, and the positions are related to the ratio of the modulation frequency of the pump to the average pulsation frequency of the output variable. These results are in good agreement with observations previously made in a far-infrared laser system.

Potassium channels and membrane potential in the modulation of intracellular calcium in vascular endothelial cells

K+ Channels and Membrane Potential in Endothelial Cells. The endothelium plays a vital role in the control of vascular functions, including modulation of tone; permeability and barrier properties; platelet adhesion and aggregation; and secretion of paracrine factors. Critical signaling events in many of these functions involve an increase in intracellular free Ca2+ concentration ([Ca2+](i)). This rise in [Ca2+](i) occurs via an interplay between several mechanisms, including release from intracellular stores, entry from the extracellular space through store depletion and second messenger-mediated processes, and the establishment of a favorable electrochemical gradient. The focus of this review centers on the role of potassium channels and membrane potential in the creation of a favorable electrochemical gradient for Ca2+ entry. In addition, evidence is examined for the existence of various classes of potassium channels and the possible influence of regional variation in expression and experimental conditions.

Tracking the states of a nonlinear and nonstationary system in the weight-space of artificial neural networks

We propose a novel interpretation and usage of Neural Network (NN) in modeling physiological signals, which are allowed to be nonlinear and/or nonstationary. The method consists of training a NN for the k-step prediction of a physiological signal, and then examining the connection-weight-space (CWS) of the NN to extract information about the signal generator mechanism. We de. ne a novel feature, Normalized Vector Separation (gamma(ij)), to measure the separation of two arbitrary states i and j in the CWS and use it to track the state changes of the generating system. The performance of the method is examined via synthetic signals and clinical EEG. Synthetic data indicates that gamma(ij) can track the system down to a SNR of 3.5 dB. Clinical data obtained from three patients undergoing carotid endarterectomy of the brain showed that EEG could be modeled (within a root-means-squared-error of 0.01) by the proposed method, and the blood perfusion state of the brain could be monitored via gamma(ij), with small NNs having no more than 21 connection weight altogether.

The Equilibrium Flux Method for the calculation of flows with non-equilbrium chemical reactions

The Equilibrium Flux Method [1] is a kinetic theory based finite volume method for calculating the flow of a compressible ideal gas. It is shown here that, in effect, the method solves the Euler equations with added pseudo-dissipative terms and that it is a natural upwinding scheme. The method can be easily modified so that the flow of a chemically reacting gas mixture can be calculated. Results from the method for a one-dimensional non-equilibrium reacting flow are shown to agree well with a conventional continuum solution. Results are also presented for the calculation of a plane two-dimensional flow, at hypersonic speed, of a dissociating gas around a blunt-nosed body.

Experimental study of the quantum driven pendulum and its classical analog in atom optics

We present experimental results for the dynamics of cold atoms in a far detuned amplitude-modulated optical standing wave. Phase-space resonances constitute distinct peaks in the atomic momentum distribution containing up to 65% of all atoms resulting from a mixed quantum chaotic phase space. We characterize the atomic behavior in classical and quantum regimes and we present the applicable quantum and classical theory, which we have developed and refined. We show experimental proof that the size and the position of the resonances in phase space can be controlled by varying several parameters, such as the modulation frequency, the scaled well depth, the modulation amplitude, and the scaled Planck’s constant of the system. We have found a surprising stability against amplitude noise. We present methods to accurately control the momentum of an ensemble of atoms using these phase-space resonances which could be used for efficient phase-space state preparation.

Modifying insect population age structure to control vector-borne disease

Age is a critical determinant of the ability of most arthropod vectors to transmit a range of human pathogens. This is due to the fact that most pathogens require a period of extrinsic incubation in the arthropod host before pathogen transmission can occur. This developmental period for the pathogen often comprises a significant proportion of the expected lifespan of the vector. As such, only a small proportion of the population that is oldest contributes to pathogen transmission. Given this, strategies that target vector age would be expected to obtain the most significant reductions in the capacity of a vector population to transmit disease. The recent identification of biological agents that shorten vector lifespan, such as Wolbachia, entomopathogenic fungi and densoviruses, offer new tools for the control of vector-borne diseases. Evaluation of the efficacy of these strategies under field conditions will be possible due to recent advances in insect age-grading techniques. Implementation of all of these strategies will require extensive field evaluation and consideration of the selective pressures that reductions in vector longevity may induce on both vector and pathogen.

Evidence of a spotted fever-like rickettsia and a potential new vector from northeastern Australia

A spotted fever-like rickettsia was identified in a Hemaphysalis tick by polymerase chain reaction (PCR) amplification and sequencing of the 16S rDNA, ompA, and ompB genes. A comparison of these nucleotide sequences with those of other spotted fever group (SFG) rickettsiae revealed that the Hemaphysalis tick rickettsia was distinct from other previously reported strains. Phylogenetic analysis based on both ompA and ompB also indicates that the strain’s closest relatives are the agents of Thai tick typhus (Rickettsia honei strain TT-118) and Flinders Island spotted fever (R. honei). This study represents the first report of an R. honei-like agent from a Hemaphysalis tick in Australia and of a spotted fever group rickettsia from Cape York Peninsula, Queensland.

Field prevalence of Wolbachia in the mosquito vector Aedes albopictus

The endosymbiotic bacteria in the genus Wolbachia have been proposed as a potential candidate to deliver pathogen-blocking genes into natural populations of medically important insects. The successful application of Wolbachia in insect vector control depends on the ability of the agent to successfully invade and maintain itself at high frequency under field conditions. Here, we evaluated the prevalence of Wolbachia infections in a field population of the Wolbachia-superinfected mosquito Aedes albopictus. A field prevalence of 100% (n = 1,016) was found in a single population in eastern Thailand via polymerase chain reaction (PCR) testing of Wolbachia both from individual parent females and their corresponding F1 offspring. This is the first report of accurate Wolbachia prevalence in a field population of an insect disease vector. The prevalence of superinfection was estimated to be 99.41%. All single-infected individual mosquitoes (n = 6) were found to harbor group A Wolbachia. For this particular population, none was found to be single-infected with group B Wolbachia. Our results also show that PCR testing of field materials alone without checking F1 offspring overestimated the natural prevalence of single infection. Thus, the confirmation of infection status by means of F1 offspring was critical to the accurate estimates of Wolbachia prevalence under field conditions.