Visualization of learning in multilayer perceptron networks using principal component analysis

This paper is concerned with the use of scientific visualization methods for the analysis of feedforward neural networks (NNs). Inevitably, the kinds of data associated with the design and implementation of neural networks are of very high dimensionality, presenting a major challenge for visualization. A method is described using the well-known statistical technique of principal component analysis (PCA). This is found to be an effective and useful method of visualizing the learning trajectories of many learning algorithms such as back-propagation and can also be used to provide insight into the learning process and the nature of the error surface.

Inter-subband redundancy prediction using neural network for video coding

High performance video codec is mandatory for multimedia applications such as video-on-demand and video conferencing. Recent research has proposed numerous video coding techniques to meet the requirement in bandwidth, delay, loss and Quality-of-Service (QoS). In this paper, we present our investigations on inter-subband self-similarity within the wavelet-decomposed video frames using neural networks, and study the performance of applying the spatial network model to all video frames over time. The goal of our proposed method is to restore the highest perceptual quality for video transmitted over a highly congested network. Our contributions in this paper are: (1) A new coding model with neural network based, inter-subband redundancy (ISR) prediction for video coding using wavelet (2) The performance of 1D and 2D ISR prediction, including multiple levels of wavelet decompositions. Our result shows a short-term quality enhancement may be obtained using both 1D and 2D ISR prediction.

Humans, dogs and parasitic zoonoses - Unravelling the relationships in a remote endemic community in Northeast India using molecular tools

Canine parasitic zoonoses pose a continuing public health problem, especially in developing countries and communities that are socioeconomically disadvantaged. Our study combined the use of conventional and molecular epidemic, logical tools to determine the role of dogs in transmission of gastrointestinal (GI) parasites such as hookworms, Giardia and Ascaris in a parasite endemic teagrowing community in northeast India. A highly sensitive and specific molecular tool was developed to detect and differentiate the zoonotic species of canine hookworm eggs directly from faeces. This allowed epidemiological screening of canine hookworm species in this community to be conducted with ease and accuracy. The zoonotic potential of canine Giardia was also investigated by characterising Giardia duodenalis recovered from humans and dogs living in the same locality and households at three different loci. Phylogenetic and epidemiological analysis provided compelling evidence to support the zoonotic transmission of canine Giardia. Molecular tools were also used to identify the species of Ascaris egg present in over 30% of dog faecal samples. The results demonstrated the role of dogs as a significant disseminator and environmental contaminator of Ascaris lumbricoides in communities where promiscuous defecation practices exist. Our study demonstrated the usefulness of combining conventional and molecular parasitological and epidemiological tools to help solve unresolved relationships with regards to parasitic zoonoses.

Designing teams for first-of-a-kind, complex systems using the initial phases of cognitive work analysis: Case study

We present a technique for team design based on cognitive work analysis (CWA). We first develop a rationale for this technique by discussing the limitations of conventional approaches for team design in light of the special characteristics of first-of-a-kind, complex systems. We then introduce the CWA-based technique for team design and provide a case study of how we used this technique to design a team for a first-of-a-kind, complex military system during the early stages of its development. In addition to illustrating the CWA-based technique by example, the case study allows us to evaluate the technique. This case study demonstrates that the CWA-based technique for team design is both feasible and useful, although empirical validation of the technique is still necessary. Applications of this work include the design of teams for first-of-a-kind, complex systems in military, medical, and industrial domains.

Spin states of C-60(3-) and C120On- (n=2, 3, 4) anions using electron spin transient nutation spectroscopy

Electron spin transient nutation (ESTN) experiments show that the spin multiplicity of the ground state of C-60(3-) in frozen solution is a doublet with S = 1/2. In purified samples, there is no evidence for excited states or other species with higher multiplicity. In the anions Of C120On- (n = 2, 3, 4), where the CW EPR experiments have shown that a mixture of species is present, ESTN experiments confirm that a doublet with S = 1/2 is associated with the 3- anion and triplets with S = 1 are associated with the 2- and 4- anions. A weak nutation peak attributable to m(s) = -1/2 1/2 transitions within a quartet state may arise from association of anions with spins of 1/2 and 1 in solute aggregates.

Characterisation of the three-dimensional structure of earthworm burrow systems using image analysis and mathematical morphology

The aim of this work was to exemplify the specific contribution of both two- and three-dimensional (31)) X-ray computed tomography to characterise earthworm burrow systems. To achieve this purpose we used 3D mathematical morphology operators to characterise burrow systems resulting from the activity of an anecic (Aporrectodea noctunia), and an endogeic species (Allolobophora chlorotica), when both species were introduced either separately or together into artificial soil cores. Images of these soil cores were obtained using a medical X-ray tomography scanner. Three-dimensional reconstructions of burrow systems were obtained using a specifically developed segmentation algorithm. To study the differences between burrow systems, a set of classical tools of mathematical morphology (granulometries) were used. So-called granulometries based on different structuring elements clearly separated the different burrow systems. They enabled us to show that burrows made by the anecic species were fatter, longer, more vertical, more continuous but less sinuous than burrows of the endogeic species. The granulometry transform of the soil matrix showed that burrows made by A. nocturna were more evenly distributed than those of A. chlorotica. Although a good discrimination was possible when only one species was introduced into the soil cores, it was not possible to separate burrows of the two species from each other in cases where species were introduced into the same soil core. This limitation, partly due to the insufficient spatial resolution of the medical scanner, precluded the use of the morphological operators to study putative interactions between the two species.

Detecting sugarcane 'orange rust' disease using EO-1 Hyperion hyperspectral imagery

This Letter evaluates several narrow-band indices from EO-1 Hyperion imagery in discriminating sugarcane areas affected by 'orange rust' ( Puccinia kuehnii ) disease. Forty spectral vegetation indices (SVIs), focusing on bands related to leaf pigments, leaf internal structure, and leaf water content, were generated from an image acquired over Mackay, Queensland, Australia. Discriminant function analysis was used to select an optimum set of indices based on their correlations with the discriminant function. The predictive ability of each index was also assessed based on the accuracy of classification. Results demonstrated that Hyperion imagery can be used to detect orange rust disease in sugarcane crops. While some indices that only used visible near-infrared (VNIR) bands (e.g. SIPI and R800/R680) offer separability, the combination of VNIR bands with the moisture-sensitive band (1660 nm) yielded increased separability of rust-affected areas. The newly formulated 'Disease-Water Stress Indices' (DWSI-1=R800/R1660; DSWI-2=R1660/R550; DWSI-5=(R800+R550)/(R1660+R680)) produced the largest correlations, indicating their superior ability to discriminate sugarcane areas affected by orange rust disease.

Using partially structured attitude measures to enhance the attitude-behavior relationship

This article recalls a classic scheme for categorizing attitude measures. One particular group of measures, those that rely on respondents' interpretations of partially structured stimuli, has virtually disappeared from attitude research. An attitude measure based on respondents' interpretation of partially structured stimuli is considered. Four studies employing such a measure demonstrate that it predicts unique variance in self-reported and actual behavior, beyond that predicted by explicit and contemporary implicit measures and regardless of whether the attitude object under consideration is wrought with social desirability concerns. Implications for conceptualizing attitude measurement and attitude-behavior relations are discussed.

Controlling chaos in a Lorenz-like system using feedback

We demonstrate that the dynamics of an autonomous chaotic laser can be controlled to a periodic or steady state under self-synchronization. In general, past the chaos threshold the dependence of the laser output on feedback applied to the pump is submerged in the Lorenz-like chaotic pulsation. However there exist specific feedback delays that stabilize the chaos to periodic behavior or even steady state. The range of control depends critically on the feedback delay time and amplitude. Our experimental results are compared with the complex Lorenz equations which show good agreement.

Oxidation of toluene in waste gas streams using mesoporous Ti-hexagonal mesoporous silica

Various mesoporous catalysts with titanium loadings between 0.5 and 4 Ti wt. % and surface areas between 600 and 1,600 m(2)/g were synthesized using the molecular designed dispersion technique. These catalysts were tested using toluene oxidation in a fixed bed reactor at temperatures between 300 and 550degreesC. The reaction products were found to be CO2 and CO with selectivity towards CO2 above 80% for all catalysts. The catalytic activity of the catalysts increases with titanium loading. The total conversion at 550degreesC was not affected by the textural porosity, but increased textural porosity did significantly reduce the ignition temperature by up to 50degreesC. The Thiele modulus was calculated to be much less than one for all these materials indicating that the reaction rate is not diffusion, limited.

Identification of "pathologs" (disease-related genes) from the RIKEN mouse cDNA dataset using human curation plus FACTS, a new biological information extraction system

Background: A major goal in the post-genomic era is to identify and characterise disease susceptibility genes and to apply this knowledge to disease prevention and treatment. Rodents and humans have remarkably similar genomes and share closely related biochemical, physiological and pathological pathways. In this work we utilised the latest information on the mouse transcriptome as revealed by the RIKEN FANTOM2 project to identify novel human disease-related candidate genes. We define a new term patholog to mean a homolog of a human disease-related gene encoding a product ( transcript, anti-sense or protein) potentially relevant to disease. Rather than just focus on Mendelian inheritance, we applied the analysis to all potential pathologs regardless of their inheritance pattern. Results: Bioinformatic analysis and human curation of 60,770 RIKEN full-length mouse cDNA clones produced 2,578 sequences that showed similarity ( 70 - 85% identity) to known human-disease genes. Using a newly developed biological information extraction and annotation tool ( FACTS) in parallel with human expert analysis of 17,051 MEDLINE scientific abstracts we identified 182 novel potential pathologs. Of these, 36 were identified by computational tools only, 49 by human expert analysis only and 97 by both methods. These pathologs were related to neoplastic ( 53%), hereditary ( 24%), immunological ( 5%), cardio-vascular (4%), or other (14%), disorders. Conclusions: Large scale genome projects continue to produce a vast amount of data with potential application to the study of human disease. For this potential to be realised we need intelligent strategies for data categorisation and the ability to link sequence data with relevant literature. This paper demonstrates the power of combining human expert annotation with FACTS, a newly developed bioinformatics tool, to identify novel pathologs from within large-scale mouse transcript datasets.