Graded Signal Functions for ARTMAP Neural Networks

This study presents an analysis of a modified ARTMAP neural network in which a graded signal function replaces the standard choice-by-difference function. The modifications are introduced mathematically and the performance of the system is studied on two benchmark examples. It is shown that the modified ARTMAP system achieves classification accuracy superior to that of standard ARTMAP, while retaining comparable complexity of the internal code.Office of Naval Research and the Defense Advanced Research Projects Agency (N00014-95-1-0409, N00014-1-95-0657

Artmap Networks for Classification of Ultrasonic Weld Inspection Signals

Inverse problems in Nondestructive Evaluation (NDE) involve estimating the characteristics of flaws from measurements obtained during an inspection. Several techniques have been developed over the years for solving the inverse problem [1]. These techniques range from calibration approaches to numerical methods based on integral equations. Signal identification and classification is one of the more popular approaches for inverse problems encountered in many practical NDE applications

Adaptive Resonance Theory (ART) for social media analytics

This chapter presents the ART-based clustering algorithms for social media analytics in detail. Sections 3.1 and 3.2 introduce Fuzzy ART and its clustering mechanisms, respectively, which provides a deep understanding of the base model that is used and extended for handling the social media clustering challenges. Important concepts such as vigilance region (VR) and its properties are explained and proven. Subsequently, Sects. 3.3-3.7 illustrate five types of ART adaptive resonance theory variants, each of which addresses the challenges in one social media analytical scenario, including automated parameter adaptation, user preference incorporation, short text clustering, heterogeneous data co-clustering and online streaming data indexing. The content of this chapter is several prior studies, including Probabilistic ART [15

Case study on the efficacy of a lanthanum-enriched clay (Phoslock®) in controlling eutrophication in Lake Het Groene Eiland (The Netherlands)

Lake Het Groene Eiland was created in the beginning of 2008 by construction of dikes for isolating it from the surrounding 220-ha water body. This so-called claustrum of 5 ha was treated using lanthanum-modified clay (Phoslock®) to control eutrophication and mitigate cyanobacterial nuisance. Cyanobacteria chlorophyll-a were significantly lower in the claustrum than those in the reference water body, where a massive bloom developed in summer, 2008. However, PO4-P and TP did not statistically differ in these two waters. TN and NO3-N were significantly lower in the claustrum, where dense submerged macrophytes beds developed. Lanthanum concentrations were elevated after the applications of the modified clay in the claustrum, but filterable lanthanum dropped rapidly below the Dutch standard of 10.1 μg l−1. During winter, dozens of Canada geese resided at the claustrum. Geese droppings contained an average of 2 mg PO4-P g−1 dry weight and 12 mg NH3-N g−1 dry weight and might present a growing source of nutrients to the water. Constructing the claustrum enabled unrestricted bathing in subsequent three summers, as no swimming bans had to be issued due to cyanobacteria blooms. However, the role of the modified clay in this positive outcome remains unclear, and longevity of the measures questionable.

A Stable Biologically Motivated Learning Mechanism for Visual Feature Extraction to Handle Facial Categorization

The brain mechanism of extracting visual features for recognizing various objects has consistently been a controversial issue in computational models of object recognition. To extract visual features, we introduce a new, biologically motivated model for facial categorization, which is an extension of the Hubel and Wiesel simple-to-complex cell hierarchy. To address the synaptic stability versus plasticity dilemma, we apply the Adaptive Resonance Theory (ART) for extracting informative intermediate level visual features during the learning process, which also makes this model stable against the destruction of previously learned information while learning new information. Such a mechanism has been suggested to be embedded within known laminar microcircuits of the cerebral cortex. To reveal the strength of the proposed visual feature learning mechanism, we show that when we use this mechanism in the training process of a well-known biologically motivated object recognition model (the HMAX model), it performs better than the HMAX model in face/non-face classification tasks. Furthermore, we demonstrate that our proposed mechanism is capable of following similar trends in performance as humans in a psychophysical experiment using a face versus non-face rapid categorization task

Evaluation of host-derived volatiles for trapping Culicoides biting midges (Diptera: Ceratopogonidae)

Culicoides biting midges (Diptera: Ceratopognidae) cause pain and distress through blood feeding, and transmit viruses that threaten both animal and human health worldwide. There are few effective tools for monitoring and control of biting midges, with semiochemical-based strategies offering the advantage of targeting host-seeking populations. In previous studies, we identified the host preference of multiple Culicoides species, including Culicoides impunctatus, as well as cattle-derived compounds that modulate the behavioral responses of C. nubeculosus under laboratory conditions. Here, we test the efficacy of these compounds, when released at different rates, in attracting C. impunctatus under field conditions in Southern Sweden. Traps releasing 1-octen-3-ol, decanal, phenol, 4-methylphenol or 3-propylphenol, when combined with carbon dioxide (CO2), captured significantly higher numbers of C. impunctatus compared to control traps baited with CO2 alone, with low release rates (0.1 mg h−1, 1 mg h−1) being generally more attractive. In contrast, traps releasing octanal or (E)-2-nonenal at 1 mg h−1 and 10 mg h−1 collected significantly lower numbers of C. impunctatus than control traps baited with CO2 only. Nonanal and 2-ethylhexanol did not affect the attraction of C. impunctatus when compared to CO2 alone at any of the release rates tested. The potential use of these semiochemicals as attractants and repellents for biting midge control is discussed

Calibration of myocardial T2 and T1 against iron concentration.

BACKGROUND: The assessment of myocardial iron using T2* cardiovascular magnetic resonance (CMR) has been validated and calibrated, and is in clinical use. However, there is very limited data assessing the relaxation parameters T1 and T2 for measurement of human myocardial iron. METHODS: Twelve hearts were examined from transfusion-dependent patients: 11 with end-stage heart failure, either following death (n=7) or cardiac transplantation (n=4), and 1 heart from a patient who died from a stroke with no cardiac iron loading. Ex-vivo R1 and R2 measurements (R1=1/T1 and R2=1/T2) at 1.5 Tesla were compared with myocardial iron concentration measured using inductively coupled plasma atomic emission spectroscopy. RESULTS: From a single myocardial slice in formalin which was repeatedly examined, a modest decrease in T2 was observed with time, from mean (± SD) 23.7 ± 0.93 ms at baseline (13 days after death and formalin fixation) to 18.5 ± 1.41 ms at day 566 (p<0.001). Raw T2 values were therefore adjusted to correct for this fall over time. Myocardial R2 was correlated with iron concentration [Fe] (R2 0.566, p<0.001), but the correlation was stronger between LnR2 and Ln[Fe] (R2 0.790, p<0.001). The relation was [Fe] = 5081•(T2)-2.22 between T2 (ms) and myocardial iron (mg/g dry weight). Analysis of T1 proved challenging with a dichotomous distribution of T1, with very short T1 (mean 72.3 ± 25.8 ms) that was independent of iron concentration in all hearts stored in formalin for greater than 12 months. In the remaining hearts stored for <10 weeks prior to scanning, LnR1 and iron concentration were correlated but with marked scatter (R2 0.517, p<0.001). A linear relationship was present between T1 and T2 in the hearts stored for a short period (R2 0.657, p<0.001). CONCLUSION: Myocardial T2 correlates well with myocardial iron concentration, which raises the possibility that T2 may provide additive information to T2* for patients with myocardial siderosis. However, ex-vivo T1 measurements are less reliable due to the severe chemical effects of formalin on T1 shortening, and therefore T1 calibration may only be practical from in-vivo human studies

Machine-Part cell formation through visual decipherable clustering of Self Organizing Map

Machine-part cell formation is used in cellular manufacturing in order to process a large variety, quality, lower work in process levels, reducing manufacturing lead-time and customer response time while retaining flexibility for new products. This paper presents a new and novel approach for obtaining machine cells and part families. In the cellular manufacturing the fundamental problem is the formation of part families and machine cells. The present paper deals with the Self Organising Map (SOM) method an unsupervised learning algorithm in Artificial Intelligence, and has been used as a visually decipherable clustering tool of machine-part cell formation. The objective of the paper is to cluster the binary machine-part matrix through visually decipherable cluster of SOM color-coding and labelling via the SOM map nodes in such a way that the part families are processed in that machine cells. The Umatrix, component plane, principal component projection, scatter plot and histogram of SOM have been reported in the present work for the successful visualization of the machine-part cell formation. Computational result with the proposed algorithm on a set of group technology problems available in the literature is also presented. The proposed SOM approach produced solutions with a grouping efficacy that is at least as good as any results earlier reported in the literature and improved the grouping efficacy for 70% of the problems and found immensely useful to both industry practitioners and researchers.Comment: 18 pages,3 table, 4 figure

Simpson's paradox visualized: The example of the Rosiglitazone meta-analysis

<p>Abstract</p> <p>Background</p> <p>Simpson's paradox is sometimes referred to in the areas of epidemiology and clinical research. It can also be found in meta-analysis of randomized clinical trials. However, though readers are able to recalculate examples from hypothetical as well as real data, they may have problems to easily figure where it emerges from.</p> <p>Method</p> <p>First, two kinds of plots are proposed to illustrate the phenomenon graphically, a scatter plot and a line graph. Subsequently, these can be overlaid, resulting in a overlay plot. The plots are applied to the recent large meta-analysis of adverse effects of rosiglitazone on myocardial infarction and to an example from the literature. A large set of meta-analyses is screened for further examples.</p> <p>Results</p> <p>As noted earlier by others, occurrence of Simpson's paradox in the meta-analytic setting, if present, is associated with imbalance of treatment arm size. This is well illustrated by the proposed plots. The rosiglitazone meta-analysis shows an effect reversion if all trials are pooled. In a sample of 157 meta-analyses, nine showed an effect reversion after pooling, though non-significant in all cases.</p> <p>Conclusion</p> <p>The plots give insight on how the imbalance of trial arm size works as a confounder, thus producing Simpson's paradox. Readers can see why meta-analytic methods must be used and what is wrong with simple pooling.</p