Cross-situational learning of object-word mapping using Neural Modeling Fields

The issue of how children learn the meaning of words is fundamental to developmental psychology. The recent attempts to develop or evolve efficient communication protocols among interacting robots or Virtual agents have brought that issue to a central place in more applied research fields, such as computational linguistics and neural networks, as well. An attractive approach to learning an object-word mapping is the so-called cross-situational learning. This learning scenario is based on the intuitive notion that a learner can determine the meaning of a word by finding something in common across all observed uses of that word. Here we show how the deterministic Neural Modeling Fields (NMF) categorization mechanism can be used by the learner as an efficient algorithm to infer the correct object-word mapping. To achieve that we first reduce the original on-line learning problem to a batch learning problem where the inputs to the NMF mechanism are all possible object-word associations that Could be inferred from the cross-situational learning scenario. Since many of those associations are incorrect, they are considered as clutter or noise and discarded automatically by a clutter detector model included in our NMF implementation. With these two key ingredients - batch learning and clutter detection - the NMF mechanism was capable to infer perfectly the correct object-word mapping. (C) 2009 Elsevier Ltd. All rights reserved.

How language can help discrimination in the Neural Modelling Fields framework

The relationship between thought and language and, in particular, the issue of whether and how language influences thought is still a matter of fierce debate. Here we consider a discrimination task scenario to study language acquisition in which an agent receives linguistic input from an external teacher, in addition to sensory stimuli from the objects that exemplify the overlapping categories that make up the environment. Sensory and linguistic input signals are fused using the Neural Modelling Fields (NMF) categorization algorithm. We find that the agent with language is capable of differentiating object features that it could not distinguish without language. In this sense, the linguistic stimuli prompt the agent to redefine and refine the discrimination capacity of its sensory channels. (C) 2007 Elsevier Ltd. All rights reserved.

Trypanosoma cruzi maxicircle heterogeneity in Chagas disease patients from Brazil

The majority of individuals in the chronic phase of Chagas disease are asymptomatic (indeterminate form, IF). Each year, similar to 3% of them develop lesions in the heart or gastrointestinal tract. Cardiomyopathy (CCHD) is the most severe manifestation of Chagas disease. The factors that determine the outcome of the infection are unknown, but certainly depend on complex interactions amongst the genetic make-up of the parasite, the host immunogenetic background and environment. In a previous study we verified that the maxicircle gene NADH dehydrogenase (mitochondrial complex 1) subunit 7 (ND7) from IF isolates had a 455 bp deletion compared with the wild type (WT) ND7 gene from CCHD strains. We proposed that ND7 could constitute a valuable target for PCR assays in the differential diagnosis of the infective strain. In the present study we evaluated this hypothesis by examination of ND7 structure in parasites from 75 patients with defined pathologies, from Southeast Brazil. We also analysed the structure of additional mitochondrial genes (ND4/CR4, COIII and COII) since the maxicircle is used for clustering Trypanosoma cruzi strains into three clades/haplogroups. We conclude that maxicircle genes do not discriminate parasite populations which induce IF or CCHD forms. Interestingly, the great majority of the analysed isolates belong to T cruzi 11 (discrete typing unit, (DTU) IIb) genotype. This scenario is at variance with the prevalence of hybrid (DTU IId) human isolates in Bolivia, Chile and Argentina. The distribution of WT and deleted ND7 and ND4 genes in T cruzi strains suggests that mutations in the two genes occurred in different ancestrals in the T cruzi 11 cluster, allowing the identification of at least three mitochondrial sub-lineages within this group. The observation that T. cruzi strains accumulate mutations in several genes coding for complex I subunits favours the hypothesis that complex I may have a limited activity in this parasite. (C) 2009 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved.

Metabolomic assessment with CE-MS of the nutraceutical effect of Cystoseira spp extracts in an animal model

There is a need of scientific evidence of claimed nutraceutical effects, but also there is a social movement towards the use of natural products and among them algae are seen as rich resources. Within this scenario, the development of methodology for rapid and reliable assessment of markers of efficiency and security of these extracts is necessary. The rat treated with streptozotocin has been proposed as the most appropriate model of systemic oxidative stress for studying antioxidant therapies. Cystoseira is a brown alga containing fucoxanthin and other carothenes whose pressure-assisted extracts were assayed to discover a possible beneficial effect on complications related to diabetes evolution in an acute but short-term model. Urine was selected as the sample and CE-TOF-MS as the analytical technique to obtain the fingerprints in a non-target metabolomic approach. Multivariate data analysis revealed a good clustering of the groups and permitted the putative assignment of compounds statistically significant in the classification. Interestingly a group of compounds associated to lysine glycation and cleavage from proteins was found to be increased in diabetic animals receiving vehicle as compared to control animals receiving vehicle (N6, N6, N6-trimethyl-L-lysine, N-methylnicotinamide, galactosylhydroxylysine, L-carnitine, N6-acetyl-N6-hydroxylysine, fructose-lysine, pipecolic acid, urocanic acid, amino-isobutanoate, formylisoglutamine. Fructoselysine significantly decreased after the treatment changing from a 24% increase to a 19% decrease. CE-MS fingerprinting of urine has provided a group of compounds different to those detected with other techniques and therefore proves the necessity of a cross-platform analysis to obtain a broad view of biological samples.

Clustering Lab value working with medical data

Data mining is a relatively new field of research that its objective is to acquire knowledge from large amounts of data. In medical and health care areas, due to regulations and due to the availability of computers, a large amount of data is becoming available [27]. On the one hand, practitioners are expected to use all this data in their work but, at the same time, such a large amount of data cannot be processed by humans in a short time to make diagnosis, prognosis and treatment schedules. A major objective of this thesis is to evaluate data mining tools in medical and health care applications to develop a tool that can help make rather accurate decisions. In this thesis, the goal is finding a pattern among patients who got pneumonia by clustering of lab data values which have been recorded every day. By this pattern we can generalize it to the patients who did not have been diagnosed by this disease whose lab values shows the same trend as pneumonia patients does. There are 10 tables which have been extracted from a big data base of a hospital in Jena for my work .In ICU (intensive care unit), COPRA system which is a patient management system has been used. All the tables and data stored in German Language database.

A scenario-driven decision support system for serious crime investigation

Consideration of a wide range of plausible crime scenarios during any crime investigation is important to seek convincing evidence and hence to minimize the likelihood of miscarriages of justice. It is equally important for crime investigators to be able to employ effective and efficient evidence-collection strategies that are likely to produce the most conclusive information under limited available resources. An intelligent decision support system that can assist human investigators by automatically constructing plausible scenarios, and reasoning with the likely best investigating actions will clearly be very helpful in addressing these challenging problems. This paper presents a system for creating scenario spaces from given evidence, based on an integrated application of techniques for compositional modelling and Bayesian network-based evidence evaluation. Methods of analysis are also provided by the use of entropy to exploit the synthesized scenario spaces in order to prioritize investigating actions and hypotheses. These theoretical developments are illustrated by realistic examples of serious crime investigation.

Knowledge based crime scenario modelling

A crucial concern in the evaluation of evidence related to a major crime is the formulation of sufficient alternative plausible scenarios that can explain the available evidence. However, software aimed at assisting human crime investigators by automatically constructing crime scenarios from evidence is difficult to develop because of the almost infinite variation of plausible crime scenarios. This paper introduces a novel knowledge driven methodology for crime scenario construction and it presents a decision support system based on it. The approach works by storing the component events of the scenarios instead of entire scenarios and by providing an algorithm that can instantiate and compose these component events into useful scenarios. The scenario composition approach is highly adaptable to unanticipated cases because it allows component events to match the case under investigation in many different ways. Given a description of the available evidence, it generates a network of plausible scenarios that can then be analysed to devise effective evidence collection strategies. The applicability of the ideas presented here are demonstrated by means of a realistic example and prototype decision support software.

Uncertainties In Future Projections Of Extreme Rainfall: The Role Of Climate Model, Emission Scenario And Randomness

Climate change has resulted in substantial variations in annual extreme rainfall quantiles in different durations and return periods. Predicting the future changes in extreme rainfall quantiles is essential for various water resources design, assessment, and decision making purposes. Current Predictions of future rainfall extremes, however, exhibit large uncertainties. According to extreme value theory, rainfall extremes are rather random variables, with changing distributions around different return periods; therefore there are uncertainties even under current climate conditions. Regarding future condition, our large-scale knowledge is obtained using global climate models, forced with certain emission scenarios. There are widely known deficiencies with climate models, particularly with respect to precipitation projections. There is also recognition of the limitations of emission scenarios in representing the future global change. Apart from these large-scale uncertainties, the downscaling methods also add uncertainty into estimates of future extreme rainfall when they convert the larger-scale projections into local scale. The aim of this research is to address these uncertainties in future projections of extreme rainfall of different durations and return periods. We plugged 3 emission scenarios with 2 global climate models and used LARS-WG, a well-known weather generator, to stochastically downscale daily climate models’ projections for the city of Saskatoon, Canada, by 2100. The downscaled projections were further disaggregated into hourly resolution using our new stochastic and non-parametric rainfall disaggregator. The extreme rainfall quantiles can be consequently identified for different durations (1-hour, 2-hour, 4-hour, 6-hour, 12-hour, 18-hour and 24-hour) and return periods (2-year, 10-year, 25-year, 50-year, 100-year) using Generalized Extreme Value (GEV) distribution. By providing multiple realizations of future rainfall, we attempt to measure the extent of total predictive uncertainty, which is contributed by climate models, emission scenarios, and downscaling/disaggregation procedures. The results show different proportions of these contributors in different durations and return periods.