The N250r is a face-sensitive event-related potential (ERP) deflection whose long-term memory sensitivity remains uncertain. We investigated the possibility that long-term memory-related voltage changes are represented in the early ERP's to faces but methodological considerations could affect how these changes appear to be manifested. We examined the effects of two peak analysis procedures in the assessment of the memory-sensitivity of the N250r elicited in an old/new recognition paradigm using analysis of variance (ANOVA) and artificial neural networks (ANN's). When latency was kept constant within subjects, ANOVA was unable to detect differences between ERP's to remembered and new faces; however, an ANN was. Network interpretation suggested that the ANN was detecting amplitude differences at occipitotemporal and frontocentral sites corresponding to the N250r. When peak latency was taken into account, ANOVA detected a significant decrease in onset latency of the N250r to remembered faces and amplitude differences were not detectable, even with an ANN. Results suggest that the N250r is sensitive to long-term memory. This effect may be a priming phenomenon that is attenuated at long lags between faces. Choice of peak analysis procedures is critical to the interpretation of phasic memory effects in ERP data.
In bare soils of semi-arid areas, surface crusting is a rather common phenomenon due to the impact of raindrops. Water infiltration measurements under ponding conditions are becoming largely applied techniques for an approximate characterization of crusted soils. In this study, the impact of crusting on soil hydraulic conductivity was assessed in a Mediterranean vineyard (western Sicily, Italy) under conventional tillage. The BEST (Beerkan Estimation of Soil Transfer parameters) algorithm was applied to the infiltration data to obtain the hydraulic conductivity of crusted and uncrusted soils. Soil hydraulic conductivity was found to vary during the year and also spatially (i.e., rows vs. interrows) due to crusting, tillage and vegetation cover. A 55 mm rainfall event resulted in a decrease of the saturated soil hydraulic conductivity, Ks, by a factor of 1.6 in the inter-row areas, due to the formation of a crusted layer at the surface. The same rainfall event did not determine a Ks reduction in the row areas (i.e., Ks decreased by a non-significant factor of 1.05) because the vegetation cover intercepted the raindrops and therefore prevented alteration of the soil surface. The
developed ring insertion methodology on crusted soil, implying pre-moistening through the periphery of the sampled surface, together with the very small insertion depth of the ring (0.01 m), prevented visible fractures. Consequently, Beerkan tests carried out along and between the vine-rows and data analysis by the BEST algorithm allowed to assess crusting-dependent reductions in hydraulic conductivity with extemporaneous measurements alone. The reliability of the tested technique was also confirmed by the results of the numerical simulation of the infiltration process in a crusted soil. Testing the Beerkan infiltration run in other crusted soils and establishing comparisons with other experimental methodologies
appear advisable to increase confidence on the reliability of the method that seems suitable for simple characterization of crusted soils.
The mobile robot path planning involves finding the shortest and least difficult path from a start to a goal position in a given environment without collisions with known obstacles.
The main idea of case-based reasoning (CBR) is a presumption that similar tasks probably also have similar solutions. New tasks are solved by adapting old proved solutions of similar tasks to new conditions. Tasks and their solutions (cases) are stored in a case base.
The focal point of this paper is the proposition of a path planning method based on CBR combined with graph algorithms in the environment represented by a rectangular grid. On the basis of the experimental results obtained, it is possible to say that case-based reasoning can significantly save computation costs, particularly in large environments. and Obsahuje seznam literatury
One broad-leaved pioneer tree, Alnus formosana, two broad-leaved understory shrubs, Ardisia crenata and Ardisia cornudentata, and four ferns with different light adaptation capabilities (ranked from high to low, Pyrrosia lingus, Asplenium antiquum, Diplazium donianum, Archangiopteris somai) were used to elucidate the light responses of photosynthetic rate and electron transport rate (ETR). Pot-grown materials received up to 3 levels of light intensity, i.e., 100%, 50% and 10% sunlight. Both gas exchange and chlorophyll (Chl) fluorescence were measured simultaneously by an equipment under constant temperature and 7 levels (0-2,000 μmol m-2 s-1) of photosynthetic photon flux density (PPFD). Plants adapted to-or acclimated to high light always had higher
light-saturation point and maximal photosynthetic rate. Even materials had a broad range of photosynthetic capacity [maximal photosynthetic rate ranging from 2 to 23 μmol(CO2) m-2 s-1], the ratio of ETR to gross photosynthetic rate (PG) was close for A. formosana and the 4 fern species when measured under constant temperature, but the PPFD varied. In addition, P. lingus and A. formosana grown under 100% sunlight and measured at different seasonal temperatures (15, 20, 25, and 30°C) showed increased ETR/P G ratio with increasing temperature and could be fitted by first- and second-order equations, respectively. With this equation, estimated and measured PG were closely correlated (r2 = 0.916 and r2 = 0.964 for P. lingus and A. formosana, respectively, p<0.001). These equations contain only the 2 easily obtained dynamic indicators, ETR and leaf temperature. Therefore, for some species with near ETR/PG ratio in differential levels of PPFD, these equations could be used to simulate dynamic variation of leaf scale photosynthetic rate under different temperature and PPFD conditions., S.-L.. Wong ... [et al.]., and Obsahuje bibliografii
Relations between two Boolean attributes derived from data can be
quantified by truth functions defined on four-fold tables corresponding to pairs of the attributes. Several classes of such quantifiers (implicational, double implicational, equivalence ones) with truth values in the unit interval were investigated in the frame of the theory of data mining methods. In the fuzzy logic theory, there are well-defined classes of fuzzy operators, namely t-norms representing various types of evaluations of fuzzy conjunction (and t-conorms representing fuzzy disjunction), and operators of fuzzy implications.
In the contribution, several types of constructions of quantifiers using fuzzy operators are described. Definitions and theorems presented by the author in previous contributions to WUPES workshops are summarized and illustrated by examples of well-known quantifiers and operators.
There is growing interest to analyze electroencephalogram (EEG) signals with the objective of classifying schizophrenic patients from the control subjects. In this study, EEG signals of 15 schizophrenic patients and 19 age-matched control subjects are recorded using twenty surface electrodes. After the preprocessing phase, several features including autoregressive (AR) model coefficients, band power and fractal dimension were extracted from their recorded signals. Three classifiers including Linear Discriminant Analysis (LDA), Multi-LDA (MLDA) and Adaptive Boosting (Adaboost) were implemented to classify the EEG features of schizophrenic and normal subjects. Leave-one (participant)-out cross validation is performed in the training phase and finally in the test phase; the results of applying the LDA, MLDA and Adaboost respectively provided 78%, 81% and 82% classification accuracies between the two groups. For further improvement, Genetic Programming (GP) is employed to select more informative features and remove the redundant ones. After applying GP on the feature vectors, the results are remarkably improved so that the classification rate of the two groups with LDA, MLDA and Adaboost classifiers yielded 82%, 84% and 93% accuracies, respectively.
Several algorithms have been developed for time series forecasting. In this paper, we develop a type of algorithm that makes use of the numerical methods for optimizing on objective function that is the Kullbak-Leibler divergence between the joint probability density function of a time series xi, X2, Xn and the product of their marginal distributions. The Grani-charlier expansion is ušed for estimating these distributions.
Using the weights that have been obtained by the neural network, and adding to them the Kullback-Leibler divergence of these weights, we obtain new weights that are ušed for forecasting the new value of Xn+k.
The paper deals with application of MF-ARTMAP neural network on
financial fraud data. The focus was on classification of data into 5 types of fraud based on expert knowledge with the aim to achieve the tool with highest classification accuracy. The fraud was characterized by 22 features and the verbal features were encoded into numerical values to be able to use them in the classification proceduře. The results show that in the čase of sufficient data (fraud) representation neural networks could be used with success; in case there are rather small examples, expert generated rules are preferred.
Hospitals must index each case of inpatient medical care with codes from the International Classification of Diseases, 9th Revision (ICD-9), under regulations from the Bureau of National Health Insurance. This paper aims to investigate the analysis of free-textual clinical medical diagnosis documents with ICD-9 codes using state-of-the-art techniques from text and visual mining fields. In this paper, ViSOM and SOM approaches inspire several analyses of clinical diagnosis records with ICD-9 codes. ViSOM and SOM are also used to obtain interesting patterns that have not been discovered with traditional, nonvisual approaches. Furthermore, we addressed three principles that can be used to help clinical doctors analyze diagnosis records effectively using the ViSOM and SOM approaches. The experiments were conducted using real diagnosis records and show that ViSOM and SOM are helpful for organizational decision-making activities.
Credit risk assessment, credit scoring and loan applications approval are one of the typical tasks that can be performed using machine learning or data mining techniques. From this viewpoint, loan applications evaluation is a classification task, in which the final decision can be either a crisp yes/no decision about the loan or a numeric score expressing the financial standing of the applicant. The knowledge to be used is inferred from data about past decisions. These data usually consist off both socio-demographic and economic characteristics of the applicant (e.g., age, income, and deposit), the characteristics of the loan, and the loan approval decision. A number of machine learning algorithms can be used for this purpose. In this paper we show how this task can be performed using the LISp- Miner system, a tool that is under development at the University of Economics, Prague. LISp-Miner is primarily focused on mining for various types of association rules, but unlike "classical" association rules proposed by Agrawal, LISp-Miner in- troduces a greater variety of different types of relations between the left-hand and right-hand sides of a rule. Two other procedures that can be used for classification task are implemented in LISp-Miner as well. We describe the 4ft-Miner and KEX procedures and show how they can be used to analyze data related to loan applications. We also compare the results obtained using the presented algorithms with results from standard rule-learning methods.