Nested by linear cointegration first provided in Granger (1981), the definition of nonlinear cointegration is presented in this paper. Sequentially, a nonlinear cointegrated economic system is introduced. What we mainly study is testing no nonlinear cointegration against nonlinear cointegration by residual-based test, which is ready for detecting stochastic trend in nonlinear autoregression models. We construct cointegrating regression along with smooth transition components from smooth transition autoregression model. Some properties are analyzed and discussed during the estimation procedure for cointegrating regression, including description of transition variable. Autoregression of order one is considered as the model of estimated residuals for residual-based test, from which the teststatistic is obtained. Critical values and asymptotic distribution of the test statistic that we request for different cointegrating regressions with different sample sizes are derived based on Monte Carlo simulation. The proposed theoretical methods and models are illustrated by an empirical example, comparing the results with linear cointegration application in Hamilton (1994). It is concluded that there exists nonlinear cointegration in our system in the final results.

The previous research have found that consumers' choices of vacations may be significantly influenced by mental imagery processing, which is considered to be high elaboration cognitive processing. Mental imagery is defined as an experience that significantly resembles the experience of perceiving some object, event, or scene, but which occurs in the absence of the appropriate stimuli for the relevant object, event, or scene. This study first aims to identify imagery-eliciting Web site features and second, to test their influence on persuasion-related outcomes such as attitude strength, confidence, and attitude resistance. Finally, this study investigates the role of individual processing style (e.g., visualizer or verbalizer) as a moderator variable. A total of 252 subjects participated in a Web-based experiment to examine the influence of selected Web site features on individual imagery processing and its effect on consumers’ attitudes and expectations. It involved a 2 (narrative vs. expository text) × 2 (pictures vs. no picture) × 2 (sounds vs. no sound) full factorial between-subjects design. The data was analyzed primarily using a structural equation modeling methodology. Structural model results revealed that the mental imagery construct strongly influenced the communication effects, which were represented by attitude strength and attitude confidence. In addition, the results of the study found that the communication effects had a significant impact on attitude resistance. This implies that the stronger attitude creates stronger resistance to a negative impact. In the context of the influence of Web site features (e.g., narrative text, pictures, and sound) inducing mental imagery processing, only pictures have a significant effect on mental imagery processing, which support positive effects of concrete pictures on mental imagery processing. In conclusion, the findings of this study show that mental imagery processing is important. Thus, we need to continue to investigate what forms of Web site designs and features best support imagery processing. More specifically, tourism marketers need to understand how certain stimuli influence mental imagery processing, and then they need to enhance Web site designs to capture potential customers.

International tourist hotel industry has becoming a professional management domain nowadays. Due to the increasingly fierce competition, hotels must develop ways to attract customers by meeting their market requirements and preferences, rather than waiting passively for the customers to come. With data mining technology, the hotels can facilitate the discovered characteristics of potential customers to make the right marketing strategies and decisions by targeting at specific groups of customers. Behind the consumers¡¦ behaviors, there are usually indicators for special consuming requirements. However, by browsing the business transaction data, one can usually learn only the consuming requirement volume and is unable to determine the implied and hidden information. This research makes use of data mining technology to explore the customers¡¦ historical data. Specifically, it applies the discovered decision rule to investigate and validate the characteristics of potentially customers ¡X customers who are more likely to book rooms of higher rate. We apply the data mining techniques to the transactional data of a hotel, collected over three years. Our research reveals that there exist characteristics rules for the potential customers and these rules do not change abruptly over the years. The application of these rules to target advertising in hotel domain is verified using the hotel transaction data collected in the subsequent year. The result shows that by targeting at customers of the discovered characteristics rules, higher response rate can be achieved.

Advances in hardware and pattern recognition techniques, along with the widespread utilization of remote sensing satellites, have urged the development of automatic target detection systems. Automatic detection of airports is particularly essential, due to the strategic importance of these targets. In this thesis, a detection method is proposed for airport runways, which is the most distinguishing element of an airport. This method, which operates on large optical satellite images, is composed of a segmentation process based on textural properties, and a runway shape detection stage. In the segmentation process, several local textural features are extracted including not only low level features such as mean, standard deviation of image intensity and gradient, but also Zernike Moments, Circular-Mellin Features, Haralick Features, as well as features involving Gabor Filters, Wavelets and Fourier Power Spectrum Analysis. Since the subset of the mentioned features, which have a role in the discrimination of airport runways from other structures and landforms, cannot be predicted, Adaboost learning algorithm is employed for both classification and determining the feature subset, due to its feature selector nature. By means of the features chosen in this way, a coarse representation of possible runway locations is obtained, as a result of the segmentation operation. Subsequently, the runway shape detection stage, based on a novel form of Hough Transform, is performed over the possible runway locations, in order to obtain final runway positions. The proposed algorithm is examined with experimental work using a comprehensive data set consisting of large and high resolution satellite images and successful results are achieved.

In this study, an automated building extraction system, which is capable of detecting buildings from satellite images using only RGB color band is implemented. The approach used in this work has four main steps: local feature extraction, feature selection, classification and post processing. There are many studies in literature that deal with the same problem. The main issue is to find the most suitable features to distinguish a building. This work presents a feature selection scheme that is connected with the classification framework of Adaboost. As well as Adaboost, four SVM kernels are used for classification. Detailed analysis regarding window type and size, feature type, feature selection, feature count and training set is done for determining the optimal parameters for the classifiers. A detailed comparison of SVM and Adaboost is done based on pixel and object performances and the results obtained are presented both numerically and visually. It is observed that SVM performs better if quadratic kernel is used than the cases using linear, RBF or polynomial kernels. SVM performance is better if features are selected either by Adaboost or by considering errors obtained on histograms of features. The performance obtained by quadratic kernel SVM operated on Adaboost selected features is found to be 38% in terms of pixel based performance criteria quality percentage and 48% in terms object based performance criteria correct detection with building detection threshold 0.4. Adaboost performed better than SVM resulting in 43% quality percentage and 67% correct detection with the same threshold.

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<p>Semantic memory is part of the long-term memory system, and there are several theories concerning this type of memory. Some of these will be described in this essay. There are also several types of neuropsychological semantic memory deficits. For example, test results have shown that patients tend to have more difficulties naming living than nonliving things, and one probable explanation is that living things are more dependent on sensory than on functional features. Description of concrete concepts is a new test of semantic memory, in which cueing is used, both to capture the maximum performance of patients, and to give insight into the access versus storage problem. The theoretical ideas and empirical results relating to this new test will be described in detail. Furthermore, other tests of semantic memory that have been commonly used will also be briefly described. In conclusion semantic memory is a complex cognitive system that needs to be studied further.</p>

Title from PDF of title page. / Document formatted into pages; contains 63 pages. / Thesis (M.S.C.S.)--University of South Florida, 2003. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: The work presented here is an important component of an on going project of developing an automated mass classification system for breast cancer screening and diagnosis for Digital Mammogram applications. Specifically, in this work the task of automatically separating mass tissue from normal breast tissue given a region of interest in a digitized mammogram is investigated. This is the crucial stage in developing a robust automated classification system because the classification depends on the accurate assessment of the tumor-normal tissue border as well as information gathered from the tumor area. In this work the Expectation Maximization (EM) method is developed and applied to high resolution digitized screen-film mammograms with the aim of segmenting normal tissue from mass tissue. / ABSTRACT: Both the raw data and summary data generated by Laws' texture analysis are investigated. Since the ultimate goal is robust classification, the merits of the tissue segmentation are assessed by its impact on the overall classification performance. Based on the 300 image dataset consisting of 97 malignant and 203 benign cases, a 63% sensitivity and 89% specificity was achieved. Although, the segmentation requires further investigation, the development and related computer coding of the EM algorithm was successful. The method was developed to take in account the input feature correlation. This development allows other researchers at this facility to investigate various input features without having the intricate understanding of the EM approach. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.

One chemoautotrophic origin of life theory posits the abiotic formation of alkyl thiols as an initial step to forming biomolecules and eventually a simple chemoautotrophic cell. The premise of this theory is that a recurring reaction on the charged surfaces of pyrite served as a primordial metabolism analogous to the reductive acetyl-CoA pathway (Wächtershäuser 1988) that was later enveloped by a primitive cellular membrane. Alkyl thiols have not previously been identified in terrestrial hot springs as unequivocally abiogenic, but they have been produced in the laboratory under hydrothermal conditions in the presence of a catalyst. I analyzed the dissolved gas content of several hot springs and conducted sterile laboratory experiments in order to evaluate the abiogenic formation of methanethiol (CH3SH), the simplest of the alkyl thiols. Specifically of interest was Cinder Pool, an acid-sulfate-chloride hot spring in Yellowstone National Park. This spring is unusual in that it contains a subaqueous molten sulfur layer (~18 m depth) and thousands of iron- vii sulfur-spherules floating on the surface, which are created by gas bubbling through the molten floor of the spring. This material could potentially serve as a reactive and catalytic surface for abiogenic CH3SH formation in Cinder Pool. Gas samples were collected from Cinder Pool and an adjacent hydrothermal feature in fall of 2011 using the bubble strip method. Two samples contained measurable quantities of CH3SH and other organic sulfur gases, with concentrations of all gases generally higher at the bottom of the pool. Laboratory microcosm experiments were conducted to replicate these findings in a sterile environment. Analog Cinder Pool water was injected into serum bottles containing different iron-sulfur compounds, including cinders collected from the pool itself, as catalytic surfaces for the CH3SH generating reaction. The bottles were then charged with hydrogen (H2), carbon dioxide (CO2), and carbon disulfide (CS2) as reaction gases and incubated for a week at temperatures between 60 and 100oC. Bottles used either powdered FeS, FeS2 (pyrite) or cinder material as a catalytic surface, and all of these surfaces were capable of catalyzing CH3SH formation. In bottles without imposed CS2, however, cinder material was the only surface that produced any detectable CH3SH. While CH3SH is central to the autotroph-first theory and has been synthesized in the laboratory (e.g. Heinen and Lauwers 1996), it has not previously been observed to form abiotically in natural systems. I have identified CH3SH in a natural hydrothermal feature where it is unlikely to have formed secondary to microbial activity, and I have duplicated these field findings in sterile laboratory experiments using the cinders as a reactive surface for formation. / text

For over half of a century, social and behavioral scientists have been investigating the construct of psychopathy. Even so, psychopathy is still a highly misunderstood personality construct. Even though it has been estimated that psychopaths make up only about 1% of the general population, they are believed to consist of 15-25% of the prison population (Hare, 1996). However, not all psychopaths are in prison. Psychopaths can also be found in such fields as the legal profession, the business world and in politics (Babiak, 1995). In terms of criminal behaviors, psychopaths are arrested at earlier ages, have a higher rate of offending, commit a wider array of offenses, are more likely to have used weapons and threatened violence, and are more likely to have used violence (Hart and Hare, 1997; Hare and McPherson, 1984; Serin, 1991; Wong, 1985). Also, once released from an institution, rates of recidivism for psychopaths are found to be higher than those for other criminals regarding both violent and FTSnon-violent criminal acts (Hemphill, Hare & Wong, 1998). Therefore, the societal importance of the psychopathy construct demanded that more research be conducted to better understand its underlying etiology, potential variants in typology, clinical course and potential treatment. Prior theories have proposed subtypes of psychopathy based on cognitive variables (passive avoidance errors) and on physiological variables (BIS/BAS) and on environmental variables (supportive upbringing or not). This study utilized self-report measures to assess the presence of psychopathy and to test the validity of the cognitive and physiological explanations for subtypes of psychopathy. A cognitive dissonance task tested the validity of the physiological theory and an alteration of a punishment task which increases the degree and strength of punishment tested the cognitive theory. Further, for the first time the construct of optimism was tested to determine it's role in parsing out two types of psychopathy.