Spectral Frame Analysis and Learning through Graph Structure

This dissertation investigates the connection between spectral analysis and frame theory. When considering the spectral properties of a frame, we present a few novel results relating to the spectral decomposition. We first show that scalable frames have the property that the inner product of the scaling coefficients and the eigenvectors must equal the inverse eigenvalues. From this, we prove a similar result when an approximate scaling is obtained. We then focus on the optimization problems inherent to the scalable frames by first showing that there is an equivalence between scaling a frame and optimization problems with a non-restrictive objective function. Various objective functions are considered, and an analysis of the solution type is presented. For linear objectives, we can encourage sparse scalings, and with barrier objective functions, we force dense solutions. We further consider frames in high dimensions, and derive various solution techniques. From here, we restrict ourselves to various frame classes, to add more specificity to the results. Using frames generated from distributions allows for the placement of probabilistic bounds on scalability. For discrete distributions (Bernoulli and Rademacher), we bound the probability of encountering an ONB, and for continuous symmetric distributions (Uniform and Gaussian), we show that symmetry is retained in the transformed domain. We also prove several hyperplane-separation results. With the theory developed, we discuss graph applications of the scalability framework. We make a connection with graph conditioning, and show the in-feasibility of the problem in the general case. After a modification, we show that any complete graph can be conditioned. We then present a modification of standard PCA (robust PCA) developed by Cand\`es, and give some background into Electron Energy-Loss Spectroscopy (EELS). We design a novel scheme for the processing of EELS through robust PCA and least-squares regression, and test this scheme on biological samples. Finally, we take the idea of robust PCA and apply the technique of kernel PCA to perform robust manifold learning. We derive the problem and present an algorithm for its solution. There is also discussion of the differences with RPCA that make theoretical guarantees difficult.

Modeling height-diameter relationship in pinus pinaster ait. in the forest intervention zone of Lomba, NE-Portugal

In this work, the relationship between diameter at breast height (d) and total height (h) of individual-tree was modeled with the aim to establish provisory height-diameter (h-d) equations for maritime pine (Pinus pinaster Ait.) stands in the Lomba ZIF, Northeast Portugal. Using data collected locally, several local and generalized h-d equations from the literature were tested and adaptations were also considered. Model fitting was conducted by using usual nonlinear least squares (nls) methods. The best local and generalized models selected, were also tested as mixed models applying a first-order conditional expectation (FOCE) approximation procedure and maximum likelihood methods to estimate fixed and random effects. For the calibration of the mixed models and in order to be consistent with the fitting procedure, the FOCE method was also used to test different sampling designs. The results showed that the local h-d equations with two parameters performed better than the analogous models with three parameters. However a unique set of parameter values for the local model can not be used to all maritime pine stands in Lomba ZIF and thus, a generalized model including covariates from the stand, in addition to d, was necessary to obtain an adequate predictive performance. No evident superiority of the generalized mixed model in comparison to the generalized model with nonlinear least squares parameters estimates was observed. On the other hand, in the case of the local model, the predictive performance greatly improved when random effects were included. The results showed that the mixed model based in the local h-d equation selected is a viable alternative for estimating h if variables from the stand are not available. Moreover, it was observed that it is possible to obtain an adequate calibrated response using only 2 to 5 additional h-d measurements in quantile (or random) trees from the distribution of d in the plot (stand). Balancing sampling effort, accuracy and straightforwardness in practical applications, the generalized model from nls fit is recommended. Examples of applications of the selected generalized equation to the forest management are presented, namely how to use it to complete missing information from forest inventory and also showing how such an equation can be incorporated in a stand-level decision support system that aims to optimize the forest management for the maximization of wood volume production in Lomba ZIF maritime pine stands.

Fatores críticos de sucesso como antecedentes da aceitação de um sistema de informação em uma universidade federal

In recent decades the public sector comes under pressure in order to improve its performance. The use of Information Technology (IT) has been a tool increasingly used in reaching that goal. Thus, it has become an important issue in public organizations, particularly in institutions of higher education, determine which factors influence the acceptance and use of technology, impacting on the success of its implementation and the desired organizational results. The Technology Acceptance Model - TAM was used as the basis for this study and is based on the constructs perceived usefulness and perceived ease of use. However, when it comes to integrated management systems due to the complexity of its implementation,organizational factors were added to thus seek further explanation of the acceptance of such systems. Thus, added to the model five TAM constructs related to critical success factors in implementing ERP systems, they are: support of top management, communication, training, cooperation, and technological complexity (BUENO and SALMERON, 2008). Based on the foregoing, launches the following research problem: What factors influence the acceptance and use of SIE / module academic at the Federal University of Para, from the users' perception of teachers and technicians? The purpose of this study was to identify the influence of organizational factors, and behavioral antecedents of behavioral intention to use the SIE / module academic UFPA in the perspective of teachers and technical users. This is applied research, exploratory and descriptive, quantitative with the implementation of a survey, and data collection occurred through a structured questionnaire applied to a sample of 229 teachers and 30 technical and administrative staff. Data analysis was carried out through descriptive statistics and structural equation modeling with the technique of partial least squares (PLS). Effected primarily to assess the measurement model, which were verified reliability, convergent and discriminant validity for all indicators and constructs. Then the structural model was analyzed using the bootstrap resampling technique like. In assessing statistical significance, all hypotheses were supported. The coefficient of determination (R ²) was high or average in five of the six endogenous variables, so the model explains 47.3% of the variation in behavioral intention. It is noteworthy that among the antecedents of behavioral intention (BI) analyzed in this study, perceived usefulness is the variable that has a greater effect on behavioral intention, followed by ease of use (PEU) and attitude (AT). Among the organizational aspects (critical success factors) studied technological complexity (TC) and training (ERT) were those with greatest effect on behavioral intention to use, although these effects were lower than those produced by behavioral factors (originating from TAM). It is pointed out further that the support of senior management (TMS) showed, among all variables, the least effect on the intention to use (BI) and was followed by communications (COM) and cooperation (CO), which exert a low effect on behavioral intention (BI). Therefore, as other studies on the TAM constructs were adequate for the present research. Thus, the study contributed towards proving evidence that the Technology Acceptance Model can be applied to predict the acceptance of integrated management systems, even in public. Keywords: Technology

Acoustic and device feature fusion for load recognition

Appliance-specific Load Monitoring (LM) provides a possible solution to the problem of energy conservation which is becoming increasingly challenging, due to growing energy demands within offices and residential spaces. It is essential to perform automatic appliance recognition and monitoring for optimal resource utilization. In this paper, we study the use of non-intrusive LM methods that rely on steady-state appliance signatures for classifying most commonly used office appliances, while demonstrating their limitation in terms of accurately discerning the low-power devices due to overlapping load signatures. We propose a multi-layer decision architecture that makes use of audio features derived from device sounds and fuse it with load signatures acquired from energy meter. For the recognition of device sounds, we perform feature set selection by evaluating the combination of time-domain and FFT-based audio features on the state of the art machine learning algorithms. Further, we demonstrate that our proposed feature set which is a concatenation of device audio feature and load signature significantly improves the device recognition accuracy in comparison to the use of steady-state load signatures only.

Time since urbanization but not encephalisation is associated with increased tolerance of human proximity in birds

The examination of links between a high degree of encephalisation (i.e., a large brain mass relative to body size) and the capacity of wildlife to inhabit anthropogenic habitats has formed the basis of several recent studies, although typically they have not uncovered any relationship. It, however, remains unclear whether encephalisation is directly related to a species' capacity to develop tolerance to human proximity (i.e., a reduction in response to approaching humans). It is also unknown whether such a relationship is related to the size of specific areas of the brain. Using published data on flight-initiation distance (FID), the distance at which animals flee from an approaching human, we estimate the degree of tolerance of human proximity for 42 bird species by comparing FIDs in urban and rural areas, with relatively high and low exposure to humans, respectively. We used a phylogenetic, comparative approach to analyse the relationship of degree of tolerance, and of FID in urban and rural populations more directly, to relative sizes of whole brains (42 species) and brain components (25 species) for the species, and examine the effect of the year that the bird species was first recorded in an urban area (year of urbanization). We demonstrate an interaction between bird habitat and year of urbanization on FIDs. Urban populations of species that have a longer history of inhabiting urban areas have lower FIDs (i.e., birds that were urbanized earlier are more tolerant), which may suggest local selection for birds with reduced responsiveness to humans in urban areas. The pattern is not seen in rural populations of the same species, providing additional evidence that it is greater exposure to humans that has resulted in this tolerance. While we found that forebrain mass and optic lobe mass are influential positive predictors of FID there was no indication that degree of tolerance itself was related to any brain size metric and hence no support for the idea that urban populations of species with larger brains are better able to habituate to human presence. This suggests that processes other than encephalisation explain the high degree of tolerance evident in urban-dwelling birds.

Internal market orientation and market/oriented behaviours

Purpose – The purpose of this empirical paper is to investigate internal marketing from a behavioural perspective. The impact of internal marketing behaviours, operationalised as an internal market orientation (IMO), on employees' marketing and other in/role behaviours (IRB) were examined. Design/methodology/approach – Survey data measuring IMO, market orientation and a range of constructs relevant to the nomological network in which they are embedded were collected from the UK retail managers. These were tested to establish their psychometric properties and the conceptual model was analysed using structural equations modelling, employing a partial least squares methodology. Findings – IMO has positive consequences for employees' market/oriented and other IRB. These, in turn, influence marketing success. Research limitations/implications – The paper provides empirical support for the long/held assumption that internal and external marketing are related and that organisations should balance their external focus with some attention to employees. Future research could measure the attitudes and behaviours of managers, employees and customers directly and explore the relationships between them. Practical implications – Firm must ensure that they do not put the needs of their employees second to those of managers and shareholders; managers must develop their listening skills and organisations must become more responsive to the needs of their employees. Originality/value – The paper contributes to the scarce body of empirical support for the role of internal marketing in services organisations. For researchers, this paper legitimises the study of internal marketing as a route to external market success; for managers, the study provides quantifiable evidence that focusing on employees' wants and needs impacts their behaviours towards the market. © 2010, Emerald Group Publishing Limited

Examining health literacy disparities in the United States: a third look at the National Assessment of Adult Literacy (NAAL)

BACKGROUND: In the United States, disparities in health literacy parallel disparities in health outcomes. Our research contributes to how diverse indicators of social inequalities (i.e., objective social class, relational social class, and social resources) contribute to understanding disparities in health literacy.

METHODS: We analyze data on respondents 18 years of age and older (N = 14,592) from the 2003 National Assessment of Adult Literacy (NAAL) restricted access data set. A series of weighted Ordinary Least Squares (OLS) regression models estimate the association between respondent's demographic characteristics, socioeconomic status (SES), relational social class, social resources and an Item Response Theory (IRT) based health literacy measure.

RESULTS: Our findings are consistent with previous research on the social and SES determinants of health literacy. However, our findings reveal the importance of relational social status for understanding health literacy disparities in the United States. Objective indicators of social status are persistent and robust indicators of health literacy. Measures of relational social status such as civic engagement (i.e., voting, volunteering, and library use) are associated with higher health literacy levels net of objective resources. Social resources including speaking English and marital status are associated with higher health literacy levels.

CONCLUSIONS: Relational indicators of social class are related to health literacy independent of objective social class indicators. Civic literacy (e.g., voting and volunteering) are predictors of health literacy and offer opportunities for health intervention. Our findings support the notion that health literacy is a social construct and suggest the need to develop a theoretically driven conceptual definition of health literacy that includes a civic literacy component.

Influence of work pressure on proactive skill development in China: the role of career networking behavior and guanxi HRM

This research examines how work pressure influences proactive skill development in the contextof the Chinese workplace. Drawing from the conservation of resources theory, we developa model which argues that career networking behavior serves as the mechanism that allowsemployees to transform work pressure into proactive skill development. We further arguethat in the context of the Chinese workplace, guanxi HRM, which is a culturally-specific workplacepractice deeply-rooted in Chinese tradition, plays a contingency role in influencing theextent to which work pressure influences career networking behavior. We test our modelusing Partial Least Squares Structural Equation Modeling for a sample of employees (N =392) in China. The results show that career networking behavior positively mediates the influenceof work pressure on proactive skill development and that guanxi HRM positively and significantlymoderates the influence of work pressure on career networking behavior. The overallfindings provide empirical support for the relevance of contextual and motivational factors inexplaining employee proactive skill development. The theoretical and practical implicationsof the findings are fully discussed.

Influencing girls' interest in information technology

The lack of skilled labour that can support digital economies is a worldwide problem, exacerbated by the lack of female participation in the Information Technology (IT) industry. Interventions that promote IT study and career opportunities for girls can be a powerful means to counter the ongoing decline in IT interest amongst females. However, the impact of such interventions is rarely being evaluated. This study is, therefore, aimed at gaining insights into the influence on IT career perceptions of one IT intervention event for secondary school girls conducted in Australia in 2014. The analysis of comprehensive survey data (n=781) using the Partial Least Squares and other statistical techniques revealed that a positive attitude toward the analysed event had significant direct and indirect positive impacts on girls' intentions to choose a career in IT. The indirect influence was supported by such mediating factors as girls' self-efficacy towards IT careers and their intentions to learn more about IT, which the event affected directly. In addition to reporting on the results of testing the research model, further insights into girls' opinions and attitudes towards the intervention and IT in general are provided in the paper.

Aplicaciones de los modelos de predicción bio-inspirados en la administración

Las organizaciones y sus entornos son sistemas complejos. Tales sistemas son difíciles de comprender y predecir. Pese a ello, la predicción es una tarea fundamental para la gestión empresarial y para la toma de decisiones que implica siempre un riesgo. Los métodos clásicos de predicción (entre los cuales están: la regresión lineal, la Autoregresive Moving Average y el exponential smoothing) establecen supuestos como la linealidad, la estabilidad para ser matemática y computacionalmente tratables. Por diferentes medios, sin embargo, se han demostrado las limitaciones de tales métodos. Pues bien, en las últimas décadas nuevos métodos de predicción han surgido con el fin de abarcar la complejidad de los sistemas organizacionales y sus entornos, antes que evitarla. Entre ellos, los más promisorios son los métodos de predicción bio-inspirados (ej. redes neuronales, algoritmos genéticos /evolutivos y sistemas inmunes artificiales). Este artículo pretende establecer un estado situacional de las aplicaciones actuales y potenciales de los métodos bio-inspirados de predicción en la administración.

Does chemometrics enhance the performance of electroanalysis?

This review explores the question whether chemometrics methods enhance the performance of electroanalytical methods. Electroanalysis has long benefited from the well-established techniques such as potentiometric titrations, polarography and voltammetry, and the more novel ones such as electronic tongues and noses, which have enlarged the scope of applications. The electroanalytical methods have been improved with the application of chemometrics for simultaneous quantitative prediction of analytes or qualitative resolution of complex overlapping responses. Typical methods include partial least squares (PLS), artificial neural networks (ANNs), and multiple curve resolution methods (MCR-ALS, N-PLS and PARAFAC). This review aims to provide the practising analyst with a broad guide to electroanalytical applications supported by chemometrics. In this context, after a general consideration of the use of a number of electroanalytical techniques with the aid of chemometrics methods, several overviews follow with each one focusing on an important field of application such as food, pharmaceuticals, pesticides and the environment. The growth of chemometrics in conjunction with electronic tongue and nose sensors is highlighted, and this is followed by an overview of the use of chemometrics for the resolution of complicated profiles for qualitative identification of analytes, especially with the use of the MCR-ALS methodology. Finally, the performance of electroanalytical methods is compared with that of some spectrophotometric procedures on the basis of figures-of-merit. This showed that electroanalytical methods can perform as well as the spectrophotometric ones. PLS-1 appears to be the method of practical choice if the %relative prediction error of not, vert, similar±10% is acceptable.

Small molecule–biopolymer interactions : Ultraviolet–visible and fluorescence spectroscopy and chemometrics

Interactions between small molecules with biopolymers e.g. the bovine serum albumin (BSA protein), are important, and significant information is recorded in the UV–vis and fluorescence spectra of their reaction mixtures. The extraction of this information is difficult conventionally and principally because there is significant overlapping of the spectra of the three analytes in the mixture. The interaction of berberine chloride (BC) and the BSA protein provides an interesting example of such complex systems. UV–vis and fluorescence spectra of BC and BSA mixtures were investigated in pH 7.4 Tris–HCl buffer at 37 °C. Two sample series were measured by each technique: (1) [BSA] was kept constant and the [BC] was varied and (2) [BC] was kept constant and the [BSA] was varied. This produced four spectral data matrices, which were combined into one expanded spectral matrix. This was processed by the multivariate curve resolution–alternating least squares method (MCR–ALS). The results produced: (1) the extracted pure BC, BSA and the BC–BSA complex spectra from the measured heavily overlapping composite responses, (2) the concentration profiles of BC, BSA and the BC–BSA complex, which are difficult to obtain by conventional means, and (3) estimates of the number of binding sites of BC.

Simultaneous determination of three synthetic glucocorticoids by differential pulse stripping voltammetry with the aid of chemometrics

A novel voltammetric method for simultaneous determination of the glucocorticoid residues prednisone, prednisolone, and dexamethasone was developed. All three compounds were reduced at a mercury electrode in a Britton-Robinson buffer (pH 3.78), and well-defined voltammetric waves were observed. However, the voltammograms of these three compounds overlapped seriously and showed nonlinear character, and thus, it was difficult to analyze the compounds individually in their mixtures. In this work, two chemometrics methods, principal component regression (PCR) and partial least squares (PLS), were applied to resolve the overlapped voltammograms, and the calibration models were established for simultaneous determination of these compounds. Under the optimum experimental conditions, the limits of detection (LOD) were 5.6, 8.3, and 16.8 µg l-1 for prednisone, prednisolone, and dexamethasone, respectively. The proposed method was also applied for the determination of these glucocorticoid residues in the rabbit plasma and human urine samples with satisfactory results.