Showing papers on "Context (language use) published in 2010"

A basic introduction to fixed‐effect and random‐effects models for meta‐analysis

Abstract: There are two popular statistical models for meta-analysis, the fixed-effect model and the random-effects model. The fact that these two models employ similar sets of formulas to compute statistics, and sometimes yield similar estimates for the various parameters, may lead people to believe that the models are interchangeable. In fact, though, the models represent fundamentally different assumptions about the data. The selection of the appropriate model is important to ensure that the various statistics are estimated correctly. Additionally, and more fundamentally, the model serves to place the analysis in context. It provides a framework for the goals of the analysis as well as for the interpretation of the statistics. In this paper we explain the key assumptions of each model, and then outline the differences between the models. We conclude with a discussion of factors to consider when choosing between the two models. Copyright © 2010 John Wiley & Sons, Ltd.

Adapting visual category models to new domains

Abstract: Domain adaptation is an important emerging topic in computer vision. In this paper, we present one of the first studies of domain shift in the context of object recognition. We introduce a method that adapts object models acquired in a particular visual domain to new imaging conditions by learning a transformation that minimizes the effect of domain-induced changes in the feature distribution. The transformation is learned in a supervised manner and can be applied to categories for which there are no labeled examples in the new domain. While we focus our evaluation on object recognition tasks, the transform-based adaptation technique we develop is general and could be applied to nonimage data. Another contribution is a new multi-domain object database, freely available for download. We experimentally demonstrate the ability of our method to improve recognition on categories with few or no target domain labels and moderate to large changes in the imaging conditions.

Identification of influential spreaders in complex networks

Abstract: Spreading of information, ideas or diseases can be conveniently modelled in the context of complex networks. An analysis now reveals that the most efficient spreaders are not always necessarily the most connected agents in a network. Instead, the position of an agent relative to the hierarchical topological organization of the network might be as important as its connectivity.

A general approach to causal mediation analysis.

Abstract: Traditionally in the social sciences, causal mediation analysis has been formulated, understood, and implemented within the framework of linear structural equation models. We argue and demonstrate that this is problematic for 3 reasons: the lack of a general definition of causal mediation effects independent of a particular statistical model, the inability to specify the key identification assumption, and the difficulty of extending the framework to nonlinear models. In this article, we propose an alternative approach that overcomes these limitations. Our approach is general because it offers the definition, identification, estimation, and sensitivity analysis of causal mediation effects without reference to any specific statistical model. Further, our approach explicitly links these 4 elements closely together within a single framework. As a result, the proposed framework can accommodate linear and nonlinear relationships, parametric and nonparametric models, continuous and discrete mediators, and various types of outcome variables. The general definition and identification result also allow us to develop sensitivity analysis in the context of commonly used models, which enables applied researchers to formally assess the robustness of their empirical conclusions to violations of the key assumption. We illustrate our approach by applying it to the Job Search Intervention Study. We also offer easy-to-use software that implements all our proposed methods.

Rethinking the concept of acculturation: implications for theory and research.

Abstract: This article presents an expanded model of acculturation among international migrants and their immediate descendants. Acculturation is proposed as a multidimensional process consisting of the confluence among heritage-cultural and receiving-cultural practices, values, and identifications. The implications of this reconceptualization for the acculturation construct, as well as for its relationship to psychosocial and health outcomes, are discussed. In particular, an expanded operationalization of acculturation is needed to address the "immigrant paradox," whereby international migrants with more exposure to the receiving cultural context report poorer mental and physical health outcomes. We discuss the role of ethnicity, cultural similarity, and discrimination in the acculturation process, offer an operational definition for context of reception, and call for studies on the role that context of reception plays in the acculturation process. The new perspective on acculturation presented in this article is intended to yield a fuller understanding of complex acculturation processes and their relationships to contextual and individual functioning.

Database access system

Abstract: An improved human user computer interface system, wherein a user characteristic or set of characteristics, such as demographic profile or societal “role”, is employed to define a scope or domain of operation. The operation itself may be a database search, to interactively define a taxonomic context for the operation, a business negotiation, or other activity. After retrieval of results, a scoring or ranking may be applied according to user define criteria, which are, for example, commensurate with the relevance to the context, but may be, for example, by date, source, or other secondary criteria. A user profile is preferably stored in a computer accessible form, and may be used to provide a history of use, persistent customization, collaborative filtering and demographic information for the user. Advantageously, user privacy and anonymity is maintained by physical and algorithmic controls over access to the personal profiles, and releasing only aggregate data without personally identifying information or of small groups.

Systematic Reviews in Health Care: Meta‐analysis in Context

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The environment and schizophrenia

Abstract: Psychotic syndromes can be understood as disorders of adaptation to social context. Although heritability is often emphasized, onset is associated with environmental factors such as early life adversity, growing up in an urban environment, minority group position and cannabis use, suggesting that exposure may have an impact on the developing 'social' brain during sensitive periods. Therefore heritability, as an index of genetic influence, may be of limited explanatory power unless viewed in the context of interaction with social effects. Longitudinal research is needed to uncover gene-environment interplay that determines how expression of vulnerability in the general population may give rise to more severe psychopathology.

High-temperature superconductivity in iron-based materials

Abstract: The surprising discovery of high-temperature superconductivity in a material containing a strong magnet—iron—has led to thousands of publications. By placing all the data in context, it becomes clear what we know and where we are headed.

Systems and methods for responding to natural language speech utterance

Abstract: Systems and methods are provided for receiving speech and non-speech communications of natural language questions and/or commands, transcribing the speech and non-speech communications to textual messages, and executing the questions and/or commands. The invention applies context, prior information, domain knowledge, and user specific profile data to achieve a natural environment for one or more users presenting questions or commands across multiple domains. The systems and methods creates, stores and uses extensive personal profile information for each user, thereby improving the reliability of determining the context of the speech and non-speech communications and presenting the expected results for a particular question or command.

Socioeconomic status and the brain: mechanistic insights from human and animal research

Abstract: Socioeconomic status (SES) influences brain development. Farah and colleagues discuss evidence that prenatal factors, parent–child interactions and cognitive stimulation mediate this effect, and consider implications for alleviating SES-related disparities in mental health and academic achievement. Human brain development occurs within a socioeconomic context and childhood socioeconomic status (SES) influences neural development — particularly of the systems that subserve language and executive function. Research in humans and in animal models has implicated prenatal factors, parent–child interactions and cognitive stimulation in the home environment in the effects of SES on neural development. These findings provide a unique opportunity for understanding how environmental factors can lead to individual differences in brain development, and for improving the programmes and policies that are designed to alleviate SES-related disparities in mental health and academic achievement.

A Unified Theory of Development: A Dialectic Integration of Nature and Nurture

Abstract: The understanding of nature and nurture within developmental science has evolved with alternating ascendance of one or the other as primary explanations for individual differences in life course trajectories of success or failure. A dialectical perspective emphasizing the interconnectedness of individual and context is suggested to interpret the evolution of developmental science in similar terms to those necessary to explain the development of individual children. A unified theory of development is proposed to integrate personal change, context, regulation, and representational models of development.

Testing for Factorial Invariance in the Context of Construct Validation

Abstract: This article describes the logic and procedures behind testing for factorial invariance across groups in the context of construct validation. The procedures include testing for configural, measurement, and structural invariance in the framework of multiple-group confirmatory factor analysis (CFA). The forward (sequential constraint imposition) approach to testing for factorial invariance is described and illustrated for the cases of first-order and second-order CFA models; computer codes in Mplus are provided. Computations of the Satorra—Bentler scaled chi-square difference, used in testing for factorial invariance under lack of multivariate normality, are also described. Some points of caution related to testing and interpreting measurement invariance are provided as well.

Chapter 29 Evaluation of Structural Equation Models Using the Partial Least Squares (PLS) Approach

Abstract: This paper gives a basic comprehension of the partial least squares approach. In this context, the aim of this paper is to develop a guide for the evalu- ation of structural equation models, using the current statistical methods method- ological knowledge by specifically considering the Partial-Least-Squares (PLS) approach's requirements. As an advantage, the PLS method demands significantly fewer requirements compared to that of covariance structure analyses, but never- theless delivers consistent estimation results. This makes PLS a valuable tool for testing theories. Another asset of the PLS approach is its ability to deal with forma- tive as well as reflective indicators, even within one structural equation model. This indicates that the PLS approach is appropriate for explorative analysis of structural equation models, too, thus offering a significant contribution to theory development. However, little knowledge is available regarding the evaluating of PLS structural equation models. To overcome this research gap a broad and detailed guideline for the assessment of reflective and formative measurement models as well as of the structural model had been developed. Moreover, to illustrate the guideline, a detailed application of the evaluation criteria had been conducted to an empirical model explaining repeat purchasing behaviour.

Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering

Abstract: Context has been recognized as an important factor to consider in personalized Recommender Systems. However, most model-based Collaborative Filtering approaches such as Matrix Factorization do not provide a straightforward way of integrating context information into the model. In this work, we introduce a Collaborative Filtering method based on Tensor Factorization, a generalization of Matrix Factorization that allows for a flexible and generic integration of contextual information by modeling the data as a User-Item-Context N-dimensional tensor instead of the traditional 2D User-Item matrix. In the proposed model, called Multiverse Recommendation, different types of context are considered as additional dimensions in the representation of the data as a tensor. The factorization of this tensor leads to a compact model of the data which can be used to provide context-aware recommendations.We provide an algorithm to address the N-dimensional factorization, and show that the Multiverse Recommendation improves upon non-contextual Matrix Factorization up to 30% in terms of the Mean Absolute Error (MAE). We also compare to two state-of-the-art context-aware methods and show that Tensor Factorization consistently outperforms them both in semi-synthetic and real-world data - improvements range from 2.5% to more than 12% depending on the data. Noticeably, our approach outperforms other methods by a wider margin whenever more contextual information is available.

Decision Making Under Uncertainty in Electricity Markets

Abstract: This paper considers stochastic programming models for decision-making under uncertainty in the context of electricity markets. It provides a brief overview of modeling and solution techniques within a mathematical programming framework. Tutorial as well as recent references are provided. This paper provides the guidelines for discussion in a panel session organized by the authors on "Decision Making under Uncertainty in Electricity Markets", scheduled for the IEEE PES 2006 General Meeting.

Receptive vocabulary differences in monolingual and bilingual children.

Abstract: Studies often report that bilingual participants possess a smaller vocabulary in the language of testing than monolinguals, especially in research with children. However, each study is based on a small sample so it is difficult to determine whether the vocabulary difference is due to sampling error. We report the results of an analysis of 1,738 children between 3 and 10 years old and demonstrate a consistent difference in receptive vocabulary between the two groups. Two preliminary analyses suggest that this difference does not change with different language pairs and is largely confined to words relevant to a home context rather than a school context.

Modeling mutual context of object and human pose in human-object interaction activities

Abstract: Detecting objects in cluttered scenes and estimating articulated human body parts are two challenging problems in computer vision. The difficulty is particularly pronounced in activities involving human-object interactions (e.g. playing tennis), where the relevant object tends to be small or only partially visible, and the human body parts are often self-occluded. We observe, however, that objects and human poses can serve as mutual context to each other – recognizing one facilitates the recognition of the other. In this paper we propose a new random field model to encode the mutual context of objects and human poses in human-object interaction activities. We then cast the model learning task as a structure learning problem, of which the structural connectivity between the object, the overall human pose, and different body parts are estimated through a structure search approach, and the parameters of the model are estimated by a new max-margin algorithm. On a sports data set of six classes of human-object interactions [12], we show that our mutual context model significantly outperforms state-of-the-art in detecting very difficult objects and human poses.

Does the technology acceptance model predict actual use? A systematic literature review

Abstract: Context: The technology acceptance model (TAM) was proposed in 1989 as a means of predicting technology usage. However, it is usually validated by using a measure of behavioural intention to use (BI) rather than actual usage. Objective: This review examines the evidence that the TAM predicts actual usage using both subjective and objective measures of actual usage. Method: We performed a systematic literature review based on a search of six digital libraries, along with vote-counting meta-analysis to analyse the overall results. Results: The search identified 79 relevant empirical studies in 73 articles. The results show that BI is likely to be correlated with actual usage. However, the TAM variables perceived ease of use (PEU) and perceived usefulness (PU) are less likely to be correlated with actual usage. Conclusion: Care should be taken using the TAM outside the context in which it has been validated.

Mind and Society

Abstract: In the process of elucidating Spencer’s First Principles and the cosmic theory of evolution, it has proved helpful already to touch on his treatment of social life in that general context. It is essential now, however, to address the theory as manifested in respect of ‘psychical’ and ‘social’ evolution, themselves intimately linked in his discussions.

Evaluation of Structural Equation Models Using the Partial Least Squares (PLS) Approach

Abstract: This paper gives a basic comprehension of the partial least squares approach. In this context, the aim of this paper is to develop a guide for the evaluation of structural equation models, using the current statistical methods methodological knowledge by specifically considering the Partial-Least-Squares (PLS) approach’s requirements. As an advantage, the PLS method demands significantly fewer requirements compared to that of covariance structure analyses, but nevertheless delivers consistent estimation results. This makes PLS a valuable tool for testing theories. Another asset of the PLS approach is its ability to deal with formative as well as reflective indicators, even within one structural equation model. This indicates that the PLS approach is appropriate for explorative analysis of structural equation models, too, thus offering a significant contribution to theory development. However, little knowledge is available regarding the evaluating of PLS structural equation models. To overcome this research gap a broad and detailed guideline for the assessment of reflective and formative measurement models as well as of the structural model had been developed. Moreover, to illustrate the guideline, a detailed application of the evaluation criteria had been conducted to an empirical model explaining repeat purchasing behaviour.

Contexts of Accommodation: Developments in Applied Sociolinguistics

Abstract: List of contributors 1. Accommodation theory: communication, context and consequence Howard Giles, Nikolas Coupland and Justine Coupland 2. Audience accommodation in the mass media Allan Bell 3. Accommodation on trial: processes of communicative accommodation in courtroom interaction Per Linell 4. Accommodation in medical consultations Richard L. Street, Jr 5. Accommodation and mental disability Heidi E. Hamilton 6. Accommodation in therapy Kathleen Ferrara 7. Accommodation in native-nonnative interactions: going beyond the 'what' to the 'why' in second-language research Jane Zuengler 8. Interethnic accommodation: the role of norms Cynthia Gallois and Victor J. Callan 9. Organisational communication and accommodation: toward some conceptual and empirical links Richard Y. Bourhis Index.

The Influence of Context on Quality Improvement Success in Health Care: A Systematic Review of the Literature

Abstract: Context: The mixed results of success among QI initiatives may be due to differences in the context of these initiatives. Methods: The business and health care literature was systematically reviewed to identify contextual factors that might influence QI success; to categorize, summarize, and synthesize these factors; and to understand the current stage of development of this research field. Findings: Forty-seven articles were included in the final review. Consistent with current theories of implementation and organization change, leadership from top management, organizational culture, data infrastructure and information systems, and years involved in QI were suggested as important to QI success. Other potentially important factors identified in this review included: physician involvement in QI, microsystem motivation to change, resources for QI, and QI team leadership. Key limitations in the existing literature were the lack of a practical conceptual model, the lack of clear definitions of contextual factors, and the lack of well-specified measures. Conclusions: Several contextual factors were shown to be important to QI success, although the current body of literature lacks adequate definitions and is characterized by considerable variability in how contextual factors are measured across studies. Future research should focus on identifying and developing measures of context tied to a conceptual model that examines context across all levels of the health care system and explores the relationships among various aspects of context.

Learning to detect roads in high-resolution aerial images

Abstract: Reliably extracting information from aerial imagery is a difficult problem with many practical applications. One specific case of this problem is the task of automatically detecting roads. This task is a difficult vision problem because of occlusions, shadows, and a wide variety of non-road objects. Despite 30 years of work on automatic road detection, no automatic or semi-automatic road detection system is currently on the market and no published method has been shown to work reliably on large datasets of urban imagery. We propose detecting roads using a neural network with millions of trainable weights which looks at a much larger context than was used in previous attempts at learning the task. The network is trained on massive amounts of data using a consumer GPU. We demonstrate that predictive performance can be substantially improved by initializing the feature detectors using recently developed unsupervised learning methods as well as by taking advantage of the local spatial coherence of the output labels.We show that our method works reliably on two challenging urban datasets that are an order of magnitude larger than what was used to evaluate previous approaches.

An overview of models of stomatal conductance at the leaf level

Abstract: Stomata play a key role in plant adaptation to changing environmental conditions as they control both water losses and CO(2) uptake. Particularly, in the context of global change, simulations of the consequences of drought on crop plants are needed to design more efficient and water-saving cropping systems. However, most of the models of stomatal conductance (g(s)) developed at the leaf level link g(s) to environmental factors or net photosynthesis (A(net)), but do not include satisfactorily the effects of drought, impairing our capacity to simulate plant functioning in conditions of limited water supply. The objective of this review was to draw an up-to-date picture of the g(s) models, from the empirical to the process-based ones, along with their mechanistic or deterministic bases. It focuses on models capable to account for multiple environmental influences with emphasis on drought conditions. We examine how models that have been proposed for well-watered conditions can be combined with those specifically designed to deal with drought conditions. Ideas for future improvements of g(s) models are discussed: the issue of co-regulation of g(s) and A(net); the roles of CO(2), absissic acid and H(2)O(2); and finally, how to better address the new challenges arising from the issue of global change.

Preprocessing techniques for context recognition from accelerometer data

Abstract: The ubiquity of communication devices such as smartphones has led to the emergence of context-aware services that are able to respond to specific user activities or contexts. These services allow communication providers to develop new, added-value services for a wide range of applications such as social networking, elderly care and near-emergency early warning systems. At the core of these services is the ability to detect specific physical settings or the context a user is in, using either internal or external sensors. For example, using built-in accelerometers, it is possible to determine whether a user is walking or running at a specific time of day. By correlating this knowledge with GPS data, it is possible to provide specific information services to users with similar daily routines. This article presents a survey of the techniques for extracting this activity information from raw accelerometer data. The techniques that can be implemented in mobile devices range from classical signal processing techniques such as FFT to contemporary string-based methods. We present experimental results to compare and evaluate the accuracy of the various techniques using real data sets collected from daily activities.