搜索结果：Support

DEFINING AND ASSESSING SOCIAL SUPPORT: Traditional Views of Social Support and Their Impact on Assessment Social Support in Young Children: Measurement, Structure and Bahavioral Impact SOCIAL SUPPORT IN THE CONTEXT OF PERSONAL RELATIONSHIPS Social Support: The Sense of Acceptance and the Role of Relationships From Self to Health: Self-Verification and Identity Disruption Social Relationships as a Source of Companionship: Implications for Older Adults' Psychological Well-Being SOCIAL SUPPORT AND STRESS COPING: Social Support, Stress and the Immune System Differentiating the Cognitive and Behavioral Aspects of Social Support SOCIAL SUPPORT APPLICATIONS AND INTERVENTIONS IN CLINICAL AND COMMUNITY SETTINGS: The Role of Coping in Support Provision: The Self- Presentational Dilemma of Victims of Life Crises Social Support During Extreme Stress: Consequences and Intervention.

Examines whether the positive association between social support and well-being is attributable more to an overall beneficial effect of support (main- or direct-effect model) or to a process of support protecting persons from potentially adverse effects of stressful events (buffering model). The review of studies is organized according to (1) whether a measure assesses support structure (the existence of relationships) or function (the extent to which one's interpersonal relationships provide particular resources) and (2) the degree of specificity (vs globality) of the scale. Special attention is given to methodological characteristics that are requisite for a fair comparison of the models. It is concluded that there is evidence consistent with both models. Evidence for the buffering model is found when the social support measure assesses the perceived availability of interpersonal resources that are responsive to the needs elicited by stressful events. Evidence for a main effect model is found when the support measure assesses a person's degree of integration in a large social network. Both conceptualizations of social support are correct in some respects, but each represents a different process through which social support may affect well-being. Implications for theories of social support processes and for the design of preventive interventions are discussed.

We present a theory of the basis of support for a social movement. Three types of support (citizenship actions, policy support and acceptance, and personal-sphere behaviors that accord with movement principles) are empirically distinct from each other and from committed activism. Drawing on theoretical work on values and norm-activation processes, we propose a value-belief-norm (VBN) theory of movement support. Individuals who accept a movement's basic values, believe that valued objects are threatened, and believe that their actions can help restore those values experience an obligation (personal norm) for pro-movement action that creates a predisposition to provide support; the particular type of support that results is dependent on the individual's capabilities and constraints. Data from a national survey of 420 respondents suggest that the VBN theory, when compared with other prevalent theories, offers the best available account of support for the environmental movement.

A measure of social support, the Social Support Questionnaire (SSQ), is described and four empirical studies employing it are described. The SSQ yields scores for (a) number of social supports, and (b) satisfaction with social support that is available. Three of the studies deal with the SSQ's psychometric properties, its correations with measures of personality and adjustment, and the relationship of the SSQ to positive and negative life changes. The fourth study was an experimental investigation of the relationship between social support and persistence in working on a complex, frustrating task. The research reported suggests that the SSQ is a reliable instrument, and that social support is (1) more strongly related to positive than negative life changes, (2) more related in a negative direction to psychological discomfort among women than men, and (3) an asset in enabling a person to persist at a task under frustrating conditions. Research and clinical implications are discussed. (Author)

The problem of empirical data modelling is germane to many engineering applications.
\nIn empirical data modelling a process of induction is used to build up a model of the
\nsystem, from which it is hoped to deduce responses of the system that have yet to be observed.
\nUltimately the quantity and quality of the observations govern the performance
\nof this empirical model. By its observational nature data obtained is finite and sampled;
\ntypically this sampling is non-uniform and due to the high dimensional nature of the
\nproblem the data will form only a sparse distribution in the input space. Consequently
\nthe problem is nearly always ill posed (Poggio et al., 1985) in the sense of Hadamard
\n(Hadamard, 1923). Traditional neural network approaches have suffered difficulties with
\ngeneralisation, producing models that can overfit the data. This is a consequence of the
\noptimisation algorithms used for parameter selection and the statistical measures used
\nto select the ’best’ model. The foundations of Support Vector Machines (SVM) have
\nbeen developed by Vapnik (1995) and are gaining popularity due to many attractive
\nfeatures, and promising empirical performance. The formulation embodies the Structural
\nRisk Minimisation (SRM) principle, which has been shown to be superior, (Gunn
\net al., 1997), to traditional Empirical Risk Minimisation (ERM) principle, employed by
\nconventional neural networks. SRM minimises an upper bound on the expected risk,
\nas opposed to ERM that minimises the error on the training data. It is this difference
\nwhich equips SVM with a greater ability to generalise, which is the goal in statistical
\nlearning. SVMs were developed to solve the classification problem, but recently they
\nhave been extended to the domain of regression problems (Vapnik et al., 1997). In the
\nliterature the terminology for SVMs can be slightly confusing. The term SVM is typically
\nused to describe classification with support vector methods and support vector
\nregression is used to describe regression with support vector methods. In this report
\nthe term SVM will refer to both classification and regression methods, and the terms
\nSupport Vector Classification (SVC) and Support Vector Regression (SVR) will be used
\nfor specification. This section continues with a brief introduction to the structural risk

Over the past 30 years investigators have called repeatedly for research on the mechanisms through which social relationships and social support improve physical and psychological well-being, both directly and as stress buffers. I describe seven possible mechanisms: social influence/social comparison, social control, role-based purpose and meaning (mattering), self-esteem, sense of control, belonging and companionship, and perceived support availability. Stress-buffering processes also involve these mechanisms. I argue that there are two broad types of support, emotional sustenance and active coping assistance, and two broad categories of supporters, significant others and experientially similar others, who specialize in supplying different types of support to distressed individuals. Emotionally sustaining behaviors and instrumental aid from significant others and empathy, active coping assistance, and role modeling from similar others should be most efficacious in alleviating the physical and emotional impacts of stressors.

Abstract— Branch support is quantified as the extra length needed to lose a branch in the consensus of near‐most‐parsimonious trees. This approach is based solely on the original data, as opposed to the data perturbation used in the bootstrap procedure. If trees have been generated by Farris's successive approximations approach to character weighting, branch support should be examined in terms of weighted extra length needed to lose a branch. The sum of all branch support values over the tree divided by the length of the most parsimonious tree[s] provides a new index, the total support index. This index is a measure of tree stability in terms of supported resolutions, which is of prime importance in cladistic analysis.

The development of a self-report measure of subjectively assessed social support, the Multidimensional Scale of Perceived Social Support (MSPSS), is described. Subjects included 136 female and 139 male university undergraduates. Three subscales, each addressing a different source of support, were identified and found to have strong factorial validity: (a) Family, (b) Friends, and (c) Significant Other. In addition, the research demonstrated that the MSPSS has good internal and test-retest reliability as well as moderate construct validity. As predicted, high levels of perceived social support were associated with low levels of depression and anxiety symptomatology as measured by the Hopkins Symptom Checklist. Gender differences with respect to the MSPSS are also presented. The value of the MSPSS as a research instrument is discussed, along with implications for future research.

Social support is defined as information leading the subject to believe that he is cared for and loved, esteemed, and a member of a network of mutual obligations. The evidence that supportive interactions among people are protective against the health consequences of life stress is reviewed. It appears that social support can protect people in crisis from a wide variety of pathological states: from low birth weight to death, from arthritis through tuberculosis to depression, alcoholism, and the social breakdown syndrome. Furthermore, social support may reduce the amount of medication required, accelerate recovery, and facilitate compliance with prescribed medical regimens.

The authors reviewed more than 70 studies concerning employees' general belief that their work organization values their contribution and cares about their well-being (perceived organizational support; POS). A meta-analysis indicated that 3 major categories of beneficial treatment received by employees (i.e., fairness, supervisor support, and organizational rewards and favorable job conditions) were associated with POS. POS, in turn, was related to outcomes favorable to employees (e.g., job satisfaction, positive mood) and the organization (e.g., affective commitment, performance, and lessened withdrawal behavior). These relationships depended on processes assumed by organizational support theory: employees' belief that the organization's actions were discretionary, feeling of obligation to aid the organization, fulfillment of socioemotional needs, and performance-reward expectancies.

Abstract We construct orthonormal bases of compactly supported wavelets, with arbitrarily high regularity. The order of regularity increases linearly with the support width. We start by reviewing the concept of multiresolution analysis as well as several algorithms in vision decomposition and reconstruction. The construction then follows from a synthesis of these different approaches.

The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world's largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas.

This chapter describes a new algorithm for training Support Vector Machines: Sequential Minimal Optimization, or SMO. Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest possible QP problems. These small QP problems are solved analytically, which avoids using a time-consuming numerical QP optimization as an inner loop. The amount of memory required for SMO is linear in the training set size, which allows SMO to handle very large training sets. Because large matrix computation is avoided, SMO scales somewhere between linear and quadratic in the training set size for various test problems, while a standard projected conjugate gradient (PCG) chunking algorithm scales somewhere between linear and cubic in the training set size. SMO's computation time is dominated by SVM evaluation, hence SMO is fastest for linear SVMs and sparse data sets. For the MNIST database, SMO is as fast as PCG chunking; while for the UCI Adult database and linear SVMs, SMO can be more than 1000 times faster than the PCG chunking algorithm.

This paper addresses the problem of the classification of hyperspectral remote sensing images by support vector machines (SVMs). First, we propose a theoretical discussion and experimental analysis aimed at understanding and assessing the potentialities of SVM classifiers in hyperdimensional feature spaces. Then, we assess the effectiveness of SVMs with respect to conventional feature-reduction-based approaches and their performances in hypersubspaces of various dimensionalities. To sustain such an analysis, the performances of SVMs are compared with those of two other nonparametric classifiers (i.e., radial basis function neural networks and the K-nearest neighbor classifier). Finally, we study the potentially critical issue of applying binary SVMs to multiclass problems in hyperspectral data. In particular, four different multiclass strategies are analyzed and compared: the one-against-all, the one-against-one, and two hierarchical tree-based strategies. Different performance indicators have been used to support our experimental studies in a detailed and accurate way, i.e., the classification accuracy, the computational time, the stability to parameter setting, and the complexity of the multiclass architecture. The results obtained on a real Airborne Visible/Infrared Imaging Spectroradiometer hyperspectral dataset allow to conclude that, whatever the multiclass strategy adopted, SVMs are a valid and effective alternative to conventional pattern recognition approaches (feature-reduction procedures combined with a classification method) for the classification of hyperspectral remote sensing data.

The initial study describing the development of the Multidimensional Scale of Perceived Social Support (MSPSS) indicated that it was a psychometrically sound instrument (Zimet, Dahlem, Zimet, & Farley, 1988). The current study attempted to extend the initial findings by demonstrating the internal reliability, factorial validity, and subscale validity of the MSPSS using three different subject groups: (a) 265 pregnant women, (b) 74 adolescents living in Europe with their families, and (c) 55 pediatric residents. The MSPSS was found to have good internal reliability across subject groups. In addition, strong factorial validity was demonstrated, confirming the three-subscale structure of the MSPSS: Family, Friends, and Significant Other. Finally, strong support was also found for the validity of the Family and Significant Other subscales.

A new regression technique based on concept of support vectors is introduced. We compare support vector regression with a committee regression technique (bagging) based on regression trees and ridge regression done in feature space. On the basis of these experiments, it is expected that SVR will have advantages in high dimensionality space because SVR optimization does not depend on the dimension&y of input space. This is a longer version of the paper appear in Advances in Neural Processing Systems 9 (proceedings of the 1996 conference)

This book is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. The book also introduces Bayesian analysis of learning and relates SVMs to Gaussian Processes and other kernel based learning methods. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc. Their first introduction in the early 1990s lead to a recent explosion of applications and deepening theoretical analysis, that has now established Support Vector Machines along with neural networks as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and application of these techniques. The concepts are introduced gradually in accessible and self-contained stages, though in each stage the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally the book will equip the practitioner to apply the techniques and an associated web site will provide pointers to updated literature, new applications, and on-line software

Support Vector Machines Basic Methods of Least Squares Support Vector Machines Bayesian Inference for LS-SVM Models Robustness Large Scale Problems LS-SVM for Unsupervised Learning LS-SVM for Recurrent Networks and Control.

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTStrong metal-support interactions. Group 8 noble metals supported on titanium dioxideS. J. Tauster, S. C. Fung, and R. L. GartenCite this: J. Am. Chem. Soc. 1978, 100, 1, 170–175Publication Date (Print):January 1, 1978Publication History Published online1 May 2002Published inissue 1 January 1978https://pubs.acs.org/doi/10.1021/ja00469a029https://doi.org/10.1021/ja00469a029research-articleACS PublicationsRequest reuse permissionsArticle Views13859Altmetric-Citations2434LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose Get e-Alerts