The fit of structural equation models with normally distributed observed and latent variables can be evaluated by examining the normalized and standardized residuals computed in Mplus. These residuals are available for the ML, MLR, and MLF estimators and can be obtained by the residual output command. Suppose that Y is the vector of dependent observed variables and η is the vector of latent var...

Leaf Area Index (LAI) is an important input variable for forest ecosystem modeling as it is a factor in predicting productivity and biomass, two key aspects of forest health. Current in situ methods of determining LAI are sometimes destructive and generally very time consuming. Other LAI derivation methods, mainly satellite-based in nature, do not provide sufficient spatial resolution or the pr...

In this paper we describe our effort on TREC Contextual Suggestion Track. Using resources such as description or websites of example suggestions to build user profile has been proven to be effective on last year’s TREC. This year we also leverage the power of using user profile. Real world opinions of the suggestions are used in our method to build both user profile and candidate suggestion pro...

This is a description of the team AG submission to the Learning to Rank Challenge. This solution has scored 4th place in the main track. The primary algorithm used is Additive Groves of regression trees.

year physiology, especially those papers emphasizing adaptive and integrative mechanisms. It is published 12 times a publishes original papers that deal with diverse area of research in applied

This article discusses the benefits and future of standards and presents the generic multi-dimensional Reference Model. First the importance and the tasks of interoperability as well as quality development and their relationship are analyzed. Especially in e-Learning their connection and interdependence is evident: Interoperability is one basic requirement for quality development. In this paper...

In this paper, we adopt various greedy result diversification strategies to the problem of feature selection for learning to rank. Our experimental evaluations using several standard datasets reveal that such diversification methods are quite effective in identifying the feature subsets in comparison to the baselines from the literature.

The context type and similarity calculation are two essential features of a distributional similarity scheme (DSS). In this paper, we propose a hierarchical semanticaware DSS that exploits semantic relation words as extra context information to guide the similarity calculation. First, we define and extract five types of semantic relations, and then develop relation-based similarities from the d...

This chapter presents a theoretical framework for evaluating next generation search engines. We focus on search engines whose results presentation is enriched with additional information and does not merely present the usual list of “10 blue links”, that is, of ten links to results, accompanied by a short description. While Web search is used as an example here, the framework can easily be appl...

For many people faced with a tough purchasing decision, the research tool of choice is a web browser. Search engines solve the general problem of finding relevant data, however it is up to the user to sort, filter, and evaluate it. Decision support methods such as LSP can turn raw data into formal evaluations, but they are generally disconnected from the Web – the most up-to-date, widely-used, ...