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Sample records for latent semantic indexing

  1. Asymmetric latent semantic indexing for gene expression experiments visualization.

    PubMed

    González, Javier; Muñoz, Alberto; Martos, Gabriel

    2016-08-01

    We propose a new method to visualize gene expression experiments inspired by the latent semantic indexing technique originally proposed in the textual analysis context. By using the correspondence word-gene document-experiment, we define an asymmetric similarity measure of association for genes that accounts for potential hierarchies in the data, the key to obtain meaningful gene mappings. We use the polar decomposition to obtain the sources of asymmetry of the similarity matrix, which are later combined with previous knowledge. Genetic classes of genes are identified by means of a mixture model applied in the genes latent space. We describe the steps of the procedure and we show its utility in the Human Cancer dataset. PMID:27427382

  2. An Explanation of the Effectiveness of Latent Semantic Indexing by Means of a Bayesian Regression Model.

    ERIC Educational Resources Information Center

    Story, Roger E.

    1996-01-01

    Discussion of the use of Latent Semantic Indexing to determine relevancy in information retrieval focuses on statistical regression and Bayesian methods. Topics include keyword searching; a multiple regression model; how the regression model can aid search methods; and limitations of this approach, including complexity, linearity, and…

  3. On matrices with low-rank-plus-shift structure: Partial SVD and latent semantic indexing

    SciTech Connect

    Zha, H.; Zhang, Z.

    1998-08-01

    The authors present a detailed analysis of matrices satisfying the so-called low-rank-plus-shift property in connection with the computation of their partial singular value decomposition. The application they have in mind is Latent Semantic Indexing for information retrieval where the term-document matrices generated from a text corpus approximately satisfy this property. The analysis is motivated by developing more efficient methods for computing and updating partial SVD of large term-document matrices and gaining deeper understanding of the behavior of the methods in the presence of noise.

  4. A Comparison of SVD, SVR, ADE and IRR for Latent Semantic Indexing

    NASA Astrophysics Data System (ADS)

    Zhang, Wen; Tang, Xijin; Yoshida, Taketoshi

    Recently, singular value decomposition (SVD) and its variants, which are singular value rescaling (SVR), approximation dimension equalization (ADE) and iterative residual rescaling (IRR), were proposed to conduct the job of latent semantic indexing (LSI). Although they are all based on linear algebraic method for tem-document matrix computation, which is SVD, the basic motivations behind them concerning LSI are different from each other. In this paper, a series of experiments are conducted to examine their effectiveness of LSI for the practical application of text mining, including information retrieval, text categorization and similarity measure. The experimental results demonstrate that SVD and SVR have better performances than other proposed LSI methods in the above mentioned applications. Meanwhile, ADE and IRR, because of the too much difference between their approximation matrix and original term-document matrix in Frobenius norm, can not derive good performances for text mining applications using LSI.

  5. Latent Semantic Analysis.

    ERIC Educational Resources Information Center

    Dumais, Susan T.

    2004-01-01

    Presents a literature review that covers the following topics related to Latent Semantic Analysis (LSA): (1) LSA overview; (2) applications of LSA, including information retrieval (IR), information filtering, cross-language retrieval, and other IR-related LSA applications; (3) modeling human memory, including the relationship of LSA to other…

  6. Latent semantic analysis.

    PubMed

    Evangelopoulos, Nicholas E

    2013-11-01

    This article reviews latent semantic analysis (LSA), a theory of meaning as well as a method for extracting that meaning from passages of text, based on statistical computations over a collection of documents. LSA as a theory of meaning defines a latent semantic space where documents and individual words are represented as vectors. LSA as a computational technique uses linear algebra to extract dimensions that represent that space. This representation enables the computation of similarity among terms and documents, categorization of terms and documents, and summarization of large collections of documents using automated procedures that mimic the way humans perform similar cognitive tasks. We present some technical details, various illustrative examples, and discuss a number of applications from linguistics, psychology, cognitive science, education, information science, and analysis of textual data in general. WIREs Cogn Sci 2013, 4:683-692. doi: 10.1002/wcs.1254 CONFLICT OF INTEREST: The author has declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website. PMID:26304272

  7. Comparing and Combining the Effectiveness of Latent Semantic Indexing and the Ordinary Vector Space Model for Information Retrieval.

    ERIC Educational Resources Information Center

    Lochbaum, Karen E.; Streeter, Lynn A.

    1989-01-01

    Describes experiments that compared a new method for automatically analyzing semantic structures in text by statistical means with the standard vector space model. Findings indicate that combining both methods improved performance over either alone. The effects of other experimental variables on retrieval performance (term weighting, suffix…

  8. From paragraph to graph: Latent semantic analysis for information visualization

    PubMed Central

    Landauer, Thomas K.; Laham, Darrell; Derr, Marcia

    2004-01-01

    Most techniques for relating textual information rely on intellectually created links such as author-chosen keywords and titles, authority indexing terms, or bibliographic citations. Similarity of the semantic content of whole documents, rather than just titles, abstracts, or overlap of keywords, offers an attractive alternative. Latent semantic analysis provides an effective dimension reduction method for the purpose that reflects synonymy and the sense of arbitrary word combinations. However, latent semantic analysis correlations with human text-to-text similarity judgments are often empirically highest at ≈300 dimensions. Thus, two- or three-dimensional visualizations are severely limited in what they can show, and the first and/or second automatically discovered principal component, or any three such for that matter, rarely capture all of the relations that might be of interest. It is our conjecture that linguistic meaning is intrinsically and irreducibly very high dimensional. Thus, some method to explore a high dimensional similarity space is needed. But the 2.7 × 107 projections and infinite rotations of, for example, a 300-dimensional pattern are impossible to examine. We suggest, however, that the use of a high dimensional dynamic viewer with an effective projection pursuit routine and user control, coupled with the exquisite abilities of the human visual system to extract information about objects and from moving patterns, can often succeed in discovering multiple revealing views that are missed by current computational algorithms. We show some examples of the use of latent semantic analysis to support such visualizations and offer views on future needs. PMID:15037748

  9. Transforming Selected Concepts into Dimensions in Latent Semantic Analysis

    ERIC Educational Resources Information Center

    Olmos, Ricardo; Jorge-Botana, Guillermo; León, José Antonio; Escudero, Inmaculada

    2014-01-01

    This study presents a new approach for transforming the latent representation derived from a Latent Semantic Analysis (LSA) space into one where dimensions have nonlatent meanings. These meanings are based on lexical descriptors, which are selected by the LSA user. The authors present three analyses that provide examples of the utility of this…

  10. Effectiveness of Automated Chinese Sentence Scoring with Latent Semantic Analysis

    ERIC Educational Resources Information Center

    Liao, Chen-Huei; Kuo, Bor-Chen; Pai, Kai-Chih

    2012-01-01

    Automated scoring by means of Latent Semantic Analysis (LSA) has been introduced lately to improve the traditional human scoring system. The purposes of the present study were to develop a LSA-based assessment system to evaluate children's Chinese sentence construction skills and to examine the effectiveness of LSA-based automated scoring function…

  11. Modeling semantic aspects for cross-media image indexing.

    PubMed

    Monay, Florent; Gatica-Perez, Daniel

    2007-10-01

    To go beyond the query-by-example paradigm in image retrieval, there is a need for semantic indexing of large image collections for intuitive text-based image search. Different models have been proposed to learn the dependencies between the visual content of an image set and the associated text captions, then allowing for the automatic creation of semantic indices for unannotated images. The task, however, remains unsolved. In this paper, we present three alternatives to learn a Probabilistic Latent Semantic Analysis model (PLSA) for annotated images, and evaluate their respective performance for automatic image indexing. Under the PLSA assumptions, an image is modeled as a mixture of latent aspects that generates both image features and text captions, and we investigate three ways to learn the mixture of aspects. We also propose a more discriminative image representation than the traditional Blob histogram, concatenating quantized local color information and quantized local texture descriptors. The first learning procedure of a PLSA model for annotated images is a standard EM algorithm, which implicitly assumes that the visual and the textual modalities can be treated equivalently. The other two models are based on an asymmetric PLSA learning, allowing to constrain the definition of the latent space on the visual or on the textual modality. We demonstrate that the textual modality is more appropriate to learn a semantically meaningful latent space, which translates into improved annotation performance. A comparison of our learning algorithms with respect to recent methods on a standard dataset is presented, and a detailed evaluation of the performance shows the validity of our framework. PMID:17699924

  12. Probabilistic latent semantic analysis for dynamic textures recognition and localization

    NASA Astrophysics Data System (ADS)

    Wang, Yong; Hu, Shiqiang

    2014-11-01

    We present a framework for dynamic textures (DTs) recognition and localization by using a model developed in the text analysis literature: probabilistic latent semantic analysis (pLSA). The novelty is revealed in three aspects. First, chaotic feature vector is introduced and characterizes each pixel intensity series. Next, the pLSA model is employed to discover the topics by using the bag of words representation. Finally, the spatial layout of DTs can be found. Experimental results are conducted on the well-known DTs datasets. The results show that the proposed method can successfully build DTs models and achieve higher accuracies in DTs recognition and effectively localize DTs.

  13. Computer assessment of interview data using latent semantic analysis.

    PubMed

    Dam, Gregory; Kaufmann, Stefan

    2008-02-01

    Clinical interviews are a powerful method for assessing students' knowledge and conceptualdevelopment. However, the analysis of the resulting data is time-consuming and can create a "bottleneck" in large-scale studies. This article demonstrates the utility of computational methods in supporting such an analysis. Thirty-four 7th-grade student explanations of the causes of Earth's seasons were assessed using latent semantic analysis (LSA). Analyses were performed on transcriptions of student responses during interviews administered, prior to (n = 21) and after (n = 13) receiving earth science instruction. An instrument that uses LSA technology was developed to identify misconceptions and assess conceptual change in students' thinking. Its accuracy, as determined by comparing its classifications to the independent coding performed by four human raters, reached 90%. Techniques for adapting LSA technology to support the analysis of interview data, as well as some limitations, are discussed. PMID:18411522

  14. Parallel Latent Semantic Analysis using a Graphics Processing Unit

    SciTech Connect

    Cui, Xiaohui; Potok, Thomas E; Cavanagh, Joseph M

    2009-01-01

    Latent Semantic Analysis (LSA) can be used to reduce the dimensions of large Term-Document datasets using Singular Value Decomposition. However, with the ever expanding size of data sets, current implementations are not fast enough to quickly and easily compute the results on a standard PC. The Graphics Processing Unit (GPU) can solve some highly parallel problems much faster than the traditional sequential processor (CPU). Thus, a deployable system using a GPU to speedup large-scale LSA processes would be a much more effective choice (in terms of cost/performance ratio) than using a computer cluster. In this paper, we presented a parallel LSA implementation on the GPU, using NVIDIA Compute Unified Device Architecture (CUDA) and Compute Unified Basic Linear Algebra Subprograms (CUBLAS). The performance of this implementation is compared to traditional LSA implementation on CPU using an optimized Basic Linear Algebra Subprograms library. For large matrices that have dimensions divisible by 16, the GPU algorithm ran five to six times faster than the CPU version.

  15. Measuring Discourse-Level Processes with Verbal Protocols and Latent Semantic Analysis

    ERIC Educational Resources Information Center

    Millis, Keith; Magliano, Joseph; Todaro, Stacey

    2006-01-01

    The present study used latent semantic analysis (LSA) to analyze verbal protocols that were collected while participants read expository passages. In the study, participants were asked to type their thoughts after reading each sentence of 2 scientific texts. The semantic similarity between the protocols and the current sentence and prior causal…

  16. MASSIVELY PARALLEL LATENT SEMANTIC ANALYSES USING A GRAPHICS PROCESSING UNIT

    SciTech Connect

    Cavanagh, J.; Cui, S.

    2009-01-01

    Latent Semantic Analysis (LSA) aims to reduce the dimensions of large term-document datasets using Singular Value Decomposition. However, with the ever-expanding size of datasets, current implementations are not fast enough to quickly and easily compute the results on a standard PC. A graphics processing unit (GPU) can solve some highly parallel problems much faster than a traditional sequential processor or central processing unit (CPU). Thus, a deployable system using a GPU to speed up large-scale LSA processes would be a much more effective choice (in terms of cost/performance ratio) than using a PC cluster. Due to the GPU’s application-specifi c architecture, harnessing the GPU’s computational prowess for LSA is a great challenge. We presented a parallel LSA implementation on the GPU, using NVIDIA® Compute Unifi ed Device Architecture and Compute Unifi ed Basic Linear Algebra Subprograms software. The performance of this implementation is compared to traditional LSA implementation on a CPU using an optimized Basic Linear Algebra Subprograms library. After implementation, we discovered that the GPU version of the algorithm was twice as fast for large matrices (1 000x1 000 and above) that had dimensions not divisible by 16. For large matrices that did have dimensions divisible by 16, the GPU algorithm ran fi ve to six times faster than the CPU version. The large variation is due to architectural benefi ts of the GPU for matrices divisible by 16. It should be noted that the overall speeds for the CPU version did not vary from relative normal when the matrix dimensions were divisible by 16. Further research is needed in order to produce a fully implementable version of LSA. With that in mind, the research we presented shows that the GPU is a viable option for increasing the speed of LSA, in terms of cost/performance ratio.

  17. A computational modeling of semantic knowledge in reading comprehension: Integrating the landscape model with latent semantic analysis.

    PubMed

    Yeari, Menahem; van den Broek, Paul

    2016-09-01

    It is a well-accepted view that the prior semantic (general) knowledge that readers possess plays a central role in reading comprehension. Nevertheless, computational models of reading comprehension have not integrated the simulation of semantic knowledge and online comprehension processes under a unified mathematical algorithm. The present article introduces a computational model that integrates the landscape model of comprehension processes with latent semantic analysis representation of semantic knowledge. In three sets of simulations of previous behavioral findings, the integrated model successfully simulated the activation and attenuation of predictive and bridging inferences during reading, as well as centrality estimations and recall of textual information after reading. Analyses of the computational results revealed new theoretical insights regarding the underlying mechanisms of the various comprehension phenomena. PMID:27383752

  18. Latent Semantic Analysis as a Method of Content-Based Image Retrieval in Medical Applications

    ERIC Educational Resources Information Center

    Makovoz, Gennadiy

    2010-01-01

    The research investigated whether a Latent Semantic Analysis (LSA)-based approach to image retrieval can map pixel intensity into a smaller concept space with good accuracy and reasonable computational cost. From a large set of M computed tomography (CT) images, a retrieval query found all images for a particular patient based on semantic…

  19. Semantic-Aware Co-Indexing for Image Retrieval.

    PubMed

    Zhang, Shiliang; Yang, Ming; Wang, Xiaoyu; Lin, Yuanqing; Tian, Qi

    2015-12-01

    In content-based image retrieval, inverted indexes allow fast access to database images and summarize all knowledge about the database. Indexing multiple clues of image contents allows retrieval algorithms search for relevant images from different perspectives, which is appealing to deliver satisfactory user experiences. However, when incorporating diverse image features during online retrieval, it is challenging to ensure retrieval efficiency and scalability. In this paper, for large-scale image retrieval, we propose a semantic-aware co-indexing algorithm to jointly embed two strong cues into the inverted indexes: 1) local invariant features that are robust to delineate low-level image contents, and 2) semantic attributes from large-scale object recognition that may reveal image semantic meanings. Specifically, for an initial set of inverted indexes of local features, we utilize semantic attributes to filter out isolated images and insert semantically similar images to this initial set. Encoding these two distinct and complementary cues together effectively enhances the discriminative capability of inverted indexes. Such co-indexing operations are totally off-line and introduce small computation overhead to online retrieval, because only local features but no semantic attributes are employed for the query. Hence, this co-indexing is different from existing image retrieval methods fusing multiple features or retrieval results. Extensive experiments and comparisons with recent retrieval methods manifest the competitive performance of our method. PMID:26539859

  20. [The hierarchical clustering analysis of hyperspectral image based on probabilistic latent semantic analysis].

    PubMed

    Yi, Wen-Bin; Shen, Li; Qi, Yin-Feng; Tang, Hong

    2011-09-01

    The paper introduces the Probabilistic Latent Semantic Analysis (PLSA) to the image clustering and an effective image clustering algorithm using the semantic information from PLSA is proposed which is used for hyperspectral images. Firstly, the ISODATA algorithm is used to obtain the initial clustering result of hyperspectral image and the clusters of the initial clustering result are considered as the visual words of the PLSA. Secondly, the object-oriented image segmentation algorithm is used to partition the hyperspectral image and segments with relatively pure pixels are regarded as documents in PLSA. Thirdly, a variety of identification methods which can estimate the best number of cluster centers is combined to get the number of latent semantic topics. Then the conditional distributions of visual words in topics and the mixtures of topics in different documents are estimated by using PLSA. Finally, the conditional probabilistic of latent semantic topics are distinguished using statistical pattern recognition method, the topic type for each visual in each document will be given and the clustering result of hyperspectral image are then achieved. Experimental results show the clusters of the proposed algorithm are better than K-MEANS and ISODATA in terms of object-oriented property and the clustering result is closer to the distribution of real spatial distribution of surface. PMID:22097851

  1. Latent morpho-semantic analysis : multilingual information retrieval with character n-grams and mutual information.

    SciTech Connect

    Bader, Brett William; Chew, Peter A.; Abdelali, Ahmed

    2008-08-01

    We describe an entirely statistics-based, unsupervised, and language-independent approach to multilingual information retrieval, which we call Latent Morpho-Semantic Analysis (LMSA). LMSA overcomes some of the shortcomings of related previous approaches such as Latent Semantic Analysis (LSA). LMSA has an important theoretical advantage over LSA: it combines well-known techniques in a novel way to break the terms of LSA down into units which correspond more closely to morphemes. Thus, it has a particular appeal for use with morphologically complex languages such as Arabic. We show through empirical results that the theoretical advantages of LMSA can translate into significant gains in precision in multilingual information retrieval tests. These gains are not matched either when a standard stemmer is used with LSA, or when terms are indiscriminately broken down into n-grams.

  2. Latent Semantics of Action Verbs Reflect Phonetic Parameters of Intensity and Emotional Content

    PubMed Central

    Petersen, Michael Kai

    2015-01-01

    Conjuring up our thoughts, language reflects statistical patterns of word co-occurrences which in turn come to describe how we perceive the world. Whether counting how frequently nouns and verbs combine in Google search queries, or extracting eigenvectors from term document matrices made up of Wikipedia lines and Shakespeare plots, the resulting latent semantics capture not only the associative links which form concepts, but also spatial dimensions embedded within the surface structure of language. As both the shape and movements of objects have been found to be associated with phonetic contrasts already in toddlers, this study explores whether articulatory and acoustic parameters may likewise differentiate the latent semantics of action verbs. Selecting 3 × 20 emotion-, face-, and hand-related verbs known to activate premotor areas in the brain, their mutual cosine similarities were computed using latent semantic analysis LSA, and the resulting adjacency matrices were compared based on two different large scale text corpora: HAWIK and TASA. Applying hierarchical clustering to identify common structures across the two text corpora, the verbs largely divide into combined mouth and hand movements versus emotional expressions. Transforming the verbs into their constituent phonemes, and projecting them into an articulatory space framed by tongue height and formant frequencies, the clustered small and large size movements appear differentiated by front versus back vowels corresponding to increasing levels of arousal. Whereas the clustered emotional verbs seem characterized by sequences of close versus open jaw produced phonemes, generating up- or downwards shifts in formant frequencies that may influence their perceived valence. Suggesting, that the latent semantics of action verbs reflect parameters of intensity and emotional polarity that appear correlated with the articulatory contrasts and acoustic characteristics of phonemes. PMID:25849977

  3. Latent semantics of action verbs reflect phonetic parameters of intensity and emotional content.

    PubMed

    Petersen, Michael Kai

    2015-01-01

    Conjuring up our thoughts, language reflects statistical patterns of word co-occurrences which in turn come to describe how we perceive the world. Whether counting how frequently nouns and verbs combine in Google search queries, or extracting eigenvectors from term document matrices made up of Wikipedia lines and Shakespeare plots, the resulting latent semantics capture not only the associative links which form concepts, but also spatial dimensions embedded within the surface structure of language. As both the shape and movements of objects have been found to be associated with phonetic contrasts already in toddlers, this study explores whether articulatory and acoustic parameters may likewise differentiate the latent semantics of action verbs. Selecting 3 × 20 emotion-, face-, and hand-related verbs known to activate premotor areas in the brain, their mutual cosine similarities were computed using latent semantic analysis LSA, and the resulting adjacency matrices were compared based on two different large scale text corpora: HAWIK and TASA. Applying hierarchical clustering to identify common structures across the two text corpora, the verbs largely divide into combined mouth and hand movements versus emotional expressions. Transforming the verbs into their constituent phonemes, and projecting them into an articulatory space framed by tongue height and formant frequencies, the clustered small and large size movements appear differentiated by front versus back vowels corresponding to increasing levels of arousal. Whereas the clustered emotional verbs seem characterized by sequences of close versus open jaw produced phonemes, generating up- or downwards shifts in formant frequencies that may influence their perceived valence. Suggesting, that the latent semantics of action verbs reflect parameters of intensity and emotional polarity that appear correlated with the articulatory contrasts and acoustic characteristics of phonemes. PMID:25849977

  4. Simulating Expert Clinical Comprehension: Adapting Latent Semantic Analysis to Accurately Extract Clinical Concepts from Psychiatric Narrative

    PubMed Central

    Cohen, Trevor; Blatter, Brett; Patel, Vimla

    2008-01-01

    Cognitive studies reveal that less-than-expert clinicians are less able to recognize meaningful patterns of data in clinical narratives. Accordingly, psychiatric residents early in training fail to attend to information that is relevant to diagnosis and the assessment of dangerousness. This manuscript presents cognitively motivated methodology for the simulation of expert ability to organize relevant findings supporting intermediate diagnostic hypotheses. Latent Semantic Analysis is used to generate a semantic space from which meaningful associations between psychiatric terms are derived. Diagnostically meaningful clusters are modeled as geometric structures within this space and compared to elements of psychiatric narrative text using semantic distance measures. A learning algorithm is defined that alters components of these geometric structures in response to labeled training data. Extraction and classification of relevant text segments is evaluated against expert annotation, with system-rater agreement approximating rater-rater agreement. A range of biomedical informatics applications for these methods are suggested. PMID:18455483

  5. Semantic overlay network for large-scale spatial information indexing

    NASA Astrophysics Data System (ADS)

    Zou, Zhiqiang; Wang, Yue; Cao, Kai; Qu, Tianshan; Wang, Zhongmin

    2013-08-01

    The increased demand for online services of spatial information poses new challenges to the combined filed of Computer Science and Geographic Information Science. Amongst others, these include fast indexing of spatial data in distributed networks. In this paper we propose a novel semantic overlay network for large-scale multi-dimensional spatial information indexing, called SON_LSII, which has a hybrid structure integrating a semantic quad-tree and Chord ring. The SON_LSII is a small world overlay network that achieves a very competitive trade-off between indexing efficiency and maintenance overhead. To create SON_LSII, we use an effective semantic clustering strategy that considers two aspects, i.e., the semantic of spatial information that peer holds in overlay network and physical network performances. Based on SON_LSII, a mapping method is used to reduce the multi-dimensional features into a single dimension and an efficient indexing algorithm is presented to support complex range queries of the spatial information with a massive number of concurrent users. The results from extensive experiments demonstrate that SON_LSII is superior to existing overlay networks in various respects, including scalability, maintenance, rate of indexing hits, indexing logical hops, and adaptability. Thus, the proposed SON_LSII can be used for large-scale spatial information indexing.

  6. Predicting Raters’ Transparency Judgments of English and Chinese Morphological Constituents using Latent Semantic Analysis

    PubMed Central

    Wang, Hsueh-Cheng; Hsu, Li-Chuan; Tien, Yi-Min; Pomplun, Marc

    2013-01-01

    The morphological constituents of English compounds (e.g., “butter” and “fly” for “butterfly”) and two-character Chinese compounds may differ in meaning from the whole word. Subjective differences and ambiguity of transparency make the judgments difficult, and a computational alternative based on a general model may be a way to average across subjective differences. The current study proposes two approaches based on Latent Semantic Analysis (Landauer & Dumais, 1997): Model 1 compares the semantic similarity between a compound word and each of its constituents, and Model 2 derives the dominant meaning of a constituent based on a clustering analysis of morphological family members (e.g., “butterfingers” or “buttermilk” for “butter”). The proposed models successfully predicted participants’ transparency ratings, and we recommend that experimenters use Model 1 for English compounds and Model 2 for Chinese compounds, due to raters’ morphological processing in different writing systems. The dominance of lexical meaning, semantic transparency, and the average similarity between all pairs within a morphological family are provided, and practical applications for future studies are discussed. PMID:23784009

  7. Validating Quantitative Measurement Using Qualitative Data: Combining Rasch Scaling and Latent Semantic Analysis in Psychiatry

    NASA Astrophysics Data System (ADS)

    Lange, Rense

    2015-02-01

    An extension of concurrent validity is proposed that uses qualitative data for the purpose of validating quantitative measures. The approach relies on Latent Semantic Analysis (LSA) which places verbal (written) statements in a high dimensional semantic space. Using data from a medical / psychiatric domain as a case study - Near Death Experiences, or NDE - we established concurrent validity by connecting NDErs qualitative (written) experiential accounts with their locations on a Rasch scalable measure of NDE intensity. Concurrent validity received strong empirical support since the variance in the Rasch measures could be predicted reliably from the coordinates of their accounts in the LSA derived semantic space (R2 = 0.33). These coordinates also predicted NDErs age with considerable precision (R2 = 0.25). Both estimates are probably artificially low due to the small available data samples (n = 588). It appears that Rasch scalability of NDE intensity is a prerequisite for these findings, as each intensity level is associated (at least probabilistically) with a well- defined pattern of item endorsements.

  8. Applying latent semantic analysis to large-scale medical image databases.

    PubMed

    Stathopoulos, Spyridon; Kalamboukis, Theodore

    2015-01-01

    Latent Semantic Analysis (LSA) although has been used successfully in text retrieval when applied to CBIR induces scalability issues with large image collections. The method so far has been used with small collections due to the high cost of storage and computational time for solving the SVD problem for a large and dense feature matrix. Here we present an effective and efficient approach of applying LSA skipping the SVD solution of the feature matrix and overcoming in this way the deficiencies of the method with large scale datasets. Early and late fusion techniques are tested and their performance is calculated. The study demonstrates that early fusion of several composite descriptors with visual words increase retrieval effectiveness. It also combines well in a late fusion for mixed (textual and visual) ad hoc and modality classification. The results reported are comparable to state of the art algorithms without including additional knowledge from the medical domain. PMID:24934416

  9. Towards a typology of business process management professionals: identifying patterns of competences through latent semantic analysis

    NASA Astrophysics Data System (ADS)

    Müller, Oliver; Schmiedel, Theresa; Gorbacheva, Elena; vom Brocke, Jan

    2016-01-01

    While researchers have analysed the organisational competences that are required for successful Business Process Management (BPM) initiatives, individual BPM competences have not yet been studied in detail. In this study, latent semantic analysis is used to examine a collection of 1507 BPM-related job advertisements in order to develop a typology of BPM professionals. This empirical analysis reveals distinct ideal types and profiles of BPM professionals on several levels of abstraction. A closer look at these ideal types and profiles confirms that BPM is a boundary-spanning field that requires interdisciplinary sets of competence that range from technical competences to business and systems competences. Based on the study's findings, it is posited that individual and organisational alignment with the identified ideal types and profiles is likely to result in high employability and organisational BPM success.

  10. A visual latent semantic approach for automatic analysis and interpretation of anaplastic medulloblastoma virtual slides.

    PubMed

    Cruz-Roa, Angel; González, Fabio; Galaro, Joseph; Judkins, Alexander R; Ellison, David; Baccon, Jennifer; Madabhushi, Anant; Romero, Eduardo

    2012-01-01

    A method for automatic analysis and interpretation of histopathology images is presented. The method uses a representation of the image data set based on bag of features histograms built from visual dictionary of Haar-based patches and a novel visual latent semantic strategy for characterizing the visual content of a set of images. One important contribution of the method is the provision of an interpretability layer, which is able to explain a particular classification by visually mapping the most important visual patterns associated with such classification. The method was evaluated on a challenging problem involving automated discrimination of medulloblastoma tumors based on image derived attributes from whole slide images as anaplastic or non-anaplastic. The data set comprised 10 labeled histopathological patient studies, 5 for anaplastic and 5 for non-anaplastic, where 750 square images cropped randomly from cancerous region from whole slide per study. The experimental results show that the new method is competitive in terms of classification accuracy achieving 0.87 in average. PMID:23285547

  11. Exploring dangerous neighborhoods: Latent Semantic Analysis and computing beyond the bounds of the familiar

    PubMed Central

    Cohen, Trevor; Blatter, Brett; Patel, Vimla

    2005-01-01

    Certain applications require computer systems to approximate intended human meaning. This is achievable in constrained domains with a finite number of concepts. Areas such as psychiatry, however, draw on concepts from the world-at-large. A knowledge structure with broad scope is required to comprehend such domains. Latent Semantic Analysis (LSA) is an unsupervised corpus-based statistical method that derives quantitative estimates of the similarity between words and documents from their contextual usage statistics. The aim of this research was to evaluate the ability of LSA to derive meaningful associations between concepts relevant to the assessment of dangerousness in psychiatry. An expert reference model of dangerousness was used to guide the construction of a relevant corpus. Derived associations between words in the corpus were evaluated qualitatively. A similarity-based scoring function was used to assign dangerousness categories to discharge summaries. LSA was shown to derive intuitive relationships between concepts and correlated significantly better than random with human categorization of psychiatric discharge summaries according to dangerousness. The use of LSA to derive a simulated knowledge structure can extend the scope of computer systems beyond the boundaries of constrained conceptual domains. PMID:16779020

  12. Annotations of Mexican bullfighting videos for semantic index

    NASA Astrophysics Data System (ADS)

    Montoya Obeso, Abraham; Oropesa Morales, Lester Arturo; Fernando Vázquez, Luis; Cocolán Almeda, Sara Ivonne; Stoian, Andrei; García Vázquez, Mireya Saraí; Zamudio Fuentes, Luis Miguel; Montiel Perez, Jesús Yalja; de la O Torres, Saul; Ramírez Acosta, Alejandro Alvaro

    2015-09-01

    The video annotation is important for web indexing and browsing systems. Indeed, in order to evaluate the performance of video query and mining techniques, databases with concept annotations are required. Therefore, it is necessary generate a database with a semantic indexing that represents the digital content of the Mexican bullfighting atmosphere. This paper proposes a scheme to make complex annotations in a video in the frame of multimedia search engine project. Each video is partitioned using our segmentation algorithm that creates shots of different length and different number of frames. In order to make complex annotations about the video, we use ELAN software. The annotations are done in two steps: First, we take note about the whole content in each shot. Second, we describe the actions as parameters of the camera like direction, position and deepness. As a consequence, we obtain a more complete descriptor of every action. In both cases we use the concepts of the TRECVid 2014 dataset. We also propose new concepts. This methodology allows to generate a database with the necessary information to create descriptors and algorithms capable to detect actions to automatically index and classify new bullfighting multimedia content.

  13. The semantic pathfinder: using an authoring metaphor for generic multimedia indexing.

    PubMed

    Snoek, Cees G M; Worring, Marcel; Geusebroek, Jan-Mark; Koelma, Dennis C; Seinstra, Frank J; Smeulders, Arnold W M

    2006-10-01

    This paper presents the semantic pathfinder architecture for generic indexing of multimedia archives. The semantic pathfinder extracts semantic concepts from video by exploring different paths through three consecutive analysis steps, which we derive from the observation that produced video is the result of an authoring-driven process. We exploit this authoring metaphor for machine-driven understanding. The pathfinder starts with the content analysis step. In this analysis step, we follow a data-driven approach of indexing semantics. The style analysis step is the second analysis step. Here, we tackle the indexing problem by viewing a video from the perspective of production. Finally, in the context analysis step, we view semantics in context. The virtue of the semantic pathfinder is its ability to learn the best path of analysis steps on a per-concept basis. To show the generality of this novel indexing approach, we develop detectors for a lexicon of 32 concepts and we evaluate the semantic pathfinder against the 2004 NIST TRECVID video retrieval benchmark, using a news archive of 64 hours. Top ranking performance in the semantic concept detection task indicates the merit of the semantic pathfinder for generic indexing of multimedia archives. PMID:16986547

  14. N400-like Potentials and Reaction Times Index Semantic Relations between Highly Repeated Individual Words

    ERIC Educational Resources Information Center

    Renoult, Louis; Debruille, J. Bruno

    2011-01-01

    The N400 ERP is an electrophysiological index of semantic processing. Its amplitude varies with the semantic category of words, their concreteness, or whether their meaning matches that of a preceding context. The results of a number of studies suggest that these effects could be markedly reduced or suppressed for stimuli that are repeated.…

  15. Detecting Growth Shape Misspecifications in Latent Growth Models: An Evaluation of Fit Indexes

    ERIC Educational Resources Information Center

    Leite, Walter L.; Stapleton, Laura M.

    2011-01-01

    In this study, the authors compared the likelihood ratio test and fit indexes for detection of misspecifications of growth shape in latent growth models through a simulation study and a graphical analysis. They found that the likelihood ratio test, MFI, and root mean square error of approximation performed best for detecting model misspecification…

  16. DeepMeSH: deep semantic representation for improving large-scale MeSH indexing

    PubMed Central

    Peng, Shengwen; You, Ronghui; Wang, Hongning; Zhai, Chengxiang; Mamitsuka, Hiroshi; Zhu, Shanfeng

    2016-01-01

    Motivation: Medical Subject Headings (MeSH) indexing, which is to assign a set of MeSH main headings to citations, is crucial for many important tasks in biomedical text mining and information retrieval. Large-scale MeSH indexing has two challenging aspects: the citation side and MeSH side. For the citation side, all existing methods, including Medical Text Indexer (MTI) by National Library of Medicine and the state-of-the-art method, MeSHLabeler, deal with text by bag-of-words, which cannot capture semantic and context-dependent information well. Methods: We propose DeepMeSH that incorporates deep semantic information for large-scale MeSH indexing. It addresses the two challenges in both citation and MeSH sides. The citation side challenge is solved by a new deep semantic representation, D2V-TFIDF, which concatenates both sparse and dense semantic representations. The MeSH side challenge is solved by using the ‘learning to rank’ framework of MeSHLabeler, which integrates various types of evidence generated from the new semantic representation. Results: DeepMeSH achieved a Micro F-measure of 0.6323, 2% higher than 0.6218 of MeSHLabeler and 12% higher than 0.5637 of MTI, for BioASQ3 challenge data with 6000 citations. Availability and Implementation: The software is available upon request. Contact: zhusf@fudan.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307646

  17. Improving chemical entity recognition through h-index based semantic similarity

    PubMed Central

    2015-01-01

    Background Our approach to the BioCreative IV challenge of recognition and classification of drug names (CHEMDNER task) aimed at achieving high levels of precision by applying semantic similarity validation techniques to Chemical Entities of Biological Interest (ChEBI) mappings. Our assumption is that the chemical entities mentioned in the same fragment of text should share some semantic relation. This validation method was further improved by adapting the semantic similarity measure to take into account the h-index of each ancestor. We applied this method in two measures, simUI and simGIC, and validated the results obtained for the competition, comparing each adapted measure to its original version. Results For the competition, we trained a Random Forest classifier that uses various scores provided by our system, including semantic similarity, which improved the F-measure obtained with the Conditional Random Fields classifiers by 4.6%. Using a notion of concept relevance based on the h-index measure, we were able to enhance our validation process so that for a fixed recall, we increased precision by excluding from the results a higher amount of false positives. We plotted precision and recall values for a range of validation thresholds using different similarity measures, obtaining higher precision values for the same recall with the measures based on the h-index. Conclusions The semantic similarity measure we introduced was more efficient at validating text mining results from machine learning classifiers than other measures. We improved the results we obtained for the CHEMDNER task by maintaining high precision values while improving the recall and F-measure. PMID:25810770

  18. Semantic extraction and processing of medical records for patient-oriented visual index

    NASA Astrophysics Data System (ADS)

    Zheng, Weilin; Dong, Wenjie; Chen, Xiangjiao; Zhang, Jianguo

    2012-02-01

    To have comprehensive and completed understanding healthcare status of a patient, doctors need to search patient medical records from different healthcare information systems, such as PACS, RIS, HIS, USIS, as a reference of diagnosis and treatment decisions for the patient. However, it is time-consuming and tedious to do these procedures. In order to solve this kind of problems, we developed a patient-oriented visual index system (VIS) to use the visual technology to show health status and to retrieve the patients' examination information stored in each system with a 3D human model. In this presentation, we present a new approach about how to extract the semantic and characteristic information from the medical record systems such as RIS/USIS to create the 3D Visual Index. This approach includes following steps: (1) Building a medical characteristic semantic knowledge base; (2) Developing natural language processing (NLP) engine to perform semantic analysis and logical judgment on text-based medical records; (3) Applying the knowledge base and NLP engine on medical records to extract medical characteristics (e.g., the positive focus information), and then mapping extracted information to related organ/parts of 3D human model to create the visual index. We performed the testing procedures on 559 samples of radiological reports which include 853 focuses, and achieved 828 focuses' information. The successful rate of focus extraction is about 97.1%.

  19. TRANSMISSION AND RISK FACTORS FOR LATENT TUBERCULOSIS INFECTIONS AMONG INDEX CASE-MATCHED HOUSEHOLD CONTACTS.

    PubMed

    Faksri, Kiatichai; Reechaipichitkul, Wipa; Pimrin, Wilailuk; Bourpoern, Janpen; Prompinij, Supapim

    2015-05-01

    An understanding of the risk factors associated with acquiring and transmitting Mycobacterium tuberculosis (MTB) is required for controlling tuberculosis (TB). We aimed to determine the risk factors and transmission factors for latent tuberculosis infection (LTBI) in northeastern Thailand. Household contact persons (n = 70) and matched index patients with pulmonary TB (n = 42) who presented to Srinagarind Hospital, Khon Kaen, Thailand were interviewed from September 1, 2012 to March 31, 2014. LTBI was determined by positive results on both a tuberculin skin test and the QuantiFERON-TB Gold In-Tube test. Multivariate analysis of host and environmental risk factors was performed. Among contact persons, being aged 20 years (adjusted OR=14.0; 95% CI: 1.2-159.5), having a family relationship with a TB subject such as being a spouse or parent (adjusted OR=24.9; 95% CI: 2.4-263.9) and exposure to a TB subject for 5 hours/day (adjusted OR=9.2; 95% CI: 1.4-58.1) were risk factors for LTBI. Having a high bacillary load (adjusted OR=2; 95% CI: 1.26-3.17) or a moderate bacillary load (adjusted OR=1.39; 95% CI: 1.04-1.84) among TB subjects correlated with increased transmissibility compared to having a low bacillary load. The type of dwelling and density of household members were not found to be risk factors for LTBI in our study. We conclude being aged 20 years and having a relationship with a TB patient as a spouse or parent were risk factors for acquiring LTBI, and having a higher bacillary load was a risk factor for transmitting TB. Keywords: latent tuberculosis infection, transmission factor, risk factor, Mycobacterium tuberculosis, interferon-gamma release assay, Thailand PMID:26521523

  20. A semantic based video indexing and retrieval system for maritime surveillance

    NASA Astrophysics Data System (ADS)

    Nguyen, Hieu T.; Ramu, Prakash; Liu, Xiaoqing; Wei, Hai; Yadegar, Jacob

    2009-05-01

    Content-based video retrieval from archived image/video is a very attractive capability of modern intelligent video surveillance systems. This paper presents an innovative Semantic-Based Video Indexing and Retrieval (SBVIR) software toolkit to help users of intelligent video surveillance to easily and rapidly search the content of large video archives to conduct video-based forensic and image intelligence. Tailored for maritime environment, SBVIR is suited for surveillance applications in harbor, sea shores, or around ships. The system comprises two major modules: a video analytic module that performs automatic target detection, tracking, classification, activities recognition, and a retrieval module that performs data indexing, and information retrieval. SBVIR is capable of detecting and tracking objects from multiple cameras robustly in condition of dynamic water background and illumination changes. The system provides hierarchical target classification among a large ontology of watercraft classes, and is capable of recognizing a variety of boat activities. Video retrieval is achieved with both query-by-keyword and query-by-example. Users can query video content using semantic concepts selected from a large dictionary of objects and activities, display the history linked to a given target/activity, and search for anomalies. The user can interact with the system and provide feedbacks to tune the system for improved accuracy and relevance of retrieved data. SBVIR has been tested for real maritime surveillance scenarios and shown to be able to generate highly-semantic metadata tags that can be used during the retrieval to provide user with relevant and accurate data in real-time.

  1. Semantics-Based Intelligent Indexing and Retrieval of Digital Images - A Case Study

    NASA Astrophysics Data System (ADS)

    Osman, Taha; Thakker, Dhavalkumar; Schaefer, Gerald

    The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they typically rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this chapter we present a semantically enabled image annotation and retrieval engine that is designed to satisfy the requirements of commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as presenting our initial thoughts on exploiting lexical databases for explicit semantic-based query expansion.

  2. Targeted Echocardiographic Screening for Latent Rheumatic Heart Disease in Northern Uganda: Evaluating Familial Risk Following Identification of an Index Case

    PubMed Central

    Aliku, Twalib; Sable, Craig; Scheel, Amy; Tompsett, Alison; Lwabi, Peter; Okello, Emmy; McCarter, Robert; Summar, Marshall; Beaton, Andrea

    2016-01-01

    Background Echocardiographic screening for detection of latent RHD has shown potential as a strategy to decrease the burden of disease. However, further research is needed to determine optimal implementation strategies. RHD results from a complex interplay between environment and host susceptibility. Family members share both and relatives of children with latent RHD may represent a high-risk group. The objective of this study was to use echocardiographic family screening to determine the relative risk of RHD among first-degree relatives of children with latent RHD compared to the risk in first-degree relatives of healthy peers. Methodology/Principal Findings Previous school-based screening data were used to identify RHD positive children and RHD negative peers. All first-degree relatives ≥ 5 years were invited for echocardiography screening (2012 World Heart Federation Criteria). Sixty RHD positive cases (30 borderline/30 definite RHD) and 67 RHD negative cases were recruited. A total of 455/667 (68%) family members were screened. Definite RHD was more common in childhood siblings of RHD positive compared to RHD negative (p = 0.05). Children with any RHD were 4.5 times as likely to have a sibling with definite RHD, a risk that increased to 5.6 times when considering only cases with definite RHD. Mothers of RHD positive and RHD negative cases had an unexpectedly high rate of latent RHD (9.3%). Conclusions/Significance Siblings of RHD positive cases with RHD are more likely to have definite RHD and the relative risk is highest if the index case has definite RHD. Future screening programs should consider implementation of sibling screening following detection of an RHD positive child. Larger screening studies of adults are needed, as data on prevalence of latent RHD outside of childhood are sparse. Future studies should prioritize implementation research to answer questions of how RHD screening can best be integrated into existing healthcare structures, ensuring

  3. Word Sense Disambiguation by Selecting the Best Semantic Type Based on Journal Descriptor Indexing: Preliminary Experiment

    PubMed Central

    Humphrey, Susanne M.; Rogers, Willie J.; Kilicoglu, Halil; Demner-Fushman, Dina; Rindflesch, Thomas C.

    2008-01-01

    An experiment was performed at the National Library of Medicine® (NLM®) in word sense disambiguation (WSD) using the Journal Descriptor Indexing (JDI) methodology. The motivation is the need to solve the ambiguity problem confronting NLM’s MetaMap system, which maps free text to terms corresponding to concepts in NLM’s Unified Medical Language System® (UMLS®) Metathesaurus®. If the text maps to more than one Metathesaurus concept at the same high confidence score, MetaMap has no way of knowing which concept is the correct mapping. We describe the JDI methodology, which is ultimately based on statistical associations between words in a training set of MEDLINE® citations and a small set of journal descriptors (assigned by humans to journals per se) assumed to be inherited by the citations. JDI is the basis for selecting the best meaning that is correlated to UMLS semantic types (STs) assigned to ambiguous concepts in the Metathesaurus. For example, the ambiguity transport has two meanings: “Biological Transport” assigned the ST Cell Function and “Patient transport” assigned the ST Health Care Activity. A JDI-based methodology can analyze text containing transport and determine which ST receives a higher score for that text, which then returns the associated meaning, presumed to apply to the ambiguity itself. We then present an experiment in which a baseline disambiguation method was compared to four versions of JDI in disambiguating 45 ambiguous strings from NLM’s WSD Test Collection. Overall average precision for the highest-scoring JDI version was 0.7873 compared to 0.2492 for the baseline method, and average precision for individual ambiguities was greater than 0.90 for 23 of them (51%), greater than 0.85 for 24 (53%), and greater than 0.65 for 35 (79%). On the basis of these results, we hope to improve performance of JDI and test its use in applications. PMID:19890434

  4. Nouns, verbs, objects, actions, and abstractions: Local fMRI activity indexes semantics, not lexical categories

    PubMed Central

    Moseley, Rachel L.; Pulvermüller, Friedemann

    2014-01-01

    Noun/verb dissociations in the literature defy interpretation due to the confound between lexical category and semantic meaning; nouns and verbs typically describe concrete objects and actions. Abstract words, pertaining to neither, are a critical test case: dissociations along lexical-grammatical lines would support models purporting lexical category as the principle governing brain organisation, whilst semantic models predict dissociation between concrete words but not abstract items. During fMRI scanning, participants read orthogonalised word categories of nouns and verbs, with or without concrete, sensorimotor meaning. Analysis of inferior frontal/insula, precentral and central areas revealed an interaction between lexical class and semantic factors with clear category differences between concrete nouns and verbs but not abstract ones. Though the brain stores the combinatorial and lexical-grammatical properties of words, our data show that topographical differences in brain activation, especially in the motor system and inferior frontal cortex, are driven by semantics and not by lexical class. PMID:24727103

  5. Hybrid Ontology for Semantic Information Retrieval Model Using Keyword Matching Indexing System

    PubMed Central

    Uthayan, K. R.; Anandha Mala, G. S.

    2015-01-01

    Ontology is the process of growth and elucidation of concepts of an information domain being common for a group of users. Establishing ontology into information retrieval is a normal method to develop searching effects of relevant information users require. Keywords matching process with historical or information domain is significant in recent calculations for assisting the best match for specific input queries. This research presents a better querying mechanism for information retrieval which integrates the ontology queries with keyword search. The ontology-based query is changed into a primary order to predicate logic uncertainty which is used for routing the query to the appropriate servers. Matching algorithms characterize warm area of researches in computer science and artificial intelligence. In text matching, it is more dependable to study semantics model and query for conditions of semantic matching. This research develops the semantic matching results between input queries and information in ontology field. The contributed algorithm is a hybrid method that is based on matching extracted instances from the queries and information field. The queries and information domain is focused on semantic matching, to discover the best match and to progress the executive process. In conclusion, the hybrid ontology in semantic web is sufficient to retrieve the documents when compared to standard ontology. PMID:25922851

  6. Hybrid ontology for semantic information retrieval model using keyword matching indexing system.

    PubMed

    Uthayan, K R; Mala, G S Anandha

    2015-01-01

    Ontology is the process of growth and elucidation of concepts of an information domain being common for a group of users. Establishing ontology into information retrieval is a normal method to develop searching effects of relevant information users require. Keywords matching process with historical or information domain is significant in recent calculations for assisting the best match for specific input queries. This research presents a better querying mechanism for information retrieval which integrates the ontology queries with keyword search. The ontology-based query is changed into a primary order to predicate logic uncertainty which is used for routing the query to the appropriate servers. Matching algorithms characterize warm area of researches in computer science and artificial intelligence. In text matching, it is more dependable to study semantics model and query for conditions of semantic matching. This research develops the semantic matching results between input queries and information in ontology field. The contributed algorithm is a hybrid method that is based on matching extracted instances from the queries and information field. The queries and information domain is focused on semantic matching, to discover the best match and to progress the executive process. In conclusion, the hybrid ontology in semantic web is sufficient to retrieve the documents when compared to standard ontology. PMID:25922851

  7. Dissociation of Event-Related Potentials Indexing Arousal and Semantic Cohesion During Emotional Word Encoding

    ERIC Educational Resources Information Center

    Dillon, Daniel G.; Cooper, Julie J.; Grent-'t-Jong, Tineke; Woldorff, Marty G.; LaBar, Kevin S.

    2006-01-01

    Event-related potential (ERP) studies have shown that emotional stimuli elicit greater amplitude late positive-polarity potentials (LPPs) than neutral stimuli. This effect has been attributed to arousal, but emotional stimuli are also more semantically coherent than uncategorized neutral stimuli. ERPs were recorded during encoding of positive,…

  8. VastMM-Tag: Semantic Indexing and Browsing of Videos for E-Learning

    ERIC Educational Resources Information Center

    Morris, Mitchell J.

    2012-01-01

    Quickly accessing the contents of a video is challenging for users, particularly for unstructured video, which contains no intentional shot boundaries, no chapters, and no apparent edited format. We approach this problem in the domain of lecture videos though the use of machine learning, to gather semantic information about the videos; and through…

  9. GeoIRIS: Geospatial Information Retrieval and Indexing System—Content Mining, Semantics Modeling, and Complex Queries

    PubMed Central

    Shyu, Chi-Ren; Klaric, Matt; Scott, Grant J.; Barb, Adrian S.; Davis, Curt H.; Palaniappan, Kannappan

    2007-01-01

    Searching for relevant knowledge across heterogeneous geospatial databases requires an extensive knowledge of the semantic meaning of images, a keen eye for visual patterns, and efficient strategies for collecting and analyzing data with minimal human intervention. In this paper, we present our recently developed content-based multimodal Geospatial Information Retrieval and Indexing System (GeoIRIS) which includes automatic feature extraction, visual content mining from large-scale image databases, and high-dimensional database indexing for fast retrieval. Using these underpinnings, we have developed techniques for complex queries that merge information from heterogeneous geospatial databases, retrievals of objects based on shape and visual characteristics, analysis of multiobject relationships for the retrieval of objects in specific spatial configurations, and semantic models to link low-level image features with high-level visual descriptors. GeoIRIS brings this diverse set of technologies together into a coherent system with an aim of allowing image analysts to more rapidly identify relevant imagery. GeoIRIS is able to answer analysts’ questions in seconds, such as “given a query image, show me database satellite images that have similar objects and spatial relationship that are within a certain radius of a landmark.” PMID:18270555

  10. Automated semantic indexing of imaging reports to support retrieval of medical images in the multimedia electronic medical record.

    PubMed

    Lowe, H J; Antipov, I; Hersh, W; Smith, C A; Mailhot, M

    1999-12-01

    This paper describes preliminary work evaluating automated semantic indexing of radiology imaging reports to represent images stored in the Image Engine multimedia medical record system at the University of Pittsburgh Medical Center. The authors used the SAPHIRE indexing system to automatically identify important biomedical concepts within radiology reports and represent these concepts with terms from the 1998 edition of the U.S. National Library of Medicine's Unified Medical Language System (UMLS) Metathesaurus. This automated UMLS indexing was then compared with manual UMLS indexing of the same reports. Human indexing identified appropriate UMLS Metathesaurus descriptors for 81% of the important biomedical concepts contained in the report set. SAPHIRE automatically identified UMLS Metathesaurus descriptors for 64% of the important biomedical concepts contained in the report set. The overall conclusions of this pilot study were that the UMLS metathesaurus provided adequate coverage of the majority of the important concepts contained within the radiology report test set and that SAPHIRE could automatically identify and translate almost two thirds of these concepts into appropriate UMLS descriptors. Further work is required to improve both the recall and precision of this automated concept extraction process. PMID:10805018

  11. Cross-language MeSH indexing using morpho-semantic normalization.

    PubMed

    Markó, Kornél; Daumke, Philipp; Schulz, Stefan; Hahn, Udo

    2003-01-01

    We consider three alternative procedures for the automatic indexing of medical documents using MeSH thesaurus identifiers as target units (document descriptors). Rather than considering complete words as the starting point of the indexing procedure, we here propose morphologically plausible subwords as basic units from which MeSH terms are derived. We describe the morphological segmentation and normalization procedures, as well as the mappings from subwords to MeSH terms, and discuss results from an evaluation carried out on a German-language corpus. PMID:14728208

  12. Indexing method of digital audiovisual medical resources with semantic Web integration.

    PubMed

    Cuggia, Marc; Mougin, Fleur; Le Beux, Pierre

    2003-01-01

    Digitalization of audio-visual resources combined with the performances of the networks offer many possibilities which are the subject of intensive work in the scientific and industrial sectors. Indexing such resources is a major challenge. Recently, the Motion Pictures Expert Group (MPEG) has been developing MPEG-7, a standard for describing multimedia content. The good of this standard is to develop a rich set of standardized tools to enable fast efficient retrieval from digital archives or filtering audiovisual broadcasts on the internet. How this kind of technologies could be used in the medical context? In this paper, we propose a simpler indexing system, based on Dublin Core standard and complaint to MPEG-7. We use MeSH and UMLS to introduce conceptual navigation. We also present a video-platform with enables to encode and give access to audio-visual resources in streaming mode. PMID:14664072

  13. Indexing method of digital audiovisual medical resources with semantic Web integration.

    PubMed

    Cuggia, Marc; Mougin, Fleur; Le Beux, Pierre

    2005-03-01

    Digitalization of audiovisual resources and network capability offer many possibilities which are the subject of intensive work in scientific and industrial sectors. Indexing such resources is a major challenge. Recently, the Motion Pictures Expert Group (MPEG) has developed MPEG-7, a standard for describing multimedia content. The goal of this standard is to develop a rich set of standardized tools to enable efficient retrieval from digital archives or the filtering of audiovisual broadcasts on the Internet. How could this kind of technology be used in the medical context? In this paper, we propose a simpler indexing system, based on the Dublin Core standard and compliant to MPEG-7. We use MeSH and the UMLS to introduce conceptual navigation. We also present a video-platform which enables encoding and gives access to audiovisual resources in streaming mode. PMID:15694622

  14. Once is Enough: N400 Indexes Semantic Integration of Novel Word Meanings from a Single Exposure in Context

    PubMed Central

    Borovsky, Arielle; Elman, Jeffrey L.; Kutas, Marta

    2012-01-01

    We investigated the impact of contextual constraint on the integration of novel word meanings into semantic memory. Adults read strongly or weakly constraining sentences ending in known or unknown (novel) words as scalp-recorded electrical brain activity was recorded. Word knowledge was assessed via a lexical decision task in which recently seen known and unknown word sentence endings served as primes for semantically related, unrelated, and synonym/identical target words. As expected, N400 amplitudes to target words preceded by known word primes were reduced by prime-target relatedness. Critically, N400 amplitudes to targets preceded by novel primes also varied with prime-target relatedness, but only when they had initially appeared in highly constraining sentences. This demonstrates for the first time that fast-mapped word representations can develop strong associations with semantically related word meanings and reveals a rapid neural process that can integrate information about word meanings into the mental lexicon of young adults. PMID:23125559

  15. Once Is Enough: N400 Indexes Semantic Integration of Novel Word Meanings from a Single Exposure in Context

    ERIC Educational Resources Information Center

    Borovsky, Arielle; Elman, Jeffrey L.; Kutas, Marta

    2012-01-01

    We investigated the impact of contextual constraint on the integration of novel word meanings into semantic memory. Adults read strongly or weakly constraining sentences ending in known or unknown (novel) words as scalp-recorded electrical brain activity was recorded. Word knowledge was assessed via a lexical decision task in which recently seen…

  16. Semantic Tools in Information Retrieval.

    ERIC Educational Resources Information Center

    Rubinoff, Morris; Stone, Don C.

    This report discusses the problem of the meansings of words used in information retrieval systems, and shows how semantic tools can aid in the communication which takes place between indexers and searchers via index terms. After treating the differing use of semantic tools in different types of systems, two tools (classification tables and…

  17. Comparing Latent Dirichlet Allocation and Latent Semantic Analysis as Classifiers

    ERIC Educational Resources Information Center

    Anaya, Leticia H.

    2011-01-01

    In the Information Age, a proliferation of unstructured text electronic documents exists. Processing these documents by humans is a daunting task as humans have limited cognitive abilities for processing large volumes of documents that can often be extremely lengthy. To address this problem, text data computer algorithms are being developed.…

  18. Semantic Data Matching: Principles and Performance

    NASA Astrophysics Data System (ADS)

    Deaton, Russell; Doan, Thao; Schweiger, Tom

    Automated and real-time management of customer relationships requires robust and intelligent data matching across widespread and diverse data sources. Simple string matching algorithms, such as dynamic programming, can handle typographical errors in the data, but are less able to match records that require contextual and experiential knowledge. Latent Semantic Indexing (LSI) (Berry et al. ; Deerwester et al. is a machine intelligence technique that can match data based upon higher order structure, and is able to handle difficult problems, such as words that have different meanings but the same spelling, are synonymous, or have multiple meanings. Essentially, the technique matches records based upon context, or mathematically quantifying when terms occur in the same record.

  19. A Semantic Relatedness Approach for Traceability Link Recovery

    SciTech Connect

    Mahmoud, Anas M.; Niu, Nan; Xu, Songhua

    2012-01-01

    Human analysts working with automated tracing tools need to directly vet candidate traceability links in order to determine the true traceability information. Currently, human intervention happens at the end of the traceability process, after candidate traceability links have already been generated. This often leads to a decline in the results accuracy. In this paper, we propose an approach, based on semantic relatedness (SR), which brings human judgment to an earlier stage of the tracing process by integrating it into the underlying retrieval mechanism. SR tries to mimic human mental model of relevance by considering a broad range of semantic relations, hence producing more semantically meaningful results. We evaluated our approach using three datasets from different application domains, and assessed the tracing results via six different performance measures concerning both result quality and browsability. The empirical evaluation results show that our SR approach achieves a significantly better performance in recovering true links than a standard Vector Space Model (VSM) in all datasets. Our approach also achieves a significantly better precision than Latent Semantic Indexing (LSI) in two of our datasets.

  20. Learning multimodal latent attributes.

    PubMed

    Fu, Yanwei; Hospedales, Timothy M; Xiang, Tao; Gong, Shaogang

    2014-02-01

    The rapid development of social media sharing has created a huge demand for automatic media classification and annotation techniques. Attribute learning has emerged as a promising paradigm for bridging the semantic gap and addressing data sparsity via transferring attribute knowledge in object recognition and relatively simple action classification. In this paper, we address the task of attribute learning for understanding multimedia data with sparse and incomplete labels. In particular, we focus on videos of social group activities, which are particularly challenging and topical examples of this task because of their multimodal content and complex and unstructured nature relative to the density of annotations. To solve this problem, we 1) introduce a concept of semilatent attribute space, expressing user-defined and latent attributes in a unified framework, and 2) propose a novel scalable probabilistic topic model for learning multimodal semilatent attributes, which dramatically reduces requirements for an exhaustive accurate attribute ontology and expensive annotation effort. We show that our framework is able to exploit latent attributes to outperform contemporary approaches for addressing a variety of realistic multimedia sparse data learning tasks including: multitask learning, learning with label noise, N-shot transfer learning, and importantly zero-shot learning. PMID:24356351

  1. Semantic Processing of Mathematical Gestures

    ERIC Educational Resources Information Center

    Lim, Vanessa K.; Wilson, Anna J.; Hamm, Jeff P.; Phillips, Nicola; Iwabuchi, Sarina J.; Corballis, Michael C.; Arzarello, Ferdinando; Thomas, Michael O. J.

    2009-01-01

    Objective: To examine whether or not university mathematics students semantically process gestures depicting mathematical functions (mathematical gestures) similarly to the way they process action gestures and sentences. Semantic processing was indexed by the N400 effect. Results: The N400 effect elicited by words primed with mathematical gestures…

  2. Towards knowledge-based retrieval of medical images. The role of semantic indexing, image content representation and knowledge-based retrieval.

    PubMed

    Lowe, H J; Antipov, I; Hersh, W; Smith, C A

    1998-01-01

    Medicine is increasingly image-intensive. The central importance of imaging technologies such as computerized tomography and magnetic resonance imaging in clinical decision making, combined with the trend to store many "traditional" clinical images such as conventional radiographs, microscopic pathology and dermatology images in digital format present both challenges and an opportunities for the designers of clinical information systems. The emergence of Multimedia Electronic Medical Record Systems (MEMRS), architectures that integrate medical images with text-based clinical data, will further hasten this trend. The development of these systems, storing a large and diverse set of medical images, suggests that in the future MEMRS will become important digital libraries supporting patient care, research and education. The representation and retrieval of clinical images within these systems is problematic as conventional database architectures and information retrieval models have, until recently, focused largely on text-based data. Medical imaging data differs in many ways from text-based medical data but perhaps the most important difference is that the information contained within imaging data is fundamentally knowledge-based. New representational and retrieval models for clinical images will be required to address this issue. Within the Image Engine multimedia medical record system project at the University of Pittsburgh we are evolving an approach to representation and retrieval of medical images which combines semantic indexing using the UMLS Metathesuarus, image content-based representation and knowledge-based image analysis. PMID:9929345

  3. Neuro-Semantics and Semantics.

    ERIC Educational Resources Information Center

    Holmes, Stewart W.

    1987-01-01

    Draws distinctions between the terms semantics (dealing with such verbal parameters as dictionaries and "laws" of logic and rhetoric), general semantics (semantics, plus the complex, dynamic, organismal properties of human beings and their physical environment), and neurosemantics (names for relations-based input from the neurosensory system, and…

  4. Manifest and Latent Variates

    ERIC Educational Resources Information Center

    Maraun, Michael D.; Halpin, Peter F.

    2008-01-01

    The clue to what latent variable models are, and to a workable account of the basis for the traditional manifest/latent variable distinction, lies in a reconsideration of the indeterminacy property of linear factor structures. In this article, the authors contend that latent variable models are not detectors of unobservable latent structures,…

  5. Language Networks Associated with Computerized Semantic Indices

    PubMed Central

    Pakhomov, Serguei V. S.; Jones, David T.; Knopman, David S.

    2014-01-01

    Tests of generative semantic verbal fluency are widely used to study organization and representation of concepts in the human brain. Previous studies demonstrated that clustering and switching behavior during verbal fluency tasks is supported by multiple brain mechanisms associated with semantic memory and executive control. Previous work relied on manual assessments of semantic relatedness between words and grouping of words into semantic clusters. We investigated a computational linguistic approach to measuring the strength of semantic relatedness between words based on latent semantic analysis of word co-occurrences in a subset of a large online encyclopedia. We computed semantic clustering indices and compared them to brain network connectivity measures obtained with task-free fMRI in a sample consisting of healthy participants and those differentially affected by cognitive impairment. We found that semantic clustering indices were associated with brain network connectivity in distinct areas including fronto-temporal, fronto-parietal and fusiform gyrus regions. This study shows that computerized semantic indices complement traditional assessments of verbal fluency to provide a more complete account of the relationship between brain and verbal behavior involved organization and retrieval of lexical information from memory. PMID:25315785

  6. Semantic Desktop

    NASA Astrophysics Data System (ADS)

    Sauermann, Leo; Kiesel, Malte; Schumacher, Kinga; Bernardi, Ansgar

    In diesem Beitrag wird gezeigt, wie der Arbeitsplatz der Zukunft aussehen könnte und wo das Semantic Web neue Möglichkeiten eröffnet. Dazu werden Ansätze aus dem Bereich Semantic Web, Knowledge Representation, Desktop-Anwendungen und Visualisierung vorgestellt, die es uns ermöglichen, die bestehenden Daten eines Benutzers neu zu interpretieren und zu verwenden. Dabei bringt die Kombination von Semantic Web und Desktop Computern besondere Vorteile - ein Paradigma, das unter dem Titel Semantic Desktop bekannt ist. Die beschriebenen Möglichkeiten der Applikationsintegration sind aber nicht auf den Desktop beschränkt, sondern können genauso in Web-Anwendungen Verwendung finden.

  7. Semantic Mapping.

    ERIC Educational Resources Information Center

    Johnson, Dale D.; And Others

    1986-01-01

    Describes semantic mapping, an effective strategy for vocabulary instruction that involves the categorical structuring of information in graphic form and requires students to relate new words to their own experience and prior knowledge. (HOD)

  8. Generalized Latent Trait Models.

    ERIC Educational Resources Information Center

    Moustaki, Irini; Knott, Martin

    2000-01-01

    Discusses a general model framework within which manifest variables with different distributions in the exponential family can be analyzed with a latent trait model. Presents a unified maximum likelihood method for estimating the parameters of the generalized latent trait model and discusses the scoring of individuals on the latent dimensions.…

  9. Principal Semantic Components of Language and the Measurement of Meaning

    PubMed Central

    Samsonovic, Alexei V.; Ascoli, Giorgio A.

    2010-01-01

    Metric systems for semantics, or semantic cognitive maps, are allocations of words or other representations in a metric space based on their meaning. Existing methods for semantic mapping, such as Latent Semantic Analysis and Latent Dirichlet Allocation, are based on paradigms involving dissimilarity metrics. They typically do not take into account relations of antonymy and yield a large number of domain-specific semantic dimensions. Here, using a novel self-organization approach, we construct a low-dimensional, context-independent semantic map of natural language that represents simultaneously synonymy and antonymy. Emergent semantics of the map principal components are clearly identifiable: the first three correspond to the meanings of “good/bad” (valence), “calm/excited” (arousal), and “open/closed” (freedom), respectively. The semantic map is sufficiently robust to allow the automated extraction of synonyms and antonyms not originally in the dictionaries used to construct the map and to predict connotation from their coordinates. The map geometric characteristics include a limited number (∼4) of statistically significant dimensions, a bimodal distribution of the first component, increasing kurtosis of subsequent (unimodal) components, and a U-shaped maximum-spread planar projection. Both the semantic content and the main geometric features of the map are consistent between dictionaries (Microsoft Word and Princeton's WordNet), among Western languages (English, French, German, and Spanish), and with previously established psychometric measures. By defining the semantics of its dimensions, the constructed map provides a foundational metric system for the quantitative analysis of word meaning. Language can be viewed as a cumulative product of human experiences. Therefore, the extracted principal semantic dimensions may be useful to characterize the general semantic dimensions of the content of mental states. This is a fundamental step toward a

  10. Generative Semantics

    ERIC Educational Resources Information Center

    Bagha, Karim Nazari

    2011-01-01

    Generative semantics is (or perhaps was) a research program within linguistics, initiated by the work of George Lakoff, John R. Ross, Paul Postal and later McCawley. The approach developed out of transformational generative grammar in the mid 1960s, but stood largely in opposition to work by Noam Chomsky and his students. The nature and genesis of…

  11. On latent fingerprint enhancement

    NASA Astrophysics Data System (ADS)

    Yoon, Soweon; Feng, Jianjiang; Jain, Anil K.

    2010-04-01

    Automatic feature extraction in latent fingerprints is a challenging problem due to poor quality of most latents, such as unclear ridge structures, overlapped lines and letters, and overlapped fingerprints. We proposed a latent fingerprint enhancement algorithm which requires manually marked region of interest (ROI) and singular points. The core of the proposed enhancement algorithm is a novel orientation field estimation algorithm, which fits orientation field model to coarse orientation field estimated from skeleton outputted by a commercial fingerprint SDK. Experimental results on NIST SD27 latent fingerprint database indicate that by incorporating the proposed enhancement algorithm, the matching accuracy of the commercial matcher was significantly improved.

  12. Textrous!: Extracting Semantic Textual Meaning from Gene Sets

    PubMed Central

    Daimon, Caitlin M.; Siddiqui, Sana; Luttrell, Louis M.; Maudsley, Stuart

    2013-01-01

    The un-biased and reproducible interpretation of high-content gene sets from large-scale genomic experiments is crucial to the understanding of biological themes, validation of experimental data, and the eventual development of plans for future experimentation. To derive biomedically-relevant information from simple gene lists, a mathematical association to scientific language and meaningful words or sentences is crucial. Unfortunately, existing software for deriving meaningful and easily-appreciable scientific textual ‘tokens’ from large gene sets either rely on controlled vocabularies (Medical Subject Headings, Gene Ontology, BioCarta) or employ Boolean text searching and co-occurrence models that are incapable of detecting indirect links in the literature. As an improvement to existing web-based informatic tools, we have developed Textrous!, a web-based framework for the extraction of biomedical semantic meaning from a given input gene set of arbitrary length. Textrous! employs natural language processing techniques, including latent semantic indexing (LSI), sentence splitting, word tokenization, parts-of-speech tagging, and noun-phrase chunking, to mine MEDLINE abstracts, PubMed Central articles, articles from the Online Mendelian Inheritance in Man (OMIM), and Mammalian Phenotype annotation obtained from Jackson Laboratories. Textrous! has the ability to generate meaningful output data with even very small input datasets, using two different text extraction methodologies (collective and individual) for the selecting, ranking, clustering, and visualization of English words obtained from the user data. Textrous!, therefore, is able to facilitate the output of quantitatively significant and easily appreciable semantic words and phrases linked to both individual gene and batch genomic data. PMID:23646135

  13. Textrous!: extracting semantic textual meaning from gene sets.

    PubMed

    Chen, Hongyu; Martin, Bronwen; Daimon, Caitlin M; Siddiqui, Sana; Luttrell, Louis M; Maudsley, Stuart

    2013-01-01

    The un-biased and reproducible interpretation of high-content gene sets from large-scale genomic experiments is crucial to the understanding of biological themes, validation of experimental data, and the eventual development of plans for future experimentation. To derive biomedically-relevant information from simple gene lists, a mathematical association to scientific language and meaningful words or sentences is crucial. Unfortunately, existing software for deriving meaningful and easily-appreciable scientific textual 'tokens' from large gene sets either rely on controlled vocabularies (Medical Subject Headings, Gene Ontology, BioCarta) or employ Boolean text searching and co-occurrence models that are incapable of detecting indirect links in the literature. As an improvement to existing web-based informatic tools, we have developed Textrous!, a web-based framework for the extraction of biomedical semantic meaning from a given input gene set of arbitrary length. Textrous! employs natural language processing techniques, including latent semantic indexing (LSI), sentence splitting, word tokenization, parts-of-speech tagging, and noun-phrase chunking, to mine MEDLINE abstracts, PubMed Central articles, articles from the Online Mendelian Inheritance in Man (OMIM), and Mammalian Phenotype annotation obtained from Jackson Laboratories. Textrous! has the ability to generate meaningful output data with even very small input datasets, using two different text extraction methodologies (collective and individual) for the selecting, ranking, clustering, and visualization of English words obtained from the user data. Textrous!, therefore, is able to facilitate the output of quantitatively significant and easily appreciable semantic words and phrases linked to both individual gene and batch genomic data. PMID:23646135

  14. Latent Variable Theory

    ERIC Educational Resources Information Center

    Borsboom, Denny

    2008-01-01

    This paper formulates a metatheoretical framework for latent variable modeling. It does so by spelling out the difference between observed and latent variables. This difference is argued to be purely epistemic in nature: We treat a variable as "observed" when the inference from data structure to variable structure can be made with certainty and as…

  15. Multimethod latent class analysis

    PubMed Central

    Nussbeck, Fridtjof W.; Eid, Michael

    2015-01-01

    Correct and, hence, valid classifications of individuals are of high importance in the social sciences as these classifications are the basis for diagnoses and/or the assignment to a treatment. The via regia to inspect the validity of psychological ratings is the multitrait-multimethod (MTMM) approach. First, a latent variable model for the analysis of rater agreement (latent rater agreement model) will be presented that allows for the analysis of convergent validity between different measurement approaches (e.g., raters). Models of rater agreement are transferred to the level of latent variables. Second, the latent rater agreement model will be extended to a more informative MTMM latent class model. This model allows for estimating (i) the convergence of ratings, (ii) method biases in terms of differential latent distributions of raters and differential associations of categorizations within raters (specific rater bias), and (iii) the distinguishability of categories indicating if categories are satisfyingly distinct from each other. Finally, an empirical application is presented to exemplify the interpretation of the MTMM latent class model. PMID:26441714

  16. Formal semantic and computer text processing, 1982

    SciTech Connect

    Meunier, J.G.; Lepage, F.

    1983-01-01

    Computer processing of large nonpreedited natural language texts has often been limited either to managing and editing or to analysing basic levels of content (indexes, concordances, clusters, etc.). Few systems approach syntactic information, even less semantic information. Because of the complexity and the originality of the underlying semantic information of any text it is not possible to import directly the AI and computational semantic concepts. It is necessary to explore news paths. The research presented here is oriented toward the understanding of certain semantic aspects in computer text processing (words and meaning representation and inference patterns). This is done through a model theoretic approach embedded in an algebraic language. The hypothesis which governs the concepts and the distinctions is the following: discourse in a text constitutes a semantic space built of an ordered set of sentences which are of different logical types and which present a specific pattern of coherence expressible in a syntactic manner. 47 references.

  17. Preserved Musical Semantic Memory in Semantic Dementia

    PubMed Central

    Weinstein, Jessica; Koenig, Phyllis; Gunawardena, Delani; McMillan, Corey; Bonner, Michael; Grossman, Murray

    2012-01-01

    Objective To understand the scope of semantic impairment in semantic dementia. Design Case study. Setting Academic medical center. Patient A man with semantic dementia, as demonstrated by clinical, neuropsychological, and imaging studies. Main Outcome Measures Music performance and magnetic resonance imaging results. Results Despite profoundly impaired semantic memory for words and objects due to left temporal lobe atrophy, this semiprofessional musician was creative and expressive in demonstrating preserved musical knowledge. Conclusion Long-term representations of words and objects in semantic memory may be dissociated from meaningful knowledge in other domains, such as music. PMID:21320991

  18. Image Annotation by Latent Community Detection and Multikernel Learning.

    PubMed

    Gu, Yun; Qian, Xueming; Li, Qing; Wang, Meng; Hong, Richang; Tian, Qi

    2015-11-01

    Automatic image annotation is an attractive service for users and administrators of online photo sharing websites. In this paper, we propose an image annotation approach that exploits latent semantic community of labels and multikernel learning (LCMKL). First, a concept graph is constructed for labels indicating the relationship between the concepts. Based on the concept graph, semantic communities are explored using an automatic community detection method. For an image to be annotated, a multikernel support vector machine is used to determine the image's latent community from its visual features. Then, a candidate label ranking based approach is determined by intracommunity and intercommunity ranking. Experiments on the NUS-WIDE database and IAPR TC-12 data set demonstrate that LCMKL outperforms some state-of-the-art approaches. PMID:26068319

  19. A Study about Placement Support Using Semantic Similarity

    ERIC Educational Resources Information Center

    Katz, Marco; van Bruggen, Jan; Giesbers, Bas; Waterink, Wim; Eshuis, Jannes; Koper, Rob

    2014-01-01

    This paper discusses Latent Semantic Analysis (LSA) as a method for the assessment of prior learning. The Accreditation of Prior Learning (APL) is a procedure to offer learners an individualized curriculum based on their prior experiences and knowledge. The placement decisions in this process are based on the analysis of student material by domain…

  20. Facilitating Image Search With a Scalable and Compact Semantic Mapping.

    PubMed

    Wang, Meng; Li, Weisheng; Liu, Dong; Ni, Bingbing; Shen, Jialie; Yan, Shuicheng

    2015-08-01

    This paper introduces a novel approach to facilitating image search based on a compact semantic embedding. A novel method is developed to explicitly map concepts and image contents into a unified latent semantic space for the representation of semantic concept prototypes. Then, a linear embedding matrix is learned that maps images into the semantic space, such that each image is closer to its relevant concept prototype than other prototypes. In our approach, the semantic concepts equated with query keywords and the images mapped into the vicinity of the prototype are retrieved by our scheme. In addition, a computationally efficient method is introduced to incorporate new semantic concept prototypes into the semantic space by updating the embedding matrix. This novelty improves the scalability of the method and allows it to be applied to dynamic image repositories. Therefore, the proposed approach not only narrows semantic gap but also supports an efficient image search process. We have carried out extensive experiments on various cross-modality image search tasks over three widely-used benchmark image datasets. Results demonstrate the superior effectiveness, efficiency, and scalability of our proposed approach. PMID:25248210

  1. Impact of latent infection treatment in indigenous populations.

    PubMed

    Yuhara, Lucia Suemi; Sacchi, Flávia Patussi Correia; Croda, Julio

    2013-01-01

    The aims of the present study were to identify risk factors associated with latent tuberculosis (TB), examine the development of active disease among contacts, and assess the effectiveness of treating latent infection in indigenous Brazilians from January 2006 to December 2011. This was a retrospective study consisting of 1,371 tuberculosis contacts, 392 of whom underwent treatment for latent infection. Morbidity-from-TB data were obtained from the Information System for Disease Notification (SINAN) database, and the contacts' data were collected from the clinical records using forms employed by Special Department of Indigenous Health (SESAI) multidisciplinary teams, according to SESAI's instructions. The variables that were associated with latent infection among the contacts were age (odds ratio [OR]: 1.03; 95% confidence interval [CI]: 1.02-1.04) and close contact with a smear-positive index case (OR: 2.26, 95% CI: 1.59-3.22). The variables associated with the development of active TB among the contacts were a tuberculin skin test (TST) ≥10 mm (relative risk [RR]: 1.12, 95% CI: 1.07-1.17), age (RR: 1.01, 95% CI: 1.00-1.03), and treatment of latent infection (RR: 0.03, 95% CI: 0.01-0.27). The estimated number of latent infection treatments needed to prevent one case of active TB among the contacts was 51 treatments (95% CI: 33-182). In contacts with TST ≥10 mm, 10 (95% CI: 6-19) latent infection treatments were necessary to prevent one case of active TB. Age and close contact with a smear-positive index case were associated with latent TB. Screening with TST is a high priority among individuals contacting smear-positive index cases. Age and TST are associated with the development of active TB among contacts, and treatment of latent infection is an effective measure to control TB in indigenous communities. PMID:23936264

  2. SEMANTICS AND CRITICAL READING.

    ERIC Educational Resources Information Center

    FLANIGAN, MICHAEL C.

    PROFICIENCY IN CRITICAL READING CAN BE ACCELERATED BY MAKING STUDENTS AWARE OF VARIOUS SEMANTIC DEVICES THAT HELP CLARIFY MEANINGS AND PURPOSES. EXCERPTS FROM THE ARTICLE "TEEN-AGE CORRUPTION" FROM THE NINTH-GRADE SEMANTICS UNIT WRITTEN BY THE PROJECT ENGLISH DEMONSTRATION CENTER AT EUCLID, OHIO, ARE USED TO ILLUSTRATE HOW SEMANTICS RELATE TO…

  3. Measuring Latent Quantities

    ERIC Educational Resources Information Center

    McDonald, Roderick P.

    2011-01-01

    A distinction is proposed between measures and predictors of latent variables. The discussion addresses the consequences of the distinction for the true-score model, the linear factor model, Structural Equation Models, longitudinal and multilevel models, and item-response models. A distribution-free treatment of calibration and…

  4. Latent Variable Interaction Modeling.

    ERIC Educational Resources Information Center

    Schumacker, Randall E.

    2002-01-01

    Used simulation to study two different approaches to latent variable interaction modeling with continuous observed variables: (1) a LISREL 8.30 program and (2) data analysis through PRELIS2 and SIMPLIS programs. Results show that parameter estimation was similar but standard errors were different. Discusses differences in ease of implementation.…

  5. Exploring MEDLINE Space with Random Indexing and Pathfinder Networks

    PubMed Central

    Cohen, Trevor

    2008-01-01

    The integration of disparate research domains is a prerequisite for the success of the translational science initiative. MEDLINE abstracts contain content from a broad range of disciplines, presenting an opportunity for the development of methods able to integrate the knowledge they contain. Latent Semantic Analysis (LSA) and related methods learn human-like associations between terms from unannotated text. However, their computational and memory demands limits their ability to address a corpus of this size. Furthermore, visualization methods previously used in conjunction with LSA have limited ability to define the local structure of the associative networks LSA learns. This paper explores these issues by (1) processing the entire MEDLINE corpus using Random Indexing, a variant of LSA, and (2) exploring learned associations using Pathfinder Networks. Meaningful associations are inferred from MEDLINE, including a drug-disease association undetected by PUBMED search. PMID:18999236

  6. Somatotopic Semantic Priming and Prediction in the Motor System

    PubMed Central

    Grisoni, Luigi; Dreyer, Felix R.; Pulvermüller, Friedemann

    2016-01-01

    The recognition of action-related sounds and words activates motor regions, reflecting the semantic grounding of these symbols in action information; in addition, motor cortex exerts causal influences on sound perception and language comprehension. However, proponents of classic symbolic theories still dispute the role of modality-preferential systems such as the motor cortex in the semantic processing of meaningful stimuli. To clarify whether the motor system carries semantic processes, we investigated neurophysiological indexes of semantic relationships between action-related sounds and words. Event-related potentials revealed that action-related words produced significantly larger stimulus-evoked (Mismatch Negativity-like) and predictive brain responses (Readiness Potentials) when presented in body-part-incongruent sound contexts (e.g., “kiss” in footstep sound context; “kick” in whistle context) than in body-part-congruent contexts, a pattern reminiscent of neurophysiological correlates of semantic priming. Cortical generators of the semantic relatedness effect were localized in areas traditionally associated with semantic memory, including left inferior frontal cortex and temporal pole, and, crucially, in motor areas, where body-part congruency of action sound–word relationships was indexed by a somatotopic pattern of activation. As our results show neurophysiological manifestations of action-semantic priming in the motor cortex, they prove semantic processing in the motor system and thus in a modality-preferential system of the human brain. PMID:26908635

  7. Somatotopic Semantic Priming and Prediction in the Motor System.

    PubMed

    Grisoni, Luigi; Dreyer, Felix R; Pulvermüller, Friedemann

    2016-05-01

    The recognition of action-related sounds and words activates motor regions, reflecting the semantic grounding of these symbols in action information; in addition, motor cortex exerts causal influences on sound perception and language comprehension. However, proponents of classic symbolic theories still dispute the role of modality-preferential systems such as the motor cortex in the semantic processing of meaningful stimuli. To clarify whether the motor system carries semantic processes, we investigated neurophysiological indexes of semantic relationships between action-related sounds and words. Event-related potentials revealed that action-related words produced significantly larger stimulus-evoked (Mismatch Negativity-like) and predictive brain responses (Readiness Potentials) when presented in body-part-incongruent sound contexts (e.g., "kiss" in footstep sound context; "kick" in whistle context) than in body-part-congruent contexts, a pattern reminiscent of neurophysiological correlates of semantic priming. Cortical generators of the semantic relatedness effect were localized in areas traditionally associated with semantic memory, including left inferior frontal cortex and temporal pole, and, crucially, in motor areas, where body-part congruency of action sound-word relationships was indexed by a somatotopic pattern of activation. As our results show neurophysiological manifestations of action-semantic priming in the motor cortex, they prove semantic processing in the motor system and thus in a modality-preferential system of the human brain. PMID:26908635

  8. Semantically Interoperable XML Data.

    PubMed

    Vergara-Niedermayr, Cristobal; Wang, Fusheng; Pan, Tony; Kurc, Tahsin; Saltz, Joel

    2013-09-01

    XML is ubiquitously used as an information exchange platform for web-based applications in healthcare, life sciences, and many other domains. Proliferating XML data are now managed through latest native XML database technologies. XML data sources conforming to common XML schemas could be shared and integrated with syntactic interoperability. Semantic interoperability can be achieved through semantic annotations of data models using common data elements linked to concepts from ontologies. In this paper, we present a framework and software system to support the development of semantic interoperable XML based data sources that can be shared through a Grid infrastructure. We also present our work on supporting semantic validated XML data through semantic annotations for XML Schema, semantic validation and semantic authoring of XML data. We demonstrate the use of the system for a biomedical database of medical image annotations and markups. PMID:25298789

  9. Semantically Interoperable XML Data

    PubMed Central

    Vergara-Niedermayr, Cristobal; Wang, Fusheng; Pan, Tony; Kurc, Tahsin; Saltz, Joel

    2013-01-01

    XML is ubiquitously used as an information exchange platform for web-based applications in healthcare, life sciences, and many other domains. Proliferating XML data are now managed through latest native XML database technologies. XML data sources conforming to common XML schemas could be shared and integrated with syntactic interoperability. Semantic interoperability can be achieved through semantic annotations of data models using common data elements linked to concepts from ontologies. In this paper, we present a framework and software system to support the development of semantic interoperable XML based data sources that can be shared through a Grid infrastructure. We also present our work on supporting semantic validated XML data through semantic annotations for XML Schema, semantic validation and semantic authoring of XML data. We demonstrate the use of the system for a biomedical database of medical image annotations and markups. PMID:25298789

  10. Biomedical semantics in the Semantic Web

    PubMed Central

    2011-01-01

    The Semantic Web offers an ideal platform for representing and linking biomedical information, which is a prerequisite for the development and application of analytical tools to address problems in data-intensive areas such as systems biology and translational medicine. As for any new paradigm, the adoption of the Semantic Web offers opportunities and poses questions and challenges to the life sciences scientific community: which technologies in the Semantic Web stack will be more beneficial for the life sciences? Is biomedical information too complex to benefit from simple interlinked representations? What are the implications of adopting a new paradigm for knowledge representation? What are the incentives for the adoption of the Semantic Web, and who are the facilitators? Is there going to be a Semantic Web revolution in the life sciences? We report here a few reflections on these questions, following discussions at the SWAT4LS (Semantic Web Applications and Tools for Life Sciences) workshop series, of which this Journal of Biomedical Semantics special issue presents selected papers from the 2009 edition, held in Amsterdam on November 20th. PMID:21388570

  11. Biomedical semantics in the Semantic Web.

    PubMed

    Splendiani, Andrea; Burger, Albert; Paschke, Adrian; Romano, Paolo; Marshall, M Scott

    2011-01-01

    The Semantic Web offers an ideal platform for representing and linking biomedical information, which is a prerequisite for the development and application of analytical tools to address problems in data-intensive areas such as systems biology and translational medicine. As for any new paradigm, the adoption of the Semantic Web offers opportunities and poses questions and challenges to the life sciences scientific community: which technologies in the Semantic Web stack will be more beneficial for the life sciences? Is biomedical information too complex to benefit from simple interlinked representations? What are the implications of adopting a new paradigm for knowledge representation? What are the incentives for the adoption of the Semantic Web, and who are the facilitators? Is there going to be a Semantic Web revolution in the life sciences?We report here a few reflections on these questions, following discussions at the SWAT4LS (Semantic Web Applications and Tools for Life Sciences) workshop series, of which this Journal of Biomedical Semantics special issue presents selected papers from the 2009 edition, held in Amsterdam on November 20th. PMID:21388570

  12. Latent effects decision analysis

    DOEpatents

    Cooper, J. Arlin; Werner, Paul W.

    2004-08-24

    Latent effects on a system are broken down into components ranging from those far removed in time from the system under study (latent) to those which closely effect changes in the system. Each component is provided with weighted inputs either by a user or from outputs of other components. A non-linear mathematical process known as `soft aggregation` is performed on the inputs to each component to provide information relating to the component. This information is combined in decreasing order of latency to the system to provide a quantifiable measure of an attribute of a system (e.g., safety) or to test hypotheses (e.g., for forensic deduction or decisions about various system design options).

  13. Semantic Networks and Social Networks

    ERIC Educational Resources Information Center

    Downes, Stephen

    2005-01-01

    Purpose: To illustrate the need for social network metadata within semantic metadata. Design/methodology/approach: Surveys properties of social networks and the semantic web, suggests that social network analysis applies to semantic content, argues that semantic content is more searchable if social network metadata is merged with semantic web…

  14. An enhanced feature set for pattern recognition based contrast enhancement of contact-less captured latent fingerprints in digitized crime scene forensics

    NASA Astrophysics Data System (ADS)

    Hildebrandt, Mario; Kiltz, Stefan; Dittmann, Jana; Vielhauer, Claus

    2014-02-01

    In crime scene forensics latent fingerprints are found on various substrates. Nowadays primarily physical or chemical preprocessing techniques are applied for enhancing the visibility of the fingerprint trace. In order to avoid altering the trace it has been shown that contact-less sensors offer a non-destructive acquisition approach. Here, the exploitation of fingerprint or substrate properties and the utilization of signal processing techniques are an essential requirement to enhance the fingerprint visibility. However, especially the optimal sensory is often substrate-dependent. An enhanced generic pattern recognition based contrast enhancement approach for scans of a chromatic white light sensor is introduced in Hildebrandt et al.1 using statistical, structural and Benford's law2 features for blocks of 50 micron. This approach achieves very good results for latent fingerprints on cooperative, non-textured, smooth substrates. However, on textured and structured substrates the error rates are very high and the approach thus unsuitable for forensic use cases. We propose the extension of the feature set with semantic features derived from known Gabor filter based exemplar fingerprint enhancement techniques by suggesting an Epsilon-neighborhood of each block in order to achieve an improved accuracy (called fingerprint ridge orientation semantics). Furthermore, we use rotation invariant Hu moments as an extension of the structural features and two additional preprocessing methods (separate X- and Y Sobel operators). This results in a 408-dimensional feature space. In our experiments we investigate and report the recognition accuracy for eight substrates, each with ten latent fingerprints: white furniture surface, veneered plywood, brushed stainless steel, aluminum foil, "Golden-Oak" veneer, non-metallic matte car body finish, metallic car body finish and blued metal. In comparison to Hildebrandt et al.,1 our evaluation shows a significant reduction of the error rates

  15. TripleRank: Ranking Semantic Web Data by Tensor Decomposition

    NASA Astrophysics Data System (ADS)

    Franz, Thomas; Schultz, Antje; Sizov, Sergej; Staab, Steffen

    The Semantic Web fosters novel applications targeting a more efficient and satisfying exploitation of the data available on the web, e.g. faceted browsing of linked open data. Large amounts and high diversity of knowledge in the Semantic Web pose the challenging question of appropriate relevance ranking for producing fine-grained and rich descriptions of the available data, e.g. to guide the user along most promising knowledge aspects. Existing methods for graph-based authority ranking lack support for fine-grained latent coherence between resources and predicates (i.e. support for link semantics in the linked data model). In this paper, we present TripleRank, a novel approach for faceted authority ranking in the context of RDF knowledge bases. TripleRank captures the additional latent semantics of Semantic Web data by means of statistical methods in order to produce richer descriptions of the available data. We model the Semantic Web by a 3-dimensional tensor that enables the seamless representation of arbitrary semantic links. For the analysis of that model, we apply the PARAFAC decomposition, which can be seen as a multi-modal counterpart to Web authority ranking with HITS. The result are groupings of resources and predicates that characterize their authority and navigational (hub) properties with respect to identified topics. We have applied TripleRank to multiple data sets from the linked open data community and gathered encouraging feedback in a user evaluation where TripleRank results have been exploited in a faceted browsing scenario.

  16. An overview of semantic compression

    NASA Astrophysics Data System (ADS)

    Schmalz, Mark S.

    2010-08-01

    approaches are considered, ranging from low-level semantic compression for text and database compaction, to high-level semantic analysis of images or video in which objects of interest have been detected, segmented, and represented compactly to facilitate indexing. In particular, we overview previous work in semantic pattern recognition, and how this has been applied to object-based compression. Discussion centers on lossless versus lossy transformations, quality of service in lossy compression, and computational efficiency.

  17. CINDI: A Virtual Library Indexing and Discovery System.

    ERIC Educational Resources Information Center

    Desai, Bipin C.; Shinghal, Raijan; Shayan, Nader R.; Zhou, Youquan

    1999-01-01

    Describes a system called CINDI (Concordia INdexing and DIscovery System) for cataloging and searching documents in a distributed virtual library. The document author registers metadata in the form of a semantic header that contains information on syntactic and semantic content, and an expert system fills the semantic header according to accepted…

  18. Semantic prosody and judgment.

    PubMed

    Hauser, David J; Schwarz, Norbert

    2016-07-01

    Some words tend to co-occur exclusively with a positive or negative context in natural language use, even though such valence patterns are not dictated by definitions or are part of the words' core meaning. These words contain semantic prosody, a subtle valenced meaning derived from co-occurrence in language. As language and thought are heavily intertwined, we hypothesized that semantic prosody can affect evaluative inferences about related ambiguous concepts. Participants inferred that an ambiguous medical outcome was more negative when it was caused, a verb with negative semantic prosody, than when it was produced, a synonymous verb with no semantic prosody (Studies 1a, 1b). Participants completed sentence fragments in a manner consistent with semantic prosody (Study 2), and semantic prosody affected various other judgments in line with evaluative inferences (estimates of an event's likelihood in Study 3). Finally, semantic prosody elicited both positive and negative evaluations of outcomes across a large set of semantically prosodic verbs (Study 4). Thus, semantic prosody can exert a strong influence on evaluative judgment. (PsycINFO Database Record PMID:27243765

  19. Semantic diversity: a measure of semantic ambiguity based on variability in the contextual usage of words.

    PubMed

    Hoffman, Paul; Lambon Ralph, Matthew A; Rogers, Timothy T

    2013-09-01

    Semantic ambiguity is typically measured by summing the number of senses or dictionary definitions that a word has. Such measures are somewhat subjective and may not adequately capture the full extent of variation in word meaning, particularly for polysemous words that can be used in many different ways, with subtle shifts in meaning. Here, we describe an alternative, computationally derived measure of ambiguity based on the proposal that the meanings of words vary continuously as a function of their contexts. On this view, words that appear in a wide range of contexts on diverse topics are more variable in meaning than those that appear in a restricted set of similar contexts. To quantify this variation, we performed latent semantic analysis on a large text corpus to estimate the semantic similarities of different linguistic contexts. From these estimates, we calculated the degree to which the different contexts associated with a given word vary in their meanings. We term this quantity a word's semantic diversity (SemD). We suggest that this approach provides an objective way of quantifying the subtle, context-dependent variations in word meaning that are often present in language. We demonstrate that SemD is correlated with other measures of ambiguity and contextual variability, as well as with frequency and imageability. We also show that SemD is a strong predictor of performance in semantic judgments in healthy individuals and in patients with semantic deficits, accounting for unique variance beyond that of other predictors. SemD values for over 30,000 English words are provided as supplementary materials. PMID:23239067

  20. A Latent Transition Model with Logistic Regression

    ERIC Educational Resources Information Center

    Chung, Hwan; Walls, Theodore A.; Park, Yousung

    2007-01-01

    Latent transition models increasingly include covariates that predict prevalence of latent classes at a given time or transition rates among classes over time. In many situations, the covariate of interest may be latent. This paper describes an approach for handling both manifest and latent covariates in a latent transition model. A Bayesian…

  1. Interpretable Probabilistic Latent Variable Models for Automatic Annotation of Clinical Text

    PubMed Central

    Kotov, Alexander; Hasan, Mehedi; Carcone, April; Dong, Ming; Naar-King, Sylvie; BroganHartlieb, Kathryn

    2015-01-01

    We propose Latent Class Allocation (LCA) and Discriminative Labeled Latent Dirichlet Allocation (DL-LDA), two novel interpretable probabilistic latent variable models for automatic annotation of clinical text. Both models separate the terms that are highly characteristic of textual fragments annotated with a given set of labels from other non-discriminative terms, but rely on generative processes with different structure of latent variables. LCA directly learns class-specific multinomials, while DL-LDA breaks them down into topics (clusters of semantically related words). Extensive experimental evaluation indicates that the proposed models outperform Naïve Bayes, a standard probabilistic classifier, and Labeled LDA, a state-of-the-art topic model for labeled corpora, on the task of automatic annotation of transcripts of motivational interviews, while the output of the proposed models can be easily interpreted by clinical practitioners. PMID:26958214

  2. The Semantic Learning Organization

    ERIC Educational Resources Information Center

    Sicilia, Miguel-Angel; Lytras, Miltiadis D.

    2005-01-01

    Purpose: The aim of this paper is introducing the concept of a "semantic learning organization" (SLO) as an extension of the concept of "learning organization" in the technological domain. Design/methodology/approach: The paper takes existing definitions and conceptualizations of both learning organizations and Semantic Web technology to develop…

  3. Communication: General Semantics Perspectives.

    ERIC Educational Resources Information Center

    Thayer, Lee, Ed.

    This book contains the edited papers from the eleventh International Conference on General Semantics, titled "A Search for Relevance." The conference questioned, as a central theme, the relevance of general semantics in a world of wars and human misery. Reacting to a fundamental Korzybski-ian principle that man's view of reality is distorted by…

  4. Aging and Semantic Activation.

    ERIC Educational Resources Information Center

    Howard, Darlene V.

    Three studies tested the theory that long term memory consists of a semantically organized network of concept nodes interconnected by leveled associations or relations, and that when a stimulus is processed, the corresponding concept node is assumed to be temporarily activated and this activation spreads to nearby semantically related nodes. In…

  5. Semantic Sensor Web

    NASA Astrophysics Data System (ADS)

    Sheth, A.; Henson, C.; Thirunarayan, K.

    2008-12-01

    Sensors are distributed across the globe leading to an avalanche of data about our environment. It is possible today to utilize networks of sensors to detect and identify a multitude of observations, from simple phenomena to complex events and situations. The lack of integration and communication between these networks, however, often isolates important data streams and intensifies the existing problem of too much data and not enough knowledge. With a view to addressing this problem, the Semantic Sensor Web (SSW) [1] proposes that sensor data be annotated with semantic metadata that will both increase interoperability and provide contextual information essential for situational knowledge. Kno.e.sis Center's approach to SSW is an evolutionary one. It adds semantic annotations to the existing standard sensor languages of the Sensor Web Enablement (SWE) defined by OGC. These annotations enhance primarily syntactic XML-based descriptions in OGC's SWE languages with microformats, and W3C's Semantic Web languages- RDF and OWL. In association with semantic annotation and semantic web capabilities including ontologies and rules, SSW supports interoperability, analysis and reasoning over heterogeneous multi-modal sensor data. In this presentation, we will also demonstrate a mashup with support for complex spatio-temporal-thematic queries [2] and semantic analysis that utilize semantic annotations, multiple ontologies and rules. It uses existing services (e.g., GoogleMap) and semantics enhanced SWE's Sensor Observation Service (SOS) over weather and road condition data from various sensors that are part of Ohio's transportation network. Our upcoming plans are to demonstrate end to end (heterogeneous sensor to application) semantics support and study scalability of SSW involving thousands of sensors to about a billion triples. Keywords: Semantic Sensor Web, Spatiotemporal thematic queries, Semantic Web Enablement, Sensor Observation Service [1] Amit Sheth, Cory Henson, Satya

  6. Order Theoretical Semantic Recommendation

    SciTech Connect

    Joslyn, Cliff A.; Hogan, Emilie A.; Paulson, Patrick R.; Peterson, Elena S.; Stephan, Eric G.; Thomas, Dennis G.

    2013-07-23

    Mathematical concepts of order and ordering relations play multiple roles in semantic technologies. Discrete totally ordered data characterize both input streams and top-k rank-ordered recommendations and query output, while temporal attributes establish numerical total orders, either over time points or in the more complex case of startend temporal intervals. But also of note are the fully partially ordered data, including both lattices and non-lattices, which actually dominate the semantic strcuture of ontological systems. Scalar semantic similarities over partially-ordered semantic data are traditionally used to return rank-ordered recommendations, but these require complementation with true metrics available over partially ordered sets. In this paper we report on our work in the foundations of partial order measurement in ontologies, with application to top-k semantic recommendation in workflows.

  7. Semantics, Pragmatics, and the Nature of Semantic Theories

    ERIC Educational Resources Information Center

    Spewak, David Charles, Jr.

    2013-01-01

    The primary concern of this dissertation is determining the distinction between semantics and pragmatics and how context sensitivity should be accommodated within a semantic theory. I approach the question over how to distinguish semantics from pragmatics from a new angle by investigating what the objects of a semantic theory are, namely…

  8. On Latent Change Model Choice in Longitudinal Studies

    ERIC Educational Resources Information Center

    Raykov, Tenko; Zajacova, Anna

    2012-01-01

    An interval estimation procedure for proportion of explained observed variance in latent curve analysis is discussed, which can be used as an aid in the process of choosing between linear and nonlinear models. The method allows obtaining confidence intervals for the R[squared] indexes associated with repeatedly followed measures in longitudinal…

  9. A Defense of Semantic Minimalism

    ERIC Educational Resources Information Center

    Kim, Su

    2012-01-01

    Semantic Minimalism is a position about the semantic content of declarative sentences, i.e., the content that is determined entirely by syntax. It is defined by the following two points: "Point 1": The semantic content is a complete/truth-conditional proposition. "Point 2": The semantic content is useful to a theory of…

  10. A Semantic Graph Query Language

    SciTech Connect

    Kaplan, I L

    2006-10-16

    Semantic graphs can be used to organize large amounts of information from a number of sources into one unified structure. A semantic query language provides a foundation for extracting information from the semantic graph. The graph query language described here provides a simple, powerful method for querying semantic graphs.

  11. Semantic Options in the Transitivity System: An Example of Textual Analysis.

    ERIC Educational Resources Information Center

    Lavid, Julia

    This article aims to discover the latent organization of a text by revealing a semantically motivated pattern of language functions that inform the theme of a story. it is shown how this pattern of linguistic features in the text provides insights into the literary effects of a description of a scene of a novel. The analysis proposed follows the…

  12. Impact of Latent Infection Treatment in Indigenous Populations

    PubMed Central

    Yuhara, Lucia Suemi; Sacchi, Flávia Patussi Correia; Croda, Julio

    2013-01-01

    The aims of the present study were to identify risk factors associated with latent tuberculosis (TB), examine the development of active disease among contacts, and assess the effectiveness of treating latent infection in indigenous Brazilians from January 2006 to December 2011. This was a retrospective study consisting of 1,371 tuberculosis contacts, 392 of whom underwent treatment for latent infection. Morbidity-from-TB data were obtained from the Information System for Disease Notification (SINAN) database, and the contacts’ data were collected from the clinical records using forms employed by Special Department of Indigenous Health (SESAI) multidisciplinary teams, according to SESAI’s instructions. The variables that were associated with latent infection among the contacts were age (odds ratio [OR]: 1.03; 95% confidence interval [CI]: 1.02–1.04) and close contact with a smear-positive index case (OR: 2.26, 95% CI: 1.59–3.22). The variables associated with the development of active TB among the contacts were a tuberculin skin test (TST) ≥10 mm (relative risk [RR]: 1.12, 95% CI: 1.07–1.17), age (RR: 1.01, 95% CI: 1.00–1.03), and treatment of latent infection (RR: 0.03, 95% CI: 0.01–0.27). The estimated number of latent infection treatments needed to prevent one case of active TB among the contacts was 51 treatments (95% CI: 33–182). In contacts with TST ≥10 mm, 10 (95% CI: 6–19) latent infection treatments were necessary to prevent one case of active TB. Age and close contact with a smear-positive index case were associated with latent TB. Screening with TST is a high priority among individuals contacting smear-positive index cases. Age and TST are associated with the development of active TB among contacts, and treatment of latent infection is an effective measure to control TB in indigenous communities. PMID:23936264

  13. Semantic Services for Wikipedia

    NASA Astrophysics Data System (ADS)

    Wang, Haofen; Penin, Thomas; Fu, Linyun; Liu, Qiaoling; Xue, Guirong; Yu, Yong

    Wikipedia, a killer application in Web 2.0, has embraced the power of collaborative editing to harness collective intelligence. It features many attractive characteristics, like entity-based link graph, abundant categorization and semi-structured layout, and can serve as an ideal data source to extract high quality and well-structured data. In this chapter, we first propose several solutions to extract knowledge from Wikipedia. We do not only consider information from the relational summaries of articles (infoboxes) but also semi-automatically extract it from the article text using the structured content available. Due to differences with information extraction from the Web, it is necessary to tackle new problems, like the lack of redundancy in Wikipedia that is dealt with by extending traditional machine learning algorithms to work with few labeled data. Furthermore, we also exploit the widespread categories as a complementary way to discover additional knowledge. Benefiting from both structured and textural information, we additionally provide a suggestion service for Wikipedia authoring. With the aim to facilitate semantic reuse, our proposal provides users with facilities such as link, categories and infobox content suggestions. The proposed enhancements can be applied to attract more contributors and lighten the burden of professional editors. Finally, we developed an enhanced search system, which can ease the process of exploiting Wikipedia. To provide a user-friendly interface, it extends the faceted search interface with relation navigation and let the user easily express his complex information needs in an interactive way. In order to achieve efficient query answering, it extends scalable IR engines to index and search both the textual and structured information with an integrated ranking support.

  14. Survey of sensible and latent heat thermal energy storage projects

    NASA Astrophysics Data System (ADS)

    Baylin, F.; Merino, M.

    1981-05-01

    Ongoing and completed research projects on sensible and latent heat thermal enegy storage for low, intermediate, and high temperature applications are reviewed. Projects in the United States and abroad are included. Several research efforts are in the index although the project descriptions are absent. Project lists are organized into four sections: short term sensible heat storage; seasonal sensible heat storage; latent heat storage; and models, economic analysis, and support studies. The organization of the Department of Energy programs managing many of these projects is also outlined. Projects are presented in a standard format that includes laboratory; funding level and period; status; project description; technical and economic parameters; and applications.

  15. Trusting Crowdsourced Geospatial Semantics

    NASA Astrophysics Data System (ADS)

    Goodhue, P.; McNair, H.; Reitsma, F.

    2015-08-01

    The degree of trust one can place in information is one of the foremost limitations of crowdsourced geospatial information. As with the development of web technologies, the increased prevalence of semantics associated with geospatial information has increased accessibility and functionality. Semantics also provides an opportunity to extend indicators of trust for crowdsourced geospatial information that have largely focused on spatio-temporal and social aspects of that information. Comparing a feature's intrinsic and extrinsic properties to associated ontologies provides a means of semantically assessing the trustworthiness of crowdsourced geospatial information. The application of this approach to unconstrained semantic submissions then allows for a detailed assessment of the trust of these features whilst maintaining the descriptive thoroughness this mode of information submission affords. The resulting trust rating then becomes an attribute of the feature, providing not only an indication as to the trustworthiness of a specific feature but is able to be aggregated across multiple features to illustrate the overall trustworthiness of a dataset.

  16. Algebraic Semantics for Narrative

    ERIC Educational Resources Information Center

    Kahn, E.

    1974-01-01

    This paper uses discussion of Edmund Spenser's "The Faerie Queene" to present a theoretical framework for explaining the semantics of narrative discourse. The algebraic theory of finite automata is used. (CK)

  17. LATENT LIFE OF ARTERIES.

    PubMed

    Carrel, A

    1910-07-23

    When a segment of artery, killed by heat, formalin or glycerin is transplanted, it undergoes a rapid degeneration. Its muscle fibers disappear while the tissue of the host reacts by building a new wall of connective tissue. When the transplanted vessel has been preserved in a condition of latent life, no degeneration of the wall occurs, or the wall undergoes only partial degeneration. The muscle fibers can keep their normal appearance, even for a long time after the operation. It is, therefore, demonstrated that arteries can be preserved outside of the body in a condition of unmanifested actual life. The best method of preservation consists of placing the vessels, immersed in vaselin, in an ice box, the temperature of which is slightly above the freezing point. From a surgical standpoint, the transplantation of preserved vessels can be used with some safety. When the arteries were kept in defibrinated blood or vaselin and in cold storage, the proportion of positive results was 75 and 80 per cent., and this can probably be increased. PMID:19867337

  18. Understanding Latent Heat of Vaporization.

    ERIC Educational Resources Information Center

    Linz, Ed

    1995-01-01

    Presents a simple exercise for students to do in the kitchen at home to determine the latent heat of vaporization of water using typical household materials. Designed to stress understanding by sacrificing precision for simplicity. (JRH)

  19. Predicting Latent Class Scores for Subsequent Analysis

    ERIC Educational Resources Information Center

    Petersen, Janne; Bandeen-Roche, Karen; Budtz-Jorgensen, Esben; Larsen, Klaus Groes

    2012-01-01

    Latent class regression models relate covariates and latent constructs such as psychiatric disorders. Though full maximum likelihood estimation is available, estimation is often in three steps: (i) a latent class model is fitted without covariates; (ii) latent class scores are predicted; and (iii) the scores are regressed on covariates. We propose…

  20. The Semantic SPASE

    NASA Astrophysics Data System (ADS)

    Hughes, S.; Crichton, D.; Thieman, J.; Ramirez, P.; King, T.; Weiss, M.

    2005-12-01

    The Semantic SPASE (Space Physics Archive Search and Extract) prototype demonstrates the use of semantic web technologies to capture, document, and manage the SPASE data model, support facet- and text-based search, and provide flexible and intuitive user interfaces. The SPASE data model, under development since late 2003 by a consortium of space physics domain experts, is intended to serve as the basis for interoperability between independent data systems. To develop the Semantic SPASE prototype, the data model was first analyzed to determine the inherit object classes and their attributes. These were entered into Stanford Medical Informatics' Protege ontology tool and annotated using definitions from the SPASE documentation. Further analysis of the data model resulted in the addition of class relationships. Finally attributes and relationships that support broad-scope interoperability were added from research associated with the Object-Oriented Data Technology task. To validate the ontology and produce a knowledge base, example data products were ingested. The capture of the data model as an ontology results in a more formal specification of the model. The Protege software is also a powerful management tool and supports plug-ins that produce several graphical notations as output. The stated purpose of the semantic web is to support machine understanding of web-based information. Protege provides an export capability to RDF/XML and RDFS/XML for this purpose. Several research efforts use RDF/XML knowledge bases to provide semantic search. MIT's Simile/Longwell project provides both facet- and text-based search using a suite of metadata browsers and the text-based search engine Lucene. Using the Protege generated RDF knowledge-base a semantic search application was easily built and deployed to run as a web application. Configuration files specify the object attributes and values to be designated as facets (i.e. search) constraints. Semantic web technologies provide

  1. Class-specific Gaussian-multinomial latent Dirichlet allocation for image annotation

    NASA Astrophysics Data System (ADS)

    Qian, Zhiming; Zhong, Ping; Wang, Runsheng

    2015-12-01

    Image annotation has been a challenging problem due to the well-known semantic gap between two heterogeneous information modalities, i.e., the visual modality referring to low-level visual features and the semantic modality referring to high-level human concepts. To bridge the semantic gap, we present an extension of latent Dirichlet allocation (LDA), denoted as class-specific Gaussian-multinomial latent Dirichlet allocation (csGM-LDA), in an effort to simulate the human's visual perception system. An analysis of previous supervised LDA models shows that the topics discovered by generative LDA models are driven by general image regularities rather than the semantic regularities for image annotation. To address this, csGM-LDA is introduced by using class supervision at the level of visual features for multimodal topic modeling. The csGM-LDA model combines the labeling strength of topic supervision with the flexibility of topic discovery, and the modeling problem can be effectively solved by a variational expectation-maximization (EM) algorithm. Moreover, as natural images usually generate an enormous size of high-dimensional data in annotation applications, an efficient descriptor based on Laplacian regularized uncorrelated tensor representation is proposed for explicitly exploiting the manifold structures in the high-order image space. Experimental results on two standard annotation datasets have shown the effectiveness of the proposed method by comparing with several state-of-the-art annotation methods.

  2. A Revised Semantic Differential Scale Distinguishing between Negative and Positive God Images

    ERIC Educational Resources Information Center

    Francis, Leslie J.; Robbins, Mandy; Gibson, Harry M.

    2006-01-01

    A sample of 755 school pupils between the ages of 11 and 18 years completed the Benson and Spilka semantic differential measure of God images. Factor analysis indicated the advantages of re-scoring the measure as an eight item unidimensional index, defining semantic space relating to God images ranging from negative affect to positive affect.…

  3. Exploring context and content links in social media: a latent space method.

    PubMed

    Qi, Guo-Jun; Aggarwal, Charu; Tian, Qi; Ji, Heng; Huang, Thomas S

    2012-05-01

    Social media networks contain both content and context-specific information. Most existing methods work with either of the two for the purpose of multimedia mining and retrieval. In reality, both content and context information are rich sources of information for mining, and the full power of mining and processing algorithms can be realized only with the use of a combination of the two. This paper proposes a new algorithm which mines both context and content links in social media networks to discover the underlying latent semantic space. This mapping of the multimedia objects into latent feature vectors enables the use of any off-the-shelf multimedia retrieval algorithms. Compared to the state-of-the-art latent methods in multimedia analysis, this algorithm effectively solves the problem of sparse context links by mining the geometric structure underlying the content links between multimedia objects. Specifically for multimedia annotation, we show that an effective algorithm can be developed to directly construct annotation models by simultaneously leveraging both context and content information based on latent structure between correlated semantic concepts. We conduct experiments on the Flickr data set, which contains user tags linked with images. We illustrate the advantages of our approach over the state-of-the-art multimedia retrieval techniques. PMID:21968917

  4. Temporal Representation in Semantic Graphs

    SciTech Connect

    Levandoski, J J; Abdulla, G M

    2007-08-07

    A wide range of knowledge discovery and analysis applications, ranging from business to biological, make use of semantic graphs when modeling relationships and concepts. Most of the semantic graphs used in these applications are assumed to be static pieces of information, meaning temporal evolution of concepts and relationships are not taken into account. Guided by the need for more advanced semantic graph queries involving temporal concepts, this paper surveys the existing work involving temporal representations in semantic graphs.

  5. Discovering biomedical semantic relations in PubMed queries for information retrieval and database curation.

    PubMed

    Huang, Chung-Chi; Lu, Zhiyong

    2016-01-01

    Identifying relevant papers from the literature is a common task in biocuration. Most current biomedical literature search systems primarily rely on matching user keywords. Semantic search, on the other hand, seeks to improve search accuracy by understanding the entities and contextual relations in user keywords. However, past research has mostly focused on semantically identifying biological entities (e.g. chemicals, diseases and genes) with little effort on discovering semantic relations. In this work, we aim to discover biomedical semantic relations in PubMed queries in an automated and unsupervised fashion. Specifically, we focus on extracting and understanding the contextual information (or context patterns) that is used by PubMed users to represent semantic relations between entities such as 'CHEMICAL-1 compared to CHEMICAL-2' With the advances in automatic named entity recognition, we first tag entities in PubMed queries and then use tagged entities as knowledge to recognize pattern semantics. More specifically, we transform PubMed queries into context patterns involving participating entities, which are subsequently projected to latent topics via latent semantic analysis (LSA) to avoid the data sparseness and specificity issues. Finally, we mine semantically similar contextual patterns or semantic relations based on LSA topic distributions. Our two separate evaluation experiments of chemical-chemical (CC) and chemical-disease (CD) relations show that the proposed approach significantly outperforms a baseline method, which simply measures pattern semantics by similarity in participating entities. The highest performance achieved by our approach is nearly 0.9 and 0.85 respectively for the CC and CD task when compared against the ground truth in terms of normalized discounted cumulative gain (nDCG), a standard measure of ranking quality. These results suggest that our approach can effectively identify and return related semantic patterns in a ranked order

  6. Discovering biomedical semantic relations in PubMed queries for information retrieval and database curation

    PubMed Central

    Huang, Chung-Chi; Lu, Zhiyong

    2016-01-01

    Identifying relevant papers from the literature is a common task in biocuration. Most current biomedical literature search systems primarily rely on matching user keywords. Semantic search, on the other hand, seeks to improve search accuracy by understanding the entities and contextual relations in user keywords. However, past research has mostly focused on semantically identifying biological entities (e.g. chemicals, diseases and genes) with little effort on discovering semantic relations. In this work, we aim to discover biomedical semantic relations in PubMed queries in an automated and unsupervised fashion. Specifically, we focus on extracting and understanding the contextual information (or context patterns) that is used by PubMed users to represent semantic relations between entities such as ‘CHEMICAL-1 compared to CHEMICAL-2.’ With the advances in automatic named entity recognition, we first tag entities in PubMed queries and then use tagged entities as knowledge to recognize pattern semantics. More specifically, we transform PubMed queries into context patterns involving participating entities, which are subsequently projected to latent topics via latent semantic analysis (LSA) to avoid the data sparseness and specificity issues. Finally, we mine semantically similar contextual patterns or semantic relations based on LSA topic distributions. Our two separate evaluation experiments of chemical-chemical (CC) and chemical–disease (CD) relations show that the proposed approach significantly outperforms a baseline method, which simply measures pattern semantics by similarity in participating entities. The highest performance achieved by our approach is nearly 0.9 and 0.85 respectively for the CC and CD task when compared against the ground truth in terms of normalized discounted cumulative gain (nDCG), a standard measure of ranking quality. These results suggest that our approach can effectively identify and return related semantic patterns in a ranked

  7. Semantic Search of Web Services

    ERIC Educational Resources Information Center

    Hao, Ke

    2013-01-01

    This dissertation addresses semantic search of Web services using natural language processing. We first survey various existing approaches, focusing on the fact that the expensive costs of current semantic annotation frameworks result in limited use of semantic search for large scale applications. We then propose a vector space model based service…

  8. Semantic Webs and Study Skills.

    ERIC Educational Resources Information Center

    Hoover, John J.; Rabideau, Debra K.

    1995-01-01

    Principles for ensuring effective use of semantic webbing in meeting study skill needs of students with learning problems are noted. Important study skills are listed, along with suggested semantic web topics for which subordinate ideas may be developed. Two semantic webs are presented, illustrating the study skills of multiple choice test-taking…

  9. GOOSE: semantic search on internet connected sensors

    NASA Astrophysics Data System (ADS)

    Schutte, Klamer; Bomhof, Freek; Burghouts, Gertjan; van Diggelen, Jurriaan; Hiemstra, Peter; van't Hof, Jaap; Kraaij, Wessel; Pasman, Huib; Smith, Arthur; Versloot, Corne; de Wit, Joost

    2013-05-01

    More and more sensors are getting Internet connected. Examples are cameras on cell phones, CCTV cameras for traffic control as well as dedicated security and defense sensor systems. Due to the steadily increasing data volume, human exploitation of all this sensor data is impossible for effective mission execution. Smart access to all sensor data acts as enabler for questions such as "Is there a person behind this building" or "Alert me when a vehicle approaches". The GOOSE concept has the ambition to provide the capability to search semantically for any relevant information within "all" (including imaging) sensor streams in the entire Internet of sensors. This is similar to the capability provided by presently available Internet search engines which enable the retrieval of information on "all" web pages on the Internet. In line with current Internet search engines any indexing services shall be utilized cross-domain. The two main challenge for GOOSE is the Semantic Gap and Scalability. The GOOSE architecture consists of five elements: (1) an online extraction of primitives on each sensor stream; (2) an indexing and search mechanism for these primitives; (3) a ontology based semantic matching module; (4) a top-down hypothesis verification mechanism and (5) a controlling man-machine interface. This paper reports on the initial GOOSE demonstrator, which consists of the MES multimedia analysis platform and the CORTEX action recognition module. It also provides an outlook into future GOOSE development.

  10. Semantator: annotating clinical narratives with semantic web ontologies.

    PubMed

    Song, Dezhao; Chute, Christopher G; Tao, Cui

    2012-01-01

    To facilitate clinical research, clinical data needs to be stored in a machine processable and understandable way. Manual annotating clinical data is time consuming. Automatic approaches (e.g., Natural Language Processing systems) have been adopted to convert such data into structured formats; however, the quality of such automatically extracted data may not always be satisfying. In this paper, we propose Semantator, a semi-automatic tool for document annotation with Semantic Web ontologies. With a loaded free text document and an ontology, Semantator supports the creation/deletion of ontology instances for any document fragment, linking/disconnecting instances with the properties in the ontology, and also enables automatic annotation by connecting to the NCBO annotator and cTAKES. By representing annotations in Semantic Web standards, Semantator supports reasoning based upon the underlying semantics of the owl:disjointWith and owl:equivalentClass predicates. We present discussions based on user experiences of using Semantator. PMID:22779043

  11. Semantator: Annotating Clinical Narratives with Semantic Web Ontologies

    PubMed Central

    Song, Dezhao; Chute, Christopher G.; Tao, Cui

    2012-01-01

    To facilitate clinical research, clinical data needs to be stored in a machine processable and understandable way. Manual annotating clinical data is time consuming. Automatic approaches (e.g., Natural Language Processing systems) have been adopted to convert such data into structured formats; however, the quality of such automatically extracted data may not always be satisfying. In this paper, we propose Semantator, a semi-automatic tool for document annotation with Semantic Web ontologies. With a loaded free text document and an ontology, Semantator supports the creation/deletion of ontology instances for any document fragment, linking/disconnecting instances with the properties in the ontology, and also enables automatic annotation by connecting to the NCBO annotator and cTAKES. By representing annotations in Semantic Web standards, Semantator supports reasoning based upon the underlying semantics of the owl:disjointWith and owl:equivalentClass predicates. We present discussions based on user experiences of using Semantator. PMID:22779043

  12. Universal Semantics in Translation

    ERIC Educational Resources Information Center

    Wang, Zhenying

    2009-01-01

    What and how we translate are questions often argued about. No matter what kind of answers one may give, priority in translation should be granted to meaning, especially those meanings that exist in all concerned languages. In this paper the author defines them as universal sememes, and the study of them as universal semantics, of which…

  13. Semantic Space Analyst

    Energy Science and Technology Software Center (ESTSC)

    2004-04-15

    The Semantic Space Analyst (SSA) is software for analyzing a text corpus, discovering relationships among terms, and allowing the user to explore that information in different ways. It includes features for displaying and laying out terms and relationships visually, for generating such maps from manual queries, for discovering differences between corpora. Data can also be exported to Microsoft Excel.

  14. Learning Semantic Query Suggestions

    NASA Astrophysics Data System (ADS)

    Meij, Edgar; Bron, Marc; Hollink, Laura; Huurnink, Bouke; de Rijke, Maarten

    An important application of semantic web technology is recognizing human-defined concepts in text. Query transformation is a strategy often used in search engines to derive queries that are able to return more useful search results than the original query and most popular search engines provide facilities that let users complete, specify, or reformulate their queries. We study the problem of semantic query suggestion, a special type of query transformation based on identifying semantic concepts contained in user queries. We use a feature-based approach in conjunction with supervised machine learning, augmenting term-based features with search history-based and concept-specific features. We apply our method to the task of linking queries from real-world query logs (the transaction logs of the Netherlands Institute for Sound and Vision) to the DBpedia knowledge base. We evaluate the utility of different machine learning algorithms, features, and feature types in identifying semantic concepts using a manually developed test bed and show significant improvements over an already high baseline. The resources developed for this paper, i.e., queries, human assessments, and extracted features, are available for download.

  15. Environmental Attitudes Semantic Differential.

    ERIC Educational Resources Information Center

    Mehne, Paul R.; Goulard, Cary J.

    This booklet is an evaluation instrument which utilizes semantic differential data to assess environmental attitudes. Twelve concepts are included: regulated access to beaches, urban planning, dune vegetation, wetlands, future cities, reclaiming wetlands for building development, city parks, commercial development of beaches, existing cities,…

  16. Taxometric and Factor Analytic Models of Anxiety Sensitivity: Integrating Approaches to Latent Structural Research

    ERIC Educational Resources Information Center

    Bernstein, Amit; Zvolensky, Michael J.; Norton, Peter J.; Schmidt, Norman B.; Taylor, Steven; Forsyth, John P.; Lewis, Sarah F.; Feldner, Matthew T.; Leen-Feldner, Ellen W.; Stewart, Sherry H.; Cox, Brian

    2007-01-01

    This study represents an effort to better understand the latent structure of anxiety sensitivity (AS), as indexed by the 16-item Anxiety Sensitivity Index (ASI; S. Reiss, R. A. Peterson, M. Gursky, & R. J. McNally, 1986), by using taxometric and factor-analytic approaches in an integrative manner. Taxometric analyses indicated that AS has a…

  17. Effects of Latent Toxoplasmosis on Autoimmune Thyroid Diseases in Pregnancy

    PubMed Central

    Kaňková, Šárka; Procházková, Lucie; Flegr, Jaroslav; Calda, Pavel; Springer, Drahomíra; Potluková, Eliška

    2014-01-01

    Background Toxoplasmosis, one of the most common zoonotic diseases worldwide, can induce various hormonal and behavioural alterations in infected hosts, and its most common form, latent toxoplasmosis, influences the course of pregnancy. Autoimmune thyroid diseases (AITD) belong to the well-defined risk factors for adverse pregnancy outcomes. The aim of this study was to investigate whether there is a link between latent toxoplasmosis and maternal AITD in pregnancy. Methods Cross-sectional study in 1248 consecutive pregnant women in the 9–12th gestational weeks. Serum thyroid-stimulating hormone (TSH), thyroperoxidase antibodies (TPOAb), and free thyroxine (FT4) were assessed by chemiluminescence; the Toxoplasma status was detected by the complement fixation test (CFT) and anti-Toxoplasma IgG enzyme-linked immunosorbent assay (ELISA). Results Overall, 22.5% of the women were positive for latent toxoplasmosis and 14.7% were screened positive for AITD. Women with latent toxoplasmosis had more often highly elevated TPOAb than the Toxoplasma-negative ones (p = 0.004), and latent toxoplasmosis was associated with decrease in serum TSH levels (p = 0.049). Moreover, we found a positive correlation between FT4 and the index of positivity for anti-Toxoplasma IgG antibodies (p = 0.033), which was even stronger in the TPOAb-positive Toxoplasma-positive women, (p = 0.014), as well as a positive correlation between FT4 and log2 CFT (p = 0.009). Conclusions Latent toxoplasmosis was associated with a mild increase in thyroid hormone production in pregnancy. The observed Toxoplasma-associated changes in the parameters of AITD are mild and do not seem to be clinically relevant; however, they could provide new clues to the complex pathogenesis of autoimmune thyroid diseases. PMID:25350671

  18. Latent Growth Modeling for Logistic Response Functions

    ERIC Educational Resources Information Center

    Choi, Jaehwa; Harring, Jeffrey R.; Hancock, Gregory R.

    2009-01-01

    Throughout much of the social and behavioral sciences, latent growth modeling (latent curve analysis) has become an important tool for understanding individuals' longitudinal change. Although nonlinear variations of latent growth models appear in the methodological and applied literature, a notable exclusion is the treatment of growth following…

  19. A Multicomponent Latent Trait Model for Diagnosis

    ERIC Educational Resources Information Center

    Embretson, Susan E.; Yang, Xiangdong

    2013-01-01

    This paper presents a noncompensatory latent trait model, the multicomponent latent trait model for diagnosis (MLTM-D), for cognitive diagnosis. In MLTM-D, a hierarchical relationship between components and attributes is specified to be applicable to permit diagnosis at two levels. MLTM-D is a generalization of the multicomponent latent trait…

  20. From Data to Semantic Information

    NASA Astrophysics Data System (ADS)

    Floridi, Luciano

    2003-06-01

    There is no consensus yet on the definition of semantic information. This paper contributes to the current debate by criticising and revising the Standard Definition of semantic Information (SDI) as meaningful data, in favour of the Dretske-Grice approach: meaningful and well-formed data constitute semantic information only if they also qualify as contingently truthful. After a brief introduction, SDI is criticised for providing necessary but insufficient conditions for the definition of semantic information. SDI is incorrect because truth-values do not supervene on semantic information, and misinformation (that is, false semantic information) is not a type of semantic information, but pseudo-information, that is not semantic information at all. This is shown by arguing that none of the reasons for interpreting misinformation as a type of semantic information is convincing, whilst there are compelling reasons to treat it as pseudo-information. As a consequence, SDI is revised to include a necessary truth-condition. The last section summarises the main results of the paper and indicates the important implications of the revised definition for the analysis of the deflationary theories of truth, the standard definition of knowledge and the classic, quantitative theory of semantic information.

  1. Latent trait cortisol (LTC) levels: reliability, validity, and stability.

    PubMed

    Doane, Leah D; Chen, Frances R; Sladek, Michael R; Van Lenten, Scott A; Granger, Douglas A

    2015-05-01

    The regulation of the hypothalamic pituitary adrenal (HPA) axis has received empirical attention as a mechanism contributing to individual differences in health and human development. A variety of sampling tactics and strategies index daily HPA axis functioning including the cortisol awakening response (CAR), the diurnal slope, and the area under the curve (AUGg). In an ethnically diverse sample (54% European-American, 23% Latino) of 82 adolescents (24% male, M age=18.05 years), we assessed salivary cortisol 45 times over the transition to college: 5 times per day, over 3 sequential days, across 3 waves (initially, 5, and 9 months later). Samples were collected at waking; 30 min, 3, and 8h post waking; and bedtime. Latent state-trait modeling indicated that the waking and 30 min post waking samples contributed to indices of within and across wave latent trait cortisol (LTC) levels. As such, a latent trait factor of cortisol was derived to reflect both within- and across-wave trait components of the variance in cortisol. LTC was distinct from the CAR, differentially predicted components of the diurnal profile across the day, and was highly stable across assessment waves (months). As preliminary evidence for convergent validity of LTC levels, childhood trauma was positively associated with LTC. Findings document the reliability, divergent and convergent validity, and stability of a latent trait factor of individual differences in HPA axis activity that may provide a cost efficient alternative to existing strategies and minimize participant burden. PMID:25705799

  2. Live Social Semantics

    NASA Astrophysics Data System (ADS)

    Alani, Harith; Szomszor, Martin; Cattuto, Ciro; van den Broeck, Wouter; Correndo, Gianluca; Barrat, Alain

    Social interactions are one of the key factors to the success of conferences and similar community gatherings. This paper describes a novel application that integrates data from the semantic web, online social networks, and a real-world contact sensing platform. This application was successfully deployed at ESWC09, and actively used by 139 people. Personal profiles of the participants were automatically generated using several Web 2.0 systems and semantic academic data sources, and integrated in real-time with face-to-face contact networks derived from wearable sensors. Integration of all these heterogeneous data layers made it possible to offer various services to conference attendees to enhance their social experience such as visualisation of contact data, and a site to explore and connect with other participants. This paper describes the architecture of the application, the services we provided, and the results we achieved in this deployment.

  3. Living With Semantic Dementia

    PubMed Central

    Sage, Karen; Wilkinson, Ray; Keady, John

    2014-01-01

    Semantic dementia is a variant of frontotemporal dementia and is a recently recognized diagnostic condition. There has been some research quantitatively examining care partner stress and burden in frontotemporal dementia. There are, however, few studies exploring the subjective experiences of family members caring for those with frontotemporal dementia. Increased knowledge of such experiences would allow service providers to tailor intervention, support, and information better. We used a case study design, with thematic narrative analysis applied to interview data, to describe the experiences of a wife and son caring for a husband/father with semantic dementia. Using this approach, we identified four themes: (a) living with routines, (b) policing and protecting, (c) making connections, and (d) being adaptive and flexible. Each of these themes were shared and extended, with the importance of routines in everyday life highlighted. The implications for policy, practice, and research are discussed. PMID:24532121

  4. Semantic interpretation of nominalizations

    SciTech Connect

    Hull, R.D.; Gomez, F.

    1996-12-31

    A computational approach to the semantic interpretation of nominalizations is described. Interpretation of normalizations involves three tasks: deciding whether the normalization is being used in a verbal or non-verbal sense; disambiguating the normalized verb when a verbal sense is used; and determining the fillers of the thematic roles of the verbal concept or predicate of the nominalization. A verbal sense can be recognized by the presence of modifiers that represent the arguments of the verbal concept. It is these same modifiers which provide the semantic clues to disambiguate the normalized verb. In the absence of explicit modifiers, heuristics are used to discriminate between verbal and non-verbal senses. A correspondence between verbs and their nominalizations is exploited so that only a small amount of additional knowledge is needed to handle the nominal form. These methods are tested in the domain of encyclopedic texts and the results are shown.

  5. Practical Semantic Astronomy

    NASA Astrophysics Data System (ADS)

    Graham, Matthew; Gray, N.; Burke, D.

    2010-01-01

    Many activities in the era of data-intensive astronomy are predicated upon some transference of domain knowledge and expertise from human to machine. The semantic infrastructure required to support this is no longer a pipe dream of computer science but a set of practical engineering challenges, more concerned with deployment and performance details than AI abstractions. The application of such ideas promises to help in such areas as contextual data access, exploiting distributed annotation and heterogeneous sources, and intelligent data dissemination and discovery. In this talk, we will review the status and use of semantic technologies in astronomy, particularly to address current problems in astroinformatics, with such projects as SKUA and AstroCollation.

  6. Structural Similarities between Brain and Linguistic Data Provide Evidence of Semantic Relations in the Brain

    PubMed Central

    Crangle, Colleen E.; Perreau-Guimaraes, Marcos; Suppes, Patrick

    2013-01-01

    This paper presents a new method of analysis by which structural similarities between brain data and linguistic data can be assessed at the semantic level. It shows how to measure the strength of these structural similarities and so determine the relatively better fit of the brain data with one semantic model over another. The first model is derived from WordNet, a lexical database of English compiled by language experts. The second is given by the corpus-based statistical technique of latent semantic analysis (LSA), which detects relations between words that are latent or hidden in text. The brain data are drawn from experiments in which statements about the geography of Europe were presented auditorily to participants who were asked to determine their truth or falsity while electroencephalographic (EEG) recordings were made. The theoretical framework for the analysis of the brain and semantic data derives from axiomatizations of theories such as the theory of differences in utility preference. Using brain-data samples from individual trials time-locked to the presentation of each word, ordinal relations of similarity differences are computed for the brain data and for the linguistic data. In each case those relations that are invariant with respect to the brain and linguistic data, and are correlated with sufficient statistical strength, amount to structural similarities between the brain and linguistic data. Results show that many more statistically significant structural similarities can be found between the brain data and the WordNet-derived data than the LSA-derived data. The work reported here is placed within the context of other recent studies of semantics and the brain. The main contribution of this paper is the new method it presents for the study of semantics and the brain and the focus it permits on networks of relations detected in brain data and represented by a semantic model. PMID:23799009

  7. Semantic Annotation for Biological Information Retrieval System

    PubMed Central

    Oshaiba, Mohamed Marouf Z.; El Houby, Enas M. F.; Salah, Akram

    2015-01-01

    Online literatures are increasing in a tremendous rate. Biological domain is one of the fast growing domains. Biological researchers face a problem finding what they are searching for effectively and efficiently. The aim of this research is to find documents that contain any combination of biological process and/or molecular function and/or cellular component. This research proposes a framework that helps researchers to retrieve meaningful documents related to their asserted terms based on gene ontology (GO). The system utilizes GO by semantically decomposing it into three subontologies (cellular component, biological process, and molecular function). Researcher has the flexibility to choose searching terms from any combination of the three subontologies. Document annotation is taking a place in this research to create an index of biological terms in documents to speed the searching process. Query expansion is used to infer semantically related terms to asserted terms. It increases the search meaningful results using the term synonyms and term relationships. The system uses a ranking method to order the retrieved documents based on the ranking weights. The proposed system achieves researchers' needs to find documents that fit the asserted terms semantically. PMID:25737720

  8. Non-semantic contributions to "semantic" redundancy gain.

    PubMed

    Shepherdson, Peter; Miller, Jeff

    2016-08-01

    Recently, two groups of researchers have reported redundancy gains (enhanced performance with multiple, redundant targets) in tasks requiring semantic categorization. Here we report two experiments aimed at determining whether the gains found by one of these groups resulted from some form of semantic coactivation. We asked undergraduate psychology students to complete choice RT tasks requiring the semantic categorization of visually presented words, and compared performance with redundant targets from the same semantic category to performance with redundant targets from different semantic categories. If the redundancy gains resulted from the combination of information at a semantic level, they should have been greater in the former than the latter situation. However, our results showed no significant differences in redundancy gain (for latency and accuracy) between same-category and different-category conditions, despite gains appearing in both conditions. Thus, we suggest that redundancy gain in the semantic categorization task may result entirely from statistical facilitation or combination of information at non-semantic levels. PMID:26339718

  9. Semantic query processing and annotation generation for content-based retrieval of histological images

    NASA Astrophysics Data System (ADS)

    Tang, Lilian H.; Hanka, Rudolf; Ip, Horace H. S.; Cheung, Kent K. T.; Lam, Ringo

    2000-05-01

    In this paper we present a semantic content representation scheme and the associated techniques for supporting (1) query by image examples or by natural language in a histological image database and (2) automatic annotation generation for images through image semantic analysis. In this research, various types of query are analyzed by either a semantic analyzer or a natural language analyzer to extract high level concepts and histological information, which are subsequently converted into an internal semantic content representation structure code-named 'Papillon.' Papillon serves not only as an intermediate representation scheme but also stores the semantic content of the image that will be used to match against the semantic index structure within the image database during query processing. During the image database population phase, all images that are going to be put into the database will go through the same processing so that every image would have its semantic content represented by a Papillon structure. Since the Papillon structure for an image contains high level semantic information of the image, it forms the basis of the technique that automatically generates textual annotation for the input images. Papillon bridges the gap between different media in the database, allows complicated intelligent browsing to be carried out efficiently, and also provides a well- defined semantic content representation scheme for different content processing engines developed for content-based retrieval.

  10. The Ontological Perspectives of the Semantic Web and the Metadata Harvesting Protocol: Applications of Metadata for Improving Web Search.

    ERIC Educational Resources Information Center

    Fast, Karl V.; Campbell, D. Grant

    2001-01-01

    Compares the implied ontological frameworks of the Open Archives Initiative Protocol for Metadata Harvesting and the World Wide Web Consortium's Semantic Web. Discusses current search engine technology, semantic markup, indexing principles of special libraries and online databases, and componentization and the distinction between data and…

  11. Semantic Analysis in Machine Translation.

    ERIC Educational Resources Information Center

    Skorokhodko, E. F.

    1970-01-01

    In many cases machine-translation does not produce satisfactory results within the framework of purely formal (morphological and syntaxic) analysis, particularly, in the case of syntaxic and lexical homonomy. An algorithm for syntaxic-semantic analysis is proposed, and its principles of operation are described. The syntaxico-semantic structure is…

  12. Semantic Feature Distinctiveness and Frequency

    ERIC Educational Resources Information Center

    Lamb, Katherine M.

    2012-01-01

    Lexical access is the process in which basic components of meaning in language, the lexical entries (words) are activated. This activation is based on the organization and representational structure of the lexical entries. Semantic features of words, which are the prominent semantic characteristics of a word concept, provide important information…

  13. Semantic Research for Digital Libraries.

    ERIC Educational Resources Information Center

    Chen, Hsinchun

    1999-01-01

    Discusses the need for semantic research in digital libraries to help overcome interoperability problems. Highlights include federal initiatives; semantic analysis; knowledge representations; human-computer interactions and information visualization; and the University of Illinois DLI (Digital Libraries Initiative) project through partnership with…

  14. The semantic planetary data system

    NASA Technical Reports Server (NTRS)

    Hughes, J. Steven; Crichton, Daniel; Kelly, Sean; Mattmann, Chris

    2005-01-01

    This paper will provide a brief overview of the PDS data model and the PDS catalog. It will then describe the implentation of the Semantic PDS including the development of the formal ontology, the generation of RDFS/XML and RDF/XML data sets, and the buiding of the semantic search application.

  15. A semantically-aided approach for online annotation and retrieval of medical images.

    PubMed

    Kyriazos, George K; Gerostathopoulos, Ilias Th; Kolias, Vassileios D; Stoitsis, John S; Nikita, Konstantina S

    2011-01-01

    The need for annotating the continuously increasing volume of medical image data is recognized from medical experts for a variety of purposes, regardless if this is medical practice, research or education. The rich information content latent in medical images can be made explicit and formal with the use of well-defined ontologies. Evolution of the Semantic Web now offers a unique opportunity of a web-based, service-oriented approach. Remote access to FMA and ICD-10 reference ontologies provides the ontological annotation framework. The proposed system utilizes this infrastructure to provide a customizable and robust annotation procedure. It also provides an intelligent search mechanism indicating the advantages of semantic over keyword search. The common representation layer discussed facilitates interoperability between institutions and systems, while semantic content enables inference and knowledge integration. PMID:22254818

  16. Indexing Learning Objects: Vocabularies and Empirical Investigation of Consistency

    ERIC Educational Resources Information Center

    Kabel, Suzanne; De Hoog, Robert; Wielinga, Bob; Anjewierden, Anjo

    2004-01-01

    In addition to the LOM standard and instructional design specifications, as well as domain specific indexing vocabularies, a structured indexing vocabulary for the more elementary learning objects is advisable in order to support retrieval tasks of developers. Furthermore, because semantic indexing is seen as a difficult task, three issues…

  17. Disorders of semantic memory.

    PubMed

    McCarthy, R A; Warrington, E K

    1994-10-29

    It is now established that selective disorders of semantic memory may arise after focal cerebral lesions. Debate and dissension remain on three principal issues: category specificity, the status of modality-dependent knowledge, and the stability and sufficiency of stored information. Theories of category specificity have focused on the frequently reported dissociation between living things and man-made objects. However, other dimensions need theoretical integration. Impairments can be both finer-grain and broader in range. A second variable of importance is stimulus modality. Reciprocal interactive dissociations between vision and language and between animals and objects will be described. These indicate that the derivation of semantic information is constrained by input modality: we appear to have evolved separable databases for the visual and the verbal world. Thirdly, an orthogonal distinction has been drawn between degradation disorders, where representations are insufficient for comprehension, and access deficits, in which representations have become unstable. These issues may have their parallel in the acquisition of knowledge by the developing child. PMID:7886158

  18. "Pre-Semantic" Cognition Revisited: Critical Differences between Semantic Aphasia and Semantic Dementia

    ERIC Educational Resources Information Center

    Jefferies, Elizabeth; Rogers, Timothy T.; Hopper, Samantha; Lambon Ralph, Matthew A.

    2010-01-01

    Patients with semantic dementia show a specific pattern of impairment on both verbal and non-verbal "pre-semantic" tasks, e.g., reading aloud, past tense generation, spelling to dictation, lexical decision, object decision, colour decision and delayed picture copying. All seven tasks are characterised by poorer performance for items that are…

  19. Automatic semantic annotation of real-world web images.

    PubMed

    Wong, R C F; Leung, C H C

    2008-11-01

    As the number of web images is increasing at a rapid rate, searching them semantically presents a significant challenge. Many raw images are constantly uploaded with little meaningful direct annotations of semantic content, limiting their search and discovery. In this paper, we present a semantic annotation technique based on the use of image parametric dimensions and metadata. Using decision trees and rule induction, we develop a rule-based approach to formulate explicit annotations for images fully automatically, so that by the use of our method, semantic query such as " sunset by the sea in autumn in New York" can be answered and indexed purely by machine. Our system is evaluated quantitatively using more than 100,000 web images. Experimental results indicate that this approach is able to deliver highly competent performance, attaining good recall and precision rates of sometimes over 80%. This approach enables a new degree of semantic richness to be automatically associated with images which previously can only be performed manually. PMID:18787242

  20. Body-part-specific representations of semantic noun categories.

    PubMed

    Carota, Francesca; Moseley, Rachel; Pulvermüller, Friedemann

    2012-06-01

    Word meaning processing in the brain involves ventrolateral temporal cortex, but a semantic contribution of the dorsal stream, especially frontocentral sensorimotor areas, has been controversial. We here examine brain activation during passive reading of object-related nouns from different semantic categories, notably animal, food, and tool words, matched for a range of psycholinguistic features. Results show ventral stream activation in temporal cortex along with category-specific activation patterns in both ventral and dorsal streams, including sensorimotor systems and adjacent pFC. Precentral activation reflected action-related semantic features of the word categories. Cortical regions implicated in mouth and face movements were sparked by food words, and hand area activation was seen for tool words, consistent with the actions implicated by the objects the words are used to speak about. Furthermore, tool words specifically activated the right cerebellum, and food words activated the left orbito-frontal and fusiform areas. We discuss our results in the context of category-specific semantic deficits in the processing of words and concepts, along with previous neuroimaging research, and conclude that specific dorsal and ventral areas in frontocentral and temporal cortex index visual and affective-emotional semantic attributes of object-related nouns and action-related affordances of their referent objects. PMID:22390464

  1. Enhancing clinical concept extraction with distributional semantics

    PubMed Central

    Cohen, Trevor; Wu, Stephen; Gonzalez, Graciela

    2011-01-01

    Extracting concepts (such as drugs, symptoms, and diagnoses) from clinical narratives constitutes a basic enabling technology to unlock the knowledge within and support more advanced reasoning applications such as diagnosis explanation, disease progression modeling, and intelligent analysis of the effectiveness of treatment. The recent release of annotated training sets of de-identified clinical narratives has contributed to the development and refinement of concept extraction methods. However, as the annotation process is labor-intensive, training data are necessarily limited in the concepts and concept patterns covered, which impacts the performance of supervised machine learning applications trained with these data. This paper proposes an approach to minimize this limitation by combining supervised machine learning with empirical learning of semantic relatedness from the distribution of the relevant words in additional unannotated text. The approach uses a sequential discriminative classifier (Conditional Random Fields) to extract the mentions of medical problems, treatments and tests from clinical narratives. It takes advantage of all Medline abstracts indexed as being of the publication type “clinical trials” to estimate the relatedness between words in the i2b2/VA training and testing corpora. In addition to the traditional features such as dictionary matching, pattern matching and part-of-speech tags, we also used as a feature words that appear in similar contexts to the word in question (that is, words that have a similar vector representation measured with the commonly used cosine metric, where vector representations are derived using methods of distributional semantics). To the best of our knowledge, this is the first effort exploring the use of distributional semantics, the semantics derived empirically from unannotated text often using vector space models, for a sequence classification task such as concept extraction. Therefore, we first

  2. Latent IBP Compound Dirichlet Allocation.

    PubMed

    Archambeau, Cedric; Lakshminarayanan, Balaji; Bouchard, Guillaume

    2015-02-01

    We introduce the four-parameter IBP compound Dirichlet process (ICDP), a stochastic process that generates sparse non-negative vectors with potentially an unbounded number of entries. If we repeatedly sample from the ICDP we can generate sparse matrices with an infinite number of columns and power-law characteristics. We apply the four-parameter ICDP to sparse nonparametric topic modelling to account for the very large number of topics present in large text corpora and the power-law distribution of the vocabulary of natural languages. The model, which we call latent IBP compound Dirichlet allocation (LIDA), allows for power-law distributions, both, in the number of topics summarising the documents and in the number of words defining each topic. It can be interpreted as a sparse variant of the hierarchical Pitman-Yor process when applied to topic modelling. We derive an efficient and simple collapsed Gibbs sampler closely related to the collapsed Gibbs sampler of latent Dirichlet allocation (LDA), making the model applicable in a wide range of domains. Our nonparametric Bayesian topic model compares favourably to the widely used hierarchical Dirichlet process and its heavy tailed version, the hierarchical Pitman-Yor process, on benchmark corpora. Experiments demonstrate that accounting for the power-distribution of real data is beneficial and that sparsity provides more interpretable results. PMID:26353244

  3. Estimating and Interpreting Latent Variable Interactions: A Tutorial for Applying the Latent Moderated Structural Equations Method

    ERIC Educational Resources Information Center

    Maslowsky, Julie; Jager, Justin; Hemken, Douglas

    2015-01-01

    Latent variables are common in psychological research. Research questions involving the interaction of two variables are likewise quite common. Methods for estimating and interpreting interactions between latent variables within a structural equation modeling framework have recently become available. The latent moderated structural equations (LMS)…

  4. Information-Theoretic Latent Distribution Modeling: Distinguishing Discrete and Continuous Latent Variable Models

    ERIC Educational Resources Information Center

    Markon, Kristian E.; Krueger, Robert F.

    2006-01-01

    Distinguishing between discrete and continuous latent variable distributions has become increasingly important in numerous domains of behavioral science. Here, the authors explore an information-theoretic approach to latent distribution modeling, in which the ability of latent distribution models to represent statistical information in observed…

  5. Optimization-Based Model Fitting for Latent Class and Latent Profile Analyses

    ERIC Educational Resources Information Center

    Huang, Guan-Hua; Wang, Su-Mei; Hsu, Chung-Chu

    2011-01-01

    Statisticians typically estimate the parameters of latent class and latent profile models using the Expectation-Maximization algorithm. This paper proposes an alternative two-stage approach to model fitting. The first stage uses the modified k-means and hierarchical clustering algorithms to identify the latent classes that best satisfy the…

  6. Mapping the Structure of Semantic Memory

    ERIC Educational Resources Information Center

    Morais, Ana Sofia; Olsson, Henrik; Schooler, Lael J.

    2013-01-01

    Aggregating snippets from the semantic memories of many individuals may not yield a good map of an individual's semantic memory. The authors analyze the structure of semantic networks that they sampled from individuals through a new snowball sampling paradigm during approximately 6 weeks of 1-hr daily sessions. The semantic networks of individuals…

  7. Semi-Nonparametric Methods for Detecting Latent Non-Normality: A Fusion of Latent Trait and Ordered Latent Class Modeling

    ERIC Educational Resources Information Center

    Schmitt, J. Eric; Mehta, Paras D.; Aggen, Steven H.; Kubarych, Thomas S.; Neale, Michael C.

    2006-01-01

    Ordered latent class analysis (OLCA) can be used to approximate unidimensional latent distributions. The main objective of this study is to evaluate the method of OLCA in detecting non-normality of an unobserved continuous variable (i.e., a common factor) used to explain the covariation between dichotomous item-level responses. Using simulation,…

  8. Semantic enrichment for medical ontologies.

    PubMed

    Lee, Yugyung; Geller, James

    2006-04-01

    The Unified Medical Language System (UMLS) contains two separate but interconnected knowledge structures, the Semantic Network (upper level) and the Metathesaurus (lower level). In this paper, we have attempted to work out better how the use of such a two-level structure in the medical field has led to notable advances in terminologies and ontologies. However, most ontologies and terminologies do not have such a two-level structure. Therefore, we present a method, called semantic enrichment, which generates a two-level ontology from a given one-level terminology and an auxiliary two-level ontology. During semantic enrichment, concepts of the one-level terminology are assigned to semantic types, which are the building blocks of the upper level of the auxiliary two-level ontology. The result of this process is the desired new two-level ontology. We discuss semantic enrichment of two example terminologies and how we approach the implementation of semantic enrichment in the medical domain. This implementation performs a major part of the semantic enrichment process with the medical terminologies, with difficult cases left to a human expert. PMID:16185937

  9. Scalable Medical Image Understanding by Fusing Cross-Modal Object Recognition with Formal Domain Semantics

    NASA Astrophysics Data System (ADS)

    Möller, Manuel; Sintek, Michael; Buitelaar, Paul; Mukherjee, Saikat; Zhou, Xiang Sean; Freund, Jörg

    Recent advances in medical imaging technology have dramatically increased the amount of clinical image data. In contrast, techniques for efficiently exploiting the rich semantic information in medical images have evolved much slower. Despite the research outcomes in image understanding, current image databases are still indexed by manually assigned subjective keywords instead of the semantics of the images. Indeed, most current content-based image search applications index image features that do not generalize well and use inflexible queries. This slow progress is due to the lack of scalable and generic information representation systems which can abstract over the high dimensional nature of medical images as well as semantically model the results of object recognition techniques. We propose a system combining medical imaging information with ontological formalized semantic knowledge that provides a basis for building universal knowledge repositories and gives clinicians fully cross-lingual and cross-modal access to biomedical information.

  10. Exploiting Recurring Structure in a Semantic Network

    NASA Technical Reports Server (NTRS)

    Wolfe, Shawn R.; Keller, Richard M.

    2004-01-01

    With the growing popularity of the Semantic Web, an increasing amount of information is becoming available in machine interpretable, semantically structured networks. Within these semantic networks are recurring structures that could be mined by existing or novel knowledge discovery methods. The mining of these semantic structures represents an interesting area that focuses on mining both for and from the Semantic Web, with surprising applicability to problems confronting the developers of Semantic Web applications. In this paper, we present representative examples of recurring structures and show how these structures could be used to increase the utility of a semantic repository deployed at NASA.

  11. WORD STATISTICS IN THE GENERATION OF SEMANTIC TOOLS FOR INFORMATION SYSTEMS.

    ERIC Educational Resources Information Center

    STONE, DON C.

    ONE OF THE PROBLEMS IN INFORMATION STORAGE AND RETRIEVAL SYSTEMS OF TECHNICAL DOCUMENTS IS THE INTERPRETATION OF WORDS USED TO INDEX DOCUMENTS. SEMANTIC TOOLS, DEFINED AS CHANNELS FOR THE COMMUNICATION OF WORD MEANINGS BETWEEN TECHNICAL EXPERTS, DOCUMENT INDEXERS, AND SEARCHERS, PROVIDE ONE METHOD OF DEALING WITH THE PROBLEM OF MULTIPLE…

  12. From autopoiesis to semantic closure.

    PubMed

    Stewart, J

    2000-01-01

    This article addresses the question of providing an adequate mathematical formulation for the concepts of autopoiesis and closure under efficient cause. What is required is metaphorically equivalent to reducing the act of writing to a set of mathematical equations, habitually effected by a human mathematician, within the ongoing function of the system itself. This, in turn, raises the question of the relationship between autopoiesis and semantics. The hypothesis suggested is that whereas semantics clearly requires autopoiesis, it may be also be the case that autopoiesis itself can only be materially realized in a system that is characterized by a semantic dimension. PMID:10818567

  13. Workspaces in the Semantic Web

    NASA Technical Reports Server (NTRS)

    Wolfe, Shawn R.; Keller, RIchard M.

    2005-01-01

    Due to the recency and relatively limited adoption of Semantic Web technologies. practical issues related to technology scaling have received less attention than foundational issues. Nonetheless, these issues must be addressed if the Semantic Web is to realize its full potential. In particular, we concentrate on the lack of scoping methods that reduce the size of semantic information spaces so they are more efficient to work with and more relevant to an agent's needs. We provide some intuition to motivate the need for such reduced information spaces, called workspaces, give a formal definition, and suggest possible methods of deriving them.

  14. High Performance Descriptive Semantic Analysis of Semantic Graph Databases

    SciTech Connect

    Joslyn, Cliff A.; Adolf, Robert D.; al-Saffar, Sinan; Feo, John T.; Haglin, David J.; Mackey, Greg E.; Mizell, David W.

    2011-06-02

    As semantic graph database technology grows to address components ranging from extant large triple stores to SPARQL endpoints over SQL-structured relational databases, it will become increasingly important to be able to understand their inherent semantic structure, whether codified in explicit ontologies or not. Our group is researching novel methods for what we call descriptive semantic analysis of RDF triplestores, to serve purposes of analysis, interpretation, visualization, and optimization. But data size and computational complexity makes it increasingly necessary to bring high performance computational resources to bear on this task. Our research group built a novel high performance hybrid system comprising computational capability for semantic graph database processing utilizing the large multi-threaded architecture of the Cray XMT platform, conventional servers, and large data stores. In this paper we describe that architecture and our methods, and present the results of our analyses of basic properties, connected components, namespace interaction, and typed paths such for the Billion Triple Challenge 2010 dataset.

  15. Attentional sensitization of unconscious cognition: task sets modulate subsequent masked semantic priming.

    PubMed

    Kiefer, Markus; Martens, Ulla

    2010-08-01

    According to classical theories, automatic processes are autonomous and independent of higher level cognitive influence. In contrast, the authors propose that automatic processing depends on attentional sensitization of task-congruent processing pathways. In 3 experiments, the authors tested this hypothesis with a modified masked semantic priming paradigm during a lexical decision task by measuring event-related potentials (ERPs): Before masked prime presentation, participants attended an induction task either to semantic or perceptual stimulus features designed to activate a semantic or perceptual task set, respectively. Semantic priming effects on the N400 ERP component, an electrophysiological index of semantic processing, were obtained when a semantic task set was induced immediately before subliminal prime presentation, whereas a previously induced perceptual task set attenuated N400 priming. Across experiments, comparable results were obtained regardless of the difficulty level and the verbal or nonverbal nature of the induction tasks. In line with the proposed attentional sensitization model, unconscious semantic processing is enhanced by a semantic and attenuated by a perceptual task set. Hence, automatic processing of unconscious stimuli is susceptible to top-down control for optimizing goal-related information processing. PMID:20677895

  16. Network-based modular latent structure analysis

    PubMed Central

    2014-01-01

    Background High-throughput expression data, such as gene expression and metabolomics data, exhibit modular structures. Groups of features in each module follow a latent factor model, while between modules, the latent factors are quasi-independent. Recovering the latent factors can shed light on the hidden regulation patterns of the expression. The difficulty in detecting such modules and recovering the latent factors lies in the high dimensionality of the data, and the lack of knowledge in module membership. Methods Here we describe a method based on community detection in the co-expression network. It consists of inference-based network construction, module detection, and interacting latent factor detection from modules. Results In simulations, the method outperformed projection-based modular latent factor discovery when the input signals were not Gaussian. We also demonstrate the method's value in real data analysis. Conclusions The new method nMLSA (network-based modular latent structure analysis) is effective in detecting latent structures, and is easy to extend to non-linear cases. The method is available as R code at http://web1.sph.emory.edu/users/tyu8/nMLSA/. PMID:25435002

  17. Rating Scale Analysis with Latent Class Models.

    ERIC Educational Resources Information Center

    Rost, Jurgen

    1988-01-01

    A general approach for analyzing rating data with latent class models is described, paralleling rating models in the framework of latent trait theory. A general rating model and a two-parameter model with location and dispersion parameters are derived and illustrated. (Author/SLD)

  18. Latent Heating from TRMM Satellite Measurements

    NASA Technical Reports Server (NTRS)

    Tao, W.-K.; Smith, E.; Olson, W.

    2005-01-01

    Rainfall production is a fundamental process within the Earth;s hydrological cycle because it represents both a principal forcing term in surface water budgets, and its energetics corollary, latent heating, is the principal source of atmospheric diabatic heating. Latent heat release itself is a consequence of phase changes between the vapor, liquid, and frozen states of water. The properties of the vertical distribution of latent heat release modulate large-scale meridional and zonal circulations with the Tropics - as well as modify the energetic efficiencies of mid-latitude weather systems. This paper highlights the retrieval of observatory, which was launched in November 1997 as a joint American-Japanese space endeavor. Since then, TRMM measurements have been providing an accurate four-dimensional amount of rainfall over the global Tropics and sub-tropics - information which can be used to estimate the spacetime structure of latent heating across the Earth's low latitudes. A set of algorithm methodologies has and continues to be developed to estimate latent heating based on rain rate profile retrievals obtained from TRMM measurements. These algorithms are briefly described followed by a discussion of the foremost latent heating products that can be generate from them. The investigation then provides an overview of how TRMM-derived latent heating information is currently being used in conjunction with global weather and climate models, concluding with remarks intended to stimulate further research on latent heating retrieval from satellites.

  19. Latent Heating Structures Derived from TRMM

    NASA Technical Reports Server (NTRS)

    Tao, W.-K.; Smith, E. A.; Adler, R.; Hou, A.; Kakar, R.; Krishnamurti, T.; Kummerow, C.; Lang, S.; Olson, W.; Satoh, S.

    2004-01-01

    Rainfall is the fundamental variable within the Earth's hydrological cycle because it is both the main forcing term leading to variations in continental and oceanic surface water budgets. The vertical distribution of latent heat release, which is accompanied with rain, modulates large-scale meridional and zonal circulations within the tropics as well as modifying the energetic efficiency of mid-latitude weather systems. Latent heat release itself is a consequence of phase changes between the vapor, liquid, and frozen states of water.This paper focuses on the retrieval of latent heat release from satellite measurements generated by the Tropical Rainfall Measuring Mission 0. The TRMM observatory, whose development was a joint US-Japan space endeavor, was launched in November 1997. TRMM measurements provide an accurate account of rainfall over the global tropics, information which can be .used to estimate the four-dimensional structure of latent heating across the entire tropical and sub-tropical regions. Various algorithm methodologies for estimating latent heating based on rain rate measurements from TRMM observations are described. The strengths and weaknesses of these algorithms, as well as the latent heating products generated by these algorithms, are also discussed along with validation analyses of the products. The investigation paper provides an overview of how TRMM-derived latent heating information is currently being used in conjunction with global weather and climate models, and concludes with remarks designed to stimulate further research on latent heating retrieval

  20. Consequences of Fitting Nonidentified Latent Class Models

    ERIC Educational Resources Information Center

    Abar, Beau; Loken, Eric

    2012-01-01

    Latent class models are becoming more popular in behavioral research. When models with a large number of latent classes relative to the number of manifest indicators are estimated, researchers must consider the possibility that the model is not identified. It is not enough to determine that the model has positive degrees of freedom. A well-known…

  1. Latent Memory for Sensitization in "Aplysia"

    ERIC Educational Resources Information Center

    Philips, Gary T.; Tzvetkova, Ekaterina I.; Marinesco, Stephane; Carew, Thomas J.

    2006-01-01

    In the analysis of memory it is commonly observed that, even after a memory is apparently forgotten, its latent presence can still be revealed in a subsequent learning task. Although well established on a behavioral level, the mechanisms underlying latent memory are not well understood. To begin to explore these mechanisms, we have used "Aplysia,"…

  2. Semantic Similarities between a Keyword Database and a Controlled Vocabulary Database: An Investigation in the Antibiotic Resistance Literature.

    ERIC Educational Resources Information Center

    Qin, Jian

    2000-01-01

    Explores similarities or dissimilarities between citation-semantic and analytic indexing based on a study of records in the Science Citation Index and MEDLINE databases on antibiotic resistance in pneumonia. Concludes that disparate indexing terms may be an advantage for better recall and precision in information retrieval. (Contains 42…

  3. A Latent Trait Look at Pretest-Posttest Validation of Criterion-referenced Test Items.

    ERIC Educational Resources Information Center

    van der Linden, Wim J.

    1981-01-01

    Using criterion-referenced test item data collected in an empirical study, differences in item selection between Cox and Vargas' pretest-posttest validity index and a latent trait approach (evaluation of the item information function for the mastery score) are analyzed. (Author/GK)

  4. Does semantic redundancy gain result from multiple semantic priming?

    PubMed

    Schröter, Hannes; Bratzke, Daniel; Fiedler, Anja; Birngruber, Teresa

    2015-10-01

    Fiedler, Schröter, and Ulrich (2013) reported faster responses to a single written word when the semantic content of this word (e.g., "elephant") matched both targets (e.g., "animal", "gray") as compared to a single target (e.g., "animal", "brown"). This semantic redundancy gain was explained by statistical facilitation due to a race of independent memory retrieval processes. The present experiment addresses one alternative explanation, namely that semantic redundancy gain results from multiple pre-activation of words that match both targets. In different blocks of trials, participants performed a redundant-targets task and a lexical decision task. The targets of the redundant-targets task served as primes in the lexical decision task. Replicating the findings of Fiedler et al., a semantic redundancy gain was observed in the redundant-targets task. Crucially, however, there was no evidence of a multiple semantic priming effect in the lexical decision task. This result suggests that semantic redundancy gain cannot be explained by multiple pre-activation of words that match both targets. PMID:26342771

  5. Epstein–Barr virus latent genes

    PubMed Central

    Kang, Myung-Soo; Kieff, Elliott

    2015-01-01

    Latent Epstein–Barr virus (EBV) infection has a substantial role in causing many human disorders. The persistence of these viral genomes in all malignant cells, yet with the expression of limited latent genes, is consistent with the notion that EBV latent genes are important for malignant cell growth. While the EBV-encoded nuclear antigen-1 (EBNA-1) and latent membrane protein-2A (LMP-2A) are critical, the EBNA-leader proteins, EBNA-2, EBNA-3A, EBNA-3C and LMP-1, are individually essential for in vitro transformation of primary B cells to lymphoblastoid cell lines. EBV-encoded RNAs and EBNA-3Bs are dispensable. In this review, the roles of EBV latent genes are summarized. PMID:25613728

  6. Open cycle latent heat engine

    SciTech Connect

    Czaja, J.

    1989-12-19

    This patent describes an open cycle latent heat engine. It comprises: an elevator passageway having an entrance at a lower level and an exit at a higher level having a substantially higher elevation than the lower level; means for inputting warm water vapor into the lower level of the elevator passageway to produce a wet adiabatic expansion of moist air rising in the passageway; a condensate remover in the region of the exit from the elevator, the condensate remover being arranged for removing water condensed from the vapor at the higher elevation of the exit: compressor passageway descending from the region of the elevator passageway exit to the region of the elevator passageway entrance; an ejector arranged at a lower region of the compressor passageway and means for extracting energy from the air circulation flow established by the elevator passageway, the compressor passageway, and the ejector.

  7. Latent classiness and other mixtures.

    PubMed

    Neale, Michael C

    2014-05-01

    The aim of this article is to laud Lindon Eaves' role in the development of mixture modeling in genetic studies. The specification of models for mixture distributions was very much in its infancy when Professor Eaves implemented it in his own FORTRAN programs, and extended it to data collected from relatives such as twins. It was his collaboration with the author of this article which led to the first implementation of mixture distribution modeling in a general-purpose structural equation modeling program, Mx, resulting in a 1996 article on linkage analysis in Behavior Genetics. Today, the popularity of these methods continues to grow, encompassing methods for genetic association, latent class analysis, growth curve mixture modeling, factor mixture modeling, regime switching, marginal maximum likelihood, genotype by environment interaction, variance component twin modeling in the absence of zygosity information, and many others. This primarily historical article concludes with some consideration of some possible future developments. PMID:24477932

  8. Latent Classiness and Other Mixtures

    PubMed Central

    Neale, Michael C.

    2014-01-01

    The aim of this article is to laud Lindon Eaves’ role in the development of mixture modeling in genetic studies. The specification of models for mixture distributions was very much in its infancy when Professor Eaves implemented it in his own FORTRAN programs, and extended it to data collected from relatives such as twins. It was his collaboration with the author of this article which led to the first implementation of mixture distribution modeling in a general-purpose structural equation modeling program, Mx, resulting in a 1996 article on linkage analysis in Behavior Genetics. Today, the popularity of these methods continues to grow, encompassing methods for genetic association, latent class analysis, growth curve mixture modeling, factor mixture modeling, regime switching, marginal maximum likelihood, genotype by environment interaction, variance component twin modeling in the absence of zygosity information, and many others. This primarily historical article concludes with some consideration of some possible future developments. PMID:24477932

  9. Latent Heating from TRMM Satellite Measurements

    NASA Technical Reports Server (NTRS)

    Tao, Wei-Kuo; Smith, E. A.; Adler, R.; Haddad, Z.; Hou, A.; Iguchi, T.; Kakar, R.; Krishnamurti, T.; Kummerow, C.; Lang, S.

    2004-01-01

    Rainfall production is the fundamental variable within the Earth's hydrological cycle because it is both the principal forcing term in surface water budgets and its energetics corollary, latent heating, is the principal source of atmospheric diabatic heating. Latent heat release itself is a consequence of phase changes between the vapor, liquid, and frozen states of water. The properties of the vertical distribution of latent heat release modulate large-scale meridional and zonal circulations within the tropics - as well as modifying the energetic efficiencies of midlatitude weather systems. This paper focuses on the retrieval of latent heat release from satellite measurements generated by the Tropical Rainfall Measuring Mission (TRMM) satellite observatory, which was launched in November 1997 as a joint American-Japanese space endeavor. Since then, TRMM measurements have been providing an accurate four-dimensional account of rainfall over the global tropics and sub-tropics, information which can be used to estimate the space-time structure of latent heating across the Earth's low latitudes. The paper examines how the observed TRMM distribution of rainfall has advanced an understanding of the global water and energy cycle and its consequent relationship to the atmospheric general circulation and climate via latent heat release. A set of algorithm methodologies that are being used to estimate latent heating based on rain rate retrievals from the TRMM observations are described. The characteristics of these algorithms and the latent heating products that can be generated from them are also described, along with validation analyses of the heating products themselves. Finally, the investigation provides an overview of how TRMM-derived latent heating information is currently being used in conjunction with global weather and climate models, concluding with remarks intended to stimulate further research on latent heating retrieval from satellites.

  10. Semplore: An IR Approach to Scalable Hybrid Query of Semantic Web Data

    NASA Astrophysics Data System (ADS)

    Zhang, Lei; Liu, Qiaoling; Zhang, Jie; Wang, Haofen; Pan, Yue; Yu, Yong

    As an extension to the current Web, Semantic Web will not only contain structured data with machine understandable semantics but also textual information. While structured queries can be used to find information more precisely on the Semantic Web, keyword searches are still needed to help exploit textual information. It thus becomes very important that we can combine precise structured queries with imprecise keyword searches to have a hybrid query capability. In addition, due to the huge volume of information on the Semantic Web, the hybrid query must be processed in a very scalable way. In this paper, we define such a hybrid query capability that combines unary tree-shaped structured queries with keyword searches. We show how existing information retrieval (IR) index structures and functions can be reused to index semantic web data and its textual information, and how the hybrid query is evaluated on the index structure using IR engines in an efficient and scalable manner. We implemented this IR approach in an engine called Semplore. Comprehensive experiments on its performance show that it is a promising approach. It leads us to believe that it may be possible to evolve current web search engines to query and search the Semantic Web. Finally, we breifly describe how Semplore is used for searching Wikipedia and an IBM customer's product information.

  11. Active maintenance of semantic representations.

    PubMed

    Nishiyama, Ryoji

    2014-12-01

    In research on verbal working memory, articulatory rehearsal, a maintenance mechanism for phonological representations, has undergone intensive and excellent study. Possible mechanisms for semantic representation have received less attention. However, several studies have reported a double dissociation in types of memory deficits (semantic memory difficulties vs. phonological memory difficulties). This suggests the separability of two maintenance mechanisms. The present study focused on this separability in individuals with normal memory abilities, using a dual-task interference paradigm. The results indicate a crossover interaction between memory and interference task effects: Preventing articulatory rehearsal more strongly disrupted the phonological memory task, whereas performing a tapping task that interfered with attentional control more strongly disrupted semantic memory. These results suggest that semantic representations are actively maintained by a mechanism other than phonological maintenance. PMID:24687734

  12. Distributed semantic networks and CLIPS

    NASA Technical Reports Server (NTRS)

    Snyder, James; Rodriguez, Tony

    1991-01-01

    Semantic networks of frames are commonly used as a method of reasoning in many problems. In most of these applications the semantic network exists as a single entity in a single process environment. Advances in workstation hardware provide support for more sophisticated applications involving multiple processes, interacting in a distributed environment. In these applications the semantic network may well be distributed over several concurrently executing tasks. This paper describes the design and implementation of a frame based, distributed semantic network in which frames are accessed both through C Language Integrated Production System (CLIPS) expert systems and procedural C++ language programs. The application area is a knowledge based, cooperative decision making model utilizing both rule based and procedural experts.

  13. Problem Solving with General Semantics.

    ERIC Educational Resources Information Center

    Hewson, David

    1996-01-01

    Discusses how to use general semantics formulations to improve problem solving at home or at work--methods come from the areas of artificial intelligence/computer science, engineering, operations research, and psychology. (PA)

  14. Semantic wireless body area networks.

    PubMed

    Nimmala, Venkatarama S R; Penders, Julien; van Hyfte, Dirk; Brands, Michael; Gyselinckx, Bert

    2008-01-01

    In this paper we introduce the concept of semantic Wireless Body Area Network (sWBAN). First the method for semantic interpretation of body sensor data is developed. This method is then illustrated for the case of ECG monitoring, providing the user with real-time monitoring and interpretation of heart activity. Finally, possible extensions of the method to data fusion and context-aware monitoring are discussed. PMID:19163441

  15. NASA and The Semantic Web

    NASA Technical Reports Server (NTRS)

    Ashish, Naveen

    2005-01-01

    We provide an overview of several ongoing NASA endeavors based on concepts, systems, and technology from the Semantic Web arena. Indeed NASA has been one of the early adopters of Semantic Web Technology and we describe ongoing and completed R&D efforts for several applications ranging from collaborative systems to airspace information management to enterprise search to scientific information gathering and discovery systems at NASA.

  16. COTARD SYNDROME IN SEMANTIC DEMENTIA

    PubMed Central

    Mendez, Mario F.; Ramírez-Bermúdez, Jesús

    2011-01-01

    Background Semantic dementia is a neurodegenerative disorder characterized by the loss of meaning of words or concepts. semantic dementia can offer potential insights into the mechanisms of content-specific delusions. Objective The authors present a rare case of semantic dementia with Cotard syndrome, a delusion characterized by nihilism or self-negation. Method The semantic deficits and other features of semantic dementia were evaluated in relation to the patient's Cotard syndrome. Results Mrs. A developed the delusional belief that she was wasting and dying. This occurred after she lost knowledge for her somatic discomforts and sensations and for the organs that were the source of these sensations. Her nihilistic beliefs appeared to emerge from her misunderstanding of her somatic sensations. Conclusion This unique patient suggests that a mechanism for Cotard syndrome is difficulty interpreting the nature and source of internal pains and sensations. We propose that loss of semantic knowledge about one's own body may lead to the delusion of nihilism or death. PMID:22054629

  17. Semantic preview benefit during reading.

    PubMed

    Hohenstein, Sven; Kliegl, Reinhold

    2014-01-01

    Word features in parafoveal vision influence eye movements during reading. The question of whether readers extract semantic information from parafoveal words was studied in 3 experiments by using a gaze-contingent display change technique. Subjects read German sentences containing 1 of several preview words that were replaced by a target word during the saccade to the preview (boundary paradigm). In the 1st experiment the preview word was semantically related or unrelated to the target. Fixation durations on the target were shorter for semantically related than unrelated previews, consistent with a semantic preview benefit. In the 2nd experiment, half the sentences were presented following the rules of German spelling (i.e., previews and targets were printed with an initial capital letter), and the other half were presented completely in lowercase. A semantic preview benefit was obtained under both conditions. In the 3rd experiment, we introduced 2 further preview conditions, an identical word and a pronounceable nonword, while also manipulating the text contrast. Whereas the contrast had negligible effects, fixation durations on the target were reliably different for all 4 types of preview. Semantic preview benefits were greater for pretarget fixations closer to the boundary (large preview space) and, although not as consistently, for long pretarget fixation durations (long preview time). The results constrain theoretical proposals about eye movement control in reading. (PsycINFO Database Record (c) 2013 APA, all rights reserved). PMID:23895448

  18. Orientation field estimation for latent fingerprint enhancement.

    PubMed

    Feng, Jianjiang; Zhou, Jie; Jain, Anil K

    2013-04-01

    Identifying latent fingerprints is of vital importance for law enforcement agencies to apprehend criminals and terrorists. Compared to live-scan and inked fingerprints, the image quality of latent fingerprints is much lower, with complex image background, unclear ridge structure, and even overlapping patterns. A robust orientation field estimation algorithm is indispensable for enhancing and recognizing poor quality latents. However, conventional orientation field estimation algorithms, which can satisfactorily process most live-scan and inked fingerprints, do not provide acceptable results for most latents. We believe that a major limitation of conventional algorithms is that they do not utilize prior knowledge of the ridge structure in fingerprints. Inspired by spelling correction techniques in natural language processing, we propose a novel fingerprint orientation field estimation algorithm based on prior knowledge of fingerprint structure. We represent prior knowledge of fingerprints using a dictionary of reference orientation patches. which is constructed using a set of true orientation fields, and the compatibility constraint between neighboring orientation patches. Orientation field estimation for latents is posed as an energy minimization problem, which is solved by loopy belief propagation. Experimental results on the challenging NIST SD27 latent fingerprint database and an overlapped latent fingerprint database demonstrate the advantages of the proposed orientation field estimation algorithm over conventional algorithms. PMID:22826508

  19. Predictive Inference Using Latent Variables with Covariates*

    PubMed Central

    Schofield, Lynne Steuerle; Junker, Brian; Taylor, Lowell J.; Black, Dan A.

    2014-01-01

    Plausible Values (PVs) are a standard multiple imputation tool for analysis of large education survey data that measures latent proficiency variables. When latent proficiency is the dependent variable, we reconsider the standard institutionally-generated PV methodology and find it applies with greater generality than shown previously. When latent proficiency is an independent variable, we show that the standard institutional PV methodology produces biased inference because the institutional conditioning model places restrictions on the form of the secondary analysts’ model. We offer an alternative approach that avoids these biases based on the mixed effects structural equations (MESE) model of Schofield (2008). PMID:25231627

  20. Extraction of latent images from printed media

    NASA Astrophysics Data System (ADS)

    Sergeyev, Vladislav; Fedoseev, Victor

    2015-12-01

    In this paper we propose an automatic technology for extraction of latent images from printed media such as documents, banknotes, financial securities, etc. This technology includes image processing by adaptively constructed Gabor filter bank for obtaining feature images, as well as subsequent stages of feature selection, grouping and multicomponent segmentation. The main advantage of the proposed technique is versatility: it allows to extract latent images made by different texture variations. Experimental results showing performance of the method over another known system for latent image extraction are given.

  1. Indexing Images.

    ERIC Educational Resources Information Center

    Rasmussen, Edie M.

    1997-01-01

    Focuses on access to digital image collections by means of manual and automatic indexing. Contains six sections: (1) Studies of Image Systems and their Use; (2) Approaches to Indexing Images; (3) Image Attributes; (4) Concept-Based Indexing; (5) Content-Based Indexing; and (6) Browsing in Image Retrieval. Contains 105 references. (AEF)

  2. Semantic Similarity in Biomedical Ontologies

    PubMed Central

    Pesquita, Catia; Faria, Daniel; Falcão, André O.; Lord, Phillip; Couto, Francisco M.

    2009-01-01

    In recent years, ontologies have become a mainstream topic in biomedical research. When biological entities are described using a common schema, such as an ontology, they can be compared by means of their annotations. This type of comparison is called semantic similarity, since it assesses the degree of relatedness between two entities by the similarity in meaning of their annotations. The application of semantic similarity to biomedical ontologies is recent; nevertheless, several studies have been published in the last few years describing and evaluating diverse approaches. Semantic similarity has become a valuable tool for validating the results drawn from biomedical studies such as gene clustering, gene expression data analysis, prediction and validation of molecular interactions, and disease gene prioritization. We review semantic similarity measures applied to biomedical ontologies and propose their classification according to the strategies they employ: node-based versus edge-based and pairwise versus groupwise. We also present comparative assessment studies and discuss the implications of their results. We survey the existing implementations of semantic similarity measures, and we describe examples of applications to biomedical research. This will clarify how biomedical researchers can benefit from semantic similarity measures and help them choose the approach most suitable for their studies. Biomedical ontologies are evolving toward increased coverage, formality, and integration, and their use for annotation is increasingly becoming a focus of both effort by biomedical experts and application of automated annotation procedures to create corpora of higher quality and completeness than are currently available. Given that semantic similarity measures are directly dependent on these evolutions, we can expect to see them gaining more relevance and even becoming as essential as sequence similarity is today in biomedical research. PMID:19649320

  3. Effective Web and Desktop Retrieval with Enhanced Semantic Spaces

    NASA Astrophysics Data System (ADS)

    Daoud, Amjad M.

    We describe the design and implementation of the NETBOOK prototype system for collecting, structuring and efficiently creating semantic vectors for concepts, noun phrases, and documents from a corpus of free full text ebooks available on the World Wide Web. Automatic generation of concept maps from correlated index terms and extracted noun phrases are used to build a powerful conceptual index of individual pages. To ensure scalabilty of our system, dimension reduction is performed using Random Projection [13]. Furthermore, we present a complete evaluation of the relative effectiveness of the NETBOOK system versus the Google Desktop [8].

  4. Retrieved Latent Heating from TRMM

    NASA Technical Reports Server (NTRS)

    Tao, Wei-Kuo; Smith, Eric A.; Houze Jr, Robert

    2008-01-01

    The global hydrological cycle is central to the Earth's climate system, with rainfall and the physics of precipitation formation acting as the key links in the cycle. Two-thirds of global rainfall occurs in the tropics with the associated latent heating (LH) accounting for three-fourths of the total heat energy available to the Earth's atmosphere. In addition, fresh water provided by tropical rainfall and its variability exerts a large impact upon the structure and motions of the upper ocean layer. In the last decade, it has been established that standard products of LH from satellite measurements, particularly TRMM measurements, would be a valuable resource for scientific research and applications. Such products would enable new insights and investigations concerning the complexities of convection system life cycles, the diabatic heating controls and feedbacks related to meso-synoptic circulations and their forecasting, the relationship of tropical patterns of LH to the global circulation and climate, and strategies for improving cloud parameterizations in environmental prediction models. The status of retrieved TRMM LH products, TRMM LH inter-comparison and validation project, current TRMM LH applications and critic issues/action items (based on previous five TRMM LH workshops) is presented in this article.

  5. Latent and delayed action polymerization systems.

    PubMed

    Naumann, Stefan; Buchmeiser, Michael R

    2014-04-01

    Various approaches to latent polymerization processes are described. In order to highlight recent advances in this field, the discussion is subdivided into chapters dedicated to diverse classes of polymers, namely polyurethanes, polyamides, polyesters, polyacrylates, epoxy resins, and metathesis-derived polymers. The described latent initiating systems encompass metal-containing as well as purely organic compounds that are activated by external triggers such as light, heat, or mechanical force. Special emphasis is put on the different chemical venues that can be taken to achieve true latency, which include masked N-heterocyclic carbenes, latent metathesis catalysts, and photolatent radical initiators, among others. Scientific challenges and the advantageous application of latent polymerization processes are discussed. PMID:24519912

  6. Latent Change Classes in Dichotomous Data.

    ERIC Educational Resources Information Center

    Formann, Anton K.; Ponocny, Ivo

    2002-01-01

    Developed a method, based on the conditional maximum likelihood principle, for establishing latent change classes in dichotomous data. Simulation studies and a real data set from an earlier study illustrate the method of parameter estimation. (SLD)

  7. Heteroscedastic Latent Trait Models for Dichotomous Data.

    PubMed

    Molenaar, Dylan

    2015-09-01

    Effort has been devoted to account for heteroscedasticity with respect to observed or latent moderator variables in item or test scores. For instance, in the multi-group generalized linear latent trait model, it could be tested whether the observed (polychoric) covariance matrix differs across the levels of an observed moderator variable. In the case that heteroscedasticity arises across the latent trait itself, existing models commonly distinguish between heteroscedastic residuals and a skewed trait distribution. These models have valuable applications in intelligence, personality and psychopathology research. However, existing approaches are only limited to continuous and polytomous data, while dichotomous data are common in intelligence and psychopathology research. Therefore, in present paper, a heteroscedastic latent trait model is presented for dichotomous data. The model is studied in a simulation study, and applied to data pertaining alcohol use and cognitive ability. PMID:25080866

  8. Reactivation of Latent Viruses in Space

    NASA Technical Reports Server (NTRS)

    Pierson, D. L.; Mehta, S. K.; Tyring, S. K.; Lugg, D. J.

    1999-01-01

    Reactivation of latent viruses is an important health risk for people working and living in physically isolated extreme environments such as Antarctica and space. Preflight quarantine does not significantly reduce the risk associated with latent viruses, however, pharmaceutical countermeasures are available for some viruses. The molecular basis of latency is not fully understood, but physical and psychosocial stresses are known to initiate the reactivation of latent viruses. Presumably, stress induced changes in selected hormones lead to alterations in the cell- mediated immune (CMI) response resulting in increased shedding of latent viruses. Limited access to space makes the use of ground-based analogs essential. The Australian Antarctic stations serve as a good stress model and simulate many aspects of space flight. Closed environmental chambers have been used to simulate space flight since the Skylab missions and have also proven to be a valuable analog of selected aspects of space flight.

  9. Web Feature Service Semantic Mediation

    NASA Astrophysics Data System (ADS)

    Hobona, G.; Bermudez, L. E.; Brackin, R.; Percivall, G. S.

    2012-12-01

    Scientists from different organizations and disciplines need to work together to find the solutions to complex problems. Multi-disciplinary science typically involves users with specialized tools and their own preferred view of the data including unique characteristics of the user's information model and symbology. Even though organizations use web services to expose data, there are still semantic inconsistencies that need to be solved. Recent activities within the OGC Interoperability Program (IP) have helped advance semantic mediation solutions when using OGC services to help solve complex problems. The OGC standards development process is influenced by the feedback of activities within the Interoperability Program, which conducts international interoperability initiatives such as Testbeds, Pilot Projects, Interoperability Experiments, and Interoperability Support Services. These activities are designed to encourage rapid development, testing, validation, demonstration and adoption of open, consensus based standards and best practices. Two recent Testbeds, the OGC Web Services Phase 8 and Phase 9, have advanced the use of semantic mediation approaches to increase semantic interoperability among geospatial communities. The Cross-Community Interoperability (CCI) thread within these two testbeds, advanced semantic mediation approaches for data discovery, access and use of heterogeneous data models and heterogeneous metadata models. This presentation will provide an overview of the interoperability program, the CCI Thread and will explain the methodology to mediate heterogeneous GML Application Profiles served via WFS, including discovery of services via a catalog standard interface and mediating symbology applicable to each application profile.

  10. Unsupervised Mining of Frequent Tags for Clinical Eligibility Text Indexing

    PubMed Central

    Miotto, Riccardo; Weng, Chunhua

    2013-01-01

    Clinical text, such as clinical trial eligibility criteria, is largely underused in state-of-the-art medical search engines due to difficulties of accurate parsing. This paper proposes a novel methodology to derive a semantic index for clinical eligibility documents based on a controlled vocabulary of frequent tags, which are automatically mined from the text. We applied this method to eligibility criteria on ClinicalTrials.gov and report that frequent tags (1) define an effective and efficient index of clinical trials and (2) are unlikely to grow radically when the repository increases. We proposed to apply the semantic index to filter clinical trial search results and we concluded that frequent tags reduce the result space more efficiently than an uncontrolled set of UMLS concepts. Overall, unsupervised mining of frequent tags from clinical text leads to an effective semantic index for the clinical eligibility documents and promotes their computational reuse. PMID:24036004

  11. Unsupervised mining of frequent tags for clinical eligibility text indexing.

    PubMed

    Miotto, Riccardo; Weng, Chunhua

    2013-12-01

    Clinical text, such as clinical trial eligibility criteria, is largely underused in state-of-the-art medical search engines due to difficulties of accurate parsing. This paper proposes a novel methodology to derive a semantic index for clinical eligibility documents based on a controlled vocabulary of frequent tags, which are automatically mined from the text. We applied this method to eligibility criteria on ClinicalTrials.gov and report that frequent tags (1) define an effective and efficient index of clinical trials and (2) are unlikely to grow radically when the repository increases. We proposed to apply the semantic index to filter clinical trial search results and we concluded that frequent tags reduce the result space more efficiently than an uncontrolled set of UMLS concepts. Overall, unsupervised mining of frequent tags from clinical text leads to an effective semantic index for the clinical eligibility documents and promotes their computational reuse. PMID:24036004

  12. Latent phenotypes pervade gene regulatory circuits

    PubMed Central

    2014-01-01

    Background Latent phenotypes are non-adaptive byproducts of adaptive phenotypes. They exist in biological systems as different as promiscuous enzymes and genome-scale metabolic reaction networks, and can give rise to evolutionary adaptations and innovations. We know little about their prevalence in the gene expression phenotypes of regulatory circuits, important sources of evolutionary innovations. Results Here, we study a space of more than sixteen million three-gene model regulatory circuits, where each circuit is represented by a genotype, and has one or more functions embodied in one or more gene expression phenotypes. We find that the majority of circuits with single functions have latent expression phenotypes. Moreover, the set of circuits with a given spectrum of functions has a repertoire of latent phenotypes that is much larger than that of any one circuit. Most of this latent repertoire can be easily accessed through a series of small genetic changes that preserve a circuit’s main functions. Both circuits and gene expression phenotypes that are robust to genetic change are associated with a greater number of latent phenotypes. Conclusions Our observations suggest that latent phenotypes are pervasive in regulatory circuits, and may thus be an important source of evolutionary adaptations and innovations involving gene regulation. PMID:24884746

  13. Graph-Theoretic Properties of Networks Based on Word Association Norms: Implications for Models of Lexical Semantic Memory.

    PubMed

    Gruenenfelder, Thomas M; Recchia, Gabriel; Rubin, Tim; Jones, Michael N

    2016-08-01

    We compared the ability of three different contextual models of lexical semantic memory (BEAGLE, Latent Semantic Analysis, and the Topic model) and of a simple associative model (POC) to predict the properties of semantic networks derived from word association norms. None of the semantic models were able to accurately predict all of the network properties. All three contextual models over-predicted clustering in the norms, whereas the associative model under-predicted clustering. Only a hybrid model that assumed that some of the responses were based on a contextual model and others on an associative network (POC) successfully predicted all of the network properties and predicted a word's top five associates as well as or better than the better of the two constituent models. The results suggest that participants switch between a contextual representation and an associative network when generating free associations. We discuss the role that each of these representations may play in lexical semantic memory. Concordant with recent multicomponent theories of semantic memory, the associative network may encode coordinate relations between concepts (e.g., the relation between pea and bean, or between sparrow and robin), and contextual representations may be used to process information about more abstract concepts. PMID:26453571

  14. Scene Classfication Based on the Semantic-Feature Fusion Fully Sparse Topic Model for High Spatial Resolution Remote Sensing Imagery

    NASA Astrophysics Data System (ADS)

    Zhu, Qiqi; Zhong, Yanfei; Zhang, Liangpei

    2016-06-01

    Topic modeling has been an increasingly mature method to bridge the semantic gap between the low-level features and high-level semantic information. However, with more and more high spatial resolution (HSR) images to deal with, conventional probabilistic topic model (PTM) usually presents the images with a dense semantic representation. This consumes more time and requires more storage space. In addition, due to the complex spectral and spatial information, a combination of multiple complementary features is proved to be an effective strategy to improve the performance for HSR image scene classification. But it should be noticed that how the distinct features are fused to fully describe the challenging HSR images, which is a critical factor for scene classification. In this paper, a semantic-feature fusion fully sparse topic model (SFF-FSTM) is proposed for HSR imagery scene classification. In SFF-FSTM, three heterogeneous features - the mean and standard deviation based spectral feature, wavelet based texture feature, and dense scale-invariant feature transform (SIFT) based structural feature are effectively fused at the latent semantic level. The combination of multiple semantic-feature fusion strategy and sparse based FSTM is able to provide adequate feature representations, and can achieve comparable performance with limited training samples. Experimental results on the UC Merced dataset and Google dataset of SIRI-WHU demonstrate that the proposed method can improve the performance of scene classification compared with other scene classification methods for HSR imagery.

  15. Semantic-JSON: a lightweight web service interface for Semantic Web contents integrating multiple life science databases

    PubMed Central

    Kobayashi, Norio; Ishii, Manabu; Takahashi, Satoshi; Mochizuki, Yoshiki; Matsushima, Akihiro; Toyoda, Tetsuro

    2011-01-01

    Global cloud frameworks for bioinformatics research databases become huge and heterogeneous; solutions face various diametric challenges comprising cross-integration, retrieval, security and openness. To address this, as of March 2011 organizations including RIKEN published 192 mammalian, plant and protein life sciences databases having 8.2 million data records, integrated as Linked Open or Private Data (LOD/LPD) using SciNetS.org, the Scientists' Networking System. The huge quantity of linked data this database integration framework covers is based on the Semantic Web, where researchers collaborate by managing metadata across public and private databases in a secured data space. This outstripped the data query capacity of existing interface tools like SPARQL. Actual research also requires specialized tools for data analysis using raw original data. To solve these challenges, in December 2009 we developed the lightweight Semantic-JSON interface to access each fragment of linked and raw life sciences data securely under the control of programming languages popularly used by bioinformaticians such as Perl and Ruby. Researchers successfully used the interface across 28 million semantic relationships for biological applications including genome design, sequence processing, inference over phenotype databases, full-text search indexing and human-readable contents like ontology and LOD tree viewers. Semantic-JSON services of SciNetS.org are provided at http://semanticjson.org. PMID:21632604

  16. Semantic-JSON: a lightweight web service interface for Semantic Web contents integrating multiple life science databases.

    PubMed

    Kobayashi, Norio; Ishii, Manabu; Takahashi, Satoshi; Mochizuki, Yoshiki; Matsushima, Akihiro; Toyoda, Tetsuro

    2011-07-01

    Global cloud frameworks for bioinformatics research databases become huge and heterogeneous; solutions face various diametric challenges comprising cross-integration, retrieval, security and openness. To address this, as of March 2011 organizations including RIKEN published 192 mammalian, plant and protein life sciences databases having 8.2 million data records, integrated as Linked Open or Private Data (LOD/LPD) using SciNetS.org, the Scientists' Networking System. The huge quantity of linked data this database integration framework covers is based on the Semantic Web, where researchers collaborate by managing metadata across public and private databases in a secured data space. This outstripped the data query capacity of existing interface tools like SPARQL. Actual research also requires specialized tools for data analysis using raw original data. To solve these challenges, in December 2009 we developed the lightweight Semantic-JSON interface to access each fragment of linked and raw life sciences data securely under the control of programming languages popularly used by bioinformaticians such as Perl and Ruby. Researchers successfully used the interface across 28 million semantic relationships for biological applications including genome design, sequence processing, inference over phenotype databases, full-text search indexing and human-readable contents like ontology and LOD tree viewers. Semantic-JSON services of SciNetS.org are provided at http://semanticjson.org. PMID:21632604

  17. The semantics of biological forms.

    PubMed

    Albertazzi, Liliana; Canal, Luisa; Dadam, James; Micciolo, Rocco

    2014-01-01

    This study analyses how certain qualitative perceptual appearances of biological forms are correlated with expressions of natural language. Making use of the Osgood semantic differential, we presented the subjects with 32 drawings of biological forms and a list of 10 pairs of connotative adjectives to be put in correlations with them merely by subjective judgments. The principal components analysis made it possible to group the semantics of forms according to two distinct axes of variability: harmony and dynamicity. Specifically, the nonspiculed, nonholed, and flat forms were perceived as harmonic and static; the rounded ones were harmonic and dynamic. The elongated forms were somewhat disharmonious and somewhat static. The results suggest the existence in the general population of a correspondence between perceptual and semantic processes, and of a nonsymbolic relation between visual forms and their adjectival expressions in natural language. PMID:25669053

  18. A Semantic Web Blackboard System

    NASA Astrophysics Data System (ADS)

    McKenzie, Craig; Preece, Alun; Gray, Peter

    In this paper, we propose a Blackboard Architecture as a means for coordinating hybrid reasoning over the Semantic Web. We describe the components of traditional blackboard systems (Knowledge Sources, Blackboard, Controller) and then explain how we have enhanced these by incorporating some of the principles of the Semantic Web to pro- duce our Semantic Web Blackboard. Much of the framework is already in place to facilitate our research: the communication protocol (HTTP); the data representation medium (RDF); a rich expressive description language (OWL); and a method of writing rules (SWRL). We further enhance this by adding our own constraint based formalism (CIF/SWRL) into the mix. We provide an example walk-though of our test-bed system, the AKTive Workgroup Builder and Blackboard(AWB+B), illustrating the interaction and cooperation of the Knowledge Sources and providing some context as to how the solution is achieved. We conclude with the strengths and weaknesses of the architecture.

  19. The Latent Structure of Dictionaries.

    PubMed

    Vincent-Lamarre, Philippe; Massé, Alexandre Blondin; Lopes, Marcos; Lord, Mélanie; Marcotte, Odile; Harnad, Stevan

    2016-07-01

    How many words-and which ones-are sufficient to define all other words? When dictionaries are analyzed as directed graphs with links from defining words to defined words, they reveal a latent structure. Recursively removing all words that are reachable by definition but that do not define any further words reduces the dictionary to a Kernel of about 10% of its size. This is still not the smallest number of words that can define all the rest. About 75% of the Kernel turns out to be its Core, a "Strongly Connected Subset" of words with a definitional path to and from any pair of its words and no word's definition depending on a word outside the set. But the Core cannot define all the rest of the dictionary. The 25% of the Kernel surrounding the Core consists of small strongly connected subsets of words: the Satellites. The size of the smallest set of words that can define all the rest-the graph's "minimum feedback vertex set" or MinSet-is about 1% of the dictionary, about 15% of the Kernel, and part-Core/part-Satellite. But every dictionary has a huge number of MinSets. The Core words are learned earlier, more frequent, and less concrete than the Satellites, which are in turn learned earlier, more frequent, but more concrete than the rest of the Dictionary. In principle, only one MinSet's words would need to be grounded through the sensorimotor capacity to recognize and categorize their referents. In a dual-code sensorimotor/symbolic model of the mental lexicon, the symbolic code could do all the rest through recombinatory definition. PMID:27424842

  20. Disrupting morphosyntactic and lexical semantic processing has opposite effects on the sample entropy of neural signals.

    PubMed

    Fonseca, André; Boboeva, Vezha; Brederoo, Sanne; Baggio, Giosuè

    2015-04-16

    Converging evidence in neuroscience suggests that syntax and semantics are dissociable in brain space and time. However, it is possible that partly disjoint cortical networks, operating in successive time frames, still perform similar types of neural computations. To test the alternative hypothesis, we collected EEG data while participants read sentences containing lexical semantic or morphosyntactic anomalies, resulting in N400 and P600 effects, respectively. Next, we reconstructed phase space trajectories from EEG time series, and we measured the complexity of the resulting dynamical orbits using sample entropy - an index of the rate at which the system generates or loses information over time. Disrupting morphosyntactic or lexical semantic processing had opposite effects on sample entropy: it increased in the N400 window for semantic anomalies, and it decreased in the P600 window for morphosyntactic anomalies. These findings point to a fundamental divergence in the neural computations supporting meaning and grammar in language. PMID:25634797

  1. Imprint of the ENSO on rainfall and latent heating variability over the Southern South China Sea from TRMM observations

    NASA Astrophysics Data System (ADS)

    Wang, Lei; Huang, Ke

    2016-04-01

    Analyses of the Tropical Rainfall Measuring Mission (TRMM) datasets revealed a prominent interannual variation in the convective-stratiform rainfall and latent heating over the southern South China Sea (SCS) during the winter monsoon between 1998 and 2010. Although the height of maximum latent heating remained nearly constant at around 7 km in all of the years, the year-to-year changes in the magnitudes of maximum latent heating over the region were noticeable. The interannual variations of the convective- stratiform rainfall and latent heating over the southern SCS were highly anti-correlated with the Niño-3 index, with more (less) rainfall and latent heating during La Niña (El Niño) years. Analysis of the large-scale environment revealed that years of active rainfall and latent heating corresponded to years of large deep convergence and relative humidity at 600 hPa. The moisture budget diagnosis indicated that the interannual variation of humidity at 600 hPa was largely modulated by the vertical moisture advection. The year-to-year changes in rainfall over the southern SCS were mainly caused by the interannual variations of the dynamic component associated with anomalous upward motions in the middle troposphere, while the interannual variations of the thermodynamic component associated with changes in surface specific humidity played a minor role. Larger latent heating over the southern SCS during La Niña years may possibly further enhance the local Hadley circulation over the SCS in the wintertime.

  2. Semantic Relatedness for Evaluation of Course Equivalencies

    ERIC Educational Resources Information Center

    Yang, Beibei

    2012-01-01

    Semantic relatedness, or its inverse, semantic distance, measures the degree of closeness between two pieces of text determined by their meaning. Related work typically measures semantics based on a sparse knowledge base such as WordNet or Cyc that requires intensive manual efforts to build and maintain. Other work is based on a corpus such as the…

  3. Metasemantics: On the Limits of Semantic Theory

    ERIC Educational Resources Information Center

    Parent, T.

    2009-01-01

    METASEMANTICS is a wake-up call for semantic theory: It reveals that some semantic questions have no adequate answer. (This is meant to be the "epistemic" point that certain semantic questions cannot be "settled"--not a metaphysical point about whether there is a fact-of-the-matter.) METASEMANTICS thus checks our default "optimism" that any…

  4. Examining Lateralized Semantic Access Using Pictures

    ERIC Educational Resources Information Center

    Lovseth, Kyle; Atchley, Ruth Ann

    2010-01-01

    A divided visual field (DVF) experiment examined the semantic processing strategies employed by the cerebral hemispheres to determine if strategies observed with written word stimuli generalize to other media for communicating semantic information. We employed picture stimuli and vary the degree of semantic relatedness between the picture pairs.…

  5. Semantic and Visual Memory After Alcohol Abuse.

    ERIC Educational Resources Information Center

    Donat, Dennis C.

    1986-01-01

    Compared the relative performance of 40 patients with a history of alcohol abuse on tasks of short-term semantic and visual memory. Performance on the visual memory tasks was impaired significantly relative to the semantic memory task in a within-subjects analysis of variance. Semantic memory was unimpaired. (Author/ABB)

  6. Semantic Weight and Verb Retrieval in Aphasia

    ERIC Educational Resources Information Center

    Barde, Laura H. F.; Schwartz, Myrna F.; Boronat, Consuelo B.

    2006-01-01

    Individuals with agrammatic aphasia may have difficulty with verb production in comparison to nouns. Additionally, they may have greater difficulty producing verbs that have fewer semantic components (i.e., are semantically "light") compared to verbs that have greater semantic weight. A connectionist verb-production model proposed by Gordon and…

  7. Chinese Character Decoding: A Semantic Bias?

    ERIC Educational Resources Information Center

    Williams, Clay; Bever, Thomas

    2010-01-01

    The effects of semantic and phonetic radicals on Chinese character decoding were examined. Our results suggest that semantic and phonetic radicals are each available for access when a corresponding task emphasizes one or the other kind of radical. But in a more neutral lexical recognition task, the semantic radical is more informative. Semantic…

  8. Bootstrapping to a Semantic Grid

    SciTech Connect

    Schwidder, Jens; Talbott, Tara; Myers, James D.

    2005-02-28

    The Scientific Annotation Middleware (SAM) is a set of components and services that enable researchers, applications, problem solving environments (PSE) and software agents to create metadata and annotations about data objects and document the semantic relationships between them. Developed starting in 2001, SAM allows applications to encode metadata within files or to manage metadata at the level of individual relationships as desired. SAM then provides mechanisms to expose metadata and relation¬ships encoded either way as WebDAV properties. In this paper, we report on work to further map this metadata into RDF and discuss the role of middleware such as SAM in bridging between traditional and semantic grid applications.

  9. Automatic Evaluation for E-Learning Using Latent Semantic Analysis: A Use Case

    ERIC Educational Resources Information Center

    Farrus, Mireia; Costa-jussa, Marta R.

    2013-01-01

    Assessment in education allows for obtaining, organizing, and presenting information about how much and how well the student is learning. The current paper aims at analysing and discussing some of the most state-of-the-art assessment systems in education. Later, this work presents a specific use case developed for the Universitat Oberta de…

  10. An Efficient Web Page Ranking for Semantic Web

    NASA Astrophysics Data System (ADS)

    Chahal, P.; Singh, M.; Kumar, S.

    2014-01-01

    With the enormous amount of information presented on the web, the retrieval of relevant information has become a serious problem and is also the topic of research for last few years. The most common tools to retrieve information from web are search engines like Google. The Search engines are usually based on keyword searching and indexing of web pages. This approach is not very efficient as the result-set of web pages obtained include large irrelevant pages. Sometimes even the entire result-set may contain lot of irrelevant pages for the user. The next generation of search engines must address this problem. Recently, many semantic web search engines have been developed like Ontolook, Swoogle, which help in searching meaningful documents presented on semantic web. In this process the ranking of the retrieved web pages is very crucial. Some attempts have been made in ranking of semantic web pages but still the ranking of these semantic web documents is neither satisfactory and nor up to the user's expectations. In this paper we have proposed a semantic web based document ranking scheme that relies not only on the keywords but also on the conceptual instances present between the keywords. As a result only the relevant page will be on the top of the result-set of searched web pages. We explore all relevant relations between the keywords exploring the user's intention and then calculate the fraction of these relations on each web page to determine their relevance. We have found that this ranking technique gives better results than those by the prevailing methods.

  11. Latent Curve Models and Latent Change Score Models Estimated in R

    ERIC Educational Resources Information Center

    Ghisletta, Paolo; McArdle, John J.

    2012-01-01

    In recent years the use of the latent curve model (LCM) among researchers in social sciences has increased noticeably, probably thanks to contemporary software developments and the availability of specialized literature. Extensions of the LCM, like the the latent change score model (LCSM), have also increased in popularity. At the same time, the R…

  12. Taxometrics, Polytomous Constructs, and the Comparison Curve Fit Index: A Monte Carlo Analysis

    ERIC Educational Resources Information Center

    Walters, Glenn D.; McGrath, Robert E.; Knight, Raymond A.

    2010-01-01

    The taxometric method effectively distinguishes between dimensional (1-class) and taxonic (2-class) latent structure, but there is virtually no information on how it responds to polytomous (3-class) latent structure. A Monte Carlo analysis showed that the mean comparison curve fit index (CCFI; Ruscio, Haslam, & Ruscio, 2006) obtained with 3…

  13. Semantic Preview Benefit during Reading

    ERIC Educational Resources Information Center

    Hohenstein, Sven; Kliegl, Reinhold

    2014-01-01

    Word features in parafoveal vision influence eye movements during reading. The question of whether readers extract semantic information from parafoveal words was studied in 3 experiments by using a gaze-contingent display change technique. Subjects read German sentences containing 1 of several preview words that were replaced by a target word…

  14. The Semantic Web in Education

    ERIC Educational Resources Information Center

    Ohler, Jason

    2008-01-01

    The semantic web or Web 3.0 makes information more meaningful to people by making it more understandable to machines. In this article, the author examines the implications of Web 3.0 for education. The author considers three areas of impact: knowledge construction, personal learning network maintenance, and personal educational administration.…

  15. Generative Semantics and Dialect Geography.

    ERIC Educational Resources Information Center

    Ney, James W.

    An extrinsic relationship between generative semantics and dialect geography should be exploited because contemporary transformational grammarians have too easily ignored the work of the dialectologist and have been too readily satisfied with what might be called armchair evidence. The work of the dialect geographers needs to be taken into…

  16. Semantic Fission through Dialect Fusion.

    ERIC Educational Resources Information Center

    Linn, Michael D.

    The linguistic atlas projects have provided much information on the regional distribution of pronunciation, vocabulary, and syntax and have given important evidence for a greater understanding of problems involved in semantic change, particularly in pointing out transition areas where dialects become fused. In a study supplementary to that…

  17. Entanglement as a Semantic Resource

    NASA Astrophysics Data System (ADS)

    Dalla Chiara, Maria Luisa; Giuntini, Roberto; Ledda, Antonio; Leporini, Roberto; Sergioli, Giuseppe

    2010-10-01

    The characteristic holistic features of the quantum theoretic formalism and the intriguing notion of entanglement can be applied to a field that is far from microphysics: logical semantics. Quantum computational logics are new forms of quantum logic that have been suggested by the theory of quantum logical gates in quantum computation. In the standard semantics of these logics, sentences denote quantum information quantities: systems of qubits ( quregisters) or, more generally, mixtures of quregisters ( qumixes), while logical connectives are interpreted as special quantum logical gates (which have a characteristic reversible and dynamic behavior). In this framework, states of knowledge may be entangled, in such a way that our information about the whole determines our information about the parts; and the procedure cannot be, generally, inverted. In spite of its appealing properties, the standard version of the quantum computational semantics is strongly “Hilbert-space dependent”. This certainly represents a shortcoming for all applications, where real and complex numbers do not generally play any significant role (as happens, for instance, in the case of natural and of artistic languages). We propose an abstract version of quantum computational semantics, where abstract qumixes, quregisters and registers are identified with some special objects (not necessarily living in a Hilbert space), while gates are reversible functions that transform qumixes into qumixes. In this framework, one can give an abstract definition of the notions of superposition and of entangled pieces of information, quite independently of any numerical values. We investigate three different forms of abstract holistic quantum computational logic.

  18. Incrementally Dissociating Syntax and Semantics

    ERIC Educational Resources Information Center

    Brennan, Jonathan R.

    2010-01-01

    A basic challenge for research into the neurobiology of language is understanding how the brain combines words to make complex representations. Linguistic theory divides this task into several computations including syntactic structure building and semantic composition. The close relationship between these computations, however, poses a strong…

  19. Semantic Annotation of Computational Components

    NASA Technical Reports Server (NTRS)

    Vanderbilt, Peter; Mehrotra, Piyush

    2004-01-01

    This paper describes a methodology to specify machine-processable semantic descriptions of computational components to enable them to be shared and reused. A particular focus of this scheme is to enable automatic compositon of such components into simple work-flows.

  20. Semantic Borders and Incomplete Understanding.

    PubMed

    Silva-Filho, Waldomiro J; Dazzani, Maria Virgínia

    2016-03-01

    In this article, we explore a fundamental issue of Cultural Psychology, that is our "capacity to make meaning", by investigating a thesis from contemporary philosophical semantics, namely, that there is a decisive relationship between language and rationality. Many philosophers think that for a person to be described as a rational agent he must understand the semantic content and meaning of the words he uses to express his intentional mental states, e.g., his beliefs and thoughts. Our argument seeks to investigate the thesis developed by Tyler Burge, according to which our mastery or understanding of the semantic content of the terms which form our beliefs and thoughts is an "incomplete understanding". To do this, we discuss, on the one hand, the general lines of anti-individualism or semantic externalism and, on the other, criticisms of the Burgean notion of incomplete understanding - one radical and the other moderate. We defend our understanding that the content of our beliefs must be described in the light of the limits and natural contingencies of our cognitive capacities and the normative nature of our rationality. At heart, anti-individualism leads us to think about the fact that we are social creatures, living in contingent situations, with important, but limited, cognitive capacities, and that we receive the main, and most important, portion of our knowledge simply from what others tell us. Finally, we conclude that this discussion may contribute to the current debate about the notion of borders. PMID:26111737

  1. Semantic Relationships between Contextual Synonyms

    ERIC Educational Resources Information Center

    Zeng, Xian-mo

    2007-01-01

    Contextual synonym is a linguistic phenomenon often applied but rarely discussed. This paper is to discuss the semantic relationships between contextual synonyms and the requirements under which words can be used as contextual synonyms between each other. The three basic relationships are embedment, intersection and non-coherence. The requirements…

  2. Synonym extraction and abbreviation expansion with ensembles of semantic spaces

    PubMed Central

    2014-01-01

    Background Terminologies that account for variation in language use by linking synonyms and abbreviations to their corresponding concept are important enablers of high-quality information extraction from medical texts. Due to the use of specialized sub-languages in the medical domain, manual construction of semantic resources that accurately reflect language use is both costly and challenging, often resulting in low coverage. Although models of distributional semantics applied to large corpora provide a potential means of supporting development of such resources, their ability to isolate synonymy from other semantic relations is limited. Their application in the clinical domain has also only recently begun to be explored. Combining distributional models and applying them to different types of corpora may lead to enhanced performance on the tasks of automatically extracting synonyms and abbreviation-expansion pairs. Results A combination of two distributional models – Random Indexing and Random Permutation – employed in conjunction with a single corpus outperforms using either of the models in isolation. Furthermore, combining semantic spaces induced from different types of corpora – a corpus of clinical text and a corpus of medical journal articles – further improves results, outperforming a combination of semantic spaces induced from a single source, as well as a single semantic space induced from the conjoint corpus. A combination strategy that simply sums the cosine similarity scores of candidate terms is generally the most profitable out of the ones explored. Finally, applying simple post-processing filtering rules yields substantial performance gains on the tasks of extracting abbreviation-expansion pairs, but not synonyms. The best results, measured as recall in a list of ten candidate terms, for the three tasks are: 0.39 for abbreviations to long forms, 0.33 for long forms to abbreviations, and 0.47 for synonyms. Conclusions This study demonstrates

  3. Chromatin organization of gammaherpesvirus latent genomes.

    PubMed

    Tempera, Italo; Lieberman, Paul M

    2010-01-01

    The gammaherpesviruses are a subclass of the herpesvirus family that establish stable latent infections in proliferating lymphoid and epithelial cells. The latent genomes are maintained as multicopy chromatinized episomes that replicate in synchrony with the cellular genome. Importantly, most of the episomes do not integrate into the host chromosome. Therefore, it is essential that the viral "minichromosome" establish a chromatin structure that is suitable for gene expression, DNA replication, and chromosome segregation. Evidence suggests that chromatin organization is important for each of these functions and plays a regulatory role in the establishment and maintenance of latent infection. Here, we review recent studies on the chromatin organization of the human gammaherpesviruses, Epstein-Barr virus (EBV) and Kaposi's sarcoma-associated herpesvirus (KSHV). We discuss the potential role of viral origins of DNA replication and viral encoded origin-binding proteins like EBNA1 and LANA in establishment of viral chromosome organization during latent infection. We also discuss the roles of host cell factors, like CTCF and cohesins, that contribute to higher-order chromosome structures that may be important for stable gene expression programs during latent infection in proliferating cells. PMID:19853673

  4. Are poker players all the same? Latent class analysis.

    PubMed

    Dufour, Magali; Brunelle, Natacha; Roy, Élise

    2015-06-01

    Poker is the gambling game that is currently gaining the most in popularity. However, there is little information on poker players' characteristics and risk factors. Furthermore, the first studies described poker players, often recruited in universities, as an homogeneous group who played in only one of the modes (land based or on the Internet). This study aims to identify, through latent class analyses, poker player subgroups. A convenience sample of 258 adult poker players was recruited across Quebec during special events or through advertising in various media. Participants filled out a series of questionnaires (Canadian Problem Gambling Index, Beck Depression, Beck Anxiety, erroneous belief and alcohol/drug consumption). The latent class analysis suggests that there are three classes of poker players. Class I (recreational poker players) includes those who have the lowest probability of engaging intensively in different game modes. Participants in class II (Internet poker players) all play poker on the Internet. This class includes the highest proportion of players who consider themselves experts or professionals. They make a living in part or in whole from poker. Class III (multiform players) includes participants with the broadest variety of poker patterns. This group is complex: these players are positioned halfway between professional and recreational players. Results indicate that poker players are not an homogeneous group identified simply on the basis of the form of poker played. The specific characteristics associated with each subgroup points to vulnerabilities that could potentially be targeted for preventive interventions. PMID:24317705

  5. Lexical Semantics and Irregular Inflection

    PubMed Central

    Huang, Yi Ting; Pinker, Steven

    2010-01-01

    Whether a word has an irregular inflection does not depend on its sound alone: compare lie-lay (recline) and lie-lied (prevaricate). Theories of morphology, particularly connectionist and symbolic models, disagree on which nonphonological factors are responsible. We test four possibilities: (1) Lexical effects, in which two lemmas differ in whether they specify an irregular form; (2) Semantic effects, in which the semantic features of a word become associated with regular or irregular forms; (3) Morphological structure effects, in which a word with a headless structure (e.g., a verb derived from a noun) blocks access to a stored irregular form; (4) Compositionality effects, in which the stored combination of an irregular word’s meaning (e.g., the verb’s inherent aspect) with the meaning of the inflection (e.g., pastness) doesn’t readily transfer to new senses with different combinations of such meanings. In four experiments, speakers were presented with existing and novel verbs and asked to rate their past-tense forms, semantic similarities, grammatical structure, and aspectual similarities. We found (1) an interaction between semantic and phonological similarity, coinciding with reported strategies of analogizing to known verbs and implicating lexical effects; (2) weak and inconsistent effects of semantic similarity; (3) robust effects of morphological structure, and (4) robust effects of aspectual compositionality. Results are consistent with theories of language that invoke lexical entries and morphological structure, and which differentiate the mode of storage of regular and irregular verbs. They also suggest how psycholinguistic processes have shaped vocabulary structure over history. PMID:21151703

  6. Latent variables may be useful in pain’s assessment

    PubMed Central

    2014-01-01

    Background Unobserved “latent” variables have the potential to minimize “measurement error” inherent to any single clinical assessment or categorical diagnosis. Objectives To demonstrate the potential utility of latent variable constructs in pain’s assessment. Design We created two latent variables representing depressive symptom-related pain (Pd) and its residual, “somatic” pain (Ps), from survey questions. Setting The Hispanic Established Population for Epidemiological Studies in the Elderly (H-EPESE) project, a longitudinal population-based cohort study. Participants Community dwelling elderly Mexican-Americans in five Southwestern U.S. states. The data were collected in the 7th HEPESE wave in 2010 (N = 1,078). Measurements Self-reported pain, Center for Epidemiological Studies Depression Scale (CES-D) scores, bedside cognitive performance measures, and informant-rated measures of basic and instrumental Activities of Daily Living. Results The model showed excellent fit [χ2 = 20.37, DF = 12; p = 0.06; Comparative fit index (CFI) = 0.998; Root mean statistical error assessment (RMSEA) = 0.025]. Ps was most strongly indicated by self-reported pain-related physician visits (r = 0.48, p ≤0.001). Pd was most strongly indicated by self-reported pain-related sleep disturbances (r = 0.65, p <0.001). Both Pd and Ps were significantly independently associated with chronic pain (> one month), regional pain and pain summed across selected regions. Pd alone was significantly independently associated with self-rated health, life satisfaction, self-reported falls, Life-space, nursing home placement, the use of opiates, and a variety of sleep related disturbances. Ps was associated with the use of NSAIDS. Neither construct was associated with declaration of a resuscitation preference, mode of resuscitation preference declaration, or with opting for a “Do Not Resuscitate” (DNR) order. Conclusion This analysis illustrates the potential of latent variables to

  7. Latent image exposure monitor using scatterometry

    SciTech Connect

    Milner, L.M.; Hickman, K.C.; Gaspar, S.M.; Bishop, K.P.; Naqvi, S.S.; McNeil, J.R.; Blain, M.; Draper, B.L.

    1992-09-01

    We discuss the use of light scattered from a latent image to control photoresist exposure dose and focus conditions which results in improved control of the critical dimension (CD) of the developed photoresist. A laser at a non-exposing wavelength is used to illuminate a latent image grating. The light diffracted from the grating is directly related to the exposure dose and focus and thus to the resultant CD in the developed resist. Modeling has been done using rigorous coupled wave analysis to predict the diffraction from a latent image as a function of the substrate optical properties and the photoactive compound (PAC) concentration distribution inside the photoresist. It is possible to use the model to solve the inverse problem: given the diffraction, to predict the parameters of the latent image and hence the developed pattern. This latent image monitor can be implemented in a stepper to monitor exposure in situ, or prior to development to predict the developed CD of a wafer for early detection of bad devices. Experimentation has been conducted using various photoresists and substrates with excellent agreement between theoretical and experimental results. The technique has been used to characterize a test pattern with a focused spot as small as 36{mu}m in diameter. Using diffracted light from a simulated closed-loop control of exposure dose, CD control was improved by as much as 4 times for substrates with variations in underlying film thickness, compared to using fixed exposure time. The latent image monitor has also been applied to wafers with rough metal substrates and focus optimization.

  8. Latent image exposure monitor using scatterometry

    SciTech Connect

    Milner, L.M.; Hickman, K.C.; Gaspar, S.M.; Bishop, K.P.; Naqvi, S.S.; McNeil, J.R. . Center for High Technology Materials); Blain, M.; Draper, B.L. )

    1992-01-01

    We discuss the use of light scattered from a latent image to control photoresist exposure dose and focus conditions which results in improved control of the critical dimension (CD) of the developed photoresist. A laser at a non-exposing wavelength is used to illuminate a latent image grating. The light diffracted from the grating is directly related to the exposure dose and focus and thus to the resultant CD in the developed resist. Modeling has been done using rigorous coupled wave analysis to predict the diffraction from a latent image as a function of the substrate optical properties and the photoactive compound (PAC) concentration distribution inside the photoresist. It is possible to use the model to solve the inverse problem: given the diffraction, to predict the parameters of the latent image and hence the developed pattern. This latent image monitor can be implemented in a stepper to monitor exposure in situ, or prior to development to predict the developed CD of a wafer for early detection of bad devices. Experimentation has been conducted using various photoresists and substrates with excellent agreement between theoretical and experimental results. The technique has been used to characterize a test pattern with a focused spot as small as 36{mu}m in diameter. Using diffracted light from a simulated closed-loop control of exposure dose, CD control was improved by as much as 4 times for substrates with variations in underlying film thickness, compared to using fixed exposure time. The latent image monitor has also been applied to wafers with rough metal substrates and focus optimization.

  9. Corporate Semantic Web: Towards the Deployment of Semantic Technologies in Enterprises

    NASA Astrophysics Data System (ADS)

    Paschke, Adrian; Coskun, Gökhan; Heese, Ralf; Luczak-Rösch, Markus; Oldakowski, Radoslaw; Schäfermeier, Ralph; Streibel, Olga

    The amount of information that companies have to produce, acquire, maintain, propagate, and use has increased dramatically over the last decades. Nowadays, companies seek more capable approaches for gaining, managing, and utilizing knowledge, and the Semantic Web offers promising solutions. While the global Semantic Web still remains an unfulfilled vision for the present, the Corporate Semantic Web idea aims at bringing semantic technologies to enterprises. The expected results are a competitive advantage for enterprises using semantic technologies and a boost for the evolution of the global Semantic Web.

  10. Comparative evolution: latent potentials for anagenetic advance.

    PubMed Central

    Stebbins, G L; Hartl, D L

    1988-01-01

    One of the principles that has emerged from experimental evolutionary studies of microorganisms is that polymorphic alleles or new mutations can sometimes possess a latent potential to respond to selection in different environments, although the alleles may be functionally equivalent or disfavored under typical conditions. We suggest that such responses to selection in microorganisms serve as experimental models of evolutionary advances that occur over much longer periods of time in higher organisms. We propose as a general evolutionary principle that anagenic advances often come from capitalizing on preexisting latent selection potentials in the presence of novel ecological opportunity. PMID:3293050