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Sample records for feature sets relative

  1. A Relation Extraction Framework for Biomedical Text Using Hybrid Feature Set

    Directory of Open Access Journals (Sweden)

    Abdul Wahab Muzaffar

    2015-01-01

    Full Text Available The information extraction from unstructured text segments is a complex task. Although manual information extraction often produces the best results, it is harder to manage biomedical data extraction manually because of the exponential increase in data size. Thus, there is a need for automatic tools and techniques for information extraction in biomedical text mining. Relation extraction is a significant area under biomedical information extraction that has gained much importance in the last two decades. A lot of work has been done on biomedical relation extraction focusing on rule-based and machine learning techniques. In the last decade, the focus has changed to hybrid approaches showing better results. This research presents a hybrid feature set for classification of relations between biomedical entities. The main contribution of this research is done in the semantic feature set where verb phrases are ranked using Unified Medical Language System (UMLS and a ranking algorithm. Support Vector Machine and Naïve Bayes, the two effective machine learning techniques, are used to classify these relations. Our approach has been validated on the standard biomedical text corpus obtained from MEDLINE 2001. Conclusively, it can be articulated that our framework outperforms all state-of-the-art approaches used for relation extraction on the same corpus.

  2. Discovering highly informative feature set over high dimensions

    KAUST Repository

    Zhang, Chongsheng; Masseglia, Florent; Zhang, Xiangliang

    2012-01-01

    For many textual collections, the number of features is often overly large. These features can be very redundant, it is therefore desirable to have a small, succinct, yet highly informative collection of features that describes the key characteristics of a dataset. Information theory is one such tool for us to obtain this feature collection. With this paper, we mainly contribute to the improvement of efficiency for the process of selecting the most informative feature set over high-dimensional unlabeled data. We propose a heuristic theory for informative feature set selection from high dimensional data. Moreover, we design data structures that enable us to compute the entropies of the candidate feature sets efficiently. We also develop a simple pruning strategy that eliminates the hopeless candidates at each forward selection step. We test our method through experiments on real-world data sets, showing that our proposal is very efficient. © 2012 IEEE.

  3. Discovering highly informative feature set over high dimensions

    KAUST Repository

    Zhang, Chongsheng

    2012-11-01

    For many textual collections, the number of features is often overly large. These features can be very redundant, it is therefore desirable to have a small, succinct, yet highly informative collection of features that describes the key characteristics of a dataset. Information theory is one such tool for us to obtain this feature collection. With this paper, we mainly contribute to the improvement of efficiency for the process of selecting the most informative feature set over high-dimensional unlabeled data. We propose a heuristic theory for informative feature set selection from high dimensional data. Moreover, we design data structures that enable us to compute the entropies of the candidate feature sets efficiently. We also develop a simple pruning strategy that eliminates the hopeless candidates at each forward selection step. We test our method through experiments on real-world data sets, showing that our proposal is very efficient. © 2012 IEEE.

  4. Annotation-based feature extraction from sets of SBML models.

    Science.gov (United States)

    Alm, Rebekka; Waltemath, Dagmar; Wolfien, Markus; Wolkenhauer, Olaf; Henkel, Ron

    2015-01-01

    Model repositories such as BioModels Database provide computational models of biological systems for the scientific community. These models contain rich semantic annotations that link model entities to concepts in well-established bio-ontologies such as Gene Ontology. Consequently, thematically similar models are likely to share similar annotations. Based on this assumption, we argue that semantic annotations are a suitable tool to characterize sets of models. These characteristics improve model classification, allow to identify additional features for model retrieval tasks, and enable the comparison of sets of models. In this paper we discuss four methods for annotation-based feature extraction from model sets. We tested all methods on sets of models in SBML format which were composed from BioModels Database. To characterize each of these sets, we analyzed and extracted concepts from three frequently used ontologies, namely Gene Ontology, ChEBI and SBO. We find that three out of the methods are suitable to determine characteristic features for arbitrary sets of models: The selected features vary depending on the underlying model set, and they are also specific to the chosen model set. We show that the identified features map on concepts that are higher up in the hierarchy of the ontologies than the concepts used for model annotations. Our analysis also reveals that the information content of concepts in ontologies and their usage for model annotation do not correlate. Annotation-based feature extraction enables the comparison of model sets, as opposed to existing methods for model-to-keyword comparison, or model-to-model comparison.

  5. Finding an optimum immuno-histochemical feature set to distinguish benign phyllodes from fibroadenoma.

    Science.gov (United States)

    Maity, Priti Prasanna; Chatterjee, Subhamoy; Das, Raunak Kumar; Mukhopadhyay, Subhalaxmi; Maity, Ashok; Maulik, Dhrubajyoti; Ray, Ajoy Kumar; Dhara, Santanu; Chatterjee, Jyotirmoy

    2013-05-01

    Benign phyllodes and fibroadenoma are two well-known breast tumors with remarkable diagnostic ambiguity. The present study is aimed at determining an optimum set of immuno-histochemical features to distinguish them by analyzing important observations on expressions of important genes in fibro-glandular tissue. Immuno-histochemically, the expressions of p63 and α-SMA in myoepithelial cells and collagen I, III and CD105 in stroma of tumors and their normal counterpart were studied. Semi-quantified features were analyzed primarily by ANOVA and ranked through F-scores for understanding relative importance of group of features in discriminating three classes followed by reduction in F-score arranged feature space dimension and application of inter-class Bhattacharyya distances to distinguish tumors with an optimum set of features. Among thirteen studied features except one all differed significantly in three study classes. F-Ranking of features revealed highest discriminative potential of collagen III (initial region). F-Score arranged feature space dimension and application of Bhattacharyya distance gave rise to a feature set of lower dimension which can discriminate benign phyllodes and fibroadenoma effectively. The work definitely separated normal breast, fibroadenoma and benign phyllodes, through an optimal set of immuno-histochemical features which are not only useful to address diagnostic ambiguity of the tumors but also to spell about malignant potentiality. Copyright © 2013 Elsevier Ltd. All rights reserved.

  6. Selecting Optimal Feature Set in High-Dimensional Data by Swarm Search

    Directory of Open Access Journals (Sweden)

    Simon Fong

    2013-01-01

    Full Text Available Selecting the right set of features from data of high dimensionality for inducing an accurate classification model is a tough computational challenge. It is almost a NP-hard problem as the combinations of features escalate exponentially as the number of features increases. Unfortunately in data mining, as well as other engineering applications and bioinformatics, some data are described by a long array of features. Many feature subset selection algorithms have been proposed in the past, but not all of them are effective. Since it takes seemingly forever to use brute force in exhaustively trying every possible combination of features, stochastic optimization may be a solution. In this paper, we propose a new feature selection scheme called Swarm Search to find an optimal feature set by using metaheuristics. The advantage of Swarm Search is its flexibility in integrating any classifier into its fitness function and plugging in any metaheuristic algorithm to facilitate heuristic search. Simulation experiments are carried out by testing the Swarm Search over some high-dimensional datasets, with different classification algorithms and various metaheuristic algorithms. The comparative experiment results show that Swarm Search is able to attain relatively low error rates in classification without shrinking the size of the feature subset to its minimum.

  7. Performance Evaluation of Feature Sets of Minutiae Quadruplets ...

    African Journals Online (AJOL)

    The features proposed in this paper are derived from minutiae quadruplets and are applicable in matching and indexing ngerprint images. In this work nineteen different possibilities of features were explored for indexing and the performances of some of the feature sets were mixed: some giving good performances on ...

  8. Spatial Relation Predicates in Topographic Feature Semantics

    Science.gov (United States)

    Varanka, Dalia E.; Caro, Holly K.

    2013-01-01

    Topographic data are designed and widely used for base maps of diverse applications, yet the power of these information sources largely relies on the interpretive skills of map readers and relational database expert users once the data are in map or geographic information system (GIS) form. Advances in geospatial semantic technology offer data model alternatives for explicating concepts and articulating complex data queries and statements. To understand and enrich the vocabulary of topographic feature properties for semantic technology, English language spatial relation predicates were analyzed in three standard topographic feature glossaries. The analytical approach drew from disciplinary concepts in geography, linguistics, and information science. Five major classes of spatial relation predicates were identified from the analysis; representations for most of these are not widely available. The classes are: part-whole (which are commonly modeled throughout semantic and linked-data networks), geometric, processes, human intention, and spatial prepositions. These are commonly found in the ‘real world’ and support the environmental science basis for digital topographical mapping. The spatial relation concepts are based on sets of relation terms presented in this chapter, though these lists are not prescriptive or exhaustive. The results of this study make explicit the concepts forming a broad set of spatial relation expressions, which in turn form the basis for expanding the range of possible queries for topographical data analysis and mapping.

  9. Extract the Relational Information of Static Features and Motion Features for Human Activities Recognition in Videos

    Directory of Open Access Journals (Sweden)

    Li Yao

    2016-01-01

    Full Text Available Both static features and motion features have shown promising performance in human activities recognition task. However, the information included in these features is insufficient for complex human activities. In this paper, we propose extracting relational information of static features and motion features for human activities recognition. The videos are represented by a classical Bag-of-Word (BoW model which is useful in many works. To get a compact and discriminative codebook with small dimension, we employ the divisive algorithm based on KL-divergence to reconstruct the codebook. After that, to further capture strong relational information, we construct a bipartite graph to model the relationship between words of different feature set. Then we use a k-way partition to create a new codebook in which similar words are getting together. With this new codebook, videos can be represented by a new BoW vector with strong relational information. Moreover, we propose a method to compute new clusters from the divisive algorithm’s projective function. We test our work on the several datasets and obtain very promising results.

  10. Comparing sociocultural features of cholera in three endemic African settings

    Science.gov (United States)

    2013-01-01

    Background Cholera mainly affects developing countries where safe water supply and sanitation infrastructure are often rudimentary. Sub-Saharan Africa is a cholera hotspot. Effective cholera control requires not only a professional assessment, but also consideration of community-based priorities. The present work compares local sociocultural features of endemic cholera in urban and rural sites from three field studies in southeastern Democratic Republic of Congo (SE-DRC), western Kenya and Zanzibar. Methods A vignette-based semistructured interview was used in 2008 in Zanzibar to study sociocultural features of cholera-related illness among 356 men and women from urban and rural communities. Similar cross-sectional surveys were performed in western Kenya (n = 379) and in SE-DRC (n = 360) in 2010. Systematic comparison across all settings considered the following domains: illness identification; perceived seriousness, potential fatality and past household episodes; illness-related experience; meaning; knowledge of prevention; help-seeking behavior; and perceived vulnerability. Results Cholera is well known in all three settings and is understood to have a significant impact on people’s lives. Its social impact was mainly characterized by financial concerns. Problems with unsafe water, sanitation and dirty environments were the most common perceived causes across settings; nonetheless, non-biomedical explanations were widespread in rural areas of SE-DRC and Zanzibar. Safe food and water and vaccines were prioritized for prevention in SE-DRC. Safe water was prioritized in western Kenya along with sanitation and health education. The latter two were also prioritized in Zanzibar. Use of oral rehydration solutions and rehydration was a top priority everywhere; healthcare facilities were universally reported as a primary source of help. Respondents in SE-DRC and Zanzibar reported cholera as affecting almost everybody without differentiating much for gender, age

  11. Breast Cancer Detection with Reduced Feature Set

    Directory of Open Access Journals (Sweden)

    Ahmet Mert

    2015-01-01

    Full Text Available This paper explores feature reduction properties of independent component analysis (ICA on breast cancer decision support system. Wisconsin diagnostic breast cancer (WDBC dataset is reduced to one-dimensional feature vector computing an independent component (IC. The original data with 30 features and reduced one feature (IC are used to evaluate diagnostic accuracy of the classifiers such as k-nearest neighbor (k-NN, artificial neural network (ANN, radial basis function neural network (RBFNN, and support vector machine (SVM. The comparison of the proposed classification using the IC with original feature set is also tested on different validation (5/10-fold cross-validations and partitioning (20%–40% methods. These classifiers are evaluated how to effectively categorize tumors as benign and malignant in terms of specificity, sensitivity, accuracy, F-score, Youden’s index, discriminant power, and the receiver operating characteristic (ROC curve with its criterion values including area under curve (AUC and 95% confidential interval (CI. This represents an improvement in diagnostic decision support system, while reducing computational complexity.

  12. Fast evaluation of patient set-up during radiotherapy by aligning features in portal and simulator images

    International Nuclear Information System (INIS)

    Bijhold, J.; Herk, M. van; Vijlbrief, R.; Lebesque, J.V.

    1991-01-01

    A new fast method is presented for the quantification of patient set-up errors during radiotherapy with external photon beams. The set-up errors are described as deviations in relative position and orientation of specified anatomical structures relative to specified field shaping devices. These deviations are determined from parameters of the image transformations that make their features in a portal image align with the corresponding features in a simulator image. Knowledge of some set-up parameters during treatment simulation is required. The method does not require accurate knowledge about the position of the portal imaging device as long as the positions of some of the field shaping devices are verified independently during treatment. By applying this method, deviations in a pelvic phantom set-up can be measured with a precision of 2 mm within 1 minute. Theoretical considerations and experiments have shown that the method is not applicable when there are out-of-plane rotations larger than 2 degrees or translations larger than 1 cm. Inter-observer variability proved to be a source of large systematic errors, which could be reduced by offering a precise protocol for the feature alignment. (author)

  13. Joint Markov Blankets in Feature Sets Extracted from Wavelet Packet Decompositions

    Directory of Open Access Journals (Sweden)

    Gert Van Dijck

    2011-07-01

    Full Text Available Since two decades, wavelet packet decompositions have been shown effective as a generic approach to feature extraction from time series and images for the prediction of a target variable. Redundancies exist between the wavelet coefficients and between the energy features that are derived from the wavelet coefficients. We assess these redundancies in wavelet packet decompositions by means of the Markov blanket filtering theory. We introduce the concept of joint Markov blankets. It is shown that joint Markov blankets are a natural extension of Markov blankets, which are defined for single features, to a set of features. We show that these joint Markov blankets exist in feature sets consisting of the wavelet coefficients. Furthermore, we prove that wavelet energy features from the highest frequency resolution level form a joint Markov blanket for all other wavelet energy features. The joint Markov blanket theory indicates that one can expect an increase of classification accuracy with the increase of the frequency resolution level of the energy features.

  14. Set of Frequent Word Item sets as Feature Representation for Text with Indonesian Slang

    Science.gov (United States)

    Sa'adillah Maylawati, Dian; Putri Saptawati, G. A.

    2017-01-01

    Indonesian slang are commonly used in social media. Due to their unstructured syntax, it is difficult to extract their features based on Indonesian grammar for text mining. To do so, we propose Set of Frequent Word Item sets (SFWI) as text representation which is considered match for Indonesian slang. Besides, SFWI is able to keep the meaning of Indonesian slang with regard to the order of appearance sentence. We use FP-Growth algorithm with adding separation sentence function into the algorithm to extract the feature of SFWI. The experiments is done with text data from social media such as Facebook, Twitter, and personal website. The result of experiments shows that Indonesian slang were more correctly interpreted based on SFWI.

  15. Level Sets and Voronoi based Feature Extraction from any Imagery

    DEFF Research Database (Denmark)

    Sharma, O.; Anton, François; Mioc, Darka

    2012-01-01

    Polygon features are of interest in many GEOProcessing applications like shoreline mapping, boundary delineation, change detection, etc. This paper presents a unique new GPU-based methodology to automate feature extraction combining level sets, or mean shift based segmentation together with Voron...

  16. Feature-based morphometry: discovering group-related anatomical patterns.

    Science.gov (United States)

    Toews, Matthew; Wells, William; Collins, D Louis; Arbel, Tal

    2010-02-01

    This paper presents feature-based morphometry (FBM), a new fully data-driven technique for discovering patterns of group-related anatomical structure in volumetric imagery. In contrast to most morphometry methods which assume one-to-one correspondence between subjects, FBM explicitly aims to identify distinctive anatomical patterns that may only be present in subsets of subjects, due to disease or anatomical variability. The image is modeled as a collage of generic, localized image features that need not be present in all subjects. Scale-space theory is applied to analyze image features at the characteristic scale of underlying anatomical structures, instead of at arbitrary scales such as global or voxel-level. A probabilistic model describes features in terms of their appearance, geometry, and relationship to subject groups, and is automatically learned from a set of subject images and group labels. Features resulting from learning correspond to group-related anatomical structures that can potentially be used as image biomarkers of disease or as a basis for computer-aided diagnosis. The relationship between features and groups is quantified by the likelihood of feature occurrence within a specific group vs. the rest of the population, and feature significance is quantified in terms of the false discovery rate. Experiments validate FBM clinically in the analysis of normal (NC) and Alzheimer's (AD) brain images using the freely available OASIS database. FBM automatically identifies known structural differences between NC and AD subjects in a fully data-driven fashion, and an equal error classification rate of 0.80 is achieved for subjects aged 60-80 years exhibiting mild AD (CDR=1). Copyright (c) 2009 Elsevier Inc. All rights reserved.

  17. EEG-based recognition of video-induced emotions: selecting subject-independent feature set.

    Science.gov (United States)

    Kortelainen, Jukka; Seppänen, Tapio

    2013-01-01

    Emotions are fundamental for everyday life affecting our communication, learning, perception, and decision making. Including emotions into the human-computer interaction (HCI) could be seen as a significant step forward offering a great potential for developing advanced future technologies. While the electrical activity of the brain is affected by emotions, offers electroencephalogram (EEG) an interesting channel to improve the HCI. In this paper, the selection of subject-independent feature set for EEG-based emotion recognition is studied. We investigate the effect of different feature sets in classifying person's arousal and valence while watching videos with emotional content. The classification performance is optimized by applying a sequential forward floating search algorithm for feature selection. The best classification rate (65.1% for arousal and 63.0% for valence) is obtained with a feature set containing power spectral features from the frequency band of 1-32 Hz. The proposed approach substantially improves the classification rate reported in the literature. In future, further analysis of the video-induced EEG changes including the topographical differences in the spectral features is needed.

  18. Relevant test set using feature selection algorithm for early detection ...

    African Journals Online (AJOL)

    The objective of feature selection is to find the most relevant features for classification. Thus, the dimensionality of the information will be reduced and may improve classification's accuracy. This paper proposed a minimum set of relevant questions that can be used for early detection of dyslexia. In this research, we ...

  19. Definitions of engineered safety features and related features for nuclear power plants

    International Nuclear Information System (INIS)

    1986-01-01

    In light water moderated, light water cooled nuclear power plants, definitions are given of engineered safety features which are designed to suppress or prevent dispersion of radioactive materials due to damage etc. of fuel at the times of power plant failures, and of related features which are designed to actuate or operate the engineered safety features. Contents are the following: scope of engineered safety features and of related features; classification of engineered safety features (direct systems and indirect systems) and of related features (auxiliaries, emergency power supply, and protective means). (Mori, K.)

  20. Feature Set Evaluation for Offline Handwriting Recognition Systems: Application to the Recurrent Neural Network Model.

    Science.gov (United States)

    Chherawala, Youssouf; Roy, Partha Pratim; Cheriet, Mohamed

    2016-12-01

    The performance of handwriting recognition systems is dependent on the features extracted from the word image. A large body of features exists in the literature, but no method has yet been proposed to identify the most promising of these, other than a straightforward comparison based on the recognition rate. In this paper, we propose a framework for feature set evaluation based on a collaborative setting. We use a weighted vote combination of recurrent neural network (RNN) classifiers, each trained with a particular feature set. This combination is modeled in a probabilistic framework as a mixture model and two methods for weight estimation are described. The main contribution of this paper is to quantify the importance of feature sets through the combination weights, which reflect their strength and complementarity. We chose the RNN classifier because of its state-of-the-art performance. Also, we provide the first feature set benchmark for this classifier. We evaluated several feature sets on the IFN/ENIT and RIMES databases of Arabic and Latin script, respectively. The resulting combination model is competitive with state-of-the-art systems.

  1. performance evaluation of feature sets of minutiae quadruplets

    African Journals Online (AJOL)

    databases. This shows that the evaluation of algorithms on just one or two databases is not sufficient to confirm the performance of tech- niques as they may be database-dependent. Much work was done to find a feature-set that would have a good performance across three. FVC databases of the FVC 2000, 2002 and. 2004 ...

  2. Using activity-related behavioural features towards more effective automatic stress detection.

    Directory of Open Access Journals (Sweden)

    Dimitris Giakoumis

    Full Text Available This paper introduces activity-related behavioural features that can be automatically extracted from a computer system, with the aim to increase the effectiveness of automatic stress detection. The proposed features are based on processing of appropriate video and accelerometer recordings taken from the monitored subjects. For the purposes of the present study, an experiment was conducted that utilized a stress-induction protocol based on the stroop colour word test. Video, accelerometer and biosignal (Electrocardiogram and Galvanic Skin Response recordings were collected from nineteen participants. Then, an explorative study was conducted by following a methodology mainly based on spatiotemporal descriptors (Motion History Images that are extracted from video sequences. A large set of activity-related behavioural features, potentially useful for automatic stress detection, were proposed and examined. Experimental evaluation showed that several of these behavioural features significantly correlate to self-reported stress. Moreover, it was found that the use of the proposed features can significantly enhance the performance of typical automatic stress detection systems, commonly based on biosignal processing.

  3. Setting-related influences on physical inactivity of older adults in residential care settings: a review.

    Science.gov (United States)

    Douma, Johanna G; Volkers, Karin M; Engels, Gwenda; Sonneveld, Marieke H; Goossens, Richard H M; Scherder, Erik J A

    2017-04-28

    Despite the detrimental effects of physical inactivity for older adults, especially aged residents of residential care settings may spend much time in inactive behavior. This may be partly due to their poorer physical condition; however, there may also be other, setting-related factors that influence the amount of inactivity. The aim of this review was to review setting-related factors (including the social and physical environment) that may contribute to the amount of older adults' physical inactivity in a wide range of residential care settings (e.g., nursing homes, assisted care facilities). Five databases were systematically searched for eligible studies, using the key words 'inactivity', 'care facilities', and 'older adults', including their synonyms and MeSH terms. Additional studies were selected from references used in articles included from the search. Based on specific eligibility criteria, a total of 12 studies were included. Quality of the included studies was assessed using the Mixed Methods Appraisal Tool (MMAT). Based on studies using different methodologies (e.g., interviews and observations), and of different quality (assessed quality range: 25-100%), we report several aspects related to the physical environment and caregivers. Factors of the physical environment that may be related to physical inactivity included, among others, the environment's compatibility with the abilities of a resident, the presence of equipment, the accessibility, security, comfort, and aesthetics of the environment/corridors, and possibly the presence of some specific areas. Caregiver-related factors included staffing levels, the available time, and the amount and type of care being provided. Inactivity levels in residential care settings may be reduced by improving several features of the physical environment and with the help of caregivers. Intervention studies could be performed in order to gain more insight into causal effects of improving setting-related factors on

  4. Fast detection of vascular plaque in optical coherence tomography images using a reduced feature set

    Science.gov (United States)

    Prakash, Ammu; Ocana Macias, Mariano; Hewko, Mark; Sowa, Michael; Sherif, Sherif

    2018-03-01

    Optical coherence tomography (OCT) images are capable of detecting vascular plaque by using the full set of 26 Haralick textural features and a standard K-means clustering algorithm. However, the use of the full set of 26 textural features is computationally expensive and may not be feasible for real time implementation. In this work, we identified a reduced set of 3 textural feature which characterizes vascular plaque and used a generalized Fuzzy C-means clustering algorithm. Our work involves three steps: 1) the reduction of a full set 26 textural feature to a reduced set of 3 textural features by using genetic algorithm (GA) optimization method 2) the implementation of an unsupervised generalized clustering algorithm (Fuzzy C-means) on the reduced feature space, and 3) the validation of our results using histology and actual photographic images of vascular plaque. Our results show an excellent match with histology and actual photographic images of vascular tissue. Therefore, our results could provide an efficient pre-clinical tool for the detection of vascular plaque in real time OCT imaging.

  5. A linear-time algorithm for Euclidean feature transform sets

    NARCIS (Netherlands)

    Hesselink, Wim H.

    2007-01-01

    The Euclidean distance transform of a binary image is the function that assigns to every pixel the Euclidean distance to the background. The Euclidean feature transform is the function that assigns to every pixel the set of background pixels with this distance. We present an algorithm to compute the

  6. Dissociation between Features and Feature Relations in Infant Memory: Effects of Memory Load.

    Science.gov (United States)

    Bhatt, Ramesh S.; Rovee-Collier, Carolyn

    1997-01-01

    Four experiments examined effects of the number of features and feature relations on learning and long-term memory in 3-month olds. Findings suggested that memory load size selectively constrained infants' long-term memory for relational information, suggesting that in infants, features and relations are psychologically distinct and that memory…

  7. Mental sets in conduct problem youth with psychopathic features: entity versus incremental theories of intelligence.

    Science.gov (United States)

    Salekin, Randall T; Lester, Whitney S; Sellers, Mary-Kate

    2012-08-01

    The purpose of the current study was to examine the effect of a motivational intervention on conduct problem youth with psychopathic features. Specifically, the current study examined conduct problem youths' mental set (or theory) regarding intelligence (entity vs. incremental) upon task performance. We assessed 36 juvenile offenders with psychopathic features and tested whether providing them with two different messages regarding intelligence would affect their functioning on a task related to academic performance. The study employed a MANOVA design with two motivational conditions and three outcomes including fluency, flexibility, and originality. Results showed that youth with psychopathic features who were given a message that intelligence grows over time, were more fluent and flexible than youth who were informed that intelligence is static. There were no significant differences between the groups in terms of originality. The implications of these findings are discussed including the possible benefits of interventions for adolescent offenders with conduct problems and psychopathic features. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

  8. Characterization of mammographic masses based on level set segmentation with new image features and patient information

    International Nuclear Information System (INIS)

    Shi Jiazheng; Sahiner, Berkman; Chan Heangping; Ge Jun; Hadjiiski, Lubomir; Helvie, Mark A.; Nees, Alexis; Wu Yita; Wei Jun; Zhou Chuan; Zhang Yiheng; Cui Jing

    2008-01-01

    Computer-aided diagnosis (CAD) for characterization of mammographic masses as malignant or benign has the potential to assist radiologists in reducing the biopsy rate without increasing false negatives. The purpose of this study was to develop an automated method for mammographic mass segmentation and explore new image based features in combination with patient information in order to improve the performance of mass characterization. The authors' previous CAD system, which used the active contour segmentation, and morphological, textural, and spiculation features, has achieved promising results in mass characterization. The new CAD system is based on the level set method and includes two new types of image features related to the presence of microcalcifications with the mass and abruptness of the mass margin, and patient age. A linear discriminant analysis (LDA) classifier with stepwise feature selection was used to merge the extracted features into a classification score. The classification accuracy was evaluated using the area under the receiver operating characteristic curve. The authors' primary data set consisted of 427 biopsy-proven masses (200 malignant and 227 benign) in 909 regions of interest (ROIs) (451 malignant and 458 benign) from multiple mammographic views. Leave-one-case-out resampling was used for training and testing. The new CAD system based on the level set segmentation and the new mammographic feature space achieved a view-based A z value of 0.83±0.01. The improvement compared to the previous CAD system was statistically significant (p=0.02). When patient age was included in the new CAD system, view-based and case-based A z values were 0.85±0.01 and 0.87±0.02, respectively. The study also demonstrated the consistency of the newly developed CAD system by evaluating the statistics of the weights of the LDA classifiers in leave-one-case-out classification. Finally, an independent test on the publicly available digital database for screening

  9. Features of standardized nursing terminology sets in Japan.

    Science.gov (United States)

    Sagara, Kaoru; Abe, Akinori; Ozaku, Hiromi Itoh; Kuwahara, Noriaki; Kogure, Kiyoshi

    2006-01-01

    This paper reports the features and relationships between standardizes nursing terminology sets used in Japan. First, we analyzed the common parts in five standardized nursing terminology sets: the Japan Nursing Practice Standard Master (JNPSM) that includes the names of nursing activities and is built by the Medical Information Center Development Center (MEDIS-DC); the labels of the Japan Classification of Nursing Practice (JCNP), built by the term advisory committee in the Japan Academy of Nursing Science; the labels of the International Classification for Nursing Practice (ICNP) translated to Japanese; the labels, domain names, and class names of the North American Nursing Diagnosis Association (NANDA) Nursing Diagnoses 2003-2004 translated to Japanese; and the terms included in the labels of Nursing Interventions Classification (NIC) translated to Japanese. Then we compared them with terms in a thesaurus dictionary, the Bunrui Goihyo, that contains general Japanese words and is built by the National Institute for Japanese Language. 1) the level of interchangeability between four standardized nursing terminology sets is quite low; 2) abbreviations and katakana words are frequently used to express nursing activities; 3) general Japanese words are usually used to express the status or situation of patients.

  10. Intelligent fault diagnosis of rolling bearing based on kernel neighborhood rough sets and statistical features

    Energy Technology Data Exchange (ETDEWEB)

    Zhu, Xiao Ran; Zhang, You Yun; Zhu, Yong Sheng [Xi' an Jiaotong Univ., Xi' an (China)

    2012-09-15

    Intelligent fault diagnosis benefits from efficient feature selection. Neighborhood rough sets are effective in feature selection. However, determining the neighborhood value accurately remains a challenge. The wrapper feature selection algorithm is designed by combining the kernel method and neighborhood rough sets to self-adaptively select sensitive features. The combination effectively solves the shortcomings in selecting the neighborhood value in the previous application process. The statistical features of time and frequency domains are used to describe the characteristic of the rolling bearing to make the intelligent fault diagnosis approach work. Three classification algorithms, namely, classification and regression tree (CART), commercial version 4.5 (C4.5), and radial basis function support vector machines (RBFSVM), are used to test UCI datasets and 10 fault datasets of rolling bearing. The results indicate that the diagnostic approach presented could effectively select the sensitive fault features and simultaneously identify the type and degree of the fault.

  11. Intelligent fault diagnosis of rolling bearing based on kernel neighborhood rough sets and statistical features

    International Nuclear Information System (INIS)

    Zhu, Xiao Ran; Zhang, You Yun; Zhu, Yong Sheng

    2012-01-01

    Intelligent fault diagnosis benefits from efficient feature selection. Neighborhood rough sets are effective in feature selection. However, determining the neighborhood value accurately remains a challenge. The wrapper feature selection algorithm is designed by combining the kernel method and neighborhood rough sets to self-adaptively select sensitive features. The combination effectively solves the shortcomings in selecting the neighborhood value in the previous application process. The statistical features of time and frequency domains are used to describe the characteristic of the rolling bearing to make the intelligent fault diagnosis approach work. Three classification algorithms, namely, classification and regression tree (CART), commercial version 4.5 (C4.5), and radial basis function support vector machines (RBFSVM), are used to test UCI datasets and 10 fault datasets of rolling bearing. The results indicate that the diagnostic approach presented could effectively select the sensitive fault features and simultaneously identify the type and degree of the fault

  12. Generalizations of the subject-independent feature set for music-induced emotion recognition.

    Science.gov (United States)

    Lin, Yuan-Pin; Chen, Jyh-Horng; Duann, Jeng-Ren; Lin, Chin-Teng; Jung, Tzyy-Ping

    2011-01-01

    Electroencephalogram (EEG)-based emotion recognition has been an intensely growing field. Yet, how to achieve acceptable accuracy on a practical system with as fewer electrodes as possible is less concerned. This study evaluates a set of subject-independent features, based on differential power asymmetry of symmetric electrode pairs [1], with emphasis on its applicability to subject variability in music-induced emotion classification problem. Results of this study have evidently validated the feasibility of using subject-independent EEG features to classify four emotional states with acceptable accuracy in second-scale temporal resolution. These features could be generalized across subjects to detect emotion induced by music excerpts not limited to the music database that was used to derive the emotion-specific features.

  13. Study of Machine-Learning Classifier and Feature Set Selection for Intent Classification of Korean Tweets about Food Safety

    Directory of Open Access Journals (Sweden)

    Yeom, Ha-Neul

    2014-09-01

    Full Text Available In recent years, several studies have proposed making use of the Twitter micro-blogging service to track various trends in online media and discussion. In this study, we specifically examine the use of Twitter to track discussions of food safety in the Korean language. Given the irregularity of keyword use in most tweets, we focus on optimistic machine-learning and feature set selection to classify collected tweets. We build the classifier model using Naive Bayes & Naive Bayes Multinomial, Support Vector Machine, and Decision Tree Algorithms, all of which show good performance. To select an optimum feature set, we construct a basic feature set as a standard for performance comparison, so that further test feature sets can be evaluated. Experiments show that precision and F-measure performance are best when using a Naive Bayes Multinomial classifier model with a test feature set defined by extracting Substantive, Predicate, Modifier, and Interjection parts of speech.

  14. Clonal sets of a binary relation

    Science.gov (United States)

    Zedam, Lemnaouar; Pérez-Fernández, Raúl; Bouremel, Hassane; De Baets, Bernard

    2018-05-01

    In a recent paper, we have introduced the notion of clone relation of a given binary relation. Intuitively, two elements are said to be "clones" if they are related in the same way w.r.t. every other element. In this paper, we generalize this notion from pairs of elements to sets of elements of any cardinality, resulting in the introduction of clonal sets. We investigate the most important properties of clonal sets, paying particular attention to the introduction of the clonal closure operator, to the analysis of the (lattice) structure of the set of clonal sets and to a distance metric expressing how close two elements are to being clones.

  15. Entropy Based Feature Selection for Fuzzy Set-Valued Information Systems

    Science.gov (United States)

    Ahmed, Waseem; Sufyan Beg, M. M.; Ahmad, Tanvir

    2018-06-01

    In Set-valued Information Systems (SIS), several objects contain more than one value for some attributes. Tolerance relation used for handling SIS sometimes leads to loss of certain information. To surmount this problem, fuzzy rough model was introduced. However, in some cases, SIS may contain some real or continuous set-values. Therefore, the existing fuzzy rough model for handling Information system with fuzzy set-values needs some changes. In this paper, Fuzzy Set-valued Information System (FSIS) is proposed and fuzzy similarity relation for FSIS is defined. Yager's relative conditional entropy was studied to find the significance measure of a candidate attribute of FSIS. Later, using these significance values, three greedy forward algorithms are discussed for finding the reduct and relative reduct for the proposed FSIS. An experiment was conducted on a sample population of the real dataset and a comparison of classification accuracies of the proposed FSIS with the existing SIS and single-valued Fuzzy Information Systems was made, which demonstrated the effectiveness of proposed FSIS.

  16. Influences of rhythm- and timbre-related musical features on characteristics of music-induced movement.

    Science.gov (United States)

    Burger, Birgitta; Thompson, Marc R; Luck, Geoff; Saarikallio, Suvi; Toiviainen, Petri

    2013-01-01

    Music makes us move. Several factors can affect the characteristics of such movements, including individual factors or musical features. For this study, we investigated the effect of rhythm- and timbre-related musical features as well as tempo on movement characteristics. Sixty participants were presented with 30 musical stimuli representing different styles of popular music, and instructed to move along with the music. Optical motion capture was used to record participants' movements. Subsequently, eight movement features and four rhythm- and timbre-related musical features were computationally extracted from the data, while the tempo was assessed in a perceptual experiment. A subsequent correlational analysis revealed that, for instance, clear pulses seemed to be embodied with the whole body, i.e., by using various movement types of different body parts, whereas spectral flux and percussiveness were found to be more distinctly related to certain body parts, such as head and hand movement. A series of ANOVAs with the stimuli being divided into three groups of five stimuli each based on the tempo revealed no significant differences between the groups, suggesting that the tempo of our stimuli set failed to have an effect on the movement features. In general, the results can be linked to the framework of embodied music cognition, as they show that body movements are used to reflect, imitate, and predict musical characteristics.

  17. Influences of rhythm- and timbre-related musical features on characteristics of music-induced movement

    Directory of Open Access Journals (Sweden)

    Birgitta eBurger

    2013-04-01

    Full Text Available Music makes us move. Several factors can affect the characteristics of such movements, including individual factors or musical features. For this study, we investigated the effect of rhythm- and timbre-related musical features as well as tempo on movement characteristics. Sixty participants were presented with 30 musical stimuli representing different styles of popular music, and instructed to move along with the music. Optical motion capture was used to record participants’ movements. Subsequently, eight movement features and four rhythm- and timbre-related musical features were computationally extracted from the data, while the tempo was assessed in a perceptual experiment. A subsequent correlational analysis revealed that, for instance, clear pulses seemed to be embodied with the whole body, i.e., by using various movement types of different body parts, whereas spectral flux and percussiveness were found to be more distinctly related to certain body parts, such as head and hand movement. A series of ANOVAs with the stimuli being divided into three groups of five stimuli each based on the tempo revealed no significant differences between the groups, suggesting that the tempo of our stimuli set failed to have an effect on the movement features. In general, the results can be linked to the framework of embodied music cognition, as they show that body movements are used to reflect, imitate, and predict musical characteristics.

  18. Guilt by Association: The 13 micron Dust Feature in Circumstellar Shells and Related Spectral Features

    Science.gov (United States)

    Sloan, G. C.; Kraemer, K. E.; Goebel, J. H.; Price, S. D.

    A study of spectra from the SWS on ISO of optically thin oxygen-rich dust shells shows that the strength of the 13 micron dust emission feature is correlated with the CO2 bands (13--17 microns) and dust emission features at 19.8 and 28.1 microns. SRb variables tend to show stronger 13 micron features than Mira variables, suggesting that the presence of the 13 micron and related features depends on pulsation mode and mass-loss rate. The absence of any correlation to dust emission features at 16.8 and 32 microns makes spinel an unlikely carrier. The most plausible carrier of the 13 micron feature remains crystalline alumina, and we suggest that the related dust features may be crystalline silicates. When dust forms in regions of low density, it may condense into crystalline grain structures.

  19. Analysis of radiological features relative to pathology in pelvic chondrosarcoma

    International Nuclear Information System (INIS)

    Zhou Jianjun; Ding Jianguo; Wang Jianhua; Zeng Mengsu; Yan Fuhua; Zhou Kangrong; Ji Yuan

    2008-01-01

    Objective: To Explore the imaging features relative to pathology of pelvic chondrosarcoma and to evaluate the clinical value. Methods: All 12 cases patients with primary pelvic chondrosarcoma confirmed by pathological examination underwent radiography, spiral CT plain scanning, MR SE-T 1 WI, FSE-T 2 WI and SE-T 1 WI enhancement scanning before operation. The imaging data was reviewed and analyzed retrospectively to compare with surgical and pathological results. Results: Eleven conventional chondrosarcoma and one dedifferentiated chondrosarcoma were located in different parts of pelvis. The diameters of the tumors ranged from 4.7 to 17.0 cm with one case less than 5.0 cm, 6 cases being 5.0-10.0 cm and 5 cases more than 10.0 cm. The CT value of 5 eases was identical or inferior to muscle with mild to moderate 'ring-and-arc' mineralization and soft mass. MR imaging depict the high water content of these lesions as very high signal intensity was detected on T 2 WI. Six cases showed typical 'ting- and-arc' fibrous tissue which enhanced persistently. Aggressive features of deep endosteal scalloping and soft-tissue extension was also found in these cases. Conclusions: Radiographic findings can suggest the diagnosis of pelvic chondrosarcoma when there is typical 'ring-and-arc' fibrous tissue, mineralization, aggressive features of deep endosteal scalloping and large soft-tissue extension. MR imaging reflect directly this pathologic structure, superior to that of CT and radiography. CT is optimal to detect the matrix mineralization, particularly when it is subtle or when the lesion is located in anatomically complex pelvic areas. (authors)

  20. An optimal set of features for predicting type IV secretion system effector proteins for a subset of species based on a multi-level feature selection approach.

    Directory of Open Access Journals (Sweden)

    Zhila Esna Ashari

    Full Text Available Type IV secretion systems (T4SS are multi-protein complexes in a number of bacterial pathogens that can translocate proteins and DNA to the host. Most T4SSs function in conjugation and translocate DNA; however, approximately 13% function to secrete proteins, delivering effector proteins into the cytosol of eukaryotic host cells. Upon entry, these effectors manipulate the host cell's machinery for their own benefit, which can result in serious illness or death of the host. For this reason recognition of T4SS effectors has become an important subject. Much previous work has focused on verifying effectors experimentally, a costly endeavor in terms of money, time, and effort. Having good predictions for effectors will help to focus experimental validations and decrease testing costs. In recent years, several scoring and machine learning-based methods have been suggested for the purpose of predicting T4SS effector proteins. These methods have used different sets of features for prediction, and their predictions have been inconsistent. In this paper, an optimal set of features is presented for predicting T4SS effector proteins using a statistical approach. A thorough literature search was performed to find features that have been proposed. Feature values were calculated for datasets of known effectors and non-effectors for T4SS-containing pathogens for four genera with a sufficient number of known effectors, Legionella pneumophila, Coxiella burnetii, Brucella spp, and Bartonella spp. The features were ranked, and less important features were filtered out. Correlations between remaining features were removed, and dimensional reduction was accomplished using principal component analysis and factor analysis. Finally, the optimal features for each pathogen were chosen by building logistic regression models and evaluating each model. The results based on evaluation of our logistic regression models confirm the effectiveness of our four optimal sets of

  1. Complex Topographic Feature Ontology Patterns

    Science.gov (United States)

    Varanka, Dalia E.; Jerris, Thomas J.

    2015-01-01

    Semantic ontologies are examined as effective data models for the representation of complex topographic feature types. Complex feature types are viewed as integrated relations between basic features for a basic purpose. In the context of topographic science, such component assemblages are supported by resource systems and found on the local landscape. Ontologies are organized within six thematic modules of a domain ontology called Topography that includes within its sphere basic feature types, resource systems, and landscape types. Context is constructed not only as a spatial and temporal setting, but a setting also based on environmental processes. Types of spatial relations that exist between components include location, generative processes, and description. An example is offered in a complex feature type ‘mine.’ The identification and extraction of complex feature types are an area for future research.

  2. A NEW STRATEGY FOR IMPROVING FEATURE SETS IN A DISCRETE HMM­BASED HANDWRITING RECOGNITION SYSTEM

    NARCIS (Netherlands)

    Grandidier, F.; Sabourin, R.; Suen, C.Y.; Gilloux, M.

    2004-01-01

    In this paper we introduce a new strategy for improving a discrete HMM­based handwriting recognition system, by integrating several information sources from specialized feature sets. For a given system, the basic idea is to keep the most discriminative features, and to replace the others with new

  3. Finding Combination of Features from Promoter Regions for Ovarian Cancer-related Gene Group Classification

    KAUST Repository

    Olayan, Rawan S.

    2012-01-01

    In classification problems, it is always important to use the suitable combination of features that will be employed by classifiers. Generating the right combination of features usually results in good classifiers. In the situation when the problem is not well understood, data items are usually described by many features in the hope that some of these may be the relevant or most relevant ones. In this study, we focus on one such problem related to genes implicated in ovarian cancer (OC). We try to recognize two important OC-related gene groups: oncogenes, which support the development and progression of OC, and oncosuppressors, which oppose such tendencies. For this, we use the properties of promoters of these genes. We identified potential “regulatory features” that characterize OC-related oncogenes and oncosuppressors promoters. In our study, we used 211 oncogenes and 39 oncosuppressors. For these, we identified 538 characteristic sequence motifs from their promoters. Promoters are annotated by these motifs and derived feature vectors used to develop classification models. We made a comparison of a number of classification models in their ability to distinguish oncogenes from oncosuppressors. Based on 10-fold cross-validation, the resultant model was able to separate the two classes with sensitivity of 96% and specificity of 100% with the complete set of features. Moreover, we developed another recognition model where we attempted to distinguish oncogenes and oncosuppressors as one group from other OC-related genes. That model achieved accuracy of 82%. We believe that the results of this study will help in discovering other OC-related oncogenes and oncosuppressors not identified as yet.

  4. Finding Combination of Features from Promoter Regions for Ovarian Cancer-related Gene Group Classification

    KAUST Repository

    Olayan, Rawan S.

    2012-12-01

    In classification problems, it is always important to use the suitable combination of features that will be employed by classifiers. Generating the right combination of features usually results in good classifiers. In the situation when the problem is not well understood, data items are usually described by many features in the hope that some of these may be the relevant or most relevant ones. In this study, we focus on one such problem related to genes implicated in ovarian cancer (OC). We try to recognize two important OC-related gene groups: oncogenes, which support the development and progression of OC, and oncosuppressors, which oppose such tendencies. For this, we use the properties of promoters of these genes. We identified potential “regulatory features” that characterize OC-related oncogenes and oncosuppressors promoters. In our study, we used 211 oncogenes and 39 oncosuppressors. For these, we identified 538 characteristic sequence motifs from their promoters. Promoters are annotated by these motifs and derived feature vectors used to develop classification models. We made a comparison of a number of classification models in their ability to distinguish oncogenes from oncosuppressors. Based on 10-fold cross-validation, the resultant model was able to separate the two classes with sensitivity of 96% and specificity of 100% with the complete set of features. Moreover, we developed another recognition model where we attempted to distinguish oncogenes and oncosuppressors as one group from other OC-related genes. That model achieved accuracy of 82%. We believe that the results of this study will help in discovering other OC-related oncogenes and oncosuppressors not identified as yet.

  5. A comparative study of different feature sets for recognition of handwritten Arabic numerals using a Multi Layer Perceptron

    OpenAIRE

    Das, Nibaran; Mollah, Ayatullah Faruk; Sarkar, Ram; Basu, Subhadip

    2010-01-01

    The work presents a comparative assessment of seven different feature sets for recognition of handwritten Arabic numerals using a Multi Layer Perceptron (MLP) based classifier. The seven feature sets employed here consist of shadow features, octant centroids, longest runs, angular distances, effective spans, dynamic centers of gravity, and some of their combinations. On experimentation with a database of 3000 samples, the maximum recognition rate of 95.80% is observed with both of two separat...

  6. The impact of image reconstruction settings on 18F-FDG PET radiomic features. Multi-scanner phantom and patient studies

    International Nuclear Information System (INIS)

    Shiri, Isaac; Abdollahi, Hamid; Rahmim, Arman; Ghaffarian, Pardis; Geramifar, Parham; Bitarafan-Rajabi, Ahmad

    2017-01-01

    The purpose of this study was to investigate the robustness of different PET/CT image radiomic features over a wide range of different reconstruction settings. Phantom and patient studies were conducted, including two PET/CT scanners. Different reconstruction algorithms and parameters including number of sub-iterations, number of subsets, full width at half maximum (FWHM) of Gaussian filter, scan time per bed position and matrix size were studied. Lesions were delineated and one hundred radiomic features were extracted. All radiomics features were categorized based on coefficient of variation (COV). Forty seven percent features showed COV ≤ 5% and 10% of which showed COV > 20%. All geometry based, 44% and 41% of intensity based and texture based features were found as robust respectively. In regard to matrix size, 56% and 6% of all features were found non-robust (COV > 20%) and robust (COV ≤ 5%) respectively. Variability and robustness of PET/CT image radiomics in advanced reconstruction settings is feature-dependent, and different settings have different effects on different features. Radiomic features with low COV can be considered as good candidates for reproducible tumour quantification in multi-center studies. (orig.)

  7. The impact of image reconstruction settings on 18F-FDG PET radiomic features. Multi-scanner phantom and patient studies

    Energy Technology Data Exchange (ETDEWEB)

    Shiri, Isaac; Abdollahi, Hamid [Iran University of Medical Sciences, Department of Medical Physics, School of Medicine, Tehran (Iran, Islamic Republic of); Rahmim, Arman [Johns Hopkins University, Department of Radiology, Baltimore, MD (United States); Johns Hopkins University, Department of Electrical and Computer Engineering, Baltimore, MD (United States); Ghaffarian, Pardis [Shahid Beheshti University of Medical Sciences, Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Tehran (Iran, Islamic Republic of); Shahid Beheshti University of Medical Sciences, PET/CT and Cyclotron Center, Masih Daneshvari Hospital, Tehran (Iran, Islamic Republic of); Geramifar, Parham [Tehran University of Medical Sciences, Research Center for Nuclear Medicine, Shariati Hospital, Tehran (Iran, Islamic Republic of); Bitarafan-Rajabi, Ahmad [Iran University of Medical Sciences, Department of Medical Physics, School of Medicine, Tehran (Iran, Islamic Republic of); Iran University of Medical Sciences, Department of Nuclear Medicine, Rajaei Cardiovascular, Medical and Research Center, Tehran (Iran, Islamic Republic of)

    2017-11-15

    The purpose of this study was to investigate the robustness of different PET/CT image radiomic features over a wide range of different reconstruction settings. Phantom and patient studies were conducted, including two PET/CT scanners. Different reconstruction algorithms and parameters including number of sub-iterations, number of subsets, full width at half maximum (FWHM) of Gaussian filter, scan time per bed position and matrix size were studied. Lesions were delineated and one hundred radiomic features were extracted. All radiomics features were categorized based on coefficient of variation (COV). Forty seven percent features showed COV ≤ 5% and 10% of which showed COV > 20%. All geometry based, 44% and 41% of intensity based and texture based features were found as robust respectively. In regard to matrix size, 56% and 6% of all features were found non-robust (COV > 20%) and robust (COV ≤ 5%) respectively. Variability and robustness of PET/CT image radiomics in advanced reconstruction settings is feature-dependent, and different settings have different effects on different features. Radiomic features with low COV can be considered as good candidates for reproducible tumour quantification in multi-center studies. (orig.)

  8. Event-related potentials reveal the relations between feature representations at different levels of abstraction.

    Science.gov (United States)

    Hannah, Samuel D; Shedden, Judith M; Brooks, Lee R; Grundy, John G

    2016-11-01

    In this paper, we use behavioural methods and event-related potentials (ERPs) to explore the relations between informational and instantiated features, as well as the relation between feature abstraction and rule type. Participants are trained to categorize two species of fictitious animals and then identify perceptually novel exemplars. Critically, two groups are given a perfectly predictive counting rule that, according to Hannah and Brooks (2009. Featuring familiarity: How a familiar feature instantiation influences categorization. Canadian Journal of Experimental Psychology/Revue Canadienne de Psychologie Expérimentale, 63, 263-275. Retrieved from http://doi.org/10.1037/a0017919), should orient them to using abstract informational features when categorizing the novel transfer items. A third group is taught a feature list rule, which should orient them to using detailed instantiated features. One counting-rule group were taught their rule before any exposure to the actual stimuli, and the other immediately after training, having learned the instantiations first. The feature-list group were also taught their rule after training. The ERP results suggest that at test, the two counting-rule groups processed items differently, despite their identical rule. This not only supports the distinction that informational and instantiated features are qualitatively different feature representations, but also implies that rules can readily operate over concrete inputs, in contradiction to traditional approaches that assume that rules necessarily act on abstract inputs.

  9. Evaluation of Mayer-Rokitansky-Kuester-Hauser syndrome with magnetic resonance imaging: Three patterns of uterine remnants and related anatomical features and clinical settings

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Yue; Lu, Jingjing; Jiang, Bo; Feng, Feng; Jin, Zhengyu [Peking Union Medical College, Chinese Academy of Medical Sciences, Department of Radiology, Peking Union Medical College Hospital, Beijing (China); Zhu, Lan; Sun, Zhijing [Chinese Academy of Medical Sciences, Department of Obstetrics and Gynaecology, Peking Union Medical College Hospital, Peking Union Medical College, Bejing (China)

    2017-12-15

    To characterize the anatomical features and clinical settings of Mayer-Rokitansky-Kuester-Hauser (MRKH) syndrome and correlate them with patterns of uterine involvement. Pelvic magnetic resonance images and medical records of 92 MRKH patients were retrospectively reviewed. Patients were subgrouped by uterine morphology: uterine agenesis, unilateral rudimentary uterus and bilateral rudimentary uteri. Uterine volume, presence of endometrium, location of ovary, endometriosis and pelvic pain were compared among groups. The mean uterine volume was 33.5 ml (17.5-90.0 ml) for unilateral uterine remnants, and 16.1 ml (3.5-21.5 ml) for bilateral uterine rudiments (p<0.01). The incidence of presence of endometrium (100% vs. 22%, p<0.001), haematometra (56% vs. 3%, p<0.001) and ovarian endometriosis (22% vs. 3%, p<0.01) was significantly increased in the group of unilateral rudimentary uteri as compared with the group of bilateral uterine remnants. Thirty-one patients (38%) showed ectopic ovaries. Pelvic pain was more common in individuals with unilateral rudimentary uterus than those who had no (56% vs. 5%, p<0.01) or bilateral uterine remnants (56% vs. 14%, p<0.05). MRKH patients with different patterns of uterine involvement may have differentiated anatomical features and clinical settings. (orig.)

  10. Organizational contextual features that influence the implementation of evidence-based practices across healthcare settings: a systematic integrative review.

    Science.gov (United States)

    Li, Shelly-Anne; Jeffs, Lianne; Barwick, Melanie; Stevens, Bonnie

    2018-05-05

    Organizational contextual features have been recognized as important determinants for implementing evidence-based practices across healthcare settings for over a decade. However, implementation scientists have not reached consensus on which features are most important for implementing evidence-based practices. The aims of this review were to identify the most commonly reported organizational contextual features that influence the implementation of evidence-based practices across healthcare settings, and to describe how these features affect implementation. An integrative review was undertaken following literature searches in CINAHL, MEDLINE, PsycINFO, EMBASE, Web of Science, and Cochrane databases from January 2005 to June 2017. English language, peer-reviewed empirical studies exploring organizational context in at least one implementation initiative within a healthcare setting were included. Quality appraisal of the included studies was performed using the Mixed Methods Appraisal Tool. Inductive content analysis informed data extraction and reduction. The search generated 5152 citations. After removing duplicates and applying eligibility criteria, 36 journal articles were included. The majority (n = 20) of the study designs were qualitative, 11 were quantitative, and 5 used a mixed methods approach. Six main organizational contextual features (organizational culture; leadership; networks and communication; resources; evaluation, monitoring and feedback; and champions) were most commonly reported to influence implementation outcomes in the selected studies across a wide range of healthcare settings. We identified six organizational contextual features that appear to be interrelated and work synergistically to influence the implementation of evidence-based practices within an organization. Organizational contextual features did not influence implementation efforts independently from other features. Rather, features were interrelated and often influenced each

  11. Management and performance features of cancer centers in Europe: A fuzzy-set analysis

    NARCIS (Netherlands)

    Wind, Anke; Lobo, Mariana Fernandes; van Dijk, Joris; Lepage-Nefkens, Isabelle; Laranja-Pontes, Jose; da Conceicao Goncalves, Vitor; van Harten, Willem H.; Rocha-Goncalves, Francisco Nuno

    2016-01-01

    The specific aim of this study is to identify the performance features of cancer centers in the European Union by using a fuzzy-set qualitative comparative analysis (fsQCA). The fsQCA method represents cases (cancer centers) as a combination of explanatory and outcome conditions. This study uses

  12. Assessing environmental features related to mental health: a reliability study of visual streetscape images.

    Science.gov (United States)

    Wu, Yu-Tzu; Nash, Paul; Barnes, Linda E; Minett, Thais; Matthews, Fiona E; Jones, Andy; Brayne, Carol

    2014-10-22

    An association between depressive symptoms and features of built environment has been reported in the literature. A remaining research challenge is the development of methods to efficiently capture pertinent environmental features in relevant study settings. Visual streetscape images have been used to replace traditional physical audits and directly observe the built environment of communities. The aim of this work is to examine the inter-method reliability of the two audit methods for assessing community environments with a specific focus on physical features related to mental health. Forty-eight postcodes in urban and rural areas of Cambridgeshire, England were randomly selected from an alphabetical list of streets hosted on a UK property website. The assessment was conducted in July and August 2012 by both physical and visual image audits based on the items in Residential Environment Assessment Tool (REAT), an observational instrument targeting the micro-scale environmental features related to mental health in UK postcodes. The assessor used the images of Google Street View and virtually "walked through" the streets to conduct the property and street level assessments. Gwet's AC1 coefficients and Bland-Altman plots were used to compare the concordance of two audits. The results of conducting the REAT by visual image audits generally correspond to direct observations. More variations were found in property level items regarding physical incivilities, with broad limits of agreement which importantly lead to most of the variation in the overall REAT score. Postcodes in urban areas had lower consistency between the two methods than rural areas. Google Street View has the potential to assess environmental features related to mental health with fair reliability and provide a less resource intense method of assessing community environments than physical audits.

  13. Specific features of goal setting in road traffic safety

    Science.gov (United States)

    Kolesov, V. I.; Danilov, O. F.; Petrov, A. I.

    2017-10-01

    Road traffic safety (RTS) management is inherently a branch of cybernetics and therefore requires clear formalization of the task. The paper aims at identification of the specific features of goal setting in RTS management under the system approach. The paper presents the results of cybernetic modeling of the cause-to-effect mechanism of a road traffic accident (RTA); in here, the mechanism itself is viewed as a complex system. A designed management goal function is focused on minimizing the difficulty in achieving the target goal. Optimization of the target goal has been performed using the Lagrange principle. The created working algorithms have passed the soft testing. The key role of the obtained solution in the tactical and strategic RTS management is considered. The dynamics of the management effectiveness indicator has been analyzed based on the ten-year statistics for Russia.

  14. Genetic Association of Major Depression With Atypical Features and Obesity-Related Immunometabolic Dysregulations

    DEFF Research Database (Denmark)

    Milaneschi, Yuri; Lamers, Femke; Peyrot, Wouter J

    2017-01-01

    Importance: The association between major depressive disorder (MDD) and obesity may stem from shared immunometabolic mechanisms particularly evident in MDD with atypical features, characterized by increased appetite and/or weight (A/W) during an active episode. Objective: To determine whether...... subgroups of patients with MDD stratified according to the A/W criterion had a different degree of genetic overlap with obesity-related traits (body mass index [BMI] and levels of C-reactive protein [CRP] and leptin). Design, Setting, and Patients: This multicenter study assembled genome-wide genotypic...... between atypical depressive symptoms and obesity-related traits may arise from shared pathophysiologic mechanisms in patients with MDD. Development of treatments effectively targeting immunometabolic dysregulations may benefit patients with depression and obesity, both syndromes with important disability....

  15. Feature Selection Methods for Zero-Shot Learning of Neural Activity

    Directory of Open Access Journals (Sweden)

    Carlos A. Caceres

    2017-06-01

    Full Text Available Dimensionality poses a serious challenge when making predictions from human neuroimaging data. Across imaging modalities, large pools of potential neural features (e.g., responses from particular voxels, electrodes, and temporal windows have to be related to typically limited sets of stimuli and samples. In recent years, zero-shot prediction models have been introduced for mapping between neural signals and semantic attributes, which allows for classification of stimulus classes not explicitly included in the training set. While choices about feature selection can have a substantial impact when closed-set accuracy, open-set robustness, and runtime are competing design objectives, no systematic study of feature selection for these models has been reported. Instead, a relatively straightforward feature stability approach has been adopted and successfully applied across models and imaging modalities. To characterize the tradeoffs in feature selection for zero-shot learning, we compared correlation-based stability to several other feature selection techniques on comparable data sets from two distinct imaging modalities: functional Magnetic Resonance Imaging and Electrocorticography. While most of the feature selection methods resulted in similar zero-shot prediction accuracies and spatial/spectral patterns of selected features, there was one exception; A novel feature/attribute correlation approach was able to achieve those accuracies with far fewer features, suggesting the potential for simpler prediction models that yield high zero-shot classification accuracy.

  16. Intuitionistic Neutrosophic Set Relations and Some of Its Properties

    OpenAIRE

    Monoranjan Bhowmik; Madhumangal Pal

    2010-01-01

    In this paper, we define intuitionistic neutrosophic set (INSs). In fact, all INSs are neutrosophic set but all neutrosophic sets are not INSs. We have shown by means of example that the definition for neutrosophic sets the complement and union are not true for INSs also give new definition of complement, union and intersection of INSs. We define the relation of INSs and four special type of INSs relations. Finally we have studied some properties of INSs relations.

  17. Consistency relations for sharp inflationary non-Gaussian features

    Energy Technology Data Exchange (ETDEWEB)

    Mooij, Sander; Palma, Gonzalo A.; Panotopoulos, Grigoris [Departamento de Física, Facultad de Ciencias Físicas y Matemáticas, Universidad de Chile, Blanco Encalada 2008, Santiago (Chile); Soto, Alex, E-mail: sander.mooij@ing.uchile.cl, E-mail: gpalmaquilod@ing.uchile.cl, E-mail: gpanotop@ing.uchile.cl, E-mail: gatogeno@gmail.com [Departamento de Física, Facultad de Ciencias, Universidad de Chile, Las Palmeras 3425, Ñuñoa, Santiago (Chile)

    2016-09-01

    If cosmic inflation suffered tiny time-dependent deviations from the slow-roll regime, these would induce the existence of small scale-dependent features imprinted in the primordial spectra, with their shapes and sizes revealing information about the physics that produced them. Small sharp features could be suppressed at the level of the two-point correlation function, making them undetectable in the power spectrum, but could be amplified at the level of the three-point correlation function, offering us a window of opportunity to uncover them in the non-Gaussian bispectrum. In this article, we show that sharp features may be analyzed using only data coming from the three point correlation function parametrizing primordial non-Gaussianity. More precisely, we show that if features appear in a particular non-Gaussian triangle configuration (e.g. equilateral, folded, squeezed), these must reappear in every other configuration according to a specific relation allowing us to correlate features across the non-Gaussian bispectrum. As a result, we offer a method to study scale-dependent features generated during inflation that depends only on data coming from measurements of non-Gaussianity, allowing us to omit data from the power spectrum.

  18. Consistency relations for sharp inflationary non-Gaussian features

    International Nuclear Information System (INIS)

    Mooij, Sander; Palma, Gonzalo A.; Panotopoulos, Grigoris; Soto, Alex

    2016-01-01

    If cosmic inflation suffered tiny time-dependent deviations from the slow-roll regime, these would induce the existence of small scale-dependent features imprinted in the primordial spectra, with their shapes and sizes revealing information about the physics that produced them. Small sharp features could be suppressed at the level of the two-point correlation function, making them undetectable in the power spectrum, but could be amplified at the level of the three-point correlation function, offering us a window of opportunity to uncover them in the non-Gaussian bispectrum. In this article, we show that sharp features may be analyzed using only data coming from the three point correlation function parametrizing primordial non-Gaussianity. More precisely, we show that if features appear in a particular non-Gaussian triangle configuration (e.g. equilateral, folded, squeezed), these must reappear in every other configuration according to a specific relation allowing us to correlate features across the non-Gaussian bispectrum. As a result, we offer a method to study scale-dependent features generated during inflation that depends only on data coming from measurements of non-Gaussianity, allowing us to omit data from the power spectrum.

  19. Estimation of minimum sample size for identification of the most important features: a case study providing a qualitative B2B sales data set

    Directory of Open Access Journals (Sweden)

    Marko Bohanec

    2017-01-01

    Full Text Available An important task in machine learning is to reduce data set dimensionality, which in turn contributes to reducing computational load and data collection costs, while improving human understanding and interpretation of models. We introduce an operational guideline for determining the minimum number of instances sufficient to identify correct ranks of features with the highest impact. We conduct tests based on qualitative B2B sales forecasting data. The results show that a relatively small instance subset is sufficient for identifying the most important features when rank is not important.

  20. Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models.

    Science.gov (United States)

    Khaligh-Razavi, Seyed-Mahdi; Henriksson, Linda; Kay, Kendrick; Kriegeskorte, Nikolaus

    2017-02-01

    Studies of the primate visual system have begun to test a wide range of complex computational object-vision models. Realistic models have many parameters, which in practice cannot be fitted using the limited amounts of brain-activity data typically available. Task performance optimization (e.g. using backpropagation to train neural networks) provides major constraints for fitting parameters and discovering nonlinear representational features appropriate for the task (e.g. object classification). Model representations can be compared to brain representations in terms of the representational dissimilarities they predict for an image set. This method, called representational similarity analysis (RSA), enables us to test the representational feature space as is (fixed RSA) or to fit a linear transformation that mixes the nonlinear model features so as to best explain a cortical area's representational space (mixed RSA). Like voxel/population-receptive-field modelling, mixed RSA uses a training set (different stimuli) to fit one weight per model feature and response channel (voxels here), so as to best predict the response profile across images for each response channel. We analysed response patterns elicited by natural images, which were measured with functional magnetic resonance imaging (fMRI). We found that early visual areas were best accounted for by shallow models, such as a Gabor wavelet pyramid (GWP). The GWP model performed similarly with and without mixing, suggesting that the original features already approximated the representational space, obviating the need for mixing. However, a higher ventral-stream visual representation (lateral occipital region) was best explained by the higher layers of a deep convolutional network and mixing of its feature set was essential for this model to explain the representation. We suspect that mixing was essential because the convolutional network had been trained to discriminate a set of 1000 categories, whose frequencies

  1. Extracted facial feature of racial closely related faces

    Science.gov (United States)

    Liewchavalit, Chalothorn; Akiba, Masakazu; Kanno, Tsuneo; Nagao, Tomoharu

    2010-02-01

    Human faces contain a lot of demographic information such as identity, gender, age, race and emotion. Human being can perceive these pieces of information and use it as an important clue in social interaction with other people. Race perception is considered the most delicacy and sensitive parts of face perception. There are many research concerning image-base race recognition, but most of them are focus on major race group such as Caucasoid, Negroid and Mongoloid. This paper focuses on how people classify race of the racial closely related group. As a sample of racial closely related group, we choose Japanese and Thai face to represents difference between Northern and Southern Mongoloid. Three psychological experiment was performed to study the strategies of face perception on race classification. As a result of psychological experiment, it can be suggested that race perception is an ability that can be learn. Eyes and eyebrows are the most attention point and eyes is a significant factor in race perception. The Principal Component Analysis (PCA) was performed to extract facial features of sample race group. Extracted race features of texture and shape were used to synthesize faces. As the result, it can be suggested that racial feature is rely on detailed texture rather than shape feature. This research is a indispensable important fundamental research on the race perception which are essential in the establishment of human-like race recognition system.

  2. Use of a New Set of Linguistic Features to Improve Automatic Assessment of Text Readability

    Science.gov (United States)

    Yoshimi, Takehiko; Kotani, Katsunori; Isahara, Hitoshi

    2012-01-01

    The present paper proposes and evaluates a readability assessment method designed for Japanese learners of EFL (English as a foreign language). The proposed readability assessment method is constructed by a regression algorithm using a new set of linguistic features that were employed separately in previous studies. The results showed that the…

  3. ClusTrack: feature extraction and similarity measures for clustering of genome-wide data sets.

    Directory of Open Access Journals (Sweden)

    Halfdan Rydbeck

    Full Text Available Clustering is a popular technique for explorative analysis of data, as it can reveal subgroupings and similarities between data in an unsupervised manner. While clustering is routinely applied to gene expression data, there is a lack of appropriate general methodology for clustering of sequence-level genomic and epigenomic data, e.g. ChIP-based data. We here introduce a general methodology for clustering data sets of coordinates relative to a genome assembly, i.e. genomic tracks. By defining appropriate feature extraction approaches and similarity measures, we allow biologically meaningful clustering to be performed for genomic tracks using standard clustering algorithms. An implementation of the methodology is provided through a tool, ClusTrack, which allows fine-tuned clustering analyses to be specified through a web-based interface. We apply our methods to the clustering of occupancy of the H3K4me1 histone modification in samples from a range of different cell types. The majority of samples form meaningful subclusters, confirming that the definitions of features and similarity capture biological, rather than technical, variation between the genomic tracks. Input data and results are available, and can be reproduced, through a Galaxy Pages document at http://hyperbrowser.uio.no/hb/u/hb-superuser/p/clustrack. The clustering functionality is available as a Galaxy tool, under the menu option "Specialized analyzis of tracks", and the submenu option "Cluster tracks based on genome level similarity", at the Genomic HyperBrowser server: http://hyperbrowser.uio.no/hb/.

  4. Set-oriented data mining in relational databases

    NARCIS (Netherlands)

    Houtsma, M.A.W.; Swami, Arun

    1995-01-01

    Data mining is an important real-life application for businesses. It is critical to find efficient ways of mining large data sets. In order to benefit from the experience with relational databases, a set-oriented approach to mining data is needed. In such an approach, the data mining operations are

  5. Working memory for visual features and conjunctions in schizophrenia.

    Science.gov (United States)

    Gold, James M; Wilk, Christopher M; McMahon, Robert P; Buchanan, Robert W; Luck, Steven J

    2003-02-01

    The visual working memory (WM) storage capacity of patients with schizophrenia was investigated using a change detection paradigm. Participants were presented with 2, 3, 4, or 6 colored bars with testing of both single feature (color, orientation) and feature conjunction conditions. Patients performed significantly worse than controls at all set sizes but demonstrated normal feature binding. Unlike controls, patient WM capacity declined at set size 6 relative to set size 4. Impairments with subcapacity arrays suggest a deficit in task set maintenance: Greater impairment for supercapacity set sizes suggests a deficit in the ability to selectively encode information for WM storage. Thus, the WM impairment in schizophrenia appears to be a consequence of attentional deficits rather than a reduction in storage capacity.

  6. Attachment insecurity and perceived importance of relational features

    NARCIS (Netherlands)

    Ren, D.; Arriaga, X.B.; Mahan, E.R.

    2017-01-01

    Chronic attachment insecurity can affect the outlook people have on relationships. This research examines how attachment insecurity relates to perceived importance of various features in a romantic relationship (e.g., intimacy, independence). Consistent with predictions, the results from Studies 1–3

  7. Feature-Specific Event-Related Potential Effects to Action- and Sound-Related Verbs during Visual Word Recognition.

    Science.gov (United States)

    Popp, Margot; Trumpp, Natalie M; Kiefer, Markus

    2016-01-01

    Grounded cognition theories suggest that conceptual representations essentially depend on modality-specific sensory and motor systems. Feature-specific brain activation across different feature types such as action or audition has been intensively investigated in nouns, while feature-specific conceptual category differences in verbs mainly focused on body part specific effects. The present work aimed at assessing whether feature-specific event-related potential (ERP) differences between action and sound concepts, as previously observed in nouns, can also be found within the word class of verbs. In Experiment 1, participants were visually presented with carefully matched sound and action verbs within a lexical decision task, which provides implicit access to word meaning and minimizes strategic access to semantic word features. Experiment 2 tested whether pre-activating the verb concept in a context phase, in which the verb is presented with a related context noun, modulates subsequent feature-specific action vs. sound verb processing within the lexical decision task. In Experiment 1, ERP analyses revealed a differential ERP polarity pattern for action and sound verbs at parietal and central electrodes similar to previous results in nouns. Pre-activation of the meaning of verbs in the preceding context phase in Experiment 2 resulted in a polarity-reversal of feature-specific ERP effects in the lexical decision task compared with Experiment 1. This parallels analogous earlier findings for primed action and sound related nouns. In line with grounded cognitions theories, our ERP study provides evidence for a differential processing of action and sound verbs similar to earlier observation for concrete nouns. Although the localizational value of ERPs must be viewed with caution, our results indicate that the meaning of verbs is linked to different neural circuits depending on conceptual feature relevance.

  8. Processing of word stress related acoustic information: A multi-feature MMN study.

    Science.gov (United States)

    Honbolygó, Ferenc; Kolozsvári, Orsolya; Csépe, Valéria

    2017-08-01

    In the present study, we investigated the processing of word stress related acoustic features in a word context. In a passive oddball multi-feature MMN experiment, we presented a disyllabic pseudo-word with two acoustically similar syllables as standard stimulus, and five contrasting deviants that differed from the standard in that they were either stressed on the first syllable or contained a vowel change. Stress was realized by an increase of f0, intensity, vowel duration or consonant duration. The vowel change was used to investigate if phonemic and prosodic changes elicit different MMN components. As a control condition, we presented non-speech counterparts of the speech stimuli. Results showed all but one feature (non-speech intensity deviant) eliciting the MMN component, which was larger for speech compared to non-speech stimuli. Two other components showed stimulus related effects: the N350 and the LDN (Late Discriminative Negativity). The N350 appeared to the vowel duration and consonant duration deviants, specifically to features related to the temporal characteristics of stimuli, while the LDN was present for all features, and it was larger for speech than for non-speech stimuli. We also found that the f0 and consonant duration features elicited a larger MMN than other features. These results suggest that stress as a phonological feature is processed based on long-term representations, and listeners show a specific sensitivity to segmental and suprasegmental cues signaling the prosodic boundaries of words. These findings support a two-stage model in the perception of stress and phoneme related acoustical information. Copyright © 2017 Elsevier B.V. All rights reserved.

  9. Age-related X-ray feature of the spine in patients with achondroplasia

    International Nuclear Information System (INIS)

    Shevtsov, V.I.; D'yachkova, G.V.; Novikova, O.S.

    1999-01-01

    Age-related X-ray features of the spine in patients with achondroplasia are studied. It gives the time course of changes in the shape of vertebrae, the specific features of apophyseal ossification, provides a quantitative account of the shorter caudal lumbar vertebral arch root distance symptom. The time course of changes in the size of the lumbosacral angle was examined. The findings suggest that there are not only considerable static changes in the spine of patients with achondroplasia, but also significant age-related features of vertebral tissue growth and differentiation [ru

  10. MRI features can predict EGFR expression in lower grade gliomas. A voxel-based radiomic analysis

    Energy Technology Data Exchange (ETDEWEB)

    Li, Yiming; Liu, Xing; Qian, Zenghui; Fan, Xing; Li, Shaowu; Jiang, Tao [Capital Medical University, Beijing Neurosurgical Institute, Beijing (China); Xu, Kaibin [Chinese Academy of Sciences, Institute of Automation, Beijing (China); Wang, Kai [Beijing Tiantan Hospital, Department of Neuroradiology, Beijing (China); Wang, Yinyan [Beijing Tiantan Hospital, Department of Neuroradiology, Beijing (China); Beijing Tiantan Hospital, Capital Medical University, Department of Neurosurgery, Beijing (China)

    2018-01-15

    To identify the magnetic resonance imaging (MRI) features associated with epidermal growth factor (EGFR) expression level in lower grade gliomas using radiomic analysis. 270 lower grade glioma patients with known EGFR expression status were randomly assigned into training (n=200) and validation (n=70) sets, and were subjected to feature extraction. Using a logistic regression model, a signature of MRI features was identified to be predictive of the EGFR expression level in lower grade gliomas in the training set, and the accuracy of prediction was assessed in the validation set. A signature of 41 MRI features achieved accuracies of 82.5% (area under the curve [AUC] = 0.90) in the training set and 90.0% (AUC = 0.95) in the validation set. This radiomic signature consisted of 25 first-order statistics or related wavelet features (including range, standard deviation, uniformity, variance), one shape and size-based feature (spherical disproportion), and 15 textural features or related wavelet features (including sum variance, sum entropy, run percentage). A radiomic signature allowing for the prediction of the EGFR expression level in patients with lower grade glioma was identified, suggesting that using tumour-derived radiological features for predicting genomic information is feasible. (orig.)

  11. Feature selection gait-based gender classification under different circumstances

    Science.gov (United States)

    Sabir, Azhin; Al-Jawad, Naseer; Jassim, Sabah

    2014-05-01

    This paper proposes a gender classification based on human gait features and investigates the problem of two variations: clothing (wearing coats) and carrying bag condition as addition to the normal gait sequence. The feature vectors in the proposed system are constructed after applying wavelet transform. Three different sets of feature are proposed in this method. First, Spatio-temporal distance that is dealing with the distance of different parts of the human body (like feet, knees, hand, Human Height and shoulder) during one gait cycle. The second and third feature sets are constructed from approximation and non-approximation coefficient of human body respectively. To extract these two sets of feature we divided the human body into two parts, upper and lower body part, based on the golden ratio proportion. In this paper, we have adopted a statistical method for constructing the feature vector from the above sets. The dimension of the constructed feature vector is reduced based on the Fisher score as a feature selection method to optimize their discriminating significance. Finally k-Nearest Neighbor is applied as a classification method. Experimental results demonstrate that our approach is providing more realistic scenario and relatively better performance compared with the existing approaches.

  12. Estimation of minimum sample size for identification of the most important features: a case study providing a qualitative B2B sales data set

    OpenAIRE

    Marko Bohanec; Mirjana Kljajić Borštnar; Marko Robnik-Šikonja

    2017-01-01

    An important task in machine learning is to reduce data set dimensionality, which in turn contributes to reducing computational load and data collection costs, while improving human understanding and interpretation of models. We introduce an operational guideline for determining the minimum number of instances sufficient to identify correct ranks of features with the highest impact. We conduct tests based on qualitative B2B sales forecasting data. The results show that a relatively small inst...

  13. Surveying alignment-free features for Ortholog detection in related yeast proteomes by using supervised big data classifiers.

    Science.gov (United States)

    Galpert, Deborah; Fernández, Alberto; Herrera, Francisco; Antunes, Agostinho; Molina-Ruiz, Reinaldo; Agüero-Chapin, Guillermin

    2018-05-03

    The development of new ortholog detection algorithms and the improvement of existing ones are of major importance in functional genomics. We have previously introduced a successful supervised pairwise ortholog classification approach implemented in a big data platform that considered several pairwise protein features and the low ortholog pair ratios found between two annotated proteomes (Galpert, D et al., BioMed Research International, 2015). The supervised models were built and tested using a Saccharomycete yeast benchmark dataset proposed by Salichos and Rokas (2011). Despite several pairwise protein features being combined in a supervised big data approach; they all, to some extent were alignment-based features and the proposed algorithms were evaluated on a unique test set. Here, we aim to evaluate the impact of alignment-free features on the performance of supervised models implemented in the Spark big data platform for pairwise ortholog detection in several related yeast proteomes. The Spark Random Forest and Decision Trees with oversampling and undersampling techniques, and built with only alignment-based similarity measures or combined with several alignment-free pairwise protein features showed the highest classification performance for ortholog detection in three yeast proteome pairs. Although such supervised approaches outperformed traditional methods, there were no significant differences between the exclusive use of alignment-based similarity measures and their combination with alignment-free features, even within the twilight zone of the studied proteomes. Just when alignment-based and alignment-free features were combined in Spark Decision Trees with imbalance management, a higher success rate (98.71%) within the twilight zone could be achieved for a yeast proteome pair that underwent a whole genome duplication. The feature selection study showed that alignment-based features were top-ranked for the best classifiers while the runners-up were

  14. Causal Set Generator and Action Computer

    OpenAIRE

    Cunningham, William; Krioukov, Dmitri

    2017-01-01

    The causal set approach to quantum gravity has gained traction over the past three decades, but numerical experiments involving causal sets have been limited to relatively small scales. The software suite presented here provides a new framework for the generation and study of causal sets. Its efficiency surpasses previous implementations by several orders of magnitude. We highlight several important features of the code, including the compact data structures, the $O(N^2)$ causal set generatio...

  15. Comparison of the effectiveness of alternative feature sets in shape retrieval of multicomponent images

    Science.gov (United States)

    Eakins, John P.; Edwards, Jonathan D.; Riley, K. Jonathan; Rosin, Paul L.

    2001-01-01

    Many different kinds of features have been used as the basis for shape retrieval from image databases. This paper investigates the relative effectiveness of several types of global shape feature, both singly and in combination. The features compared include well-established descriptors such as Fourier coefficients and moment invariants, as well as recently-proposed measures of triangularity and ellipticity. Experiments were conducted within the framework of the ARTISAN shape retrieval system, and retrieval effectiveness assessed on a database of over 10,000 images, using 24 queries and associated ground truth supplied by the UK Patent Office . Our experiments revealed only minor differences in retrieval effectiveness between different measures, suggesting that a wide variety of shape feature combinations can provide adequate discriminating power for effective shape retrieval in multi-component image collections such as trademark registries. Marked differences between measures were observed for some individual queries, suggesting that there could be considerable scope for improving retrieval effectiveness by providing users with an improved framework for searching multi-dimensional feature space.

  16. Simulation and Verification of Synchronous Set Relations in Rewriting Logic

    Science.gov (United States)

    Rocha, Camilo; Munoz, Cesar A.

    2011-01-01

    This paper presents a mathematical foundation and a rewriting logic infrastructure for the execution and property veri cation of synchronous set relations. The mathematical foundation is given in the language of abstract set relations. The infrastructure consists of an ordersorted rewrite theory in Maude, a rewriting logic system, that enables the synchronous execution of a set relation provided by the user. By using the infrastructure, existing algorithm veri cation techniques already available in Maude for traditional asynchronous rewriting, such as reachability analysis and model checking, are automatically available to synchronous set rewriting. The use of the infrastructure is illustrated with an executable operational semantics of a simple synchronous language and the veri cation of temporal properties of a synchronous system.

  17. Relations between Automatically Extracted Motion Features and the Quality of Mother-Infant Interactions at 4 and 13 Months.

    Science.gov (United States)

    Egmose, Ida; Varni, Giovanna; Cordes, Katharina; Smith-Nielsen, Johanne; Væver, Mette S; Køppe, Simo; Cohen, David; Chetouani, Mohamed

    2017-01-01

    Bodily movements are an essential component of social interactions. However, the role of movement in early mother-infant interaction has received little attention in the research literature. The aim of the present study was to investigate the relationship between automatically extracted motion features and interaction quality in mother-infant interactions at 4 and 13 months. The sample consisted of 19 mother-infant dyads at 4 months and 33 mother-infant dyads at 13 months. The coding system Coding Interactive Behavior (CIB) was used for rating the quality of the interactions. Kinetic energy of upper-body, arms and head motion was calculated and used as segmentation in order to extract coarse- and fine-grained motion features. Spearman correlations were conducted between the composites derived from the CIB and the coarse- and fine-grained motion features. At both 4 and 13 months, longer durations of maternal arm motion and infant upper-body motion were associated with more aversive interactions, i.e., more parent-led interactions and more infant negativity. Further, at 4 months, the amount of motion silence was related to more adaptive interactions, i.e., more sensitive and child-led interactions. Analyses of the fine-grained motion features showed that if the mother coordinates her head movements with her infant's head movements, the interaction is rated as more adaptive in terms of less infant negativity and less dyadic negative states. We found more and stronger correlations between the motion features and the interaction qualities at 4 compared to 13 months. These results highlight that motion features are related to the quality of mother-infant interactions. Factors such as infant age and interaction set-up are likely to modify the meaning and importance of different motion features.

  18. Chemical-induced disease relation extraction with various linguistic features.

    Science.gov (United States)

    Gu, Jinghang; Qian, Longhua; Zhou, Guodong

    2016-01-01

    Understanding the relations between chemicals and diseases is crucial in various biomedical tasks such as new drug discoveries and new therapy developments. While manually mining these relations from the biomedical literature is costly and time-consuming, such a procedure is often difficult to keep up-to-date. To address these issues, the BioCreative-V community proposed a challenging task of automatic extraction of chemical-induced disease (CID) relations in order to benefit biocuration. This article describes our work on the CID relation extraction task on the BioCreative-V tasks. We built a machine learning based system that utilized simple yet effective linguistic features to extract relations with maximum entropy models. In addition to leveraging various features, the hypernym relations between entity concepts derived from the Medical Subject Headings (MeSH)-controlled vocabulary were also employed during both training and testing stages to obtain more accurate classification models and better extraction performance, respectively. We demoted relation extraction between entities in documents to relation extraction between entity mentions. In our system, pairs of chemical and disease mentions at both intra- and inter-sentence levels were first constructed as relation instances for training and testing, then two classification models at both levels were trained from the training examples and applied to the testing examples. Finally, we merged the classification results from mention level to document level to acquire final relations between chemicals and diseases. Our system achieved promisingF-scores of 60.4% on the development dataset and 58.3% on the test dataset using gold-standard entity annotations, respectively. Database URL:https://github.com/JHnlp/BC5CIDTask. © The Author(s) 2016. Published by Oxford University Press.

  19. Setting-related influences on physical inactivity of older adults in residential care settings : a review

    NARCIS (Netherlands)

    Douma, Johanna G.; Volkers, Karin M.; Engels, Gwenda; Sonneveld, Marieke H.; Goossens, Richard H. M.; Scherder, Erik J. A.

    2017-01-01

    Background: Despite the detrimental effects of physical inactivity for older adults, especially aged residents of residential care settings may spend much time in inactive behavior. This may be partly due to their poorer physical condition; however, there may also be other, setting-related factors

  20. The Roles of Feature-Specific Task Set and Bottom-Up Salience in Attentional Capture: An ERP Study

    Science.gov (United States)

    Eimer, Martin; Kiss, Monika; Press, Clare; Sauter, Disa

    2009-01-01

    We investigated the roles of top-down task set and bottom-up stimulus salience for feature-specific attentional capture. Spatially nonpredictive cues preceded search arrays that included a color-defined target. For target-color singleton cues, behavioral spatial cueing effects were accompanied by cue-induced N2pc components, indicative of…

  1. Auditing SNOMED CT hierarchical relations based on lexical features of concepts in non-lattice subgraphs.

    Science.gov (United States)

    Cui, Licong; Bodenreider, Olivier; Shi, Jay; Zhang, Guo-Qiang

    2018-02-01

    We introduce a structural-lexical approach for auditing SNOMED CT using a combination of non-lattice subgraphs of the underlying hierarchical relations and enriched lexical attributes of fully specified concept names. Our goal is to develop a scalable and effective approach that automatically identifies missing hierarchical IS-A relations. Our approach involves 3 stages. In stage 1, all non-lattice subgraphs of SNOMED CT's IS-A hierarchical relations are extracted. In stage 2, lexical attributes of fully-specified concept names in such non-lattice subgraphs are extracted. For each concept in a non-lattice subgraph, we enrich its set of attributes with attributes from its ancestor concepts within the non-lattice subgraph. In stage 3, subset inclusion relations between the lexical attribute sets of each pair of concepts in each non-lattice subgraph are compared to existing IS-A relations in SNOMED CT. For concept pairs within each non-lattice subgraph, if a subset relation is identified but an IS-A relation is not present in SNOMED CT IS-A transitive closure, then a missing IS-A relation is reported. The September 2017 release of SNOMED CT (US edition) was used in this investigation. A total of 14,380 non-lattice subgraphs were extracted, from which we suggested a total of 41,357 missing IS-A relations. For evaluation purposes, 200 non-lattice subgraphs were randomly selected from 996 smaller subgraphs (of size 4, 5, or 6) within the "Clinical Finding" and "Procedure" sub-hierarchies. Two domain experts confirmed 185 (among 223) suggested missing IS-A relations, a precision of 82.96%. Our results demonstrate that analyzing the lexical features of concepts in non-lattice subgraphs is an effective approach for auditing SNOMED CT. Copyright © 2017 Elsevier Inc. All rights reserved.

  2. The relation between intercultural competence, personality features and students’ intellectual development

    Directory of Open Access Journals (Sweden)

    Gridunova Marina V.

    2017-01-01

    Full Text Available In the light of globalisation processes accompanied by an increase in interethnic tensions, the research on personality features that contribute to a more efficient functioning in the intercultural context has become fairly topical. The aim of the conducted research was to explore the relation between intercultural competence, personality features and the level of intellectual development of students (N=121, 45% male students of a general education secondary school in Moscow. Bennett’s developmental model of intercultural sensitivity was used as the basis for studying intercultural competence, while the Scale of intercultural sensitivity was used as a diagnostic instrument. Personality features were defined in accordance with the Five Factor Model and diagnosed via the shorter version of the Five Factors questionnaire. The level of mental (intellectual development was measured using the normative School test of intellectual development (STID-2. Based on research results, it has been established that personality features such as conscientiousness, extraversion and neuroticism are related to the indicators of intercultural competence in the examined students, whereby the intensity of the relations is by far higher in the group of students with the lower level of intellectual development. At the same time, the students whose level of intellectual development is higher are more inclined towards accepting cultural differences, while those with the lower level of intellectual development tend to absolutise them.

  3. Influence of Familiar Features on Diagnosis: Instantiated Features in an Applied Setting

    Science.gov (United States)

    Dore, Kelly L.; Brooks, Lee R.; Weaver, Bruce; Norman, Geoffrey R.

    2012-01-01

    Medical diagnosis can be viewed as a categorization task. There are two mechanisms whereby humans make categorical judgments: "analytical reasoning," based on explicit consideration of features and "nonanalytical reasoning," an unconscious holistic process of matching against prior exemplars. However, there is evidence that prior experience can…

  4. What goes through the gate? Exploring interference with visual feature binding.

    Science.gov (United States)

    Ueno, Taiji; Mate, Judit; Allen, Richard J; Hitch, Graham J; Baddeley, Alan D

    2011-05-01

    A series of experiments explored the mechanisms determining the encoding and storage of features and objects in visual working memory. We contrasted the effects of three types of visual suffix on cued recall of a display of colored shapes. The suffix was presented after the display and before the recall cue. The latter was either the color or shape of one of the objects and signaled recall of the object's other feature. In Experiments 1 and 2, we found a larger effect of 'plausible' suffixes comprising features (color and shape) drawn from the experimental set, relative to the effect of 'implausible' suffixes comprising features outside the experimental set. Experiment 3 extended this pattern by showing that 'semi-plausible' suffixes containing only one feature (either color or shape) from the experimental set had an equivalent effect to those with both features from the set. Reduction in accuracy was mainly due to an increase in recall of suffix features, rather than within-display confusions. The findings suggest a feature-based filtering process in visual working memory, with any stimuli that pass through this filter serving to directly overwrite existing object representations. Copyright © 2011 Elsevier Ltd. All rights reserved.

  5. Chemical name extraction based on automatic training data generation and rich feature set.

    Science.gov (United States)

    Yan, Su; Spangler, W Scott; Chen, Ying

    2013-01-01

    The automation of extracting chemical names from text has significant value to biomedical and life science research. A major barrier in this task is the difficulty of getting a sizable and good quality data to train a reliable entity extraction model. Another difficulty is the selection of informative features of chemical names, since comprehensive domain knowledge on chemistry nomenclature is required. Leveraging random text generation techniques, we explore the idea of automatically creating training sets for the task of chemical name extraction. Assuming the availability of an incomplete list of chemical names, called a dictionary, we are able to generate well-controlled, random, yet realistic chemical-like training documents. We statistically analyze the construction of chemical names based on the incomplete dictionary, and propose a series of new features, without relying on any domain knowledge. Compared to state-of-the-art models learned from manually labeled data and domain knowledge, our solution shows better or comparable results in annotating real-world data with less human effort. Moreover, we report an interesting observation about the language for chemical names. That is, both the structural and semantic components of chemical names follow a Zipfian distribution, which resembles many natural languages.

  6. Beliefs about unmet interpersonal needs mediate the relation between conflictual family relations and borderline personality features in young adult females.

    Science.gov (United States)

    Kalpakci, Allison; Venta, Amanda; Sharp, Carla

    2014-01-01

    Central to most theories of borderline personality disorder (BPD) is the notion that the family environment interacts with genetically-based vulnerabilities to influence the development of BPD, with particular attention given to risk conferred by conflictual familial relations. However, the extent to which family conflict may relate to the development of BPD via related interpersonal beliefs is currently unknown. This study sought to test the hypothesis that the concurrent relation between conflictual family relations and borderline features in female college students is explained by beliefs associated with real or perceived unmet interpersonal needs (captured by Joiner's [2005] Interpersonal Psychological Theory, specifically thwarted belongingness and perceived burdensomeness). The sample included 267 female undergraduates ages 18-25 years (M = 20.86; SD = 1.80). Level of borderline personality features, unmet interpersonal needs, and family conflict were assessed. Bivariate analyses revealed significant relations between both thwarted belongingness and perceived burdensomeness, conflictual family relations, and borderline features. Multivariate analyses revealed that thwarted belongingness and perceived burdensomeness both mediated the relation between family conflict and borderline personality features, thus supporting a multiple mediation model. This cross-sectional study is a preliminary step towards confirming the broad theoretical hypothesis that conflictual family relations relate to beliefs about thwarted belongingness and perceived burdensomeness, which, in turn, relate to borderline personality pathology. Limitations and areas of future research are discussed.

  7. Features of the reproductive setting of men and women which are patients of the programs of assisted reproductive technologies (ARTs

    Directory of Open Access Journals (Sweden)

    A. V. Kaminsky

    2017-10-01

    Full Text Available Infertility refers to those states that significantly affect the psycho-emotional status of a person, causing the state of chronic stress. In turn, chronic stress can lead to the development of stress-induced infertility. The aim of the study was to identify features of the reproductive setting of men and women who are patients of assisted reproductive technology (ART programs in connection with reproductive behavior. Material and methods. Under supervision, there were 233 women and men who needed infertility treatment using ART methods, and 142 fertile women and men who had already had births, and applied for pre-gestational preparation before planning another pregnancy. Methods of psychological testing are used. Results. It has been established that the reproductive setting of infertile men and women is uncertain (contradictory; in it there is a discrepancy and ambivalence in the content of affective, cognitive and conative components. Reproductive testing of individuals having children is definite (harmonious; there is consistency in the content of affective, cognitive and conative components. There are gender differences in the components of the reproductive setting, both infertile and those with children. There is a connection between the type of reproductive setting and the personality characteristics, the relation to the spouse, the motives for the birth of the child. Conclusions. The reproductive settings of infertile men and women who are patients of the ART are different from those of mothers and fathers with newborn babies and require psychological correction.

  8. kmer-SVM: a web server for identifying predictive regulatory sequence features in genomic data sets

    Science.gov (United States)

    Fletez-Brant, Christopher; Lee, Dongwon; McCallion, Andrew S.; Beer, Michael A.

    2013-01-01

    Massively parallel sequencing technologies have made the generation of genomic data sets a routine component of many biological investigations. For example, Chromatin immunoprecipitation followed by sequence assays detect genomic regions bound (directly or indirectly) by specific factors, and DNase-seq identifies regions of open chromatin. A major bottleneck in the interpretation of these data is the identification of the underlying DNA sequence code that defines, and ultimately facilitates prediction of, these transcription factor (TF) bound or open chromatin regions. We have recently developed a novel computational methodology, which uses a support vector machine (SVM) with kmer sequence features (kmer-SVM) to identify predictive combinations of short transcription factor-binding sites, which determine the tissue specificity of these genomic assays (Lee, Karchin and Beer, Discriminative prediction of mammalian enhancers from DNA sequence. Genome Res. 2011; 21:2167–80). This regulatory information can (i) give confidence in genomic experiments by recovering previously known binding sites, and (ii) reveal novel sequence features for subsequent experimental testing of cooperative mechanisms. Here, we describe the development and implementation of a web server to allow the broader research community to independently apply our kmer-SVM to analyze and interpret their genomic datasets. We analyze five recently published data sets and demonstrate how this tool identifies accessory factors and repressive sequence elements. kmer-SVM is available at http://kmersvm.beerlab.org. PMID:23771147

  9. Matroidal Structure of Generalized Rough Sets Based on Tolerance Relations

    Directory of Open Access Journals (Sweden)

    Hui Li

    2014-01-01

    of the generalized rough set based on the tolerance relation. The matroid can also induce a new relation. We investigate the connection between the original tolerance relation and the induced relation.

  10. Online feature selection with streaming features.

    Science.gov (United States)

    Wu, Xindong; Yu, Kui; Ding, Wei; Wang, Hao; Zhu, Xingquan

    2013-05-01

    We propose a new online feature selection framework for applications with streaming features where the knowledge of the full feature space is unknown in advance. We define streaming features as features that flow in one by one over time whereas the number of training examples remains fixed. This is in contrast with traditional online learning methods that only deal with sequentially added observations, with little attention being paid to streaming features. The critical challenges for Online Streaming Feature Selection (OSFS) include 1) the continuous growth of feature volumes over time, 2) a large feature space, possibly of unknown or infinite size, and 3) the unavailability of the entire feature set before learning starts. In the paper, we present a novel Online Streaming Feature Selection method to select strongly relevant and nonredundant features on the fly. An efficient Fast-OSFS algorithm is proposed to improve feature selection performance. The proposed algorithms are evaluated extensively on high-dimensional datasets and also with a real-world case study on impact crater detection. Experimental results demonstrate that the algorithms achieve better compactness and higher prediction accuracy than existing streaming feature selection algorithms.

  11. Set optimization and applications the state of the art : from set relations to set-valued risk measures

    CERN Document Server

    Heyde, Frank; Löhne, Andreas; Rudloff, Birgit; Schrage, Carola

    2015-01-01

    This volume presents five surveys with extensive bibliographies and six original contributions on set optimization and its applications in mathematical finance and game theory. The topics range from more conventional approaches that look for minimal/maximal elements with respect to vector orders or set relations, to the new complete-lattice approach that comprises a coherent solution concept for set optimization problems, along with existence results, duality theorems, optimality conditions, variational inequalities and theoretical foundations for algorithms. Modern approaches to scalarization methods can be found as well as a fundamental contribution to conditional analysis. The theory is tailor-made for financial applications, in particular risk evaluation and [super-]hedging for market models with transaction costs, but it also provides a refreshing new perspective on vector optimization. There is no comparable volume on the market, making the book an invaluable resource for researchers working in vector o...

  12. The feature-weighted receptive field: an interpretable encoding model for complex feature spaces.

    Science.gov (United States)

    St-Yves, Ghislain; Naselaris, Thomas

    2017-06-20

    We introduce the feature-weighted receptive field (fwRF), an encoding model designed to balance expressiveness, interpretability and scalability. The fwRF is organized around the notion of a feature map-a transformation of visual stimuli into visual features that preserves the topology of visual space (but not necessarily the native resolution of the stimulus). The key assumption of the fwRF model is that activity in each voxel encodes variation in a spatially localized region across multiple feature maps. This region is fixed for all feature maps; however, the contribution of each feature map to voxel activity is weighted. Thus, the model has two separable sets of parameters: "where" parameters that characterize the location and extent of pooling over visual features, and "what" parameters that characterize tuning to visual features. The "where" parameters are analogous to classical receptive fields, while "what" parameters are analogous to classical tuning functions. By treating these as separable parameters, the fwRF model complexity is independent of the resolution of the underlying feature maps. This makes it possible to estimate models with thousands of high-resolution feature maps from relatively small amounts of data. Once a fwRF model has been estimated from data, spatial pooling and feature tuning can be read-off directly with no (or very little) additional post-processing or in-silico experimentation. We describe an optimization algorithm for estimating fwRF models from data acquired during standard visual neuroimaging experiments. We then demonstrate the model's application to two distinct sets of features: Gabor wavelets and features supplied by a deep convolutional neural network. We show that when Gabor feature maps are used, the fwRF model recovers receptive fields and spatial frequency tuning functions consistent with known organizational principles of the visual cortex. We also show that a fwRF model can be used to regress entire deep

  13. Girth 5 graphs from relative difference sets

    DEFF Research Database (Denmark)

    Jørgensen, Leif Kjær

    2005-01-01

    We consider the problem of construction of graphs with given degree $k$ and girth 5 and as few vertices as possible. We give a construction of a family of girth 5 graphs based on relative difference sets. This family contains the smallest known graph of degree 8 and girth 5 which was constructed ...

  14. Obscene Video Recognition Using Fuzzy SVM and New Sets of Features

    Directory of Open Access Journals (Sweden)

    Alireza Behrad

    2013-02-01

    Full Text Available In this paper, a novel approach for identifying normal and obscene videos is proposed. In order to classify different episodes of a video independently and discard the need to process all frames, first, key frames are extracted and skin regions are detected for groups of video frames starting with key frames. In the second step, three different features including 1- structural features based on single frame information, 2- features based on spatiotemporal volume and 3-motion-based features, are extracted for each episode of video. The PCA-LDA method is then applied to reduce the size of structural features and select more distinctive features. For the final step, we use fuzzy or a Weighted Support Vector Machine (WSVM classifier to identify video episodes. We also employ a multilayer Kohonen network as an initial clustering algorithm to increase the ability to discriminate between the extracted features into two classes of videos. Features based on motion and periodicity characteristics increase the efficiency of the proposed algorithm in videos with bad illumination and skin colour variation. The proposed method is evaluated using 1100 videos in different environmental and illumination conditions. The experimental results show a correct recognition rate of 94.2% for the proposed algorithm.

  15. Girth 5 graphs from relative difference sets

    DEFF Research Database (Denmark)

    Jørgensen, Leif Kjær

    We consider the problem of construction of graphs with given degree and girth 5 and as few vertices as possible. We give a construction of a family of girth 5 graphs based on relative difference sets. This family contains the smallest known graph of degree 8 and girth 5 which was constructed by G...

  16. Popular Nutrition-Related Mobile Apps: A Feature Assessment.

    Science.gov (United States)

    Franco, Rodrigo Zenun; Fallaize, Rosalind; Lovegrove, Julie A; Hwang, Faustina

    2016-08-01

    A key challenge in human nutrition is the assessment of usual food intake. This is of particular interest given recent proposals of eHealth personalized interventions. The adoption of mobile phones has created an opportunity for assessing and improving nutrient intake as they can be used for digitalizing dietary assessments and providing feedback. In the last few years, hundreds of nutrition-related mobile apps have been launched and installed by millions of users. This study aims to analyze the main features of the most popular nutrition apps and to compare their strategies and technologies for dietary assessment and user feedback. Apps were selected from the two largest online stores of the most popular mobile operating systems-the Google Play Store for Android and the iTunes App Store for iOS-based on popularity as measured by the number of installs and reviews. The keywords used in the search were as follows: calorie(s), diet, diet tracker, dietician, dietitian, eating, fit, fitness, food, food diary, food tracker, health, lose weight, nutrition, nutritionist, weight, weight loss, weight management, weight watcher, and ww calculator. The inclusion criteria were as follows: English language, minimum number of installs (1 million for Google Play Store) or reviews (7500 for iTunes App Store), relation to nutrition (ie, diet monitoring or recommendation), and independence from any device (eg, wearable) or subscription. A total of 13 apps were classified as popular for inclusion in the analysis. Nine apps offered prospective recording of food intake using a food diary feature. Food selection was available via text search or barcode scanner technologies. Portion size selection was only textual (ie, without images or icons). All nine of these apps were also capable of collecting physical activity (PA) information using self-report, the global positioning system (GPS), or wearable integrations. Their outputs focused predominantly on energy balance between dietary

  17. A novel relational regularization feature selection method for joint regression and classification in AD diagnosis.

    Science.gov (United States)

    Zhu, Xiaofeng; Suk, Heung-Il; Wang, Li; Lee, Seong-Whan; Shen, Dinggang

    2017-05-01

    In this paper, we focus on joint regression and classification for Alzheimer's disease diagnosis and propose a new feature selection method by embedding the relational information inherent in the observations into a sparse multi-task learning framework. Specifically, the relational information includes three kinds of relationships (such as feature-feature relation, response-response relation, and sample-sample relation), for preserving three kinds of the similarity, such as for the features, the response variables, and the samples, respectively. To conduct feature selection, we first formulate the objective function by imposing these three relational characteristics along with an ℓ 2,1 -norm regularization term, and further propose a computationally efficient algorithm to optimize the proposed objective function. With the dimension-reduced data, we train two support vector regression models to predict the clinical scores of ADAS-Cog and MMSE, respectively, and also a support vector classification model to determine the clinical label. We conducted extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to validate the effectiveness of the proposed method. Our experimental results showed the efficacy of the proposed method in enhancing the performances of both clinical scores prediction and disease status identification, compared to the state-of-the-art methods. Copyright © 2015 Elsevier B.V. All rights reserved.

  18. Causal Relations and Feature Similarity in Children's Inductive Reasoning

    Science.gov (United States)

    Hayes, Brett K.; Thompson, Susan P.

    2007-01-01

    Four experiments examined the development of property induction on the basis of causal relations. In the first 2 studies, 5-year-olds, 8-year-olds, and adults were presented with triads in which a target instance was equally similar to 2 inductive bases but shared a causal antecedent feature with 1 of them. All 3 age groups used causal relations…

  19. Hippocampal sleep features: relations to human memory function

    Directory of Open Access Journals (Sweden)

    Michele eFerrara

    2012-04-01

    Full Text Available The recent spread of intracranial EEG recordings techniques for presurgical evaluation of drug-resistant epileptic patients is providing new information on the activity of different brain structures during both wakefulness and sleep. The interest has been mainly focused on the medial temporal lobe, and in particular the hippocampal formation, whose peculiar local sleep features have been recently described, providing support to the idea that sleep is not a spatially global phenomenon. The study of the hippocampal sleep electrophysiology is particularly interesting because of its central role in the declarative memory formation. Recent data indicate that sleep contributes to memory formation. Therefore, it is relevant to understand whether specific pattern of activity taking place during sleep are related to memory consolidation processes. Fascinating similarities between different states of consciousness (wakefulness, REM sleep, NREM sleep in some electrophysiological mechanisms underlying cognitive processes have been reported. For instance, large-scale synchrony in gamma activity is important for waking memory and perception processes, and its changes during sleep may be the neurophysiological substrate of sleep-related deficits of declarative memory. Hippocampal activity seems to specifically support memory consolidation during sleep, through specific coordinated neurophysiological events (slow waves, spindles, ripples that would facilitate the integration of new information into the pre-existing cortical networks. A few studies indeed provided direct evidence that rhinal ripples as well as slow hippocampal oscillations are correlated with memory consolidation in humans. More detailed electrophysiological investigations assessing the specific relations between different types of memory consolidation and hippocampal EEG features are in order. These studies will add an important piece of knowledge to the elucidation of the ultimate sleep

  20. Hippocampal Sleep Features: Relations to Human Memory Function

    Science.gov (United States)

    Ferrara, Michele; Moroni, Fabio; De Gennaro, Luigi; Nobili, Lino

    2012-01-01

    The recent spread of intracranial electroencephalographic (EEG) recording techniques for presurgical evaluation of drug-resistant epileptic patients is providing new information on the activity of different brain structures during both wakefulness and sleep. The interest has been mainly focused on the medial temporal lobe, and in particular the hippocampal formation, whose peculiar local sleep features have been recently described, providing support to the idea that sleep is not a spatially global phenomenon. The study of the hippocampal sleep electrophysiology is particularly interesting because of its central role in the declarative memory formation. Recent data indicate that sleep contributes to memory formation. Therefore, it is relevant to understand whether specific patterns of activity taking place during sleep are related to memory consolidation processes. Fascinating similarities between different states of consciousness (wakefulness, REM sleep, non-REM sleep) in some electrophysiological mechanisms underlying cognitive processes have been reported. For instance, large-scale synchrony in gamma activity is important for waking memory and perception processes, and its changes during sleep may be the neurophysiological substrate of sleep-related deficits of declarative memory. Hippocampal activity seems to specifically support memory consolidation during sleep, through specific coordinated neurophysiological events (slow waves, spindles, ripples) that would facilitate the integration of new information into the pre-existing cortical networks. A few studies indeed provided direct evidence that rhinal ripples as well as slow hippocampal oscillations are correlated with memory consolidation in humans. More detailed electrophysiological investigations assessing the specific relations between different types of memory consolidation and hippocampal EEG features are in order. These studies will add an important piece of knowledge to the elucidation of the ultimate

  1. Personalized features for attention detection in children with Attention Deficit Hyperactivity Disorder.

    Science.gov (United States)

    Fahimi, Fatemeh; Guan, Cuntai; Wooi Boon Goh; Kai Keng Ang; Choon Guan Lim; Tih Shih Lee

    2017-07-01

    Measuring attention from electroencephalogram (EEG) has found applications in the treatment of Attention Deficit Hyperactivity Disorder (ADHD). It is of great interest to understand what features in EEG are most representative of attention. Intensive research has been done in the past and it has been proven that frequency band powers and their ratios are effective features in detecting attention. However, there are still unanswered questions, like, what features in EEG are most discriminative between attentive and non-attentive states? Are these features common among all subjects or are they subject-specific and must be optimized for each subject? Using Mutual Information (MI) to perform subject-specific feature selection on a large data set including 120 ADHD children, we found that besides theta beta ratio (TBR) which is commonly used in attention detection and neurofeedback, the relative beta power and theta/(alpha+beta) (TBAR) are also equally significant and informative for attention detection. Interestingly, we found that the relative theta power (which is also commonly used) may not have sufficient discriminative information itself (it is informative only for 3.26% of ADHD children). We have also demonstrated that although these features (relative beta power, TBR and TBAR) are the most important measures to detect attention on average, different subjects have different set of most discriminative features.

  2. Setting and changing feature priorities in visual short-term memory.

    Science.gov (United States)

    Kalogeropoulou, Zampeta; Jagadeesh, Akshay V; Ohl, Sven; Rolfs, Martin

    2017-04-01

    Many everyday tasks require prioritizing some visual features over competing ones, both during the selection from the rich sensory input and while maintaining information in visual short-term memory (VSTM). Here, we show that observers can change priorities in VSTM when, initially, they attended to a different feature. Observers reported from memory the orientation of one of two spatially interspersed groups of black and white gratings. Using colored pre-cues (presented before stimulus onset) and retro-cues (presented after stimulus offset) predicting the to-be-reported group, we manipulated observers' feature priorities independently during stimulus encoding and maintenance, respectively. Valid pre-cues reliably increased observers' performance (reduced guessing, increased report precision) as compared to neutral ones; invalid pre-cues had the opposite effect. Valid retro-cues also consistently improved performance (by reducing random guesses), even if the unexpected group suddenly became relevant (invalid-valid condition). Thus, feature-based attention can reshape priorities in VSTM protecting information that would otherwise be forgotten.

  3. Some Syntactic Features of Relative Constructions in the Greek New Testament

    OpenAIRE

    Herman C du Toit

    2016-01-01

    In the Greek New Testament, relative sentences that are introduced by relative pronouns alone, apart from the adverbial uses, are the most frequent subordinate sentence type. The research reported on in this paper aimed to investigate and describe a number of syntactic features of relative constructions in the Greek New Testament, taking account, among others, of some typological parameters that have been developed in the general linguistics literature for these constructions.The results indi...

  4. A new set of wavelet- and fractals-based features for Gleason grading of prostate cancer histopathology images

    Science.gov (United States)

    Mosquera Lopez, Clara; Agaian, Sos

    2013-02-01

    Prostate cancer detection and staging is an important step towards patient treatment selection. Advancements in digital pathology allow the application of new quantitative image analysis algorithms for computer-assisted diagnosis (CAD) on digitized histopathology images. In this paper, we introduce a new set of features to automatically grade pathological images using the well-known Gleason grading system. The goal of this study is to classify biopsy images belonging to Gleason patterns 3, 4, and 5 by using a combination of wavelet and fractal features. For image classification we use pairwise coupling Support Vector Machine (SVM) classifiers. The accuracy of the system, which is close to 97%, is estimated through three different cross-validation schemes. The proposed system offers the potential for automating classification of histological images and supporting prostate cancer diagnosis.

  5. Visualisation of variable binding pockets on protein surfaces by probabilistic analysis of related structure sets

    Directory of Open Access Journals (Sweden)

    Ashford Paul

    2012-03-01

    Full Text Available Abstract Background Protein structures provide a valuable resource for rational drug design. For a protein with no known ligand, computational tools can predict surface pockets that are of suitable size and shape to accommodate a complementary small-molecule drug. However, pocket prediction against single static structures may miss features of pockets that arise from proteins' dynamic behaviour. In particular, ligand-binding conformations can be observed as transiently populated states of the apo protein, so it is possible to gain insight into ligand-bound forms by considering conformational variation in apo proteins. This variation can be explored by considering sets of related structures: computationally generated conformers, solution NMR ensembles, multiple crystal structures, homologues or homology models. It is non-trivial to compare pockets, either from different programs or across sets of structures. For a single structure, difficulties arise in defining particular pocket's boundaries. For a set of conformationally distinct structures the challenge is how to make reasonable comparisons between them given that a perfect structural alignment is not possible. Results We have developed a computational method, Provar, that provides a consistent representation of predicted binding pockets across sets of related protein structures. The outputs are probabilities that each atom or residue of the protein borders a predicted pocket. These probabilities can be readily visualised on a protein using existing molecular graphics software. We show how Provar simplifies comparison of the outputs of different pocket prediction algorithms, of pockets across multiple simulated conformations and between homologous structures. We demonstrate the benefits of use of multiple structures for protein-ligand and protein-protein interface analysis on a set of complexes and consider three case studies in detail: i analysis of a kinase superfamily highlights the

  6. Visualisation of variable binding pockets on protein surfaces by probabilistic analysis of related structure sets.

    Science.gov (United States)

    Ashford, Paul; Moss, David S; Alex, Alexander; Yeap, Siew K; Povia, Alice; Nobeli, Irene; Williams, Mark A

    2012-03-14

    Protein structures provide a valuable resource for rational drug design. For a protein with no known ligand, computational tools can predict surface pockets that are of suitable size and shape to accommodate a complementary small-molecule drug. However, pocket prediction against single static structures may miss features of pockets that arise from proteins' dynamic behaviour. In particular, ligand-binding conformations can be observed as transiently populated states of the apo protein, so it is possible to gain insight into ligand-bound forms by considering conformational variation in apo proteins. This variation can be explored by considering sets of related structures: computationally generated conformers, solution NMR ensembles, multiple crystal structures, homologues or homology models. It is non-trivial to compare pockets, either from different programs or across sets of structures. For a single structure, difficulties arise in defining particular pocket's boundaries. For a set of conformationally distinct structures the challenge is how to make reasonable comparisons between them given that a perfect structural alignment is not possible. We have developed a computational method, Provar, that provides a consistent representation of predicted binding pockets across sets of related protein structures. The outputs are probabilities that each atom or residue of the protein borders a predicted pocket. These probabilities can be readily visualised on a protein using existing molecular graphics software. We show how Provar simplifies comparison of the outputs of different pocket prediction algorithms, of pockets across multiple simulated conformations and between homologous structures. We demonstrate the benefits of use of multiple structures for protein-ligand and protein-protein interface analysis on a set of complexes and consider three case studies in detail: i) analysis of a kinase superfamily highlights the conserved occurrence of surface pockets at the active

  7. Some Syntactic Features of Relative Constructions in the Greek New Testament

    Directory of Open Access Journals (Sweden)

    Herman C du Toit

    2016-07-01

    Full Text Available In the Greek New Testament, relative sentences that are introduced by relative pronouns alone, apart from the adverbial uses, are the most frequent subordinate sentence type. The research reported on in this paper aimed to investigate and describe a number of syntactic features of relative constructions in the Greek New Testament, taking account, among others, of some typological parameters that have been developed in the general linguistics literature for these constructions.The results indicate that relative constructions in the Greek New Testament have a variety of features, all of which have counterparts in some modern (or other ancient languages, despite the differences. The relative sentence in the Greek New Testament is mostly postnominal, and the relative pronoun-type is used in those cases for encoding the role of the coreferential element in the relative sentence. Phrases expressing a variety of syntactic functions in a sentence (e.g. subject, direct object, etc. are accessible to relativisation, that is, they can be represented by relative pronouns. Nominal elements serve mostly as antecedents of relative sentences, although sentences appear in that function as well.A variety of syntactic types of relative sentences can be distinguished, including the prenominal participial, postnominal finite/participial, circumnominal, free relative, adverbial, prejoined, postjoined, sentential and conjoined types. These can be linked in a systematic way to the four functions of relative sentences in the New Testament, i.e. identifying, appositive, adverbial and continuative.Relative sentences also play a role in communicative strategies. Prejoined relative sentences, for example, are most suitable for exposition and theme-building, especially in the correlative diptych construction.

  8. Impact of load-related neural processes on feature binding in visuospatial working memory.

    Directory of Open Access Journals (Sweden)

    Nicole A Kochan

    Full Text Available BACKGROUND: The capacity of visual working memory (WM is substantially limited and only a fraction of what we see is maintained as a temporary trace. The process of binding visual features has been proposed as an adaptive means of minimising information demands on WM. However the neural mechanisms underlying this process, and its modulation by task and load effects, are not well understood. OBJECTIVE: To investigate the neural correlates of feature binding and its modulation by WM load during the sequential phases of encoding, maintenance and retrieval. METHODS AND FINDINGS: 18 young healthy participants performed a visuospatial WM task with independent factors of load and feature conjunction (object identity and position in an event-related functional MRI study. During stimulus encoding, load-invariant conjunction-related activity was observed in left prefrontal cortex and left hippocampus. During maintenance, greater activity for task demands of feature conjunction versus single features, and for increased load was observed in left-sided regions of the superior occipital cortex, precuneus and superior frontal cortex. Where these effects were expressed in overlapping cortical regions, their combined effect was additive. During retrieval, however, an interaction of load and feature conjunction was observed. This modulation of feature conjunction activity under increased load was expressed through greater deactivation in medial structures identified as part of the default mode network. CONCLUSIONS AND SIGNIFICANCE: The relationship between memory load and feature binding qualitatively differed through each phase of the WM task. Of particular interest was the interaction of these factors observed within regions of the default mode network during retrieval which we interpret as suggesting that at low loads, binding processes may be 'automatic' but at higher loads it becomes a resource-intensive process leading to disengagement of activity in this

  9. Making sense of large data sets without annotations: analyzing age-related correlations from lung CT scans

    Science.gov (United States)

    Dicente Cid, Yashin; Mamonov, Artem; Beers, Andrew; Thomas, Armin; Kovalev, Vassili; Kalpathy-Cramer, Jayashree; Müller, Henning

    2017-03-01

    The analysis of large data sets can help to gain knowledge about specific organs or on specific diseases, just as big data analysis does in many non-medical areas. This article aims to gain information from 3D volumes, so the visual content of lung CT scans of a large number of patients. In the case of the described data set, only little annotation is available on the patients that were all part of an ongoing screening program and besides age and gender no information on the patient and the findings was available for this work. This is a scenario that can happen regularly as image data sets are produced and become available in increasingly large quantities but manual annotations are often not available and also clinical data such as text reports are often harder to share. We extracted a set of visual features from 12,414 CT scans of 9,348 patients that had CT scans of the lung taken in the context of a national lung screening program in Belarus. Lung fields were segmented by two segmentation algorithms and only cases where both algorithms were able to find left and right lung and had a Dice coefficient above 0.95 were analyzed. This assures that only segmentations of good quality were used to extract features of the lung. Patients ranged in age from 0 to 106 years. Data analysis shows that age can be predicted with a fairly high accuracy for persons under 15 years. Relatively good results were also obtained between 30 and 65 years where a steady trend is seen. For young adults and older people the results are not as good as variability is very high in these groups. Several visualizations of the data show the evolution patters of the lung texture, size and density with age. The experiments allow learning the evolution of the lung and the gained results show that even with limited metadata we can extract interesting information from large-scale visual data. These age-related changes (for example of the lung volume, the density histogram of the tissue) can also be

  10. Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain-computer interface.

    Science.gov (United States)

    Siuly; Li, Yan; Paul Wen, Peng

    2014-03-01

    Motor imagery (MI) tasks classification provides an important basis for designing brain-computer interface (BCI) systems. If the MI tasks are reliably distinguished through identifying typical patterns in electroencephalography (EEG) data, a motor disabled people could communicate with a device by composing sequences of these mental states. In our earlier study, we developed a cross-correlation based logistic regression (CC-LR) algorithm for the classification of MI tasks for BCI applications, but its performance was not satisfactory. This study develops a modified version of the CC-LR algorithm exploring a suitable feature set that can improve the performance. The modified CC-LR algorithm uses the C3 electrode channel (in the international 10-20 system) as a reference channel for the cross-correlation (CC) technique and applies three diverse feature sets separately, as the input to the logistic regression (LR) classifier. The present algorithm investigates which feature set is the best to characterize the distribution of MI tasks based EEG data. This study also provides an insight into how to select a reference channel for the CC technique with EEG signals considering the anatomical structure of the human brain. The proposed algorithm is compared with eight of the most recently reported well-known methods including the BCI III Winner algorithm. The findings of this study indicate that the modified CC-LR algorithm has potential to improve the identification performance of MI tasks in BCI systems. The results demonstrate that the proposed technique provides a classification improvement over the existing methods tested. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  11. Molecular features related to HIV integrase inhibition obtained from structure- and ligand-based approaches.

    Directory of Open Access Journals (Sweden)

    Luciana L de Carvalho

    Full Text Available Among several biological targets to treat AIDS, HIV integrase is a promising enzyme that can be employed to develop new anti-HIV agents. The aim of this work is to propose a mechanistic interpretation of HIV-1 integrase inhibition and to rationalize the molecular features related to the binding affinity of studied ligands. A set of 79 HIV-1 integrase inhibitors and its relationship with biological activity are investigated employing 2D and 3D QSAR models, docking analysis and DFT studies. Analyses of docking poses and frontier molecular orbitals revealed important features on the main ligand-receptor interactions. 2D and 3D models presenting good internal consistency, predictive power and stability were obtained in all cases. Significant correlation coefficients (r(2 = 0.908 and q(2= 0.643 for 2D model; r(2= 0.904 and q(2= 0.719 for 3D model were obtained, indicating the potential of these models for untested compounds. The generated holograms and contribution maps revealed important molecular requirements to HIV-1 IN inhibition and several evidences for molecular modifications. The final models along with information resulting from molecular orbitals, 2D contribution and 3D contour maps should be useful in the design of new inhibitors with increased potency and selectivity within the chemical diversity of the data.

  12. DYNAMIC FEATURE SELECTION FOR WEB USER IDENTIFICATION ON LINGUISTIC AND STYLISTIC FEATURES OF ONLINE TEXTS

    Directory of Open Access Journals (Sweden)

    A. A. Vorobeva

    2017-01-01

    Full Text Available The paper deals with identification and authentication of web users participating in the Internet information processes (based on features of online texts.In digital forensics web user identification based on various linguistic features can be used to discover identity of individuals, criminals or terrorists using the Internet to commit cybercrimes. Internet could be used as a tool in different types of cybercrimes (fraud and identity theft, harassment and anonymous threats, terrorist or extremist statements, distribution of illegal content and information warfare. Linguistic identification of web users is a kind of biometric identification, it can be used to narrow down the suspects, identify a criminal and prosecute him. Feature set includes various linguistic and stylistic features extracted from online texts. We propose dynamic feature selection for each web user identification task. Selection is based on calculating Manhattan distance to k-nearest neighbors (Relief-f algorithm. This approach improves the identification accuracy and minimizes the number of features. Experiments were carried out on several datasets with different level of class imbalance. Experiment results showed that features relevance varies in different set of web users (probable authors of some text; features selection for each set of web users improves identification accuracy by 4% at the average that is approximately 1% higher than with the use of static set of features. The proposed approach is most effective for a small number of training samples (messages per user.

  13. Prevalence and correlates of binge eating disorder related features in the community.

    Science.gov (United States)

    Mustelin, Linda; Bulik, Cynthia M; Kaprio, Jaakko; Keski-Rahkonen, Anna

    2017-02-01

    Binge eating disorder (BED) is associated with high levels of obesity and psychological suffering, but little is known about 1) the distribution of features of BED in the general population and 2) their consequences for weight development and psychological distress in young adulthood. We investigated the prevalence of features of BED and their association with body mass index (BMI) and psychological distress among men (n = 2423) and women (n = 2825) from the longitudinal community-based FinnTwin16 cohort (born 1975-1979). Seven eating-related cognitions and behaviors similar to the defining features of BED were extracted from the Eating Disorder Inventory-2 and were assessed at a mean age of 24. BMI and psychological distress, measured with the General Health Questionnaire, were assessed at ages 24 and 34. We assessed prevalence of the features and their association with BMI and psychological distress cross-sectionally and prospectively. More than half of our participants reported at least one feature of BED; clustering of several features in one individual was less common, particularly among men. The most frequently reported feature was 'stuffing oneself with food', whereas the least common was 'eating or drinking in secrecy'. All individual features of BED and their clustering particularly were associated with higher BMI and more psychological distress cross-sectionally. Prospectively, the clustering of features of BED predicted increase in psychological distress but not additional weight gain when baseline BMI was accounted for. In summary, although some features of BED were common, the clustering of several features in one individual was not. The features were cumulatively associated with BMI and psychological distress and predicted further increase in psychological distress over ten years of follow-up. Copyright © 2016. Published by Elsevier Ltd.

  14. Music preferences based on audio features, and its relation to personality

    OpenAIRE

    Dunn, Greg

    2009-01-01

    Recent studies have summarized reported music preferences by genre into four broadly defined categories, which relate to various personality characteristics. Other research has indicated that genre classification is ambiguous and inconsistent. This ambiguity suggests that research relating personality to music preferences based on genre could benefit from a more objective definition of music. This problem is addressed by investigating how music preferences linked to objective audio features r...

  15. Parenting, relational aggression, and borderline personality features: associations over time in a Russian longitudinal sample.

    Science.gov (United States)

    Nelson, David A; Coyne, Sarah M; Swanson, Savannah M; Hart, Craig H; Olsen, Joseph A

    2014-08-01

    Crick, Murray-Close, and Woods (2005) encouraged the study of relational aggression as a developmental precursor to borderline personality features in children and adolescents. A longitudinal study is needed to more fully explore this association, to contrast potential associations with physical aggression, and to assess generalizability across various cultural contexts. In addition, parenting is of particular interest in the prediction of aggression or borderline personality disorder. Early aggression and parenting experiences may differ in their long-term prediction of aggression or borderline features, which may have important implications for early intervention. The currrent study incorporated a longitudinal sample of preschool children (84 boys, 84 girls) living in intact, two-parent biological households in Voronezh, Russia. Teachers provided ratings of children's relational and physical aggression in preschool. Mothers and fathers also self-reported their engagement in authoritative, authoritarian, permissive, and psychological controlling forms of parenting with their preschooler. A decade later, 70.8% of the original child participants consented to a follow-up study in which they completed self-reports of relational and physical aggression and borderline personality features. The multivariate results of this study showed that preschool relational aggression in girls predicted adolescent relational aggression. Preschool aversive parenting (i.e., authoritarian, permissive, and psychologically controlling forms) significantly predicted aggression and borderline features in adolescent females. For adolescent males, preschool authoritative parenting served as a protective factor against aggression and borderline features, whereas authoritarian parenting was a risk factor for later aggression.

  16. Effective traffic features selection algorithm for cyber-attacks samples

    Science.gov (United States)

    Li, Yihong; Liu, Fangzheng; Du, Zhenyu

    2018-05-01

    By studying the defense scheme of Network attacks, this paper propose an effective traffic features selection algorithm based on k-means++ clustering to deal with the problem of high dimensionality of traffic features which extracted from cyber-attacks samples. Firstly, this algorithm divide the original feature set into attack traffic feature set and background traffic feature set by the clustering. Then, we calculates the variation of clustering performance after removing a certain feature. Finally, evaluating the degree of distinctiveness of the feature vector according to the result. Among them, the effective feature vector is whose degree of distinctiveness exceeds the set threshold. The purpose of this paper is to select out the effective features from the extracted original feature set. In this way, it can reduce the dimensionality of the features so as to reduce the space-time overhead of subsequent detection. The experimental results show that the proposed algorithm is feasible and it has some advantages over other selection algorithms.

  17. Method to assess the temporal persistence of potential biometric features: Application to oculomotor, gait, face and brain structure databases

    Science.gov (United States)

    Nixon, Mark S.; Komogortsev, Oleg V.

    2017-01-01

    We introduce the intraclass correlation coefficient (ICC) to the biometric community as an index of the temporal persistence, or stability, of a single biometric feature. It requires, as input, a feature on an interval or ratio scale, and which is reasonably normally distributed, and it can only be calculated if each subject is tested on 2 or more occasions. For a biometric system, with multiple features available for selection, the ICC can be used to measure the relative stability of each feature. We show, for 14 distinct data sets (1 synthetic, 8 eye-movement-related, 2 gait-related, and 2 face-recognition-related, and one brain-structure-related), that selecting the most stable features, based on the ICC, resulted in the best biometric performance generally. Analyses based on using only the most stable features produced superior Rank-1-Identification Rate (Rank-1-IR) performance in 12 of 14 databases (p = 0.0065, one-tailed), when compared to other sets of features, including the set of all features. For Equal Error Rate (EER), using a subset of only high-ICC features also produced superior performance in 12 of 14 databases (p = 0. 0065, one-tailed). In general, then, for our databases, prescreening potential biometric features, and choosing only highly reliable features yields better performance than choosing lower ICC features or than choosing all features combined. We also determined that, as the ICC of a group of features increases, the median of the genuine similarity score distribution increases and the spread of this distribution decreases. There was no statistically significant similar relationships for the impostor distributions. We believe that the ICC will find many uses in biometric research. In case of the eye movement-driven biometrics, the use of reliable features, as measured by ICC, allowed to us achieve the authentication performance with EER = 2.01%, which was not possible before. PMID:28575030

  18. Dose Relations between Goal Setting, Theory-Based Correlates of Goal Setting and Increases in Physical Activity during a Workplace Trial

    Science.gov (United States)

    Dishman, Rod K.; Vandenberg, Robert J.; Motl, Robert W.; Wilson, Mark G.; DeJoy, David M.

    2010-01-01

    The effectiveness of an intervention depends on its dose and on moderators of dose, which usually are not studied. The purpose of the study is to determine whether goal setting and theory-based moderators of goal setting had dose relations with increases in goal-related physical activity during a successful workplace intervention. A…

  19. Systems-Level Annotation of a Metabolomics Data Set Reduces 25 000 Features to Fewer than 1000 Unique Metabolites.

    Science.gov (United States)

    Mahieu, Nathaniel G; Patti, Gary J

    2017-10-03

    When using liquid chromatography/mass spectrometry (LC/MS) to perform untargeted metabolomics, it is now routine to detect tens of thousands of features from biological samples. Poor understanding of the data, however, has complicated interpretation and masked the number of unique metabolites actually being measured in an experiment. Here we place an upper bound on the number of unique metabolites detected in Escherichia coli samples analyzed with one untargeted metabolomics method. We first group multiple features arising from the same analyte, which we call "degenerate features", using a context-driven annotation approach. Surprisingly, this analysis revealed thousands of previously unreported degeneracies that reduced the number of unique analytes to ∼2961. We then applied an orthogonal approach to remove nonbiological features from the data using the 13 C-based credentialing technology. This further reduced the number of unique analytes to less than 1000. Our 90% reduction in data is 5-fold greater than previously published studies. On the basis of the results, we propose an alternative approach to untargeted metabolomics that relies on thoroughly annotated reference data sets. To this end, we introduce the creDBle database ( http://creDBle.wustl.edu ), which contains accurate mass, retention time, and MS/MS fragmentation data as well as annotations of all credentialed features.

  20. External and internal facial features modulate processing of vertical but not horizontal spatial relations.

    Science.gov (United States)

    Meinhardt, Günter; Kurbel, David; Meinhardt-Injac, Bozana; Persike, Malte

    2018-03-22

    Some years ago an asymmetry was reported for the inversion effect for horizontal (H) and vertical (V) relational face manipulations (Goffaux & Rossion, 2007). Subsequent research examined whether a specific disruption of long-range relations underlies the H/V inversion asymmetry (Sekunova & Barton, 2008). Here, we tested how detection of changes in interocular distance (H) and eye height (V) depends on cardinal internal features and external feature surround. Results replicated the H/V inversion asymmetry. Moreover, we found very different face cue dependencies for both change types. Performance and inversion effects did not depend on the presence of other face cues for detecting H changes. In contrast, accuracy for detecting V changes strongly depended on internal and external features, showing cumulative improvement when more cues were added. Inversion effects were generally large, and larger with external feature surround. The cue independence in detecting H relational changes indicates specialized local processing tightly tuned to the eyes region, while the strong cue dependency in detecting V relational changes indicates a global mechanism of cue integration across different face regions. These findings suggest that the H/V asymmetry of the inversion effect rests on an H/V anisotropy of face cue dependency, since only the global V mechanism suffers from disruption of cue integration as the major effect of face inversion. Copyright © 2018. Published by Elsevier Ltd.

  1. An age-related deficit in spatial-feature reference memory in homing pigeons (Columba livia).

    Science.gov (United States)

    Coppola, Vincent J; Flaim, Mary E; Carney, Samantha N; Bingman, Verner P

    2015-03-01

    Age-related memory decline in mammals has been well documented. By contrast, very little is known about memory decline in birds as they age. In the current study we trained younger and older homing pigeons on a reference memory task in which a goal location could be encoded by spatial and feature cues. Consistent with a previous working memory study, the results revealed impaired acquisition of combined spatial-feature reference memory in older compared to younger pigeons. Following memory acquisition, we used cue-conflict probe trials to provide an initial assessment of possible age-related differences in cue preference. Both younger and older pigeons displayed a similarly modest preference for feature over spatial cues. Copyright © 2014 Elsevier B.V. All rights reserved.

  2. Multiple song features are related to paternal effort in common nightingales.

    Science.gov (United States)

    Bartsch, Conny; Weiss, Michael; Kipper, Silke

    2015-06-18

    Sexual ornamentation may be related to the degree of paternal care and the 'good-parent' model predicts that male secondary characters honestly advertise paternal investment. In most birds, males are involved in bringing up the young and successful reproduction highly depends on male contribution during breeding. In passerines, male song is indicative of male attributes and for few species it has been shown that song features also signal paternal investment to females. Males of nightingales Luscinia megarhynchos are famous for their elaborate singing but so far there is only little knowledge on the role of male song in intersexual communication, and it is unknown whether male song predicts male parenting abilities. Using RFID technology to record male feeding visits to the nest, we found that nightingale males substantially contribute to chick feeding. Also, we analyzed male nocturnal song with focus on song features that have been shown to signal male quality before. We found that several song features, namely measures of song complexity and song sequencing, were correlated with male feeding rates. Moreover, the combination of these song features had strong predictive power for male contribution to nestling feeding. Since male nightingales are involved in chick rearing, paternal investment might be a crucial variable for female mate choice in this species. Females may assess future paternal care on the basis of song features identified in our study and thus these features may have evolved to signal direct benefits to females. Additionally we underline the importance of multiple acoustic cues for female mating decisions especially in species with complex song such as the nightingale.

  3. Which features are important for effectiveness of sport- and health-related apps?

    NARCIS (Netherlands)

    Joan Dallinga; Marije Baart de la Faille-Deutekom; Mark Janssen; Steven Vos

    2017-01-01

    In this presentation we presented the results of expert meetings. The aim was to identify which features in sport- and health-related apps contribute to effectiveness of apps. A nominal group technique was used.

  4. Ceramic coatings: A phenomenological modeling for damping behavior related to microstructural features

    International Nuclear Information System (INIS)

    Tassini, N.; Patsias, S.; Lambrinou, K.

    2006-01-01

    Recent research has shown that both stiffness and damping of ceramic coatings exhibit different non-linearities. These properties strongly depend on the microstructure, which is characterized by heterogeneous sets of elastic elements with mesoscopic sizes and shapes, as in non-linear mesoscopic elastic materials. To predict the damping properties of this class of materials, we have implemented a phenomenological model that characterizes their elastic properties. The model is capable of reproducing the basic features of the observed damping behavior for zirconia coatings prepared by air plasma spraying and electron-beam physical-vapor-deposition

  5. Features Related to Faunal Activity

    NARCIS (Netherlands)

    Kooistra, M.J.; Pulleman, M.M.

    2010-01-01

    Soil fauna plays an important role in transporting and altering various soil components, in particular the decomposition of organic matter and the development of soil structure. Fauna-induced features are found in all types of soils and can be so abundant that they determine the nature and intensity

  6. Opening the Learning Process: The Potential Role of Feature Film in Teaching Employment Relations

    Science.gov (United States)

    Lafferty, George

    2016-01-01

    This paper explores the potential of feature film to encourage more inclusive, participatory and open learning in the area of employment relations. Evaluations of student responses in a single postgraduate course over a five-year period revealed how feature film could encourage participatory learning processes in which students reexamined their…

  7. Algorithms for Learning Preferences for Sets of Objects

    Science.gov (United States)

    Wagstaff, Kiri L.; desJardins, Marie; Eaton, Eric

    2010-01-01

    A method is being developed that provides for an artificial-intelligence system to learn a user's preferences for sets of objects and to thereafter automatically select subsets of objects according to those preferences. The method was originally intended to enable automated selection, from among large sets of images acquired by instruments aboard spacecraft, of image subsets considered to be scientifically valuable enough to justify use of limited communication resources for transmission to Earth. The method is also applicable to other sets of objects: examples of sets of objects considered in the development of the method include food menus, radio-station music playlists, and assortments of colored blocks for creating mosaics. The method does not require the user to perform the often-difficult task of quantitatively specifying preferences; instead, the user provides examples of preferred sets of objects. This method goes beyond related prior artificial-intelligence methods for learning which individual items are preferred by the user: this method supports a concept of setbased preferences, which include not only preferences for individual items but also preferences regarding types and degrees of diversity of items in a set. Consideration of diversity in this method involves recognition that members of a set may interact with each other in the sense that when considered together, they may be regarded as being complementary, redundant, or incompatible to various degrees. The effects of such interactions are loosely summarized in the term portfolio effect. The learning method relies on a preference representation language, denoted DD-PREF, to express set-based preferences. In DD-PREF, a preference is represented by a tuple that includes quality (depth) functions to estimate how desired a specific value is, weights for each feature preference, the desired diversity of feature values, and the relative importance of diversity versus depth. The system applies statistical

  8. A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest

    Directory of Open Access Journals (Sweden)

    Nantian Huang

    2016-09-01

    Full Text Available The prediction accuracy of short-term load forecast (STLF depends on prediction model choice and feature selection result. In this paper, a novel random forest (RF-based feature selection method for STLF is proposed. First, 243 related features were extracted from historical load data and the time information of prediction points to form the original feature set. Subsequently, the original feature set was used to train an RF as the original model. After the training process, the prediction error of the original model on the test set was recorded and the permutation importance (PI value of each feature was obtained. Then, an improved sequential backward search method was used to select the optimal forecasting feature subset based on the PI value of each feature. Finally, the optimal forecasting feature subset was used to train a new RF model as the final prediction model. Experiments showed that the prediction accuracy of RF trained by the optimal forecasting feature subset was higher than that of the original model and comparative models based on support vector regression and artificial neural network.

  9. Face Alignment via Regressing Local Binary Features.

    Science.gov (United States)

    Ren, Shaoqing; Cao, Xudong; Wei, Yichen; Sun, Jian

    2016-03-01

    This paper presents a highly efficient and accurate regression approach for face alignment. Our approach has two novel components: 1) a set of local binary features and 2) a locality principle for learning those features. The locality principle guides us to learn a set of highly discriminative local binary features for each facial landmark independently. The obtained local binary features are used to jointly learn a linear regression for the final output. This approach achieves the state-of-the-art results when tested on the most challenging benchmarks to date. Furthermore, because extracting and regressing local binary features are computationally very cheap, our system is much faster than previous methods. It achieves over 3000 frames per second (FPS) on a desktop or 300 FPS on a mobile phone for locating a few dozens of landmarks. We also study a key issue that is important but has received little attention in the previous research, which is the face detector used to initialize alignment. We investigate several face detectors and perform quantitative evaluation on how they affect alignment accuracy. We find that an alignment friendly detector can further greatly boost the accuracy of our alignment method, reducing the error up to 16% relatively. To facilitate practical usage of face detection/alignment methods, we also propose a convenient metric to measure how good a detector is for alignment initialization.

  10. Temporal feature integration for music genre classification

    DEFF Research Database (Denmark)

    Meng, Anders; Ahrendt, Peter; Larsen, Jan

    2007-01-01

    , but they capture neither the temporal dynamics nor dependencies among the individual feature dimensions. Here, a multivariate autoregressive feature model is proposed to solve this problem for music genre classification. This model gives two different feature sets, the diagonal autoregressive (DAR......) and multivariate autoregressive (MAR) features which are compared against the baseline mean-variance as well as two other temporal feature integration techniques. Reproducibility in performance ranking of temporal feature integration methods were demonstrated using two data sets with five and eleven music genres...

  11. Feature-based attention is functionally distinct from relation-based attention: The double dissociation between color-based capture and color-relation-based capture of attention.

    Science.gov (United States)

    Du, Feng; Jiao, Jun

    2016-04-01

    The present study used a spatial blink task and a cuing task to examine the boundary between feature-based capture and relation-based capture. Feature-based capture occurs when distractors match the target feature such as target color. The occurrence of relation-based capture is contingent upon the feature relation between target and distractor (e.g., color relation). The results show that color distractors that match the target-nontarget color relation do not consistently capture attention when they appear outside of the attentional window, but distractors appearing outside the attentional window that match the target color consistently capture attention. In contrast, color distractors that best match the target-nontarget color relation but not the target color, are more likely to capture attention when they appear within the attentional window. Consistently, color cues that match the target-nontarget color relation produce a cuing effect when they appear within the attentional window, while target-color matched cues do not. Such a double dissociation between color-based capture and color-relation-based capture indicates functionally distinct mechanisms for these 2 types of attentional selection. This also indicates that the spatial blink task and the uninformative cuing task are measuring distinctive aspects of involuntary attention. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  12. Classification Influence of Features on Given Emotions and Its Application in Feature Selection

    Science.gov (United States)

    Xing, Yin; Chen, Chuang; Liu, Li-Long

    2018-04-01

    In order to solve the problem that there is a large amount of redundant data in high-dimensional speech emotion features, we analyze deeply the extracted speech emotion features and select better features. Firstly, a given emotion is classified by each feature. Secondly, the recognition rate is ranked in descending order. Then, the optimal threshold of features is determined by rate criterion. Finally, the better features are obtained. When applied in Berlin and Chinese emotional data set, the experimental results show that the feature selection method outperforms the other traditional methods.

  13. Construction of a century solar chromosphere data set for solar activity related research

    Science.gov (United States)

    Lin, Ganghua; Wang, Xiao Fan; Yang, Xiao; Liu, Suo; Zhang, Mei; Wang, Haimin; Liu, Chang; Xu, Yan; Tlatov, Andrey; Demidov, Mihail; Borovik, Aleksandr; Golovko, Aleksey

    2017-06-01

    This article introduces our ongoing project "Construction of a Century Solar Chromosphere Data Set for Solar Activity Related Research". Solar activities are the major sources of space weather that affects human lives. Some of the serious space weather consequences, for instance, include interruption of space communication and navigation, compromising the safety of astronauts and satellites, and damaging power grids. Therefore, the solar activity research has both scientific and social impacts. The major database is built up from digitized and standardized film data obtained by several observatories around the world and covers a time span of more than 100 years. After careful calibration, we will develop feature extraction and data mining tools and provide them together with the comprehensive database for the astronomical community. Our final goal is to address several physical issues: filament behavior in solar cycles, abnormal behavior of solar cycle 24, large-scale solar eruptions, and sympathetic remote brightenings. Significant signs of progress are expected in data mining algorithms and software development, which will benefit the scientific analysis and eventually advance our understanding of solar cycles.

  14. Construction of a century solar chromosphere data set for solar activity related research

    Directory of Open Access Journals (Sweden)

    Ganghua Lin

    2017-06-01

    Full Text Available This article introduces our ongoing project “Construction of a Century Solar Chromosphere Data Set for Solar Activity Related Research”. Solar activities are the major sources of space weather that affects human lives. Some of the serious space weather consequences, for instance, include interruption of space communication and navigation, compromising the safety of astronauts and satellites, and damaging power grids. Therefore, the solar activity research has both scientific and social impacts. The major database is built up from digitized and standardized film data obtained by several observatories around the world and covers a timespan more than 100 years. After careful calibration, we will develop feature extraction and data mining tools and provide them together with the comprehensive database for the astronomical community. Our final goal is to address several physical issues: filament behavior in solar cycles, abnormal behavior of solar cycle 24, large-scale solar eruptions, and sympathetic remote brightenings. Significant progresses are expected in data mining algorithms and software development, which will benefit the scientific analysis and eventually advance our understanding of solar cycles.

  15. Fuzzy Relational Compression Applied on Feature Vectors for Infant Cry Recognition

    Science.gov (United States)

    Reyes-Galaviz, Orion Fausto; Reyes-García, Carlos Alberto

    Data compression is always advisable when it comes to handling and processing information quickly and efficiently. There are two main problems that need to be solved when it comes to handling data; store information in smaller spaces and processes it in the shortest possible time. When it comes to infant cry analysis (ICA), there is always the need to construct large sound repositories from crying babies. Samples that have to be analyzed and be used to train and test pattern recognition algorithms; making this a time consuming task when working with uncompressed feature vectors. In this work, we show a simple, but efficient, method that uses Fuzzy Relational Product (FRP) to compresses the information inside a feature vector, building with this a compressed matrix that will help us recognize two kinds of pathologies in infants; Asphyxia and Deafness. We describe the sound analysis, which consists on the extraction of Mel Frequency Cepstral Coefficients that generate vectors which will later be compressed by using FRP. There is also a description of the infant cry database used in this work, along with the training and testing of a Time Delay Neural Network with the compressed features, which shows a performance of 96.44% with our proposed feature vector compression.

  16. Multiple-Features-Based Semisupervised Clustering DDoS Detection Method

    Directory of Open Access Journals (Sweden)

    Yonghao Gu

    2017-01-01

    Full Text Available DDoS attack stream from different agent host converged at victim host will become very large, which will lead to system halt or network congestion. Therefore, it is necessary to propose an effective method to detect the DDoS attack behavior from the massive data stream. In order to solve the problem that large numbers of labeled data are not provided in supervised learning method, and the relatively low detection accuracy and convergence speed of unsupervised k-means algorithm, this paper presents a semisupervised clustering detection method using multiple features. In this detection method, we firstly select three features according to the characteristics of DDoS attacks to form detection feature vector. Then, Multiple-Features-Based Constrained-K-Means (MF-CKM algorithm is proposed based on semisupervised clustering. Finally, using MIT Laboratory Scenario (DDoS 1.0 data set, we verify that the proposed method can improve the convergence speed and accuracy of the algorithm under the condition of using a small amount of labeled data sets.

  17. Spatial features register: toward standardization of spatial features

    Science.gov (United States)

    Cascio, Janette

    1994-01-01

    As the need to share spatial data increases, more than agreement on a common format is needed to ensure that the data is meaningful to both the importer and the exporter. Effective data transfer also requires common definitions of spatial features. To achieve this, part 2 of the Spatial Data Transfer Standard (SDTS) provides a model for a spatial features data content specification and a glossary of features and attributes that fit this model. The model provides a foundation for standardizing spatial features. The glossary now contains only a limited subset of hydrographic and topographic features. For it to be useful, terms and definitions must be included for other categories, such as base cartographic, bathymetric, cadastral, cultural and demographic, geodetic, geologic, ground transportation, international boundaries, soils, vegetation, water, and wetlands, and the set of hydrographic and topographic features must be expanded. This paper will review the philosophy of the SDTS part 2 and the current plans for creating a national spatial features register as one mechanism for maintaining part 2.

  18. Feasibility of using the International Classification of Functioning, Disability and Health Core Set for evaluation of fall-related risk factors in acute rehabilitation settings.

    Science.gov (United States)

    Huang, Shih W; Lin, Li F; Chou, Lin C; Wu, Mei J; Liao, Chun D; Liou, Tsan H

    2016-04-01

    Previously, we reported the use of an International Classification of Functioning (ICF) core set that can provide a holistic framework for evaluating the risk factors of falls; however, data on the feasibility of applying this core set are lacking. To investigate the feasibility of applying the fall-related ICF risk-factor core set in the case of patients in an acute-rehabilitation setting. A cross-sectional and descriptive correlational design. Acute-rehabilitation ward. A total of 273 patients who experienced fall at acute-rehabilitation ward. The data on falls were collected from the hospital's Nursing Information System (NIS) and the fall-reporting system (Adverse Event Reporting System, AERS) between 2010 and 2013. The relationship of both systems to the fall-related ICF core set was analyzed to assess the feasibility of their clinical application. We evaluated the feasibility of using the fall-related ICF risk-factor core set by using the frequency and the percentage of the fall patients in of the listed categories. The fall-related ICF risk-factor core set category b735 (muscle tone functions) exhibited a high feasibility (85.95%) for clinical application, and the category b730 (muscle power functions) covered 77.11% of the patients. The feasibility of application of the category d410 (change basic body position) was also high in the case of all fall patients (81.69%). In the acute-rehabilitation setting, the feasibility of application of the fall-related ICF risk-factor core set is high. The fall-related ICF risk-factor core set can help multidisciplinary teams develop fall-prevention strategies in acute rehabilitation wards.

  19. Intonational meaning in institutional settings: the role of syntagmatic relations

    Science.gov (United States)

    Wichmann, Anne

    2010-12-01

    This paper addresses the power of intonation to convey interpersonal or attitudinal meaning. Speakers have been shown to accommodate to each other in the course of conversation, and this convergence may be perceived as a sign of empathy. Accommodation often involves paradigmatic choices—choosing the same words, gestures, regional accent or melodic pattern, but this paper suggests that affective meaning can also be conveyed syntagmatically through the relationship between prosodic features in successive utterances. The paper also addresses the use of prosody in situations of conflict, particularly in institutional settings. The requirement of the more powerful participant to exercise control may conflict with the expression of empathy. Situations are described where divergent rather than convergent behaviour is more successful both in keeping control and in maintaining rapport.

  20. Improving Naive Bayes with Online Feature Selection for Quick Adaptation to Evolving Feature Usefulness

    Energy Technology Data Exchange (ETDEWEB)

    Pon, R K; Cardenas, A F; Buttler, D J

    2007-09-19

    The definition of what makes an article interesting varies from user to user and continually evolves even for a single user. As a result, for news recommendation systems, useless document features can not be determined a priori and all features are usually considered for interestingness classification. Consequently, the presence of currently useless features degrades classification performance [1], particularly over the initial set of news articles being classified. The initial set of document is critical for a user when considering which particular news recommendation system to adopt. To address these problems, we introduce an improved version of the naive Bayes classifier with online feature selection. We use correlation to determine the utility of each feature and take advantage of the conditional independence assumption used by naive Bayes for online feature selection and classification. The augmented naive Bayes classifier performs 28% better than the traditional naive Bayes classifier in recommending news articles from the Yahoo! RSS feeds.

  1. The relative importance of geophysical constraints, amenity values, and farm-related factors in the dynamics of grassland set-aside

    DEFF Research Database (Denmark)

    Odgaard, Mette Vestergaard; Moeslund, Jesper Erenskjold; Bøcher, Peder Klith

    2013-01-01

    This study aimed at quantifying the spatial distribution of set-aside – highly valuable biodiversity reservoirs – in a typical lowland agricultural region (Denmark), just before and after the set-aside policy change (years 2007 and 2008), to assess which factors drive farmers’ set-aside priorities......, and to elaborate the potential consequence of the set-aside spatial transformation from 2007 to 2008 on nature. Multiple regressions were used to test if and how set-aside is linked to three potential groups of drivers: (1) geophysical constraints (topographic and edaphic constraints on the farming......-suitability of an area), (2) amenity values (nature conservation and aesthetic values), and (3) farming-related factors (e.g., field size and livestock density). The spatial distribution of set-aside was influenced by both geophysical constraints and amenity values and only some extent farming-related factors. More...

  2. Complement Set Reference after Implicitly Small Quantities: An Event-Related Potentials Study

    Science.gov (United States)

    Ingram, Joanne; Ferguson, Heather J.

    2018-01-01

    An anaphoric reference to the complement-set is a reference to the set that does not fulfil the predicate of the preceding sentence. Preferred reference to the complement-set has been found in eye movements when a character's implicit desire for a high amount has been denied using a negative emotion. We recorded event-related potentials to examine…

  3. Clinical features of IgG4-related rhinosinusitis.

    Science.gov (United States)

    Hanaoka, Machiko; Kammisawa, Terumi; Koizumi, Satomi; Kuruma, Sawako; Chiba, Kazuro; Kikuyama, Masataka; Shirakura, Satoshi; Sugimoto, Taro; Hishima, Tsunekazu

    2017-09-01

    IgG4-related disease is a systemic disease that affects various organs of the body. Aim of this study is to elucidate the clinical characteristics of IgG4-related rhinosinusitis. Clinical features, laboratory findings, radiological and endoscopic findings, associated disease, treatment and prognosis were retrospectively examined in 10 patients with IgG4-related rhinosinusitis. The age was 59.1±11.3 years old and male-to-female ratio was 1:1. The chief nasal complaints were hyposmia (n=4), nasal obstruction (n=3), and nothing (n=3). Serum IgG4 levels were elevated in all patients and the value was 740.4±472.4mg/dl. Other IgG4-related diseases were associated in all 10 patients, including IgG4-related sialadenitis (n=6), IgG4-related dacryoadenitis (n=5), and autoimmune pancreatitis (n=5). Imaging findings on CT/MRI were obstruction of the way of elimination (n=10), thickening of the sinus mucous membrane (n=10), and fluid in the sinus (n=6). All of the cases had bilateral findings. Nasal endoscopic findings were chiefly deviated nasal septum (n=5), polyps (n=4), edema of the mucous membrane (n=3). Histologically, abundant infiltration of IgG4 positive plasma cell and lymphocyte and an elevated IgG4+/IgG+ cell ration was detected in all 8 patients and 5 patients, respectively. Endoscopic sinus surgery was performed in 8 patients. Eight patients were treated with steroid therapy for other associated IgG4-related diseases. Symptoms improved in all 6 patients after an initial treatment (endoscopic surgery (n=5) and steroids (n=1)), but one patient suffered relapse. IgG4-related rhinosinusitis is a distinct entity of IgG4-related disease, and is associated in patients with multiple IgG4-related diseases. Copyright © 2017 Medical University of Bialystok. Published by Elsevier B.V. All rights reserved.

  4. Practices Regarding Rape-related Pregnancy in U.S. Abortion Care Settings.

    Science.gov (United States)

    Perry, Rachel; Murphy, Molly; Rankin, Kristin M; Cowett, Allison; Harwood, Bryna

    2016-01-01

    We aimed to explore current practices regarding screening for rape and response to disclosure of rape-related pregnancy in the abortion care setting. We performed a cross-sectional, nonprobability survey of U.S. abortion providers. Individuals were recruited in person and via emailed invitations to professional organization member lists. Questions in this web-based survey pertained to providers' practice setting, how they identify rape-related pregnancy, the availability of support services, and their experiences with law enforcement. Providers were asked their perceptions of barriers to care for women who report rape-related pregnancy. Surveys were completed by 279 providers (21% response rate). Most respondents were female (93.1%), and the majority were physicians in a clinical role (69.4%). One-half (49.8%) reported their practice screens for pregnancy resulting from rape, although fewer (34.8%) reported that screening is the method through which most patients with this history are identified. Most (80.6%) refer women with rape-related pregnancy to support services such as rape crisis centers. Relatively few (19.7%) have a specific protocol for care of women who report rape-related pregnancy. Clinics that screen were 79% more likely to have a protocol for care than centers that do not screen. Although the majority (67.4%) reported barriers to identification of women with rape-related pregnancy, fewer (33.3%) reported barriers to connecting them to support services. Practices for identifying and providing care to women with rape-related pregnancy in the abortion care setting are variable. Further research should address barriers to care provision, as well as identifying protocols for care. Copyright © 2016 Jacobs Institute of Women's Health. Published by Elsevier Inc. All rights reserved.

  5. 3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set.

    Science.gov (United States)

    Popuri, Karteek; Cobzas, Dana; Murtha, Albert; Jägersand, Martin

    2012-07-01

    Brain tumor segmentation is a required step before any radiation treatment or surgery. When performed manually, segmentation is time consuming and prone to human errors. Therefore, there have been significant efforts to automate the process. But, automatic tumor segmentation from MRI data is a particularly challenging task. Tumors have a large diversity in shape and appearance with intensities overlapping the normal brain tissues. In addition, an expanding tumor can also deflect and deform nearby tissue. In our work, we propose an automatic brain tumor segmentation method that addresses these last two difficult problems. We use the available MRI modalities (T1, T1c, T2) and their texture characteristics to construct a multidimensional feature set. Then, we extract clusters which provide a compact representation of the essential information in these features. The main idea in this work is to incorporate these clustered features into the 3D variational segmentation framework. In contrast to previous variational approaches, we propose a segmentation method that evolves the contour in a supervised fashion. The segmentation boundary is driven by the learned region statistics in the cluster space. We incorporate prior knowledge about the normal brain tissue appearance during the estimation of these region statistics. In particular, we use a Dirichlet prior that discourages the clusters from the normal brain region to be in the tumor region. This leads to a better disambiguation of the tumor from brain tissue. We evaluated the performance of our automatic segmentation method on 15 real MRI scans of brain tumor patients, with tumors that are inhomogeneous in appearance, small in size and in proximity to the major structures in the brain. Validation with the expert segmentation labels yielded encouraging results: Jaccard (58%), Precision (81%), Recall (67%), Hausdorff distance (24 mm). Using priors on the brain/tumor appearance, our proposed automatic 3D variational

  6. Feature extraction using convolutional neural network for classifying breast density in mammographic images

    Science.gov (United States)

    Thomaz, Ricardo L.; Carneiro, Pedro C.; Patrocinio, Ana C.

    2017-03-01

    Breast cancer is the leading cause of death for women in most countries. The high levels of mortality relate mostly to late diagnosis and to the direct proportionally relationship between breast density and breast cancer development. Therefore, the correct assessment of breast density is important to provide better screening for higher risk patients. However, in modern digital mammography the discrimination among breast densities is highly complex due to increased contrast and visual information for all densities. Thus, a computational system for classifying breast density might be a useful tool for aiding medical staff. Several machine-learning algorithms are already capable of classifying small number of classes with good accuracy. However, machinelearning algorithms main constraint relates to the set of features extracted and used for classification. Although well-known feature extraction techniques might provide a good set of features, it is a complex task to select an initial set during design of a classifier. Thus, we propose feature extraction using a Convolutional Neural Network (CNN) for classifying breast density by a usual machine-learning classifier. We used 307 mammographic images downsampled to 260x200 pixels to train a CNN and extract features from a deep layer. After training, the activation of 8 neurons from a deep fully connected layer are extracted and used as features. Then, these features are feedforward to a single hidden layer neural network that is cross-validated using 10-folds to classify among four classes of breast density. The global accuracy of this method is 98.4%, presenting only 1.6% of misclassification. However, the small set of samples and memory constraints required the reuse of data in both CNN and MLP-NN, therefore overfitting might have influenced the results even though we cross-validated the network. Thus, although we presented a promising method for extracting features and classifying breast density, a greater database is

  7. HIV-Related discrimination in European health care settings.

    Science.gov (United States)

    Nöstlinger, Christiana; Rojas Castro, Daniela; Platteau, Tom; Dias, Sonia; Le Gall, Jean

    2014-03-01

    This cross-sectional European study assessed self-reported HIV-related discrimination and its associated factors in health care settings. Socio-demographics, health status, support needs relating to sexual and reproductive health (SRH), and self-reported HIV-related discrimination were measured using an anonymous survey in a sample of 1549 people living with HIV from 14 countries. Thirty-two per cent of the participants had experienced HIV-related discrimination during the previous 3 years; almost half of them felt discriminated against by health care providers. For this type of discrimination, logistic regression analysis revealed significant associations with not being a migrant (OR: 2.0; IC 1.0-3.7; psex practices (OR: 1.8; IC 1.0-3.2; pgender had a protective effect (OR: 0.2; IC 0.0-0.9; pdiscrimination. Improving health care providers' communication skills, and fostering openness about SRH topics in HIV care could contribute to destigmatization of PLHIV.

  8. Analysis of wheezes using wavelet higher order spectral features.

    Science.gov (United States)

    Taplidou, Styliani A; Hadjileontiadis, Leontios J

    2010-07-01

    Wheezes are musical breath sounds, which usually imply an existing pulmonary obstruction, such as asthma and chronic obstructive pulmonary disease (COPD). Although many studies have addressed the problem of wheeze detection, a limited number of scientific works has focused in the analysis of wheeze characteristics, and in particular, their time-varying nonlinear characteristics. In this study, an effort is made to reveal and statistically analyze the nonlinear characteristics of wheezes and their evolution over time, as they are reflected in the quadratic phase coupling of their harmonics. To this end, the continuous wavelet transform (CWT) is used in combination with third-order spectra to define the analysis domain, where the nonlinear interactions of the harmonics of wheezes and their time variations are revealed by incorporating instantaneous wavelet bispectrum and bicoherence, which provide with the instantaneous biamplitude and biphase curves. Based on this nonlinear information pool, a set of 23 features is proposed for the nonlinear analysis of wheezes. Two complementary perspectives, i.e., general and detailed, related to average performance and to localities, respectively, were used in the construction of the feature set, in order to embed trends and local behaviors, respectively, seen in the nonlinear interaction of the harmonic elements of wheezes over time. The proposed feature set was evaluated on a dataset of wheezes, acquired from adult patients with diagnosed asthma and COPD from a lung sound database. The statistical evaluation of the feature set revealed discrimination ability between the two pathologies for all data subgroupings. In particular, when the total breathing cycle was examined, all 23 features, but one, showed statistically significant difference between the COPD and asthma pathologies, whereas for the subgroupings of inspiratory and expiratory phases, 18 out of 23 and 22 out of 23 features exhibited discrimination power, respectively

  9. A feature-based approach to modeling protein-protein interaction hot spots.

    Science.gov (United States)

    Cho, Kyu-il; Kim, Dongsup; Lee, Doheon

    2009-05-01

    Identifying features that effectively represent the energetic contribution of an individual interface residue to the interactions between proteins remains problematic. Here, we present several new features and show that they are more effective than conventional features. By combining the proposed features with conventional features, we develop a predictive model for interaction hot spots. Initially, 54 multifaceted features, composed of different levels of information including structure, sequence and molecular interaction information, are quantified. Then, to identify the best subset of features for predicting hot spots, feature selection is performed using a decision tree. Based on the selected features, a predictive model for hot spots is created using support vector machine (SVM) and tested on an independent test set. Our model shows better overall predictive accuracy than previous methods such as the alanine scanning methods Robetta and FOLDEF, and the knowledge-based method KFC. Subsequent analysis yields several findings about hot spots. As expected, hot spots have a larger relative surface area burial and are more hydrophobic than other residues. Unexpectedly, however, residue conservation displays a rather complicated tendency depending on the types of protein complexes, indicating that this feature is not good for identifying hot spots. Of the selected features, the weighted atomic packing density, relative surface area burial and weighted hydrophobicity are the top 3, with the weighted atomic packing density proving to be the most effective feature for predicting hot spots. Notably, we find that hot spots are closely related to pi-related interactions, especially pi . . . pi interactions.

  10. A feature-based approach to modeling protein–protein interaction hot spots

    Science.gov (United States)

    Cho, Kyu-il; Kim, Dongsup; Lee, Doheon

    2009-01-01

    Identifying features that effectively represent the energetic contribution of an individual interface residue to the interactions between proteins remains problematic. Here, we present several new features and show that they are more effective than conventional features. By combining the proposed features with conventional features, we develop a predictive model for interaction hot spots. Initially, 54 multifaceted features, composed of different levels of information including structure, sequence and molecular interaction information, are quantified. Then, to identify the best subset of features for predicting hot spots, feature selection is performed using a decision tree. Based on the selected features, a predictive model for hot spots is created using support vector machine (SVM) and tested on an independent test set. Our model shows better overall predictive accuracy than previous methods such as the alanine scanning methods Robetta and FOLDEF, and the knowledge-based method KFC. Subsequent analysis yields several findings about hot spots. As expected, hot spots have a larger relative surface area burial and are more hydrophobic than other residues. Unexpectedly, however, residue conservation displays a rather complicated tendency depending on the types of protein complexes, indicating that this feature is not good for identifying hot spots. Of the selected features, the weighted atomic packing density, relative surface area burial and weighted hydrophobicity are the top 3, with the weighted atomic packing density proving to be the most effective feature for predicting hot spots. Notably, we find that hot spots are closely related to π–related interactions, especially π · · · π interactions. PMID:19273533

  11. Adaptable Value-Set Analysis for Low-Level Code

    OpenAIRE

    Brauer, Jörg; Hansen, René Rydhof; Kowalewski, Stefan; Larsen, Kim G.; Olesen, Mads Chr.

    2012-01-01

    This paper presents a framework for binary code analysis that uses only SAT-based algorithms. Within the framework, incremental SAT solving is used to perform a form of weakly relational value-set analysis in a novel way, connecting the expressiveness of the value sets to computational complexity. Another key feature of our framework is that it translates the semantics of binary code into an intermediate representation. This allows for a straightforward translation of the program semantics in...

  12. Gulf-Wide Information System, Environmental Sensitivity Index Socio-Economic Features, Geographic NAD83, LDWF (2001) [esi_socecon_LDWF_2001

    Data.gov (United States)

    Louisiana Geographic Information Center — This data set contains socio-economic features in coastal Louisiana. Feature-specific contact, type, and source information are stored in relational data tables...

  13. Feature Optimization for Long-Range Visual Homing in Changing Environments

    Directory of Open Access Journals (Sweden)

    Qidan Zhu

    2014-02-01

    Full Text Available This paper introduces a feature optimization method for robot long-range feature-based visual homing in changing environments. To cope with the changing environmental appearance, the optimization procedure is introduced to distinguish the most relevant features for feature-based visual homing, including the spatial distribution, selection and updating. In the previous research on feature-based visual homing, less effort has been spent on the way to improve the feature distribution to get uniformly distributed features, which are closely related to homing performance. This paper presents a modified feature extraction algorithm to decrease the influence of anisotropic feature distribution. In addition, the feature selection and updating mechanisms, which have hardly drawn any attention in the domain of feature-based visual homing, are crucial in improving homing accuracy and in maintaining the representation of changing environments. To verify the feasibility of the proposal, several comprehensive evaluations are conducted. The results indicate that the feature optimization method can find optimal feature sets for feature-based visual homing, and adapt the appearance representation to the changing environments as well.

  14. Combining heterogeneous features for colonic polyp detection in CTC based on semi-definite programming

    Science.gov (United States)

    Wang, Shijun; Yao, Jianhua; Petrick, Nicholas A.; Summers, Ronald M.

    2009-02-01

    Colon cancer is the second leading cause of cancer-related deaths in the United States. Computed tomographic colonography (CTC) combined with a computer aided detection system provides a feasible combination for improving colonic polyps detection and increasing the use of CTC for colon cancer screening. To distinguish true polyps from false positives, various features extracted from polyp candidates have been proposed. Most of these features try to capture the shape information of polyp candidates or neighborhood knowledge about the surrounding structures (fold, colon wall, etc.). In this paper, we propose a new set of shape descriptors for polyp candidates based on statistical curvature information. These features, called histogram of curvature features, are rotation, translation and scale invariant and can be treated as complementing our existing feature set. Then in order to make full use of the traditional features (defined as group A) and the new features (group B) which are highly heterogeneous, we employed a multiple kernel learning method based on semi-definite programming to identify an optimized classification kernel based on the combined set of features. We did leave-one-patient-out test on a CTC dataset which contained scans from 50 patients (with 90 6-9mm polyp detections). Experimental results show that a support vector machine (SVM) based on the combined feature set and the semi-definite optimization kernel achieved higher FROC performance compared to SVMs using the two groups of features separately. At a false positive per patient rate of 7, the sensitivity on 6-9mm polyps using the combined features improved from 0.78 (Group A) and 0.73 (Group B) to 0.82 (p<=0.01).

  15. Automated age-related macular degeneration classification in OCT using unsupervised feature learning

    Science.gov (United States)

    Venhuizen, Freerk G.; van Ginneken, Bram; Bloemen, Bart; van Grinsven, Mark J. J. P.; Philipsen, Rick; Hoyng, Carel; Theelen, Thomas; Sánchez, Clara I.

    2015-03-01

    Age-related Macular Degeneration (AMD) is a common eye disorder with high prevalence in elderly people. The disease mainly affects the central part of the retina, and could ultimately lead to permanent vision loss. Optical Coherence Tomography (OCT) is becoming the standard imaging modality in diagnosis of AMD and the assessment of its progression. However, the evaluation of the obtained volumetric scan is time consuming, expensive and the signs of early AMD are easy to miss. In this paper we propose a classification method to automatically distinguish AMD patients from healthy subjects with high accuracy. The method is based on an unsupervised feature learning approach, and processes the complete image without the need for an accurate pre-segmentation of the retina. The method can be divided in two steps: an unsupervised clustering stage that extracts a set of small descriptive image patches from the training data, and a supervised training stage that uses these patches to create a patch occurrence histogram for every image on which a random forest classifier is trained. Experiments using 384 volume scans show that the proposed method is capable of identifying AMD patients with high accuracy, obtaining an area under the Receiver Operating Curve of 0:984. Our method allows for a quick and reliable assessment of the presence of AMD pathology in OCT volume scans without the need for accurate layer segmentation algorithms.

  16. Accounts of bullying on Twitter in relation to dentofacial features and orthodontic treatment.

    Science.gov (United States)

    Chan, A; Antoun, J S; Morgaine, K C; Farella, M

    2017-04-01

    Social media offers an accessible resource for gaining valuable insights into the social culture of bullying. The purpose of this study was to qualitatively analyse Twitter posts for common themes relating to dentofacial features, braces and bullying. Twitter's database was searched from 2010 to 2014 using keywords relevant to bullying, teeth and orthodontics. Two investigators assessed the Twitter posts, and selected those that conveyed the experiences or opinions of bullying victims. The posts were qualitatively analysed using thematic analysis. Of the 548 posts screened, 321 were included in the final sample. Four primary categories relating to 'dental-related bullying' were identified: (i) morphological features, (ii) psychological and psychosocial impact, (iii) coping mechanisms and (iv) the role of family. Bullied individuals reported a diverse range of psychological impacts and coping mechanisms. Secondary categories were also identified. Family members, for example, were found to play both a contributory and mediatory role in bullying. In summary, social media can provide new and valuable information about the causal factors and social issues associated with oral health-related bullying. Importantly, some coping mechanisms may mitigate the negative effects of bullying. © 2017 John Wiley & Sons Ltd.

  17. A Mean-Shift-Based Feature Descriptor for Wide Baseline Stereo Matching

    Directory of Open Access Journals (Sweden)

    Yiwen Dou

    2015-01-01

    Full Text Available We propose a novel Mean-Shift-based building approach in wide baseline. Initially, scale-invariance feature transform (SIFT approach is used to extract relatively stable feature points. As to each matching SIFT feature point, it needs a reasonable neighborhood range so as to choose feature points set. Subsequently, in view of selecting repeatable and high robust feature points, Mean-Shift controls corresponding feature scale. At last, our approach is employed to depth image acquirement in wide baseline and Graph Cut algorithm optimizes disparity information. Compared with the existing methods such as SIFT, speeded up robust feature (SURF, and normalized cross-correlation (NCC, the presented approach has the advantages of higher robustness and accuracy rate. Experimental results on low resolution image and weak feature description in wide baseline confirm the validity of our approach.

  18. Feature selection, statistical modeling and its applications to universal JPEG steganalyzer

    Energy Technology Data Exchange (ETDEWEB)

    Jalan, Jaikishan [Iowa State Univ., Ames, IA (United States)

    2009-01-01

    Steganalysis deals with identifying the instances of medium(s) which carry a message for communication by concealing their exisitence. This research focuses on steganalysis of JPEG images, because of its ubiquitous nature and low bandwidth requirement for storage and transmission. JPEG image steganalysis is generally addressed by representing an image with lower-dimensional features such as statistical properties, and then training a classifier on the feature set to differentiate between an innocent and stego image. Our approach is two fold: first, we propose a new feature reduction technique by applying Mahalanobis distance to rank the features for steganalysis. Many successful steganalysis algorithms use a large number of features relative to the size of the training set and suffer from a ”curse of dimensionality”: large number of feature values relative to training data size. We apply this technique to state-of-the-art steganalyzer proposed by Tom´as Pevn´y (54) to understand the feature space complexity and effectiveness of features for steganalysis. We show that using our approach, reduced-feature steganalyzers can be obtained that perform as well as the original steganalyzer. Based on our experimental observation, we then propose a new modeling technique for steganalysis by developing a Partially Ordered Markov Model (POMM) (23) to JPEG images and use its properties to train a Support Vector Machine. POMM generalizes the concept of local neighborhood directionality by using a partial order underlying the pixel locations. We show that the proposed steganalyzer outperforms a state-of-the-art steganalyzer by testing our approach with many different image databases, having a total of 20000 images. Finally, we provide a software package with a Graphical User Interface that has been developed to make this research accessible to local state forensic departments.

  19. Validation of the Care-Related Quality of Life Instrument in different study settings: findings from The Older Persons and Informal Caregivers Survey Minimum DataSet (TOPICS-MDS).

    Science.gov (United States)

    Lutomski, J E; van Exel, N J A; Kempen, G I J M; Moll van Charante, E P; den Elzen, W P J; Jansen, A P D; Krabbe, P F M; Steunenberg, B; Steyerberg, E W; Olde Rikkert, M G M; Melis, R J F

    2015-05-01

    Validity is a contextual aspect of a scale which may differ across sample populations and study protocols. The objective of our study was to validate the Care-Related Quality of Life Instrument (CarerQol) across two different study design features, sampling framework (general population vs. different care settings) and survey mode (interview vs. written questionnaire). Data were extracted from The Older Persons and Informal Caregivers Minimum DataSet (TOPICS-MDS, www.topics-mds.eu ), a pooled public-access data set with information on >3,000 informal caregivers throughout the Netherlands. Meta-correlations and linear mixed models between the CarerQol's seven dimensions (CarerQol-7D) and caregiver's level of happiness (CarerQol-VAS) and self-rated burden (SRB) were performed. The CarerQol-7D dimensions were correlated to the CarerQol-VAS and SRB in the pooled data set and the subgroups. The strength of correlations between CarerQol-7D dimensions and SRB was weaker among caregivers who were interviewed versus those who completed a written questionnaire. The directionality of associations between the CarerQol-VAS, SRB and the CarerQol-7D dimensions in the multivariate model supported the construct validity of the CarerQol in the pooled population. Significant interaction terms were observed in several dimensions of the CarerQol-7D across sampling frame and survey mode, suggesting meaningful differences in reporting levels. Although good scientific practice emphasises the importance of re-evaluating instrument properties in individual research studies, our findings support the validity and applicability of the CarerQol instrument in a variety of settings. Due to minor differential reporting, pooling CarerQol data collected using mixed administration modes should be interpreted with caution; for TOPICS-MDS, meta-analytic techniques may be warranted.

  20. MetaFIND: A feature analysis tool for metabolomics data

    Directory of Open Access Journals (Sweden)

    Cunningham Pádraig

    2008-11-01

    Full Text Available Abstract Background Metabolomics, or metabonomics, refers to the quantitative analysis of all metabolites present within a biological sample and is generally carried out using NMR spectroscopy or Mass Spectrometry. Such analysis produces a set of peaks, or features, indicative of the metabolic composition of the sample and may be used as a basis for sample classification. Feature selection may be employed to improve classification accuracy or aid model explanation by establishing a subset of class discriminating features. Factors such as experimental noise, choice of technique and threshold selection may adversely affect the set of selected features retrieved. Furthermore, the high dimensionality and multi-collinearity inherent within metabolomics data may exacerbate discrepancies between the set of features retrieved and those required to provide a complete explanation of metabolite signatures. Given these issues, the latter in particular, we present the MetaFIND application for 'post-feature selection' correlation analysis of metabolomics data. Results In our evaluation we show how MetaFIND may be used to elucidate metabolite signatures from the set of features selected by diverse techniques over two metabolomics datasets. Importantly, we also show how MetaFIND may augment standard feature selection and aid the discovery of additional significant features, including those which represent novel class discriminating metabolites. MetaFIND also supports the discovery of higher level metabolite correlations. Conclusion Standard feature selection techniques may fail to capture the full set of relevant features in the case of high dimensional, multi-collinear metabolomics data. We show that the MetaFIND 'post-feature selection' analysis tool may aid metabolite signature elucidation, feature discovery and inference of metabolic correlations.

  1. Development of Feature Set, Classification Implementation and Applications for Vowel Migration/Modification in Sung Filipino (Tagalog Texts and Perceived Intelligibility

    Directory of Open Access Journals (Sweden)

    Virginia B. Bustos

    2009-12-01

    Full Text Available With the emergence of research on real-time visual feedback to supplement vocal pedagogy, the utilization of technology in the world of music is now seen to accelerate skills learning and enhance cognitive development. The researchers of this project aim to further analyze vowel intelligibility and develop software applications intended to be used not only by professional singers but also by individuals who wish to improve their singing capability. Data in the form of sung vowels and song pieces were obtained from 46 singers. A Listening Test was then conducted on these samples to obtain the ground truth for vowel classification based on human perception. Simulation of the human auditory perception of sung Filipino vowels was performed using formant frequencies and Mel-frequency cepstral coefficients as feature vector inputs to a two-stage Discriminant Analysis classifier. The setup resulted in an over-all Training Set accuracy of 89.4% and an over-all Test Set accuracy of 90.9%. The accuracy of the classifier, measured in terms of the correspondence of vowel classifications obtained from the classifier with the results of the Listening Test, reached 92.3%. Using information obtained from the classifier, offline and online/real-time software applications were developed. The main application features include the display of the spectral envelope and spectrogram, pitch and vibrato analysis and direct feedback on the classification of the sung vowel. These features were recommended by singers who were surveyed and were incorporated in the applications to aid singers to adjust formant locations, directly determine listener’s perception of sung vowels, perform modeling effectively and carry out vowel migration.

  2. Feature Selection for Chemical Sensor Arrays Using Mutual Information

    Science.gov (United States)

    Wang, X. Rosalind; Lizier, Joseph T.; Nowotny, Thomas; Berna, Amalia Z.; Prokopenko, Mikhail; Trowell, Stephen C.

    2014-01-01

    We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best features and upper bounds on classification performance. We selected feature sets that exhibit the maximal mutual information with the identity of the chemicals. The selected features closely match those found to perform well in the previous study using a wrapper approach to conduct an exhaustive search of all permitted feature combinations. By comparing the classification performance of support vector machines (using features selected by mutual information) with the performance observed in the previous study, we found that while our approach does not always give the maximum possible classification performance, it always selects features that achieve classification performance approaching the optimum obtained by exhaustive search. We performed further classification using the selected feature set with some common classifiers and found that, for the selected features, Bayesian Networks gave the best performance. Finally, we compared the observed classification performances with the performance of classifiers using randomly selected features. We found that the selected features consistently outperformed randomly selected features for all tested classifiers. The mutual information filter approach is therefore a computationally efficient method for selecting near optimal features for chemical sensor arrays. PMID:24595058

  3. Human Papillomavirus-related Carcinoma with Adenoid Cystic-like Features of the Sinonasal Tract

    DEFF Research Database (Denmark)

    Andreasen, Simon; Bishop, J; Hansen, T V O

    2017-01-01

    with adenoid cystic carcinoma (ACC), a rare and aggressive carcinoma originating in the minor salivary glands. Termed HPV-related carcinoma with ACC-like features, only 9 cases have been reported. To clarify the occurrence of these tumours we screened a large material for presence of HPV-related ACC....... For the distinction between ACC and HPV-related ACC-like carcinoma, p16, MYB immunohistochemistry, or investigation of MYB, MYBL1, and NFIB gene status are valuable. This article is protected by copyright. All rights reserved....

  4. SIP-FS: a novel feature selection for data representation

    Directory of Open Access Journals (Sweden)

    Yiyou Guo

    2018-02-01

    Full Text Available Abstract Multiple features are widely used to characterize real-world datasets. It is desirable to select leading features with stability and interpretability from a set of distinct features for a comprehensive data description. However, most of existing feature selection methods focus on the predictability (e.g., prediction accuracy of selected results yet neglect stability. To obtain compact data representation, a novel feature selection method is proposed to improve stability, and interpretability without sacrificing predictability (SIP-FS. Instead of mutual information, generalized correlation is adopted in minimal redundancy maximal relevance to measure the relation between different feature types. Several feature types (each contains a certain number of features can then be selected and evaluated quantitatively to determine what types contribute to a specific class, thereby enhancing the so-called interpretability of features. Moreover, stability is introduced in the criterion of SIP-FS to obtain consistent results of ranking. We conduct experiments on three publicly available datasets using one-versus-all strategy to select class-specific features. The experiments illustrate that SIP-FS achieves significant performance improvements in terms of stability and interpretability with desirable prediction accuracy and indicates advantages over several state-of-the-art approaches.

  5. SEEDLING DISCRIMINATION USING SHAPE FEATURES DERIVED FROM A DISTANCE TRANSFORM

    DEFF Research Database (Denmark)

    Mosgaard Giselsson, Thomas; Jørgensen, Rasmus Nyholm; Midtiby, Henrik

    NN, Naive-Bayes, Linear SVM, Non-linear SVM). A set of well known features is used for comparison. This feature set will be referred to as Standard Feature Set (SFS). The used dataset consisted of 139 samples of Corn Flower (Centaura cyanus L.) and 63 samples of Night Shade (Solanum nigrum L.). The highest...

  6. An Evaluation of optional timing/synchronization features to support selection of an optimum design for the DCS digital communication network

    Science.gov (United States)

    Bradley, D. B.; Cain, J. B., III; Williard, M. W.

    1978-01-01

    The task was to evaluate the ability of a set of timing/synchronization subsystem features to provide a set of desirable characteristics for the evolving Defense Communications System digital communications network. The set of features related to the approaches by which timing/synchronization information could be disseminated throughout the network and the manner in which this information could be utilized to provide a synchronized network. These features, which could be utilized in a large number of different combinations, included mutual control, directed control, double ended reference links, independence of clock error measurement and correction, phase reference combining, and self organizing.

  7. Signs of helicity in solar prominences and related features

    Science.gov (United States)

    Martin, S.

    This review illustrates several ways to identify the chirality (handedness) of solar prominences (filaments) from their structure and the structure of their surrounding magnetic fields in the chromosphere and corona. For prominences, these structural elements include the axial magnetic field direction, orientation of barbs, and direction of the prominence fine structure. The surrounding structures include the pattern of fibrils beneath the prominences and the pattern of coronal loops above the prominences. These ways of identifying chirality are then interpreted in terms of the formal definitions of helicity to yield a consistent set of one-to-one helicity relationships for all features. The helicity of some prominences can also be independently determined during their eruption by their fine structure, apparent crossings in the line-of-sight of different parts of the same prominence, and by large- scale twist of the prominence structure. Unlike observations of prominences (filaments) observed prior to eruption, in some cases evidence of both signs of helicity are found within the same erupting prominence. This indicates the continued application of forces on the prominences during the eruption process or the possible introduction of force(s) not present during earlier stages of their evolution.

  8. Prostate cancer multi-feature analysis using trans-rectal ultrasound images

    International Nuclear Information System (INIS)

    Mohamed, S S; Salama, M M A; Kamel, M; El-Saadany, E F; Rizkalla, K; Chin, J

    2005-01-01

    This note focuses on extracting and analysing prostate texture features from trans-rectal ultrasound (TRUS) images for tissue characterization. One of the principal contributions of this investigation is the use of the information of the images' frequency domain features and spatial domain features to attain a more accurate diagnosis. Each image is divided into regions of interest (ROIs) by the Gabor multi-resolution analysis, a crucial stage, in which segmentation is achieved according to the frequency response of the image pixels. The pixels with a similar response to the same filter are grouped to form one ROI. Next, from each ROI two different statistical feature sets are constructed; the first set includes four grey level dependence matrix (GLDM) features and the second set consists of five grey level difference vector (GLDV) features. These constructed feature sets are then ranked by the mutual information feature selection (MIFS) algorithm. Here, the features that provide the maximum mutual information of each feature and class (cancerous and non-cancerous) and the minimum mutual information of the selected features are chosen, yeilding a reduced feature subset. The two constructed feature sets, GLDM and GLDV, as well as the reduced feature subset, are examined in terms of three different classifiers: the condensed k-nearest neighbour (CNN), the decision tree (DT) and the support vector machine (SVM). The accuracy classification results range from 87.5% to 93.75%, where the performance of the SVM and that of the DT are significantly better than the performance of the CNN. (note)

  9. Exploring the Moderating Role of Problematic Substance Use in the Relations Between Borderline and Antisocial Personality Features and Intimate Partner Violence.

    Science.gov (United States)

    Armenti, Nicholas A; Snead, Alexandra L; Babcock, Julia C

    2018-02-01

    Borderline and antisocial personality features relate to multiple externalizing behaviors, including intimate partner violence (IPV). However, not all individuals with borderline and antisocial traits perpetrate IPV. The strength of the personality-IPV link may be related to problematic substance use. We examined borderline and antisocial personality features, problematic substance use, and IPV in a community sample of couples. Positive relations between both borderline and antisocial features and IPV were stronger in conditions of high problematic alcohol use relative to low problematic alcohol use. Alcohol misuse may be an important factor to consider for IPV reduction in men with these personality features.

  10. Gambling disorder in financial markets: Clinical and treatment-related features.

    Science.gov (United States)

    Shin, Young-Chul; Choi, Sam-Wook; Ha, Juwon; Choi, Jung-Seok; Kim, Dai-Jin

    2015-12-01

    To date, few studies have examined the clinical manifestation of disordered gamblers in financial markets. This study examined the differences in the clinical and treatment-related features of gambling disorder between financial markets and horse races. Subjects who met the DSM-IV criteria for pathological gambling (PG) and who sought treatment were assessed by retrospective chart review. One hundred forty-four subjects were included in this sample, which consisted of the following groups: financial markets (n = 45; 28.6%) and horse races (n = 99; 71.4%). Multiple similar manifestations were found between the groups, including severity of PG, age of PG onset, amounts of gambling debts, drinking days per week, depressive mood, duration of seeking treatment after the onset of PG, and treatment follow-up duration. However, disordered gamblers who invested in the financial market were significantly more likely to be educated (p = 0.003), live with their spouses (p = 0.007), have full-time jobs (p = 0.006), and they were more likely to participate in the first type of gambling than the horse races group (pfinancial markets group received the anti-craving medication less often than the horse races group (p = 0.04). These findings suggest that disordered gamblers in financial markets show different socio-demographic, clinical and treatment-related features compared with the horse race gamblers, despite a similar severity of gambling disorder. Understanding these differential manifestations may provide insight into prevention and treatment development for specific types of gambling.

  11. Counting SET-free sets

    OpenAIRE

    Harman, Nate

    2016-01-01

    We consider the following counting problem related to the card game SET: How many $k$-element SET-free sets are there in an $n$-dimensional SET deck? Through a series of algebraic reformulations and reinterpretations, we show the answer to this question satisfies two polynomiality conditions.

  12. A Python tool to set up relative free energy calculations in GROMACS.

    Science.gov (United States)

    Klimovich, Pavel V; Mobley, David L

    2015-11-01

    Free energy calculations based on molecular dynamics (MD) simulations have seen a tremendous growth in the last decade. However, it is still difficult and tedious to set them up in an automated manner, as the majority of the present-day MD simulation packages lack that functionality. Relative free energy calculations are a particular challenge for several reasons, including the problem of finding a common substructure and mapping the transformation to be applied. Here we present a tool, alchemical-setup.py, that automatically generates all the input files needed to perform relative solvation and binding free energy calculations with the MD package GROMACS. When combined with Lead Optimization Mapper (LOMAP; Liu et al. in J Comput Aided Mol Des 27(9):755-770, 2013), recently developed in our group, alchemical-setup.py allows fully automated setup of relative free energy calculations in GROMACS. Taking a graph of the planned calculations and a mapping, both computed by LOMAP, our tool generates the topology and coordinate files needed to perform relative free energy calculations for a given set of molecules, and provides a set of simulation input parameters. The tool was validated by performing relative hydration free energy calculations for a handful of molecules from the SAMPL4 challenge (Mobley et al. in J Comput Aided Mol Des 28(4):135-150, 2014). Good agreement with previously published results and the straightforward way in which free energy calculations can be conducted make alchemical-setup.py a promising tool for automated setup of relative solvation and binding free energy calculations.

  13. Feature Scaling via Second-Order Cone Programming

    Directory of Open Access Journals (Sweden)

    Zhizheng Liang

    2016-01-01

    Full Text Available Feature scaling has attracted considerable attention during the past several decades because of its important role in feature selection. In this paper, a novel algorithm for learning scaling factors of features is proposed. It first assigns a nonnegative scaling factor to each feature of data and then adopts a generalized performance measure to learn the optimal scaling factors. It is of interest to note that the proposed model can be transformed into a convex optimization problem: second-order cone programming (SOCP. Thus the scaling factors of features in our method are globally optimal in some sense. Several experiments on simulated data, UCI data sets, and the gene data set are conducted to demonstrate that the proposed method is more effective than previous methods.

  14. Health Communication in Social Media: Message Features Predicting User Engagement on Diabetes-Related Facebook Pages.

    Science.gov (United States)

    Rus, Holly M; Cameron, Linda D

    2016-10-01

    Social media provides unprecedented opportunities for enhancing health communication and health care, including self-management of chronic conditions such as diabetes. Creating messages that engage users is critical for enhancing message impact and dissemination. This study analyzed health communications within ten diabetes-related Facebook pages to identify message features predictive of user engagement. The Common-Sense Model of Illness Self-Regulation and established health communication techniques guided content analyses of 500 Facebook posts. Each post was coded for message features predicted to engage users and numbers of likes, shares, and comments during the week following posting. Multi-level, negative binomial regressions revealed that specific features predicted different forms of engagement. Imagery emerged as a strong predictor; messages with images had higher rates of liking and sharing relative to messages without images. Diabetes consequence information and positive identity predicted higher sharing while negative affect, social support, and crowdsourcing predicted higher commenting. Negative affect, crowdsourcing, and use of external links predicted lower sharing while positive identity predicted lower commenting. The presence of imagery weakened or reversed the positive relationships of several message features with engagement. Diabetes control information and negative affect predicted more likes in text-only messages, but fewer likes when these messages included illustrative imagery. Similar patterns of imagery's attenuating effects emerged for the positive relationships of consequence information, control information, and positive identity with shares and for positive relationships of negative affect and social support with comments. These findings hold promise for guiding communication design in health-related social media.

  15. First evidence of a prospective relation between avoidance of internal states and borderline personality disorder features in adolescents.

    Science.gov (United States)

    Sharp, Carla; Kalpakci, Allison; Mellick, William; Venta, Amanda; Temple, Jeff R

    2015-03-01

    At least two leading developmental models of borderline personality disorder (BPD) emphasize the role of accurate reflection and understanding of internal states as significant to the development of BPD features (Fonagy, Int J Psycho-Anal 72:639-656, 1991; Linehan, Cognitive-behavioral treatment of borderline personality disorder, 1993). The current study used the construct of experiential avoidance (EA) to operationalize avoidance of internal states and sought to examine (1) the concurrent relations between EA and borderline features in a large and diverse community sample; and (2) the prospective relation between EA and borderline features over a 1-year follow-up, controlling for baseline levels of borderline features. N = 881 adolescents recruited from public schools in a large metropolitan area participated in baseline assessments and N = 730 completed follow-up assessments. Two main findings were reported. First, EA was associated with borderline features, depressive, and anxiety symptoms at the bivariate level, but when all variables were considered together, depression and anxiety no longer remained significantly associated with borderline features, suggesting that the relations among these symptom clusters may be accounted for by EA as a cross-cutting underlying psychological process. Second, EA predicted levels of borderline symptoms at 1-year follow-up, controlling for baseline levels of borderline symptoms, and symptoms of anxiety and depression. Results are interpreted against the background of developmental theories of borderline personality disorder.

  16. Detection of fraudulent emails by employing advanced feature abundance

    Directory of Open Access Journals (Sweden)

    Sarwat Nizamani

    2014-11-01

    Full Text Available In this paper, we present a fraudulent email detection model using advanced feature choice. We extracted various kinds of features and compared the performance of each category of features with the others in terms of the fraudulent email detection rate. The different types of features are incorporated step by step. The detection of fraudulent email has been considered as a classification problem and it is evaluated using various state-of-the art algorithms and on CCM (Nizamani et al., 2011 [1] which is authors’ previous cluster based classification model. The experiments have been performed on diverse feature sets and the different classification methods. The comparison of the results is also presented and the evaluation show that for the fraudulent email detection tasks, the feature set is more important regardless of classification method. The results of the study suggest that the task of fraudulent emails detection requires the better choice of feature set; while the choice of classification method is of less importance.

  17. A new MCNP trademark test set

    International Nuclear Information System (INIS)

    Brockhoff, R.C.; Hendricks, J.S.

    1994-09-01

    The MCNP test set is used to test the MCNP code after installation on various computer platforms. For MCNP4 and MCNP4A this test set included 25 test problems designed to test as many features of the MCNP code as possible. A new and better test set has been devised to increase coverage of the code from 85% to 97% with 28 problems. The new test set is as fast as and shorter than the MCNP4A test set. The authors describe the methodology for devising the new test set, the features that were not covered in the MCNP4A test set, and the changes in the MCNP4A test set that have been made for MCNP4B and its developmental versions. Finally, new bugs uncovered by the new test set and a compilation of all known MCNP4A bugs are presented

  18. Cancer survival classification using integrated data sets and intermediate information.

    Science.gov (United States)

    Kim, Shinuk; Park, Taesung; Kon, Mark

    2014-09-01

    Although numerous studies related to cancer survival have been published, increasing the prediction accuracy of survival classes still remains a challenge. Integration of different data sets, such as microRNA (miRNA) and mRNA, might increase the accuracy of survival class prediction. Therefore, we suggested a machine learning (ML) approach to integrate different data sets, and developed a novel method based on feature selection with Cox proportional hazard regression model (FSCOX) to improve the prediction of cancer survival time. FSCOX provides us with intermediate survival information, which is usually discarded when separating survival into 2 groups (short- and long-term), and allows us to perform survival analysis. We used an ML-based protocol for feature selection, integrating information from miRNA and mRNA expression profiles at the feature level. To predict survival phenotypes, we used the following classifiers, first, existing ML methods, support vector machine (SVM) and random forest (RF), second, a new median-based classifier using FSCOX (FSCOX_median), and third, an SVM classifier using FSCOX (FSCOX_SVM). We compared these methods using 3 types of cancer tissue data sets: (i) miRNA expression, (ii) mRNA expression, and (iii) combined miRNA and mRNA expression. The latter data set included features selected either from the combined miRNA/mRNA profile or independently from miRNAs and mRNAs profiles (IFS). In the ovarian data set, the accuracy of survival classification using the combined miRNA/mRNA profiles with IFS was 75% using RF, 86.36% using SVM, 84.09% using FSCOX_median, and 88.64% using FSCOX_SVM with a balanced 22 short-term and 22 long-term survivor data set. These accuracies are higher than those using miRNA alone (70.45%, RF; 75%, SVM; 75%, FSCOX_median; and 75%, FSCOX_SVM) or mRNA alone (65.91%, RF; 63.64%, SVM; 72.73%, FSCOX_median; and 70.45%, FSCOX_SVM). Similarly in the glioblastoma multiforme data, the accuracy of miRNA/mRNA using IFS

  19. Ab initio calculation of reaction energies. III. Basis set dependence of relative energies on the FH2 and H2CO potential energy surfaces

    International Nuclear Information System (INIS)

    Frisch, M.J.; Binkley, J.S.; Schaefer, H.F. III

    1984-01-01

    The relative energies of the stationary points on the FH 2 and H 2 CO nuclear potential energy surfaces relevant to the hydrogen atom abstraction, H 2 elimination and 1,2-hydrogen shift reactions have been examined using fourth-order Moller--Plesset perturbation theory and a variety of basis sets. The theoretical absolute zero activation energy for the F+H 2 →FH+H reaction is in better agreement with experiment than previous theoretical studies, and part of the disagreement between earlier theoretical calculations and experiment is found to result from the use of assumed rather than calculated zero-point vibrational energies. The fourth-order reaction energy for the elimination of hydrogen from formaldehyde is within 2 kcal mol -1 of the experimental value using the largest basis set considered. The qualitative features of the H 2 CO surface are unchanged by expansion of the basis set beyond the polarized triple-zeta level, but diffuse functions and several sets of polarization functions are found to be necessary for quantitative accuracy in predicted reaction and activation energies. Basis sets and levels of perturbation theory which represent good compromises between computational efficiency and accuracy are recommended

  20. Handwriting: Feature Correlation Analysis for Biometric Hashes

    Science.gov (United States)

    Vielhauer, Claus; Steinmetz, Ralf

    2004-12-01

    In the application domain of electronic commerce, biometric authentication can provide one possible solution for the key management problem. Besides server-based approaches, methods of deriving digital keys directly from biometric measures appear to be advantageous. In this paper, we analyze one of our recently published specific algorithms of this category based on behavioral biometrics of handwriting, the biometric hash. Our interest is to investigate to which degree each of the underlying feature parameters contributes to the overall intrapersonal stability and interpersonal value space. We will briefly discuss related work in feature evaluation and introduce a new methodology based on three components: the intrapersonal scatter (deviation), the interpersonal entropy, and the correlation between both measures. Evaluation of the technique is presented based on two data sets of different size. The method presented will allow determination of effects of parameterization of the biometric system, estimation of value space boundaries, and comparison with other feature selection approaches.

  1. Handwriting: Feature Correlation Analysis for Biometric Hashes

    Directory of Open Access Journals (Sweden)

    Ralf Steinmetz

    2004-04-01

    Full Text Available In the application domain of electronic commerce, biometric authentication can provide one possible solution for the key management problem. Besides server-based approaches, methods of deriving digital keys directly from biometric measures appear to be advantageous. In this paper, we analyze one of our recently published specific algorithms of this category based on behavioral biometrics of handwriting, the biometric hash. Our interest is to investigate to which degree each of the underlying feature parameters contributes to the overall intrapersonal stability and interpersonal value space. We will briefly discuss related work in feature evaluation and introduce a new methodology based on three components: the intrapersonal scatter (deviation, the interpersonal entropy, and the correlation between both measures. Evaluation of the technique is presented based on two data sets of different size. The method presented will allow determination of effects of parameterization of the biometric system, estimation of value space boundaries, and comparison with other feature selection approaches.

  2. A keyword spotting model using perceptually significant energy features

    Science.gov (United States)

    Umakanthan, Padmalochini

    The task of a keyword recognition system is to detect the presence of certain words in a conversation based on the linguistic information present in human speech. Such keyword spotting systems have applications in homeland security, telephone surveillance and human-computer interfacing. General procedure of a keyword spotting system involves feature generation and matching. In this work, new set of features that are based on the psycho-acoustic masking nature of human speech are proposed. After developing these features a time aligned pattern matching process was implemented to locate the words in a set of unknown words. A word boundary detection technique based on frame classification using the nonlinear characteristics of speech is also addressed in this work. Validation of this keyword spotting model was done using widely acclaimed Cepstral features. The experimental results indicate the viability of using these perceptually significant features as an augmented feature set in keyword spotting.

  3. Green Settings for Children in Preschools

    DEFF Research Database (Denmark)

    Lerstrup, Inger Elisabeth

    settings for preschools. The intent is to facilitate transfer of knowledge from preschools to planners and managers of green settings such as woodland, parks, green lots and playgrounds. The central concept applied is that of affordances, here defined as the meaningful action possibilities......This Danish study investigates the relationship between children in preschool (age range 3-6.5 years) and the outdoor environments they use. The main aim is to describe and analyse the outdoor features of significance for children’s activities and of importance for design and management of green...... between forest features and manufactured features, a detailed account of the affordances of ditches, and a description of the forest sites used by a Danish forest preschool. Children were attracted to features with changing and not fully explored action possibilities; forest features added variation...

  4. A level-set method for pathology segmentation in fluorescein angiograms and en face retinal images of patients with age-related macular degeneration

    Science.gov (United States)

    Mohammad, Fatimah; Ansari, Rashid; Shahidi, Mahnaz

    2013-03-01

    The visibility and continuity of the inner segment outer segment (ISOS) junction layer of the photoreceptors on spectral domain optical coherence tomography images is known to be related to visual acuity in patients with age-related macular degeneration (AMD). Automatic detection and segmentation of lesions and pathologies in retinal images is crucial for the screening, diagnosis, and follow-up of patients with retinal diseases. One of the challenges of using the classical level-set algorithms for segmentation involves the placement of the initial contour. Manually defining the contour or randomly placing it in the image may lead to segmentation of erroneous structures. It is important to be able to automatically define the contour by using information provided by image features. We explored a level-set method which is based on the classical Chan-Vese model and which utilizes image feature information for automatic contour placement for the segmentation of pathologies in fluorescein angiograms and en face retinal images of the ISOS layer. This was accomplished by exploiting a priori knowledge of the shape and intensity distribution allowing the use of projection profiles to detect the presence of pathologies that are characterized by intensity differences with surrounding areas in retinal images. We first tested our method by applying it to fluorescein angiograms. We then applied our method to en face retinal images of patients with AMD. The experimental results included demonstrate that the proposed method provided a quick and improved outcome as compared to the classical Chan-Vese method in which the initial contour is randomly placed, thus indicating the potential to provide a more accurate and detailed view of changes in pathologies due to disease progression and treatment.

  5. Maladaptive Personality and Neuropsychological Features of Highly Relationally Aggressive Adolescent Girls

    Directory of Open Access Journals (Sweden)

    Michael Savage

    2017-07-01

    Full Text Available The maladaptive personality and neuropsychological features of highly relationally aggressive females were examined in a group of 30 grade 6, 7, and 8 girls and group-matched controls. Employing a multistage cluster sampling procedure a group of highly, yet almost exclusively, relationally aggressive females were identified and matched on a number of variables to a group of nonaggressive females. Parents of the students in both groups completed the Coolidge Personality and Neuropsychological Inventory, a 200-item DSM-IV-TR aligned, parent-as-respondent, standardized measure of children’s psychological functioning. It was found that high levels of relational aggression, in the absence of physical and verbal aggression, were associated with symptoms of DSM-IV-TR Axis I oppositional defiant disorder and conduct disorder. The highly relationally aggressive group also exhibited a wide variety of personality traits associated with DSM-IV-TR Axis II paranoid, borderline, narcissistic, histrionic, schizotypal, and passive aggressive personality disorders that were not exhibited by the matched controls. Implications of these findings are discussed.

  6. Changes in computed tomography features following preoperative chemotherapy for nephroblastoma: relation to histopathological classification

    International Nuclear Information System (INIS)

    Olsen, Oeystein E.; Jeanes, Annmarie C.; Roebuck, Derek J.; Owens, Catherine M.; Sebire, Neil J.; Risdon, Rupert A.; Michalski, Anthony J.

    2004-01-01

    The objective of this study is to assess computed tomography (CT) changes, both volume estimates and subjective features, following preoperative chemotherapy for nephroblastoma (Wilms' tumour) in patients treated on the United Kingdom Children's Cancer Study Group Wilms' Tumour Study-3 (UKW-3) protocol and to compare CT changes and histopathological classification. Twenty-one nephroblastomas in 15 patients treated on UKW-3 were included. All patients were examined by CT before and after preoperative chemotherapy treatment. CT images were reviewed (estimated volume change and subjectively assessed features). CT changes were compared to histopathological classification. Of the 21 tumours, all five high-risk tumours decreased in volume following chemotherapy (median -79%; range -37 to -91%). The sole low-risk tumour decreased in volume by 98%. Ten intermediate-risk tumours decreased in volume (median -72%; range -6 to -98%) and five intermediate-risk tumours increased (median +110%; range +11 to +164%). None of the five high-risk tumours, compared to 15/16 intermediate or low-risk tumours, became less dense and/or more homogeneous, or virtually disappeared, following chemotherapy. Volume change following chemotherapy did not relate to histopathological risk group. Changes in subjectively assessed qualitative CT features were more strongly related to histopathological risk group. (orig.)

  7. Election Districts and Precincts, PrecinctPoly-The data set is a polygon feature consisting of 220 segments representing voter precinct boundaries., Published in 1991, Davis County Government.

    Data.gov (United States)

    NSGIC Local Govt | GIS Inventory — Election Districts and Precincts dataset current as of 1991. PrecinctPoly-The data set is a polygon feature consisting of 220 segments representing voter precinct...

  8. Yield in almond is related more to the abundance of flowers than the relative number of flowers that set fruit

    Directory of Open Access Journals (Sweden)

    Sergio Tombesi

    2016-11-01

    Full Text Available Almond tree yield is a function of the number of flowers on a tree and the percentage of flowers that set fruit. Almonds are borne on spurs (short proleptic shoots that can have both leaves and flowers. Almond tree spur dynamics research has documented that previous year spur leaf area is a predictive parameter for year-to-year spur survival, spur flowering and to a lesser extent spur fruiting, while previous year fruit bearing has a negative impact on subsequent year flowering. However, a question remained about whether yields are more dependent on flower numbers or relative fruit set of the flowers that are present. The aim of the present work was to compare the importance of flower abundance with that of relative fruit set in determining the productivity of a population of tagged spurs in almond trees over a 6-year period. Overall tree yield among years was more sensitive to total number of flowers on a tree rather than relative fruit set. These results emphasize the importance of maintaining large populations of healthy flowering spurs for sustained high production in almond orchards.

  9. HIV-related stigma and psychological distress: the harmful effects of specific stigma manifestations in various social settings.

    Science.gov (United States)

    Stutterheim, Sarah E; Pryor, John B; Bos, Arjan E R; Hoogendijk, Robert; Muris, Peter; Schaalma, Herman P

    2009-11-13

    Recent research has shown that experiences of stigmatization have an adverse impact on the psychological well being of people living with HIV/AIDS (PLWHA). Most studies investigating this relationship employ an aggregate measure of stigma. Although this approach provides useful information about the psychological implications of HIV-related stigma in general, it neglects to acknowledge the possibility that some manifestations in specific settings may be psychologically more detrimental than others. The present study examines which specific stigma experiences are most strongly related to psychological distress across a number of social settings. A cross-sectional survey was administered to 667 PLWHA in the Netherlands. We examined participants' experiences of 11 manifestations of HIV-related stigma in six social settings. Linear regression analyses were conducted to determine which setting-specific manifestations best predict psychological distress after controlling for marital status, education and health status. Three manifestations in family settings, namely receiving advice to conceal one's status, being avoided and being treated with exaggerated kindness, and one manifestation in healthcare settings, namely awkward social interaction, best predicted psychological distress in PLWHA. Manifestations of HIV-related stigma vary according to setting. Certain manifestations in specific social settings impact the psychological well being of PLWHA more than others. In this study, certain experiences of stigmatization with PLWHA's families and in healthcare settings were more strongly related to psychological distress than experiences occurring in other social settings. These findings suggest that stigma reduction interventions focusing on these influential settings may benefit the psychological well being of PLWHA.

  10. Action recognition using mined hierarchical compound features.

    Science.gov (United States)

    Gilbert, Andrew; Illingworth, John; Bowden, Richard

    2011-05-01

    The field of Action Recognition has seen a large increase in activity in recent years. Much of the progress has been through incorporating ideas from single-frame object recognition and adapting them for temporal-based action recognition. Inspired by the success of interest points in the 2D spatial domain, their 3D (space-time) counterparts typically form the basic components used to describe actions, and in action recognition the features used are often engineered to fire sparsely. This is to ensure that the problem is tractable; however, this can sacrifice recognition accuracy as it cannot be assumed that the optimum features in terms of class discrimination are obtained from this approach. In contrast, we propose to initially use an overcomplete set of simple 2D corners in both space and time. These are grouped spatially and temporally using a hierarchical process, with an increasing search area. At each stage of the hierarchy, the most distinctive and descriptive features are learned efficiently through data mining. This allows large amounts of data to be searched for frequently reoccurring patterns of features. At each level of the hierarchy, the mined compound features become more complex, discriminative, and sparse. This results in fast, accurate recognition with real-time performance on high-resolution video. As the compound features are constructed and selected based upon their ability to discriminate, their speed and accuracy increase at each level of the hierarchy. The approach is tested on four state-of-the-art data sets, the popular KTH data set to provide a comparison with other state-of-the-art approaches, the Multi-KTH data set to illustrate performance at simultaneous multiaction classification, despite no explicit localization information provided during training. Finally, the recent Hollywood and Hollywood2 data sets provide challenging complex actions taken from commercial movie sequences. For all four data sets, the proposed hierarchical

  11. A Feature Subset Selection Method Based On High-Dimensional Mutual Information

    Directory of Open Access Journals (Sweden)

    Chee Keong Kwoh

    2011-04-01

    Full Text Available Feature selection is an important step in building accurate classifiers and provides better understanding of the data sets. In this paper, we propose a feature subset selection method based on high-dimensional mutual information. We also propose to use the entropy of the class attribute as a criterion to determine the appropriate subset of features when building classifiers. We prove that if the mutual information between a feature set X and the class attribute Y equals to the entropy of Y , then X is a Markov Blanket of Y . We show that in some cases, it is infeasible to approximate the high-dimensional mutual information with algebraic combinations of pairwise mutual information in any forms. In addition, the exhaustive searches of all combinations of features are prerequisite for finding the optimal feature subsets for classifying these kinds of data sets. We show that our approach outperforms existing filter feature subset selection methods for most of the 24 selected benchmark data sets.

  12. Feature displacement interpolation

    DEFF Research Database (Denmark)

    Nielsen, Mads; Andresen, Per Rønsholt

    1998-01-01

    Given a sparse set of feature matches, we want to compute an interpolated dense displacement map. The application may be stereo disparity computation, flow computation, or non-rigid medical registration. Also estimation of missing image data, may be phrased in this framework. Since the features...... often are very sparse, the interpolation model becomes crucial. We show that a maximum likelihood estimation based on the covariance properties (Kriging) show properties more expedient than methods such as Gaussian interpolation or Tikhonov regularizations, also including scale......-selection. The computational complexities are identical. We apply the maximum likelihood interpolation to growth analysis of the mandibular bone. Here, the features used are the crest-lines of the object surface....

  13. Novel gene sets improve set-level classification of prokaryotic gene expression data.

    Science.gov (United States)

    Holec, Matěj; Kuželka, Ondřej; Železný, Filip

    2015-10-28

    Set-level classification of gene expression data has received significant attention recently. In this setting, high-dimensional vectors of features corresponding to genes are converted into lower-dimensional vectors of features corresponding to biologically interpretable gene sets. The dimensionality reduction brings the promise of a decreased risk of overfitting, potentially resulting in improved accuracy of the learned classifiers. However, recent empirical research has not confirmed this expectation. Here we hypothesize that the reported unfavorable classification results in the set-level framework were due to the adoption of unsuitable gene sets defined typically on the basis of the Gene ontology and the KEGG database of metabolic networks. We explore an alternative approach to defining gene sets, based on regulatory interactions, which we expect to collect genes with more correlated expression. We hypothesize that such more correlated gene sets will enable to learn more accurate classifiers. We define two families of gene sets using information on regulatory interactions, and evaluate them on phenotype-classification tasks using public prokaryotic gene expression data sets. From each of the two gene-set families, we first select the best-performing subtype. The two selected subtypes are then evaluated on independent (testing) data sets against state-of-the-art gene sets and against the conventional gene-level approach. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. Novel gene sets defined on the basis of regulatory interactions improve set-level classification of gene expression data. The experimental scripts and other material needed to reproduce the experiments are available at http://ida.felk.cvut.cz/novelgenesets.tar.gz.

  14. Cosmic ray exposure ages of features and events at the Apollo landing sites

    International Nuclear Information System (INIS)

    Arvidson, R.; Crozaz, G.; Drozd, R.J.; Hohenberg, C.M.; Morgan, C.J.

    1975-01-01

    Cosmic ray exposure ages of lunar samples have been used to date surface features related to impact cratering and downslope movement of material. Only when multiple samples related to a feature have the same rare gas exposure age, or when a single sample has the same 81 Kr-Kr and track exposure age can a feature be considered reliably dated. Because any single lunar sample is likely to have had a complex history, assignment of ages to features based upon only one determination by any method should be avoided. Based on the above criteria, there are only five well-dated lunar features: Cone Crater (Apollo 14) 26 m.y., North Ray Crater (Apollo 16) 50 m.y., South Ray Crater (Apollo 16) 2 m.y., the emplacement of the Station 6 boulders (Apollo 17) 22 m.y., and the emplacement of the Station 7 boulder (Apollo 17) 28 m.y. Other features are tentatively dated or have limits set on their ages: Bench Crater (Apollo 12) =50 m.y. (Auth.)

  15. Textural features for image classification

    Science.gov (United States)

    Haralick, R. M.; Dinstein, I.; Shanmugam, K.

    1973-01-01

    Description of some easily computable textural features based on gray-tone spatial dependances, and illustration of their application in category-identification tasks of three different kinds of image data - namely, photomicrographs of five kinds of sandstones, 1:20,000 panchromatic aerial photographs of eight land-use categories, and ERTS multispectral imagery containing several land-use categories. Two kinds of decision rules are used - one for which the decision regions are convex polyhedra (a piecewise-linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89% for the photomicrographs, 82% for the aerial photographic imagery, and 83% for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.

  16. Empirical evaluation of cross-site reproducibility in radiomic features for characterizing prostate MRI

    Science.gov (United States)

    Chirra, Prathyush; Leo, Patrick; Yim, Michael; Bloch, B. Nicolas; Rastinehad, Ardeshir R.; Purysko, Andrei; Rosen, Mark; Madabhushi, Anant; Viswanath, Satish

    2018-02-01

    The recent advent of radiomics has enabled the development of prognostic and predictive tools which use routine imaging, but a key question that still remains is how reproducible these features may be across multiple sites and scanners. This is especially relevant in the context of MRI data, where signal intensity values lack tissue specific, quantitative meaning, as well as being dependent on acquisition parameters (magnetic field strength, image resolution, type of receiver coil). In this paper we present the first empirical study of the reproducibility of 5 different radiomic feature families in a multi-site setting; specifically, for characterizing prostate MRI appearance. Our cohort comprised 147 patient T2w MRI datasets from 4 different sites, all of which were first pre-processed to correct acquisition-related for artifacts such as bias field, differing voxel resolutions, as well as intensity drift (non-standardness). 406 3D voxel wise radiomic features were extracted and evaluated in a cross-site setting to determine how reproducible they were within a relatively homogeneous non-tumor tissue region; using 2 different measures of reproducibility: Multivariate Coefficient of Variation and Instability Score. Our results demonstrated that Haralick features were most reproducible between all 4 sites. By comparison, Laws features were among the least reproducible between sites, as well as performing highly variably across their entire parameter space. Similarly, the Gabor feature family demonstrated good cross-site reproducibility, but for certain parameter combinations alone. These trends indicate that despite extensive pre-processing, only a subset of radiomic features and associated parameters may be reproducible enough for use within radiomics-based machine learning classifier schemes.

  17. Feature Selection for Audio Surveillance in Urban Environment

    Directory of Open Access Journals (Sweden)

    KIKTOVA Eva

    2014-05-01

    Full Text Available This paper presents the work leading to the acoustic event detection system, which is designed to recognize two types of acoustic events (shot and breaking glass in urban environment. For this purpose, a huge front-end processing was performed for the effective parametric representation of an input sound. MFCC features and features computed during their extraction (MELSPEC and FBANK, then MPEG-7 audio descriptors and other temporal and spectral characteristics were extracted. High dimensional feature sets were created and in the next phase reduced by the mutual information based selection algorithms. Hidden Markov Model based classifier was applied and evaluated by the Viterbi decoding algorithm. Thus very effective feature sets were identified and also the less important features were found.

  18. Features analysis for identification of date and party hubs in protein interaction network of Saccharomyces Cerevisiae

    Directory of Open Access Journals (Sweden)

    Araabi Babak N

    2010-12-01

    Full Text Available Abstract Background It has been understood that biological networks have modular organizations which are the sources of their observed complexity. Analysis of networks and motifs has shown that two types of hubs, party hubs and date hubs, are responsible for this complexity. Party hubs are local coordinators because of their high co-expressions with their partners, whereas date hubs display low co-expressions and are assumed as global connectors. However there is no mutual agreement on these concepts in related literature with different studies reporting their results on different data sets. We investigated whether there is a relation between the biological features of Saccharomyces Cerevisiae's proteins and their roles as non-hubs, intermediately connected, party hubs, and date hubs. We propose a classifier that separates these four classes. Results We extracted different biological characteristics including amino acid sequences, domain contents, repeated domains, functional categories, biological processes, cellular compartments, disordered regions, and position specific scoring matrix from various sources. Several classifiers are examined and the best feature-sets based on average correct classification rate and correlation coefficients of the results are selected. We show that fusion of five feature-sets including domains, Position Specific Scoring Matrix-400, cellular compartments level one, and composition pairs with two and one gaps provide the best discrimination with an average correct classification rate of 77%. Conclusions We study a variety of known biological feature-sets of the proteins and show that there is a relation between domains, Position Specific Scoring Matrix-400, cellular compartments level one, composition pairs with two and one gaps of Saccharomyces Cerevisiae's proteins, and their roles in the protein interaction network as non-hubs, intermediately connected, party hubs and date hubs. This study also confirms the

  19. Bariatric surgery patients’ perceptions of weight-related stigma in healthcare settings impair post-surgery dietary adherence

    Directory of Open Access Journals (Sweden)

    Danielle M. Raves

    2016-10-01

    Full Text Available Background: Weight-related stigma is reported frequently by higher body-weight patients in healthcare settings. Bariatric surgery triggers profound weight loss. This weight loss may therefore alleviate patients’ experiences of weight-related stigma within healthcare settings. In non-clinical settings, weight-related stigma is associated with weight-inducing eating patterns. Dietary adherence is a major challenge after bariatric surgery.Objectives: (1 Evaluate the relationship between weight-related stigma and post-surgical dietary adherence; (2 understand if weight loss reduces weight-related stigma, thereby improving post-surgical dietary adherence; and (3 explore provider and patient perspectives on adherence and stigma in healthcare settings. Design: This mixed methods study contrasts survey responses from 300 postoperative bariatric patients with ethnographic data based on interviews with 35 patients and extensive multi-year participant-observation within a clinic setting. The survey measured experiences of weight-related stigma, including from healthcare professionals, on the Interpersonal Sources of Weight Stigma scale and internalized stigma based on the Weight Bias Internalization Scale. Dietary adherence measures included patient self-reports, non-disordered eating patterns reported on the Disordered Eating after Bariatric Surgery scale, and food frequencies. Regression was used to assess the relationships among post-surgical stigma, dietary adherence, and weight loss. Qualitative analyses consisted of thematic analysis.Results: The quantitative data show that internalized stigma and general experiences of weight-related stigma predict worse dietary adherence, even after weight is lost. The qualitative data show patients did not generally recognize this connection, and health professionals explained it as poor patient compliance.Conclusion: Reducing perceptions of weight-related stigma in healthcare settings and weight bias

  20. Efficient Generation and Selection of Combined Features for Improved Classification

    KAUST Repository

    Shono, Ahmad N.

    2014-05-01

    This study contributes a methodology and associated toolkit developed to allow users to experiment with the use of combined features in classification problems. Methods are provided for efficiently generating combined features from an original feature set, for efficiently selecting the most discriminating of these generated combined features, and for efficiently performing a preliminary comparison of the classification results when using the original features exclusively against the results when using the selected combined features. The potential benefit of considering combined features in classification problems is demonstrated by applying the developed methodology and toolkit to three sample data sets where the discovery of combined features containing new discriminating information led to improved classification results.

  1. The relation between rumination and temporal features of emotion intensity.

    Science.gov (United States)

    Résibois, Maxime; Kalokerinos, Elise K; Verleysen, Gregory; Kuppens, Peter; Van Mechelen, Iven; Fossati, Philippe; Verduyn, Philippe

    2018-03-01

    Intensity profiles of emotional experience over time have been found to differ primarily in explosiveness (i.e. whether the profile has a steep vs. a gentle start) and accumulation (i.e. whether intensity increases over time vs. goes back to baseline). However, the determinants of these temporal features remain poorly understood. In two studies, we examined whether emotion regulation strategies are predictive of the degree of explosiveness and accumulation of negative emotional episodes. Participants were asked to draw profiles reflecting changes in the intensity of emotions elicited either by negative social feedback in the lab (Study 1) or by negative events in daily life (Study 2). In addition, trait (Study 1 & 2), and state (Study 2) usage of a set of emotion regulation strategies was assessed. Multilevel analyses revealed that trait rumination (especially the brooding component) was positively associated with emotion accumulation (Study 1 & 2). State rumination was also positively associated with emotion accumulation and, to a lesser extent, with emotion explosiveness (Study 2). These results provide support for emotion regulation theories, which hypothesise that rumination is a central mechanism underlying the maintenance of negative emotions.

  2. Default settings of computerized physician order entry system order sets drive ordering habits.

    Science.gov (United States)

    Olson, Jordan; Hollenbeak, Christopher; Donaldson, Keri; Abendroth, Thomas; Castellani, William

    2015-01-01

    Computerized physician order entry (CPOE) systems are quickly becoming ubiquitous, and groups of orders ("order sets") to allow for easy order input are a common feature. This provides a streamlined mechanism to view, modify, and place groups of related orders. This often serves as an electronic equivalent of a specialty requisition. A characteristic, of these order sets is that specific orders can be predetermined to be "preselected" or "defaulted-on" whenever the order set is used while others are "optional" or "defaulted-off" (though there is typically the option is to "deselect" defaulted-on tests in a given situation). While it seems intuitive that the defaults in an order set are often accepted, additional study is required to understand the impact of these "default" settings in an order set on ordering habits. This study set out to quantify the effect of changing the default settings of an order set. For quality improvement purposes, order sets dealing with transfusions were recently reviewed and modified to improve monitoring of outcome. Initially, the order for posttransfusion hematocrits and platelet count had the default setting changed from "optional" to "preselected." The default settings for platelet count was later changed back to "optional," allowing for a natural experiment to study the effect of the default selections of an order set on clinician ordering habits. Posttransfusion hematocrit values were ordered for 8.3% of red cell transfusions when the default order set selection was "off" and for 57.4% of transfusions when the default selection was "preselected" (P default order set selection was "optional," increased to 59.4% when the default was changed to "preselected" (P default selection was returned to "optional." The posttransfusion platelet count rates during the two "optional" periods: 7.0% versus 7.5% - were not statistically different (P = 0.620). Default settings in CPOE order sets can significantly influence physician selection of

  3. All Set! Evidence of Simultaneous Attentional Control Settings for Multiple Target Colors

    Science.gov (United States)

    Irons, Jessica L.; Folk, Charles L.; Remington, Roger W.

    2012-01-01

    Although models of visual search have often assumed that attention can only be set for a single feature or property at a time, recent studies have suggested that it may be possible to maintain more than one attentional control setting. The aim of the present study was to investigate whether spatial attention could be guided by multiple attentional…

  4. Influence of the relative rotational speed on component features in micro rotary swaging

    Directory of Open Access Journals (Sweden)

    Ishkina Svetlana

    2015-01-01

    Full Text Available Micro rotary swaging is a cold forming process for production of micro components with determined geometry and surface. It is also possible to change the microstructure of wires and hence the material properties. Swaging dies revolve around the work piece with an overlaid radial oscillation. Newly developed tools (Flat Surface Dies, FSD feature plain surfaces and do not represent the geometry of the formed part as in conventional swaging. Using these tools allows for producing wires with triangle geometry (cross section as well as a circular shape. To test the influence of FSD on material properties by micro swaging a new method is investigated: the variation of the relative speed between the specimen and dies in infeed rotary swaging. During this specific process copper (C11000 and steel (304 Alloy wires with diameter d0 = 1 mm are formed. It is noticed that the mechanical characteristics such as ductility and strength differ from the characteristics after conventional swaging. Moreover this approach enables new possibilities to influence the geometry and the surface quality of wires. The impact of the relative speed on the processed wire features is described in this paper.

  5. Textural features for radar image analysis

    Science.gov (United States)

    Shanmugan, K. S.; Narayanan, V.; Frost, V. S.; Stiles, J. A.; Holtzman, J. C.

    1981-01-01

    Texture is seen as an important spatial feature useful for identifying objects or regions of interest in an image. While textural features have been widely used in analyzing a variety of photographic images, they have not been used in processing radar images. A procedure for extracting a set of textural features for characterizing small areas in radar images is presented, and it is shown that these features can be used in classifying segments of radar images corresponding to different geological formations.

  6. Automatic Feature Detection, Description and Matching from Mobile Laser Scanning Data and Aerial Imagery

    Science.gov (United States)

    Hussnain, Zille; Oude Elberink, Sander; Vosselman, George

    2016-06-01

    In mobile laser scanning systems, the platform's position is measured by GNSS and IMU, which is often not reliable in urban areas. Consequently, derived Mobile Laser Scanning Point Cloud (MLSPC) lacks expected positioning reliability and accuracy. Many of the current solutions are either semi-automatic or unable to achieve pixel level accuracy. We propose an automatic feature extraction method which involves utilizing corresponding aerial images as a reference data set. The proposed method comprise three steps; image feature detection, description and matching between corresponding patches of nadir aerial and MLSPC ortho images. In the data pre-processing step the MLSPC is patch-wise cropped and converted to ortho images. Furthermore, each aerial image patch covering the area of the corresponding MLSPC patch is also cropped from the aerial image. For feature detection, we implemented an adaptive variant of Harris-operator to automatically detect corner feature points on the vertices of road markings. In feature description phase, we used the LATCH binary descriptor, which is robust to data from different sensors. For descriptor matching, we developed an outlier filtering technique, which exploits the arrangements of relative Euclidean-distances and angles between corresponding sets of feature points. We found that the positioning accuracy of the computed correspondence has achieved the pixel level accuracy, where the image resolution is 12cm. Furthermore, the developed approach is reliable when enough road markings are available in the data sets. We conclude that, in urban areas, the developed approach can reliably extract features necessary to improve the MLSPC accuracy to pixel level.

  7. Using Gazetteers to Extract Sets of Keywords from Free-Flowing Texts

    Directory of Open Access Journals (Sweden)

    Adam Crymble

    2015-12-01

    Full Text Available If you have a copy of a text in electronic format stored on your computer, it is relatively easy to keyword search for a single term. Often you can do this by using the built-in search features in your favourite text editor. However, scholars are increasingly needing to find instances of many terms within a text or texts. For example, a scholar may want to use a gazetteer to extract all mentions of English placenames within a collection of texts so that those places can later be plotted on a map. Alternatively, they may want to extract all male given names, all pronouns, stop words, or any other set of words. Using those same built-in search features to achieve this more complex goal is time consuming and clunky. This lesson will teach you how to use Python to extract a set of keywords very quickly and systematically from a set of texts. It is expected that once you have completed this lesson, you will be able to generalise the skills to extract custom sets of keywords from any set of locally saved files.

  8. Cost-benefit evaluation of containment related engineered safety features of Indian pressurized heavy water reactors

    International Nuclear Information System (INIS)

    Bajaj, S.S.; Bhawal, R.N.; Rustagi, R.S.

    1984-01-01

    The typical containment system for a commercial nuclear reactor uses several engineered safety features to achieve its objective of limiting the release of radioactive fission products to the environment in the event of postulated accident conditions. The design of containment systems and associated features for Indian Pressurized Heavy Water Reactors (PHWRs) has undergone progressive improvement in successive projects. In particular, the current design adopted for the Narora Atomic Power Project (NAPP) has seen several notable improvements. The paper reports on a cost-benefit study in respect of three containment related engineered safety features and subsystems of NAPP, viz. (i) secondary containment envelope, (ii) primary containment filtration and pump-back system, and (iii) secondary containment filtration, recirculation and purge system. The effect of each of these systems in reducing the environmental releases of radioactivity following a design basis accident is presented. The corresponding reduction in population exposure and the associated monetary value of this reduction in exposure are also given. The costs of the features and subsystem under consideration are then compared with the monetary value of the exposures saved, as well as other non-quantified benefits, to arrive at conclusions regarding the usefulness of each subsystem. This study clearly establishes for the secondary containment envelope the benefit in terms of reduction in public exposure giving a quantitative justification for the costs involved. In the case of the other two subsystems, which involve relatively low costs, while all benefits have not been quantified, their desirability is justified on qualitative considerations. It is concluded that the engineered safety features adopted in the current containment system design of Indian PHWRs contribute to reducing radiation exposures during accident conditions in accordance with the ALARA ('as low as reasonably achievable') principle

  9. A Transform-Based Feature Extraction Approach for Motor Imagery Tasks Classification

    Science.gov (United States)

    Khorshidtalab, Aida; Mesbah, Mostefa; Salami, Momoh J. E.

    2015-01-01

    In this paper, we present a new motor imagery classification method in the context of electroencephalography (EEG)-based brain–computer interface (BCI). This method uses a signal-dependent orthogonal transform, referred to as linear prediction singular value decomposition (LP-SVD), for feature extraction. The transform defines the mapping as the left singular vectors of the LP coefficient filter impulse response matrix. Using a logistic tree-based model classifier; the extracted features are classified into one of four motor imagery movements. The proposed approach was first benchmarked against two related state-of-the-art feature extraction approaches, namely, discrete cosine transform (DCT) and adaptive autoregressive (AAR)-based methods. By achieving an accuracy of 67.35%, the LP-SVD approach outperformed the other approaches by large margins (25% compared with DCT and 6 % compared with AAR-based methods). To further improve the discriminatory capability of the extracted features and reduce the computational complexity, we enlarged the extracted feature subset by incorporating two extra features, namely, Q- and the Hotelling’s \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$T^{2}$ \\end{document} statistics of the transformed EEG and introduced a new EEG channel selection method. The performance of the EEG classification based on the expanded feature set and channel selection method was compared with that of a number of the state-of-the-art classification methods previously reported with the BCI IIIa competition data set. Our method came second with an average accuracy of 81.38%. PMID:27170898

  10. Discovery and fusion of salient multimodal features toward news story segmentation

    Science.gov (United States)

    Hsu, Winston; Chang, Shih-Fu; Huang, Chih-Wei; Kennedy, Lyndon; Lin, Ching-Yung; Iyengar, Giridharan

    2003-12-01

    In this paper, we present our new results in news video story segmentation and classification in the context of TRECVID video retrieval benchmarking event 2003. We applied and extended the Maximum Entropy statistical model to effectively fuse diverse features from multiple levels and modalities, including visual, audio, and text. We have included various features such as motion, face, music/speech types, prosody, and high-level text segmentation information. The statistical fusion model is used to automatically discover relevant features contributing to the detection of story boundaries. One novel aspect of our method is the use of a feature wrapper to address different types of features -- asynchronous, discrete, continuous and delta ones. We also developed several novel features related to prosody. Using the large news video set from the TRECVID 2003 benchmark, we demonstrate satisfactory performance (F1 measures up to 0.76 in ABC news and 0.73 in CNN news), present how these multi-level multi-modal features construct the probabilistic framework, and more importantly observe an interesting opportunity for further improvement.

  11. Automatic topic identification of health-related messages in online health community using text classification.

    Science.gov (United States)

    Lu, Yingjie

    2013-01-01

    To facilitate patient involvement in online health community and obtain informative support and emotional support they need, a topic identification approach was proposed in this paper for identifying automatically topics of the health-related messages in online health community, thus assisting patients in reaching the most relevant messages for their queries efficiently. Feature-based classification framework was presented for automatic topic identification in our study. We first collected the messages related to some predefined topics in a online health community. Then we combined three different types of features, n-gram-based features, domain-specific features and sentiment features to build four feature sets for health-related text representation. Finally, three different text classification techniques, C4.5, Naïve Bayes and SVM were adopted to evaluate our topic classification model. By comparing different feature sets and different classification techniques, we found that n-gram-based features, domain-specific features and sentiment features were all considered to be effective in distinguishing different types of health-related topics. In addition, feature reduction technique based on information gain was also effective to improve the topic classification performance. In terms of classification techniques, SVM outperformed C4.5 and Naïve Bayes significantly. The experimental results demonstrated that the proposed approach could identify the topics of online health-related messages efficiently.

  12. Task representation in individual and joint settings

    Directory of Open Access Journals (Sweden)

    Wolfgang ePrinz

    2015-05-01

    Full Text Available This paper outlines a framework for task representation and discusses applications to interference tasks in individual and joint settings. The framework is derived from the Theory of Event Coding. This theory regards task sets as transient assemblies of event codes in which stimulus and response codes interact and shape each other in particular ways. On the one hand, stimulus and response codes compete with each other within their respective subsets (horizontal interactions. On the other hand, stimulus and response code cooperate with each other (vertical interactions. Code interactions instantiating competition and cooperation apply to two time scales: on-line performance (i.e., doing the task and off-line implementation (i.e., setting the task. Interference arises when stimulus and response codes overlap in features that are irrelevant for stimulus identification, but relevant for response selection. To resolve this dilemma, the feature profiles of event codes may become restructured in various ways. The framework is applied to three kinds of interference paradigms. Special emphasis is given to joint settings where tasks are shared between two participants. Major conclusions derived from these applications include: (1 Response competition is the chief driver of interference. Likewise, different modes of response competition give rise to different patterns of interference. (2 The type of features in which stimulus and response codes overlap is also a crucial factor. Different types of such features give likewise rise to different patterns of interference. (3 Task sets for joint settings conflate intraindividual conflicts between responses (what, with interindividual conflicts between responding agents (whom. Features of response codes may, therefore, not only address responses, but also responding agents (both physically and socially.

  13. Task representation in individual and joint settings

    Science.gov (United States)

    Prinz, Wolfgang

    2015-01-01

    This paper outlines a framework for task representation and discusses applications to interference tasks in individual and joint settings. The framework is derived from the Theory of Event Coding (TEC). This theory regards task sets as transient assemblies of event codes in which stimulus and response codes interact and shape each other in particular ways. On the one hand, stimulus and response codes compete with each other within their respective subsets (horizontal interactions). On the other hand, stimulus and response code cooperate with each other (vertical interactions). Code interactions instantiating competition and cooperation apply to two time scales: on-line performance (i.e., doing the task) and off-line implementation (i.e., setting the task). Interference arises when stimulus and response codes overlap in features that are irrelevant for stimulus identification, but relevant for response selection. To resolve this dilemma, the feature profiles of event codes may become restructured in various ways. The framework is applied to three kinds of interference paradigms. Special emphasis is given to joint settings where tasks are shared between two participants. Major conclusions derived from these applications include: (1) Response competition is the chief driver of interference. Likewise, different modes of response competition give rise to different patterns of interference; (2) The type of features in which stimulus and response codes overlap is also a crucial factor. Different types of such features give likewise rise to different patterns of interference; and (3) Task sets for joint settings conflate intraindividual conflicts between responses (what), with interindividual conflicts between responding agents (whom). Features of response codes may, therefore, not only address responses, but also responding agents (both physically and socially). PMID:26029085

  14. Classification of breast masses in ultrasound images using self-adaptive differential evolution extreme learning machine and rough set feature selection.

    Science.gov (United States)

    Prabusankarlal, Kadayanallur Mahadevan; Thirumoorthy, Palanisamy; Manavalan, Radhakrishnan

    2017-04-01

    A method using rough set feature selection and extreme learning machine (ELM) whose learning strategy and hidden node parameters are optimized by self-adaptive differential evolution (SaDE) algorithm for classification of breast masses is investigated. A pathologically proven database of 140 breast ultrasound images, including 80 benign and 60 malignant, is used for this study. A fast nonlocal means algorithm is applied for speckle noise removal, and multiresolution analysis of undecimated discrete wavelet transform is used for accurate segmentation of breast lesions. A total of 34 features, including 29 textural and five morphological, are applied to a [Formula: see text]-fold cross-validation scheme, in which more relevant features are selected by quick-reduct algorithm, and the breast masses are discriminated into benign or malignant using SaDE-ELM classifier. The diagnosis accuracy of the system is assessed using parameters, such as accuracy (Ac), sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), Matthew's correlation coefficient (MCC), and area ([Formula: see text]) under receiver operating characteristics curve. The performance of the proposed system is also compared with other classifiers, such as support vector machine and ELM. The results indicated that the proposed SaDE algorithm has superior performance with [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] compared to other classifiers.

  15. Reliability in content analysis: The case of semantic feature norms classification.

    Science.gov (United States)

    Bolognesi, Marianna; Pilgram, Roosmaryn; van den Heerik, Romy

    2017-12-01

    Semantic feature norms (e.g., STIMULUS: car → RESPONSE: ) are commonly used in cognitive psychology to look into salient aspects of given concepts. Semantic features are typically collected in experimental settings and then manually annotated by the researchers into feature types (e.g., perceptual features, taxonomic features, etc.) by means of content analyses-that is, by using taxonomies of feature types and having independent coders perform the annotation task. However, the ways in which such content analyses are typically performed and reported are not consistent across the literature. This constitutes a serious methodological problem that might undermine the theoretical claims based on such annotations. In this study, we first offer a review of some of the released datasets of annotated semantic feature norms and the related taxonomies used for content analysis. We then provide theoretical and methodological insights in relation to the content analysis methodology. Finally, we apply content analysis to a new dataset of semantic features and show how the method should be applied in order to deliver reliable annotations and replicable coding schemes. We tackle the following issues: (1) taxonomy structure, (2) the description of categories, (3) coder training, and (4) sustainability of the coding scheme-that is, comparison of the annotations provided by trained versus novice coders. The outcomes of the project are threefold: We provide methodological guidelines for semantic feature classification; we provide a revised and adapted taxonomy that can (arguably) be applied to both concrete and abstract concepts; and we provide a dataset of annotated semantic feature norms.

  16. Feature singletons attract spatial attention independently of feature priming.

    Science.gov (United States)

    Yashar, Amit; White, Alex L; Fang, Wanghaoming; Carrasco, Marisa

    2017-08-01

    People perform better in visual search when the target feature repeats across trials (intertrial feature priming [IFP]). Here, we investigated whether repetition of a feature singleton's color modulates stimulus-driven shifts of spatial attention by presenting a probe stimulus immediately after each singleton display. The task alternated every two trials between a probe discrimination task and a singleton search task. We measured both stimulus-driven spatial attention (via the distance between the probe and singleton) and IFP (via repetition of the singleton's color). Color repetition facilitated search performance (IFP effect) when the set size was small. When the probe appeared at the singleton's location, performance was better than at the opposite location (stimulus-driven attention effect). The magnitude of this attention effect increased with the singleton's set size (which increases its saliency) but did not depend on whether the singleton's color repeated across trials, even when the previous singleton had been attended as a search target. Thus, our findings show that repetition of a salient singleton's color affects performance when the singleton is task relevant and voluntarily attended (as in search trials). However, color repetition does not affect performance when the singleton becomes irrelevant to the current task, even though the singleton does capture attention (as in probe trials). Therefore, color repetition per se does not make a singleton more salient for stimulus-driven attention. Rather, we suggest that IFP requires voluntary selection of color singletons in each consecutive trial.

  17. Determining an Estimate of an Equivalence Relation for Moderate and Large Sized Sets

    Directory of Open Access Journals (Sweden)

    Leszek Klukowski

    2017-01-01

    Full Text Available This paper presents two approaches to determining estimates of an equivalence relation on the basis of pairwise comparisons with random errors. Obtaining such an estimate requires the solution of a discrete programming problem which minimizes the sum of the differences between the form of the relation and the comparisons. The problem is NP hard and can be solved with the use of exact algorithms for sets of moderate size, i.e. about 50 elements. In the case of larger sets, i.e. at least 200 comparisons for each element, it is necessary to apply heuristic algorithms. The paper presents results (a statistical preprocessing, which enable us to determine the optimal or a near-optimal solution with acceptable computational cost. They include: the development of a statistical procedure producing comparisons with low probabilities of errors and a heuristic algorithm based on such comparisons. The proposed approach guarantees the applicability of such estimators for any size of set. (original abstract

  18. Disruption of visual feature binding in working memory.

    Science.gov (United States)

    Ueno, Taiji; Allen, Richard J; Baddeley, Alan D; Hitch, Graham J; Saito, Satoru

    2011-01-01

    In a series of five experiments, we studied the effect of a visual suffix on the retention in short-term visual memory of both individual visual features and objects involving the binding of two features. Experiments 1A, 1B, and 2 involved suffixes consisting of features external to the to-be-remembered set and revealed a modest but equivalent disruption on individual and bound feature conditions. Experiments 3A and 3B involved suffixes comprising features that could potentially have formed part of the to-be-remembered set (but did not on that trial). Both experiments showed greater disruption of retention for objects comprising bound features than for their individual features. The results are interpreted as differentiating two components of suffix interference, one affecting memory for features and bindings equally, the other affecting memory for bindings. The general component is tentatively identified with the attentional cost of operating a filter to prevent the suffix from entering visual working memory, whereas the specific component is attributed to the particular fragility of bound representations when the filter fails.

  19. Distinct features of trampoline-related orthopedic injuries in children aged under 6 years.

    Science.gov (United States)

    Choi, Eun Seok; Hong, Jin Heon; Sim, Jae Ang

    2018-02-01

    Concern has been growing about trampoline-related injuries among young children. Several published policy statements have repeatedly recommended that children younger than 6 years should not use trampolines. However, few studies have investigated the injuries caused by trampoline-related accidents among young children. This study aimed to identify the distinct features of trampoline-related orthopedic injuries in children younger than 6 years. We retrospectively reviewed the medical records of pediatric patients aged between 0 and 16 years who visited our regional emergency center due to trampoline-related orthopedic injuries between 2012 and 2015. Patients were divided into two groups: a preschool group (younger than 6 years) and a school group (older than 6 years). We compared the features of the injuries in the two groups. Among 208 patients, 108 (52%) were male and 100 (48%) were female. The mean age was 5.4 years. The preschool group accounted for 66%. There were no seasonal variations. Fractures were sustained in 96 patients (46%). The anatomical locations of injuries differed significantly between the two age groups. Proximal tibia fractures were more frequent in the preschool group than the school group (34% and 6%, respectively). Distal tibia fractures were more prevalent in the school group than the preschool group (44% vs. 13%, respectively). Surgical treatment was needed more frequently in the school group (p = 0.035, hazard ratio 2.52, 95% confidence interval: 1.03-6.17). Most of the injuries (82%) occurred at trampoline parks. The anatomical locations of trampoline-related orthopedic injuries differed significantly between age groups. Fractures were more common around the knee in younger children and the ankle in older children. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Relating price strategies and price-setting practices

    NARCIS (Netherlands)

    Ingenbleek, P.T.M.; Lans, van der I.A.

    2013-01-01

    Purpose - This article addresses the relationship between price strategies and price-setting practices. The first derive from a normative tradition in the pricing literature and the latter from a descriptive tradition. Price strategies are visible in the market, whereas price-setting practices are

  1. Use of liquefaction-induced features for paleoseismic analysis - An overview of how seismic liquefaction features can be distinguished from other features and how their regional distribution and properties of source sediment can be used to infer the location and strength of Holocene paleo-earthquakes

    Science.gov (United States)

    Obermeier, S.F.

    1996-01-01

    Liquefaction features can be used in many field settings to estimate the recurrence interval and magnitude of strong earthquakes through much of the Holocene. These features include dikes, craters, vented sand, sills, and laterally spreading landslides. The relatively high seismic shaking level required for their formation makes them particularly valuable as records of strong paleo-earthquakes. This state-of-the-art summary for using liquefaction-induced features for paleoseismic interpretation and analysis takes into account both geological and geotechnical engineering perspectives. The driving mechanism for formation of the features is primarily the increased pore-water pressure associated with liquefaction of sand-rich sediment. The role of this mechanism is often supplemented greatly by the direct action of seismic shaking at the ground surface, which strains and breaks the clay-rich cap that lies immediately above the sediment that liquefied. Discussed in the text are the processes involved in formation of the features, as well as their morphology and characteristics in field settings. Whether liquefaction occurs is controlled mainly by sediment grain size, sediment packing, depth to the water table, and strength and duration of seismic shaking. Formation of recognizable features in the field generally requires a low-permeability cap above the sediment that liquefied. Field manifestations are controlled largely by the severity of liquefaction and the thickness and properties of the low-permeability cap. Criteria are presented for determining whether observed sediment deformation in the field originated by seismically induced liquefaction. These criteria have been developed mainly by observing historic effects of liquefaction in varied field settings. The most important criterion is that a seismic liquefaction origin requires widespread, regional development of features around a core area where the effects are most severe. In addition, the features must have a

  2. Degeneracy relations in QCD and the equivalence of two systematic all-orders methods for setting the renormalization scale

    Directory of Open Access Journals (Sweden)

    Huan-Yu Bi

    2015-09-01

    Full Text Available The Principle of Maximum Conformality (PMC eliminates QCD renormalization scale-setting uncertainties using fundamental renormalization group methods. The resulting scale-fixed pQCD predictions are independent of the choice of renormalization scheme and show rapid convergence. The coefficients of the scale-fixed couplings are identical to the corresponding conformal series with zero β-function. Two all-orders methods for systematically implementing the PMC-scale setting procedure for existing high order calculations are discussed in this article. One implementation is based on the PMC-BLM correspondence (PMC-I; the other, more recent, method (PMC-II uses the Rδ-scheme, a systematic generalization of the minimal subtraction renormalization scheme. Both approaches satisfy all of the principles of the renormalization group and lead to scale-fixed and scheme-independent predictions at each finite order. In this work, we show that PMC-I and PMC-II scale-setting methods are in practice equivalent to each other. We illustrate this equivalence for the four-loop calculations of the annihilation ratio Re+e− and the Higgs partial width Γ(H→bb¯. Both methods lead to the same resummed (‘conformal’ series up to all orders. The small scale differences between the two approaches are reduced as additional renormalization group {βi}-terms in the pQCD expansion are taken into account. We also show that special degeneracy relations, which underly the equivalence of the two PMC approaches and the resulting conformal features of the pQCD series, are in fact general properties of non-Abelian gauge theory.

  3. Are Haar-like Rectangular Features for Biometric Recognition Reducible?

    DEFF Research Database (Denmark)

    Nasrollahi, Kamal; Moeslund, Thomas B.

    2013-01-01

    Biometric recognition is still a very difficult task in real-world scenarios wherein unforeseen changes in degradations factors like noise, occlusion, blurriness and illumination can drastically affect the extracted features from the biometric signals. Very recently Haar-like rectangular features...... which have usually been used for object detection were introduced for biometric recognition resulting in systems that are robust against most of the mentioned degradations [9]. The problem with these features is that one can define many different such features for a given biometric signal...... and it is not clear whether all of these features are required for the actual recognition or not. This is exactly what we are dealing with in this paper: How can an initial set of Haar-like rectangular features, that have been used for biometric recognition, be reduced to a set of most influential features...

  4. Using affective knowledge to generate and validate a set of emotion-related, action words.

    Science.gov (United States)

    Portch, Emma; Havelka, Jelena; Brown, Charity; Giner-Sorolla, Roger

    2015-01-01

    Emotion concepts are built through situated experience. Abstract word meaning is grounded in this affective knowledge, giving words the potential to evoke emotional feelings and reactions (e.g., Vigliocco et al., 2009). In the present work we explore whether words differ in the extent to which they evoke 'specific' emotional knowledge. Using a categorical approach, in which an affective 'context' is created, it is possible to assess whether words proportionally activate knowledge relevant to different emotional states (e.g., 'sadness', 'anger', Stevenson, Mikels & James, 2007a). We argue that this method may be particularly effective when assessing the emotional meaning of action words (e.g., Schacht & Sommer, 2009). In study 1 we use a constrained feature generation task to derive a set of action words that participants associated with six, basic emotional states (see full list in Appendix S1). Generation frequencies were taken to indicate the likelihood that the word would evoke emotional knowledge relevant to the state to which it had been paired. In study 2 a rating task was used to assess the strength of association between the six most frequently generated, or 'typical', action words and corresponding emotion labels. Participants were presented with a series of sentences, in which action words (typical and atypical) and labels were paired e.g., "If you are feeling 'sad' how likely would you be to act in the following way?" … 'cry.' Findings suggest that typical associations were robust. Participants always gave higher ratings to typical vs. atypical action word and label pairings, even when (a) rating direction was manipulated (the label or verb appeared first in the sentence), and (b) the typical behaviours were to be performed by the rater themselves, or others. Our findings suggest that emotion-related action words vary in the extent to which they evoke knowledge relevant for different emotional states. When measuring affective grounding, it may then be

  5. Automatic feature design for optical character recognition using an evolutionary search procedure.

    Science.gov (United States)

    Stentiford, F W

    1985-03-01

    An automatic evolutionary search is applied to the problem of feature extraction in an OCR application. A performance measure based on feature independence is used to generate features which do not appear to suffer from peaking effects [17]. Features are extracted from a training set of 30 600 machine printed 34 class alphanumeric characters derived from British mail. Classification results on the training set and a test set of 10 200 characters are reported for an increasing number of features. A 1.01 percent forced decision error rate is obtained on the test data using 316 features. The hardware implementation should be cheap and fast to operate. The performance compares favorably with current low cost OCR page readers.

  6. Using Relational Histogram Features and Action Labelled Data to Learn Preconditions for Means-End Actions

    DEFF Research Database (Denmark)

    Fichtl, Severin; Kraft, Dirk; Krüger, Norbert

    2015-01-01

    The outcome of many complex manipulation ac- tions is contingent on the spatial relationships among pairs of objects, e.g. if an object is “inside” or “on top” of another. Recognising these spatial relationships requires a vision system which can extract appropriate features from the vision input...... that capture and represent the spatial relationships in an easily accessible way. We are interested in learning to predict the success of “means end” actions that manipulate two objects at once, from exploratory actions, and the observed sensorimo- tor contingencies. In this paper, we use relational histogram...... features and illustrate their effect on learning to predict a variety of “means end” actions’ outcomes. The results show that our vision features can make the learning problem significantly easier, leading to increased learning rates and higher maximum performance. This work is in particular important...

  7. Quantitative prediction of drug side effects based on drug-related features.

    Science.gov (United States)

    Niu, Yanqing; Zhang, Wen

    2017-09-01

    Unexpected side effects of drugs are great concern in the drug development, and the identification of side effects is an important task. Recently, machine learning methods are proposed to predict the presence or absence of interested side effects for drugs, but it is difficult to make the accurate prediction for all of them. In this paper, we transform side effect profiles of drugs as their quantitative scores, by summing up their side effects with weights. The quantitative scores may measure the dangers of drugs, and thus help to compare the risk of different drugs. Here, we attempt to predict quantitative scores of drugs, namely the quantitative prediction. Specifically, we explore a variety of drug-related features and evaluate their discriminative powers for the quantitative prediction. Then, we consider several feature combination strategies (direct combination, average scoring ensemble combination) to integrate three informative features: chemical substructures, targets, and treatment indications. Finally, the average scoring ensemble model which produces the better performances is used as the final quantitative prediction model. Since weights for side effects are empirical values, we randomly generate different weights in the simulation experiments. The experimental results show that the quantitative method is robust to different weights, and produces satisfying results. Although other state-of-the-art methods cannot make the quantitative prediction directly, the prediction results can be transformed as the quantitative scores. By indirect comparison, the proposed method produces much better results than benchmark methods in the quantitative prediction. In conclusion, the proposed method is promising for the quantitative prediction of side effects, which may work cooperatively with existing state-of-the-art methods to reveal dangers of drugs.

  8. High-level intuitive features (HLIFs) for intuitive skin lesion description.

    Science.gov (United States)

    Amelard, Robert; Glaister, Jeffrey; Wong, Alexander; Clausi, David A

    2015-03-01

    A set of high-level intuitive features (HLIFs) is proposed to quantitatively describe melanoma in standard camera images. Melanoma is the deadliest form of skin cancer. With rising incidence rates and subjectivity in current clinical detection methods, there is a need for melanoma decision support systems. Feature extraction is a critical step in melanoma decision support systems. Existing feature sets for analyzing standard camera images are comprised of low-level features, which exist in high-dimensional feature spaces and limit the system's ability to convey intuitive diagnostic rationale. The proposed HLIFs were designed to model the ABCD criteria commonly used by dermatologists such that each HLIF represents a human-observable characteristic. As such, intuitive diagnostic rationale can be conveyed to the user. Experimental results show that concatenating the proposed HLIFs with a full low-level feature set increased classification accuracy, and that HLIFs were able to separate the data better than low-level features with statistical significance. An example of a graphical interface for providing intuitive rationale is given.

  9. Automated Experiments on Ad Privacy Settings

    Directory of Open Access Journals (Sweden)

    Datta Amit

    2015-04-01

    Full Text Available To partly address people’s concerns over web tracking, Google has created the Ad Settings webpage to provide information about and some choice over the profiles Google creates on users. We present AdFisher, an automated tool that explores how user behaviors, Google’s ads, and Ad Settings interact. AdFisher can run browser-based experiments and analyze data using machine learning and significance tests. Our tool uses a rigorous experimental design and statistical analysis to ensure the statistical soundness of our results. We use AdFisher to find that the Ad Settings was opaque about some features of a user’s profile, that it does provide some choice on ads, and that these choices can lead to seemingly discriminatory ads. In particular, we found that visiting webpages associated with substance abuse changed the ads shown but not the settings page. We also found that setting the gender to female resulted in getting fewer instances of an ad related to high paying jobs than setting it to male. We cannot determine who caused these findings due to our limited visibility into the ad ecosystem, which includes Google, advertisers, websites, and users. Nevertheless, these results can form the starting point for deeper investigations by either the companies themselves or by regulatory bodies.

  10. Everyday science & science every day: Science-related talk & activities across settings

    Science.gov (United States)

    Zimmerman, Heather

    To understand the development of science-related thinking, acting, and learning in middle childhood, I studied youth in schools, homes, and other neighborhood settings over a three-year period. The research goal was to analyze how multiple everyday experiences influence children's participation in science-related practices and their thinking about science and scientists. Ethnographic and interaction analysis methodologies were to study the cognition and social interactions of the children as they participated in activities with peers, family, and teachers (n=128). Interviews and participant self-documentation protocols elucidated the participants' understandings of science. An Everyday Expertise (Bell et al., 2006) theoretical framework was employed to study the development of science understandings on three analytical planes: individual learner, social groups, and societal/community resources. Findings came from a cross-case analysis of urban science learners and from two within-case analyses of girls' science-related practices as they transitioned from elementary to middle school. Results included: (1) children participated actively in science across settings---including in their homes as well as in schools, (2) children's interests in science were not always aligned to the school science content, pedagogy, or school structures for participation, yet children found ways to engage with science despite these differences through crafting multiple pathways into science, (3) urban parents were active supporters of STEM-related learning environments through brokering access to social and material resources, (4) the youth often found science in their daily activities that formal education did not make use of, and (5) children's involvement with science-related practices can be developed into design principles to reach youth in culturally relevant ways.

  11. Multi-task feature learning by using trace norm regularization

    Directory of Open Access Journals (Sweden)

    Jiangmei Zhang

    2017-11-01

    Full Text Available Multi-task learning can extract the correlation of multiple related machine learning problems to improve performance. This paper considers applying the multi-task learning method to learn a single task. We propose a new learning approach, which employs the mixture of expert model to divide a learning task into several related sub-tasks, and then uses the trace norm regularization to extract common feature representation of these sub-tasks. A nonlinear extension of this approach by using kernel is also provided. Experiments conducted on both simulated and real data sets demonstrate the advantage of the proposed approach.

  12. Relation of borderline personality features to preincarceration HIV risk behaviors of jail inmates: Evidence for gender differences?

    Science.gov (United States)

    Adams, Leah M; Stuewig, Jeffrey B; Tangney, June P

    2016-01-01

    The present study explored the relationship between borderline personality disorder (BPD) features and preincarceration HIV risk behaviors in a sample of 499 (70% male) jail inmates, as well as gender differences in these associations. Elevated levels of BPD symptomatology were present among male and female participants, though there was considerable variance observed in participants' BPD symptoms and HIV risk behaviors. In the full sample, BPD features were positively associated with a variety of HIV risk behaviors, including injection drug use and frequency of unprotected sex with high risk partners and under high risk circumstances. Gender moderated only 2 relationships between BPD features and HIV risk behaviors, with a stronger association between BPD features and number of sexual partners, and BPD features and frequency of unprotected sex while under the influence of alcohol or drugs for women, relative to men. Taken together, these findings suggest that programs targeting HIV risk within correctional populations may benefit from considering the role of BPD features, particularly emotion regulation difficulties and impulsivity, in influencing HIV risk behaviors among both women and men. (c) 2016 APA, all rights reserved).

  13. Quantitative analysis and relevant features of the scientific literature related to SAXS and SANS

    International Nuclear Information System (INIS)

    Craievich, Aldo F; Fischer, Hannes

    2010-01-01

    We present and discuss here numerical information derived from a systematic searching of scientific papers related to SAXS and SANS published in indexed journals - from 1945 until nowadays - recorded by the Web of Science Data Bank (WoS). We have detected interesting features regarding the time dependence of the number of papers/year, N(t), indicating the existence of three well-defined periods of historical evolution with rather well-defined boundaries. All three periods exhibit a positive and approximately linear variation of N(t) but, at the two transitions between periods, the rate of growth exhibits clear and strong increases. Differences of the historical evolutions in the numbers of papers/year related to SAXS and to SANS were established. The different behaviours regarding the numbers of papers/year related to SAXS and to SANS and the existence of three different and well defined periods for N(t) can be qualitatively understood as a consequence of the progressive and increasing availability along the last three decades of very brilliant synchrotrons, last generation commercial X-ray sources, new neutron facilities, powerful computers and novel theoretical approaches for SAS data analysis. The rates of growth in the number of papers/year published by authors from a set of different countries are approximately constant along the last two decades. For other countries we have detected a slowing down effect in the number of papers/year while a clear acceleration could be noticed for the production of SAS papers by authors from several emerging countries. These opposite trends compensate in such a way that the number of SAS (SAXS+SAXS) articles published per year all around the world maintained a vigorous linear growth - during more than 20 years - at a constant rate of 60 papers/year, without any indication of eventual saturation. The observed distribution of articles among different journals indicates that a very high fraction of the volume of SAS research is

  14. Etiological features of borderline personality related characteristics in a birth cohort of 12-year-old children.

    Science.gov (United States)

    Belsky, Daniel W; Caspi, Avshalom; Arseneault, Louise; Bleidorn, Wiebke; Fonagy, Peter; Goodman, Marianne; Houts, Renate; Moffitt, Terrie E

    2012-02-01

    It has been reported that borderline personality related characteristics can be observed in children, and that these characteristics are associated with increased risk for the development of borderline personality disorder. It is not clear whether borderline personality related characteristics in children share etiological features with adult borderline personality disorder. We investigated the etiology of borderline personality related characteristics in a longitudinal cohort study of 1,116 pairs of same-sex twins followed from birth through age 12 years. Borderline personality related characteristics measured at age 12 years were highly heritable, were more common in children who had exhibited poor cognitive function, impulsivity, and more behavioral and emotional problems at age 5 years, and co-occurred with symptoms of conduct disorder, depression, anxiety, and psychosis. Exposure to harsh treatment in the family environment through age 10 years predicted borderline personality related characteristics at age 12 years. This association showed evidence of environmental mediation and was stronger among children with a family history of psychiatric illness, consistent with diathesis-stress models of borderline etiology. Results indicate that borderline personality related characteristics in children share etiological features with borderline personality disorder in adults and suggest that inherited and environmental risk factors make independent and interactive contributions to borderline etiology.

  15. Gene Ontology and KEGG Enrichment Analyses of Genes Related to Age-Related Macular Degeneration

    Directory of Open Access Journals (Sweden)

    Jian Zhang

    2014-01-01

    Full Text Available Identifying disease genes is one of the most important topics in biomedicine and may facilitate studies on the mechanisms underlying disease. Age-related macular degeneration (AMD is a serious eye disease; it typically affects older adults and results in a loss of vision due to retina damage. In this study, we attempt to develop an effective method for distinguishing AMD-related genes. Gene ontology and KEGG enrichment analyses of known AMD-related genes were performed, and a classification system was established. In detail, each gene was encoded into a vector by extracting enrichment scores of the gene set, including it and its direct neighbors in STRING, and gene ontology terms or KEGG pathways. Then certain feature-selection methods, including minimum redundancy maximum relevance and incremental feature selection, were adopted to extract key features for the classification system. As a result, 720 GO terms and 11 KEGG pathways were deemed the most important factors for predicting AMD-related genes.

  16. When do letter features migrate? A boundary condition for feature-integration theory.

    Science.gov (United States)

    Butler, B E; Mewhort, D J; Browse, R A

    1991-01-01

    Feature-integration theory postulates that a lapse of attention will allow letter features to change position and to recombine as illusory conjunctions (Treisman & Paterson, 1984). To study such errors, we used a set of uppercase letters known to yield illusory conjunctions in each of three tasks. The first, a bar-probe task, showed whole-character mislocations but not errors based on feature migration and recombination. The second, a two-alternative forced-choice detection task, allowed subjects to focus on the presence or absence of subletter features and showed illusory conjunctions based on feature migration and recombination. The third was also a two-alternative forced-choice detection task, but we manipulated the subjects' knowledge of the shape of the stimuli: In the case-certain condition, the stimuli were always in uppercase, but in the case-uncertain condition, the stimuli could appear in either upper- or lowercase. Subjects in the case-certain condition produced illusory conjunctions based on feature recombination, whereas subjects in the case-uncertain condition did not. The results suggest that when subjects can view the stimuli as feature groups, letter features regroup as illusory conjunctions; when subjects encode the stimuli as letters, whole items may be mislocated, but subletter features are not. Thus, illusory conjunctions reflect the subject's processing strategy, rather than the architecture of the visual system.

  17. Featureous: infrastructure for feature-centric analysis of object-oriented software

    DEFF Research Database (Denmark)

    Olszak, Andrzej; Jørgensen, Bo Nørregaard

    2010-01-01

    The decentralized nature of collaborations between objects in object-oriented software makes it difficult to understand how user-observable program features are implemented and how their implementations relate to each other. It is worthwhile to improve this situation, since feature-centric program...... understanding and modification are essential during software evolution and maintenance. In this paper, we present an infrastructure built on top of the NetBeans IDE called Featureous that allows for rapid construction of tools for feature-centric analysis of object-oriented software. Our infrastructure...... encompasses a lightweight feature location mechanism, a number of analytical views and an API allowing for addition of third-party extensions. To form a common conceptual framework for future feature-centric extensions, we propose to structure feature centric analysis along three dimensions: perspective...

  18. Visualization of Penile Suspensory Ligamentous System Based on Visible Human Data Sets

    Science.gov (United States)

    Chen, Xianzhuo; Wu, Yi; Tao, Ling; Yan, Yan; Pang, Jun; Zhang, Shaoxiang; Li, Shirong

    2017-01-01

    Background The aim of this study was to use a three-dimensional (3D) visualization technology to illustrate and describe the anatomical features of the penile suspensory ligamentous system based on the Visible Human data sets and to explore the suspensory mechanism of the penis for the further improvement of the penis-lengthening surgery. Material/Methods Cross-sectional images retrieved from the first Chinese Visible Human (CVH-1), third Chinese Visible Human (CVH-3), and Visible Human Male (VHM) data sets were used to segment the suspensory ligamentous system and its adjacent structures. The magnetic resonance imaging (MRI) images of this system were studied and compared with those from the Visible Human data sets. The 3D models reconstructed from the Visible Human data sets were used to provide morphological features of the penile suspensory ligamentous system and its related structures. Results The fundiform ligament was a superficial, loose, fibro-fatty tissue which originated from Scarpa’s fascia superiorly and continued to the scrotal septum inferiorly. The suspensory ligament and arcuate pubic ligament were dense fibrous connective tissues which started from the pubic symphysis and terminated by attaching to the tunica albuginea of the corpora cavernosa. Furthermore, the arcuate pubic ligament attached to the inferior rami of the pubis laterally. Conclusions The 3D model based on Visible Human data sets can be used to clarify the anatomical features of the suspensory ligamentous system, thereby contributing to the improvement of penis-lengthening surgery. PMID:28530218

  19. Automatic extraction of ontological relations from Arabic text

    Directory of Open Access Journals (Sweden)

    Mohammed G.H. Al Zamil

    2014-12-01

    The proposed methodology has been designed to analyze Arabic text using lexical semantic patterns of the Arabic language according to a set of features. Next, the features have been abstracted and enriched with formal descriptions for the purpose of generalizing the resulted rules. The rules, then, have formulated a classifier that accepts Arabic text, analyzes it, and then displays related concepts labeled with its designated relationship. Moreover, to resolve the ambiguity of homonyms, a set of machine translation, text mining, and part of speech tagging algorithms have been reused. We performed extensive experiments to measure the effectiveness of our proposed tools. The results indicate that our proposed methodology is promising for automating the process of extracting ontological relations.

  20. Distant supervision for neural relation extraction integrated with word attention and property features.

    Science.gov (United States)

    Qu, Jianfeng; Ouyang, Dantong; Hua, Wen; Ye, Yuxin; Li, Ximing

    2018-04-01

    Distant supervision for neural relation extraction is an efficient approach to extracting massive relations with reference to plain texts. However, the existing neural methods fail to capture the critical words in sentence encoding and meanwhile lack useful sentence information for some positive training instances. To address the above issues, we propose a novel neural relation extraction model. First, we develop a word-level attention mechanism to distinguish the importance of each individual word in a sentence, increasing the attention weights for those critical words. Second, we investigate the semantic information from word embeddings of target entities, which can be developed as a supplementary feature for the extractor. Experimental results show that our model outperforms previous state-of-the-art baselines. Copyright © 2018 Elsevier Ltd. All rights reserved.

  1. Anatomical Features of the Interscapular Area Where Wet Cupping Therapy Is Done and Its Possible Relation to Acupuncture Meridians.

    Science.gov (United States)

    Ghods, Roshanak; Sayfouri, Nasrin; Ayati, Mohammad Hossein

    2016-12-01

    Although wet cupping has been a treatment for centuries, its mechanism of action is not well understood. Because the anatomical features of the wet-cupping area might play a role in its mechanism, we focus on the features of the interscapular area in which a common type of wet-cupping therapy (WCT), called Hijamat-e-Aam in Iranian medicine, is usually applied and discuss the possible relation of those features to the acupuncture meridians. We gathered and analyzed data from reliable textbooks on modern medicine with a focus on the anatomical features of the interscapular area, topics related to WTC in Iranian medicine, and acupuncture sources obtained by searching PubMed, Google-Scholar, and Science Direct. The interscapular area used for WCT was found to have special features: brown adipose tissue, immediate proximity to sympathetic ganglia, passage of the thoracic duct, two important acupuncture meridians, and proximity to the main vessel divisions carrying blood from the heart and the brain. These features indicate that the interscapular application of WCT not only discharges waste materials through a shifting of blood to the site after application of a traction force but also invigorates the body's metabolism, increases immunity, and regulates blood biochemistry, which are desired therapeutic effects of WCT. Copyright © 2016. Published by Elsevier B.V.

  2. Anatomical Features of the Interscapular Area Where Wet Cupping Therapy Is Done and Its Possible Relation to Acupuncture Meridians

    Directory of Open Access Journals (Sweden)

    Roshanak Ghods

    2016-12-01

    Full Text Available Although wet cupping has been a treatment for centuries, its mechanism of action is not well understood. Because the anatomical features of the wet-cupping area might play a role in its mechanism, we focus on the features of the interscapular area in which a common type of wet-cupping therapy (WCT, called Hijamat-e-Aam in Iranian medicine, is usually applied and discuss the possible relation of those features to the acupuncture meridians. We gathered and analyzed data from reliable textbooks on modern medicine with a focus on the anatomical features of the interscapular area, topics related to WTC in Iranian medicine, and acupuncture sources obtained by searching PubMed, Google-Scholar, and Science Direct. The interscapular area used for WCT was found to have special features: brown adipose tissue, immediate proximity to sympathetic ganglia, passage of the thoracic duct, two important acupuncture meridians, and proximity to the main vessel divisions carrying blood from the heart and the brain. These features indicate that the interscapular application of WCT not only discharges waste materials through a shifting of blood to the site after application of a traction force but also invigorates the body’s metabolism, increases immunity, and regulates blood biochemistry, which are desired therapeutic effects of WCT.

  3. Cardiovascular disease prediction: do pulmonary disease-related chest CT features have added value?

    International Nuclear Information System (INIS)

    Jairam, Pushpa M.; Jong, Pim A. de; Mali, Willem P.T.M.; Isgum, Ivana; Graaf, Yolanda van der

    2015-01-01

    Certain pulmonary diseases are associated with cardiovascular disease (CVD). Therefore we investigated the incremental predictive value of pulmonary, mediastinal and pleural features over cardiovascular imaging findings. A total of 10,410 patients underwent diagnostic chest CT for non-cardiovascular indications. Using a case-cohort approach, we visually graded CTs from the cases and from an approximately 10 % random sample of the baseline cohort (n = 1,203) for cardiovascular, pulmonary, mediastinal and pleural findings. The incremental value of pulmonary disease-related CT findings above cardiovascular imaging findings in cardiovascular event risk prediction was quantified by comparing discrimination and reclassification. During a mean follow-up of 3.7 years (max. 7.0 years), 1,148 CVD events (cases) were identified. Addition of pulmonary, mediastinal and pleural features to a cardiovascular imaging findings-based prediction model led to marginal improvement of discrimination (increase in c-index from 0.72 (95 % CI 0.71-0.74) to 0.74 (95 % CI 0.72-0.75)) and reclassification measures (net reclassification index 6.5 % (p < 0.01)). Pulmonary, mediastinal and pleural features have limited predictive value in the identification of subjects at high risk of CVD events beyond cardiovascular findings on diagnostic chest CT scans. (orig.)

  4. Cardiovascular disease prediction: do pulmonary disease-related chest CT features have added value?

    Energy Technology Data Exchange (ETDEWEB)

    Jairam, Pushpa M. [University Medical Center Utrecht, Julius Center for Health Sciences and Primary Care, Utrecht (Netherlands); University Medical Center Utrecht, Department of Radiology, Utrecht (Netherlands); Jong, Pim A. de; Mali, Willem P.T.M. [University Medical Center Utrecht, Department of Radiology, Utrecht (Netherlands); Isgum, Ivana [University Medical Center Utrecht, Image Sciences Institute, Utrecht (Netherlands); Graaf, Yolanda van der [University Medical Center Utrecht, Julius Center for Health Sciences and Primary Care, Utrecht (Netherlands); Collaboration: PROVIDI study-group

    2015-06-01

    Certain pulmonary diseases are associated with cardiovascular disease (CVD). Therefore we investigated the incremental predictive value of pulmonary, mediastinal and pleural features over cardiovascular imaging findings. A total of 10,410 patients underwent diagnostic chest CT for non-cardiovascular indications. Using a case-cohort approach, we visually graded CTs from the cases and from an approximately 10 % random sample of the baseline cohort (n = 1,203) for cardiovascular, pulmonary, mediastinal and pleural findings. The incremental value of pulmonary disease-related CT findings above cardiovascular imaging findings in cardiovascular event risk prediction was quantified by comparing discrimination and reclassification. During a mean follow-up of 3.7 years (max. 7.0 years), 1,148 CVD events (cases) were identified. Addition of pulmonary, mediastinal and pleural features to a cardiovascular imaging findings-based prediction model led to marginal improvement of discrimination (increase in c-index from 0.72 (95 % CI 0.71-0.74) to 0.74 (95 % CI 0.72-0.75)) and reclassification measures (net reclassification index 6.5 % (p < 0.01)). Pulmonary, mediastinal and pleural features have limited predictive value in the identification of subjects at high risk of CVD events beyond cardiovascular findings on diagnostic chest CT scans. (orig.)

  5. JCE Feature Columns

    Science.gov (United States)

    Holmes, Jon L.

    1999-05-01

    The Features area of JCE Online is now readily accessible through a single click from our home page. In the Features area each column is linked to its own home page. These column home pages also have links to them from the online Journal Table of Contents pages or from any article published as part of that feature column. Using these links you can easily find abstracts of additional articles that are related by topic. Of course, JCE Online+ subscribers are then just one click away from the entire article. Finding related articles is easy because each feature column "site" contains links to the online abstracts of all the articles that have appeared in the column. In addition, you can find the mission statement for the column and the email link to the column editor that I mentioned above. At the discretion of its editor, a feature column site may contain additional resources. As an example, the Chemical Information Instructor column edited by Arleen Somerville will have a periodically updated bibliography of resources for teaching and using chemical information. Due to the increase in the number of these resources available on the WWW, it only makes sense to publish this information online so that you can get to these resources with a simple click of the mouse. We expect that there will soon be additional information and resources at several other feature column sites. Following in the footsteps of the Chemical Information Instructor, up-to-date bibliographies and links to related online resources can be made available. We hope to extend the online component of our feature columns with moderated online discussion forums. If you have a suggestion for an online resource you would like to see included, let the feature editor or JCE Online (jceonline@chem.wisc.edu) know about it. JCE Internet Features JCE Internet also has several feature columns: Chemical Education Resource Shelf, Conceptual Questions and Challenge Problems, Equipment Buyers Guide, Hal's Picks, Mathcad

  6. Biopsychosocial impact of the voice in relation to the psychological features in female student teachers.

    NARCIS (Netherlands)

    Meulenbroek, L.F.P.; Thomas, G.; Kooijman, P.G.C.; Jong, F.I.C.R.S. de

    2010-01-01

    OBJECTIVE: The aim of the study was to assess biopsychosocial impact of the voice in relation to the psychological features in female student teachers. METHODS: This research was a cross-sectional study in 755 student teachers using general questionnaires, the Voice Handicap Inventory (VHI), Type D

  7. Detection of Fraudulent Emails by Employing Advanced Feature Abundance

    DEFF Research Database (Denmark)

    Nizamani, Sarwat; Memon, Nasrullah; Glasdam, Mathies

    2014-01-01

    In this paper, we present a fraudulent email detection model using advanced feature choice. We extracted various kinds of features and compared the performance of each category of features with the others in terms of the fraudulent email detection rate. The different types of features...... are incorporated step by step. The detection of fraudulent email has been considered as a classification problem and it is evaluated using various state-of-the art algorithms and on CCM [1] which is authors' previous cluster based classification model. The experiments have been performed on diverse feature sets...... and the different classification methods. The comparison of the results is also presented and the evaluations shows that for the fraudulent email detection tasks, the feature set is more important regardless of classification method. The results of the study suggest that the task of fraudulent emails detection...

  8. Hypothesis testing for differentially correlated features.

    Science.gov (United States)

    Sheng, Elisa; Witten, Daniela; Zhou, Xiao-Hua

    2016-10-01

    In a multivariate setting, we consider the task of identifying features whose correlations with the other features differ across conditions. Such correlation shifts may occur independently of mean shifts, or differences in the means of the individual features across conditions. Previous approaches for detecting correlation shifts consider features simultaneously, by computing a correlation-based test statistic for each feature. However, since correlations involve two features, such approaches do not lend themselves to identifying which feature is the culprit. In this article, we instead consider a serial testing approach, by comparing columns of the sample correlation matrix across two conditions, and removing one feature at a time. Our method provides a novel perspective and favorable empirical results compared with competing approaches. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  9. Contingent attentional capture across multiple feature dimensions in a temporal search task.

    Science.gov (United States)

    Ito, Motohiro; Kawahara, Jun I

    2016-01-01

    The present study examined whether attention can be flexibly controlled to monitor two different feature dimensions (shape and color) in a temporal search task. Specifically, we investigated the occurrence of contingent attentional capture (i.e., interference from task-relevant distractors) and resulting set reconfiguration (i.e., enhancement of single task-relevant set). If observers can restrict searches to a specific value for each relevant feature dimension independently, the capture and reconfiguration effect should only occur when the single relevant distractor in each dimension appears. Participants identified a target letter surrounded by a non-green square or a non-square green frame. The results revealed contingent attentional capture, as target identification accuracy was lower when the distractor contained a target-defining feature than when it contained a nontarget feature. Resulting set reconfiguration was also obtained in that accuracy was superior when the current target's feature (e.g., shape) corresponded to the defining feature of the present distractor (shape) than when the current target's feature did not match the distractor's feature (color). This enhancement was not due to perceptual priming. The present study demonstrated that the principles of contingent attentional capture and resulting set reconfiguration held even when multiple target feature dimensions were monitored. Copyright © 2015 Elsevier B.V. All rights reserved.

  10. Analysis of the Westland Data Set

    Science.gov (United States)

    Wen, Fang; Willett, Peter; Deb, Somnath

    2001-01-01

    The "Westland" set of empirical accelerometer helicopter data with seeded and labeled faults is analyzed with the aim of condition monitoring. The autoregressive (AR) coefficients from a simple linear model encapsulate a great deal of information in a relatively few measurements; and it has also been found that augmentation of these by harmonic and other parameters call improve classification significantly. Several techniques have been explored, among these restricted Coulomb energy (RCE) networks, learning vector quantization (LVQ), Gaussian mixture classifiers and decision trees. A problem with these approaches, and in common with many classification paradigms, is that augmentation of the feature dimension can degrade classification ability. Thus, we also introduce the Bayesian data reduction algorithm (BDRA), which imposes a Dirichlet prior oil training data and is thus able to quantify probability of error in all exact manner, such that features may be discarded or coarsened appropriately.

  11. Feature selection using genetic algorithms for fetal heart rate analysis

    International Nuclear Information System (INIS)

    Xu, Liang; Redman, Christopher W G; Georgieva, Antoniya; Payne, Stephen J

    2014-01-01

    The fetal heart rate (FHR) is monitored on a paper strip (cardiotocogram) during labour to assess fetal health. If necessary, clinicians can intervene and assist with a prompt delivery of the baby. Data-driven computerized FHR analysis could help clinicians in the decision-making process. However, selecting the best computerized FHR features that relate to labour outcome is a pressing research problem. The objective of this study is to apply genetic algorithms (GA) as a feature selection method to select the best feature subset from 64 FHR features and to integrate these best features to recognize unfavourable FHR patterns. The GA was trained on 404 cases and tested on 106 cases (both balanced datasets) using three classifiers, respectively. Regularization methods and backward selection were used to optimize the GA. Reasonable classification performance is shown on the testing set for the best feature subset (Cohen's kappa values of 0.45 to 0.49 using different classifiers). This is, to our knowledge, the first time that a feature selection method for FHR analysis has been developed on a database of this size. This study indicates that different FHR features, when integrated, can show good performance in predicting labour outcome. It also gives the importance of each feature, which will be a valuable reference point for further studies. (paper)

  12. Using listener-based perceptual features as intermediate representations in music information retrieval.

    Science.gov (United States)

    Friberg, Anders; Schoonderwaldt, Erwin; Hedblad, Anton; Fabiani, Marco; Elowsson, Anders

    2014-10-01

    The notion of perceptual features is introduced for describing general music properties based on human perception. This is an attempt at rethinking the concept of features, aiming to approach the underlying human perception mechanisms. Instead of using concepts from music theory such as tones, pitches, and chords, a set of nine features describing overall properties of the music was selected. They were chosen from qualitative measures used in psychology studies and motivated from an ecological approach. The perceptual features were rated in two listening experiments using two different data sets. They were modeled both from symbolic and audio data using different sets of computational features. Ratings of emotional expression were predicted using the perceptual features. The results indicate that (1) at least some of the perceptual features are reliable estimates; (2) emotion ratings could be predicted by a small combination of perceptual features with an explained variance from 75% to 93% for the emotional dimensions activity and valence; (3) the perceptual features could only to a limited extent be modeled using existing audio features. Results clearly indicated that a small number of dedicated features were superior to a "brute force" model using a large number of general audio features.

  13. On some classical problems of descriptive set theory

    International Nuclear Information System (INIS)

    Kanovei, Vladimir G; Lyubetskii, Vasilii A

    2003-01-01

    The centenary of P.S. Novikov's birth provides an inspiring motivation to present, with full proofs and from a modern standpoint, the presumably definitive solutions of some classical problems in descriptive set theory which were formulated by Luzin [Lusin] and, to some extent, even earlier by Hadamard, Borel, and Lebesgue and relate to regularity properties of point sets. The solutions of these problems began in the pioneering works of Aleksandrov [Alexandroff], Suslin [Souslin], and Luzin (1916-17) and evolved in the fundamental studies of Goedel, Novikov, Cohen, and their successors. Main features of this branch of mathematics are that, on the one hand, it is an ordinary mathematical theory studying natural properties of point sets and functions and rather distant from general set theory or intrinsic problems of mathematical logic like consistency or Goedel's theorems, and on the other hand, it has become a subject of applications of the most subtle tools of modern mathematical logic

  14. Identifying relevant feature-action associations for grasping unmodelled objects

    DEFF Research Database (Denmark)

    Thomsen, Mikkel Tang; Kraft, Dirk; Krüger, Norbert

    2015-01-01

    content. The method is provided with a large and structured set of visual features, motivated by the visual hierarchy in primates and finds relevant feature action associations automatically. We apply our method in a simulated environment on three different object sets for the case of grasp affordance...... learning. For box objects, we achieve a 0.90 success probability, 0.80 for round objects and up to 0.75 for open objects, when presented with novel objects. In this work, we in particular demonstrate the effect of choosing appropriate feature representations. We demonstrate a significant performance...

  15. Pharmacy students' preference for using mobile devices in a clinical setting for practice-related tasks.

    Science.gov (United States)

    Richard, Craig A H; Hastings, Justine F; Bryant, Jennifer E

    2015-03-25

    To examine pharmacy students' ownership of, use of, and preference for using a mobile device in a practice setting. Eighty-one pharmacy students were recruited and completed a pretest that collected information about their demographics and mobile devices and also had them rank the iPhone, iPad mini, and iPad for preferred use in a pharmacy practice setting. Students used the 3 devices to perform pharmacy practice-related tasks and then completed a posttest to again rank the devices for preferred use in a pharmacy practice setting. The iPhone was the most commonly owned mobile device (59.3% of students), and the iPad mini was the least commonly owned (18.5%). About 70% of the students used their mobile devices at least once a week in a pharmacy practice setting. The iPhone was the most commonly used device in a practice setting (46.9% of students), and the iPod Touch was the least commonly used device (1.2%). The iPad mini was the most preferred device for use in a pharmacy practice setting prior to performing pharmacy practice-related tasks (49.4% of students), and was preferred by significantly more students after performing the tasks (70.4%). Pharmacy students commonly use their mobile devices in pharmacy practice settings and most selected the iPad mini as the preferred device for use in a practice setting even though it was the device owned by the fewest students.

  16. Feature selection using angle modulated simulated Kalman filter for peak classification of EEG signals.

    Science.gov (United States)

    Adam, Asrul; Ibrahim, Zuwairie; Mokhtar, Norrima; Shapiai, Mohd Ibrahim; Mubin, Marizan; Saad, Ismail

    2016-01-01

    In the existing electroencephalogram (EEG) signals peak classification research, the existing models, such as Dumpala, Acir, Liu, and Dingle peak models, employ different set of features. However, all these models may not be able to offer good performance for various applications and it is found to be problem dependent. Therefore, the objective of this study is to combine all the associated features from the existing models before selecting the best combination of features. A new optimization algorithm, namely as angle modulated simulated Kalman filter (AMSKF) will be employed as feature selector. Also, the neural network random weight method is utilized in the proposed AMSKF technique as a classifier. In the conducted experiment, 11,781 samples of peak candidate are employed in this study for the validation purpose. The samples are collected from three different peak event-related EEG signals of 30 healthy subjects; (1) single eye blink, (2) double eye blink, and (3) eye movement signals. The experimental results have shown that the proposed AMSKF feature selector is able to find the best combination of features and performs at par with the existing related studies of epileptic EEG events classification.

  17. Cross-sensory mapping of feature values in the size-brightness correspondence can be more relative than absolute

    OpenAIRE

    Walker, Laura; Walker, Peter

    2016-01-01

    A role for conceptual representations in cross-sensory correspondences has been linked to the relative (context-sensitive) mapping of feature values, whereas a role for sensory-perceptual representations has been linked to their absolute (context-insensitive) mapping. Demonstrating the relative nature of the automatic mapping underlying a cross-sensory correspondence therefore offers one way of confirming its conceptual basis. After identifying several prerequisites for relative and absolute ...

  18. Feature Extraction from 3D Point Cloud Data Based on Discrete Curves

    Directory of Open Access Journals (Sweden)

    Yi An

    2013-01-01

    Full Text Available Reliable feature extraction from 3D point cloud data is an important problem in many application domains, such as reverse engineering, object recognition, industrial inspection, and autonomous navigation. In this paper, a novel method is proposed for extracting the geometric features from 3D point cloud data based on discrete curves. We extract the discrete curves from 3D point cloud data and research the behaviors of chord lengths, angle variations, and principal curvatures at the geometric features in the discrete curves. Then, the corresponding similarity indicators are defined. Based on the similarity indicators, the geometric features can be extracted from the discrete curves, which are also the geometric features of 3D point cloud data. The threshold values of the similarity indicators are taken from [0,1], which characterize the relative relationship and make the threshold setting easier and more reasonable. The experimental results demonstrate that the proposed method is efficient and reliable.

  19. Feature-based RNN target recognition

    Science.gov (United States)

    Bakircioglu, Hakan; Gelenbe, Erol

    1998-09-01

    Detection and recognition of target signatures in sensory data obtained by synthetic aperture radar (SAR), forward- looking infrared, or laser radar, have received considerable attention in the literature. In this paper, we propose a feature based target classification methodology to detect and classify targets in cluttered SAR images, that makes use of selective signature data from sensory data, together with a neural network technique which uses a set of trained networks based on the Random Neural Network (RNN) model (Gelenbe 89, 90, 91, 93) which is trained to act as a matched filter. We propose and investigate radial features of target shapes that are invariant to rotation, translation, and scale, to characterize target and clutter signatures. These features are then used to train a set of learning RNNs which can be used to detect targets within clutter with high accuracy, and to classify the targets or man-made objects from natural clutter. Experimental data from SAR imagery is used to illustrate and validate the proposed method, and to calculate Receiver Operating Characteristics which illustrate the performance of the proposed algorithm.

  20. Iris recognition based on key image feature extraction.

    Science.gov (United States)

    Ren, X; Tian, Q; Zhang, J; Wu, S; Zeng, Y

    2008-01-01

    In iris recognition, feature extraction can be influenced by factors such as illumination and contrast, and thus the features extracted may be unreliable, which can cause a high rate of false results in iris pattern recognition. In order to obtain stable features, an algorithm was proposed in this paper to extract key features of a pattern from multiple images. The proposed algorithm built an iris feature template by extracting key features and performed iris identity enrolment. Simulation results showed that the selected key features have high recognition accuracy on the CASIA Iris Set, where both contrast and illumination variance exist.

  1. Coding of visual object features and feature conjunctions in the human brain.

    Science.gov (United States)

    Martinovic, Jasna; Gruber, Thomas; Müller, Matthias M

    2008-01-01

    Object recognition is achieved through neural mechanisms reliant on the activity of distributed coordinated neural assemblies. In the initial steps of this process, an object's features are thought to be coded very rapidly in distinct neural assemblies. These features play different functional roles in the recognition process--while colour facilitates recognition, additional contours and edges delay it. Here, we selectively varied the amount and role of object features in an entry-level categorization paradigm and related them to the electrical activity of the human brain. We found that early synchronizations (approx. 100 ms) increased quantitatively when more image features had to be coded, without reflecting their qualitative contribution to the recognition process. Later activity (approx. 200-400 ms) was modulated by the representational role of object features. These findings demonstrate that although early synchronizations may be sufficient for relatively crude discrimination of objects in visual scenes, they cannot support entry-level categorization. This was subserved by later processes of object model selection, which utilized the representational value of object features such as colour or edges to select the appropriate model and achieve identification.

  2. Model-Based Learning of Local Image Features for Unsupervised Texture Segmentation

    Science.gov (United States)

    Kiechle, Martin; Storath, Martin; Weinmann, Andreas; Kleinsteuber, Martin

    2018-04-01

    Features that capture well the textural patterns of a certain class of images are crucial for the performance of texture segmentation methods. The manual selection of features or designing new ones can be a tedious task. Therefore, it is desirable to automatically adapt the features to a certain image or class of images. Typically, this requires a large set of training images with similar textures and ground truth segmentation. In this work, we propose a framework to learn features for texture segmentation when no such training data is available. The cost function for our learning process is constructed to match a commonly used segmentation model, the piecewise constant Mumford-Shah model. This means that the features are learned such that they provide an approximately piecewise constant feature image with a small jump set. Based on this idea, we develop a two-stage algorithm which first learns suitable convolutional features and then performs a segmentation. We note that the features can be learned from a small set of images, from a single image, or even from image patches. The proposed method achieves a competitive rank in the Prague texture segmentation benchmark, and it is effective for segmenting histological images.

  3. Pharmacy Students’ Preference for Using Mobile Devices in a Clinical Setting for Practice-Related Tasks

    Science.gov (United States)

    Hastings, Justine F.; Bryant, Jennifer E.

    2015-01-01

    Objective. To examine pharmacy students’ ownership of, use of, and preference for using a mobile device in a practice setting. Methods. Eighty-one pharmacy students were recruited and completed a pretest that collected information about their demographics and mobile devices and also had them rank the iPhone, iPad mini, and iPad for preferred use in a pharmacy practice setting. Students used the 3 devices to perform pharmacy practice-related tasks and then completed a posttest to again rank the devices for preferred use in a pharmacy practice setting. Results. The iPhone was the most commonly owned mobile device (59.3% of students), and the iPad mini was the least commonly owned (18.5%). About 70% of the students used their mobile devices at least once a week in a pharmacy practice setting. The iPhone was the most commonly used device in a practice setting (46.9% of students), and the iPod Touch was the least commonly used device (1.2%). The iPad mini was the most preferred device for use in a pharmacy practice setting prior to performing pharmacy practice-related tasks (49.4% of students), and was preferred by significantly more students after performing the tasks (70.4%). Conclusion. Pharmacy students commonly use their mobile devices in pharmacy practice settings and most selected the iPad mini as the preferred device for use in a practice setting even though it was the device owned by the fewest students. PMID:25861103

  4. "One Problem Became Another": Disclosure of Rape-Related Pregnancy in the Abortion Care Setting.

    Science.gov (United States)

    Perry, Rachel; Murphy, Molly; Haider, Sadia; Harwood, Bryna

    2015-01-01

    We sought to explore the experiences of women who disclosed that their pregnancies resulted from rape in the abortion care setting, as well as the experiences of professionals involved in care of women with rape-related pregnancy. In-depth interviews were conducted with 9 patients who had terminated rape-related pregnancies and 12 professionals working in abortion care or rape crisis advocacy (5 abortion providers, 4 rape crisis center advocates, 2 social workers, and 1 clinic administrator). Transcribed interviews were coded and analyzed for themes related to the experiences of disclosing rape and the consequences of disclosure in the abortion care setting. Patients and professionals involved in care of women with rape-related pregnancy described opportunities arising from disclosure, including interpersonal (explaining abortion decision making in the context of assault, belief, and caring by providers), as well as structural opportunities (funding assistance, legal options, and mental health options). Whereas most patients did not choose to pursue all three structural opportunities, both patients and professionals emphasized the importance of offering them. The most important consequence of disclosure for patients was being believed and feeling that providers cared about them. Rape-related pregnancy disclosure in the abortion care setting can lead to opportunities for interpersonal support and open options for funding, legal recourse, and mental health care. Those working in abortion care should create environments conducive to disclosure and opportunities for rape survivors to access these additional options if they desire. Copyright © 2015 Jacobs Institute of Women's Health. Published by Elsevier Inc. All rights reserved.

  5. Grade-related differences in strategy use in multidigit division in two instructional settings.

    Science.gov (United States)

    Hickendorff, Marian; Torbeyns, Joke; Verschaffel, Lieven

    2017-11-23

    We aimed to investigate upper elementary children's strategy use in the domain of multidigit division in two instructional settings: the Netherlands and Flanders (Belgium). A cross-sectional sample of 119 Dutch and 122 Flemish fourth to sixth graders solved a varied set of multidigit division problems. With latent class analysis, three distinct strategy profiles were identified: children consistently using number-based strategies, children combining the use of column-based and number-based strategies, and children combining the use of digit-based and number-based strategies. The relation between children's strategy profiles and their instructional setting (country) and grade were generally in line with instructional differences, but large individual differences remained. Furthermore, Dutch children more frequently made adaptive strategy choices and realistic solutions than their Flemish peers. These results complement and refine previous findings on children's strategy use in relation to mathematics instruction. Statement of contribution What is already known? Mathematics education reform emphasizes variety, adaptivity, and insight in arithmetic strategies. Countries have different instructional trajectories for multidigit division. Mixed results on the impact of instruction on children's strategy use in multidigit division. What does this study add? Latent class analysis identified three meaningful strategy profiles in children from grades 4-6. These strategy profiles substantially differed between children. Dutch and Flemish children's strategy use is related to their instructional trajectory. © 2017 The Authors. British Journal of Developmental Psychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.

  6. Dressing-related trauma: clinical sequelae and resource utilization in a UK setting

    Directory of Open Access Journals (Sweden)

    Charlesworth B

    2014-04-01

    Full Text Available Bruce Charlesworth,1 Claire Pilling,1 Paul Chadwick,2 Martyn Butcher31Adelphi Values, Macclesfield, 2Salford Royal Foundation Trust, Salford, 3Northern Devon Healthcare Trust, Devon, UKBackground: Dressings are the mainstay of wound care management; however, adherence of the dressing to the wound or periwound skin is common and can lead to dressing-related pain and trauma. Dressing-related trauma is recognized as a clinical and economic burden to patients and health care providers. This study was conducted to garner expert opinion on clinical sequelae and resource use associated with dressing-related trauma in a UK setting.Methods: This was an exploratory study with two phases: qualitative pilot interviews with six wound care specialists to explore dressing-related trauma concepts, sequelae, and resource utilization; and online quantitative research with 30 wound care specialists to validate and quantify the concepts, sequelae, and resource utilization explored in the first phase of the study. Data were collected on mean health care professional time, material costs, pharmaceutical costs, and inpatient management per sequela occurrence until resolution. Data were analyzed to give total costs per sequela and concept occurrence.Results: The results demonstrate that dressing-related trauma is a clinically relevant concept. The main types of dressing-related trauma concepts included skin reactions, adherence to the wound, skin stripping, maceration, drying, and plugging of the wound. These were the foundation for a number of clinical sequelae, including wound enlargement, increased exudate, bleeding, infection, pain, itching/excoriation, edema, dermatitis, inflammation, and anxiety. Mean total costs range from £56 to £175 for the complete onward management of each occurrence of the six main concepts.Conclusion: These results provide insight into the hidden costs of dressing-related trauma in a UK setting. This research successfully conceptualized

  7. Internal versus external features in triggering the brain waveforms for conjunction and feature faces in recognition.

    Science.gov (United States)

    Nie, Aiqing; Jiang, Jingguo; Fu, Qiao

    2014-08-20

    Previous research has found that conjunction faces (whose internal features, e.g. eyes, nose, and mouth, and external features, e.g. hairstyle and ears, are from separate studied faces) and feature faces (partial features of these are studied) can produce higher false alarms than both old and new faces (i.e. those that are exactly the same as the studied faces and those that have not been previously presented) in recognition. The event-related potentials (ERPs) that relate to conjunction and feature faces at recognition, however, have not been described as yet; in addition, the contributions of different facial features toward ERPs have not been differentiated. To address these issues, the present study compared the ERPs elicited by old faces, conjunction faces (the internal and the external features were from two studied faces), old internal feature faces (whose internal features were studied), and old external feature faces (whose external features were studied) with those of new faces separately. The results showed that old faces not only elicited an early familiarity-related FN400, but a more anterior distributed late old/new effect that reflected recollection. Conjunction faces evoked similar late brain waveforms as old internal feature faces, but not to old external feature faces. These results suggest that, at recognition, old faces hold higher familiarity than compound faces in the profiles of ERPs and internal facial features are more crucial than external ones in triggering the brain waveforms that are characterized as reflecting the result of familiarity.

  8. Spatial analysis of geologic and hydrologic features relating to sinkhole occurrence in Jefferson County, West Virginia

    Science.gov (United States)

    Doctor, Daniel H.; Doctor, Katarina Z.

    2012-01-01

    In this study the influence of geologic features related to sinkhole susceptibility was analyzed and the results were mapped for the region of Jefferson County, West Virginia. A model of sinkhole density was constructed using Geographically Weighted Regression (GWR) that estimated the relations among discrete geologic or hydrologic features and sinkhole density at each sinkhole location. Nine conditioning factors on sinkhole occurrence were considered as independent variables: distance to faults, fold axes, fracture traces oriented along bedrock strike, fracture traces oriented across bedrock strike, ponds, streams, springs, quarries, and interpolated depth to groundwater. GWR model parameter estimates for each variable were evaluated for significance, and the results were mapped. The results provide visual insight into the influence of these variables on localized sinkhole density, and can be used to provide an objective means of weighting conditioning factors in models of sinkhole susceptibility or hazard risk.

  9. Predicting protein amidation sites by orchestrating amino acid sequence features

    Science.gov (United States)

    Zhao, Shuqiu; Yu, Hua; Gong, Xiujun

    2017-08-01

    Amidation is the fourth major category of post-translational modifications, which plays an important role in physiological and pathological processes. Identifying amidation sites can help us understanding the amidation and recognizing the original reason of many kinds of diseases. But the traditional experimental methods for predicting amidation sites are often time-consuming and expensive. In this study, we propose a computational method for predicting amidation sites by orchestrating amino acid sequence features. Three kinds of feature extraction methods are used to build a feature vector enabling to capture not only the physicochemical properties but also position related information of the amino acids. An extremely randomized trees algorithm is applied to choose the optimal features to remove redundancy and dependence among components of the feature vector by a supervised fashion. Finally the support vector machine classifier is used to label the amidation sites. When tested on an independent data set, it shows that the proposed method performs better than all the previous ones with the prediction accuracy of 0.962 at the Matthew's correlation coefficient of 0.89 and area under curve of 0.964.

  10. New antithrombotic agents in the ambulatory setting.

    Science.gov (United States)

    Gibbs, Neville M; Weightman, William M; Watts, Stephen A

    2014-12-01

    Many patients presenting for surgical or other procedures in an ambulatory setting are taking new antiplatelet or anticoagulant agents. This review assesses how the novel features of these new agents affect the management of antithrombotic therapy in the ambulatory setting. There have been very few studies investigating the relative risks of continuing or ceasing new antithrombotic agents. Recent reviews indicate that the new antithrombotic agents offer greater efficacy or ease of administration but are more difficult to monitor or reverse. They emphasize the importance of assessing the bleeding risk of the procedure, the thrombotic risk if the agent is ceased, and patient factors that increase the likelihood of bleeding. The timing of cessation of the agent, if required, depends on its pharmacokinetics and patients' bleeding risks. Patients at high risk of thrombotic complications may require bridging therapy. Once agreed upon, the perioperative plan should be made clear to all involved. As there are few clinical studies to guide management, clinicians must make rational decisions in relation to continuing or ceasing new antithrombotic agents. This requires knowledge of their pharmacokinetics, and a careful multidisciplinary assessment of the relative thrombotic and bleeding risks in individual patients.

  11. Using affective knowledge to generate and validate a set of emotion-related, action words

    Directory of Open Access Journals (Sweden)

    Emma Portch

    2015-07-01

    Full Text Available Emotion concepts are built through situated experience. Abstract word meaning is grounded in this affective knowledge, giving words the potential to evoke emotional feelings and reactions (e.g., Vigliocco et al., 2009. In the present work we explore whether words differ in the extent to which they evoke ‘specific’ emotional knowledge. Using a categorical approach, in which an affective ‘context’ is created, it is possible to assess whether words proportionally activate knowledge relevant to different emotional states (e.g., ‘sadness’, ‘anger’, Stevenson, Mikels & James, 2007a. We argue that this method may be particularly effective when assessing the emotional meaning of action words (e.g., Schacht & Sommer, 2009. In study 1 we use a constrained feature generation task to derive a set of action words that participants associated with six, basic emotional states (see full list in Appendix S1. Generation frequencies were taken to indicate the likelihood that the word would evoke emotional knowledge relevant to the state to which it had been paired. In study 2 a rating task was used to assess the strength of association between the six most frequently generated, or ‘typical’, action words and corresponding emotion labels. Participants were presented with a series of sentences, in which action words (typical and atypical and labels were paired e.g., “If you are feeling ‘sad’ how likely would you be to act in the following way?” … ‘cry.’ Findings suggest that typical associations were robust. Participants always gave higher ratings to typical vs. atypical action word and label pairings, even when (a rating direction was manipulated (the label or verb appeared first in the sentence, and (b the typical behaviours were to be performed by the rater themselves, or others. Our findings suggest that emotion-related action words vary in the extent to which they evoke knowledge relevant for different emotional states. When

  12. Fast Computation of Categorical Richness on Raster Data Sets and Related Problems

    DEFF Research Database (Denmark)

    de Berg, Mark; Tsirogiannis, Constantinos; Wilkinson, Bryan

    2015-01-01

    that runs in O(n) time and one for circular windows that runs in O((1+K/r)n) time, where K is the number of different categories appearing in G. The algorithms are not only very efficient in theory, but also in practice: our experiments show that our algorithms can handle raster data sets of hundreds...... of millions of cells. The categorical richness problem is related to colored range counting, where the goal is to preprocess a colored point set such that we can efficiently count the number of colors appearing inside a query range. We present a data structure for colored range counting in R^2 for the case......In many scientific fields, it is common to encounter raster data sets consisting of categorical data, such as soil type or land usage of a terrain. A problem that arises in the presence of such data is the following: given a raster G of n cells storing categorical data, compute for every cell c...

  13. Prominent feature extraction for review analysis: an empirical study

    Science.gov (United States)

    Agarwal, Basant; Mittal, Namita

    2016-05-01

    Sentiment analysis (SA) research has increased tremendously in recent times. SA aims to determine the sentiment orientation of a given text into positive or negative polarity. Motivation for SA research is the need for the industry to know the opinion of the users about their product from online portals, blogs, discussion boards and reviews and so on. Efficient features need to be extracted for machine-learning algorithm for better sentiment classification. In this paper, initially various features are extracted such as unigrams, bi-grams and dependency features from the text. In addition, new bi-tagged features are also extracted that conform to predefined part-of-speech patterns. Furthermore, various composite features are created using these features. Information gain (IG) and minimum redundancy maximum relevancy (mRMR) feature selection methods are used to eliminate the noisy and irrelevant features from the feature vector. Finally, machine-learning algorithms are used for classifying the review document into positive or negative class. Effects of different categories of features are investigated on four standard data-sets, namely, movie review and product (book, DVD and electronics) review data-sets. Experimental results show that composite features created from prominent features of unigram and bi-tagged features perform better than other features for sentiment classification. mRMR is a better feature selection method as compared with IG for sentiment classification. Boolean Multinomial Naïve Bayes) algorithm performs better than support vector machine classifier for SA in terms of accuracy and execution time.

  14. Fundus Autofluorescence Features of Optic Disc Pit Related ...

    African Journals Online (AJOL)

    chorioretinopathy, retina telangiectasia and diffuse and macula retina dystrophies.[1,3‑8] These features give useful clinical and prognostic information, making FAF a desired day‑to‑day clinical tool. Fundus autoflorescence signals can be detected using 3 different systems, the Delori's fundus. Fundus Autofluorescence ...

  15. Metastrategies in large-scale bargaining settings

    NARCIS (Netherlands)

    Hennes, D.; Jong, S. de; Tuyls, K.; Gal, Y.

    2015-01-01

    This article presents novel methods for representing and analyzing a special class of multiagent bargaining settings that feature multiple players, large action spaces, and a relationship among players' goals, tasks, and resources. We show how to reduce these interactions to a set of bilateral

  16. Multi-Stage Recognition of Speech Emotion Using Sequential Forward Feature Selection

    Directory of Open Access Journals (Sweden)

    Liogienė Tatjana

    2016-07-01

    Full Text Available The intensive research of speech emotion recognition introduced a huge collection of speech emotion features. Large feature sets complicate the speech emotion recognition task. Among various feature selection and transformation techniques for one-stage classification, multiple classifier systems were proposed. The main idea of multiple classifiers is to arrange the emotion classification process in stages. Besides parallel and serial cases, the hierarchical arrangement of multi-stage classification is most widely used for speech emotion recognition. In this paper, we present a sequential-forward-feature-selection-based multi-stage classification scheme. The Sequential Forward Selection (SFS and Sequential Floating Forward Selection (SFFS techniques were employed for every stage of the multi-stage classification scheme. Experimental testing of the proposed scheme was performed using the German and Lithuanian emotional speech datasets. Sequential-feature-selection-based multi-stage classification outperformed the single-stage scheme by 12–42 % for different emotion sets. The multi-stage scheme has shown higher robustness to the growth of emotion set. The decrease in recognition rate with the increase in emotion set for multi-stage scheme was lower by 10–20 % in comparison with the single-stage case. Differences in SFS and SFFS employment for feature selection were negligible.

  17. Ontology patterns for complex topographic feature yypes

    Science.gov (United States)

    Varanka, Dalia E.

    2011-01-01

    Complex feature types are defined as integrated relations between basic features for a shared meaning or concept. The shared semantic concept is difficult to define in commonly used geographic information systems (GIS) and remote sensing technologies. The role of spatial relations between complex feature parts was recognized in early GIS literature, but had limited representation in the feature or coverage data models of GIS. Spatial relations are more explicitly specified in semantic technology. In this paper, semantics for topographic feature ontology design patterns (ODP) are developed as data models for the representation of complex features. In the context of topographic processes, component assemblages are supported by resource systems and are found on local landscapes. The topographic ontology is organized across six thematic modules that can account for basic feature types, resource systems, and landscape types. Types of complex feature attributes include location, generative processes and physical description. Node/edge networks model standard spatial relations and relations specific to topographic science to represent complex features. To demonstrate these concepts, data from The National Map of the U. S. Geological Survey was converted and assembled into ODP.

  18. Adversarial Feature Selection Against Evasion Attacks.

    Science.gov (United States)

    Zhang, Fei; Chan, Patrick P K; Biggio, Battista; Yeung, Daniel S; Roli, Fabio

    2016-03-01

    Pattern recognition and machine learning techniques have been increasingly adopted in adversarial settings such as spam, intrusion, and malware detection, although their security against well-crafted attacks that aim to evade detection by manipulating data at test time has not yet been thoroughly assessed. While previous work has been mainly focused on devising adversary-aware classification algorithms to counter evasion attempts, only few authors have considered the impact of using reduced feature sets on classifier security against the same attacks. An interesting, preliminary result is that classifier security to evasion may be even worsened by the application of feature selection. In this paper, we provide a more detailed investigation of this aspect, shedding some light on the security properties of feature selection against evasion attacks. Inspired by previous work on adversary-aware classifiers, we propose a novel adversary-aware feature selection model that can improve classifier security against evasion attacks, by incorporating specific assumptions on the adversary's data manipulation strategy. We focus on an efficient, wrapper-based implementation of our approach, and experimentally validate its soundness on different application examples, including spam and malware detection.

  19. Prototype Theory Based Feature Representation for PolSAR Images

    OpenAIRE

    Huang Xiaojing; Yang Xiangli; Huang Pingping; Yang Wen

    2016-01-01

    This study presents a new feature representation approach for Polarimetric Synthetic Aperture Radar (PolSAR) image based on prototype theory. First, multiple prototype sets are generated using prototype theory. Then, regularized logistic regression is used to predict similarities between a test sample and each prototype set. Finally, the PolSAR image feature representation is obtained by ensemble projection. Experimental results of an unsupervised classification of PolSAR images show that our...

  20. Hybrid feature selection for supporting lightweight intrusion detection systems

    Science.gov (United States)

    Song, Jianglong; Zhao, Wentao; Liu, Qiang; Wang, Xin

    2017-08-01

    Redundant and irrelevant features not only cause high resource consumption but also degrade the performance of Intrusion Detection Systems (IDS), especially when coping with big data. These features slow down the process of training and testing in network traffic classification. Therefore, a hybrid feature selection approach in combination with wrapper and filter selection is designed in this paper to build a lightweight intrusion detection system. Two main phases are involved in this method. The first phase conducts a preliminary search for an optimal subset of features, in which the chi-square feature selection is utilized. The selected set of features from the previous phase is further refined in the second phase in a wrapper manner, in which the Random Forest(RF) is used to guide the selection process and retain an optimized set of features. After that, we build an RF-based detection model and make a fair comparison with other approaches. The experimental results on NSL-KDD datasets show that our approach results are in higher detection accuracy as well as faster training and testing processes.

  1. Feature Import Vector Machine: A General Classifier with Flexible Feature Selection.

    Science.gov (United States)

    Ghosh, Samiran; Wang, Yazhen

    2015-02-01

    The support vector machine (SVM) and other reproducing kernel Hilbert space (RKHS) based classifier systems are drawing much attention recently due to its robustness and generalization capability. General theme here is to construct classifiers based on the training data in a high dimensional space by using all available dimensions. The SVM achieves huge data compression by selecting only few observations which lie close to the boundary of the classifier function. However when the number of observations are not very large (small n ) but the number of dimensions/features are large (large p ), then it is not necessary that all available features are of equal importance in the classification context. Possible selection of an useful fraction of the available features may result in huge data compression. In this paper we propose an algorithmic approach by means of which such an optimal set of features could be selected. In short, we reverse the traditional sequential observation selection strategy of SVM to that of sequential feature selection. To achieve this we have modified the solution proposed by Zhu and Hastie (2005) in the context of import vector machine (IVM), to select an optimal sub-dimensional model to build the final classifier with sufficient accuracy.

  2. AVC: Selecting discriminative features on basis of AUC by maximizing variable complementarity.

    Science.gov (United States)

    Sun, Lei; Wang, Jun; Wei, Jinmao

    2017-03-14

    The Receiver Operator Characteristic (ROC) curve is well-known in evaluating classification performance in biomedical field. Owing to its superiority in dealing with imbalanced and cost-sensitive data, the ROC curve has been exploited as a popular metric to evaluate and find out disease-related genes (features). The existing ROC-based feature selection approaches are simple and effective in evaluating individual features. However, these approaches may fail to find real target feature subset due to their lack of effective means to reduce the redundancy between features, which is essential in machine learning. In this paper, we propose to assess feature complementarity by a trick of measuring the distances between the misclassified instances and their nearest misses on the dimensions of pairwise features. If a misclassified instance and its nearest miss on one feature dimension are far apart on another feature dimension, the two features are regarded as complementary to each other. Subsequently, we propose a novel filter feature selection approach on the basis of the ROC analysis. The new approach employs an efficient heuristic search strategy to select optimal features with highest complementarities. The experimental results on a broad range of microarray data sets validate that the classifiers built on the feature subset selected by our approach can get the minimal balanced error rate with a small amount of significant features. Compared with other ROC-based feature selection approaches, our new approach can select fewer features and effectively improve the classification performance.

  3. Feature selection and nearest centroid classification for protein mass spectrometry

    Directory of Open Access Journals (Sweden)

    Levner Ilya

    2005-03-01

    Full Text Available Abstract Background The use of mass spectrometry as a proteomics tool is poised to revolutionize early disease diagnosis and biomarker identification. Unfortunately, before standard supervised classification algorithms can be employed, the "curse of dimensionality" needs to be solved. Due to the sheer amount of information contained within the mass spectra, most standard machine learning techniques cannot be directly applied. Instead, feature selection techniques are used to first reduce the dimensionality of the input space and thus enable the subsequent use of classification algorithms. This paper examines feature selection techniques for proteomic mass spectrometry. Results This study examines the performance of the nearest centroid classifier coupled with the following feature selection algorithms. Student-t test, Kolmogorov-Smirnov test, and the P-test are univariate statistics used for filter-based feature ranking. From the wrapper approaches we tested sequential forward selection and a modified version of sequential backward selection. Embedded approaches included shrunken nearest centroid and a novel version of boosting based feature selection we developed. In addition, we tested several dimensionality reduction approaches, namely principal component analysis and principal component analysis coupled with linear discriminant analysis. To fairly assess each algorithm, evaluation was done using stratified cross validation with an internal leave-one-out cross-validation loop for automated feature selection. Comprehensive experiments, conducted on five popular cancer data sets, revealed that the less advocated sequential forward selection and boosted feature selection algorithms produce the most consistent results across all data sets. In contrast, the state-of-the-art performance reported on isolated data sets for several of the studied algorithms, does not hold across all data sets. Conclusion This study tested a number of popular feature

  4. Extraction of features from sleep EEG for Bayesian assessment of brain development.

    Directory of Open Access Journals (Sweden)

    Vitaly Schetinin

    Full Text Available Brain development can be evaluated by experts analysing age-related patterns in sleep electroencephalograms (EEG. Natural variations in the patterns, noise, and artefacts affect the evaluation accuracy as well as experts' agreement. The knowledge of predictive posterior distribution allows experts to estimate confidence intervals within which decisions are distributed. Bayesian approach to probabilistic inference has provided accurate estimates of intervals of interest. In this paper we propose a new feature extraction technique for Bayesian assessment and estimation of predictive distribution in a case of newborn brain development assessment. The new EEG features are verified within the Bayesian framework on a large EEG data set including 1,100 recordings made from newborns in 10 age groups. The proposed features are highly correlated with brain maturation and their use increases the assessment accuracy.

  5. Imaging features of microvascular invasion in hepatocellular carcinoma developed after direct-acting antiviral therapy in HCV-related cirrhosis

    Energy Technology Data Exchange (ETDEWEB)

    Renzulli, Matteo; Brocchi, Stefano; Golfieri, Rita [Sant' Orsola-Malpighi Hospital, Department of Diagnostic Medicine and Prevention, Bologna (Italy); Buonfiglioli, Federica; Conti, Fabio; Verucchi, Gabriella; Andreone, Pietro [University of Bologna, Research Centre for the Study of Hepatitis, Department of Medical and Surgical Sciences DIMEC, Bologna (Italy); Serio, Ilaria [Sant' Orsola-Malpighi Hospital, Department of Digestive Diseases, Bologna (Italy); Foschi, Francesco Giuseppe [Ospedale di Faenza, Division of Internal Medicine, Faenza (Italy); Caraceni, Paolo; Mazzella, Giuseppe [University of Bologna, Department of Medical and Surgical Sciences (DIMEC), Bologna (Italy); Brillanti, Stefano [University of Bologna, Research Centre for the Study of Hepatitis, Department of Medical and Surgical Sciences DIMEC, Bologna (Italy); U.O. di Gastroenterologia, Bologna (Italy)

    2018-02-15

    To evaluate imaging features of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) developed after direct-acting antiviral (DAA) therapy in HCV-related cirrhosis. Retrospective cohort study on 344 consecutive patients with HCV-related cirrhosis treated with DAA and followed for 48-74 weeks. Using established imaging criteria for MVI, HCC features were analysed and compared with those in nodules not occurring after DAA. After DAA, HCC developed in 29 patients (single nodule, 18 and multinodular, 11). Median interval between therapy end and HCC diagnosis was 82 days (0-318). Forty-one HCC nodules were detected (14 de novo, 27 recurrent): maximum diameter was 10-20 mm in 27, 20-50 mm in 13, and > 50 mm in 1. Imaging features of MVI were present in 29/41 nodules (70.7%, CI: 54-84), even in 17/29 nodules with 10-20 mm diameter (58.6%, CI: 39-76). MVI was present in only 17/51 HCC nodules that occurred before DAA treatment (33.3%, CI: 22-47) (p= 0.0007). MVI did not correlate with history of previous HCC. HCC occurs rapidly after DAA therapy, and aggressive features of MVI characterise most neoplastic nodules. Close imaging evaluations are needed after DAA in cirrhotic patients. (orig.)

  6. Location of silicic caldera formation in arc settings

    Energy Technology Data Exchange (ETDEWEB)

    Hughes, Gwyneth R; Mahood, Gail A [Department of Geological and Environmental Sciences, Stanford University, 450 Serra, Mall, Building 320, Stanford, CA 94305-2115 (United States)

    2008-10-01

    Silicic calderas are the surface expressions of silicic magma chambers, and thus their study may yield information about what tectonic and crustal features favor the generation of evolved magma. The goal of this study is to determine whether silicic calderas in arc settings are preferentially located behind the volcanic front. After a global analysis of young, arc-related calderas, we find that silicic calderas at continental margins do form over a wide area behind the front, as compared to other types of arc volcanoes.

  7. Sequence features responsible for intron retention in human

    Directory of Open Access Journals (Sweden)

    Sakabe Noboru

    2007-02-01

    Full Text Available Abstract Background One of the least common types of alternative splicing is the complete retention of an intron in a mature transcript. Intron retention (IR is believed to be the result of intron, rather than exon, definition associated with failure of the recognition of weak splice sites flanking short introns. Although studies on individual retained introns have been published, few systematic surveys of large amounts of data have been conducted on the mechanisms that lead to IR. Results TTo understand how sequence features are associated with or control IR, and to produce a generalized model that could reveal previously unknown signals that regulate this type of alternative splicing, we partitioned intron retention events observed in human cDNAs into two groups based on the relative abundance of both isoforms and compared relevant features. We found that a higher frequency of IR in human is associated with individual introns that have weaker splice sites, genes with shorter intron lengths, higher expression levels and lower density of both a set of exon splicing silencers (ESSs and the intronic splicing enhancer GGG. Both groups of retained introns presented events conserved in mouse, in which the retained introns were also short and presented weaker splice sites. Conclusion Although our results confirmed that weaker splice sites are associated with IR, they showed that this feature alone cannot explain a non-negligible fraction of events. Our analysis suggests that cis-regulatory elements are likely to play a crucial role in regulating IR and also reveals previously unknown features that seem to influence its occurrence. These results highlight the importance of considering the interplay among these features in the regulation of the relative frequency of IR.

  8. New Hybrid Features Selection Method: A Case Study on Websites Phishing

    Directory of Open Access Journals (Sweden)

    Khairan D. Rajab

    2017-01-01

    Full Text Available Phishing is one of the serious web threats that involves mimicking authenticated websites to deceive users in order to obtain their financial information. Phishing has caused financial damage to the different online stakeholders. It is massive in the magnitude of hundreds of millions; hence it is essential to minimize this risk. Classifying websites into “phishy” and legitimate types is a primary task in data mining that security experts and decision makers are hoping to improve particularly with respect to the detection rate and reliability of the results. One way to ensure the reliability of the results and to enhance performance is to identify a set of related features early on so the data dimensionality reduces and irrelevant features are discarded. To increase reliability of preprocessing, this article proposes a new feature selection method that combines the scores of multiple known methods to minimize discrepancies in feature selection results. The proposed method has been applied to the problem of website phishing classification to show its pros and cons in identifying relevant features. Results against a security dataset reveal that the proposed preprocessing method was able to derive new features datasets which when mined generate high competitive classifiers with reference to detection rate when compared to results obtained from other features selection methods.

  9. Feature Evaluation for Building Facade Images - AN Empirical Study

    Science.gov (United States)

    Yang, M. Y.; Förstner, W.; Chai, D.

    2012-08-01

    The classification of building facade images is a challenging problem that receives a great deal of attention in the photogrammetry community. Image classification is critically dependent on the features. In this paper, we perform an empirical feature evaluation task for building facade images. Feature sets we choose are basic features, color features, histogram features, Peucker features, texture features, and SIFT features. We present an approach for region-wise labeling using an efficient randomized decision forest classifier and local features. We conduct our experiments with building facade image classification on the eTRIMS dataset, where our focus is the object classes building, car, door, pavement, road, sky, vegetation, and window.

  10. Solving problems by interrogating sets of knowledge systems: Toward a theory of multiple knowledge systems

    Science.gov (United States)

    Dekorvin, Andre

    1989-01-01

    The main purpose is to develop a theory for multiple knowledge systems. A knowledge system could be a sensor or an expert system, but it must specialize in one feature. The problem is that we have an exhaustive list of possible answers to some query (such as what object is it). By collecting different feature values, in principle, it should be possible to give an answer to the query, or at least narrow down the list. Since a sensor, or for that matter an expert system, does not in most cases yield a precise value for the feature, uncertainty must be built into the model. Also, researchers must have a formal mechanism to be able to put the information together. Researchers chose to use the Dempster-Shafer approach to handle the problems mentioned above. Researchers introduce the concept of a state of recognition and point out that there is a relation between receiving updates and defining a set valued Markov Chain. Also, deciding what the value of the next set valued variable is can be phrased in terms of classical decision making theory such as minimizing the maximum regret. Other related problems are examined.

  11. 3-D FEATURE-BASED MATCHING BY RSTG APPROACH

    Directory of Open Access Journals (Sweden)

    J.-J. Jaw

    2012-07-01

    Full Text Available 3-D feature matching is the essential kernel in a fully automated feature-based LiDAR point cloud registration. After feasible procedures of feature acquisition, connecting corresponding features in different data frames is imperative to be solved. The objective addressed in this paper is developing an approach coined RSTG to retrieve corresponding counterparts of unsorted multiple 3-D features extracted from sets of LiDAR point clouds. RSTG stands for the four major processes, "Rotation alignment"; "Scale estimation"; "Translation alignment" and "Geometric check," strategically formulated towards finding out matching solution with high efficiency and leading to accomplishing the 3-D similarity transformation among all sets. The workable types of features to RSTG comprise points, lines, planes and clustered point groups. Each type of features can be employed exclusively or combined with others, if sufficiently supplied, throughout the matching scheme. The paper gives a detailed description of the matching methodology and discusses on the matching effects based on the statistical assessment which revealed that the RSTG approach reached an average matching rate of success up to 93% with around 6.6% of statistical type 1 error. Notably, statistical type 2 error, the critical indicator of matching reliability, was kept 0% throughout all the experiments.

  12. Event-related potential correlates of suspicious thoughts in individuals with schizotypal personality features.

    Science.gov (United States)

    Li, Xue-bing; Huang, Jia; Cheung, Eric F C; Gong, Qi-yong; Chan, Raymond C K

    2011-01-01

    Suspiciousness is a common feature of schizophrenia. However, suspicious thoughts are also commonly experienced by the general population. This study aimed to examine the underlying neural mechanism of suspicious thoughts in individuals with and without schizotypal personality disorder (SPD)-proneness, using an event-related potential (ERP) paradigm. Electroencephalography (EEG) was recorded when the "feeling of being seen through" was evoked in the participants. The findings showed a prominent positive deflection of the difference wave within the time window 250-400 ms after stimuli presentation in both SPD-prone and non-SPD-prone groups. Furthermore, the P3 amplitude was significantly reduced in the SPD-prone group compared to the non-SPD-prone group. The current density analysis also indicated hypoactivity in both frontal and temporal regions in the SPD-prone group, suggesting that the frontotemporal cortical network may play a role in the onset of suspicious thoughts. The P3 of difference wave was inversely correlated with the cognitive-perception factor and the suspiciousness/paranoid ideation trait, which provided preliminary electrophysiological evidence for the association of suspiciousness with SPD features.

  13. STYLISTIC FEATURES OF ADVERTISING TEXTS OF INFORMATIVE AND COMPARATIVE TYPES

    Directory of Open Access Journals (Sweden)

    Poddubskaya, O.N.

    2016-06-01

    Full Text Available The relevance of this article is related to the fact that nowadays advertising has a very strong impact both on the consumer market, political and cultural life of society, and on the language and its development as a system. Advertising has given rise to the development of a special set of stylistic features of a text, formed under the influence of reviving advertising traditions in the Russian language and under the active impact of energetic and pushy European advertising. The purpose of this study is to explore stylistic features of informative and comparative advertising texts. The object of research is Russian-language advertising in printed media and on television. In the end of the article we made conclusions about groups of language means used for different stylistic devices in informative and comparative advertising texts. Analysis of stylistic features of modern informative and comparative advertising texts can be of great interest to specialists in the field of theoretical studies of modern advertising.

  14. First-Class Object Sets

    DEFF Research Database (Denmark)

    Ernst, Erik

    Typically, objects are monolithic entities with a fixed interface. To increase the flexibility in this area, this paper presents first-class object sets as a language construct. An object set offers an interface which is a disjoint union of the interfaces of its member objects. It may also be used...... for a special kind of method invocation involving multiple objects in a dynamic lookup process. With support for feature access and late-bound method calls object sets are similar to ordinary objects, only more flexible. The approach is made precise by means of a small calculus, and the soundness of its type...

  15. Mechanism-based biomarker gene sets for glutathione depletion-related hepatotoxicity in rats

    International Nuclear Information System (INIS)

    Gao Weihua; Mizukawa, Yumiko; Nakatsu, Noriyuki; Minowa, Yosuke; Yamada, Hiroshi; Ohno, Yasuo; Urushidani, Tetsuro

    2010-01-01

    Chemical-induced glutathione depletion is thought to be caused by two types of toxicological mechanisms: PHO-type glutathione depletion [glutathione conjugated with chemicals such as phorone (PHO) or diethyl maleate (DEM)], and BSO-type glutathione depletion [i.e., glutathione synthesis inhibited by chemicals such as L-buthionine-sulfoximine (BSO)]. In order to identify mechanism-based biomarker gene sets for glutathione depletion in rat liver, male SD rats were treated with various chemicals including PHO (40, 120 and 400 mg/kg), DEM (80, 240 and 800 mg/kg), BSO (150, 450 and 1500 mg/kg), and bromobenzene (BBZ, 10, 100 and 300 mg/kg). Liver samples were taken 3, 6, 9 and 24 h after administration and examined for hepatic glutathione content, physiological and pathological changes, and gene expression changes using Affymetrix GeneChip Arrays. To identify differentially expressed probe sets in response to glutathione depletion, we focused on the following two courses of events for the two types of mechanisms of glutathione depletion: a) gene expression changes occurring simultaneously in response to glutathione depletion, and b) gene expression changes after glutathione was depleted. The gene expression profiles of the identified probe sets for the two types of glutathione depletion differed markedly at times during and after glutathione depletion, whereas Srxn1 was markedly increased for both types as glutathione was depleted, suggesting that Srxn1 is a key molecule in oxidative stress related to glutathione. The extracted probe sets were refined and verified using various compounds including 13 additional positive or negative compounds, and they established two useful marker sets. One contained three probe sets (Akr7a3, Trib3 and Gstp1) that could detect conjugation-type glutathione depletors any time within 24 h after dosing, and the other contained 14 probe sets that could detect glutathione depletors by any mechanism. These two sets, with appropriate scoring

  16. Formal Features of Headlines: notes on ten spanish speaking newspapers

    Directory of Open Access Journals (Sweden)

    Juan Nadal Palazón

    2013-02-01

    Full Text Available As a reaction to the few existing descriptions of headlines, descriptions which in some respects often fail the empirical proof of comparing them to the observable newspapers reality, it is proposed an updated set of the most distinctive formal features of headlines, according to their distribution within a large corpus of current newspapers in Spanish. The set is summarized in four constant and four variable features. The constant featuresrelatively homogeneous throughout the corpus (although some of its variants have certain conditions— are: expressive bimembration, ellipsis, nominal structures and historical present. On the other hand, the variable traits —which show a not so regular distribution— are: impersonal third person, verb in starting position, quoting conditional and anthroponyms abbreviated by initializations. The analysis is based on a corpus of 3 689 recent headlines published in Spanish in the printed editions of the following newspapers: El País, from Madrid (Spain; La Opinión, from Los Angeles (United States; El Universal, from Mexico City (Mexico; La Nación, from San Jose (Costa Rica; Hoy, from Santo Domingo (Dominican Republic; El Tiempo, from Bogota (Colombia; El Nacional, from Caracas (Venezuela; El Comercio, from Lima (Peru; El Mercurio, from Santiago (Chile, and Clarín, from Buenos Aires (Argentina. Where appropriate, the diatopic factor is considered, and the inaccuracy of some frequent approaches is also demonstrated.

  17. Online Feature Transformation Learning for Cross-Domain Object Category Recognition.

    Science.gov (United States)

    Zhang, Xuesong; Zhuang, Yan; Wang, Wei; Pedrycz, Witold

    2017-06-09

    In this paper, we introduce a new research problem termed online feature transformation learning in the context of multiclass object category recognition. The learning of a feature transformation is viewed as learning a global similarity metric function in an online manner. We first consider the problem of online learning a feature transformation matrix expressed in the original feature space and propose an online passive aggressive feature transformation algorithm. Then these original features are mapped to kernel space and an online single kernel feature transformation (OSKFT) algorithm is developed to learn a nonlinear feature transformation. Based on the OSKFT and the existing Hedge algorithm, a novel online multiple kernel feature transformation algorithm is also proposed, which can further improve the performance of online feature transformation learning in large-scale application. The classifier is trained with k nearest neighbor algorithm together with the learned similarity metric function. Finally, we experimentally examined the effect of setting different parameter values in the proposed algorithms and evaluate the model performance on several multiclass object recognition data sets. The experimental results demonstrate the validity and good performance of our methods on cross-domain and multiclass object recognition application.

  18. Identification of DNA-Binding Proteins Using Mixed Feature Representation Methods.

    Science.gov (United States)

    Qu, Kaiyang; Han, Ke; Wu, Song; Wang, Guohua; Wei, Leyi

    2017-09-22

    DNA-binding proteins play vital roles in cellular processes, such as DNA packaging, replication, transcription, regulation, and other DNA-associated activities. The current main prediction method is based on machine learning, and its accuracy mainly depends on the features extraction method. Therefore, using an efficient feature representation method is important to enhance the classification accuracy. However, existing feature representation methods cannot efficiently distinguish DNA-binding proteins from non-DNA-binding proteins. In this paper, a multi-feature representation method, which combines three feature representation methods, namely, K-Skip-N-Grams, Information theory, and Sequential and structural features (SSF), is used to represent the protein sequences and improve feature representation ability. In addition, the classifier is a support vector machine. The mixed-feature representation method is evaluated using 10-fold cross-validation and a test set. Feature vectors, which are obtained from a combination of three feature extractions, show the best performance in 10-fold cross-validation both under non-dimensional reduction and dimensional reduction by max-relevance-max-distance. Moreover, the reduced mixed feature method performs better than the non-reduced mixed feature technique. The feature vectors, which are a combination of SSF and K-Skip-N-Grams, show the best performance in the test set. Among these methods, mixed features exhibit superiority over the single features.

  19. Identification of DNA-Binding Proteins Using Mixed Feature Representation Methods

    Directory of Open Access Journals (Sweden)

    Kaiyang Qu

    2017-09-01

    Full Text Available DNA-binding proteins play vital roles in cellular processes, such as DNA packaging, replication, transcription, regulation, and other DNA-associated activities. The current main prediction method is based on machine learning, and its accuracy mainly depends on the features extraction method. Therefore, using an efficient feature representation method is important to enhance the classification accuracy. However, existing feature representation methods cannot efficiently distinguish DNA-binding proteins from non-DNA-binding proteins. In this paper, a multi-feature representation method, which combines three feature representation methods, namely, K-Skip-N-Grams, Information theory, and Sequential and structural features (SSF, is used to represent the protein sequences and improve feature representation ability. In addition, the classifier is a support vector machine. The mixed-feature representation method is evaluated using 10-fold cross-validation and a test set. Feature vectors, which are obtained from a combination of three feature extractions, show the best performance in 10-fold cross-validation both under non-dimensional reduction and dimensional reduction by max-relevance-max-distance. Moreover, the reduced mixed feature method performs better than the non-reduced mixed feature technique. The feature vectors, which are a combination of SSF and K-Skip-N-Grams, show the best performance in the test set. Among these methods, mixed features exhibit superiority over the single features.

  20. The perception of naturalness correlates with low-level visual features of environmental scenes.

    Directory of Open Access Journals (Sweden)

    Marc G Berman

    Full Text Available Previous research has shown that interacting with natural environments vs. more urban or built environments can have salubrious psychological effects, such as improvements in attention and memory. Even viewing pictures of nature vs. pictures of built environments can produce similar effects. A major question is: What is it about natural environments that produces these benefits? Problematically, there are many differing qualities between natural and urban environments, making it difficult to narrow down the dimensions of nature that may lead to these benefits. In this study, we set out to uncover visual features that related to individuals' perceptions of naturalness in images. We quantified naturalness in two ways: first, implicitly using a multidimensional scaling analysis and second, explicitly with direct naturalness ratings. Features that seemed most related to perceptions of naturalness were related to the density of contrast changes in the scene, the density of straight lines in the scene, the average color saturation in the scene and the average hue diversity in the scene. We then trained a machine-learning algorithm to predict whether a scene was perceived as being natural or not based on these low-level visual features and we could do so with 81% accuracy. As such we were able to reliably predict subjective perceptions of naturalness with objective low-level visual features. Our results can be used in future studies to determine if these features, which are related to naturalness, may also lead to the benefits attained from interacting with nature.

  1. Credit scoring using ensemble of various classifiers on reduced feature set

    Directory of Open Access Journals (Sweden)

    Dahiya Shashi

    2015-01-01

    Full Text Available Credit scoring methods are widely used for evaluating loan applications in financial and banking institutions. Credit score identifies if applicant customers belong to good risk applicant group or a bad risk applicant group. These decisions are based on the demographic data of the customers, overall business by the customer with bank, and loan payment history of the loan applicants. The advantages of using credit scoring models include reducing the cost of credit analysis, enabling faster credit decisions and diminishing possible risk. Many statistical and machine learning techniques such as Logistic Regression, Support Vector Machines, Neural Networks and Decision tree algorithms have been used independently and as hybrid credit scoring models. This paper proposes an ensemble based technique combining seven individual models to increase the classification accuracy. Feature selection has also been used for selecting important attributes for classification. Cross classification was conducted using three data partitions. German credit dataset having 1000 instances and 21 attributes is used in the present study. The results of the experiments revealed that the ensemble model yielded a very good accuracy when compared to individual models. In all three different partitions, the ensemble model was able to classify more than 80% of the loan customers as good creditors correctly. Also, for 70:30 partition there was a good impact of feature selection on the accuracy of classifiers. The results were improved for almost all individual models including the ensemble model.

  2. On the comparisons of tropical relative humidity in the lower and middle troposphere among COSMIC radio occultations, MERRA and ECMWF data sets

    Science.gov (United States)

    Vergados, P.; Mannucci, A. J.; Ao, C. O.; Jiang, J. H.; Su, H.

    2015-01-01

    The spatial variability of the tropical tropospheric relative humidity (RH) throughout the vertical extent of the troposphere is examined using Global Positioning System Radio Occultation (GPSRO) observations from the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) mission. These high vertical resolution observations capture the detailed structure and moisture budget of the Hadley Cell circulation. We compare the COSMIC observations with the European Center for Medium-range Weather Forecast (ECMWF) Re-Analysis Interim (ERA-Interim) and the Modern-Era Retrospective analysis for Research and Applications (MERRA) climatologies. Qualitatively, the spatial pattern of RH in all data sets matches up remarkably well, capturing distinct features of the general circulation. However, RH discrepancies exist between ERA-Interim and COSMIC data sets, which are noticeable across the tropical boundary layer. Specifically, ERA-Interim shows a drier Inter Tropical Convergence Zone (ITCZ) by 15-20% compared both to COSMIC and MERRA data sets, but this difference decreases with altitude. Unlike ECMWF, MERRA shows an excellent agreement with the COSMIC observations except above 400 hPa, where GPSRO observations capture drier air by 5-10%. RH climatologies were also used to evaluate intraseasonal variability. The results indicate that the tropical middle troposphere at ±5-25° is most sensitive to seasonal variations. COSMIC and MERRA data sets capture the same magnitude of the seasonal variability, but ERA-Interim shows a weaker seasonal fluctuation up to 10% in the middle troposphere inside the dry air subsidence regions of the Hadley Cell. Over the ITCZ, RH varies by maximum 9% between winter and summer.

  3. Celluloid devils: a research study of male nurses in feature films.

    Science.gov (United States)

    Stanley, David

    2012-11-01

    To report a study of how male nurses are portrayed in feature films. It was hypothesized that male nurses are frequently portrayed negatively or stereotypically in the film media, potentially having a negative impact on male nurse recruitment and the public's perception of male nurses. An interpretive, qualitative methodology guided by insights into hegemonic masculinity and structured around a set of collective case studies (films) was used to examine the portrayal of male nurses in feature films made in the Western world from 1900 to 2007. Over 36,000 feature film synopses were reviewed (via CINAHL, ProQuest and relevant movie-specific literature) for the keyword 'nurse' and 'nursing' with an additional search for films from 1900 to 2010 for the word 'male nurse'. Identified films were labelled as 'cases' and analysed collectively to determine key attributes related to men in nursing and explore them for the emergence of concepts and themes related to the image of male nurses in films. A total of 13 relevant cases (feature films) were identified with 12 being made in the USA. Most films portrayed male nurses negatively and in ways opposed to hegemonic masculinity, as effeminate, homosexual, homicidal, corrupt or incompetent. Few film images of male nurses show them in traditional masculine roles or as clinically competent or self-confident professionals.   Feature films predominantly portray male nurses negatively. Given the popularity of feature films, there may be negative effects on recruitment and on the public's perception of male nurses. © 2012 Blackwell Publishing Ltd.

  4. Joint Concept Correlation and Feature-Concept Relevance Learning for Multilabel Classification.

    Science.gov (United States)

    Zhao, Xiaowei; Ma, Zhigang; Li, Zhi; Li, Zhihui

    2018-02-01

    In recent years, multilabel classification has attracted significant attention in multimedia annotation. However, most of the multilabel classification methods focus only on the inherent correlations existing among multiple labels and concepts and ignore the relevance between features and the target concepts. To obtain more robust multilabel classification results, we propose a new multilabel classification method aiming to capture the correlations among multiple concepts by leveraging hypergraph that is proved to be beneficial for relational learning. Moreover, we consider mining feature-concept relevance, which is often overlooked by many multilabel learning algorithms. To better show the feature-concept relevance, we impose a sparsity constraint on the proposed method. We compare the proposed method with several other multilabel classification methods and evaluate the classification performance by mean average precision on several data sets. The experimental results show that the proposed method outperforms the state-of-the-art methods.

  5. Generalised Brown Clustering and Roll-up Feature Generation

    DEFF Research Database (Denmark)

    Derczynski, Leon; Chester, Sean

    2016-01-01

    active set size. Moreover, the generalisation permits a novel approach to feature selection from Brown clusters: We show that the standard approach of shearing the Brown clustering output tree at arbitrary bitlengths is lossy and that features should be chosen instead by rolling up Generalised Brown...

  6. Iris recognition using possibilistic fuzzy matching on local features.

    Science.gov (United States)

    Tsai, Chung-Chih; Lin, Heng-Yi; Taur, Jinshiuh; Tao, Chin-Wang

    2012-02-01

    In this paper, we propose a novel possibilistic fuzzy matching strategy with invariant properties, which can provide a robust and effective matching scheme for two sets of iris feature points. In addition, the nonlinear normalization model is adopted to provide more accurate position before matching. Moreover, an effective iris segmentation method is proposed to refine the detected inner and outer boundaries to smooth curves. For feature extraction, the Gabor filters are adopted to detect the local feature points from the segmented iris image in the Cartesian coordinate system and to generate a rotation-invariant descriptor for each detected point. After that, the proposed matching algorithm is used to compute a similarity score for two sets of feature points from a pair of iris images. The experimental results show that the performance of our system is better than those of the systems based on the local features and is comparable to those of the typical systems.

  7. Classification Using Markov Blanket for Feature Selection

    DEFF Research Database (Denmark)

    Zeng, Yifeng; Luo, Jian

    2009-01-01

    Selecting relevant features is in demand when a large data set is of interest in a classification task. It produces a tractable number of features that are sufficient and possibly improve the classification performance. This paper studies a statistical method of Markov blanket induction algorithm...... for filtering features and then applies a classifier using the Markov blanket predictors. The Markov blanket contains a minimal subset of relevant features that yields optimal classification performance. We experimentally demonstrate the improved performance of several classifiers using a Markov blanket...... induction as a feature selection method. In addition, we point out an important assumption behind the Markov blanket induction algorithm and show its effect on the classification performance....

  8. Pairwise Constraint-Guided Sparse Learning for Feature Selection.

    Science.gov (United States)

    Liu, Mingxia; Zhang, Daoqiang

    2016-01-01

    Feature selection aims to identify the most informative features for a compact and accurate data representation. As typical supervised feature selection methods, Lasso and its variants using L1-norm-based regularization terms have received much attention in recent studies, most of which use class labels as supervised information. Besides class labels, there are other types of supervised information, e.g., pairwise constraints that specify whether a pair of data samples belong to the same class (must-link constraint) or different classes (cannot-link constraint). However, most of existing L1-norm-based sparse learning methods do not take advantage of the pairwise constraints that provide us weak and more general supervised information. For addressing that problem, we propose a pairwise constraint-guided sparse (CGS) learning method for feature selection, where the must-link and the cannot-link constraints are used as discriminative regularization terms that directly concentrate on the local discriminative structure of data. Furthermore, we develop two variants of CGS, including: 1) semi-supervised CGS that utilizes labeled data, pairwise constraints, and unlabeled data and 2) ensemble CGS that uses the ensemble of pairwise constraint sets. We conduct a series of experiments on a number of data sets from University of California-Irvine machine learning repository, a gene expression data set, two real-world neuroimaging-based classification tasks, and two large-scale attribute classification tasks. Experimental results demonstrate the efficacy of our proposed methods, compared with several established feature selection methods.

  9. Feature-level domain adaptation

    DEFF Research Database (Denmark)

    Kouw, Wouter M.; Van Der Maaten, Laurens J P; Krijthe, Jesse H.

    2016-01-01

    -level domain adaptation (flda), that models the dependence between the two domains by means of a feature-level transfer model that is trained to describe the transfer from source to target domain. Subsequently, we train a domain-adapted classifier by minimizing the expected loss under the resulting transfer...... modeled via a dropout distribution, which allows the classiffier to adapt to differences in the marginal probability of features in the source and the target domain. Our experiments on several real-world problems show that flda performs on par with state-of-the-art domainadaptation techniques.......Domain adaptation is the supervised learning setting in which the training and test data are sampled from different distributions: training data is sampled from a source domain, whilst test data is sampled from a target domain. This paper proposes and studies an approach, called feature...

  10. Feature Importance for Human Epithelial (HEp-2 Cell Image Classification

    Directory of Open Access Journals (Sweden)

    Vibha Gupta

    2018-02-01

    Full Text Available Indirect Immuno-Fluorescence (IIF microscopy imaging of human epithelial (HEp-2 cells is a popular method for diagnosing autoimmune diseases. Considering large data volumes, computer-aided diagnosis (CAD systems, based on image-based classification, can help in terms of time, effort, and reliability of diagnosis. Such approaches are based on extracting some representative features from the images. This work explores the selection of the most distinctive features for HEp-2 cell images using various feature selection (FS methods. Considering that there is no single universally optimal feature selection technique, we also propose hybridization of one class of FS methods (filter methods. Furthermore, the notion of variable importance for ranking features, provided by another type of approaches (embedded methods such as Random forest, Random uniform forest is exploited to select a good subset of features from a large set, such that addition of new features does not increase classification accuracy. In this work, we have also, with great consideration, designed class-specific features to capture morphological visual traits of the cell patterns. We perform various experiments and discussions to demonstrate the effectiveness of FS methods along with proposed and a standard feature set. We achieve state-of-the-art performance even with small number of features, obtained after the feature selection.

  11. Identification of informative features for predicting proinflammatory potentials of engine exhausts.

    Science.gov (United States)

    Wang, Chia-Chi; Lin, Ying-Chi; Lin, Yuan-Chung; Jhang, Syu-Ruei; Tung, Chun-Wei

    2017-08-18

    The immunotoxicity of engine exhausts is of high concern to human health due to the increasing prevalence of immune-related diseases. However, the evaluation of immunotoxicity of engine exhausts is currently based on expensive and time-consuming experiments. It is desirable to develop efficient methods for immunotoxicity assessment. To accelerate the development of safe alternative fuels, this study proposed a computational method for identifying informative features for predicting proinflammatory potentials of engine exhausts. A principal component regression (PCR) algorithm was applied to develop prediction models. The informative features were identified by a sequential backward feature elimination (SBFE) algorithm. A total of 19 informative chemical and biological features were successfully identified by SBFE algorithm. The informative features were utilized to develop a computational method named FS-CBM for predicting proinflammatory potentials of engine exhausts. FS-CBM model achieved a high performance with correlation coefficient values of 0.997 and 0.943 obtained from training and independent test sets, respectively. The FS-CBM model was developed for predicting proinflammatory potentials of engine exhausts with a large improvement on prediction performance compared with our previous CBM model. The proposed method could be further applied to construct models for bioactivities of mixtures.

  12. Ageing and feature binding in visual working memory: The role of presentation time.

    Science.gov (United States)

    Rhodes, Stephen; Parra, Mario A; Logie, Robert H

    2016-01-01

    A large body of research has clearly demonstrated that healthy ageing is accompanied by an associative memory deficit. Older adults exhibit disproportionately poor performance on memory tasks requiring the retention of associations between items (e.g., pairs of unrelated words). In contrast to this robust deficit, older adults' ability to form and temporarily hold bound representations of an object's surface features, such as colour and shape, appears to be relatively well preserved. However, the findings of one set of experiments suggest that older adults may struggle to form temporary bound representations in visual working memory when given more time to study objects. However, these findings were based on between-participant comparisons across experimental paradigms. The present study directly assesses the role of presentation time in the ability of younger and older adults to bind shape and colour in visual working memory using a within-participant design. We report new evidence that giving older adults longer to study memory objects does not differentially affect their immediate memory for feature combinations relative to individual features. This is in line with a growing body of research suggesting that there is no age-related impairment in immediate memory for colour-shape binding.

  13. Function of One Regular Separable Relation Set Decided for the Minimal Covering in Multiple Valued Logic

    Directory of Open Access Journals (Sweden)

    Liu Yu Zhen

    2016-01-01

    Full Text Available Multiple-valued logic is an important branch of the computer science and technology. Multiple-valued logic studies the theory, multiple-valued circuit & multiple-valued system, and the applications of multiple-valued logic included.In the theory of multiple-valued logic, one primary and important problem is the completeness of function sets, which can be solved depending on the decision for all the precomplete sets(also called maximal closed sets of K-valued function sets noted by PK*, and another is the decision for Sheffer function, which can be totally solved by picking out all of the minimal covering of the precomplete sets. In the function structure theory of multi-logic, decision on Sheffer function is an important role. It contains structure and decision of full multi-logic and partial multi-logic. Its decision is closely related to decision of completeness of function which can be done by deciding the minimal covering of full multi-logic and partial-logic. By theory of completeness of partial multi-logic, we prove that function of one regular separable relation is not minimal covering of PK* under the condition of m = 2, σ = e.

  14. Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification.

    Science.gov (United States)

    Fan, Jianqing; Feng, Yang; Jiang, Jiancheng; Tong, Xin

    We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taking as input the newly transformed or augmented features. This procedure trains models equipped with local complexity and global simplicity, thereby avoiding the curse of dimensionality while creating a flexible nonlinear decision boundary. The resulting method is called Feature Augmentation via Nonparametrics and Selection (FANS). We motivate FANS by generalizing the Naive Bayes model, writing the log ratio of joint densities as a linear combination of those of marginal densities. It is related to generalized additive models, but has better interpretability and computability. Risk bounds are developed for FANS. In numerical analysis, FANS is compared with competing methods, so as to provide a guideline on its best application domain. Real data analysis demonstrates that FANS performs very competitively on benchmark email spam and gene expression data sets. Moreover, FANS is implemented by an extremely fast algorithm through parallel computing.

  15. Clustering-based Feature Learning on Variable Stars

    Science.gov (United States)

    Mackenzie, Cristóbal; Pichara, Karim; Protopapas, Pavlos

    2016-04-01

    The success of automatic classification of variable stars depends strongly on the lightcurve representation. Usually, lightcurves are represented as a vector of many descriptors designed by astronomers called features. These descriptors are expensive in terms of computing, require substantial research effort to develop, and do not guarantee a good classification. Today, lightcurve representation is not entirely automatic; algorithms must be designed and manually tuned up for every survey. The amounts of data that will be generated in the future mean astronomers must develop scalable and automated analysis pipelines. In this work we present a feature learning algorithm designed for variable objects. Our method works by extracting a large number of lightcurve subsequences from a given set, which are then clustered to find common local patterns in the time series. Representatives of these common patterns are then used to transform lightcurves of a labeled set into a new representation that can be used to train a classifier. The proposed algorithm learns the features from both labeled and unlabeled lightcurves, overcoming the bias using only labeled data. We test our method on data sets from the Massive Compact Halo Object survey and the Optical Gravitational Lensing Experiment; the results show that our classification performance is as good as and in some cases better than the performance achieved using traditional statistical features, while the computational cost is significantly lower. With these promising results, we believe that our method constitutes a significant step toward the automation of the lightcurve classification pipeline.

  16. CLUSTERING-BASED FEATURE LEARNING ON VARIABLE STARS

    International Nuclear Information System (INIS)

    Mackenzie, Cristóbal; Pichara, Karim; Protopapas, Pavlos

    2016-01-01

    The success of automatic classification of variable stars depends strongly on the lightcurve representation. Usually, lightcurves are represented as a vector of many descriptors designed by astronomers called features. These descriptors are expensive in terms of computing, require substantial research effort to develop, and do not guarantee a good classification. Today, lightcurve representation is not entirely automatic; algorithms must be designed and manually tuned up for every survey. The amounts of data that will be generated in the future mean astronomers must develop scalable and automated analysis pipelines. In this work we present a feature learning algorithm designed for variable objects. Our method works by extracting a large number of lightcurve subsequences from a given set, which are then clustered to find common local patterns in the time series. Representatives of these common patterns are then used to transform lightcurves of a labeled set into a new representation that can be used to train a classifier. The proposed algorithm learns the features from both labeled and unlabeled lightcurves, overcoming the bias using only labeled data. We test our method on data sets from the Massive Compact Halo Object survey and the Optical Gravitational Lensing Experiment; the results show that our classification performance is as good as and in some cases better than the performance achieved using traditional statistical features, while the computational cost is significantly lower. With these promising results, we believe that our method constitutes a significant step toward the automation of the lightcurve classification pipeline

  17. CLUSTERING-BASED FEATURE LEARNING ON VARIABLE STARS

    Energy Technology Data Exchange (ETDEWEB)

    Mackenzie, Cristóbal; Pichara, Karim [Computer Science Department, Pontificia Universidad Católica de Chile, Santiago (Chile); Protopapas, Pavlos [Institute for Applied Computational Science, Harvard University, Cambridge, MA (United States)

    2016-04-01

    The success of automatic classification of variable stars depends strongly on the lightcurve representation. Usually, lightcurves are represented as a vector of many descriptors designed by astronomers called features. These descriptors are expensive in terms of computing, require substantial research effort to develop, and do not guarantee a good classification. Today, lightcurve representation is not entirely automatic; algorithms must be designed and manually tuned up for every survey. The amounts of data that will be generated in the future mean astronomers must develop scalable and automated analysis pipelines. In this work we present a feature learning algorithm designed for variable objects. Our method works by extracting a large number of lightcurve subsequences from a given set, which are then clustered to find common local patterns in the time series. Representatives of these common patterns are then used to transform lightcurves of a labeled set into a new representation that can be used to train a classifier. The proposed algorithm learns the features from both labeled and unlabeled lightcurves, overcoming the bias using only labeled data. We test our method on data sets from the Massive Compact Halo Object survey and the Optical Gravitational Lensing Experiment; the results show that our classification performance is as good as and in some cases better than the performance achieved using traditional statistical features, while the computational cost is significantly lower. With these promising results, we believe that our method constitutes a significant step toward the automation of the lightcurve classification pipeline.

  18. Extending GIS Technology to Study Karst Features of Southeastern Minnesota

    Science.gov (United States)

    Gao, Y.; Tipping, R. G.; Alexander, E. C.; Alexander, S. C.

    2001-12-01

    This paper summarizes ongoing research on karst feature distribution of southeastern Minnesota. The main goals of this interdisciplinary research are: 1) to look for large-scale patterns in the rate and distribution of sinkhole development; 2) to conduct statistical tests of hypotheses about the formation of sinkholes; 3) to create management tools for land-use managers and planners; and 4) to deliver geomorphic and hydrogeologic criteria for making scientifically valid land-use policies and ethical decisions in karst areas of southeastern Minnesota. Existing county and sub-county karst feature datasets of southeastern Minnesota have been assembled into a large GIS-based database capable of analyzing the entire data set. The central database management system (DBMS) is a relational GIS-based system interacting with three modules: GIS, statistical and hydrogeologic modules. ArcInfo and ArcView were used to generate a series of 2D and 3D maps depicting karst feature distributions in southeastern Minnesota. IRIS ExplorerTM was used to produce satisfying 3D maps and animations using data exported from GIS-based database. Nearest-neighbor analysis has been used to test sinkhole distributions in different topographic and geologic settings. All current nearest-neighbor analyses testify that sinkholes in southeastern Minnesota are not evenly distributed in this area (i.e., they tend to be clustered). More detailed statistical methods such as cluster analysis, histograms, probability estimation, correlation and regression have been used to study the spatial distributions of some mapped karst features of southeastern Minnesota. A sinkhole probability map for Goodhue County has been constructed based on sinkhole distribution, bedrock geology, depth to bedrock, GIS buffer analysis and nearest-neighbor analysis. A series of karst features for Winona County including sinkholes, springs, seeps, stream sinks and outcrop has been mapped and entered into the Karst Feature Database

  19. Deep-learning derived features for lung nodule classification with limited datasets

    Science.gov (United States)

    Thammasorn, P.; Wu, W.; Pierce, L. A.; Pipavath, S. N.; Lampe, P. D.; Houghton, A. M.; Haynor, D. R.; Chaovalitwongse, W. A.; Kinahan, P. E.

    2018-02-01

    Only a few percent of indeterminate nodules found in lung CT images are cancer. However, enabling earlier diagnosis is important to avoid invasive procedures or long-time surveillance to those benign nodules. We are evaluating a classification framework using radiomics features derived with a machine learning approach from a small data set of indeterminate CT lung nodule images. We used a retrospective analysis of 194 cases with pulmonary nodules in the CT images with or without contrast enhancement from lung cancer screening clinics. The nodules were contoured by a radiologist and texture features of the lesion were calculated. In addition, sematic features describing shape were categorized. We also explored a Multiband network, a feature derivation path that uses a modified convolutional neural network (CNN) with a Triplet Network. This was trained to create discriminative feature representations useful for variable-sized nodule classification. The diagnostic accuracy was evaluated for multiple machine learning algorithms using texture, shape, and CNN features. In the CT contrast-enhanced group, the texture or semantic shape features yielded an overall diagnostic accuracy of 80%. Use of a standard deep learning network in the framework for feature derivation yielded features that substantially underperformed compared to texture and/or semantic features. However, the proposed Multiband approach of feature derivation produced results similar in diagnostic accuracy to the texture and semantic features. While the Multiband feature derivation approach did not outperform the texture and/or semantic features, its equivalent performance indicates promise for future improvements to increase diagnostic accuracy. Importantly, the Multiband approach adapts readily to different size lesions without interpolation, and performed well with relatively small amount of training data.

  20. Real-time hypothesis driven feature extraction on parallel processing architectures

    DEFF Research Database (Denmark)

    Granmo, O.-C.; Jensen, Finn Verner

    2002-01-01

    the problem of higher-order feature-content/feature-feature correlation, causally complexly interacting features are identified through Bayesian network d-separation analysis and combined into joint features. When used on a moderately complex object-tracking case, the technique is able to select...... extraction, which selectively extract relevant features one-by-one, have in some cases achieved real-time performance on single processing element architectures. In this paperwe propose a novel technique which combines the above two approaches. Features are selectively extracted in parallelizable sets...

  1. Acoustic features of objects matched by an echolocating bottlenose dolphin.

    Science.gov (United States)

    Delong, Caroline M; Au, Whitlow W L; Lemonds, David W; Harley, Heidi E; Roitblat, Herbert L

    2006-03-01

    The focus of this study was to investigate how dolphins use acoustic features in returning echolocation signals to discriminate among objects. An echolocating dolphin performed a match-to-sample task with objects that varied in size, shape, material, and texture. After the task was completed, the features of the object echoes were measured (e.g., target strength, peak frequency). The dolphin's error patterns were examined in conjunction with the between-object variation in acoustic features to identify the acoustic features that the dolphin used to discriminate among the objects. The present study explored two hypotheses regarding the way dolphins use acoustic information in echoes: (1) use of a single feature, or (2) use of a linear combination of multiple features. The results suggested that dolphins do not use a single feature across all object sets or a linear combination of six echo features. Five features appeared to be important to the dolphin on four or more sets: the echo spectrum shape, the pattern of changes in target strength and number of highlights as a function of object orientation, and peak and center frequency. These data suggest that dolphins use multiple features and integrate information across echoes from a range of object orientations.

  2. An Extended HITS Algorithm on Bipartite Network for Features Extraction of Online Customer Reviews

    Directory of Open Access Journals (Sweden)

    Chen Liu

    2018-05-01

    Full Text Available How to acquire useful information intelligently in the age of information explosion has become an important issue. In this context, sentiment analysis emerges with the growth of the need of information extraction. One of the most important tasks of sentiment analysis is feature extraction of entities in consumer reviews. This paper first constitutes a directed bipartite feature-sentiment relation network with a set of candidate features-sentiment pairs that is extracted by dependency syntax analysis from consumer reviews. Then, a novel method called MHITS which combines PMI with weighted HITS algorithm is proposed to rank these candidate product features to find out real product features. Empirical experiments indicate the effectiveness of our approach across different kinds and various data sizes of product. In addition, the effect of the proposed algorithm is not the same for the corpus with different proportions of the word pair that includes the “bad”, “good”, “poor”, “pretty good”, “not bad” these general collocation words.

  3. Ultrasonic features and radionuclide correlation in liver cell adenoma and focal nodular hyperlasia.

    Science.gov (United States)

    Sandler, M A; Petrocelli, R D; Marks, D S; Lopez, R

    1980-05-01

    Ultrasonic features of three cases of liver cell adenoma (LCA) and two cases of focal nodular hyperplasia (FNH) are presented. These tumors have similar sonographic appearances presenting either as solid masses or containing sonolucent areas due to hemorrhage or necrosis. Although these ultrasonic features in patients with an area of decreased activity on 99mTc-sulfur colloid (Tc-SC) liver scans are not specific for LCA or FNH, such findings in the appropriate clinical setting are suggestive of these lesions. The combination of a solid mass on ultrasonography and a normal Tc-SC radioisotope liver study may be relatively specific for uncomplicated FNH.

  4. Ultrasonic features and radionuclide correlation in liver cell adenoma and focal nodular hyperplasia

    International Nuclear Information System (INIS)

    Sandler, M.A.; Petrocelli, R.D.; Marks, D.S.; Lopez, R.

    1980-01-01

    Ultrasonic features of three cases of liver cell adenoma (LCA) and two cases of focal nodular hyperplasia (FNH) are presented. These tumors have similar sonographic appearances presenting either as solid masses or containing sonolucent areas due to hemorrhage or necrosis. Although these ultrasonic features in patients wth an area of decreased activity on /sup 99m/Tc-sulfur colloid (Tc-SC) liver scans are not specific for LCA or FNH, such findings in the appropriate clinical setting are suggestive of these lesions. The combination of a solid mass on ultrasonography and a normal Tc-SC radioisotope liver study may be relatively specific for uncomplicated FNH

  5. Categories of relations as models of quantum theory

    Directory of Open Access Journals (Sweden)

    Chris Heunen

    2015-11-01

    Full Text Available Categories of relations over a regular category form a family of models of quantum theory. Using regular logic, many properties of relations over sets lift to these models, including the correspondence between Frobenius structures and internal groupoids. Over compact Hausdorff spaces, this lifting gives continuous symmetric encryption. Over a regular Mal'cev category, this correspondence gives a characterization of categories of completely positive maps, enabling the formulation of quantum features. These models are closer to Hilbert spaces than relations over sets in several respects: Heisenberg uncertainty, impossibility of broadcasting, and behavedness of rank one morphisms.

  6. On the use of wavelet for extracting feature patterns from Multitemporal google earth satellite data sets

    Science.gov (United States)

    Lasaponara, R.

    2012-04-01

    The great amount of multispectral VHR satellite images, even available free of charge in Google earth has opened new strategic challenges in the field of remote sensing for archaeological studies. These challenges substantially deal with: (i) the strategic exploitation of satellite data as much as possible, (ii) the setting up of effective and reliable automatic and/or semiautomatic data processing strategies and (iii) the integration with other data sources from documentary resources to the traditional ground survey, historical documentation, geophysical prospection, etc. VHR satellites provide high resolution data which can improve knowledge on past human activities providing precious qualitative and quantitative information developed to such an extent that currently they share many of the physical characteristics of aerial imagery. This makes them ideal for investigations ranging from a local to a regional scale (see. for example, Lasaponara and Masini 2006a,b, 2007a, 2011; Masini and Lasaponara 2006, 2007, Sparavigna, 2010). Moreover, satellite data are still the only data source for research performed in areas where aerial photography is restricted because of military or political reasons. Among the main advantages of using satellite remote sensing compared to traditional field archaeology herein we briefly focalize on the use of wavelet data processing for enhancing google earth satellite data with particular reference to multitemporal datasets. Study areas selected from Southern Italy, Middle East and South America are presented and discussed. Results obtained point out the use of automatic image enhancement can successfully applied as first step of supervised classification and intelligent data analysis for semiautomatic identification of features of archaeological interest. Reference Lasaponara R, Masini N (2006a) On the potential of panchromatic and multispectral Quickbird data for archaeological prospection. Int J Remote Sens 27: 3607-3614. Lasaponara R

  7. Identification of features of electronic prescribing systems to support quality and safety in primary care using a modified Delphi process.

    Science.gov (United States)

    Sweidan, Michelle; Williamson, Margaret; Reeve, James F; Harvey, Ken; O'Neill, Jennifer A; Schattner, Peter; Snowdon, Teri

    2010-04-15

    Electronic prescribing is increasingly being used in primary care and in hospitals. Studies on the effects of e-prescribing systems have found evidence for both benefit and harm. The aim of this study was to identify features of e-prescribing software systems that support patient safety and quality of care and that are useful to the clinician and the patient, with a focus on improving the quality use of medicines. Software features were identified by a literature review, key informants and an expert group. A modified Delphi process was used with a 12-member multidisciplinary expert group to reach consensus on the expected impact of the features in four domains: patient safety, quality of care, usefulness to the clinician and usefulness to the patient. The setting was electronic prescribing in general practice in Australia. A list of 114 software features was developed. Most of the features relate to the recording and use of patient data, the medication selection process, prescribing decision support, monitoring drug therapy and clinical reports. The expert group rated 78 of the features (68%) as likely to have a high positive impact in at least one domain, 36 features (32%) as medium impact, and none as low or negative impact. Twenty seven features were rated as high positive impact across 3 or 4 domains including patient safety and quality of care. Ten features were considered "aspirational" because of a lack of agreed standards and/or suitable knowledge bases. This study defines features of e-prescribing software systems that are expected to support safety and quality, especially in relation to prescribing and use of medicines in general practice. The features could be used to develop software standards, and could be adapted if necessary for use in other settings and countries.

  8. Oversampling the Minority Class in the Feature Space.

    Science.gov (United States)

    Perez-Ortiz, Maria; Gutierrez, Pedro Antonio; Tino, Peter; Hervas-Martinez, Cesar

    2016-09-01

    The imbalanced nature of some real-world data is one of the current challenges for machine learning researchers. One common approach oversamples the minority class through convex combination of its patterns. We explore the general idea of synthetic oversampling in the feature space induced by a kernel function (as opposed to input space). If the kernel function matches the underlying problem, the classes will be linearly separable and synthetically generated patterns will lie on the minority class region. Since the feature space is not directly accessible, we use the empirical feature space (EFS) (a Euclidean space isomorphic to the feature space) for oversampling purposes. The proposed method is framed in the context of support vector machines, where the imbalanced data sets can pose a serious hindrance. The idea is investigated in three scenarios: 1) oversampling in the full and reduced-rank EFSs; 2) a kernel learning technique maximizing the data class separation to study the influence of the feature space structure (implicitly defined by the kernel function); and 3) a unified framework for preferential oversampling that spans some of the previous approaches in the literature. We support our investigation with extensive experiments over 50 imbalanced data sets.

  9. Single and Multiple Object Tracking Using a Multi-Feature Joint Sparse Representation.

    Science.gov (United States)

    Hu, Weiming; Li, Wei; Zhang, Xiaoqin; Maybank, Stephen

    2015-04-01

    In this paper, we propose a tracking algorithm based on a multi-feature joint sparse representation. The templates for the sparse representation can include pixel values, textures, and edges. In the multi-feature joint optimization, noise or occlusion is dealt with using a set of trivial templates. A sparse weight constraint is introduced to dynamically select the relevant templates from the full set of templates. A variance ratio measure is adopted to adaptively adjust the weights of different features. The multi-feature template set is updated adaptively. We further propose an algorithm for tracking multi-objects with occlusion handling based on the multi-feature joint sparse reconstruction. The observation model based on sparse reconstruction automatically focuses on the visible parts of an occluded object by using the information in the trivial templates. The multi-object tracking is simplified into a joint Bayesian inference. The experimental results show the superiority of our algorithm over several state-of-the-art tracking algorithms.

  10. Differential privacy-based evaporative cooling feature selection and classification with relief-F and random forests.

    Science.gov (United States)

    Le, Trang T; Simmons, W Kyle; Misaki, Masaya; Bodurka, Jerzy; White, Bill C; Savitz, Jonathan; McKinney, Brett A

    2017-09-15

    Classification of individuals into disease or clinical categories from high-dimensional biological data with low prediction error is an important challenge of statistical learning in bioinformatics. Feature selection can improve classification accuracy but must be incorporated carefully into cross-validation to avoid overfitting. Recently, feature selection methods based on differential privacy, such as differentially private random forests and reusable holdout sets, have been proposed. However, for domains such as bioinformatics, where the number of features is much larger than the number of observations p≫n , these differential privacy methods are susceptible to overfitting. We introduce private Evaporative Cooling, a stochastic privacy-preserving machine learning algorithm that uses Relief-F for feature selection and random forest for privacy preserving classification that also prevents overfitting. We relate the privacy-preserving threshold mechanism to a thermodynamic Maxwell-Boltzmann distribution, where the temperature represents the privacy threshold. We use the thermal statistical physics concept of Evaporative Cooling of atomic gases to perform backward stepwise privacy-preserving feature selection. On simulated data with main effects and statistical interactions, we compare accuracies on holdout and validation sets for three privacy-preserving methods: the reusable holdout, reusable holdout with random forest, and private Evaporative Cooling, which uses Relief-F feature selection and random forest classification. In simulations where interactions exist between attributes, private Evaporative Cooling provides higher classification accuracy without overfitting based on an independent validation set. In simulations without interactions, thresholdout with random forest and private Evaporative Cooling give comparable accuracies. We also apply these privacy methods to human brain resting-state fMRI data from a study of major depressive disorder. Code

  11. A Novel Rough Set Reduct Algorithm for Medical Domain Based on Bee Colony Optimization

    OpenAIRE

    Suguna, N.; Thanushkodi, K.

    2010-01-01

    Feature selection refers to the problem of selecting relevant features which produce the most predictive outcome. In particular, feature selection task is involved in datasets containing huge number of features. Rough set theory has been one of the most successful methods used for feature selection. However, this method is still not able to find optimal subsets. This paper proposes a new feature selection method based on Rough set theory hybrid with Bee Colony Optimization (BCO) in an attempt...

  12. Temporal resolution for the perception of features and conjunctions.

    Science.gov (United States)

    Bodelón, Clara; Fallah, Mazyar; Reynolds, John H

    2007-01-24

    The visual system decomposes stimuli into their constituent features, represented by neurons with different feature selectivities. How the signals carried by these feature-selective neurons are integrated into coherent object representations is unknown. To constrain the set of possible integrative mechanisms, we quantified the temporal resolution of perception for color, orientation, and conjunctions of these two features. We find that temporal resolution is measurably higher for each feature than for their conjunction, indicating that time is required to integrate features into a perceptual whole. This finding places temporal limits on the mechanisms that could mediate this form of perceptual integration.

  13. A study on feature analysis for musical instrument classification.

    Science.gov (United States)

    Deng, Jeremiah D; Simmermacher, Christian; Cranefield, Stephen

    2008-04-01

    In tackling data mining and pattern recognition tasks, finding a compact but effective set of features has often been found to be a crucial step in the overall problem-solving process. In this paper, we present an empirical study on feature analysis for recognition of classical instrument, using machine learning techniques to select and evaluate features extracted from a number of different feature schemes. It is revealed that there is significant redundancy between and within feature schemes commonly used in practice. Our results suggest that further feature analysis research is necessary in order to optimize feature selection and achieve better results for the instrument recognition problem.

  14. Neuron's eye view: Inferring features of complex stimuli from neural responses.

    Directory of Open Access Journals (Sweden)

    Xin Chen

    2017-08-01

    Full Text Available Experiments that study neural encoding of stimuli at the level of individual neurons typically choose a small set of features present in the world-contrast and luminance for vision, pitch and intensity for sound-and assemble a stimulus set that systematically varies along these dimensions. Subsequent analysis of neural responses to these stimuli typically focuses on regression models, with experimenter-controlled features as predictors and spike counts or firing rates as responses. Unfortunately, this approach requires knowledge in advance about the relevant features coded by a given population of neurons. For domains as complex as social interaction or natural movement, however, the relevant feature space is poorly understood, and an arbitrary a priori choice of features may give rise to confirmation bias. Here, we present a Bayesian model for exploratory data analysis that is capable of automatically identifying the features present in unstructured stimuli based solely on neuronal responses. Our approach is unique within the class of latent state space models of neural activity in that it assumes that firing rates of neurons are sensitive to multiple discrete time-varying features tied to the stimulus, each of which has Markov (or semi-Markov dynamics. That is, we are modeling neural activity as driven by multiple simultaneous stimulus features rather than intrinsic neural dynamics. We derive a fast variational Bayesian inference algorithm and show that it correctly recovers hidden features in synthetic data, as well as ground-truth stimulus features in a prototypical neural dataset. To demonstrate the utility of the algorithm, we also apply it to cluster neural responses and demonstrate successful recovery of features corresponding to monkeys and faces in the image set.

  15. A Hybrid Feature Selection Approach for Arabic Documents Classification

    NARCIS (Netherlands)

    Habib, Mena Badieh; Sarhan, Ahmed A. E.; Salem, Abdel-Badeeh M.; Fayed, Zaki T.; Gharib, Tarek F.

    Text Categorization (classification) is the process of classifying documents into a predefined set of categories based on their content. Text categorization algorithms usually represent documents as bags of words and consequently have to deal with huge number of features. Feature selection tries to

  16. Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm.

    Science.gov (United States)

    Khushaba, Rami N; Kodagoda, Sarath; Lal, Sara; Dissanayake, Gamini

    2011-01-01

    Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount of drowsiness-related information extracted from a set of electroencephalogram (EEG), electrooculogram (EOG), and electrocardiogram (ECG) signals during a simulation driving test. Specifically, we develop an efficient fuzzy mutual-information (MI)- based wavelet packet transform (FMIWPT) feature-extraction method for classifying the driver drowsiness state into one of predefined drowsiness levels. The proposed method estimates the required MI using a novel approach based on fuzzy memberships providing an accurate-information content-estimation measure. The quality of the extracted features was assessed on datasets collected from 31 drivers on a simulation test. The experimental results proved the significance of FMIWPT in extracting features that highly correlate with the different drowsiness levels achieving a classification accuracy of 95%-- 97% on an average across all subjects.

  17. A threshold auto-adjustment algorithm of feature points extraction based on grid

    Science.gov (United States)

    Yao, Zili; Li, Jun; Dong, Gaojie

    2018-02-01

    When dealing with high-resolution digital images, detection of feature points is usually the very first important step. Valid feature points depend on the threshold. If the threshold is too low, plenty of feature points will be detected, and they may be aggregated in the rich texture regions, which consequently not only affects the speed of feature description, but also aggravates the burden of following processing; if the threshold is set high, the feature points in poor texture area will lack. To solve these problems, this paper proposes a threshold auto-adjustment method of feature extraction based on grid. By dividing the image into numbers of grid, threshold is set in every local grid for extracting the feature points. When the number of feature points does not meet the threshold requirement, the threshold will be adjusted automatically to change the final number of feature points The experimental results show that feature points produced by our method is more uniform and representative, which avoids the aggregation of feature points and greatly reduces the complexity of following work.

  18. On the comparisons of tropical relative humidity in the lower and middle troposphere among COSMIC radio occultations and MERRA and ECMWF data sets

    Science.gov (United States)

    Vergados, P.; Mannucci, A. J.; Ao, C. O.; Jiang, J. H.; Su, H.

    2015-04-01

    The spatial variability of the tropical tropospheric relative humidity (RH) throughout the vertical extent of the troposphere is examined using Global Positioning System Radio Occultation (GPSRO) observations from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission. These high vertical resolution observations capture the detailed structure and moisture budget of the Hadley Cell circulation. We compare the COSMIC observations with the European Center for Medium-range Weather Forecast (ECMWF) Reanalysis Interim (ERA-Interim) and the Modern-Era Retrospective analysis for Research and Applications (MERRA) climatologies. Qualitatively, the spatial pattern of RH in all data sets matches up remarkably well, capturing distinct features of the general circulation. However, RH discrepancies exist between ERA-Interim and COSMIC data sets that are noticeable across the tropical boundary layer. Specifically, ERA-Interim shows a drier Intertropical Convergence Zone (ITCZ) by 15-20% compared to both COSMIC and MERRA data sets, but this difference decreases with altitude. Unlike ECMWF, MERRA shows an excellent agreement with the COSMIC observations except above 400 hPa, where GPSRO observations capture drier air by 5-10%. RH climatologies were also used to evaluate intraseasonal variability. The results indicate that the tropical middle troposphere at ±5-25° is most sensitive to seasonal variations. COSMIC and MERRA data sets capture the same magnitude of the seasonal variability, but ERA-Interim shows a weaker seasonal fluctuation up to 10% in the middle troposphere inside the dry air subsidence regions of the Hadley Cell. Over the ITCZ, RH varies by maximum 9% between winter and summer.

  19. Genome-wide association meta-analysis of neuropathologic features of Alzheimer's disease and related dementias.

    Directory of Open Access Journals (Sweden)

    Gary W Beecham

    2014-09-01

    Full Text Available Alzheimer's disease (AD and related dementias are a major public health challenge and present a therapeutic imperative for which we need additional insight into molecular pathogenesis. We performed a genome-wide association study and analysis of known genetic risk loci for AD dementia using neuropathologic data from 4,914 brain autopsies. Neuropathologic data were used to define clinico-pathologic AD dementia or controls, assess core neuropathologic features of AD (neuritic plaques, NPs; neurofibrillary tangles, NFTs, and evaluate commonly co-morbid neuropathologic changes: cerebral amyloid angiopathy (CAA, Lewy body disease (LBD, hippocampal sclerosis of the elderly (HS, and vascular brain injury (VBI. Genome-wide significance was observed for clinico-pathologic AD dementia, NPs, NFTs, CAA, and LBD with a number of variants in and around the apolipoprotein E gene (APOE. GalNAc transferase 7 (GALNT7, ATP-Binding Cassette, Sub-Family G (WHITE, Member 1 (ABCG1, and an intergenic region on chromosome 9 were associated with NP score; and Potassium Large Conductance Calcium-Activated Channel, Subfamily M, Beta Member 2 (KCNMB2 was strongly associated with HS. Twelve of the 21 non-APOE genetic risk loci for clinically-defined AD dementia were confirmed in our clinico-pathologic sample: CR1, BIN1, CLU, MS4A6A, PICALM, ABCA7, CD33, PTK2B, SORL1, MEF2C, ZCWPW1, and CASS4 with 9 of these 12 loci showing larger odds ratio in the clinico-pathologic sample. Correlation of effect sizes for risk of AD dementia with effect size for NFTs or NPs showed positive correlation, while those for risk of VBI showed a moderate negative correlation. The other co-morbid neuropathologic features showed only nominal association with the known AD loci. Our results discovered new genetic associations with specific neuropathologic features and aligned known genetic risk for AD dementia with specific neuropathologic changes in the largest brain autopsy study of AD and related

  20. Sending and Receiving Text Messages with Sexual Content: Relations with Early Sexual Activity and Borderline Personality Features in Late Adolescence.

    Science.gov (United States)

    Brinkley, Dawn Y; Ackerman, Robert A; Ehrenreich, Samuel E; Underwood, Marion K

    2017-05-01

    This research examined adolescents' written text messages with sexual content to investigate how sexting relates to sexual activity and borderline personality features. Participants (N = 181, 85 girls) completed a measure of borderline personality features prior to 10 th grade and were subsequently given smartphones configured to capture the content of their text messages. Four days of text messaging were micro-coded for content related to sex. Following 12 th grade, participants reported on their sexual activity and again completed a measure of borderline personality features. Results showed that engaging in sexting at age 16 was associated with reporting an early sexual debut, having sexual intercourse experience, having multiple sex partners, and engaging in drug use in combination with sexual activity two years later. Girls engaging in sex talk were more likely to have had sexual intercourse by age 18. Text messaging about hypothetical sex in grade 10 also predicted borderline personality features at age 18. These findings suggest that sending text messages with sexual content poses risks for adolescents. Programs to prevent risky sexual activity and to promote psychological health could be enhanced by teaching adolescents to use digital communication responsibly.

  1. Sending and Receiving Text Messages with Sexual Content: Relations with Early Sexual Activity and Borderline Personality Features in Late Adolescence

    Science.gov (United States)

    Brinkley, Dawn Y.; Ackerman, Robert A.; Ehrenreich, Samuel E.; Underwood, Marion K.

    2017-01-01

    This research examined adolescents’ written text messages with sexual content to investigate how sexting relates to sexual activity and borderline personality features. Participants (N = 181, 85 girls) completed a measure of borderline personality features prior to 10th grade and were subsequently given smartphones configured to capture the content of their text messages. Four days of text messaging were micro-coded for content related to sex. Following 12th grade, participants reported on their sexual activity and again completed a measure of borderline personality features. Results showed that engaging in sexting at age 16 was associated with reporting an early sexual debut, having sexual intercourse experience, having multiple sex partners, and engaging in drug use in combination with sexual activity two years later. Girls engaging in sex talk were more likely to have had sexual intercourse by age 18. Text messaging about hypothetical sex in grade 10 also predicted borderline personality features at age 18. These findings suggest that sending text messages with sexual content poses risks for adolescents. Programs to prevent risky sexual activity and to promote psychological health could be enhanced by teaching adolescents to use digital communication responsibly. PMID:28824224

  2. The body of the analyst and the analytic setting: reflections on the embodied setting and the symbiotic transference.

    Science.gov (United States)

    Lemma, Alessandra

    2014-04-01

    In this paper the author questions whether the body of the analyst may be helpfully conceptualized as an embodied feature of the setting and suggests that this may be especially helpful for understanding patients who develop a symbiotic transference and for whom any variance in the analyst's body is felt to be profoundly destabilizing. In such cases the patient needs to relate to the body of the analyst concretely and exclusively as a setting 'constant' and its meaning for the patient may thus remain inaccessible to analysis for a long time. When the separateness of the body of the analyst reaches the patient's awareness because of changes in the analyst's appearance or bodily state, it then mobilizes primitive anxieties in the patient. It is only when the body of the analyst can become a dynamic variable between them (i.e., part of the process) that it can be used by the patient to further the exploration of their own mind. Copyright © 2014 Institute of Psychoanalysis.

  3. Comparative Study on Feature Selection and Fusion Schemes for Emotion Recognition from Speech

    Directory of Open Access Journals (Sweden)

    Santiago Planet

    2012-09-01

    Full Text Available The automatic analysis of speech to detect affective states may improve the way users interact with electronic devices. However, the analysis only at the acoustic level could be not enough to determine the emotion of a user in a realistic scenario. In this paper we analyzed the spontaneous speech recordings of the FAU Aibo Corpus at the acoustic and linguistic levels to extract two sets of features. The acoustic set was reduced by a greedy procedure selecting the most relevant features to optimize the learning stage. We compared two versions of this greedy selection algorithm by performing the search of the relevant features forwards and backwards. We experimented with three classification approaches: Naïve-Bayes, a support vector machine and a logistic model tree, and two fusion schemes: decision-level fusion, merging the hard-decisions of the acoustic and linguistic classifiers by means of a decision tree; and feature-level fusion, concatenating both sets of features before the learning stage. Despite the low performance achieved by the linguistic data, a dramatic improvement was achieved after its combination with the acoustic information, improving the results achieved by this second modality on its own. The results achieved by the classifiers using the parameters merged at feature level outperformed the classification results of the decision-level fusion scheme, despite the simplicity of the scheme. Moreover, the extremely reduced set of acoustic features obtained by the greedy forward search selection algorithm improved the results provided by the full set.

  4. An investigation of face and fingerprint feature-fusion guidelines

    CSIR Research Space (South Africa)

    Brown, Dane

    2016-05-01

    Full Text Available There are a lack of multi-modal biometric fusion guidelines at the feature-level. This paper investigates face and fingerprint features in the form of their strengths and weaknesses. This serves as a set of guidelines to authors that are planning...

  5. [Mobile geriatric rehabilitation in nursing homes, in short-term care facilities and private homes : Setting-specific analysis of nationwide treatment documentation (Part 2)].

    Science.gov (United States)

    Pippel, Kristina; Meinck, M; Lübke, N

    2017-06-01

    Mobile geriatric rehabilitation can be provided in the setting of nursing homes, short-term care (STC) facilities and exclusively in private homes. This study analyzed the common features and differences of mobile rehabilitation interventions in various settings. Stratified by setting 1,879 anonymized mobile geriatric rehabilitation treatments between 2011 and 2014 from 11 participating institutions were analyzed with respect to patient, process and outcome-related features. Significant differences between the settings nursing home (n = 514, 27 %), STC (n = 167, 9 %) and private homes (n = 1198, 64 %) were evident for mean age (83 years, 83 years and 80 years, respectively), percentage of women (72 %, 64 % and 55 %), degree of dependency on pre-existing care (92 %, 76 % and 64 %), total treatment sessions (TS, 38 TS, 42 TS and 41 TS), treatment duration (54 days, 61 days and 58 days) as well as the Barthel index at the start of rehabilitation (34 points, 39 points and 46 points) and the gain in the Barthel index (15 points, 21 points and 18 points), whereby the gain in the capacity for self-sufficiency was significant in all settings. The setting-specific evaluation of mobile geriatric rehabilitation showed differences for relevant patient, process and outcome-related features. Compared to inpatient rehabilitation mobile rehabilitation in all settings made an above average contribution to the rehabilitation of patients with pre-existing dependency on care. The gains in the capacity for self-sufficiency achieved in all settings support the efficacy of mobile geriatric rehabilitation under the current prerequisites for applicability.

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

    Science.gov (United States)

    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

  7. Aerodynamic features of flames in premixed gases

    Science.gov (United States)

    Oppenheim, A. K.

    1984-01-01

    A variety of experimentally established flame phenomena in premixed gases are interpreted by relating them to basic aerodynamic properties of the flow field. On this basis the essential mechanism of some well known characteristic features of flames stabilized in the wake of a bluff-body or propagating in ducts are revealed. Elementary components of the flame propagation process are shown to be: rotary motion, self-advancement, and expansion. Their consequences are analyzed under a most strict set of idealizations that permit the flow field to be treated as potential in character, while the flame is modelled as a Stefan-like interface capable of exerting a feed-back effect upon the flow field. The results provide an insight into the fundamental fluid-mechanical reasons for the experimentally observed distortions of the flame front, rationalizing in particular its ability to sustain relatively high flow velocities at amazingly low normal burning speeds.

  8. Credible Set Estimation, Analysis, and Applications in Synthetic Aperture Radar Canonical Feature Extraction

    Science.gov (United States)

    2015-03-26

    83 5.1 Marginal PMFs for the cylinder scene at coarse zoom. . . . . . . . . . . . . . . 85 5.2 SAR image of a Nissan Sentra with canonical...of a Nissan Sentra with canonical features extracted by the SPLIT algorithm. 5.2.4 Experiment Summary. A notional algorithm is presented in Figure 5.3

  9. A Grounded Theory Study of HIV-Related Stigma in U.S.-Based Health Care Settings.

    Science.gov (United States)

    Davtyan, Mariam; Olshansky, Ellen F; Brown, Brandon; Lakon, Cynthia

    Despite progress made in the treatment and care of people living with HIV (PLWH), HIV-related stigma has remained persistent. Health care settings and workers have been identified as important sources of stigma. Studies have addressed the construct of stigma in U.S. health care settings, but mainly from the perspectives of PLWH. We used Grounded Theory to understand how health care workers conceptualized HIV-related stigma and to develop a model to project a purposive view of stigma in health care settings. Our model indicates that stigma may be rooted in historically derogatory representations of HIV and intensified by power inequalities. Stigma may be triggered by fear, inadequate clinical education and training, unintentional behaviors, and limited contact with PLWH. Study participants perceived stigma as injurious to patient and provider health outcomes. Additional research on provider perceptions of stigma and programs that encourage empowerment, communication, and training may be necessary for stigma reduction. Copyright © 2017 Association of Nurses in AIDS Care. Published by Elsevier Inc. All rights reserved.

  10. Basic feature of host rock and its relation to the formation of leachable sandstone type uranium deposit in Shihongtan

    International Nuclear Information System (INIS)

    Quan Zhigao; Zhang Jiamin; Ji Haijun; Sun Yanhuan; Zhang Fa

    2012-01-01

    Basic feature of sedimentology and petrology and lithogeochemistry of middle Jurassic Xishanyao formation were discussed for Shihongtan uranium deposit in the paper. The relation between host rock and ore formation was analysed. It is indicated that the formation of Shihongtan uranium deposit de-ponds on the following host features in sedimentology, petrology, lithogeochemistry and the intense oxidized epigenetic alteration under hot dry climate condition during the formation of peneplain caused by the slow tilting uplift. (authors)

  11. Validation of the Care-Related Quality of Life Instrument in different study settings : findings from The Older Persons and Informal Caregivers Survey Minimum DataSet (TOPICS-MDS)

    NARCIS (Netherlands)

    Lutomski, J. E.; van Exel, N. J. A.; Kempen, G. I. J. M.; van Charante, E. P. Moll; den Elzen, W. P. J.; Jansen, A. P. D.; Krabbe, P. F. M.; Steunenberg, B.; Steyerberg, E. W.; Rikkert, M. G. M. Olde; Melis, R. J. F.

    PURPOSE: Validity is a contextual aspect of a scale which may differ across sample populations and study protocols. The objective of our study was to validate the Care-Related Quality of Life Instrument (CarerQol) across two different study design features, sampling framework (general population vs.

  12. Validation of the Care-Related Quality of Life Instrument in different study settings: findings from The Older Persons and Informal Caregivers Survey Minimum DataSet (TOPICS-MDS)

    NARCIS (Netherlands)

    Lutomski, J.E.; Exel, N.J. van; Kempen, G.I.; Charante, E.P. Moll van; Elzen, W.P. den; Jansen, A.P.; Krabbe, P.F.M.; Steunenberg, B.; Steyerberg, E.W.; Olde Rikkert, M.G.M.; Melis, R.J.F.

    2015-01-01

    PURPOSE: Validity is a contextual aspect of a scale which may differ across sample populations and study protocols. The objective of our study was to validate the Care-Related Quality of Life Instrument (CarerQol) across two different study design features, sampling framework (general population vs.

  13. Cloud field classification based on textural features

    Science.gov (United States)

    Sengupta, Sailes Kumar

    1989-01-01

    An essential component in global climate research is accurate cloud cover and type determination. Of the two approaches to texture-based classification (statistical and textural), only the former is effective in the classification of natural scenes such as land, ocean, and atmosphere. In the statistical approach that was adopted, parameters characterizing the stochastic properties of the spatial distribution of grey levels in an image are estimated and then used as features for cloud classification. Two types of textural measures were used. One is based on the distribution of the grey level difference vector (GLDV), and the other on a set of textural features derived from the MaxMin cooccurrence matrix (MMCM). The GLDV method looks at the difference D of grey levels at pixels separated by a horizontal distance d and computes several statistics based on this distribution. These are then used as features in subsequent classification. The MaxMin tectural features on the other hand are based on the MMCM, a matrix whose (I,J)th entry give the relative frequency of occurrences of the grey level pair (I,J) that are consecutive and thresholded local extremes separated by a given pixel distance d. Textural measures are then computed based on this matrix in much the same manner as is done in texture computation using the grey level cooccurrence matrix. The database consists of 37 cloud field scenes from LANDSAT imagery using a near IR visible channel. The classification algorithm used is the well known Stepwise Discriminant Analysis. The overall accuracy was estimated by the percentage or correct classifications in each case. It turns out that both types of classifiers, at their best combination of features, and at any given spatial resolution give approximately the same classification accuracy. A neural network based classifier with a feed forward architecture and a back propagation training algorithm is used to increase the classification accuracy, using these two classes

  14. Health-related quality of life of irritable bowel syndrome patients in different cultural settings

    Science.gov (United States)

    Faresjö, Åshild; Anastasiou, Foteini; Lionis, Christos; Johansson, Saga; Wallander, Mari-Ann; Faresjö, Tomas

    2006-01-01

    Background Persons with Irritable bowel syndrome (IBS) are seriously affected in their everyday life. The effect across different cultural settings of IBS on their quality of life has been little studied. The aim was to compare health-related quality of life (HRQOL) of individuals suffering from IBS in two different cultural settings; Crete, Greece and Linköping, Sweden. Methods This study is a sex and age-matched case-control study, with n = 30 Cretan IBS cases and n = 90 Swedish IBS cases and a Swedish control group (n = 300) randomly selected from the general population. Health-related quality of life, measured by SF-36 and demographics, life style indicators and co-morbidity, was measured. Results Cretan IBS cases reported lower HRQOL on most dimensions of SF-36 in comparison to the Swedish IBS cases. Significant differences were found for the dimensions mental health (p cultural environments could perceive their disease differently and that the disease might affect their everyday life and quality of life in a different way. The Cretan population, and especially women, are more seriously affected mentally by their disease than Swedish IBS cases. Coping with IBS in everyday life might be more problematic in the Cretan environment than in the Swedish setting. PMID:16566821

  15. Health-related quality of life of irritable bowel syndrome patients in different cultural settings.

    Science.gov (United States)

    Faresjö, Ashild; Anastasiou, Foteini; Lionis, Christos; Johansson, Saga; Wallander, Mari-Ann; Faresjö, Tomas

    2006-03-27

    Persons with Irritable bowel syndrome (IBS) are seriously affected in their everyday life. The effect across different cultural settings of IBS on their quality of life has been little studied. The aim was to compare health-related quality of life (HRQOL) of individuals suffering from IBS in two different cultural settings; Crete, Greece and Linköping, Sweden. This study is a sex and age-matched case-control study, with n = 30 Cretan IBS cases and n = 90 Swedish IBS cases and a Swedish control group (n = 300) randomly selected from the general population. Health-related quality of life, measured by SF-36 and demographics, life style indicators and co-morbidity, was measured. Cretan IBS cases reported lower HRQOL on most dimensions of SF-36 in comparison to the Swedish IBS cases. Significant differences were found for the dimensions mental health (p cultural environments could perceive their disease differently and that the disease might affect their everyday life and quality of life in a different way. The Cretan population, and especially women, are more seriously affected mentally by their disease than Swedish IBS cases. Coping with IBS in everyday life might be more problematic in the Cretan environment than in the Swedish setting.

  16. Quality related principles of the South African beef classification ...

    African Journals Online (AJOL)

    This paper addresses the principles related to different grading and classification systems of the world with specific focus on beef quality related outcomes. The paper uses the definitions that classification is a set of descriptive terms describing features of the carcass that are useful as guidelines to those involved in the ...

  17. Imagiologic features and the relative imaging factors in hepatolenticular degeneration

    International Nuclear Information System (INIS)

    Gao Wenqing; Liu Pengcheng; Huang Rong; Yan Weiqiang; Zhao Yan; Liu Yuanjian; Luo Lili; Zou Liqiu; Liu Hanqiao

    2002-01-01

    Objective: To study the CT, MR and ultrasound features of hepatolenticular degeneration (HLD), and investigate relative factors affecting the imaging manifestations. Methods: Fifty-four HLD were reported, and the 35 male and 19 female patients ranged in the age from 3 to 40 years. CT was performed in 29 patients, MR in 11, both CT and MR in 5, ultrasound in 26. Six cases were hospitalized for 3 times, and 9 for twice. Results: (1) The putamen was affected on MR in all cases (100%), the caudate nucleus in 8. The thalami in 5, the globus pallidum in 2, the red nucleus in 2, the substantia nigra in 3, the midbrain in 1, the pons in 2, the white matter of frontal lobi in 1. According to the different basal ganglia involved in brain, resembling 'woodpecker' or 'butterfly spreading the wing' in appearance were showed on the MR images respectively. (2) Positive signs were found by CT scans in 18 cases (72%), but negative in 7 cases (28%). It is important manifestation that low density in brain occurred bilaterally and symmetrically. (3) The sonographic changes of chronic liver disease were showed on US in all 26 cases. Among the number, 12 cases were regarded as cirrhosis at the same time. Conclusion: (1) T 2 signal intensity and CT density changes are often not parallel to the clinical symptoms. T 1 WI is suitable for the follow-up, but quantitative analysis is still difficult. (2) Damage of liver occurs almost in all HLD, and earlier than that of brain. On the early stage, the liver damage is reversible, the brain lesions are symmetric. Moderately, the liver damage changes static. Lately, the brain presents atrophic. (3) The investigation suggests that there are 4 factors affecting CT and MR imaging features: the systemic disease resulting from metabolic disorder and the selected affinity caused by gene defect, deposition of copper together with cellular damage, endogenous and autonomous discharge of copper and histiocyte repaired, and extro-generate expelled copper

  18. Facial expression recognition in the wild based on multimodal texture features

    Science.gov (United States)

    Sun, Bo; Li, Liandong; Zhou, Guoyan; He, Jun

    2016-11-01

    Facial expression recognition in the wild is a very challenging task. We describe our work in static and continuous facial expression recognition in the wild. We evaluate the recognition results of gray deep features and color deep features, and explore the fusion of multimodal texture features. For the continuous facial expression recognition, we design two temporal-spatial dense scale-invariant feature transform (SIFT) features and combine multimodal features to recognize expression from image sequences. For the static facial expression recognition based on video frames, we extract dense SIFT and some deep convolutional neural network (CNN) features, including our proposed CNN architecture. We train linear support vector machine and partial least squares classifiers for those kinds of features on the static facial expression in the wild (SFEW) and acted facial expression in the wild (AFEW) dataset, and we propose a fusion network to combine all the extracted features at decision level. The final achievement we gained is 56.32% on the SFEW testing set and 50.67% on the AFEW validation set, which are much better than the baseline recognition rates of 35.96% and 36.08%.

  19. Feature selection for domain knowledge representation through multitask learning

    CSIR Research Space (South Africa)

    Rosman, Benjamin S

    2014-10-01

    Full Text Available represent stimuli of interest, and rich feature sets which increase the dimensionality of the space and thus the difficulty of the learning problem. We focus on a multitask reinforcement learning setting, where the agent is learning domain knowledge...

  20. Career Pathways for Related Service Paratherapists Working in Early Intervention and Other Education Settings.

    Science.gov (United States)

    Longhurst, Thomas M.

    1997-01-01

    Discusses issues in personnel training practices for paraprofessionals providing related services in early intervention and education settings. The term paratherapist is used to refer to paraprofessionals working under the supervision of professionals in physical therapy, occupational therapy, and speech-language pathology. Presents a philosophy…

  1. FEATURE EVALUATION FOR BUILDING FACADE IMAGES – AN EMPIRICAL STUDY

    Directory of Open Access Journals (Sweden)

    M. Y. Yang

    2012-08-01

    Full Text Available The classification of building facade images is a challenging problem that receives a great deal of attention in the photogrammetry community. Image classification is critically dependent on the features. In this paper, we perform an empirical feature evaluation task for building facade images. Feature sets we choose are basic features, color features, histogram features, Peucker features, texture features, and SIFT features. We present an approach for region-wise labeling using an efficient randomized decision forest classifier and local features. We conduct our experiments with building facade image classification on the eTRIMS dataset, where our focus is the object classes building, car, door, pavement, road, sky, vegetation, and window.

  2. A static analysis tool set for assembler code verification

    International Nuclear Information System (INIS)

    Dhodapkar, S.D.; Bhattacharjee, A.K.; Sen, Gopa

    1991-01-01

    Software Verification and Validation (V and V) is an important step in assuring reliability and quality of the software. The verification of program source code forms an important part of the overall V and V activity. The static analysis tools described here are useful in verification of assembler code. The tool set consists of static analysers for Intel 8086 and Motorola 68000 assembly language programs. The analysers examine the program source code and generate information about control flow within the program modules, unreachable code, well-formation of modules, call dependency between modules etc. The analysis of loops detects unstructured loops and syntactically infinite loops. Software metrics relating to size and structural complexity are also computed. This report describes the salient features of the design, implementation and the user interface of the tool set. The outputs generated by the analyser are explained using examples taken from some projects analysed by this tool set. (author). 7 refs., 17 figs

  3. Graphical matching rules for cardinality based service feature diagrams

    Directory of Open Access Journals (Sweden)

    Faiza Kanwal

    2017-03-01

    Full Text Available To provide efficient services to end-users, variability and commonality among the features of the product line is a challenge for industrialist and researchers. Feature modeling provides great services to deal with variability and commonality among the features of product line. Cardinality based service feature diagrams changed the basic framework of service feature diagrams by putting constraints to them, which make service specifications more flexible, but apart from their variation in selection third party services may have to be customizable. Although to control variability, cardinality based service feature diagrams provide high level visual notations. For specifying variability, the use of cardinality based service feature diagrams raises the problem of matching a required feature diagram against the set of provided diagrams.

  4. Feature Selection based on Machine Learning in MRIs for Hippocampal Segmentation

    Science.gov (United States)

    Tangaro, Sabina; Amoroso, Nicola; Brescia, Massimo; Cavuoti, Stefano; Chincarini, Andrea; Errico, Rosangela; Paolo, Inglese; Longo, Giuseppe; Maglietta, Rosalia; Tateo, Andrea; Riccio, Giuseppe; Bellotti, Roberto

    2015-01-01

    Neurodegenerative diseases are frequently associated with structural changes in the brain. Magnetic resonance imaging (MRI) scans can show these variations and therefore can be used as a supportive feature for a number of neurodegenerative diseases. The hippocampus has been known to be a biomarker for Alzheimer disease and other neurological and psychiatric diseases. However, it requires accurate, robust, and reproducible delineation of hippocampal structures. Fully automatic methods are usually the voxel based approach; for each voxel a number of local features were calculated. In this paper, we compared four different techniques for feature selection from a set of 315 features extracted for each voxel: (i) filter method based on the Kolmogorov-Smirnov test; two wrapper methods, respectively, (ii) sequential forward selection and (iii) sequential backward elimination; and (iv) embedded method based on the Random Forest Classifier on a set of 10 T1-weighted brain MRIs and tested on an independent set of 25 subjects. The resulting segmentations were compared with manual reference labelling. By using only 23 feature for each voxel (sequential backward elimination) we obtained comparable state-of-the-art performances with respect to the standard tool FreeSurfer.

  5. Feature Selection via Chaotic Antlion Optimization.

    Directory of Open Access Journals (Sweden)

    Hossam M Zawbaa

    Full Text Available Selecting a subset of relevant properties from a large set of features that describe a dataset is a challenging machine learning task. In biology, for instance, the advances in the available technologies enable the generation of a very large number of biomarkers that describe the data. Choosing the more informative markers along with performing a high-accuracy classification over the data can be a daunting task, particularly if the data are high dimensional. An often adopted approach is to formulate the feature selection problem as a biobjective optimization problem, with the aim of maximizing the performance of the data analysis model (the quality of the data training fitting while minimizing the number of features used.We propose an optimization approach for the feature selection problem that considers a "chaotic" version of the antlion optimizer method, a nature-inspired algorithm that mimics the hunting mechanism of antlions in nature. The balance between exploration of the search space and exploitation of the best solutions is a challenge in multi-objective optimization. The exploration/exploitation rate is controlled by the parameter I that limits the random walk range of the ants/prey. This variable is increased iteratively in a quasi-linear manner to decrease the exploration rate as the optimization progresses. The quasi-linear decrease in the variable I may lead to immature convergence in some cases and trapping in local minima in other cases. The chaotic system proposed here attempts to improve the tradeoff between exploration and exploitation. The methodology is evaluated using different chaotic maps on a number of feature selection datasets. To ensure generality, we used ten biological datasets, but we also used other types of data from various sources. The results are compared with the particle swarm optimizer and with genetic algorithm variants for feature selection using a set of quality metrics.

  6. Relations between mental workload and decision-making in an organizational setting

    Directory of Open Access Journals (Sweden)

    María Soria-Oliver

    2017-05-01

    Full Text Available Asbtract Background The complexity of current organizations implies a potential overload for workers. For this reason, it is of interest to study the effects that mental workload has on the performance of complex tasks in professional settings. Objective The objective of this study is to empirically analyze the relation between the quality of decision-making, on the one hand, and the expected and real mental workload, on the other. Methods The study uses an ex post facto prospective design with a sample of 176 professionals from a higher education organization. Expected mental workload (Pre-Task WL and real mental workload (Post-Task WL were measured with the unweighted NASA-Task Load Index (NASA-TLX questionnaire; difference between real WL and expected WL (Differential WL was also calculated; quality of decision-making was measured by means of the Decision-Making Questionnaire. Results General quality of decision-making and Pre-Task WL relation is compatible with an inverted U pattern, with slight variations depending on the specific dimension of decision-making that is considered. There were no verifiable relations between Post-Task WL and decision-making. The subjects whose expected WL matched the real WL showed worse quality in decision-making than subjects with high or low Differential WL. Conclusions The relations between mental workload and decision-making reveal a complex pattern, with evidence of nonlinear relations.

  7. Deep Restricted Kernel Machines Using Conjugate Feature Duality.

    Science.gov (United States)

    Suykens, Johan A K

    2017-08-01

    The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal component analysis (PCA), matrix singular value decomposition, and Parzen-type models. A key element is to first characterize these kernel machines in terms of so-called conjugate feature duality, yielding a representation with visible and hidden units. It is shown how this is related to the energy form in restricted Boltzmann machines, with continuous variables in a nonprobabilistic setting. In this new framework of so-called restricted kernel machine (RKM) representations, the dual variables correspond to hidden features. Deep RKM are obtained by coupling the RKMs. The method is illustrated for deep RKM, consisting of three levels with a least squares support vector machine regression level and two kernel PCA levels. In its primal form also deep feedforward neural networks can be trained within this framework.

  8. Person Re-Identification by Camera Correlation Aware Feature Augmentation.

    Science.gov (United States)

    Chen, Ying-Cong; Zhu, Xiatian; Zheng, Wei-Shi; Lai, Jian-Huang

    2018-02-01

    The challenge of person re-identification (re-id) is to match individual images of the same person captured by different non-overlapping camera views against significant and unknown cross-view feature distortion. While a large number of distance metric/subspace learning models have been developed for re-id, the cross-view transformations they learned are view-generic and thus potentially less effective in quantifying the feature distortion inherent to each camera view. Learning view-specific feature transformations for re-id (i.e., view-specific re-id), an under-studied approach, becomes an alternative resort for this problem. In this work, we formulate a novel view-specific person re-identification framework from the feature augmentation point of view, called Camera coR relation Aware Feature augmenTation (CRAFT). Specifically, CRAFT performs cross-view adaptation by automatically measuring camera correlation from cross-view visual data distribution and adaptively conducting feature augmentation to transform the original features into a new adaptive space. Through our augmentation framework, view-generic learning algorithms can be readily generalized to learn and optimize view-specific sub-models whilst simultaneously modelling view-generic discrimination information. Therefore, our framework not only inherits the strength of view-generic model learning but also provides an effective way to take into account view specific characteristics. Our CRAFT framework can be extended to jointly learn view-specific feature transformations for person re-id across a large network with more than two cameras, a largely under-investigated but realistic re-id setting. Additionally, we present a domain-generic deep person appearance representation which is designed particularly to be towards view invariant for facilitating cross-view adaptation by CRAFT. We conducted extensively comparative experiments to validate the superiority and advantages of our proposed framework over state

  9. The age related slow and fast contributions to the overall changes in tibialis anterior contractile features disclosed by maximal single twitch scan.

    Science.gov (United States)

    Orizio, Claudio; Cogliati, Marta; Bissolotti, Luciano; Diemont, Bertrand; Gobbo, Massimiliano; Celichowski, Jan

    2016-01-01

    This work aimed to verify if maximal electrically evoked single twitch (STmax) scan discloses the relative functional weight of fast and slow small bundles of fibres (SBF) in determining the contractile features of tibialis anterior (TA) with ageing. SBFs were recruited by TA main motor point stimulation through 60 increasing levels of stimulation (LS): 20 stimuli at 2Hz for each LS. The lowest and highest LS provided the least ST and STmax, respectively. The scanned STmax was decomposed into individual SBF STs. They were identified when twitches from adjacent LS were significantly different and then subtracted from each other. Nine young (Y) and eleven old (O) subjects were investigated. Contraction time (CT) and STarea/STpeak (A/PT) were calculated per each SBF ST. 143 and 155 SBF STs were obtained in Y and O, respectively. Y: CT and A/PT range: 45-105ms and 67-183mNs/mN, respectively. Literature data set TA fast fibres at 34% so, from the arrays of CT and A/PT, 65ms and 100mNs/mN were identified as the upper limit for SBF fast ST classification. O: no SBF ST could be classified as fast. STmax scan reveals age-related changes in the relative contribution of fast and slow SBFs to the overall muscle mechanics. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  10. TOWARD SEMANTIC WEB INFRASTRUCTURE FOR SPATIAL FEATURES' INFORMATION

    Directory of Open Access Journals (Sweden)

    R. Arabsheibani

    2015-12-01

    Full Text Available The Web and its capabilities can be employed as a tool for data and information integration if comprehensive datasets and appropriate technologies and standards enable the web with interpretation and easy alignment of data and information. Semantic Web along with the spatial functionalities enable the web to deal with the huge amount of data and information. The present study investigate the advantages and limitations of the Spatial Semantic Web and compare its capabilities with relational models in order to build a spatial data infrastructure. An architecture is proposed and a set of criteria is defined for the efficiency evaluation. The result demonstrate that when using the data with special characteristics such as schema dynamicity, sparse data or available relations between the features, the spatial semantic web and graph databases with spatial operations are preferable.

  11. NASA's Global Change Master Directory: Discover and Access Earth Science Data Sets, Related Data Services, and Climate Diagnostics

    Science.gov (United States)

    Aleman, Alicia; Olsen, Lola; Ritz, Scott; Morahan, Michael; Cepero, Laurel; Stevens, Tyler

    2011-01-01

    NASA's Global Change Master Directory provides the scientific community with the ability to discover, access, and use Earth science data, data-related services, and climate diagnostics worldwide. The GCMD offers descriptions of Earth science data sets using the Directory Interchange Format (DIF) metadata standard; Earth science related data services are described using the Service Entry Resource Format (SERF); and climate visualizations are described using the Climate Diagnostic (CD) standard. The DIF, SERF and CD standards each capture data attributes used to determine whether a data set, service, or climate visualization is relevant to a user's needs. Metadata fields include: title, summary, science keywords, service keywords, data center, data set citation, personnel, instrument, platform, quality, related URL, temporal and spatial coverage, data resolution and distribution information. In addition, nine valuable sets of controlled vocabularies have been developed to assist users in normalizing the search for data descriptions. An update to the GCMD's search functionality is planned to further capitalize on the controlled vocabularies during database queries. By implementing a dynamic keyword "tree", users will have the ability to search for data sets by combining keywords in new ways. This will allow users to conduct more relevant and efficient database searches to support the free exchange and re-use of Earth science data. http://gcmd.nasa.gov/

  12. An Incremental Classification Algorithm for Mining Data with Feature Space Heterogeneity

    Directory of Open Access Journals (Sweden)

    Yu Wang

    2014-01-01

    Full Text Available Feature space heterogeneity often exists in many real world data sets so that some features are of different importance for classification over different subsets. Moreover, the pattern of feature space heterogeneity might dynamically change over time as more and more data are accumulated. In this paper, we develop an incremental classification algorithm, Supervised Clustering for Classification with Feature Space Heterogeneity (SCCFSH, to address this problem. In our approach, supervised clustering is implemented to obtain a number of clusters such that samples in each cluster are from the same class. After the removal of outliers, relevance of features in each cluster is calculated based on their variations in this cluster. The feature relevance is incorporated into distance calculation for classification. The main advantage of SCCFSH lies in the fact that it is capable of solving a classification problem with feature space heterogeneity in an incremental way, which is favorable for online classification tasks with continuously changing data. Experimental results on a series of data sets and application to a database marketing problem show the efficiency and effectiveness of the proposed approach.

  13. Assessment of features for automatic CTG analysis based on expert annotation.

    Science.gov (United States)

    Chudácek, Vacláv; Spilka, Jirí; Lhotská, Lenka; Janku, Petr; Koucký, Michal; Huptych, Michal; Bursa, Miroslav

    2011-01-01

    Cardiotocography (CTG) is the monitoring of fetal heart rate (FHR) and uterine contractions (TOCO) since 1960's used routinely by obstetricians to detect fetal hypoxia. The evaluation of the FHR in clinical settings is based on an evaluation of macroscopic morphological features and so far has managed to avoid adopting any achievements from the HRV research field. In this work, most of the ever-used features utilized for FHR characterization, including FIGO, HRV, nonlinear, wavelet, and time and frequency domain features, are investigated and the features are assessed based on their statistical significance in the task of distinguishing the FHR into three FIGO classes. Annotation derived from the panel of experts instead of the commonly utilized pH values was used for evaluation of the features on a large data set (552 records). We conclude the paper by presenting the best uncorrelated features and their individual rank of importance according to the meta-analysis of three different ranking methods. Number of acceleration and deceleration, interval index, as well as Lempel-Ziv complexity and Higuchi's fractal dimension are among the top five features.

  14. Cosine and sine operators related to orthogonal polynomial sets on the interval [-1, 1

    International Nuclear Information System (INIS)

    Appl, Thomas; Schiller, Diethard H

    2005-01-01

    The quantization of phase is still an open problem. In the approach of Susskind and Glogower, the so-called cosine and sine operators play a fundamental role. Their eigenstates in the Fock representation are related to the Chebyshev polynomials of the second kind. Here we introduce more general cosine and sine operators whose eigenfunctions in the Fock basis are related in a similar way to arbitrary orthogonal polynomial sets on the interval [-1, 1]. To each polynomial set defined in terms of a weight function there corresponds a pair of cosine and sine operators. Depending on the symmetry of the weight function, we distinguish generalized or extended operators. Their eigenstates are used to define cosine and sine representations and probability distributions. We also consider the arccosine and arcsine operators and use their eigenstates to define cosine-phase and sine-phase distributions, respectively. Specific, numerical and graphical results are given for the classical orthogonal polynomials and for particular Fock and coherent states

  15. Characterizing the nature of visual conscious access: the distinction between features and locations.

    Science.gov (United States)

    Huang, Liqiang

    2010-08-24

    The difference between the roles of features and locations has been a central topic in the theoretical debates on visual attention. A recent theory proposed that momentary visual awareness is limited to one Boolean map, that is the linkage of one feature per dimension with a set of locations (L. Huang & H. Pashler, 2007). This theory predicts that: (a) access to the features of a set of objects is inefficient whereas access to their locations is efficient; (b) shuffling the locations of objects disrupts access to their features whereas shuffling the features of objects has little impact on access to their locations. Both of these predictions were confirmed in Experiments 1 and 2. Experiments 3 and 4 showed that this feature/location distinction remains when the task involves the detection of changes to old objects rather than the coding of new objects. Experiments 5 and 6 showed that, in a pre-specified set, one missing location can be readily detected, but detecting one missing color is difficult. Taken together, multiple locations seem to be accessed and represented together as a holistic pattern, but features have to be handled as separate labels, one at a time, and do not constitute a pattern in featural space.

  16. Integration of educational methods and physical settings: Design ...

    African Journals Online (AJOL)

    ... setting without having an architectural background. The theoretical framework of the research allows designers to consider key features and users' possible activities in High/ Scope settings and shape their designs accordingly. Keywords: daily activity; design; High/Scope education; interior space; teaching method ...

  17. Permutation importance: a corrected feature importance measure.

    Science.gov (United States)

    Altmann, André; Toloşi, Laura; Sander, Oliver; Lengauer, Thomas

    2010-05-15

    In life sciences, interpretability of machine learning models is as important as their prediction accuracy. Linear models are probably the most frequently used methods for assessing feature relevance, despite their relative inflexibility. However, in the past years effective estimators of feature relevance have been derived for highly complex or non-parametric models such as support vector machines and RandomForest (RF) models. Recently, it has been observed that RF models are biased in such a way that categorical variables with a large number of categories are preferred. In this work, we introduce a heuristic for normalizing feature importance measures that can correct the feature importance bias. The method is based on repeated permutations of the outcome vector for estimating the distribution of measured importance for each variable in a non-informative setting. The P-value of the observed importance provides a corrected measure of feature importance. We apply our method to simulated data and demonstrate that (i) non-informative predictors do not receive significant P-values, (ii) informative variables can successfully be recovered among non-informative variables and (iii) P-values computed with permutation importance (PIMP) are very helpful for deciding the significance of variables, and therefore improve model interpretability. Furthermore, PIMP was used to correct RF-based importance measures for two real-world case studies. We propose an improved RF model that uses the significant variables with respect to the PIMP measure and show that its prediction accuracy is superior to that of other existing models. R code for the method presented in this article is available at http://www.mpi-inf.mpg.de/ approximately altmann/download/PIMP.R CONTACT: altmann@mpi-inf.mpg.de, laura.tolosi@mpi-inf.mpg.de Supplementary data are available at Bioinformatics online.

  18. Particle swarm optimization based feature enhancement and feature selection for improved emotion recognition in speech and glottal signals.

    Science.gov (United States)

    Muthusamy, Hariharan; Polat, Kemal; Yaacob, Sazali

    2015-01-01

    In the recent years, many research works have been published using speech related features for speech emotion recognition, however, recent studies show that there is a strong correlation between emotional states and glottal features. In this work, Mel-frequency cepstralcoefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), perceptual linear predictive (PLP) features, gammatone filter outputs, timbral texture features, stationary wavelet transform based timbral texture features and relative wavelet packet energy and entropy features were extracted from the emotional speech (ES) signals and its glottal waveforms(GW). Particle swarm optimization based clustering (PSOC) and wrapper based particle swarm optimization (WPSO) were proposed to enhance the discerning ability of the features and to select the discriminating features respectively. Three different emotional speech databases were utilized to gauge the proposed method. Extreme learning machine (ELM) was employed to classify the different types of emotions. Different experiments were conducted and the results show that the proposed method significantly improves the speech emotion recognition performance compared to previous works published in the literature.

  19. Bayesian analogy with relational transformations.

    Science.gov (United States)

    Lu, Hongjing; Chen, Dawn; Holyoak, Keith J

    2012-07-01

    How can humans acquire relational representations that enable analogical inference and other forms of high-level reasoning? Using comparative relations as a model domain, we explore the possibility that bottom-up learning mechanisms applied to objects coded as feature vectors can yield representations of relations sufficient to solve analogy problems. We introduce Bayesian analogy with relational transformations (BART) and apply the model to the task of learning first-order comparative relations (e.g., larger, smaller, fiercer, meeker) from a set of animal pairs. Inputs are coded by vectors of continuous-valued features, based either on human magnitude ratings, normed feature ratings (De Deyne et al., 2008), or outputs of the topics model (Griffiths, Steyvers, & Tenenbaum, 2007). Bootstrapping from empirical priors, the model is able to induce first-order relations represented as probabilistic weight distributions, even when given positive examples only. These learned representations allow classification of novel instantiations of the relations and yield a symbolic distance effect of the sort obtained with both humans and other primates. BART then transforms its learned weight distributions by importance-guided mapping, thereby placing distinct dimensions into correspondence. These transformed representations allow BART to reliably solve 4-term analogies (e.g., larger:smaller::fiercer:meeker), a type of reasoning that is arguably specific to humans. Our results provide a proof-of-concept that structured analogies can be solved with representations induced from unstructured feature vectors by mechanisms that operate in a largely bottom-up fashion. We discuss potential implications for algorithmic and neural models of relational thinking, as well as for the evolution of abstract thought. Copyright 2012 APA, all rights reserved.

  20. Identification of the proteins related to SET-mediated hepatic cytotoxicity of trichloroethylene by proteomic analysis.

    Science.gov (United States)

    Ren, Xiaohu; Yang, Xifei; Hong, Wen-Xu; Huang, Peiwu; Wang, Yong; Liu, Wei; Ye, Jinbo; Huang, Haiyan; Huang, Xinfeng; Shen, Liming; Yang, Linqing; Zhuang, Zhixiong; Liu, Jianjun

    2014-05-16

    Trichloroethylene (TCE) is an effective solvent for a variety of organic materials. Since the wide use of TCE as industrial degreasing of metals, adhesive paint and polyvinyl chloride production, TCE has turned into an environmental and occupational toxicant. Exposure to TCE could cause severe hepatotoxicity; however, the toxic mechanisms of TCE remain poorly understood. Recently, we reported that SET protein mediated TCE-induced cytotoxicity in L-02 cells. Here, we further identified the proteins related to SET-mediated hepatic cytotoxicity of TCE using the techniques of DIGE (differential gel electrophoresis) and MALDI-TOF-MS/MS. Among the 20 differential proteins identified, 8 were found to be modulated by SET in TCE-induced cytotoxicity and three of them (cofilin-1, peroxiredoxin-2 and S100-A11) were validated by Western-blot analysis. The functional analysis revealed that most of the identified SET-modulated proteins are apoptosis-associated proteins. These data indicated that these proteins may be involved in SET-mediated hepatic cytotoxicity of TCE in L-02 cells. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  1. Infants' Developing Sensitivity to Object Function: Attention to Features and Feature Correlations

    Science.gov (United States)

    Baumgartner, Heidi A.; Oakes, Lisa M.

    2011-01-01

    When learning object function, infants must detect relations among features--for example, that squeezing is associated with squeaking or that objects with wheels roll. Previously, Perone and Oakes (2006) found 10-month-old infants were sensitive to relations between object appearances and actions, but not to relations between appearances and…

  2. Breast image feature learning with adaptive deconvolutional networks

    Science.gov (United States)

    Jamieson, Andrew R.; Drukker, Karen; Giger, Maryellen L.

    2012-03-01

    Feature extraction is a critical component of medical image analysis. Many computer-aided diagnosis approaches employ hand-designed, heuristic lesion extracted features. An alternative approach is to learn features directly from images. In this preliminary study, we explored the use of Adaptive Deconvolutional Networks (ADN) for learning high-level features in diagnostic breast mass lesion images with potential application to computer-aided diagnosis (CADx) and content-based image retrieval (CBIR). ADNs (Zeiler, et. al., 2011), are recently-proposed unsupervised, generative hierarchical models that decompose images via convolution sparse coding and max pooling. We trained the ADNs to learn multiple layers of representation for two breast image data sets on two different modalities (739 full field digital mammography (FFDM) and 2393 ultrasound images). Feature map calculations were accelerated by use of GPUs. Following Zeiler et. al., we applied the Spatial Pyramid Matching (SPM) kernel (Lazebnik, et. al., 2006) on the inferred feature maps and combined this with a linear support vector machine (SVM) classifier for the task of binary classification between cancer and non-cancer breast mass lesions. Non-linear, local structure preserving dimension reduction, Elastic Embedding (Carreira-Perpiñán, 2010), was then used to visualize the SPM kernel output in 2D and qualitatively inspect image relationships learned. Performance was found to be competitive with current CADx schemes that use human-designed features, e.g., achieving a 0.632+ bootstrap AUC (by case) of 0.83 [0.78, 0.89] for an ultrasound image set (1125 cases).

  3. Evaluating the Stability of Feature Selectors that Optimize Feature Subset Cardinality

    Czech Academy of Sciences Publication Activity Database

    Somol, Petr; Novovičová, Jana

    2008-01-01

    Roč. 2008, č. 5342 (2008), s. 956-966 ISSN 0302-9743. [Joint IAPR International Workshops SSPR 2008 and SPR 2008. Orlando , 04.12.2008-06.12.2008] R&D Projects: GA AV ČR 1ET400750407; GA MŠk 1M0572; GA ČR GA102/07/1594 Grant - others:GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : Feature selection * stability * relative weighted consistency measure * sequential search * floating search Subject RIV: IN - Informatics, Computer Science http://library.utia.cas.cz/separaty/2008/RO/somol-evaluating the stability of feature selectors that optimize feature subset cardinality.pdf

  4. Normalization of relative and incomplete temporal expressions in clinical narratives.

    Science.gov (United States)

    Sun, Weiyi; Rumshisky, Anna; Uzuner, Ozlem

    2015-09-01

    To improve the normalization of relative and incomplete temporal expressions (RI-TIMEXes) in clinical narratives. We analyzed the RI-TIMEXes in temporally annotated corpora and propose two hypotheses regarding the normalization of RI-TIMEXes in the clinical narrative domain: the anchor point hypothesis and the anchor relation hypothesis. We annotated the RI-TIMEXes in three corpora to study the characteristics of RI-TMEXes in different domains. This informed the design of our RI-TIMEX normalization system for the clinical domain, which consists of an anchor point classifier, an anchor relation classifier, and a rule-based RI-TIMEX text span parser. We experimented with different feature sets and performed an error analysis for each system component. The annotation confirmed the hypotheses that we can simplify the RI-TIMEXes normalization task using two multi-label classifiers. Our system achieves anchor point classification, anchor relation classification, and rule-based parsing accuracy of 74.68%, 87.71%, and 57.2% (82.09% under relaxed matching criteria), respectively, on the held-out test set of the 2012 i2b2 temporal relation challenge. Experiments with feature sets reveal some interesting findings, such as: the verbal tense feature does not inform the anchor relation classification in clinical narratives as much as the tokens near the RI-TIMEX. Error analysis showed that underrepresented anchor point and anchor relation classes are difficult to detect. We formulate the RI-TIMEX normalization problem as a pair of multi-label classification problems. Considering only RI-TIMEX extraction and normalization, the system achieves statistically significant improvement over the RI-TIMEX results of the best systems in the 2012 i2b2 challenge. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  5. Clinicopathological features of Riedel's thyroiditis associated with IgG4-related disease in Japan.

    Science.gov (United States)

    Takeshima, Ken; Inaba, Hidefumi; Ariyasu, Hiroyuki; Furukawa, Yasushi; Doi, Asako; Nishi, Masahiro; Hirokawa, Mitsuyoshi; Yoshida, Akira; Imai, Ryoukichi; Akamizu, Takashi

    2015-01-01

    Riedel's thyroiditis (RT) is a rare chronic fibrosing disorder characterized by a hard, infiltrative lesion in the thyroid gland, which is often associated with multifocal fibrosclerosis. Immunoglobulin G4-related disease (IgG4-RD) is typified by infiltration of IgG4-positive plasma cells into multiple organs, resulting in tissue fibrosis and organ dysfunction. In order to evaluate the clinicopathological features of RT and its relationship with IgG4-RD, we performed a Japanese literature search using the keywords "Riedel" and "Riedel's thyroiditis." We used the electronic databases Medline and Igaku Chuo Zasshi, the latter of which is the largest medical literature database in Japan. The diagnosis of RT was based on the presence of a fibroinflammatory process with extension into surrounding tissues. Only 10 patients in Japan fulfilled RT diagnostic criteria during the 25-year period between 1988 and 2012. Two patients with confirmed IgG4/IgG immunohistochemical findings demonstrated 43 and 13 IgG4-positive plasma cells per high-power field, respectively, and the IgG4-positive/IgG-positive plasma cell ratios of 20% and less than 5%. Of the 10 patients with RT, two received glucocorticoids, one of whom experienced marked shrinkage of the thyroid lesion. One patient had extra-thyroid involvement in the form of retroperitoneal fibrosis. Although the clinicopathological features of RT suggest that IgG4-RD may be the underlying condition in some cases, further investigation is needed to clarify the etiology of RT in relation to IgG4-RD.

  6. Classification of health webpages as expert and non expert with a reduced set of cross-language features.

    Science.gov (United States)

    Grabar, Natalia; Krivine, Sonia; Jaulent, Marie-Christine

    2007-10-11

    Making the distinction between expert and non expert health documents can help users to select the information which is more suitable for them, according to whether they are familiar or not with medical terminology. This issue is particularly important for the information retrieval area. In our work we address this purpose through stylistic corpus analysis and the application of machine learning algorithms. Our hypothesis is that this distinction can be performed on the basis of a small number of features and that such features can be language and domain independent. The used features were acquired in source corpus (Russian language, diabetes topic) and then tested on target (French language, pneumology topic) and source corpora. These cross-language features show 90% precision and 93% recall with non expert documents in source language; and 85% precision and 74% recall with expert documents in target language.

  7. Detection of Vandalism in Wikipedia using Metadata Features – Implementation in Simple English and Albanian sections

    Directory of Open Access Journals (Sweden)

    Arsim Susuri

    2017-03-01

    Full Text Available In this paper, we evaluate a list of classifiers in order to use them in the detection of vandalism by focusing on metadata features. Our work is focused on two low resource data sets (Simple English and Albanian from Wikipedia. The aim of this research is to prove that this form of vandalism detection applied in one data set (language can be extended into another data set (language. Article views data sets in Wikipedia have been used rarely for the purpose of detecting vandalism. We will show the benefits of using article views data set with features from the article revisions data set with the aim of improving the detection of vandalism. The key advantage of using metadata features is that these metadata features are language independent and simple to extract because they require minimal processing. This paper shows that application of vandalism models across low resource languages is possible, and vandalism can be detected through view patterns of articles.

  8. Rotation, scale, and translation invariant pattern recognition using feature extraction

    Science.gov (United States)

    Prevost, Donald; Doucet, Michel; Bergeron, Alain; Veilleux, Luc; Chevrette, Paul C.; Gingras, Denis J.

    1997-03-01

    A rotation, scale and translation invariant pattern recognition technique is proposed.It is based on Fourier- Mellin Descriptors (FMD). Each FMD is taken as an independent feature of the object, and a set of those features forms a signature. FMDs are naturally rotation invariant. Translation invariance is achieved through pre- processing. A proper normalization of the FMDs gives the scale invariance property. This approach offers the double advantage of providing invariant signatures of the objects, and a dramatic reduction of the amount of data to process. The compressed invariant feature signature is next presented to a multi-layered perceptron neural network. This final step provides some robustness to the classification of the signatures, enabling good recognition behavior under anamorphically scaled distortion. We also present an original feature extraction technique, adapted to optical calculation of the FMDs. A prototype optical set-up was built, and experimental results are presented.

  9. A set for relational reasoning: Facilitation of algebraic modeling by a fraction task.

    Science.gov (United States)

    DeWolf, Melissa; Bassok, Miriam; Holyoak, Keith J

    2016-12-01

    Recent work has identified correlations between early mastery of fractions and later math achievement, especially in algebra. However, causal connections between aspects of reasoning with fractions and improved algebra performance have yet to be established. The current study investigated whether relational reasoning with fractions facilitates subsequent algebraic reasoning using both pre-algebra students and adult college students. Participants were first given either a relational reasoning fractions task or a fraction algebra procedures control task. Then, all participants solved word problems and constructed algebraic equations in either multiplication or division format. The word problems and the equation construction tasks involved simple multiplicative comparison statements such as "There are 4 times as many students as teachers in a classroom." Performance on the algebraic equation construction task was enhanced for participants who had previously completed the relational fractions task compared with those who completed the fraction algebra procedures task. This finding suggests that relational reasoning with fractions can establish a relational set that promotes students' tendency to model relations using algebraic expressions. Copyright © 2016 Elsevier Inc. All rights reserved.

  10. Feature Set Fusion for Spoof Iris Detection

    Directory of Open Access Journals (Sweden)

    P. V. L. Suvarchala

    2018-04-01

    Full Text Available Iris recognition is considered as one of the most promising noninvasive biometric systems providing automated human identification. Numerous programs, like unique ID program in India - Aadhar, include iris biometric to provide distinctive identity identification to citizens. The active area is usually captured under non ideal imaging conditions. It usually suffers from poor brightness, low contrast, blur due to camera or subject's relative movement and eyelid eyelash occlusions. Besides the technical challenges, iris recognition started facing sophisticated threats like spoof attacks. Therefore it is vital that the integrity of such large scale iris deployments must be preserved. This paper presents the development of a new spoof resistant approach which exploits the statistical dependencies of both general eye and localized iris regions in textural domain using spatial gray level dependence matrix (SGLDM, gray level run length matrix (GLRLM and contourlets in transform domain. We did experiments on publicly available fake and lens iris image databases. Correct classification rate obtained with ATVS-FIr iris database is 100% while it is 95.63% and 88.83% with IITD spoof iris databases respectively.

  11. Classifying Written Texts Through Rhythmic Features

    NARCIS (Netherlands)

    Balint, Mihaela; Dascalu, Mihai; Trausan-Matu, Stefan

    2016-01-01

    Rhythm analysis of written texts focuses on literary analysis and it mainly considers poetry. In this paper we investigate the relevance of rhythmic features for categorizing texts in prosaic form pertaining to different genres. Our contribution is threefold. First, we define a set of rhythmic

  12. Influence of contact definitions in assessment of the relative importance of social settings in disease transmission risk.

    Directory of Open Access Journals (Sweden)

    Kirsty J Bolton

    Full Text Available BACKGROUND: Realistic models of disease transmission incorporating complex population heterogeneities require input from quantitative population mixing studies. We use contact diaries to assess the relative importance of social settings in respiratory pathogen spread using three measures of person contact hours (PCH as proxies for transmission risk with an aim to inform bipartite network models of respiratory pathogen transmission. METHODS AND FINDINGS: Our survey examines the contact behaviour for a convenience sample of 65 adults, with each encounter classified as occurring in a work, retail, home, social, travel or "other" setting. The diary design allows for extraction of PCH-interaction (cumulative time in face-face conversational or touch interaction with contacts--analogous to the contact measure used in several existing surveys--as well as PCH-setting (product of time spent in setting and number of people present and PCH-reach (product of time spent in setting and number of people in close proximity. Heterogeneities in day-dependent distribution of risk across settings are analysed using partitioning and cluster analyses and compared between days and contact measures. Although home is typically the highest-risk setting when PCH measures isolate two-way interactions, its relative importance compared to social and work settings may reduce when adopting a more inclusive contact measure that considers the number and duration of potential exposure events. CONCLUSIONS: Heterogeneities in location-dependent contact behaviour as measured by contact diary studies depend on the adopted contact definition. We find that contact measures isolating face-face conversational or touch interactions suggest that contact in the home dominates, whereas more inclusive contact measures indicate that home and work settings may be of higher importance. In the absence of definitive knowledge of the contact required to facilitate transmission of various

  13. A novel automated spike sorting algorithm with adaptable feature extraction.

    Science.gov (United States)

    Bestel, Robert; Daus, Andreas W; Thielemann, Christiane

    2012-10-15

    To study the electrophysiological properties of neuronal networks, in vitro studies based on microelectrode arrays have become a viable tool for analysis. Although in constant progress, a challenging task still remains in this area: the development of an efficient spike sorting algorithm that allows an accurate signal analysis at the single-cell level. Most sorting algorithms currently available only extract a specific feature type, such as the principal components or Wavelet coefficients of the measured spike signals in order to separate different spike shapes generated by different neurons. However, due to the great variety in the obtained spike shapes, the derivation of an optimal feature set is still a very complex issue that current algorithms struggle with. To address this problem, we propose a novel algorithm that (i) extracts a variety of geometric, Wavelet and principal component-based features and (ii) automatically derives a feature subset, most suitable for sorting an individual set of spike signals. Thus, there is a new approach that evaluates the probability distribution of the obtained spike features and consequently determines the candidates most suitable for the actual spike sorting. These candidates can be formed into an individually adjusted set of spike features, allowing a separation of the various shapes present in the obtained neuronal signal by a subsequent expectation maximisation clustering algorithm. Test results with simulated data files and data obtained from chick embryonic neurons cultured on microelectrode arrays showed an excellent classification result, indicating the superior performance of the described algorithm approach. Copyright © 2012 Elsevier B.V. All rights reserved.

  14. Attentional Selection of Feature Conjunctions Is Accomplished by Parallel and Independent Selection of Single Features.

    Science.gov (United States)

    Andersen, Søren K; Müller, Matthias M; Hillyard, Steven A

    2015-07-08

    Experiments that study feature-based attention have often examined situations in which selection is based on a single feature (e.g., the color red). However, in more complex situations relevant stimuli may not be set apart from other stimuli by a single defining property but by a specific combination of features. Here, we examined sustained attentional selection of stimuli defined by conjunctions of color and orientation. Human observers attended to one out of four concurrently presented superimposed fields of randomly moving horizontal or vertical bars of red or blue color to detect brief intervals of coherent motion. Selective stimulus processing in early visual cortex was assessed by recordings of steady-state visual evoked potentials (SSVEPs) elicited by each of the flickering fields of stimuli. We directly contrasted attentional selection of single features and feature conjunctions and found that SSVEP amplitudes on conditions in which selection was based on a single feature only (color or orientation) exactly predicted the magnitude of attentional enhancement of SSVEPs when attending to a conjunction of both features. Furthermore, enhanced SSVEP amplitudes elicited by attended stimuli were accompanied by equivalent reductions of SSVEP amplitudes elicited by unattended stimuli in all cases. We conclude that attentional selection of a feature-conjunction stimulus is accomplished by the parallel and independent facilitation of its constituent feature dimensions in early visual cortex. The ability to perceive the world is limited by the brain's processing capacity. Attention affords adaptive behavior by selectively prioritizing processing of relevant stimuli based on their features (location, color, orientation, etc.). We found that attentional mechanisms for selection of different features belonging to the same object operate independently and in parallel: concurrent attentional selection of two stimulus features is simply the sum of attending to each of those

  15. A Rough Set Approach of Mechanical Fault Diagnosis for Five-Plunger Pump

    Directory of Open Access Journals (Sweden)

    Jiangping Wang

    2013-01-01

    Full Text Available Five-plunger pumps are widely used in oil field to recover petroleum due to their reliability and relatively low cost. Petroleum production is, to a great extent, dependent upon the running condition of the pumps. Closely monitoring the condition of the pumps and carrying out timely system diagnosis whenever a fault symptom is detected would help to reduce the production downtime and improve overall productivity. In this paper, a rough set approach of mechanical fault diagnosis is proposed to identify the five-plunger pump faults. The details of the approach, together with the basic concepts of the rough sets theory, are presented. The rough classifier is a set of decision rules derived from lower and upper approximations of decision classes. The definitions of these approximations are based on the indiscernibility relation in the set of objects. The spectrum features of vibration signals are abstracted as the attributes of the learning samples. The minimum decision rule set is used to classify technical states of the considered object. The diagnostic investigation is done on data from a five-plunger pump in outdoor conditions on a real industrial object. Results show that the approach can effectively identify the different operating states of the pump.

  16. IgG4-related disease: a systemic condition with characteristic microscopic features

    DEFF Research Database (Denmark)

    Detlefsen, Sönke

    2013-01-01

    that a significant proportion of the AIP patients had a variety of extrapancreatic fibroinflammatory lesions, and that AIP therefore was the pancreatic manifestation of a systemic disease. Among these extrapancreatic manifestations, the extrahepatic bile ducts, salivary glands, thyroid, lymph nodes......During the first decade of the 21st century, IgG4-related disease (IgG4-RD), a fibroinflammatory condition occurring at multiple sites of the body, has been newly recognized. As indicated by its name, elevation of IgG4 in the serum and tissue is a common denominator of IgG4-RD. Since...... diseases on their own, others have been included under the umbrella of "multifocal fibrosclerosis". Biopsies or resection specimens from affected organs in IgG4-RD reveal several common microscopic features irrespective of the site of the lesion. Cellular and storiform fibrosis, lymphoplasmacytic...

  17. Feature selection is the ReliefF for multiple instance learning

    NARCIS (Netherlands)

    Zafra, A.; Pechenizkiy, M.; Ventura, S.

    2010-01-01

    Dimensionality reduction and feature selection in particular are known to be of a great help for making supervised learning more effective and efficient. Many different feature selection techniques have been proposed for the traditional settings, where each instance is expected to have a label. In

  18. Quantification of the 3D relative movement of external marker sets vs. bones based on magnetic resonance imaging.

    Science.gov (United States)

    Sangeux, M; Marin, F; Charleux, F; Dürselen, L; Ho Ba Tho, M C

    2006-11-01

    Most in vivo knee kinematic analyses are based on external markers attached to the shank and the thigh. Literature data show that markers positioning and soft tissues artifacts affect the kinematic parameters of the bones true movement. Most of the techniques of quantification used were invasive. The aim of the present study was to develop and apply a non-invasive methodology to compute the relative movement between the bones and the markers. Magnetic resonance imaging acquisitions were performed on the right knee of eleven volunteers without knee injury. The subjects were equipped with external magnetic resonance imaging-compatible marker sets. A foot drive device allowed the subjects to perform an actively loaded knee extension. The whole volume of the subject's knee was processed for four sequentially held knee flexion positions during the knee movement. The bones and external marker sets geometry were reconstructed from magnetic resonance imaging images. Then a registration algorithm was applied to the bones and the relative movement of the thigh and shank marker sets with respect to their underlying bones was computed. The protocol resulted in a good geometrical accuracy and reproducibility. Marker sets movement differ from that of the bones with a maximum of 22 mm in translation and 15 degrees in rotation and it affects the knee kinematics. Marker sets relative movement modify the knee movement finite helical axes direction (range 10-35 degrees ) and localization (range 0-40 mm). The methodology developed can evaluate external marker set system to be used for kinematic analysis in a clinical environment.

  19. FEATURES CONCERNING THE ESTABLISHMENT OF AUTHORIZED INDIVIDUAL AND FAMILY FIRMS

    Directory of Open Access Journals (Sweden)

    CLAUDIA ISAC

    2014-12-01

    Full Text Available This paper presents recent legislative changes relating to the establishment and organization of small firms as: The individual firm, the family firm, Authorized individuals (PFA. Thus, in the first part of the paper I present the main features and advantages of the three types of firms, and a comparison between them. The paper continues with the necessary documents for setting up the companies and highlights their role in economic advances. In the second part of the paper, I did a statistical analysis of the evolution of the number of firms of this type and the sectors in which they operate.

  20. Clinical features of symptomatic patellofemoral joint osteoarthritis

    Science.gov (United States)

    2012-01-01

    Introduction Patellofemoral joint osteoarthritis (OA) is common and leads to pain and disability. However, current classification criteria do not distinguish between patellofemoral and tibiofemoral joint OA. The objective of this study was to provide empirical evidence of the clinical features of patellofemoral joint OA (PFJOA) and to explore the potential for making a confident clinical diagnosis in the community setting. Methods This was a population-based cross-sectional study of 745 adults aged ≥50 years with knee pain. Information on risk factors and clinical signs and symptoms was gathered by a self-complete questionnaire, and standardised clinical interview and examination. Three radiographic views of the knee were obtained (weight-bearing semi-flexed posteroanterior, supine skyline and lateral) and individuals were classified into four subsets (no radiographic OA, isolated PFJOA, isolated tibiofemoral joint OA, combined patellofemoral/tibiofemoral joint OA) according to two different cut-offs: 'any OA' and 'moderate to severe OA'. A series of binary logistic and multinomial regression functions were performed to compare the clinical features of each subset and their ability in combination to discriminate PFJOA from other subsets. Results Distinctive clinical features of moderate to severe isolated PFJOA included a history of dramatic swelling, valgus deformity, markedly reduced quadriceps strength, and pain on patellofemoral joint compression. Mild isolated PFJOA was barely distinguished from no radiographic OA (AUC 0.71, 95% CI 0.66, 0.76) with only difficulty descending stairs and coarse crepitus marginally informative over age, sex and body mass index. Other cardinal signs of knee OA - the presence of effusion, bony enlargement, reduced flexion range of movement, mediolateral instability and varus deformity - were indicators of tibiofemoral joint OA. Conclusions Early isolated PFJOA is clinically manifest in symptoms and self-reported functional

  1. Information Theory for Gabor Feature Selection for Face Recognition

    Directory of Open Access Journals (Sweden)

    Shen Linlin

    2006-01-01

    Full Text Available A discriminative and robust feature—kernel enhanced informative Gabor feature—is proposed in this paper for face recognition. Mutual information is applied to select a set of informative and nonredundant Gabor features, which are then further enhanced by kernel methods for recognition. Compared with one of the top performing methods in the 2004 Face Verification Competition (FVC2004, our methods demonstrate a clear advantage over existing methods in accuracy, computation efficiency, and memory cost. The proposed method has been fully tested on the FERET database using the FERET evaluation protocol. Significant improvements on three of the test data sets are observed. Compared with the classical Gabor wavelet-based approaches using a huge number of features, our method requires less than 4 milliseconds to retrieve a few hundreds of features. Due to the substantially reduced feature dimension, only 4 seconds are required to recognize 200 face images. The paper also unified different Gabor filter definitions and proposed a training sample generation algorithm to reduce the effects caused by unbalanced number of samples available in different classes.

  2. Information Theory for Gabor Feature Selection for Face Recognition

    Science.gov (United States)

    Shen, Linlin; Bai, Li

    2006-12-01

    A discriminative and robust feature—kernel enhanced informative Gabor feature—is proposed in this paper for face recognition. Mutual information is applied to select a set of informative and nonredundant Gabor features, which are then further enhanced by kernel methods for recognition. Compared with one of the top performing methods in the 2004 Face Verification Competition (FVC2004), our methods demonstrate a clear advantage over existing methods in accuracy, computation efficiency, and memory cost. The proposed method has been fully tested on the FERET database using the FERET evaluation protocol. Significant improvements on three of the test data sets are observed. Compared with the classical Gabor wavelet-based approaches using a huge number of features, our method requires less than 4 milliseconds to retrieve a few hundreds of features. Due to the substantially reduced feature dimension, only 4 seconds are required to recognize 200 face images. The paper also unified different Gabor filter definitions and proposed a training sample generation algorithm to reduce the effects caused by unbalanced number of samples available in different classes.

  3. Late Pleistocene dune-sourced alluvial fans in coastal settings: Sedimentary facies and related processes (Mallorca, Western Mediterranean)

    Science.gov (United States)

    Pomar, F.; del Valle, L.; Fornós, J. J.; Gómez-Pujol, L.

    2018-05-01

    Aeolian-alluvial sedimentary interaction results in the formation of deposits characterized by typical alluvial sedimentary structures, but is composed of conspicuous amounts of aeolian sediments. The literature on this topic is limited and most works relate more with continental aeolian dunes or fluvial dune interference with fan bodies. Furthermore, there is a lack of examples of aeolian-alluvial sedimentary interference in coastal settings. In the western Mediterranean, there are many Pleistocene alluvial fan deposits built up partly by sediment originating from coastal dunes dismantled by alluvial streams. Very often, these deposits show a continuous sedimentary sequence through which we can derive the contribution and predominance of coastal, alluvial-colluvial and aeolian processes and their controls on landscape formation. This is an outstanding feature within coastal systems since it shows marine sediments reworked and integrated within coastal dune fields by aeolian transport, and the latter built up into alluvial fan bodies. In this sense, aeolian-alluvial interaction is the geomorphic-sedimentary expression of the coexistence and overlapping of alluvial and aeolian environments resulting in deposits sharing sedimentary features from both environments. The aim of this paper is to unravel the contribution of coastal dunes in the construction of alluvial fans bodies and identify the main sedimentary facies that constitute these deposits, as well as their climatic controls. For this reason, Es Caló fan (northern Mallorca) has been selected due to its well-exposed deposits exhibiting the alternation of aeolian, alluvial and colluvial deposits. Sedimentological and stratigraphic analyses based on 33 logs and complementary analyses demonstrate that most of the facies constituting the fan body are made up completely of marine bioclastic sands. These deposits record an alluvial fan sedimentary environment characterized by sediments inputs that do not proceed

  4. The gas-hydrate-related seabed features in the Palm Ridge off southwest Taiwan

    Science.gov (United States)

    Su, Zheng-Wei; Hsu, Shu-Kun; Tsai, Ching-Hui; Chen, Song-Chuen; Lin, Hsiao-Shan

    2016-04-01

    The offshore area of the SW Taiwan is located in the convergence zone between the northern continental margin of the South China Sea and the Manila subduction complex. Our study area, the Palm Ridge, is located in the passive continental margin. According to the geophysical, geochemical and geothermal data, abundant gas hydrate may exist in the offshore area of SW Taiwan. In this study, we will study the relation between the seabed features and the gas hydrate formation of the Palm Ridge. The data used in this study include high-resolution sidescan sonar images, sub-bottom profiles, echo sounder system, multi-beam bathymetric data, multi-channel reflection seismic and submarine photography in the Palm Ridge. Our results show the existing authigenic carbonates, gas seepages and gas plumes are mainly distributed in the bathymetric high of the Palm Ridge. Numerous submarine landslides have occurred in the place where the BSR distribution is not continuous. We suggest that it may be because of rapid slope failure, causing the change of the gas hydrate stability zone. We also found several faults on the R3.1 anticline structure east of the deformation front. These features imply that abundant deep methane gases have migrated to shallow strata, causing submarine landslides or collapse. The detailed relationship of gas migration and submarine landslides need further studies.

  5. Setting semantics: conceptual set can determine the physical properties that capture attention.

    Science.gov (United States)

    Goodhew, Stephanie C; Kendall, William; Ferber, Susanne; Pratt, Jay

    2014-08-01

    The ability of a stimulus to capture visuospatial attention depends on the interplay between its bottom-up saliency and its relationship to an observer's top-down control set, such that stimuli capture attention if they match the predefined properties that distinguish a searched-for target from distractors (Folk, Remington, & Johnston, Journal of Experimental Psychology: Human Perception & Performance, 18, 1030-1044 1992). Despite decades of research on this phenomenon, however, the vast majority has focused exclusively on matches based on low-level physical properties. Yet if contingent capture is indeed a "top-down" influence on attention, then semantic content should be accessible and able to determine which physical features capture attention. Here we tested this prediction by examining whether a semantically defined target could create a control set for particular features. To do this, we had participants search to identify a target that was differentiated from distractors by its meaning (e.g., the word "red" among color words all written in black). Before the target array, a cue was presented, and it was varied whether the cue appeared in the physical color implied by the target word. Across three experiments, we found that cues that embodied the meaning of the word produced greater cuing than cues that did not. This suggests that top-down control sets activate content that is semantically associated with the target-defining property, and this content in turn has the ability to exogenously orient attention.

  6. The Airway Microbiome in Severe Asthma: Associations with Disease Features and Severity

    Science.gov (United States)

    Huang, Yvonne J.; Nariya, Snehal; Harris, Jeffrey M.; Lynch, Susan V.; Choy, David F.; Arron, Joseph R.; Boushey, Homer

    2015-01-01

    Background Asthma is heterogeneous, and airway dysbiosis is associated with clinical features in mild-moderate asthma. Whether similar relationships exist among patients with severe asthma is unknown. Objective To evaluate relationships between the bronchial microbiome and features of severe asthma. Methods Bronchial brushings from 40 participants in the BOBCAT study (Bronchoscopic Exploratory Research Study of Biomarkers in Corticosteroid-refractory Asthma) were evaluated using 16S rRNA-based methods. Relationships to clinical and inflammatory features were analyzed among microbiome-profiled subjects. Secondarily, bacterial compositional profiles were compared between severe asthmatics, and previously studied healthy controls (n=7), and mild-moderate asthma subjects (n=41). Results In severe asthma, bronchial bacterial composition was associated with several disease-related features, including body-mass index (BMI; Bray-Curtis distance PERMANOVA, p < 0.05), changes in Asthma Control Questionnaire (ACQ) scores (p < 0.01), sputum total leukocytes (p = 0.06) and bronchial biopsy eosinophils (per mm2; p = 0.07). Bacterial communities associated with worsening ACQ and sputum total leukocytes (predominantly Proteobacteria) differed markedly from those associated with BMI (Bacteroidetes/Firmicutes). In contrast, improving/stable ACQ and bronchial epithelial gene expression of FKBP5, an indicator of steroid responsiveness, correlated with Actinobacteria. Mostly negative correlations were observed between biopsy eosinophils and Proteobacteria. No taxa were associated with a T-helper type 2-related epithelial gene expression signature, but expression of Th17-related genes was associated with Proteobacteria. Severe asthma subjects, compared to healthy controls or mild-moderate asthmatics, were significantly enriched in Actinobacteria, although the largest differences observed involved a Klebsiella genus member (7.8 fold-increase in severe asthma, padj < 0.001) Conclusions

  7. Contingent Attentional Capture by Top-Down Control Settings: Converging Evidence from Event-Related Potentials

    Science.gov (United States)

    Lien, Mei-Ching; Ruthruff, Eric; Goodin, Zachary; Remington, Roger W.

    2008-01-01

    Theories of attentional control are divided over whether the capture of spatial attention depends primarily on stimulus salience or is contingent on attentional control settings induced by task demands. The authors addressed this issue using the N2-posterior-contralateral (N2pc) effect, a component of the event-related brain potential thought to…

  8. Manifold regularized multitask feature learning for multimodality disease classification.

    Science.gov (United States)

    Jie, Biao; Zhang, Daoqiang; Cheng, Bo; Shen, Dinggang

    2015-02-01

    Multimodality based methods have shown great advantages in classification of Alzheimer's disease (AD) and its prodromal stage, that is, mild cognitive impairment (MCI). Recently, multitask feature selection methods are typically used for joint selection of common features across multiple modalities. However, one disadvantage of existing multimodality based methods is that they ignore the useful data distribution information in each modality, which is essential for subsequent classification. Accordingly, in this paper we propose a manifold regularized multitask feature learning method to preserve both the intrinsic relatedness among multiple modalities of data and the data distribution information in each modality. Specifically, we denote the feature learning on each modality as a single task, and use group-sparsity regularizer to capture the intrinsic relatedness among multiple tasks (i.e., modalities) and jointly select the common features from multiple tasks. Furthermore, we introduce a new manifold-based Laplacian regularizer to preserve the data distribution information from each task. Finally, we use the multikernel support vector machine method to fuse multimodality data for eventual classification. Conversely, we also extend our method to the semisupervised setting, where only partial data are labeled. We evaluate our method using the baseline magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography (FDG-PET), and cerebrospinal fluid (CSF) data of subjects from AD neuroimaging initiative database. The experimental results demonstrate that our proposed method can not only achieve improved classification performance, but also help to discover the disease-related brain regions useful for disease diagnosis. © 2014 Wiley Periodicals, Inc.

  9. Health-related quality of life of irritable bowel syndrome patients in different cultural settings

    Directory of Open Access Journals (Sweden)

    Johansson Saga

    2006-03-01

    Full Text Available Abstract Background Persons with Irritable bowel syndrome (IBS are seriously affected in their everyday life. The effect across different cultural settings of IBS on their quality of life has been little studied. The aim was to compare health-related quality of life (HRQOL of individuals suffering from IBS in two different cultural settings; Crete, Greece and Linköping, Sweden. Methods This study is a sex and age-matched case-control study, with n = 30 Cretan IBS cases and n = 90 Swedish IBS cases and a Swedish control group (n = 300 randomly selected from the general population. Health-related quality of life, measured by SF-36 and demographics, life style indicators and co-morbidity, was measured. Results Cretan IBS cases reported lower HRQOL on most dimensions of SF-36 in comparison to the Swedish IBS cases. Significant differences were found for the dimensions mental health (p Conclusion The results from this study tentatively support that the claim that similar individuals having the same disease, e.g. IBS, but living in different cultural environments could perceive their disease differently and that the disease might affect their everyday life and quality of life in a different way. The Cretan population, and especially women, are more seriously affected mentally by their disease than Swedish IBS cases. Coping with IBS in everyday life might be more problematic in the Cretan environment than in the Swedish setting.

  10. Is there a relation between the 2D Causal Set action and the Lorentzian Gauss-Bonnet theorem?

    Science.gov (United States)

    Benincasa, Dionigi M. T.

    2011-07-01

    We investigate the relation between the two dimensional Causal Set action, Script S, and the Lorentzian Gauss-Bonnet theorem (LGBT). We give compelling reasons why the answer to the title's question is no. In support of this point of view we calculate the causal set inspired action of causal intervals in some two dimensional spacetimes: Minkowski, the flat cylinder and the flat trousers.

  11. Is there a relation between the 2D Causal Set action and the Lorentzian Gauss-Bonnet theorem?

    International Nuclear Information System (INIS)

    Benincasa, Dionigi M T

    2011-01-01

    We investigate the relation between the two dimensional Causal Set action, S, and the Lorentzian Gauss-Bonnet theorem (LGBT). We give compelling reasons why the answer to the title's question is no. In support of this point of view we calculate the causal set inspired action of causal intervals in some two dimensional spacetimes: Minkowski, the flat cylinder and the flat trousers.

  12. Benchmarking of protein descriptor sets in proteochemometric modeling (part 2): modeling performance of 13 amino acid descriptor sets

    Science.gov (United States)

    2013-01-01

    Background While a large body of work exists on comparing and benchmarking descriptors of molecular structures, a similar comparison of protein descriptor sets is lacking. Hence, in the current work a total of 13 amino acid descriptor sets have been benchmarked with respect to their ability of establishing bioactivity models. The descriptor sets included in the study are Z-scales (3 variants), VHSE, T-scales, ST-scales, MS-WHIM, FASGAI, BLOSUM, a novel protein descriptor set (termed ProtFP (4 variants)), and in addition we created and benchmarked three pairs of descriptor combinations. Prediction performance was evaluated in seven structure-activity benchmarks which comprise Angiotensin Converting Enzyme (ACE) dipeptidic inhibitor data, and three proteochemometric data sets, namely (1) GPCR ligands modeled against a GPCR panel, (2) enzyme inhibitors (NNRTIs) with associated bioactivities against a set of HIV enzyme mutants, and (3) enzyme inhibitors (PIs) with associated bioactivities on a large set of HIV enzyme mutants. Results The amino acid descriptor sets compared here show similar performance (set differences ( > 0.3 log units RMSE difference and >0.7 difference in MCC). Combining different descriptor sets generally leads to better modeling performance than utilizing individual sets. The best performers were Z-scales (3) combined with ProtFP (Feature), or Z-Scales (3) combined with an average Z-Scale value for each target, while ProtFP (PCA8), ST-Scales, and ProtFP (Feature) rank last. Conclusions While amino acid descriptor sets capture different aspects of amino acids their ability to be used for bioactivity modeling is still – on average – surprisingly similar. Still, combining sets describing complementary information consistently leads to small but consistent improvement in modeling performance (average MCC 0.01 better, average RMSE 0.01 log units lower). Finally, performance differences exist between the targets compared thereby underlining that

  13. Digital database of mining-related features at selected historic and active phosphate mines, Bannock, Bear Lake, Bingham, and Caribou counties, Idaho

    Science.gov (United States)

    Causey, J. Douglas; Moyle, Phillip R.

    2001-01-01

    This report provides a description of data and processes used to produce a spatial database that delineates mining-related features in areas of historic and active phosphate mining in the core of the southeastern Idaho phosphate resource area. The data have varying degrees of accuracy and attribution detail. Classification of areas by type of mining-related activity at active mines is generally detailed; however, the spatial coverage does not differentiate mining-related surface disturbance features at many of the closed or inactive mines. Nineteen phosphate mine sites are included in the study. A total of 5,728 hc (14,154 ac), or more than 57 km2 (22 mi2), of phosphate mining-related surface disturbance are documented in the spatial coverage of the core of the southeast Idaho phosphate resource area. The study includes 4 active phosphate mines—Dry Valley, Enoch Valley, Rasmussen Ridge, and Smoky Canyon—and 15 historic phosphate mines—Ballard, Champ, Conda, Diamond Gulch, Gay, Georgetown Canyon, Henry, Home Canyon, Lanes Creek, Maybe Canyon, Mountain Fuel, Trail Canyon, Rattlesnake Canyon, Waterloo, and Wooley Valley. Spatial data on the inactive historic mines is relatively up-to-date; however, spatially described areas for active mines are based on digital maps prepared in early 1999. The inactive Gay mine has the largest total area of disturbance: 1,917 hc (4,736 ac) or about 19 km2 (7.4 mi2). It encompasses over three times the disturbance area of the next largest mine, the Conda mine with 607 hc (1,504 ac), and it is nearly four times the area of the Smoky Canyon mine, the largest of the active mines with 497 hc (1,228 ac). The wide range of phosphate mining-related surface disturbance features (approximately 80) were reduced to 13 types or features used in this study—adit and pit, backfilled mine pit, facilities, mine pit, ore stockpile, railroad, road, sediment catchment, tailings or tailings pond, topsoil stockpile, water reservoir, and disturbed

  14. Electrocardiographic features of patients with earthquake related posttraumatic stress disorder

    OpenAIRE

    İlhan, Erkan; Kaplan, Abdullah; Güvenç, Tolga Sinan; Biteker, Murat; Karabulut, Evindar; Işıklı, Serhan

    2013-01-01

    AIM: To analyze electrocardiographic features of patients diagnosed with posttraumatic stress disorder (PTSD) after the Van-Erciş earthquake, with a shock measuring 7.2 on the Richter scale that took place in Turkey in October 2011.

  15. Manifold regularized multi-task feature selection for multi-modality classification in Alzheimer's disease.

    Science.gov (United States)

    Jie, Biao; Zhang, Daoqiang; Cheng, Bo; Shen, Dinggang

    2013-01-01

    Accurate diagnosis of Alzheimer's disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment, MCI), is very important for possible delay and early treatment of the disease. Recently, multi-modality methods have been used for fusing information from multiple different and complementary imaging and non-imaging modalities. Although there are a number of existing multi-modality methods, few of them have addressed the problem of joint identification of disease-related brain regions from multi-modality data for classification. In this paper, we proposed a manifold regularized multi-task learning framework to jointly select features from multi-modality data. Specifically, we formulate the multi-modality classification as a multi-task learning framework, where each task focuses on the classification based on each modality. In order to capture the intrinsic relatedness among multiple tasks (i.e., modalities), we adopted a group sparsity regularizer, which ensures only a small number of features to be selected jointly. In addition, we introduced a new manifold based Laplacian regularization term to preserve the geometric distribution of original data from each task, which can lead to the selection of more discriminative features. Furthermore, we extend our method to the semi-supervised setting, which is very important since the acquisition of a large set of labeled data (i.e., diagnosis of disease) is usually expensive and time-consuming, while the collection of unlabeled data is relatively much easier. To validate our method, we have performed extensive evaluations on the baseline Magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) data of Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our experimental results demonstrate the effectiveness of the proposed method.

  16. A unified framework for image retrieval using keyword and visual features.

    Science.gov (United States)

    Jing, Feng; Li, Mingling; Zhang, Hong-Jiang; Zhang, Bo

    2005-07-01

    In this paper, a unified image retrieval framework based on both keyword annotations and visual features is proposed. In this framework, a set of statistical models are built based on visual features of a small set of manually labeled images to represent semantic concepts and used to propagate keywords to other unlabeled images. These models are updated periodically when more images implicitly labeled by users become available through relevance feedback. In this sense, the keyword models serve the function of accumulation and memorization of knowledge learned from user-provided relevance feedback. Furthermore, two sets of effective and efficient similarity measures and relevance feedback schemes are proposed for query by keyword scenario and query by image example scenario, respectively. Keyword models are combined with visual features in these schemes. In particular, a new, entropy-based active learning strategy is introduced to improve the efficiency of relevance feedback for query by keyword. Furthermore, a new algorithm is proposed to estimate the keyword features of the search concept for query by image example. It is shown to be more appropriate than two existing relevance feedback algorithms. Experimental results demonstrate the effectiveness of the proposed framework.

  17. A feature-based approach to modeling protein-DNA interactions.

    Directory of Open Access Journals (Sweden)

    Eilon Sharon

    Full Text Available Transcription factor (TF binding to its DNA target site is a fundamental regulatory interaction. The most common model used to represent TF binding specificities is a position specific scoring matrix (PSSM, which assumes independence between binding positions. However, in many cases, this simplifying assumption does not hold. Here, we present feature motif models (FMMs, a novel probabilistic method for modeling TF-DNA interactions, based on log-linear models. Our approach uses sequence features to represent TF binding specificities, where each feature may span multiple positions. We develop the mathematical formulation of our model and devise an algorithm for learning its structural features from binding site data. We also developed a discriminative motif finder, which discovers de novo FMMs that are enriched in target sets of sequences compared to background sets. We evaluate our approach on synthetic data and on the widely used TF chromatin immunoprecipitation (ChIP dataset of Harbison et al. We then apply our algorithm to high-throughput TF ChIP data from mouse and human, reveal sequence features that are present in the binding specificities of mouse and human TFs, and show that FMMs explain TF binding significantly better than PSSMs. Our FMM learning and motif finder software are available at http://genie.weizmann.ac.il/.

  18. Automatic activation of alcohol cues by child maltreatment related words: a replication attempt in a different treatment setting.

    Science.gov (United States)

    Potthast, Nadine; Neuner, Frank; Catani, Claudia

    2017-01-03

    A growing body of research attempts to clarify the underlying mechanisms of the association between emotional maltreatment and alcohol dependence (AD). In a preceding study, we found considerable support for a specific priming effect in subjects with AD and emotional abuse experiences receiving alcohol rehabilitation treatment. We concluded that maltreatment related cues can automatically activate an associative memory network comprising cues eliciting craving as well as alcohol-related responses. Generalizability of the results to other treatment settings remains unclear because of considerable differences in German treatment settings as well as insufficiently clarified influences of selection effects. As replication studies in other settings are necessary, the current study aimed to replicate the specific priming effect in a qualified detoxification sample. 22 AD subjects (n = 10 with emotional abuse vs. n = 12 without emotional abuse) participated in a priming experiment. Comparison data from 34 healthy control subjects were derived from the prior study. Contrary to our hypothesis, we did not find a specific priming effect. We could not replicate the result of an automatic network activation by maltreatment related words in a sample of subjects with AD and emotional abuse experiences receiving qualified detoxification treatment. This discrepancy might be attributed to reasons related to treatment settings as well as to methodological limitations. Future work is required to determine the generalizability of the specific priming effect before valid conclusions regarding automatic activation can be drawn.

  19. Interplay of a multiplicity of security features

    Science.gov (United States)

    Moser, Jean-Frederic

    2000-04-01

    The great variety of existing security features can cause difficulty in choosing the adequate set for a particular security document. Considering the cost/benefit aspects with respect to the overall protection performance requested, a choice has to be made, for example, between either few features of high-security value or numerous many, less- resistant features. Another choice is the high versus low complexity of one particular features. A study aimed at providing a decision basis is a challenging matter because it involves human factors. Attention, perception, physiology of seeing and habits - to name some of the factors - are intangibles and are subject to evaluations involving normally a great number of experiments, if they are to be representative. The opportunity was given for a case study with the introduction of new Swiss banknotes between 1995 and 1998, because the new banknotes represent a novelty in the sense of the multiplicity and interplay of its optical security features. We have analyzed 652 articles which appeared in the press media concerning the new banknotes, seeking especially for peoples' reaction towards the security features.

  20. Improving mass candidate detection in mammograms via feature maxima propagation and local feature selection.

    Science.gov (United States)

    Melendez, Jaime; Sánchez, Clara I; van Ginneken, Bram; Karssemeijer, Nico

    2014-08-01

    Mass candidate detection is a crucial component of multistep computer-aided detection (CAD) systems. It is usually performed by combining several local features by means of a classifier. When these features are processed on a per-image-location basis (e.g., for each pixel), mismatching problems may arise while constructing feature vectors for classification, which is especially true when the behavior expected from the evaluated features is a peaked response due to the presence of a mass. In this study, two of these problems, consisting of maxima misalignment and differences of maxima spread, are identified and two solutions are proposed. The first proposed method, feature maxima propagation, reproduces feature maxima through their neighboring locations. The second method, local feature selection, combines different subsets of features for different feature vectors associated with image locations. Both methods are applied independently and together. The proposed methods are included in a mammogram-based CAD system intended for mass detection in screening. Experiments are carried out with a database of 382 digital cases. Sensitivity is assessed at two sets of operating points. The first one is the interval of 3.5-15 false positives per image (FPs/image), which is typical for mass candidate detection. The second one is 1 FP/image, which allows to estimate the quality of the mass candidate detector's output for use in subsequent steps of the CAD system. The best results are obtained when the proposed methods are applied together. In that case, the mean sensitivity in the interval of 3.5-15 FPs/image significantly increases from 0.926 to 0.958 (p < 0.0002). At the lower rate of 1 FP/image, the mean sensitivity improves from 0.628 to 0.734 (p < 0.0002). Given the improved detection performance, the authors believe that the strategies proposed in this paper can render mass candidate detection approaches based on image location classification more robust to feature

  1. Feature-based Alignment of Volumetric Multi-modal Images

    Science.gov (United States)

    Toews, Matthew; Zöllei, Lilla; Wells, William M.

    2014-01-01

    This paper proposes a method for aligning image volumes acquired from different imaging modalities (e.g. MR, CT) based on 3D scale-invariant image features. A novel method for encoding invariant feature geometry and appearance is developed, based on the assumption of locally linear intensity relationships, providing a solution to poor repeatability of feature detection in different image modalities. The encoding method is incorporated into a probabilistic feature-based model for multi-modal image alignment. The model parameters are estimated via a group-wise alignment algorithm, that iteratively alternates between estimating a feature-based model from feature data, then realigning feature data to the model, converging to a stable alignment solution with few pre-processing or pre-alignment requirements. The resulting model can be used to align multi-modal image data with the benefits of invariant feature correspondence: globally optimal solutions, high efficiency and low memory usage. The method is tested on the difficult RIRE data set of CT, T1, T2, PD and MP-RAGE brain images of subjects exhibiting significant inter-subject variability due to pathology. PMID:24683955

  2. Proscene: A feature-rich framework for interactive environments

    Directory of Open Access Journals (Sweden)

    Jean Pierre Charalambos

    2017-01-01

    Full Text Available We introduce Proscene, a feature-rich, open-source framework for interactive environments. The design of Proscene comprises a three-layered onion-like software architecture, promoting different possible development scenarios. The framework innermost layer decouples user gesture parsing from user-defined actions. The in-between layer implements a feature-rich set of widely-used motion actions allowing the selection and manipulation of objects, including the scene viewpoint. The outermost layer exposes those features as a Processing library. The results have shown the feasibility of our approach together with the simplicity and flexibility of the Proscene framework API.

  3. An EEG-Based Person Authentication System with Open-Set Capability Combining Eye Blinking Signals.

    Science.gov (United States)

    Wu, Qunjian; Zeng, Ying; Zhang, Chi; Tong, Li; Yan, Bin

    2018-01-24

    The electroencephalogram (EEG) signal represents a subject's specific brain activity patterns and is considered as an ideal biometric given its superior forgery prevention. However, the accuracy and stability of the current EEG-based person authentication systems are still unsatisfactory in practical application. In this paper, a multi-task EEG-based person authentication system combining eye blinking is proposed, which can achieve high precision and robustness. Firstly, we design a novel EEG-based biometric evoked paradigm using self- or non-self-face rapid serial visual presentation (RSVP). The designed paradigm could obtain a distinct and stable biometric trait from EEG with a lower time cost. Secondly, the event-related potential (ERP) features and morphological features are extracted from EEG signals and eye blinking signals, respectively. Thirdly, convolutional neural network and back propagation neural network are severally designed to gain the score estimation of EEG features and eye blinking features. Finally, a score fusion technology based on least square method is proposed to get the final estimation score. The performance of multi-task authentication system is improved significantly compared to the system using EEG only, with an increasing average accuracy from 92.4% to 97.6%. Moreover, open-set authentication tests for additional imposters and permanence tests for users are conducted to simulate the practical scenarios, which have never been employed in previous EEG-based person authentication systems. A mean false accepted rate (FAR) of 3.90% and a mean false rejected rate (FRR) of 3.87% are accomplished in open-set authentication tests and permanence tests, respectively, which illustrate the open-set authentication and permanence capability of our systems.

  4. Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation.

    Science.gov (United States)

    Brosch, Tom; Tang, Lisa Y W; Youngjin Yoo; Li, David K B; Traboulsee, Anthony; Tam, Roger

    2016-05-01

    We propose a novel segmentation approach based on deep 3D convolutional encoder networks with shortcut connections and apply it to the segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. Our model is a neural network that consists of two interconnected pathways, a convolutional pathway, which learns increasingly more abstract and higher-level image features, and a deconvolutional pathway, which predicts the final segmentation at the voxel level. The joint training of the feature extraction and prediction pathways allows for the automatic learning of features at different scales that are optimized for accuracy for any given combination of image types and segmentation task. In addition, shortcut connections between the two pathways allow high- and low-level features to be integrated, which enables the segmentation of lesions across a wide range of sizes. We have evaluated our method on two publicly available data sets (MICCAI 2008 and ISBI 2015 challenges) with the results showing that our method performs comparably to the top-ranked state-of-the-art methods, even when only relatively small data sets are available for training. In addition, we have compared our method with five freely available and widely used MS lesion segmentation methods (EMS, LST-LPA, LST-LGA, Lesion-TOADS, and SLS) on a large data set from an MS clinical trial. The results show that our method consistently outperforms these other methods across a wide range of lesion sizes.

  5. MRI features of tuberculosis of peripheral joints

    Energy Technology Data Exchange (ETDEWEB)

    Sawlani, V.; Chandra, T.; Mishra, R.N.; Aggarwal, A.; Jain, U.K.; Gujral, R.B. E-mail: gujralrb@sgpgi.ac.in

    2003-10-01

    The aim of this article is to present the magnetic resonance imaging (MRI) features of peripheral tubercular arthritis. The clinical presentation of peripheral tubercular arthritis is variable and simulates other chronic inflammatory arthritic disorders. MRI is a highly sensitive technique which demonstrates fine anatomical details and identifies the early changes of arthritis, which are not visible on radiographs. The MRI features of tubercular arthritis include synovitis, effusion, central and peripheral erosions, active and chronic pannus, abscess, bone chips and hypo-intense synovium. These imaging features in an appropriate clinical setting may help in the diagnosis of tubercular arthritis. Early diagnosis and treatment can effectively eliminate the long-term morbidity of joints affected by tuberculosis.

  6. MRI features of tuberculosis of peripheral joints

    International Nuclear Information System (INIS)

    Sawlani, V.; Chandra, T.; Mishra, R.N.; Aggarwal, A.; Jain, U.K.; Gujral, R.B.

    2003-01-01

    The aim of this article is to present the magnetic resonance imaging (MRI) features of peripheral tubercular arthritis. The clinical presentation of peripheral tubercular arthritis is variable and simulates other chronic inflammatory arthritic disorders. MRI is a highly sensitive technique which demonstrates fine anatomical details and identifies the early changes of arthritis, which are not visible on radiographs. The MRI features of tubercular arthritis include synovitis, effusion, central and peripheral erosions, active and chronic pannus, abscess, bone chips and hypo-intense synovium. These imaging features in an appropriate clinical setting may help in the diagnosis of tubercular arthritis. Early diagnosis and treatment can effectively eliminate the long-term morbidity of joints affected by tuberculosis

  7. Is there a relation between the 2D Causal Set action and the Lorentzian Gauss-Bonnet theorem?

    Energy Technology Data Exchange (ETDEWEB)

    Benincasa, Dionigi M T, E-mail: db1808@ic.ac.uk [Theoretical Physics Group, Blackett Laboratory, Imperial College, Prince Consort Rd., London SW7 2AZ (United Kingdom)

    2011-07-08

    We investigate the relation between the two dimensional Causal Set action, S, and the Lorentzian Gauss-Bonnet theorem (LGBT). We give compelling reasons why the answer to the title's question is no. In support of this point of view we calculate the causal set inspired action of causal intervals in some two dimensional spacetimes: Minkowski, the flat cylinder and the flat trousers.

  8. Discriminative topological features reveal biological network mechanisms

    Directory of Open Access Journals (Sweden)

    Levovitz Chaya

    2004-11-01

    Full Text Available Abstract Background Recent genomic and bioinformatic advances have motivated the development of numerous network models intending to describe graphs of biological, technological, and sociological origin. In most cases the success of a model has been evaluated by how well it reproduces a few key features of the real-world data, such as degree distributions, mean geodesic lengths, and clustering coefficients. Often pairs of models can reproduce these features with indistinguishable fidelity despite being generated by vastly different mechanisms. In such cases, these few target features are insufficient to distinguish which of the different models best describes real world networks of interest; moreover, it is not clear a priori that any of the presently-existing algorithms for network generation offers a predictive description of the networks inspiring them. Results We present a method to assess systematically which of a set of proposed network generation algorithms gives the most accurate description of a given biological network. To derive discriminative classifiers, we construct a mapping from the set of all graphs to a high-dimensional (in principle infinite-dimensional "word space". This map defines an input space for classification schemes which allow us to state unambiguously which models are most descriptive of a given network of interest. Our training sets include networks generated from 17 models either drawn from the literature or introduced in this work. We show that different duplication-mutation schemes best describe the E. coli genetic network, the S. cerevisiae protein interaction network, and the C. elegans neuronal network, out of a set of network models including a linear preferential attachment model and a small-world model. Conclusions Our method is a first step towards systematizing network models and assessing their predictability, and we anticipate its usefulness for a number of communities.

  9. Stackel spaces of an electrovacuum with isotropic complete sets. Formulation of problem and basic relations

    International Nuclear Information System (INIS)

    Bagrov, V.G.; Evseevich, A.A.; Obukhov, V.V.; Osetrin, K.E.

    1987-01-01

    The authors consider the problem of the classification of the Stackel spaces of the electrovacuum with isotropic complete sets. The metrics of the spaces are represented in a form that is convenient for their investigation. We obtain necessary relations for the construction of the field equations

  10. Spatial database of mining-related features in 2001 at selected phosphate mines, Bannock, Bear Lake, Bingham, and Caribou Counties, Idaho

    Science.gov (United States)

    Moyle, Phillip R.; Kayser, Helen Z.

    2006-01-01

    This report describes the spatial database, PHOSMINE01, and the processes used to delineate mining-related features (active and inactive/historical) in the core of the southeastern Idaho phosphate resource area. The spatial data have varying degrees of accuracy and attribution detail. Classification of areas by type of mining-related activity at active mines is generally detailed; however, for many of the closed or inactive mines the spatial coverage does not differentiate mining-related surface disturbance features. Nineteen phosphate mine sites are included in the study, three active phosphate mines - Enoch Valley (nearing closure), Rasmussen Ridge, and Smoky Canyon - and 16 inactive (or historical) phosphate mines - Ballard, Champ, Conda, Diamond Gulch, Dry Valley, Gay, Georgetown Canyon, Henry, Home Canyon, Lanes Creek, Maybe Canyon, Mountain Fuel, Trail Canyon, Rattlesnake, Waterloo, and Wooley Valley. Approximately 6,000 hc (15,000 ac), or 60 km2 (23 mi2) of phosphate mining-related surface disturbance are documented in the spatial coverage. Spatial data for the inactive mines is current because no major changes have occurred; however, the spatial data for active mines were derived from digital maps prepared in early 2001 and therefore recent activity is not included. The inactive Gay Mine has the largest total area of disturbance, 1,900 hc (4,700 ac) or about 19 km2 (7.4 mi2). It encompasses over three times the disturbance area of the next largest mine, the Conda Mine with 610 hc (1,500 ac), and it is nearly four times the area of the Smoky Canyon Mine, the largest of the active mines with about 550 hc (1,400 ac). The wide range of phosphate mining-related surface disturbance features (141) from various industry maps were reduced to 15 types or features based on a generic classification system used for this study: mine pit; backfilled mine pit; waste rock dump; adit and waste rock dump; ore stockpile; topsoil stockpile; tailings or tailings pond; sediment

  11. FEATURES OF DYNAMIC CHANGE OF INNER DISEASE-RELATION TYPE IN COHORT OF PATIENTS SUFFERING FROM ADDICTIONS

    Directory of Open Access Journals (Sweden)

    A. Z. Grigoryan

    2014-12-01

    Full Text Available Aim. Pathoplastic modification of addictive disorders with affective spectrum violations, leads to the formation of the psychopathological cluster that have specific structural and dynamic features.Methods and results. In order to assess the dynamics of change of disease-relation type by LOBY questionnaire, 100 patients from «Zaporozhye Regional Narcological Dispensary» suffering from polydrug usage and affective spectrum disorders were examined in the following clinical periods: withdrawal state, further inpatient and outpatient follow-up.Conclusion. The solidity of background psychopathological disorders in perspective of their affiliation to somatogenically-organic register, for the entire study contingent was found. Dynamics of change of disease-relation type illustrates partially reversible character of these disorders.

  12. Relational and conjunctive binding functions dissociate in short-term memory.

    Science.gov (United States)

    Parra, Mario A; Fabi, Katia; Luzzi, Simona; Cubelli, Roberto; Hernandez Valdez, Maria; Della Sala, Sergio

    2015-02-01

    Remembering complex events requires binding features within unified objects (conjunctions) and holding associations between objects (relations). Recent studies suggest that the two functions dissociate in long-term memory (LTM). Less is known about their functional organization in short-term memory (STM). The present study investigated this issue in patient AE affected by a stroke which caused damage to brain regions known to be relevant for relational functions both in LTM and in STM (i.e., the hippocampus). The assessment involved a battery of standard neuropsychological tasks and STM binding tasks. One STM binding task (Experiment 1) presented common objects and common colors forming either pairs (relations) or integrated objects (conjunctions). Free recall of relations or conjunctions was assessed. A second STM binding task used random polygons and non-primary colors instead (Experiment 2). Memory was assessed by selecting the features that made up the relations or the conjunctions from a set of single polygons and a set of single colors. The neuropsychological assessment revealed impaired delayed memory in AE. AE's pronounced relational STM binding deficits contrasted with his completely preserved conjunctive binding functions in both Experiments 1 and 2. Only 2.35% and 1.14% of the population were expected to have a discrepancy more extreme than that presented by AE in Experiments 1 and 2, respectively. Processing relations and conjunctions of very elementary nonspatial features in STM led to dissociating performances in AE. These findings may inform current theories of memory decline such as those linked to cognitive aging.

  13. Understanding Legacy Features with Featureous

    DEFF Research Database (Denmark)

    Olszak, Andrzej; Jørgensen, Bo Nørregaard

    2011-01-01

    Java programs called Featureous that addresses this issue. Featureous allows a programmer to easily establish feature-code traceability links and to analyze their characteristics using a number of visualizations. Featureous is an extension to the NetBeans IDE, and can itself be extended by third...

  14. Time-Frequency Feature Representation Using Multi-Resolution Texture Analysis and Acoustic Activity Detector for Real-Life Speech Emotion Recognition

    Directory of Open Access Journals (Sweden)

    Kun-Ching Wang

    2015-01-01

    Full Text Available The classification of emotional speech is mostly considered in speech-related research on human-computer interaction (HCI. In this paper, the purpose is to present a novel feature extraction based on multi-resolutions texture image information (MRTII. The MRTII feature set is derived from multi-resolution texture analysis for characterization and classification of different emotions in a speech signal. The motivation is that we have to consider emotions have different intensity values in different frequency bands. In terms of human visual perceptual, the texture property on multi-resolution of emotional speech spectrogram should be a good feature set for emotion classification in speech. Furthermore, the multi-resolution analysis on texture can give a clearer discrimination between each emotion than uniform-resolution analysis on texture. In order to provide high accuracy of emotional discrimination especially in real-life, an acoustic activity detection (AAD algorithm must be applied into the MRTII-based feature extraction. Considering the presence of many blended emotions in real life, in this paper make use of two corpora of naturally-occurring dialogs recorded in real-life call centers. Compared with the traditional Mel-scale Frequency Cepstral Coefficients (MFCC and the state-of-the-art features, the MRTII features also can improve the correct classification rates of proposed systems among different language databases. Experimental results show that the proposed MRTII-based feature information inspired by human visual perception of the spectrogram image can provide significant classification for real-life emotional recognition in speech.

  15. Feature-based Ontology Mapping from an Information Receivers’ Viewpoint

    DEFF Research Database (Denmark)

    Glückstad, Fumiko Kano; Mørup, Morten

    2012-01-01

    This paper compares four algorithms for computing feature-based similarities between concepts respectively possessing a distinctive set of features. The eventual purpose of comparing these feature-based similarity algorithms is to identify a candidate term in a Target Language (TL) that can...... optimally convey the original meaning of a culturally-specific Source Language (SL) concept to a TL audience by aligning two culturally-dependent domain-specific ontologies. The results indicate that the Bayesian Model of Generalization [1] performs best, not only for identifying candidate translation terms...

  16. A generic approach for the automatic verification of featured, parameterised systems

    OpenAIRE

    Miller, A.; Calder, M.

    2005-01-01

    A general technique is presented that allows property based feature analysis of systems consisting of an arbitrary number of components. Each component may have an arbitrary set of safe features. The components are defined in a guarded command form and the technique combines model checking and abstraction. Features must fulfill certain criteria in order to be safe, the criteria express constraints on the variables which occur in feature guards. The main result is a generalisation theorem whic...

  17. DDAH2 mRNA expression is inversely associated with some cardiovascular risk-related features in healthy young adults.

    Science.gov (United States)

    Puchau, Blanca; Hermsdorff, Helen Hermana M; Zulet, M Angeles; Martínez, J Alfredo

    2009-01-01

    The purpose of this study was to evaluate whether the mRNA expression profiles of three genes (PRMT1, DDAH2 and NOS3) are related to ADMA metabolism and signalling, and the potential relationships with anthropometrical, biochemical, lifestyle and inflammatory indicators in healthy young adults. An emphasis on the putative effect of different mRNA expression on cardiovascular risk-related features was paid. Anthropometrical measurements as well as lifestyle features were analyzed in 120 healthy young adults. Fasting blood samples were collected for the measurement of glucose and lipid profiles as well as the concentrations of selected inflammatory markers. Profiles of mRNA expression were assessed for PRMT1, DDAH2 and NOS3 genes from peripheral blood mononuclear cells. Regarding inflammatory biomarkers, DDAH2 was inversely associated with IL-6 and TNF-alpha. Moreover, subjects in the highest quintile of DDAH2 mRNA expression showed a reduced risk to have higher values of waist circumference, and to be more prone to show higher values of HDL-c. Interestingly, DDAH2 gene expression seemed to be related with some anthropometrical, biochemical, lifestyle and inflammatory indicators linked to cardiovascular risk in apparently healthy young adults, emerging as a potential disease marker.

  18. DDAH2 mRNA Expression Is Inversely Associated with Some Cardiovascular Risk-Related Features in Healthy Young Adults

    Directory of Open Access Journals (Sweden)

    Blanca Puchau

    2009-01-01

    Full Text Available The purpose of this study was to evaluate whether the mRNA expression profiles of three genes (PRMT1, DDAH2 and NOS3 are related to ADMA metabolism and signalling, and the potential relationships with anthropometrical, biochemical, lifestyle and inflammatory indicators in healthy young adults. An emphasis on the putative effect of different mRNA expression on cardiovascular risk-related features was paid. Anthropometrical measurements as well as lifestyle features were analyzed in 120 healthy young adults. Fasting blood samples were collected for the measurement of glucose and lipid profiles as well as the concentrations of selected inflammatory markers. Profiles of mRNA expression were assessed for PRMT1, DDAH2 and NOS3 genes from peripheral blood mononuclear cells. Regarding inflammatory biomarkers, DDAH2 was inversely associated with IL-6 and TNF-α. Moreover, subjects in the highest quintile of DDAH2 mRNA expression showed a reduced risk to have higher values of waist circumference, and to be more prone to show higher values of HDL-c. Interestingly, DDAH2 gene expression seemed to be related with some anthropometrical, biochemical, lifestyle and inflammatory indicators linked to cardiovascular risk in apparently healthy young adults, emerging as a potential disease marker.

  19. Complex Urban LiDAR Data Set

    OpenAIRE

    Jeong, Jinyong; Cho, Younggun; Shin, Young-Sik; Roh, Hyunchul; Kim, Ayoung

    2018-01-01

    This paper presents a Light Detection and Ranging (LiDAR) data set that targets complex urban environments. Urban environments with high-rise buildings and congested traffic pose a significant challenge for many robotics applications. The presented data set is unique in the sense it is able to capture the genuine features of an urban environment (e.g. metropolitan areas, large building complexes and underground parking lots). Data of two-dimensional (2D) and threedimensional (3D) LiDAR, which...

  20. Do features of public open spaces vary according to neighbourhood socio-economic status?

    Science.gov (United States)

    Crawford, David; Timperio, Anna; Giles-Corti, Billie; Ball, Kylie; Hume, Clare; Roberts, Rebecca; Andrianopoulos, Nick; Salmon, Jo

    2008-12-01

    This study examined the relations between neighbourhood socio-economic status and features of public open spaces (POS) hypothesised to influence children's physical activity. Data were from the first follow-up of the Children Living in Active Neighbourhoods (CLAN) Study, which involved 540 families of 5-6 and 10-12-year-old children in Melbourne, Australia. The Socio-Economic Index for Areas Index (SEIFA) of Relative Socio-economic Advantage/Disadvantage was used to assign a socioeconomic index score to each child's neighbourhood, based on postcode. Participant addresses were geocoded using a Geographic Information System. The Open Space 2002 spatial data set was used to identify all POS within an 800 m radius of each participant's home. The features of each of these POS (1497) were audited. Variability of POS features was examined across quintiles of neighbourhood SEIFA. Compared with POS in lower socioeconomic neighbourhoods, POS in the highest socioeconomic neighbourhoods had more amenities (e.g. picnic tables and drink fountains) and were more likely to have trees that provided shade, a water feature (e.g. pond, creek), walking and cycling paths, lighting, signage regarding dog access and signage restricting other activities. There were no differences across neighbourhoods in the number of playgrounds or the number of recreation facilities (e.g. number of sports catered for on courts and ovals, the presence of other facilities such as athletics tracks, skateboarding facility and swimming pool). This study suggests that POS in high socioeconomic neighbourhoods possess more features that are likely to promote physical activity amongst children.

  1. Set theory essentials

    CERN Document Server

    Milewski, Emil G

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Set Theory includes elementary logic, sets, relations, functions, denumerable and non-denumerable sets, cardinal numbers, Cantor's theorem, axiom of choice, and order relations.

  2. Age-related changes of adaptive and neuropsychological features in persons with Down Syndrome.

    Directory of Open Access Journals (Sweden)

    Alessandro Ghezzo

    Full Text Available Down Syndrome (DS is characterised by premature aging and an accelerated decline of cognitive functions in the vast majority of cases. As the life expectancy of DS persons is rapidly increasing, this decline is becoming a dramatic health problem. The aim of this study was to thoroughly evaluate a group of 67 non-demented persons with DS of different ages (11 to 66 years, from a neuropsychological, neuropsychiatric and psychomotor point of view in order to evaluate in a cross-sectional study the age-related adaptive and neuropsychological features, and to possibly identify early signs predictive of cognitive decline. The main finding of this study is that both neuropsychological functions and adaptive skills are lower in adult DS persons over 40 years old, compared to younger ones. In particular, language and short memory skills, frontal lobe functions, visuo-spatial abilities and adaptive behaviour appear to be the more affected domains. A growing deficit in verbal comprehension, along with social isolation, loss of interest and greater fatigue in daily tasks, are the main features found in older, non demented DS persons evaluated in our study. It is proposed that these signs can be alarm bells for incipient dementia, and that neuro-cognitive rehabilitation and psycho-pharmacological interventions must start as soon as the fourth decade (or even earlier in DS persons, i.e. at an age where interventions can have the greatest efficacy.

  3. Analysis of respiratory events in obstructive sleep apnea syndrome: Inter-relations and association to simple nocturnal features.

    Science.gov (United States)

    Ghandeharioun, H; Rezaeitalab, F; Lotfi, R

    2016-01-01

    This study carefully evaluates the association of different respiration-related events to each other and to simple nocturnal features in obstructive sleep apnea-hypopnea syndrome (OSAS). The events include apneas, hypopneas, respiratory event-related arousals and snores. We conducted a statistical study on 158 adults who underwent polysomnography between July 2012 and May 2014. To monitor relevance, along with linear statistical strategies like analysis of variance and bootstrapping a correlation coefficient standard error, the non-linear method of mutual information is also applied to illuminate vague results of linear techniques. Based on normalized mutual information weights (NMIW), indices of apnea are 1.3 times more relevant to AHI values than those of hypopnea. NMIW for the number of blood oxygen desaturation below 95% is considerable (0.531). The next relevant feature is "respiratory arousals index" with NMIW of 0.501. Snore indices (0.314), and BMI (0.203) take the next place. Based on NMIW values, snoring events are nearly one-third (29.9%) more dependent to hypopneas than RERAs. 1. The more sever the OSAS is, the more frequently the apneic events happen. 2. The association of snore with hypopnea/RERA revealed which is routinely ignored in regression-based OSAS modeling. 3. The statistical dependencies of oximetry features potentially can lead to home-based screening of OSAS. 4. Poor ESS-AHI relevance in the database under study indicates its disability for the OSA diagnosis compared to oximetry. 5. Based on poor RERA-snore/ESS relevance, detailed history of the symptoms plus polysomnography is suggested for accurate diagnosis of RERAs. Copyright © 2015 Sociedade Portuguesa de Pneumologia. Published by Elsevier España, S.L.U. All rights reserved.

  4. Application of eigen value expansion to feature extraction from MRI images

    International Nuclear Information System (INIS)

    Kinosada, Yasutomi; Takeda, Kan; Nakagawa, Tsuyoshi

    1991-01-01

    The eigen value expansion technique was utilized for feature extraction of magnetic resonance (MR) images. The eigen value expansion is an orthonormal transformation method which decomposes a set of images into some statistically uncorrelated images. The technique was applied to MR images obtained with various imaging parameters at the same anatomical site. It generated one mean image and another set of images called bases for the images. Each basis corresponds to a feature in the images. A basis is, therefore, utilized for the feature extraction from MR images and a weighted sum of bases is also used for the feature enhancement. Furthermore, any MR image with specific feature can be obtained from a linear combination of the mean image and all of the bases. Images of hemorrhaged brain with a spin echo sequence and a series of cinematic cerebro spinal fluid flow images with ECG gated gradient refocused echo sequence were employed to estimate the ability of the feature extraction and the contrast enhancement. Results showed us that proposed application of an eigen value expansion technique to the feature extraction of MR images is good enough to clinical use and superior to other feature extraction methods such as producing a calculated MR image with a given TR and TE or the matched-filter method in processing speed and reproducibility of results. (author)

  5. Receptive fields selection for binary feature description.

    Science.gov (United States)

    Fan, Bin; Kong, Qingqun; Trzcinski, Tomasz; Wang, Zhiheng; Pan, Chunhong; Fua, Pascal

    2014-06-01

    Feature description for local image patch is widely used in computer vision. While the conventional way to design local descriptor is based on expert experience and knowledge, learning-based methods for designing local descriptor become more and more popular because of their good performance and data-driven property. This paper proposes a novel data-driven method for designing binary feature descriptor, which we call receptive fields descriptor (RFD). Technically, RFD is constructed by thresholding responses of a set of receptive fields, which are selected from a large number of candidates according to their distinctiveness and correlations in a greedy way. Using two different kinds of receptive fields (namely rectangular pooling area and Gaussian pooling area) for selection, we obtain two binary descriptors RFDR and RFDG .accordingly. Image matching experiments on the well-known patch data set and Oxford data set demonstrate that RFD significantly outperforms the state-of-the-art binary descriptors, and is comparable with the best float-valued descriptors at a fraction of processing time. Finally, experiments on object recognition tasks confirm that both RFDR and RFDG successfully bridge the performance gap between binary descriptors and their floating-point competitors.

  6. Preventing diabetes in the clinical setting.

    Science.gov (United States)

    Burnet, Deborah L; Elliott, Lorrie D; Quinn, Michael T; Plaut, Andrea J; Schwartz, Mindy A; Chin, Marshall H

    2006-01-01

    Translating lessons from clinical trials on the prevention or delay of type 2 diabetes to populations in nonstudy settings remains a challenge. The purpose of this paper is to review, from the perspective of practicing clinicians, available evidence on lifestyle interventions or medication to prevent or delay the onset of type 2 diabetes. A MEDLINE search identified 4 major diabetes prevention trials using lifestyle changes and 3 using prophylactic medications. We reviewed the study design, key components, and outcomes for each study, focusing on aspects of the interventions potentially adaptable to clinical settings. The lifestyle intervention studies set modest goals for weight loss and physical activity. Individualized counseling helped participants work toward their own goals; behavioral contracting and self-monitoring were key features, and family and social context were emphasized. Study staff made vigorous follow-up efforts for subjects having less success. Actual weight loss by participants was modest; yet, the reduction in diabetes incidence was quite significant. Prophylactic medication also reduced diabetes risk; however, lifestyle changes were more effective and are recommended as first-line strategy. Cost-effectiveness analyses have shown both lifestyle and medication interventions to be beneficial, especially as they might be implemented in practice. Strong evidence exists for the prevention or delay of type 2 diabetes through lifestyle changes. Components of these programs may be adaptable for use in clinical settings. This evidence supports broader implementation and increased reimbursement for provider services related to nutrition and physical activity to forestall morbidity from type 2 diabetes.

  7. Classification of visual and linguistic tasks using eye-movement features.

    Science.gov (United States)

    Coco, Moreno I; Keller, Frank

    2014-03-07

    The role of the task has received special attention in visual-cognition research because it can provide causal explanations of goal-directed eye-movement responses. The dependency between visual attention and task suggests that eye movements can be used to classify the task being performed. A recent study by Greene, Liu, and Wolfe (2012), however, fails to achieve accurate classification of visual tasks based on eye-movement features. In the present study, we hypothesize that tasks can be successfully classified when they differ with respect to the involvement of other cognitive domains, such as language processing. We extract the eye-movement features used by Greene et al. as well as additional features from the data of three different tasks: visual search, object naming, and scene description. First, we demonstrated that eye-movement responses make it possible to characterize the goals of these tasks. Then, we trained three different types of classifiers and predicted the task participants performed with an accuracy well above chance (a maximum of 88% for visual search). An analysis of the relative importance of features for classification accuracy reveals that just one feature, i.e., initiation time, is sufficient for above-chance performance (a maximum of 79% accuracy in object naming). Crucially, this feature is independent of task duration, which differs systematically across the three tasks we investigated. Overall, the best task classification performance was obtained with a set of seven features that included both spatial information (e.g., entropy of attention allocation) and temporal components (e.g., total fixation on objects) of the eye-movement record. This result confirms the task-dependent allocation of visual attention and extends previous work by showing that task classification is possible when tasks differ in the cognitive processes involved (purely visual tasks such as search vs. communicative tasks such as scene description).

  8. Different underlying mechanisms for face emotion and gender processing during feature-selective attention: Evidence from event-related potential studies.

    Science.gov (United States)

    Wang, Hailing; Ip, Chengteng; Fu, Shimin; Sun, Pei

    2017-05-01

    Face recognition theories suggest that our brains process invariant (e.g., gender) and changeable (e.g., emotion) facial dimensions separately. To investigate whether these two dimensions are processed in different time courses, we analyzed the selection negativity (SN, an event-related potential component reflecting attentional modulation) elicited by face gender and emotion during a feature selective attention task. Participants were instructed to attend to a combination of face emotion and gender attributes in Experiment 1 (bi-dimensional task) and to either face emotion or gender in Experiment 2 (uni-dimensional task). The results revealed that face emotion did not elicit a substantial SN, whereas face gender consistently generated a substantial SN in both experiments. These results suggest that face gender is more sensitive to feature-selective attention and that face emotion is encoded relatively automatically on SN, implying the existence of different underlying processing mechanisms for invariant and changeable facial dimensions. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Robust Feature Selection from Microarray Data Based on Cooperative Game Theory and Qualitative Mutual Information

    Directory of Open Access Journals (Sweden)

    Atiyeh Mortazavi

    2016-01-01

    Full Text Available High dimensionality of microarray data sets may lead to low efficiency and overfitting. In this paper, a multiphase cooperative game theoretic feature selection approach is proposed for microarray data classification. In the first phase, due to high dimension of microarray data sets, the features are reduced using one of the two filter-based feature selection methods, namely, mutual information and Fisher ratio. In the second phase, Shapley index is used to evaluate the power of each feature. The main innovation of the proposed approach is to employ Qualitative Mutual Information (QMI for this purpose. The idea of Qualitative Mutual Information causes the selected features to have more stability and this stability helps to deal with the problem of data imbalance and scarcity. In the third phase, a forward selection scheme is applied which uses a scoring function to weight each feature. The performance of the proposed method is compared with other popular feature selection algorithms such as Fisher ratio, minimum redundancy maximum relevance, and previous works on cooperative game based feature selection. The average classification accuracy on eleven microarray data sets shows that the proposed method improves both average accuracy and average stability compared to other approaches.

  10. Application of biclustering of gene expression data and gene set enrichment analysis methods to identify potentially disease causing nanomaterials

    Directory of Open Access Journals (Sweden)

    Andrew Williams

    2015-12-01

    Full Text Available Background: The presence of diverse types of nanomaterials (NMs in commerce is growing at an exponential pace. As a result, human exposure to these materials in the environment is inevitable, necessitating the need for rapid and reliable toxicity testing methods to accurately assess the potential hazards associated with NMs. In this study, we applied biclustering and gene set enrichment analysis methods to derive essential features of altered lung transcriptome following exposure to NMs that are associated with lung-specific diseases. Several datasets from public microarray repositories describing pulmonary diseases in mouse models following exposure to a variety of substances were examined and functionally related biclusters of genes showing similar expression profiles were identified. The identified biclusters were then used to conduct a gene set enrichment analysis on pulmonary gene expression profiles derived from mice exposed to nano-titanium dioxide (nano-TiO2, carbon black (CB or carbon nanotubes (CNTs to determine the disease significance of these data-driven gene sets.Results: Biclusters representing inflammation (chemokine activity, DNA binding, cell cycle, apoptosis, reactive oxygen species (ROS and fibrosis processes were identified. All of the NM studies were significant with respect to the bicluster related to chemokine activity (DAVID; FDR p-value = 0.032. The bicluster related to pulmonary fibrosis was enriched in studies where toxicity induced by CNT and CB studies was investigated, suggesting the potential for these materials to induce lung fibrosis. The pro-fibrogenic potential of CNTs is well established. Although CB has not been shown to induce fibrosis, it induces stronger inflammatory, oxidative stress and DNA damage responses than nano-TiO2 particles.Conclusion: The results of the analysis correctly identified all NMs to be inflammogenic and only CB and CNTs as potentially fibrogenic. In addition to identifying several

  11. New features in MEDM

    International Nuclear Information System (INIS)

    Evans, K. Jr.

    1999-01-01

    MEDM, which is derived from Motif Editor and Display Manager, is the primary graphical interface to the EPICS control system. This paper describes new features that have been added to MEDM in the last two years. These features include new editing capabilities, a PV Info dialog box, a means of specifying limits and precision, a new implementation of the Cartesian Plot, new features for several objects, new capability for the Related Display, help, a user-configurable Execute Menu, reconfigured start-up options, and availability for Windows 95/98/NT. Over one hundred bugs have been fixed, and the program is quite stable and in extensive use

  12. 3p interstitial deletion including PRICKLE2 in identical twins with autistic features.

    Science.gov (United States)

    Okumura, Akihisa; Yamamoto, Toshiyuki; Miyajima, Masakazu; Shimojima, Keiko; Kondo, Satoshi; Abe, Shinpei; Ikeno, Mitsuru; Shimizu, Toshiaki

    2014-11-01

    Microdeletion and microduplication syndromes without characteristic dysmorphic features are difficult to diagnose without chromosomal microarrays. We describe the clinical course and genetic findings of monozygotic twins with intellectual disabilities and autistic features associated with mild facial dysmorphism and microdeletion of chromosome 3p14. The postnatal course of the second twin was complicated by intestinal malrotation, whereas that of the first twin was unremarkable. Both twins had several mild dysmorphic features including upswept frontal hair, low-set posterior rotated ears, arched down-slanting eyebrows, prominent forehead, epicanthic folds, micrognathia, hypertelorism, broad nasal bridge, short philtrum, and camptodactyly of the bilateral fifth fingers. They had autistic features such as poor eye contact and no social smile, stereotyped behaviors, and preference for solitary play. Array comparative genomic hybridization analysis revealed de novo 6.88-Mb deletions of 3p14 (chr3: 60,472,496-67,385,119) involving 17 genes in both twins. The deleted region contained 17 genes, five of which are known or presumed to be related to central nervous system disorders: FEZF2, SYNPR, ATXN7, PRICKLE2, and MAGI1. We consider that PRICKLE2 is the most likely causative gene for the autistic features exhibited by these individuals. Copyright © 2014 Elsevier Inc. All rights reserved.

  13. Saliency image of feature building for image quality assessment

    Science.gov (United States)

    Ju, Xinuo; Sun, Jiyin; Wang, Peng

    2011-11-01

    The purpose and method of image quality assessment are quite different for automatic target recognition (ATR) and traditional application. Local invariant feature detectors, mainly including corner detectors, blob detectors and region detectors etc., are widely applied for ATR. A saliency model of feature was proposed to evaluate feasibility of ATR in this paper. The first step consisted of computing the first-order derivatives on horizontal orientation and vertical orientation, and computing DoG maps in different scales respectively. Next, saliency images of feature were built based auto-correlation matrix in different scale. Then, saliency images of feature of different scales amalgamated. Experiment were performed on a large test set, including infrared images and optical images, and the result showed that the salient regions computed by this model were consistent with real feature regions computed by mostly local invariant feature extraction algorithms.

  14. Boosting Discriminant Learners for Gait Recognition Using MPCA Features

    Directory of Open Access Journals (Sweden)

    Haiping Lu

    2009-01-01

    Full Text Available This paper proposes a boosted linear discriminant analysis (LDA solution on features extracted by the multilinear principal component analysis (MPCA to enhance gait recognition performance. Three-dimensional gait objects are projected in the MPCA space first to obtain low-dimensional tensorial features. Then, lower-dimensional vectorial features are obtained through discriminative feature selection. These feature vectors are then fed into an LDA-style booster, where several regularized and weakened LDA learners work together to produce a strong learner through a novel feature weighting and sampling process. The LDA learner employs a simple nearest-neighbor classifier with a weighted angle distance measure for classification. The experimental results on the NIST/USF “Gait Challenge” data-sets show that the proposed solution has successfully improved the gait recognition performance and outperformed several state-of-the-art gait recognition algorithms.

  15. The perceptual processing capacity of summary statistics between and within feature dimensions

    Science.gov (United States)

    Attarha, Mouna; Moore, Cathleen M.

    2015-01-01

    The simultaneous–sequential method was used to test the processing capacity of statistical summary representations both within and between feature dimensions. Sixteen gratings varied with respect to their size and orientation. In Experiment 1, the gratings were equally divided into four separate smaller sets, one of which with a mean size that was larger or smaller than the other three sets, and one of which with a mean orientation that was tilted more leftward or rightward. The task was to report the mean size and orientation of the oddball sets. This therefore required four summary representations for size and another four for orientation. The sets were presented at the same time in the simultaneous condition or across two temporal frames in the sequential condition. Experiment 1 showed evidence of a sequential advantage, suggesting that the system may be limited with respect to establishing multiple within-feature summaries. Experiment 2 eliminates the possibility that some aspect of the task, other than averaging, was contributing to this observed limitation. In Experiment 3, the same 16 gratings appeared as one large superset, and therefore the task only required one summary representation for size and another one for orientation. Equal simultaneous–sequential performance indicated that between-feature summaries are capacity free. These findings challenge the view that within-feature summaries drive a global sense of visual continuity across areas of the peripheral visual field, and suggest a shift in focus to seeking an understanding of how between-feature summaries in one area of the environment control behavior. PMID:26360153

  16. A Meta-Heuristic Regression-Based Feature Selection for Predictive Analytics

    Directory of Open Access Journals (Sweden)

    Bharat Singh

    2014-11-01

    Full Text Available A high-dimensional feature selection having a very large number of features with an optimal feature subset is an NP-complete problem. Because conventional optimization techniques are unable to tackle large-scale feature selection problems, meta-heuristic algorithms are widely used. In this paper, we propose a particle swarm optimization technique while utilizing regression techniques for feature selection. We then use the selected features to classify the data. Classification accuracy is used as a criterion to evaluate classifier performance, and classification is accomplished through the use of k-nearest neighbour (KNN and Bayesian techniques. Various high dimensional data sets are used to evaluate the usefulness of the proposed approach. Results show that our approach gives better results when compared with other conventional feature selection algorithms.

  17. Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients.

    Directory of Open Access Journals (Sweden)

    Nicole A Capela

    Full Text Available Human activity recognition (HAR, using wearable sensors, is a growing area with the potential to provide valuable information on patient mobility to rehabilitation specialists. Smartphones with accelerometer and gyroscope sensors are a convenient, minimally invasive, and low cost approach for mobility monitoring. HAR systems typically pre-process raw signals, segment the signals, and then extract features to be used in a classifier. Feature selection is a crucial step in the process to reduce potentially large data dimensionality and provide viable parameters to enable activity classification. Most HAR systems are customized to an individual research group, including a unique data set, classes, algorithms, and signal features. These data sets are obtained predominantly from able-bodied participants. In this paper, smartphone accelerometer and gyroscope sensor data were collected from populations that can benefit from human activity recognition: able-bodied, elderly, and stroke patients. Data from a consecutive sequence of 41 mobility tasks (18 different tasks were collected for a total of 44 participants. Seventy-six signal features were calculated and subsets of these features were selected using three filter-based, classifier-independent, feature selection methods (Relief-F, Correlation-based Feature Selection, Fast Correlation Based Filter. The feature subsets were then evaluated using three generic classifiers (Naïve Bayes, Support Vector Machine, j48 Decision Tree. Common features were identified for all three populations, although the stroke population subset had some differences from both able-bodied and elderly sets. Evaluation with the three classifiers showed that the feature subsets produced similar or better accuracies than classification with the entire feature set. Therefore, since these feature subsets are classifier-independent, they should be useful for developing and improving HAR systems across and within populations.

  18. Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients.

    Science.gov (United States)

    Capela, Nicole A; Lemaire, Edward D; Baddour, Natalie

    2015-01-01

    Human activity recognition (HAR), using wearable sensors, is a growing area with the potential to provide valuable information on patient mobility to rehabilitation specialists. Smartphones with accelerometer and gyroscope sensors are a convenient, minimally invasive, and low cost approach for mobility monitoring. HAR systems typically pre-process raw signals, segment the signals, and then extract features to be used in a classifier. Feature selection is a crucial step in the process to reduce potentially large data dimensionality and provide viable parameters to enable activity classification. Most HAR systems are customized to an individual research group, including a unique data set, classes, algorithms, and signal features. These data sets are obtained predominantly from able-bodied participants. In this paper, smartphone accelerometer and gyroscope sensor data were collected from populations that can benefit from human activity recognition: able-bodied, elderly, and stroke patients. Data from a consecutive sequence of 41 mobility tasks (18 different tasks) were collected for a total of 44 participants. Seventy-six signal features were calculated and subsets of these features were selected using three filter-based, classifier-independent, feature selection methods (Relief-F, Correlation-based Feature Selection, Fast Correlation Based Filter). The feature subsets were then evaluated using three generic classifiers (Naïve Bayes, Support Vector Machine, j48 Decision Tree). Common features were identified for all three populations, although the stroke population subset had some differences from both able-bodied and elderly sets. Evaluation with the three classifiers showed that the feature subsets produced similar or better accuracies than classification with the entire feature set. Therefore, since these feature subsets are classifier-independent, they should be useful for developing and improving HAR systems across and within populations.

  19. Mapping Phonetic Features for Voice-Driven Sound Synthesis

    Science.gov (United States)

    Janer, Jordi; Maestre, Esteban

    In applications where the human voice controls the synthesis of musical instruments sounds, phonetics convey musical information that might be related to the sound of the imitated musical instrument. Our initial hypothesis is that phonetics are user- and instrument-dependent, but they remain constant for a single subject and instrument. We propose a user-adapted system, where mappings from voice features to synthesis parameters depend on how subjects sing musical articulations, i.e. note to note transitions. The system consists of two components. First, a voice signal segmentation module that automatically determines note-to-note transitions. Second, a classifier that determines the type of musical articulation for each transition based on a set of phonetic features. For validating our hypothesis, we run an experiment where subjects imitated real instrument recordings with their voice. Performance recordings consisted of short phrases of saxophone and violin performed in three grades of musical articulation labeled as: staccato, normal, legato. The results of a supervised training classifier (user-dependent) are compared to a classifier based on heuristic rules (user-independent). Finally, from the previous results we show how to control the articulation in a sample-concatenation synthesizer by selecting the most appropriate samples.

  20. Predicting age groups of Twitter users based on language and metadata features.

    Directory of Open Access Journals (Sweden)

    Antonio A Morgan-Lopez

    Full Text Available Health organizations are increasingly using social media, such as Twitter, to disseminate health messages to target audiences. Determining the extent to which the target audience (e.g., age groups was reached is critical to evaluating the impact of social media education campaigns. The main objective of this study was to examine the separate and joint predictive validity of linguistic and metadata features in predicting the age of Twitter users. We created a labeled dataset of Twitter users across different age groups (youth, young adults, adults by collecting publicly available birthday announcement tweets using the Twitter Search application programming interface. We manually reviewed results and, for each age-labeled handle, collected the 200 most recent publicly available tweets and user handles' metadata. The labeled data were split into training and test datasets. We created separate models to examine the predictive validity of language features only, metadata features only, language and metadata features, and words/phrases from another age-validated dataset. We estimated accuracy, precision, recall, and F1 metrics for each model. An L1-regularized logistic regression model was conducted for each age group, and predicted probabilities between the training and test sets were compared for each age group. Cohen's d effect sizes were calculated to examine the relative importance of significant features. Models containing both Tweet language features and metadata features performed the best (74% precision, 74% recall, 74% F1 while the model containing only Twitter metadata features were least accurate (58% precision, 60% recall, and 57% F1 score. Top predictive features included use of terms such as "school" for youth and "college" for young adults. Overall, it was more challenging to predict older adults accurately. These results suggest that examining linguistic and Twitter metadata features to predict youth and young adult Twitter users may

  1. Oscillospira and related bacteria - from metagenomics species to metabolic features

    DEFF Research Database (Denmark)

    Gophna, Uri; Konikoff, Tom; Nielsen, Henrik Bjørn

    2017-01-01

    and manual metabolic pathway curation to decipher key metabolic features of this intriguing bacterial genus. We infer that Oscillospira species are butyrate producers, and at least some of them have the ability to utilize glucuronate, a common animal-derived sugar that is both produced by the human host...

  2. Feature-Space Clustering for fMRI Meta-Analysis

    DEFF Research Database (Denmark)

    Goutte, Cyril; Hansen, Lars Kai; Liptrot, Mathew G.

    2001-01-01

    MRI sequences containing several hundreds of images, it is sometimes necessary to invoke feature extraction to reduce the dimensionality of the data space. A second interesting application is in the meta-analysis of fMRI experiment, where features are obtained from a possibly large number of single......-voxel analyses. In particular this allows the checking of the differences and agreements between different methods of analysis. Both approaches are illustrated on a fMRI data set involving visual stimulation, and we show that the feature space clustering approach yields nontrivial results and, in particular......, shows interesting differences between individual voxel analysis performed with traditional methods. © 2001 Wiley-Liss, Inc....

  3. Manifold Regularized Multi-Task Feature Selection for Multi-Modality Classification in Alzheimer’s Disease

    Science.gov (United States)

    Jie, Biao; Cheng, Bo

    2014-01-01

    Accurate diagnosis of Alzheimer’s disease (AD), as well as its pro-dromal stage (i.e., mild cognitive impairment, MCI), is very important for possible delay and early treatment of the disease. Recently, multi-modality methods have been used for fusing information from multiple different and complementary imaging and non-imaging modalities. Although there are a number of existing multi-modality methods, few of them have addressed the problem of joint identification of disease-related brain regions from multi-modality data for classification. In this paper, we proposed a manifold regularized multi-task learning framework to jointly select features from multi-modality data. Specifically, we formulate the multi-modality classification as a multi-task learning framework, where each task focuses on the classification based on each modality. In order to capture the intrinsic relatedness among multiple tasks (i.e., modalities), we adopted a group sparsity regularizer, which ensures only a small number of features to be selected jointly. In addition, we introduced a new manifold based Laplacian regularization term to preserve the geometric distribution of original data from each task, which can lead to the selection of more discriminative features. Furthermore, we extend our method to the semi-supervised setting, which is very important since the acquisition of a large set of labeled data (i.e., diagnosis of disease) is usually expensive and time-consuming, while the collection of unlabeled data is relatively much easier. To validate our method, we have performed extensive evaluations on the baseline Magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) data of Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Our experimental results demonstrate the effectiveness of the proposed method. PMID:24505676

  4. Shapes and features of the primordial bispectrum

    Energy Technology Data Exchange (ETDEWEB)

    Gong, Jinn-Ouk [Asia Pacific Center for Theoretical Physics, Cheongam-ro 67, Pohang, 37673 (Korea, Republic of); Palma, Gonzalo A.; Sypsas, Spyros, E-mail: jinn-ouk.gong@apctp.org, E-mail: gpalmaquilod@ing.uchile.cl, E-mail: s.sypsas@gmail.com [Departamento de Física, FCFM, Universidad de Chile, Blanco Encalada 2008, Santiago, 837.0415 Chile (Chile)

    2017-05-01

    If time-dependent disruptions from slow-roll occur during inflation, the correlation functions of the primordial curvature perturbation should have scale-dependent features, a case which is marginally supported from the cosmic microwave background (CMB) data. We offer a new approach to analyze the appearance of such features in the primordial bispectrum that yields new consistency relations and justifies the search of oscillating patterns modulated by orthogonal and local templates. Under the assumption of sharp features, we find that the cubic couplings of the curvature perturbation can be expressed in terms of the bispectrum in two specific momentum configurations, for example local and equilateral. This allows us to derive consistency relations among different bispectrum shapes, which in principle could be tested in future CMB surveys. Furthermore, based on the form of the consistency relations, we construct new two-parameter templates for features that include all the known shapes.

  5. Fault-tolerant feature-based estimation of space debris rotational motion during active removal missions

    Science.gov (United States)

    Biondi, Gabriele; Mauro, Stefano; Pastorelli, Stefano; Sorli, Massimo

    2018-05-01

    One of the key functionalities required by an Active Debris Removal mission is the assessment of the target kinematics and inertial properties. Passive sensors, such as stereo cameras, are often included in the onboard instrumentation of a chaser spacecraft for capturing sequential photographs and for tracking features of the target surface. A plenty of methods, based on Kalman filtering, are available for the estimation of the target's state from feature positions; however, to guarantee the filter convergence, they typically require continuity of measurements and the capability of tracking a fixed set of pre-defined features of the object. These requirements clash with the actual tracking conditions: failures in feature detection often occur and the assumption of having some a-priori knowledge about the shape of the target could be restrictive in certain cases. The aim of the presented work is to propose a fault-tolerant alternative method for estimating the angular velocity and the relative magnitudes of the principal moments of inertia of the target. Raw data regarding the positions of the tracked features are processed to evaluate corrupted values of a 3-dimentional parameter which entirely describes the finite screw motion of the debris and which primarily is invariant on the particular set of considered features of the object. Missing values of the parameter are completely restored exploiting the typical periodicity of the rotational motion of an uncontrolled satellite: compressed sensing techniques, typically adopted for recovering images or for prognostic applications, are herein used in a completely original fashion for retrieving a kinematic signal that appears sparse in the frequency domain. Due to its invariance about the features, no assumptions are needed about the target's shape and continuity of the tracking. The obtained signal is useful for the indirect evaluation of an attitude signal that feeds an unscented Kalman filter for the estimation of

  6. Spatiotemporal Features for Asynchronous Event-based Data

    Directory of Open Access Journals (Sweden)

    Xavier eLagorce

    2015-02-01

    Full Text Available Bio-inspired asynchronous event-based vision sensors are currently introducing a paradigm shift in visual information processing. These new sensors rely on a stimulus-driven principle of light acquisition similar to biological retinas. They are event-driven and fully asynchronous, thereby reducing redundancy and encoding exact times of input signal changes, leading to a very precise temporal resolution. Approaches for higher-level computer vision often rely on the realiable detection of features in visual frames, but similar definitions of features for the novel dynamic and event-based visual input representation of silicon retinas have so far been lacking. This article addresses the problem of learning and recognizing features for event-based vision sensors, which capture properties of truly spatiotemporal volumes of sparse visual event information. A novel computational architecture for learning and encoding spatiotemporal features is introduced based on a set of predictive recurrent reservoir networks, competing via winner-take-all selection. Features are learned in an unsupervised manner from real-world input recorded with event-based vision sensors. It is shown that the networks in the architecture learn distinct and task-specific dynamic visual features, and can predict their trajectories over time.

  7. Feature selection for anomaly–based network intrusion detection using cluster validity indices

    CSIR Research Space (South Africa)

    Naidoo, Tyrone

    2015-09-01

    Full Text Available data, which is rarely available in operational networks. It uses normalized cluster validity indices as an objective function that is optimized over the search space of candidate feature subsets via a genetic algorithm. Feature sets produced...

  8. Feature ranking and rank aggregation for automatic sleep stage classification: a comparative study.

    Science.gov (United States)

    Najdi, Shirin; Gharbali, Ali Abdollahi; Fonseca, José Manuel

    2017-08-18

    Nowadays, sleep quality is one of the most important measures of healthy life, especially considering the huge number of sleep-related disorders. Identifying sleep stages using polysomnographic (PSG) signals is the traditional way of assessing sleep quality. However, the manual process of sleep stage classification is time-consuming, subjective and costly. Therefore, in order to improve the accuracy and efficiency of the sleep stage classification, researchers have been trying to develop automatic classification algorithms. Automatic sleep stage classification mainly consists of three steps: pre-processing, feature extraction and classification. Since classification accuracy is deeply affected by the extracted features, a poor feature vector will adversely affect the classifier and eventually lead to low classification accuracy. Therefore, special attention should be given to the feature extraction and selection process. In this paper the performance of seven feature selection methods, as well as two feature rank aggregation methods, were compared. Pz-Oz EEG, horizontal EOG and submental chin EMG recordings of 22 healthy males and females were used. A comprehensive feature set including 49 features was extracted from these recordings. The extracted features are among the most common and effective features used in sleep stage classification from temporal, spectral, entropy-based and nonlinear categories. The feature selection methods were evaluated and compared using three criteria: classification accuracy, stability, and similarity. Simulation results show that MRMR-MID achieves the highest classification performance while Fisher method provides the most stable ranking. In our simulations, the performance of the aggregation methods was in the average level, although they are known to generate more stable results and better accuracy. The Borda and RRA rank aggregation methods could not outperform significantly the conventional feature ranking methods. Among

  9. ImSET 3.1: Impact of Sector Energy Technologies Model Description and User's Guide

    Energy Technology Data Exchange (ETDEWEB)

    Scott, Michael J.; Livingston, Olga V.; Balducci, Patrick J.; Roop, Joseph M.; Schultz, Robert W.

    2009-05-22

    This 3.1 version of the Impact of Sector Energy Technologies (ImSET) model represents the next generation of the previously-built ImSET model (ImSET 2.0) that was developed in 2005 to estimate the macroeconomic impacts of energy-efficient technology in buildings. In particular, a special-purpose version of the Benchmark National Input-Output (I-O) model was designed specifically to estimate the national employment and income effects of the deployment of Office of Energy Efficiency and Renewable Energy (EERE)–developed energy-saving technologies. In comparison with the previous versions of the model, this version features the use of the U.S. Bureau of Economic Analysis 2002 national input-output table and the central processing code has been moved from the FORTRAN legacy operating environment to a modern C++ code. ImSET is also easier to use than extant macroeconomic simulation models and incorporates information developed by each of the EERE offices as part of the requirements of the Government Performance and Results Act. While it does not include the ability to model certain dynamic features of markets for labor and other factors of production featured in the more complex models, for most purposes these excluded features are not critical. The analysis is credible as long as the assumption is made that relative prices in the economy would not be substantially affected by energy efficiency investments. In most cases, the expected scale of these investments is small enough that neither labor markets nor production cost relationships should seriously affect national prices as the investments are made. The exact timing of impacts on gross product, employment, and national wage income from energy efficiency investments is not well-enough understood that much special insight can be gained from the additional dynamic sophistication of a macroeconomic simulation model. Thus, we believe that this version of ImSET is a cost-effective solution to estimating the economic

  10. SoFoCles: feature filtering for microarray classification based on gene ontology.

    Science.gov (United States)

    Papachristoudis, Georgios; Diplaris, Sotiris; Mitkas, Pericles A

    2010-02-01

    Marker gene selection has been an important research topic in the classification analysis of gene expression data. Current methods try to reduce the "curse of dimensionality" by using statistical intra-feature set calculations, or classifiers that are based on the given dataset. In this paper, we present SoFoCles, an interactive tool that enables semantic feature filtering in microarray classification problems with the use of external, well-defined knowledge retrieved from the Gene Ontology. The notion of semantic similarity is used to derive genes that are involved in the same biological path during the microarray experiment, by enriching a feature set that has been initially produced with legacy methods. Among its other functionalities, SoFoCles offers a large repository of semantic similarity methods that are used in order to derive feature sets and marker genes. The structure and functionality of the tool are discussed in detail, as well as its ability to improve classification accuracy. Through experimental evaluation, SoFoCles is shown to outperform other classification schemes in terms of classification accuracy in two real datasets using different semantic similarity computation approaches.

  11. Features Students Really Expect from Learning Analytics

    Science.gov (United States)

    Schumacher, Clara; Ifenthaler, Dirk

    2016-01-01

    In higher education settings more and more learning is facilitated through online learning environments. To support and understand students' learning processes better, learning analytics offers a promising approach. The purpose of this study was to investigate students' expectations toward features of learning analytics systems. In a first…

  12. Mammographic and sonographic features of fat necrosis of the breast

    International Nuclear Information System (INIS)

    Upadhyaya, Vidya S; Uppoor, Raghuraj; Shetty, Lathika

    2013-01-01

    Imaging features of fat necrosis vary depending on its stage of evolution and can mimic malignancy in late stages. Imaging may suffice to differentiate fat necrosis in the early stages from malignancy and thus avoid unnecessary biopsy. In this pictorial essay, we present combination of benign features in mammography and/or ultrasonography (USG) that can lead to imaging diagnosis of fat necrosis. The follow-up imaging features of fat necrosis which mirror its pathophysiological evolution have also been demonstrated. To summarize, in the appropriate clinical setting, no mammographic features suspicious for malignancy should be present. When the typical mammographic features are not present, USG can aid with the diagnosis and follow up USG can confirm it

  13. Designing attractive gamification features for collaborative storytelling websites.

    Science.gov (United States)

    Hsu, Shang Hwa; Chang, Jen-Wei; Lee, Chun-Chia

    2013-06-01

    Gamification design is considered as the predictor of collaborative storytelling websites' success. Although aforementioned studies have mentioned a broad range of factors that may influence gamification, they neither depicted the actual design features nor relative attractiveness among them. This study aims to identify attractive gamification features for collaborative storytelling websites. We first constructed a hierarchical system structure of gamification design of collaborative storytelling websites and conducted a focus group interview with eighteen frequent users to identify 35gamification features. After that, this study determined the relative attractiveness of these gamification features by administrating an online survey to 6333 collaborative storytelling websites users. The results indicated that the top 10 most attractive gamification features could account for more than 50% of attractiveness among these 35 gamification features. The feature of unpredictable time pressure is important to website users, yet not revealed in previous relevant studies. Implications of the findings were discussed.

  14. Desired features of smartphone applications promoting physical activity.

    Science.gov (United States)

    Rabin, Carolyn; Bock, Beth

    2011-12-01

    Approximately one-third of adults in the United States are physically inactive. This is a significant public health concern as physical activity (PA) can influence the risk of cardiovascular disease, diabetes, and certain forms of cancer. To minimize these health risks, effective PA interventions must be developed and disseminated to the vast number of individuals who remain sedentary. Smartphone technology presents an exciting opportunity for delivering PA interventions remotely. Although a number of PA applications are currently available for smartphones, these "apps" are not based on established theories of health behavior change and most do not include evidence-based features (e.g., reinforcement and goal setting). Our aim was to collect formative data to develop a smartphone PA app that is empirically and theoretically-based and incorporates user preferences. We recruited 15 sedentary adults to test three currently available PA smartphone apps and provide qualitative and quantitative feedback. Findings indicate that users have a number of specific preferences with regard to PA app features, including that apps provide automatic tracking of PA (e.g., steps taken and calories burned), track progress toward PA goals, and integrate a music feature. Participants also preferred that PA apps be flexible enough to be used with several types of PA, and have well-documented features and user-friendly interfaces (e.g., a one-click main page). When queried by the researcher, most participants endorsed including goal-setting and problem-solving features. These findings provide a blue print for developing a smartphone PA app that incorporates evidence-based components and user preferences.

  15. T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results.

    Science.gov (United States)

    Nketiah, Gabriel; Elschot, Mattijs; Kim, Eugene; Teruel, Jose R; Scheenen, Tom W; Bathen, Tone F; Selnæs, Kirsten M

    2017-07-01

    To evaluate the diagnostic relevance of T2-weighted (T2W) MRI-derived textural features relative to quantitative physiological parameters derived from diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI in Gleason score (GS) 3+4 and 4+3 prostate cancers. 3T multiparametric-MRI was performed on 23 prostate cancer patients prior to prostatectomy. Textural features [angular second moment (ASM), contrast, correlation, entropy], apparent diffusion coefficient (ADC), and DCE pharmacokinetic parameters (K trans and V e ) were calculated from index tumours delineated on the T2W, DW, and DCE images, respectively. The association between the textural features and prostatectomy GS and the MRI-derived parameters, and the utility of the parameters in differentiating between GS 3+4 and 4+3 prostate cancers were assessed statistically. ASM and entropy correlated significantly (p textural features correlated insignificantly with K trans and V e . GS 4+3 cancers had significantly lower ASM and higher entropy than 3+4 cancers, but insignificant differences in median ADC, K trans , and V e . The combined texture-MRI parameters yielded higher classification accuracy (91%) than the individual parameter sets. T2W MRI-derived textural features could serve as potential diagnostic markers, sensitive to the pathological differences in prostate cancers. • T2W MRI-derived textural features correlate significantly with Gleason score and ADC. • T2W MRI-derived textural features differentiate Gleason score 3+4 from 4+3 cancers. • T2W image textural features could augment tumour characterization.

  16. Associations between the settings of exercise habits and health-related outcomes in community-dwelling older adults

    OpenAIRE

    Makino, Keitaro; Ihira, Hikaru; Mizumoto, Atsushi; Shimizu, Kotaro; Ishida, Toyoaki; Furuna, Taketo

    2015-01-01

    [Purpose] The purpose of this study was to examine the associations between the settings of exercise habits and health-related outcomes in community-dwelling older adults. [Subjects] A total of 304 Japanese community-dwelling older adults (70.3 ? 4.1?years; 113 males and 191 females) participated in this study. [Methods] Demographic characteristics, medical conditions, exercise habits, and health-related outcomes were assessed by face-to-face interviews and self-reported questionnaires. Older...

  17. Tumor recognition in wireless capsule endoscopy images using textural features and SVM-based feature selection.

    Science.gov (United States)

    Li, Baopu; Meng, Max Q-H

    2012-05-01

    Tumor in digestive tract is a common disease and wireless capsule endoscopy (WCE) is a relatively new technology to examine diseases for digestive tract especially for small intestine. This paper addresses the problem of automatic recognition of tumor for WCE images. Candidate color texture feature that integrates uniform local binary pattern and wavelet is proposed to characterize WCE images. The proposed features are invariant to illumination change and describe multiresolution characteristics of WCE images. Two feature selection approaches based on support vector machine, sequential forward floating selection and recursive feature elimination, are further employed to refine the proposed features for improving the detection accuracy. Extensive experiments validate that the proposed computer-aided diagnosis system achieves a promising tumor recognition accuracy of 92.4% in WCE images on our collected data.

  18. Axiomatic set theory

    CERN Document Server

    Suppes, Patrick

    1972-01-01

    This clear and well-developed approach to axiomatic set theory is geared toward upper-level undergraduates and graduate students. It examines the basic paradoxes and history of set theory and advanced topics such as relations and functions, equipollence, finite sets and cardinal numbers, rational and real numbers, and other subjects. 1960 edition.

  19. Feature selection model based on clustering and ranking in pipeline for microarray data

    Directory of Open Access Journals (Sweden)

    Barnali Sahu

    2017-01-01

    Full Text Available Most of the available feature selection techniques in the literature are classifier bound. It means a group of features tied to the performance of a specific classifier as applied in wrapper and hybrid approach. Our objective in this study is to select a set of generic features not tied to any classifier based on the proposed framework. This framework uses attribute clustering and feature ranking techniques in pipeline in order to remove redundant features. On each uncovered cluster, signal-to-noise ratio, t-statistics and significance analysis of microarray are independently applied to select the top ranked features. Both filter and evolutionary wrapper approaches have been considered for feature selection and the data set with selected features are given to ensemble of predefined statistically different classifiers. The class labels of the test data are determined using majority voting technique. Moreover, with the aforesaid objectives, this paper focuses on obtaining a stable result out of various classification models. Further, a comparative analysis has been performed to study the classification accuracy and computational time of the current approach and evolutionary wrapper techniques. It gives a better insight into the features and further enhancing the classification accuracy with less computational time.

  20. Use of alarm features in referral of febrile children to the emergency department : an observational study

    NARCIS (Netherlands)

    van Ierland, Yvette; Elshout, Gijs; Moll, Henritte A.; Nijman, Ruud G.; Vergouwe, Yvonne; van der Lei, Johan; Berger, Marjolein Y.; Oostenbrink, Rianne

    Background The diagnostic value of alarm features of serious infections in low prevalence settings is unclear. Aim To explore to what extent alarm features play a role in referral to the emergency department (ED) by GPs who face a febrile child during out-of-hours care. Design and setting

  1. Heartbeat Classification Using Abstract Features From the Abductive Interpretation of the ECG.

    Science.gov (United States)

    Teijeiro, Tomas; Felix, Paulo; Presedo, Jesus; Castro, Daniel

    2018-03-01

    This paper aims to prove that automatic beat classification on ECG signals can be effectively solved with a pure knowledge-based approach, using an appropriate set of abstract features obtained from the interpretation of the physiological processes underlying the signal. A set of qualitative morphological and rhythm features are obtained for each heartbeat as a result of the abductive interpretation of the ECG. Then, a QRS clustering algorithm is applied in order to reduce the effect of possible errors in the interpretation. Finally, a rule-based classifier assigns a tag to each cluster. The method has been tested with the MIT-BIH Arrhythmia Database records, showing a significantly better performance than any other automatic approach in the state-of-the-art, and even improving most of the assisted approaches that require the intervention of an expert in the process. The most relevant issues in ECG classification, related to a large extent to the variability of the signal patterns between different subjects and even in the same subject over time, will be overcome by changing the reasoning paradigm. This paper demonstrates the power of an abductive framework for time-series interpretation to make a qualitative leap in the significance of the information extracted from the ECG by automatic methods.

  2. Reductio ad discrimen: Where features come from

    Directory of Open Access Journals (Sweden)

    Elizabeth Cowper

    2015-04-01

    Full Text Available This paper addresses two fundamental questions about the nature of formal features in phonology and morphosyntax: what is their expressive power, and where do they come from? To answer these questions, we begin with the most restrictive possible hypothesis (all features are privative, and are wholly dictated by Universal Grammar, with no room for cross-linguistic variation, and examine the extent to which empirical evidence from a variety of languages compels a retreat from this position. We argue that there is little to be gained by positing a universal set of specific features, and propose instead that the crucial contribution of UG is the language learner's ability to construct features by identifying correlations between contrasts at different levels of linguistic structure. This view resonates with current research on how the interaction between UG and external 'third factors' shapes the structure of language, while at the same time harking back to the Saussurean notion that contrast is the central function of linguistic representations.

  3. Spectral-based features ranking for gamelan instruments identification using filter techniques

    Directory of Open Access Journals (Sweden)

    Diah P Wulandari

    2013-03-01

    Full Text Available In this paper, we describe an approach of spectral-based features ranking for Javanese gamelaninstruments identification using filter techniques. The model extracted spectral-based features set of thesignal using Short Time Fourier Transform (STFT. The rank of the features was determined using the fivealgorithms; namely ReliefF, Chi-Squared, Information Gain, Gain Ratio, and Symmetric Uncertainty. Then,we tested the ranked features by cross validation using Support Vector Machine (SVM. The experimentshowed that Gain Ratio algorithm gave the best result, it yielded accuracy of 98.93%.

  4. Cross-Domain Semi-Supervised Learning Using Feature Formulation.

    Science.gov (United States)

    Xingquan Zhu

    2011-12-01

    Semi-Supervised Learning (SSL) traditionally makes use of unlabeled samples by including them into the training set through an automated labeling process. Such a primitive Semi-Supervised Learning (pSSL) approach suffers from a number of disadvantages including false labeling and incapable of utilizing out-of-domain samples. In this paper, we propose a formative Semi-Supervised Learning (fSSL) framework which explores hidden features between labeled and unlabeled samples to achieve semi-supervised learning. fSSL regards that both labeled and unlabeled samples are generated from some hidden concepts with labeling information partially observable for some samples. The key of the fSSL is to recover the hidden concepts, and take them as new features to link labeled and unlabeled samples for semi-supervised learning. Because unlabeled samples are only used to generate new features, but not to be explicitly included in the training set like pSSL does, fSSL overcomes the inherent disadvantages of the traditional pSSL methods, especially for samples not within the same domain as the labeled instances. Experimental results and comparisons demonstrate that fSSL significantly outperforms pSSL-based methods for both within-domain and cross-domain semi-supervised learning.

  5. Estimating the CCSD basis-set limit energy from small basis sets: basis-set extrapolations vs additivity schemes

    Energy Technology Data Exchange (ETDEWEB)

    Spackman, Peter R.; Karton, Amir, E-mail: amir.karton@uwa.edu.au [School of Chemistry and Biochemistry, The University of Western Australia, Perth, WA 6009 (Australia)

    2015-05-15

    Coupled cluster calculations with all single and double excitations (CCSD) converge exceedingly slowly with the size of the one-particle basis set. We assess the performance of a number of approaches for obtaining CCSD correlation energies close to the complete basis-set limit in conjunction with relatively small DZ and TZ basis sets. These include global and system-dependent extrapolations based on the A + B/L{sup α} two-point extrapolation formula, and the well-known additivity approach that uses an MP2-based basis-set-correction term. We show that the basis set convergence rate can change dramatically between different systems(e.g.it is slower for molecules with polar bonds and/or second-row elements). The system-dependent basis-set extrapolation scheme, in which unique basis-set extrapolation exponents for each system are obtained from lower-cost MP2 calculations, significantly accelerates the basis-set convergence relative to the global extrapolations. Nevertheless, we find that the simple MP2-based basis-set additivity scheme outperforms the extrapolation approaches. For example, the following root-mean-squared deviations are obtained for the 140 basis-set limit CCSD atomization energies in the W4-11 database: 9.1 (global extrapolation), 3.7 (system-dependent extrapolation), and 2.4 (additivity scheme) kJ mol{sup –1}. The CCSD energy in these approximations is obtained from basis sets of up to TZ quality and the latter two approaches require additional MP2 calculations with basis sets of up to QZ quality. We also assess the performance of the basis-set extrapolations and additivity schemes for a set of 20 basis-set limit CCSD atomization energies of larger molecules including amino acids, DNA/RNA bases, aromatic compounds, and platonic hydrocarbon cages. We obtain the following RMSDs for the above methods: 10.2 (global extrapolation), 5.7 (system-dependent extrapolation), and 2.9 (additivity scheme) kJ mol{sup –1}.

  6. Estimating the CCSD basis-set limit energy from small basis sets: basis-set extrapolations vs additivity schemes

    International Nuclear Information System (INIS)

    Spackman, Peter R.; Karton, Amir

    2015-01-01

    Coupled cluster calculations with all single and double excitations (CCSD) converge exceedingly slowly with the size of the one-particle basis set. We assess the performance of a number of approaches for obtaining CCSD correlation energies close to the complete basis-set limit in conjunction with relatively small DZ and TZ basis sets. These include global and system-dependent extrapolations based on the A + B/L α two-point extrapolation formula, and the well-known additivity approach that uses an MP2-based basis-set-correction term. We show that the basis set convergence rate can change dramatically between different systems(e.g.it is slower for molecules with polar bonds and/or second-row elements). The system-dependent basis-set extrapolation scheme, in which unique basis-set extrapolation exponents for each system are obtained from lower-cost MP2 calculations, significantly accelerates the basis-set convergence relative to the global extrapolations. Nevertheless, we find that the simple MP2-based basis-set additivity scheme outperforms the extrapolation approaches. For example, the following root-mean-squared deviations are obtained for the 140 basis-set limit CCSD atomization energies in the W4-11 database: 9.1 (global extrapolation), 3.7 (system-dependent extrapolation), and 2.4 (additivity scheme) kJ mol –1 . The CCSD energy in these approximations is obtained from basis sets of up to TZ quality and the latter two approaches require additional MP2 calculations with basis sets of up to QZ quality. We also assess the performance of the basis-set extrapolations and additivity schemes for a set of 20 basis-set limit CCSD atomization energies of larger molecules including amino acids, DNA/RNA bases, aromatic compounds, and platonic hydrocarbon cages. We obtain the following RMSDs for the above methods: 10.2 (global extrapolation), 5.7 (system-dependent extrapolation), and 2.9 (additivity scheme) kJ mol –1

  7. Discriminative semi-supervised feature selection via manifold regularization.

    Science.gov (United States)

    Xu, Zenglin; King, Irwin; Lyu, Michael Rung-Tsong; Jin, Rong

    2010-07-01

    Feature selection has attracted a huge amount of interest in both research and application communities of data mining. We consider the problem of semi-supervised feature selection, where we are given a small amount of labeled examples and a large amount of unlabeled examples. Since a small number of labeled samples are usually insufficient for identifying the relevant features, the critical problem arising from semi-supervised feature selection is how to take advantage of the information underneath the unlabeled data. To address this problem, we propose a novel discriminative semi-supervised feature selection method based on the idea of manifold regularization. The proposed approach selects features through maximizing the classification margin between different classes and simultaneously exploiting the geometry of the probability distribution that generates both labeled and unlabeled data. In comparison with previous semi-supervised feature selection algorithms, our proposed semi-supervised feature selection method is an embedded feature selection method and is able to find more discriminative features. We formulate the proposed feature selection method into a convex-concave optimization problem, where the saddle point corresponds to the optimal solution. To find the optimal solution, the level method, a fairly recent optimization method, is employed. We also present a theoretic proof of the convergence rate for the application of the level method to our problem. Empirical evaluation on several benchmark data sets demonstrates the effectiveness of the proposed semi-supervised feature selection method.

  8. A Probabilistic Feature Map-Based Localization System Using a Monocular Camera

    Directory of Open Access Journals (Sweden)

    Hyungjin Kim

    2015-08-01

    Full Text Available Image-based localization is one of the most widely researched localization techniques in the robotics and computer vision communities. As enormous image data sets are provided through the Internet, many studies on estimating a location with a pre-built image-based 3D map have been conducted. Most research groups use numerous image data sets that contain sufficient features. In contrast, this paper focuses on image-based localization in the case of insufficient images and features. A more accurate localization method is proposed based on a probabilistic map using 3D-to-2D matching correspondences between a map and a query image. The probabilistic feature map is generated in advance by probabilistic modeling of the sensor system as well as the uncertainties of camera poses. Using the conventional PnP algorithm, an initial camera pose is estimated on the probabilistic feature map. The proposed algorithm is optimized from the initial pose by minimizing Mahalanobis distance errors between features from the query image and the map to improve accuracy. To verify that the localization accuracy is improved, the proposed algorithm is compared with the conventional algorithm in a simulation and realenvironments

  9. A Probabilistic Feature Map-Based Localization System Using a Monocular Camera.

    Science.gov (United States)

    Kim, Hyungjin; Lee, Donghwa; Oh, Taekjun; Choi, Hyun-Taek; Myung, Hyun

    2015-08-31

    Image-based localization is one of the most widely researched localization techniques in the robotics and computer vision communities. As enormous image data sets are provided through the Internet, many studies on estimating a location with a pre-built image-based 3D map have been conducted. Most research groups use numerous image data sets that contain sufficient features. In contrast, this paper focuses on image-based localization in the case of insufficient images and features. A more accurate localization method is proposed based on a probabilistic map using 3D-to-2D matching correspondences between a map and a query image. The probabilistic feature map is generated in advance by probabilistic modeling of the sensor system as well as the uncertainties of camera poses. Using the conventional PnP algorithm, an initial camera pose is estimated on the probabilistic feature map. The proposed algorithm is optimized from the initial pose by minimizing Mahalanobis distance errors between features from the query image and the map to improve accuracy. To verify that the localization accuracy is improved, the proposed algorithm is compared with the conventional algorithm in a simulation and realenvironments.

  10. Setting the Record Straight. The Truth About Fad Diets.

    Science.gov (United States)

    Wheat Foods Council, Parker, CO.

    The Setting the Record Straight information packet presents facts to set the record straight about nutrition and debunk fad diets. The kit features materials designed to communicate the importance of balanced eating. Materials include: a time line of fad diets; four reproducible fad diet book review handouts that show the misleading claims rampant…

  11. Features of opposition of offender and worker of militia under various conditions

    Directory of Open Access Journals (Sweden)

    Bondarenko V.V.

    2013-01-01

    Full Text Available An analysis and generalization of knowledge of features of origin and motion of situations of the armed collision of employees of law enforcement authorities and offenders is conducted. 82 workers of practical subdivisions of internal affairs organs took part in research between workers, who have already necessary to clash and detain criminals with a plain weapon. Canvassed on the specially developed questionnaire. It is set that for the workers of militia the insufficient level of the special theoretical knowledge of features of conduct of offenders and abilities of determination of degree of danger is formed. The aggregate of factors, influencing on a decision-making by an offender in relation to attacking militiaman is certain. It is found out that on a decision to accomplish an attack determining influence is rendered by internal factors: psychological state of offender in the moment of collision, his preparedness and level of motivation.

  12. The Memory Is in the Details: Relations between Memory for the Specific Features of Events and Long-Term Recall during Infancy

    Science.gov (United States)

    Bauer, Patricia J.; Lukowski, Angela F.

    2010-01-01

    The second year of life is marked by pronounced changes in the length of time over which events are remembered. We tested whether the age-related differences are related to differences in memory for the specific features of events. In our study, 16- and 20-month-olds were tested for immediate and long-term recall of individual actions and temporal…

  13. Electrocardiographic features of patients with earthquake related posttraumatic stress disorder

    Science.gov (United States)

    İlhan, Erkan; Kaplan, Abdullah; Güvenç, Tolga Sinan; Biteker, Murat; Karabulut, Evindar; Işıklı, Serhan

    2013-01-01

    AIM: To analyze electrocardiographic features of patients diagnosed with posttraumatic stress disorder (PTSD) after the Van-Erciş earthquake, with a shock measuring 7.2 on the Richter scale that took place in Turkey in October 2011. METHODS: Surface electrocardiograms of 12 patients with PTSD admitted to Van Erciş State Hospital (Van, Turkey) from February 2012 to May 2012 were examined. Psychiatric interviews of the sex and age matched control subjects, who had experienced the earthquake, confirmed the absence of any known diagnosable psychiatric conditions in the control group. RESULTS: A wide range of electrocardiogram (ECG) parameters, such as P-wave dispersion, QT dispersion, QT interval, Tpeak to Tend interval, intrinsicoid deflection durations and other traditional parameters were similar in both groups. There was no one with an abnormal P wave axis, short or long PR interval, long or short QT interval, negative T wave in lateral leads, abnormal T wave axis, abnormal left or right intrinsicoid deflection duration, low voltage, left bundle branch block, right bundle branch block, left posterior hemiblock, left or right axis deviation, left ventricular hypertrophy, right or left atrial enlargement and pathological q(Q) wave in either group. CONCLUSION: The study showed no direct effect of earthquake related PTSD on surface ECG in young patients. So, we propose that PTSD has no direct effect on surface ECG but may cause electrocardiographic changes indirectly by triggering atherosclerosis and/or contributing to the ongoing atherosclerotic process. PMID:23538549

  14. Examining the design features of a communication-rich, problem-centred mathematics professional development

    Science.gov (United States)

    de Araujo, Zandra; Orrill, Chandra Hawley; Jacobson, Erik

    2018-04-01

    While there is considerable scholarship describing principles for effective professional development, there have been few attempts to examine these principles in practice. In this paper, we identify and examine the particular design features of a mathematics professional development experience provided for middle grades teachers over 14 weeks. The professional development was grounded in a set of mathematical tasks that each had one right answer, but multiple solution paths. The facilitator engaged participants in problem solving and encouraged participants to work collaboratively to explore different solution paths. Through analysis of this collaborative learning environment, we identified five design features for supporting teacher learning of important mathematics and pedagogy in a problem-solving setting. We discuss these design features in depth and illustrate them by presenting an elaborated example from the professional development. This study extends the existing guidance for the design of professional development by examining and operationalizing the relationships among research-based features of effective professional development and the enacted features of a particular design.

  15. Age-related distance esotropia: Clinical features and therapeutic outcomes.

    Science.gov (United States)

    Gómez de Liaño Sánchez, P; Olavarri González, G; Merino Sanz, P; Escribano Villafruela, J C

    2016-12-01

    To describe the clinical characteristics and surgical outcomes of a group of patients with age-related distance esotropia (ARDE). A retrospective study was conducted on a consecutive case series of 16 adult patients diagnosed with ARDE between 2008 and 2015. The clinical features evaluated included mean age and gender, primary position deviations at distance and near, measured in prism dioptres (pd), treatment offered in each case, and post-surgical deviations. Ductions and versions were full, with no evidence of lateral rectus paresis. None of these patients had any obvious underlying neurological disorder, such as, high myopia or thyroid disease. A good result is considered to be the disappearance of diplopia in all positions of gaze. A total of 16 patients (11 females [68.8%]) were identified. The mean age at diagnosis was 78.19±6.77 years. The mean initial esodeviation was 2.25±3.08 pd at near (-4 to +8 pd) and 9.5±4.18 pd at distance (2 to 18 pd). Treatment was not necessary in 5 cases because the symptoms were intermittent or well-tolerated. Of the 11 patients with symptoms, one was corrected with an external base therapeutic prism. Botulinum toxin was administered in another patient, without satisfactory results. Unilateral medial rectus muscle recession was performed on one patient, and unilateral lateral rectus plication on 7 patients, indicating prisms before surgery. One patient refused surgery despite continuous diplopia in far vision. After a mean follow-up of 16.5 months, all operated patients were asymptomatic. Not all patients with ARDE require treatment, as the tolerance to diplopia varies from one subject to another. Both medial rectus weakening and lateral rectus strengthening provides excellent results. Crown Copyright © 2016. Publicado por Elsevier España, S.L.U. All rights reserved.

  16. Rehabilitation-Related Research on Disability and Employer Practices Using Individual-Based National and Administrative Data Sets

    Science.gov (United States)

    Nazarov, Zafar E.; Erickson, William A.; Bruyère, Susanne M.

    2014-01-01

    Objective: It is useful to examine workplace factors influencing employment outcomes of individuals with disabilities and the interplay of disability, employment-related, and employer characteristics to inform rehabilitation practice. Design: A number of large national survey and administrative data sets provide information on employers and can…

  17. Gender-related clinical and immunological features of extremely low birth weight infants

    Directory of Open Access Journals (Sweden)

    G. N. Chistyakova

    2016-01-01

    Full Text Available Examinations were made in 35 boys and 39 girls with extremely low birth weight in order to identify gender-related clinical and immunological features. A comparison group consisted of 31 full-term newborns with early uncomplicated adaptation. The investigators determined the number of lymphocyte subpopulations and cytokine-producing cells (CD3+INF-γ+, CD3+IL-4+ by flow cytometry and the serum levels of cytokines (IL-4, IFN-γ and neopterin by enzyme immunoassay. A study of immunological parameters revealed that the boys had a smaller number of CD3+, CD4+, and CD8+ subpopulations, a reduced content of CD3+IL-4+ cells at birth, and low IL-4 production on the first day of life compared to the full-term neonates. The girls were recorded to have higher levels of neopterin and B cells on the first day of life, the quantitative characteristics of T lymphocytes were consistent with those in the full-term infants. The findings are indicative of the greater functional immaturity of the immune system in the preterm boys. 

  18. Design Features of Modern Mechanical Ventilators.

    Science.gov (United States)

    MacIntyre, Neil

    2016-12-01

    A positive-pressure breath ideally should provide a V T that is adequate for gas exchange and appropriate muscle unloading while minimizing any risk for injury or discomfort. The latest generation of ventilators uses sophisticated feedback systems to sculpt positive-pressure breaths according to patient effort and respiratory system mechanics. Currently, however, these new control strategies are not totally closed-loop systems. This is because the automatic input variables remain limited, some clinician settings are still required, and the specific features of the perfect breath design still are not entirely clear. Despite these limitations, there are some rationale for many of these newer feedback features. Copyright © 2016 Elsevier Inc. All rights reserved.

  19. An exponential combination procedure for set-based association tests in sequencing studies.

    Science.gov (United States)

    Chen, Lin S; Hsu, Li; Gamazon, Eric R; Cox, Nancy J; Nicolae, Dan L

    2012-12-07

    State-of-the-art next-generation-sequencing technologies can facilitate in-depth explorations of the human genome by investigating both common and rare variants. For the identification of genetic factors that are associated with disease risk or other complex phenotypes, methods have been proposed for jointly analyzing variants in a set (e.g., all coding SNPs in a gene). Variants in a properly defined set could be associated with risk or phenotype in a concerted fashion, and by accumulating information from them, one can improve power to detect genetic risk factors. Many set-based methods in the literature are based on statistics that can be written as the summation of variant statistics. Here, we propose taking the summation of the exponential of variant statistics as the set summary for association testing. From both Bayesian and frequentist perspectives, we provide theoretical justification for taking the sum of the exponential of variant statistics because it is particularly powerful for sparse alternatives-that is, compared with the large number of variants being tested in a set, only relatively few variants are associated with disease risk-a distinctive feature of genetic data. We applied the exponential combination gene-based test to a sequencing study in anticancer pharmacogenomics and uncovered mechanistic insights into genes and pathways related to chemotherapeutic susceptibility for an important class of oncologic drugs. Copyright © 2012 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

  20. Temporality of Features in Near-Death Experience Narratives

    Directory of Open Access Journals (Sweden)

    Charlotte Martial

    2017-06-01

    Full Text Available Background: After an occurrence of a Near-Death Experience (NDE, Near-Death Experiencers (NDErs usually report extremely rich and detailed narratives. Phenomenologically, a NDE can be described as a set of distinguishable features. Some authors have proposed regular patterns of NDEs, however, the actual temporality sequence of NDE core features remains a little explored area.Objectives: The aim of the present study was to investigate the frequency distribution of these features (globally and according to the position of features in narratives as well as the most frequently reported temporality sequences of features.Methods: We collected 154 French freely expressed written NDE narratives (i.e., Greyson NDE scale total score ≥ 7/32. A text analysis was conducted on all narratives in order to infer temporal ordering and frequency distribution of NDE features.Results: Our analyses highlighted the following most frequently reported sequence of consecutive NDE features: Out-of-Body Experience, Experiencing a tunnel, Seeing a bright light, Feeling of peace. Yet, this sequence was encountered in a very limited number of NDErs.Conclusion: These findings may suggest that NDEs temporality sequences can vary across NDErs. Exploring associations and relationships among features encountered during NDEs may complete the rigorous definition and scientific comprehension of the phenomenon.